Water quality monitoring is critical for sustainable aquaculture, as it directly affects fish health, growth, and productivity. Traditional monitoring methods, reliant on manual sampling and laboratory testing, are labor-intensive, time-consuming, and subjective. Although numerous artificial intelligence (AI) techniques have been proposed to forecast individual water quality parameters, the challenge of assessing overall water quality by combining multiple sensor inputs into a meaningful quality category remains underexplored. To address this gap, we propose a two-stage approach consisting of two complementary systems: a Mamdani–Assilian fuzzy inference system (MAFIS) for water quality labeling and a Takagi–Sugeno–Kang compact adaptive neuro-fuzzy system (TSK-CANFS) for predictive modeling. MAFIS utilizes expert-defined membership functions to label water quality based on individual parameter values, effectively generating labeled datasets for training. TSK-CANFS, on the other hand, leverages a novel rule generation mechanism and the sliding window approaches to construct a reduced fuzzy rule base for efficient and accurate water quality prediction. Experiments on real-world water quality datasets demonstrate that the proposed MAFIS and TSK-CANFS achieve competitive performance compared to state-of-the-art methods. Combining data-driven learning with expert-defined reasoning, our approach enhances interpretability, scalability, and accuracy, offering a practical solution for sustainable aquaculture management.

Accidents involving escalators in mass rapid transit (MRT) systems pose a serious risk to public safety, often resulting from clothing or footwear getting caught, or large items toppling during movement. Despite the availability of passive warnings, such as signage and audio announcements, these methods often go unnoticed by commuters and lack the ability to adapt to real-time risks. Existing computer vision solutions are either too computationally intensive for deployment on edge devices or lack sufficient accuracy for practical use. To address these challenges, this study proposes a real-time, lightweight object detection system using a pruned YOLOv7-Tiny model, optimized for deployment on the NVIDIA Jetson Nano edge computing platform. The system is designed to identify safety-critical items, such as general footwear, high heels, long skirts, suitcases, strollers, and shopping trolleys, in real-time. Upon detection, it issues visual and auditory alerts, and in cases involving large items, sends email notifications to station personnel. Model pruning significantly reduces computational overhead while maintaining high accuracy. Experimental results demonstrate that the system achieves a mean average precision (mAP) of 94.69%, outperforming conventional detection models while maintaining real-time performance. These results highlight the system’s potential for enhancing passenger safety and operational efficiency in resource-constrained public transit environments.

Bird excrement deposited on solar panels can lead to hotspots, significantly reducing the efficiency of solar power plants. This article presents a novel solution to this problem leveraging unmanned aerial vehicle (UAV) systems for the automated geolocation and removal of bird excrement across large-scale solar power facilities. First, a UAV executes a predefined flight path to capture sequential aerial images of the plant. These images are subsequently stitched to produce a high-definition orthomosaic of the entire facility. An advanced detection framework based on YOLOv7, enhanced with an attention module, is employed to accurately detect bird excrement by reducing background noise and highlighting key features. An additional prediction head is integrated to improve detection of smaller bird excrements. To compute precise geolocation of the detected excrement, the midpoint pixel coordinates of the excrement along with the azimuth angle and actual ground distance (AGD) relative to a ground control point (GCP) is used. This article further proposes a cleaning technique that employs a traveling salesman problem (TSP) approximation algorithm to efficiently optimize flight path of the cleaning UAV. Experimental results indicate the system achieves an average detection precision (AP) of 93.91% and GPS coordinate accuracy with an average error of 0.149 m, demonstrating the efficacy of the proposed method in both geolocation and removal of bird excrement from solar panels.

Navigation assistance is an active research area, where one aim is to foster independent living for people with vision impairments. Despite the fact that many navigation assistants use advanced technologies and methods, we found that they did not explicitly address two essential requirements in a navigation assistant - portability and convenience. It is equally imperative in designing a navigation assistant for the visually impaired that the device is portable and convenient to use without much training. Some navigation assistants do not provide users with detailed information about the obstacle types that can be detected, which is essential to make informed decisions when navigating in real-time. To address these gaps, we propose DeepNAVI, a smartphone-based navigation assistant that leverages deep learning competence. Besides providing information about the type of obstacles present, our system can also provide information about their position, distance from the user, motion status, and scene information. All this information is offered to users through audio mode without compromising portability and convenience. With a small model size and rapid inference time, our navigation assistant can be deployed on a portable device such as a smartphone and work seamlessly in a real-time environment. We conducted a pilot test with a user to assess the usefulness and practicality of the system. Our testing results indicate that our system has the potential to be a practical and useful navigation assistant for the visually impaired.

Displays viewed from extreme angles become perceptually distorted due to perspective projections. Perspective correction has been discussed as a remedy, yet there are few general and practical implementations available to users. This work explores modern browser technology for realizing perspective correction in practice, and whether partial perspective corrections is feasible. Three prototypes are explored, two where the corrections are configured manually and one where the corrections are determined automatically through camera-based vantage point measurements. Browser transformations are used to perform perspective corrections to content in real-time.

Low contrast between text and background and its effect with low vision is relatively well-understood. Many tools exist for helping web designers check contrast limits. Most of these tools identify contrast problems but give limited advice on how to rectify the problems. Moreover, website accessibility audits reveal that insufficient color contrast still is a recurring issue in practice. A framework was therefore developed that automatically proposes color adjustments to problematic text-background color pairs on web pages. These suggestions adhere to contrast requirements and are aligned with the visual design profile. The framework allows the developers to visually inspect the suggestions and amend color definitions in projects.

Deep learning models have recently gained popularity in the research community due to their high classification success rates. In this paper, we proposed an EfficientNet-Lite based scene recognition model for scene recognition as a part of the smartphone-based navigation support application for the blind and visually impaired. We created a custom dataset with both indoor and outdoor scenes for training and testing of the model. The main objective of this work is to support people with visual impairments navigate by providing information about the scene via a smartphone application. The results from the experiment show encouraging performance from the proposed model. As a proof of concept, a prototype app was developed on the Android platform. However, the model can be implemented and deployed in any modern smartphone with good processing power.

Most contrast checking tools allow designers to verify if a set of colors satisfy the minimum contrast requirements for text and background colors. However, such tools usually do not provide the designer with any clues on how the colors should be adjusted to ensure enough contrast. This demo shows our web-based contrast tool which suggests how existing set of colors could be adjusted to satisfy minimum contrast requirements while maintaining the gist of the original visual design.

Abrasive holograms allow people to experiment with impressive quasi-holography and create hologram artwork through simple means of creating reflective scratches on sheets of plastic. Most of the reported accounts of abrasive holography address the creation of three-dimensional illustrations. This work explores abrasive holograms for creating arbitrary holographic animations through scratch patterns. The opportunities and limitations of scratch patterns are investigated. A model for classifying abrasive holographs is introduced. The classification framework is used to derive two new scratch pattern generation methods allowing the creation of arbitrary animations.

A novel framework that allows designers without 3D-modelling experience to draw three-dimensional panoramic sketches by hand with the help of a support lines is proposed. Sketches are viewed with panoramic viewing software, giving observers interactive three-dimensional 360-degree immersed experiences.

Klout, a famous App, could measure people's social network influence power. Klout score is measured according to the data from past 90 days and an individual who has high Klout score is thought as having high social influence power. Lots of businesses or organizations like to hire high Klout score people to help them to diffuse their brand images. However, Klout score cannot tell us who has high influence power in a specific short time period. For example, it is possible that some of the users might always have high influence power on Monday or on Monday morning. These time-dependent influential users probably have low Klout scores in average but have high influence power in some specific time periods. Businesses should not just know who are the high Klout score users but also they should identify who are the time-dependent influential users because all of them may have some sort of power to influence other users' buying decisions. In this study, a framework based on frequent pattern mining is proposed to find the time-dependent influential users. First of all, the framework will divide a predefined long time period into successive short time segments and then influential transactions that contain Facebook fans' influence power data will be defined in each time segment. From the frequent patterns, the proper time for time-dependent influence users to spread information can be found. A theoretical experiment is given to verify the effectiveness of the proposed framework.

Equirectangular panoramas are popular tools for achieving 360 degree immersed viewing experiences. A panorama captures a scene from one point and panoramic viewers allow the user to control the viewing direction, but the viewer is not allowed to move around. This study proposes a strategy for transforming equirectangular panoramic images with the effect of moving freely in three dimensions. The strategy assumes that the panorama is an enclosed space comprising a flat ground and flat vertical walls. A equirectuangular Hough transform is proposed for detecting the boundaries of the respective planes. The panoramic image is then decomposed into the respective planes, the viewing point is translated and a new panoramic image based on the new viewing position is composed. Preliminary proof of concept test shows that the strategy allows free translation within simple panoramic images.

Many skin detection approaches have been proposed in the image analysis literature. Some are simple and static; the others are dynamic and rely on complex machine learning algorithms and training data. Generally the simple approaches are preferred. We hypothesize that the developers' choice for the simple approaches are due to the reasonable quality of results and ease of implementation, since the results of more sophisticated results are not readily available. This paper explores the skin color of a large number of hand samples using color space visualization. The results suggest that a static method may suffice for many applications, but that a small set of rules is not enough to capture the details of skin. Moreover, the results suggest that successful skin detection does not depend on the color space used as there are no apparent advantages of using a perceptual uniform color space such as CIElab. A skin detection approach based on RGB-color table-lookup is proposed that is able to capture the complex skin color cluster shape. The method is practical and simple to implement with minimal computational cost. The lookup table is released into the public domain.

The fusioning of data from unreliable sensors has received much research attention. The main stream of research assesses the reliability of a sensor by comparing its readings to the ground truth in an online or offline manner. For instance, the Weighted Majority Algorithm is a representative example of a large class of similar legacy algorithms. Recently, some advances have been achieved in identifying unreliable sensors without any knowledge of the ground truth which seems a paradox in itself. In this paper, we present a simple mechanism for solving the problem using Tsetlin-like Learning Automata (LA). Our approach leverages a Random Walk (RW) inspired by Tsetlin LA so that to gradually learn the identity of the reliable and unreliable sensors. In this perspective, we resort to a team of RWs, where a distinct RW is associated with each sensor. By virtue of the limited memory requirement of our devised LA, we achieve adaptive behavior at the cost of negligible loss in the accuracy.

Dyslexic users often do not exhibit spelling and reading skills at a level required to perform effective search. To explore whether autocomplete functions reduce the impact of dyslexia on information searching, 20 participants with dyslexia and 20 controls solved 10 predefined tasks in the search engine Google. Eye‐tracking and screen‐capture documented the searches. There were no significant differences between the dyslexic students and the controls in time usage, number of queries, query lengths, or the use of the autocomplete function. However, participants with dyslexia made more misspellings and looked less at the screen and the autocomplete suggestions lists while entering the queries. The results indicate that although the autocomplete function supported the participants in the search process, a more extensive use of the autocomplete function would have reduced misspellings. Further, the high tolerance for spelling errors considerably reduced the effect of dyslexia, and may be as important as the autocomplete function.

Calendars can be important memory aids for individuals with cognitive disabilities. This study compared the effect of the popular two-dimensional table calendar view and the simpler list view. A controlled experiment was conducted involving 10 individuals with cognitive disabilities and 10 controls. The results show that the list view gave significantly fewer errors and shorter searching times, while editing took longer with the list view.

Finger lengths have fascinated scientific researchers for a long time and people hardly notice different underpinnings of the relation between the shape and size of our body to the scientific meanings. The idea behind the shape of the palm length of the fingers indicates something profound about people sexual proclivities and vulnerability to certain diseases. In order to investigate and intervene in this health concern we measure the digit ratio that is the ratio of the lengths of different digits. The index finger (2D) and ring finger (4D) for both the hands are typically measured from the midpoint of the bottom crease where the finger joins the hand to the tip of the finger. This study uses smart phone camera to take the hand image and then measure relative lengths of the second (2D) and the fourth (4D) fingers. Their ratio is calculated by dividing the length of the index finger by the length of the ring finger of the hand. This finger reading foretells information of an individual's psychology, motivations, health and social behavior such as homosexuality, cancers, musical ability, aggressive personality, passive personality traits, development disorder such as Dyslexia (which can be termed as literacy deficiencies), athletic ability and dexterity innate ability in key cognitive areas.

There is a vast body of research based on 2D:4D finger ratio measurements within the fields of medicine, physiology, and several other disciplines. There are well-known conventions for how such finger measures should be conducted, and all such measurements are to this date manual. Manual measurements are time consuming and prone to human error, different practices and subjective influences. This study proposes a novel algorithm for automatically computing the 2D:4D finger ratios using images of hand pairs. The algorithm is simple to implement and robust to image noise. Initial experimental assessments indicate that the algorithm successfully manages to accurately determine 2D:4D finger ratios. The proposed algorithm could help improve the research fields that make use of digit ratio measurements. Areas of algorithm improvements are also identified.

Sufficient colour contrast between text and background is necessary for the text to be readable. WCAG2.0 defines contrast guidelines that ensure web pages can be read by people with a visual acuity of as low as 20/80. However, the WCAG2.0 specification is hard to understand for web-designers and they often perform colour checks after the design is finished using colour contrast calculators. It is then often too late to make significant changes to the design. This paper proposes a new colour selection scheme that integrates contrast constraints into the main design phase by only allowing designers to select colour pairs that contains sufficient contrast. The mechanism is compatible with the colour harmony systems commonly used by designers. The tool gives designers an easily perceivable overview of the design space of available colours. The paper briefly describes a prototype of a colour picker tool that implements the proposed scheme.

Finger length ratios have received much attention among researchers as the 2D:4D ratio has been linked to several physical and mental characteristics. This study explores the feasibility of using a Smartphone as an instrument for measuring finger length ratios. The approach taken in this study is to use the Smartphone camera to take freehand photos of the hand which is subsequently subjected to image analysis. Measurement procedures include hand near and far from the body, palms up or down, or hands in mid air versus hands resting on a flat surface. Experimental evaluations show that the most accurate measurements are achieved by resting the hand on a surface with the palm facing up. These results are comparable to those achieved with conventional procedures with an error of 1%.

June 2012, 11% of the overall population in Taiwan was over the age of 65. This ratio is higher than the average figure for the United Nations (8%). Critical issues concerning elderly in healthcare include fall detection, loneliness prevention and retard of obliviousness. In this study we design type-2 fuzzy models that utilize smart phone tri-axial accelerometer signals to detect fall incidents and identify abnormal gaits among elderly. Once a fall incident is detected an alarm is sent to notify the medical staff for taking any necessary treatment. When the proposed system is used as a pedometer, all the tri-axial accelerometer signals are used to identify the gaits during walking. Based on the proposed type-2 fuzzy models, the walking gaits can be identified as normal, left-tilted, and right-tilted. Experimental results from type-2 fuzzy models reveal that the accuracy rates in identifying normal walking and fall over are 92.3% and 100%, respectively, exceeding what are obtained using type-1 fuzzy models.

Manually operated medical equipment, including drug infusion pumps, are often subject to input errors. Human operators copy data from a prescription into the relevant form field on the equipment panels. This process is error prone and time consuming. A computer supported memory aid is proposed where the user remembers phrases instead of value sequences. The proposed strategy speeds up the task of setting up medical equipment while reducing the chances of human errors.

Conventional water quality monitoring and bird species observation were recorded using pen and paper. Moreover, experiments can usually only be conducted in limited areas due to the high cost of the water monitoring equipment. Consequently, not enough data could be collected to find the a biotic interactions between water quality factors and bird habitat. To resolve this problem, the proposed smart phone based system can transmit the water quality measurements and observed quantity of each bird species to the back-end server via a 3G network. This server functions as a ubiquitous information center where all measured and observed data are stored. Fuzzy C-means is applied to cluster water quality factors and bird species. The clustering results of bird quantity are then used to calculate the Simpson's diversity indices. Based on the results from the water quality factors and biodiversity index pattern trees (PTs) are constructed to find the a biotic interactions between them. The PTs can be further transformed into IF-THEN rules to provide references for researchers to conduct environmental protection. Experimental results are given to verify the applicability of the proposed system in finding the a biotic interactions.

This paper describes a model that discovers association rules from a medical database to help doctors treat and diagnose a group of patients who show similar prehistoric medical symptoms. The proposed data mining procedure consists of two modules. The first is a clustering module that is based on a neural network, Adaptive Resonance Theory 2 (ART2), which performs affinity grouping tasks on a large amount of medical records. The other module employs fuzzy set theory to extract fuzzy association rules for each homogeneous cluster of data records. In addition, an example is given to illustrate this model. Simulation results show that the proposed algorithm can be used to obtain the desired results with a reduced processing time.

With the constant stress from work load and daily life people may show symptoms of melancholia. However, most people are reluctant to describe it or may not know that they already have it. In this paper a novel system is proposed to discover clues from patient's interaction with psychologist or from self-recorded voice or text messages. A user friendly interface is provided for patients to input text messages or record a voice file by mobile phones or other input devices. A speech-to-text conversion software is used to convert voice mails to simple text files in advance. Based on the text files, a data mining model is used to discover frequent keywords mentioned in the text or speech files. The association rules can be used to help psychologists diagnose patients' degree of melancholia. Experimental results show that the proposed system can effectively discover melancholia keywords.

In order to navigate autonomously, a mobile robot needs to build an environment map where the robot is navigating. Currently, the sensors are mounted on the robot to detect if the obstacles exist and then the map immediate surrounding of the robot is built to help for navigation path planning. The map created by this method is a local map that may cause global navigation problem which a global coverage map is needed to solve such a problem. In this study, a sensor network is deployed for building global environment map. All the sensor locations are assumed known. The navigation space is divided into grids and a grid is to be detected if obstacles exist by one or a number of sensors. Fuzzy set concept is used to introduce a tool useful for sensor perception. Those sensors work as a team to explore all the space and then the global fuzzy map is constructed. The experiments show that the fuzzy map is more practical and helps the path planning problem to be solved more efficiently.

Information retrieval systems should provide users quick access to desired information. There are no established ways for inexperienced users to explicitly express queries for retrieving images from ecological databases. This study proposes an entropy-based feature selection strategy for finding images of interest from databases. Six visual features are used to represent birds, and hence used to formulate search queries. The proposed method is tested on a real world bird database and the experimental results demonstrate the effectiveness of the presented work.

Several research institutions and governmental departments provide ocean images for research purposes. For example, Argo, a worldwide ocean research organization, produces ocean salinity and temperature images and researchers can download those images from the Internet. One may build an image system to store ocean images and retrieve them later for further research, for example, to predict future salinity or temperature variation. Image retrieval technology is therefore important. This paper describes an ocean image retrieval system based on content-based image retrieval. Currently, content-based image retrieval technology does not exploit high-level semantics, and it is hard to obtain predictive information from retrieved images. Our improvement involves a spatial reference method that is used to help get the spatial relationships between objects for a certain image. This allows the spatial semantics between the query image and images in database to be considered. Spatial association rules are also mined and are subsequently used as a basis for retrieving additional images. As the spatial semantics in both the query image and spatial association rules, the retrieved images are more accurate. The experimental results verify that the system effectively predicts the occurrence of salinity or temperature variations.

This study elaborates the influence patterns of different backup strategies on AODV-based routing protocols. Although some backup routing strategies yield good data delivery rates, they suffer from low efficiency. To make the process of data salvation more efficiently in case of link failure, we explore the possibility of combining the AODV-BR strategy and on-demand node-disjoint multi-path routing protocols. This article proposes an improved approach named NDAMR (node-disjoint alternative multiple path), a routing protocol that maintains the only two shortest backup paths in the source and destination nodes. The NDAMR can alleviate the redundancy-frames overhead during the process of data salvation by the neighboring intermediate nodes. Our simulation results have demonstrated that NDAMR delivers good data delivery performance while restricting the impacts of transmission collision and contention.

A novel and more effective region-based image retrieval strategy is presented based on semantic ontology. An unsupervised segmentation algorithm splits images into regions that are subsequently used as basis by the ontology-based strategy. The approach comprises three stages, namely automatic region generation, categorization and ontology construction. When receiving a query for a specific object, the search engine will, in addition to conventionally matched images, also find candidates through the semantic ontology using low level features. The proposed approach can thus find a richer set of related candidate images than traditional image retrieval approaches. This strategy is particularly useful for vague queries encountered by inexperienced users that are not trained in searching for images by the means of low-level features. The experimental results demonstrate the effectiveness of the proposed approach.

Ocean salinity and temperature variations have been observed for decades to clarify their effect to global climate changes. Data mining techniques are effective in extracting implicit and useful information from large databases. Discovering salinity and temperature variation patterns from Argo ocean data will in turn help reveal the spatio-temporal relationship between salinity and temperature variations. However, some of the discovered patterns are trivial because they are already known to the oceanographer. In this study, the water mass (a water body with the same salinity and temperature), the mined salinity and temperature patterns and an entropy importance measure are combined to discriminate important patterns from trivial patterns. This study measures both the patterns with variations in both antecedent and consequent parts that belong to separate clusters, and that belong to the same cluster. A pattern is classified as important if its importance measure exceeds a predefined threshold. The important patterns are transformed into fuzzy rules in a fuzzy inference model to obtain more accurate salinity and temperature variation predictions. Simulation results verify the effectiveness of the proposed model.

Mobile devices with limited interaction controls often employ cyclic scrolling for retrieval tasks. In this paper the scrolling behaviour of users entering text using a tree-key input technique based on two-phase cyclic character scrolling is studied. The results show that users have a tendency to scroll more from left-to-right than from right-to-left. However, users do also use the right-to-left functionality to both speed up their text entry task by choosing the shortest path and to make navigational corrections, suggesting that it is appropriate to provide bidirectional scrolling functionality in user interfaces on constrained mobile devices. In situations where a device architect is constrained to providing only unidirectional scrolling, the results suggest that a right-directional design is preferred over a left-directional design.

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

An improved shape based image retrieval strategy based on gray relational analysis and association rules is proposed. The choice of a suitable object representation and retrieval scheme is essential for efficient retrieval. In addition, a two-stage relevance feedback mechanism based on the GM(1, N) method and association rules is incorporated to improve the retrieval accuracy. The GM(1, N) method is used to build the re-query example for subsequent retrievals. The retrieval log files stored on the server are used for offline mining of association rules. The association rules mined from users' retrieval history can further reveal users' image searching behavior. The effectiveness of the proposed model is demonstrated on the FISH dataset.

An improved scheme for integrity protection of binary images representing text documents based on the topologies of these images is proposed. The image skeleton and the inverse skeleton are found through thinning and the skeleton signature is combined with watermark information. The result is encrypted asymmetrically and hidden in embeddable locations of the image. A series of attack experiments are conducted to demonstrate that the approach is capable of detecting tampering. Even single malicious pixel modifications are detected. The approach has a lower computation cost than previous methods.

Providing users with easy access to interesting information is one important goal of information retrieval. However, traditional information retrieval techniques are not suitable for ecological database queries since users are unable to clearly describe the target features. To overcome the problem, an information retrieval system running on handheld devices allowing users to issue queries according to their perceptions or impressions of the target bird is proposed. Six visual features allow users to express their visual perception of a bird and the query will be formulated based on the chosen features. Texture descriptions are also used when searching for the target in the bird database. A bird-information ontology based on the biodiversity is exploited to extend the search space. The methodology is illustrated in this paper. The experimental results demonstrate the effectiveness of the strategy and its applicability in other domains.

The ARGO project archives huge quantities of upper ocean salinity/temperature time series measurements that are related to climate issues such as global warming. Fuzzy inter-transaction association rules are derived from ARGO data using a reduced prefix-projected itemset algorithm that has a small space and time complexity. After mining the frequent 1-itemsets the proposed algorithm exploits a reduced prefix projection strategy to extract the frequent inter-itemsets. Based on the extracted fuzzy inter-transaction association rules a fuzzy inference model is proposed for identifying salinity/temperature anomalies. Experimental results verify that the proposed model is effective in predicting the occurrence of abnormal salinity/temperature variations.

A strategy for entering text using two-handed game controllers with two analogue joysticks is proposed where the QWERTY keyboard layout is used as a spatial mnemonic. The technique is inspired by two-finger QWERTY typing where the fingers are represented by the two joysticks. Characters are organized into a QWERTY layout with the joystick resting position conceptually located where the index fingers are in touch position. The user moves the relevant joystick in the direction of the desired character. The technique is easy to learn for users already familiar with QWERTY two-finger typing or touch-typing. Furthermore, text can potentially be entered with limited visual feedback, and the bimanual nature of the approach implies a potential for high input speed as the operation of each hand can be overlapped. The technique can be realized with commonly available off-the-shelf hardware and it is especially applicable to online gamers communicating textually. Experimental evaluations show that text can be entered at a mean rate of 6.75 words per minute with less than one hour of practice.

A strategy for locating television (TV) commercials in TV programs is proposed. Based on the observation that most TV commercials do not have subtitles, the first stage exploits six subtitle constraints and an adaptive neurofuzzy inference system model to determine whether a frame contains a subtitle or not. The second stage involves locating the mark-in/mark-out points using a genetic algorithm. An interactive user interface allows users to efficiently identify and fine-tune the exact boundaries separating the commercials from the program content. Furthermore, erroneous boundaries are manually corrected. Experimental results show that the precision rate and recall rates exceed 90%.

This study addresses to what degree spatial mnemonics can be used to assist users to memorise or infer a set of text input chords. Users mentally visualise the appearance of each character as a 3x3 pixel grid. This grid is input as a sequence of three chords using one, two or three fingers to construct each chord. Experiments show that users are able to use the strategy after a few minutes of instruction, and that some subjects enter text without help after three hours of practice. Further, the experiments show that text can be input at a mean rate of 5.9 words per minute (9.9 words per minute for the fastest subject) after 3 hours of practice. On the downside, the approach suffers from a relatively high error rate of about 10% as subjects often resort to trial and error when recalling character patterns.

In this paper a load-balancing strategy for distributed Web-applications is proposed. The approach assumes uneven traffic with extreme peaks that cannot be served with a given infrastructure. The approach presented herein is based on distributing the load temporally in time. In online Web-based applications the perceived response time is more important than the actual response time and various strategies for manipulating the perception of response time are discussed. The technique is relatively easy to incorporate with existing Web-applications using modern Web-technology such as AJAX. Simulation results demonstrate the effectiveness of the approach

A novel data mining techniques for finding interesting spatial-temporal patterns in ocean data is presented. The data consist of time series measurements of upper ocean science variables (e.g., salinity and temperature). Extracting interesting patterns from ocean variables is of important for understanding the relationship between ocean salinity/ temperature structures and climate variability. Association rules mining is applied in the search for these spatial-temporal patterns. Most traditional data mining models focus on mining association rules among attributes within one transaction. For example, if salinity rose, then temperature rises. However, people may be interested in discovering additional relations among transactions and take context such as time or location into consideration. An example of such a rule might be "if the salinity in area A rose from 5% to 7%, then the temperature in area B will rise from 0% to 2.5% in the next month." In this case, the associated salinity/temperature variations among different locations and days are revealed. To overcome these issues, a multi-dimensional inter-transaction association rules mining framework was developed. Unlike other mining algorithms that suffer from a large number of inter-transaction items, the proposed Apriori-like method treats each event from the ocean science data as a transaction and applies the MBC (minimum bounding cube) to form inter-transactions within the maxspan. Since there is no need to slide the maxspan window, the proposed method is easy to implement and it is computationally efficient.

A general input paradigm for blind and visually impaired users is proposed. Input is classified into three categories, namely input of text using Braille chords, input of digits and navigation and editing of text. A numpad input device is used for all the three modes of input. The required mode is automatically detected by analyzing the spatial and temporal properties of the device usage. Braille input chords are resolved dynamically and are therefore not bound to specific keys. Low-cost and widely available standalone numeric keypads are used as input devices.

Task scheduling is an important aspect of parallel programming. Most of the heuristics for this NP-hard problem are based on a very simple system model of the target parallel system. Experiments revealed the inappropriateness of this classic model to obtain accurate and efficient schedules for real-systems. In order to overcome this shortcoming, a new scheduling model was proposed that considers the contention for communication resources. Even though the accuracy and efficiency improved with the consideration of contention, the new contention model is still not good enough. The crucial aspect is the involvement of the processor in communication. This paper investigates the involvement of the processor in communication and its impact on task scheduling. A new system model is proposed based on the contention model that is aware of the processor involvement. The challenges for the scheduling techniques are analyzed and two scheduling algorithms are proposed. Experiments on real parallel systems show the significantly improved accuracy and efficiency of the new model and algorithms.

In this paper a methodology for representing text-input strategies for miniature and mobile devices is proposed. The methodology is based on representing text-input strategies as graphs. Graph representations allow different static mobile text-input strategies to be represented in a uniform manner. Further, different strategies are easily compared as the graph representation allows various characteristics to be extracted. The methodology incorporates KSPC (KeyStrokes Per Character), checking for error recoverability and correctness. We also propose an error recovery measure - the mean error recovery distance (MERD). The methodology can be expanded to include additional evaluation measures and it is feasible to implement design-tool support. Finally, the methodology is demonstrated on several text entry designs from the literature.

A novel strategy for employing schedules obtained using standard static scheduling algorithms in a battery powered multiprocessor environment is investigated. The strategy is able to dynamically respond to deteriorating batteries and operate with fewer processors according to the battery levels of the system. The nature of the proposed approach allows poorly performing batteries to recover through self charge. The strategy therefore maximizes the battery life and the operation time of the device.

There are two problems as we use conventional Boolean association rules mining algorithm to discover temporal association rules over the stock market to predict stock price variation. The first problem is that the discovered rules only consider associations between the presence and absence of variations of stock prices and the second problem is that the associations among stock price variations are within the same transaction day. For example, if stock A raises, then stock B raises the same day. This Boolean temporal association rule reveals no information of quantitative variations of stock prices and can only predict price trend in the same day. In this paper, we deal with the problem of mining temporal association rules in stock databases containing quantitative price variations to discover the associations among different transactions day. Our algorithm first employs data discretization concept to partition quantitative attributes into intervals and an adaptive a priori method that cooperates with time sliding window concept and prefix tree is developed to find quantitative temporal association rules. An example of such a rule might be "if stock A price variation raised 5% to 7% and stock B raised 2.5% to 5% the same day, then stock C will raise 0% to 2.5% in the next two days." In this case, the stock price variation is taking into consideration and the associated stock price variations belong to different transaction days. As compared with conventional methods, more useful results can be found from the proposed quantitative temporal association rules.