Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies.

The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties. Though there are many MT evaluation metrics that generally correlate with human judgments, they are not directly useful in identifying specific shortcomings of MT systems. In this demo, we present a benchmarking interface that enables improved evaluation of specific MT systems in isolation or multiple MT systems collectively by quantitatively evaluating their performance on many tasks across multiple domains and evaluation metrics. Further, it facilitates effective debugging and error analysis of MT output via the use of dynamic filters that help users hone in on problem sentences with specific properties, such as genre, topic, sentence length, etc. The interface can be extended to include additional filters such as lexical, morphological, and syntactic features. Aside from helping debug MT output, it can also help in identifying problems in reference translations and evaluation metrics.

Multiple researchers recently proposed the use of the digital compass embedded in mobile devices for touchless interaction in the 3D space around them. These methods overcome several limits imposed by other interaction techniques and were evaluated for a variety of uses. However, they do not support collaborative settings and are prone to dynamic noise caused by external conditions, as with most other sensor-based interaction techniques. In this paper, we propose the use of frequency-modulated electromagnets as an input medium for magnetic interaction to overcome its various constraints and further enable multi-user and two-handed input. Furthermore, we demonstrated the hardware design specifications of a novel input device, referred to as electromagnetic stylus, which is prototyped to conduct a user-study on the proposed method. Experimental results indicate that gestures performed simultaneously by four electromagnetic styli can accurately be recognized using a single magnetic field sensor, and dynamic noises can be substantially reduced.

Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs, but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates.

In ubiquitous computing environments, the information processing is integrated into everyday objects that are ideally small, inexpensive and wirelessly networked devices. Contemporary human-computer interaction models are not adequate to control miniaturized devices, which are distributed throughout everyday life and activities. This post-desktop model requires natural gesture-based interaction with distributed devices in an egocentric manner as opposed to the current device-centric interaction. In this work, we have utilized the recently proposed touch-less gesture-based interaction method based on magnetic field to provide a hardware basis for a wearable input controller. Furthermore, we have discussed how the proposed device can allow natural interaction with other devices within a ubiquitous computing environment such as personal area network.

In this work, we have propose a novel design for a basic mobile phone, which is focused on the essence of mobile communication and connectivity, based on a silent speech interface and auditory feedback. This assistive interface takes the advantages of voice control systems while discarding its disadvantages such as the background noise, privacy and social acceptance. The proposed device utilizes low-cost and commercially available hardware components. Thus, it would be affordable and accessible by majority of users including disabled, elderly and illiterate people.

Playing musical instruments such as chordophones, percussions and keyboard types accompany with harmonic interaction of player's hand with the instruments. In this work, we present a novel approach that enables the user to imitate the music playing gestures around mobile devices. In our approach, touch-less gestures, which change magnetic field around the device, are employed for interaction. The activity of playing an instrument can be transparently pursued by moving a tiny magnet in hand around new generation of mobile phones equipped with embedded digital compass (magnetic sensor). The phonation intentions of the user can be simulated on the mobile device by capturing the gestural pattern using magnetic sensor. The proposed method allows digital imitation of a broad number of instruments while still being able to sense musical hits and relative plectrum gestures. It provides a framework for extending interaction space with music applications beyond physical boundaries of small mobile devices, and to 3D space around the device. This can allow for a more natural, comfortable and flexible interaction. We present several mobile music applications developed based on the proposed method for Apple iPhone 3GS.

In this work, we present a method for controlling call alert functionality in mobile phones. It has happened for almost everybody experiencing a situation that call alert functionality is not proper for actual ambient context, leading to missing a phone call or disturbing others by a loud ring. In this work, we use audio and physical movement analysis to distinguish between different situations in which a mobile phone may ring, and adjust the call alert functionality accordingly. Considering the fact that mobile phones are usually carried in a pocket or bag, capturing ambient audio is not usually practically perfect. The novelty in our work is using information about physical movements of user of mobile device in addition to analysis of ambient audio. Analysis of user movements is based on information captured by acceleration sensors integrated in mobile phone. The call alert functionality is then adjusted based on a combination of ambient audio level and physical activities of user.

In this work, we present a new technique for efficient use of 3D space around a mobile device for interaction with the device. Around Device Interaction (ADI) enables extending interaction space of small mobile and tangible devices beyond their physical boundary. Our proposed method is based on using compass (magnetic field) sensor integrated in mobile devices (e.g. iPhone 3GS, G1 Android). In this method, a properly shaped permanent magnet (e.g. in the shape of a rod, pen or a ring) is used for interaction. The user makes coarse gestures in the 3D space around the device using the magnet. Movement of the magnet affects the magnetic field sensed by the compass sensor integrated in the device. The temporal pattern of the gesture is then used as a basis for sending different interaction commands to the mobile device. Zooming, turning pages, accepting/rejecting calls, clicking items, controlling a music player, and game interaction are some example use cases. The proposed method does not impose changes in hardware specifications of the mobile device, and unlike optical methods is not limited by occlusion problems.

We present a new technique based on using embedded compass (magnetic) sensor for efficient use of 3D space around a mobile device for interaction with the device. Around Device Interaction (ADI) enables extending interaction space of small mobile and tangible devices beyond their physical boundary. Our proposed method is based on using compass (magnetic field) sensor integrated in new mobile devices (e.g. iPhone 3GS, G1/2 Android). In this method, a properly shaped permanent magnet (e.g. a rod, pen or a ring) is used for interaction. The user makes coarse gestures in 3D space around the device using the magnet. Movement of the magnet affects magnetic field sensed by the compass sensor integrated in the device. The temporal pattern of the gesture is then used as a basis for sending different interaction commands to the mobile device. The proposed method does not impose changes in hardware and physical specifications of the mobile device, and unlike optical methods is not limited by occlusion problems. Therefore, it allows for efficient use of 3D space around device, including back of device. Zooming, turning pages, accepting/rejecting calls, clicking items, controlling a music player, and mobile game interaction are some example use cases. Initial evaluation of our algorithm using a prototype application developed for iPhone shows convincing gesture classification results.

In this work, we present a new approach for text (mainly digit) entry based on digit shaped gestures created in 3D space around a mobile device. Some new mobile devices such as Apple iPhone 3GS and Google Android are equipped with magnetic (compass) sensor. The main idea is to influence the magnetic sensor using a magnet taken in hand. The user draws (writes) digits in the 3D space around the device using the magnet taken in hand. Movement of the magnet changes temporal pattern of magnetic field around the device which is sensed and registered by the magnetic (compass) sensor. The registered pattern is then compared against already recorded templates for different digits. Such a text (digit) entry approach can be especially useful for small mobile devices in which it is hard to operate small buttons or touch screen. Using our technique, the text entry space extends beyond physical boundaries of the device. A demonstrator for this approach is implemented on Apple iPhone 3GS platform. It demonstrates registering a few templates for different digits, and recognizing digits written in the space around the device

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.