The Lempel-Ziv-Welch (LZW) algorithm achieves outstanding performance and is widely used in text encoding. However, when the bit-width for each code is limited and the amount of data to be encoded is huge, the dictionary will be prematurely full. As a result, the new string cannot be added to the dictionary in the later encoding process and thus the performance is compromised. In this paper, an improved LZW algorithm is proposed to address this problem. In the proposed algorithm, the string is added to the dictionary only if the frequency of the string reaches the threshold. In this way, the dictionary is left only for the common strings. The experiments on the test patterns consisting of 300,000 characters show that the proposed algorithm further enhances the compression rates and is efficient for data compression. For example, the compression rate of the proposed algorithm is 6.0% larger than that of the LZW algorithm when each codeword contains 12 bits.

In this paper, we propose an AI-FML agent for robotic game of Go and AIoT real-world co-learning applications. The fuzzy machine learning mechanisms are adopted in the proposed model, including fuzzy markup language (FML)-based genetic learning (GFML), eXtreme Gradient Boost (XGBoost), and a seven-layered deep fuzzy neural network (DFNN) with backpropagation learning, to predict the win rate of the game of Go as Black or White. This paper uses Google AlphaGo Master sixty games as the dataset to evaluate the performance of the fuzzy machine learning, and the desired output dataset were predicted by Facebook AI Research (FAIR) ELF Open Go AI bot. In addition, we use IEEE 1855 standard for FML to describe the knowledge base and rule base of the Open Go Darkforest (OGD) prediction platform in order to infer the win rate of the game. Next, the proposed AI-FML agent publishes the inferred result to communicate with the robot Kebbi Air based on MQTT protocol to achieve the goal of human and smart machine co-learning. From Sept. 2019 to Jan. 2020, we introduced the AI-FML agent into the teaching and learning fields in Taiwan. The experimental results show the robots and students can co-learn AI tools and FML applications effectively. In addition, XGBoost outperforms the other machine learning methods but DFNN has the most obvious progress after learning. In the future, we hope to deploy the AI-FML agent to more available robot and human co-learning platforms through the established AI-FML International Academy in the world.

In this paper, we propose an AI-FML agent with a patch learning (PL) mechanism for the robotic game of Go applications. The proposed AI-FML agent contains three kinds of intelligence, including a perception intelligence, a cognition intelligence, and computational intelligence, for the robotic application. Additionally, we embed the PL mechanism into the AI-FML agent. The method for running PL involves three steps. It first trains an initial global model, then trains a patch model for each identified patch, and finally updates the global model using the training data that do not fall into any patch. This paper adopts the Google DeepMind Master 60 games to be the training data and testing data set. The experimental results show the AI-FML agent with the patch learning mechanism can improve the performance of regression for the robotic game of Go applications.

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

This paper addresses the spectral efficiency in dynamic femtocell networks, where users leave and/or enter their serving femtocells frequently. The overall system bandwidth is divided into several subchannels. The spectral efficiency would be decreased if no appropriate resource allocation designed to manage the topology variation due to user mobility. We thus propose a three-step dynamic frequency allocation algorithm to manage the co-channel interference more effectively. First, a weighted clustering of femtocells is centrally utilized to avoid the co-channel interference among clusters. Secondly, each FBS assigns subchannels to its served users. Thirdly, if there is a new user and no available subchannel for assignment, spectrum stealing is processed by reusing a subchannel, which belongs to another cluster while satisfying the interference constraints. Numerical results demonstrate that the proposed algorithm can not only enhance the system throughput but also decrease the outage probability of user accesses.

BACKGROUND: Observation of gene expression changes implying gene regulations using a repetitive experiment in time course has become more and more important. However, there is no effective method which can handle such kind of data. For instance, in a clinical/biological progression like inflammatory response or cancer formation, a great number of differentially expressed genes at different time points could be identified through a large-scale microarray approach. For each repetitive experiment with different samples, converting the microarray datasets into transactional databases with significant singleton genes at each time point would allow sequential patterns implying gene regulations to be identified. Although traditional sequential pattern mining methods have been successfully proposed and widely used in different interesting topics, like mining customer purchasing sequences from a transactional database, to our knowledge, the methods are not suitable for such biological dataset because every transaction in the converted database may contain too many items/genes. RESULTS: In this paper, we propose a new algorithm called CTGR-Span (Cross-Timepoint Gene Regulation Sequential pattern) to efficiently mine CTGR-SPs (Cross-Timepoint Gene Regulation Sequential Patterns) even on larger datasets where traditional algorithms are infeasible. The CTGR-Span includes several biologically designed parameters based on the characteristics of gene regulation. We perform an optimal parameter tuning process using a GO enrichment analysis to yield CTGR-SPs more meaningful biologically. The proposed method was evaluated with two publicly available human time course microarray datasets and it was shown that it outperformed the traditional methods in terms of execution efficiency. After evaluating with previous literature, the resulting patterns also strongly correlated with the experimental backgrounds of the datasets used in this study. CONCLUSIONS: We propose an efficient CTGR-Span to mine several biologically meaningful CTGR-SPs. We postulate that the biologist can benefit from our new algorithm since the patterns implying gene regulations could provide further insights into the mechanisms of novel gene regulations during a biological or clinical progression. The Java source code, program tutorial and other related materials used in this program are available at http://websystem.csie.ncku.edu.tw/CTGR-Span.rar.

Sequential pattern mining techniques have been widely used in different topics of interest, such as mining customer purchasing sequences from a transactional database. Notably, observation of gene expressions to discover gene regulations during biological or clinical progression via microarray approaches has become the dominant trend. By converting microarray datasets into the format of transactional databases, sequential patterns implying gene regulations could be identified. However, there exists no effective method in current studies that can handle such kind of dataset as every transaction may contain too many items/genes and the resultant patterns are very susceptible to item order. We propose a new method called CTGR-Span (Cross-Timepoint Gene Regulation Sequential Patterns) to efficiently mine CTGR-SPs (cross-timepoint gene regulation sequential patterns). The proposed method was experimented with two publicly available human time course microarray datasets and it outperformed traditional methods over 2,000 times in terms of the execution efficiency. Furthermore, via a Gene Ontology enrichment analysis, the resultant patterns are more meaningful biologically compared to previous literature reports. Hence, it could provide biologists more insights into the mechanisms of novel gene regulations in certain disease progressions.

In this short paper we describe an approach and primary results for obtaining bounds on mutual information between release times and capture times for a set of M identical quanta traveling from a source to a target. The first-passage times are assumed independent and identically distributed and the launch times are constrained. One major application of area is intercellular molecular signaling in biological systems whereby a cell (or group of cells) must deliver some message (such as developmental instructions) over distance with reasonable certainty to another cell (or group of cells). Another application area is communication between components of nano/molecular computers. However, the model can also be applied to any communication systems wherein indistinguishable signals have random transit latencies.

We derive the maximum mutual information for an additive exponential noise (AEN) channel with a peak input constraint. We find that the optimizing input density is mixed (with singularities) similar to previous results for AEN channels with a mean input constraint. Likewise, the maximum mutual information takes a similar form, though obviously the maximum for the peak constraint is smaller than for the corresponding mean-constrained channel. This model is inspired by multiple biological phenomena and processes which can be abstracted as follows: inscribed matter is sent by an emitter, moves through a medium, and arrives eventually at its destination receptor. The inscribed matter can convey information in a variety of ways such as the number of signaling quanta - molecules, macromolecular complexes, organelles, cells and tissues - that are emitted as well as the detailed pattern of their release. However, rather than focus on a general class of emitter-receptor systems or a particular exemplar of biomedical importance, our ultimate goal is to provide bounds on the potential efficacy of timed-release signaling for any system which emits identical signaling quanta. That is, we seek to apply one of the most potent aspects of information theory to biological signaling - mechanism blindness - in the hopes of gaining insights applicable to diverse systems that span a wide range of spatiotemporal scales.

Flash memory is widely adopted in various consumer products for information storage, especially for embedded systems. With strong demands on product designs for overhead control and performance requirements, vendors must have an effective design for the mapping of logical block addresses (LBA's) and physical addresses of data over flash memory. This paper targets such an essential issue by proposing a configurable mapping method that could trade the main-memory overhead with the system performance under the best needs of vendors. A series of experiments is conducted to provide insights on different configurations and the proposed method, compared to existing implementations.