Miracle-dz/UniFAR-FTUs huggingface dataset:
MagiTact: interaction with mobile devices based on compass (magnetic) sensor

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.
Enhancing atlas based segmentation with multiclass linear classifiers.

PURPOSE To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. METHODS In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. RESULTS Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. CONCLUSIONS The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.
The generalized railroad crossing: a case study in formal verification of real-time systems

A new solution to the generalized railroad crossing problem, based on timed automata, invariants and simulation mappings, is presented and evaluated. The solution shows formally the correspondence between four system descriptions: an axiomatic specification, an operational specification, a discrete system implementation, and a system implementation that works with a continuous gate model. <<ETX>>
BISINDO Hand-Sign Detection Using Transfer Learning

This paper presents the development of a BISINDO hand sign language recognition system using transfer learning methods. The system aims to address the communication challenges faced by the deaf community in interacting with the majority of people and alleviate the feelings of isolation experienced by the deaf and mute society. Two models, SSD MobileNet v2 FPNLite 320x320 and SSD MobileNet v2 FPNLite 640x640, were trained for 20,000 steps using a custom dataset of BISINDO alphabets (A-Z). The results demonstrate the successful detection and recognition of BISINDO alphabet characters by both models. The second model, trained with SSD MobileNet v2 FPNLite 640x640 pre-trained model, exhibited slightly higher accuracy with improved mAP and AR scores. These findings highlight the potential of computer vision technology to facilitate real-time detection and recognition of BISINDO hand signs, enabling effective communication between deaf and hearing individuals. Future research can focus on refining the models, expanding the dataset, and incorporating more comprehensive sign gestures to enhance the system’s accuracy and usability. This research contributes to advancing assistive technologies for the deaf and hard-of-hearing community, fostering inclusivity and accessibility in society.
Learning with Positive and Unlabeled Examples Using Topic-Sensitive PLSA

It is often difficult and time-consuming to provide a large amount of positive and negative examples for training a classification system in many applications such as information retrieval. Instead, users often find it easier to indicate just a few positive examples of what he or she likes, and thus, these are the only labeled examples available for the learning system. A large amount of unlabeled data are easier to obtain. How to make use of the positive and unlabeled data for learning is a critical problem in machine learning and information retrieval. Several approaches for solving this problem have been proposed in the past, but most of these methods do not work well when only a small amount of labeled positive data are available. In this paper, we propose a novel algorithm called Topic-Sensitive pLSA to solve this problem. This algorithm extends the original probabilistic latent semantic analysis (pLSA), which is a purely unsupervised framework, by injecting a small amount of supervision information from the user. The supervision from users is in the form of indicating which documents fit the users' interests. The supervision is encoded into a set of constraints. By introducing the penalty terms for these constraints, we propose an objective function that trades off the likelihood of the observed data and the enforcement of the constraints. We develop an iterative algorithm that can obtain the local optimum of the objective function. Experimental evaluation on three data corpora shows that the proposed method can improve the performance especially only with a small amount of labeled positive data.
SIGMA: A Dataset for Text-to-Code Semantic Parsing with Statistical Analysis

In the semantic parsing domain, significant progress has been achieved in Text-to-SQL and question-answering tasks, both focused on extracting information from data sources in their native format. However, the inherent constraints of their formal meaning representations, such as SQL programming language, hinder their ability to analyze data from various perspectives, such as conducting statistical analyses. To address this limitation and inspire research in this field, we design SIGMA, a new dataset for Text-to-Code semantic parsing with statistical analysis. SIGMA consists of 6000 questions with corresponding Python code labels. The Python code labels in our dataset cover 4 types of query patterns, which return data in their original format, and 40 types of statistical analysis patterns, which perform statistical operations on the data. We evaluated the SIGMA dataset using three different baseline models: LGESQL, 5mBoP, and SLSQL. The experimental results show that the LGESQL model with ELECTRA outperforms all other models, achieving 83.37% structure accuracy. In terms of execution accuracy, the 5mBoP model, when combined with GraPPa and T5, reaches 76.38%.
Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding

Anomaly detection plays a pivotal role in diverse real-world applications such as cybersecurity, fault detection, network monitoring, predictive maintenance, and highly automated driving. However, obtaining labeled anomalous data can be a formidable challenge, especially when anomalies exhibit temporal evolution. This paper introduces LATAM (Long short-term memory Autoen-coder with Temporal Attention Mechanism) for few-shot anomaly detection, with the aim of enhancing detection performance in scenarios with limited labeled anomaly data. LATAM effectively captures temporal dependencies and emphasizes significant patterns in multivariate time series data. In our investigation, we comprehensively evaluate LATAM against other anomaly detection models, particularly assessing its capability in few-shot learning scenarios where we have minimal examples from the normal class and none from the anomalous class in the training data. Our experimental results, derived from real-world photovoltaic inverter data, highlight LATAM's superiority, showcasing a substantial 27% mean F1 score improvement, even when trained on a mere two-week dataset. Furthermore, LATAM demonstrates remarkable results on the open-source SWaT dataset, achieving a 12% boost in accuracy with only two days of training data. Moreover, we introduce a simple yet effective dynamic thresholding mechanism, further enhancing the anomaly detection capabilities of LATAM. This underscores LATAM's efficacy in addressing the challenges posed by limited labeled anomalies in practical scenarios and it proves valuable for downstream tasks involving temporal representation and time series prediction, extending its utility beyond anomaly detection applications.
Additively Manufactured Dielectric Meta-Lens Antenna for Millimeter-Wave 5G Base-Stations

This paper presents the design and development of a compact 3D-printed dielectric meta-lens (DML) antenna intended for ultra-wideband operation in the millimeter-wave frequency range. Initially, cylindrical shaped dielectric unit cells having periodicity of $0.28 \lambda_{0}$ (where, $\lambda_{0}$ is the free-space wavelength at the design frequency i.e. 28 GHz) is designed. Next, the DML structure is crafted by arranging the unit cells in an array of $20 \times 20$ according to the phase distribution required on the lens aperture resulting in an aperture size of $5.6 \lambda_{0} \times 5.6 \lambda_{0}$. The complete DML antenna setup includes a dielectric horn antenna (DHA) positioned at a separation distance of 52 mm from the proposed DML, oriented along the -z-direction held in place with the help of a 3D-printed support structure. Once the DML antenna is fabricated it is assessed for its performance in an anechoic chamber. The experimental results indicate that the antenna attains a maximum measured gain of 23.9 dBi at 28 GHz, signifying a substantial gain enhancement of 9 dB. The measured 3-dB gain bandwidth of 34.4 % is achieved for the proposed DML antenna. Furthermore, the antenna demonstrates an impressive maximum aperture efficiency of 92 %.
SliceFormer: Deep Dense Depth Estimation from a Single Indoor Omnidirectional Image Using a Slice-Based Transformer

In this research, we tackle the task of estimating depth from a single indoor omnidirectional image. Acknowledging gravity's critical influence in artificially constructed indoor environments, we process the input from equirectangular projection by dividing it into vertical slices. These slices are then utilized as patch embeddings for the transformer encoder, a strategy designed to recreate an equirectangular depth map. Our architecture is evaluated against leading models using real-world datasets, namely Stanford2D3D and Matterport3D, demonstrating its superior performance. These results underscore the significance of our gravity-aligned approach for depth estimation in omnidirectional images, especially in man-made settings.
Managing Infrastructures of Limited Resources

It is hardly impossible to limit traffic by administrative measures. There are, however, chances of using innovative procedures of traffic telematics in order to manage traffic demands and, above all, traffic flows in an optimal way on the basis of available resources. A conclusion concerning the expected resources capacities can be made by predicting the presence of a mobile object at a certain location at a certain time. On the basis of the comparison of available and finally required resources it is possible to give effective anticipatory routing recommendations within a large area.
An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations.
Police Eyes: Real world automated detection of traffic violations

Dangerous lane changing, illegal overtaking, and driving in the wrong lane account for a high percentage of the total accidents that occur on the road, second only to accidents due to over-speeding. Automated traffic applications typically encompass the detection and segmentation of moving vehicles as a crucial process. Background subtraction and shadow detection are amongst the most challenging tasks involved in the segmentation of foreground blobs in dynamic environments. An effective balance between accuracy and speed is required to process a continuous feed of high resolution images from multiple cameras. Police Eyes is a mobile, real-time traffic surveillance system we have developed to enable automatic detection of traffic violations. Police Eyes would be useful to police for enforcing traffic laws and would also increase compliance with traffic laws even in the absence of police. The system detects illegal crossings of solid lines using image processing and efficient computer vision techniques on image sequences acquired from IP cameras. The automatic solid line crossing detection system can be used at locations where the traffic violations are notoriously high and are known to create traffic congestion and avoidable accidents. The system can be installed on an embankment, at an intersection area, at a lane change restriction area, at a no parking area or anywhere there is an observed pattern of drivers intentionally violating traffic laws. We have installed the system in an industrial grade embedded PC and deployed it in a police mannequin. Results of an empirical field evaluation show that the system performs well in a variety of real-world traffic scenes.
Distributed Weighted Least Squares Estimator Based on ADMM

This paper is concerned with a distributed tracking estimator with nonlinear measurement, which aims to achieve a consensus estimate over the entire network. To this end, we first employ a named sigma point transform (SPT) technique to convert measurement into Cartesian coordinates. In this setting, each node retains a weighted least squares (WLS) style without any prior distribution information, so that the centralized WLS filter is decomposed into several local parallel optimization problems with consensus-based constraints. To yield a closed-form expression in both the estimate and error covariance (information matrix), we use the alternating direction method of multiplier (ADMM) technique to perform an inner iteration. The proposed estimator is found to increase the tracking accuracy over alternative approach in a dynamic scenario.
Low-Power Variable-Gain CMOS Trans Impedance Amplifier for Near Infra-Red Spectroscopy Applications

A novel low-power and variable gain CMOS Trans Impedance Amplifier (LP-VG-TIA) circuit design for Near Infra-Red Spectroscopy (NIRS) Applications is presented in this paper. The proposed low power and variable gain CMOS Trans Impedance Amplifier achieves a variable gain from 55.06 dBΩ to 119.81 dBΩ with a minimum bandwidth of 12.46 MHz and a maximum bandwidth of 25.33 MHz. The variable gain control is used to achieve different values of the gain and the bandwidth for multiple bio-medical sensor applications. The input-referred noise with photodiode (PD) model with 1pF capacitance is 1.6pArms. The maximum input photocurrent is 1uAp-p. The proposed LP-VG-TIA consumes 95.3 µW at a room temperature of 27 °C from a 1V power supply. The proposed circuit has been implemented using a GPDK 45nm CMOS process. The proposed low power and variable gain TIA outperforms the existing TIAs in the literature.
A Synthetic Dataset for Deep Learning Recognition of Pathological Iris Affected by Coloboma

Biometric recognition systems might not work for people suffering from alteration of physical characteristics. This can also happen for well-known iris recognition systems. In this paper, we describe the creation of a synthetic dataset of eyes suffering from Coloboma, a congenital abnormality of eye membranes characterized by a “keyhole” appearance of the pupil. Due to the rarity of the disease, we apply image processing techniques on a dataset of healthy eyes to artificially simulate the effects of Coloboma. The pupil is distorted to simulate Coloboma on each of these images and the iris is compressed in the direction of the defect. A preliminary evaluation based on k-means has been performed. The dataset will be adopted for designing “non-excluding” iris recognition systems.
A Novel Deep Learning Multi-Modal Sentiment Analysis Model for English and Egyptian Arabic Dialects Using Audio and Text

As emotions play an important role in human interaction, the need for sentiment analysis has become crucial for human-computer interaction. This paper proposes a new model named Audio-Text Fusion (ATFusion), for sentiment analysis that utilizes text and speech data to detect emotions. The model comprises local classifiers for audio and text inputs, followed by fusion using Group Gated Fusion (GGF) technique. Convolutional neural network (CNN), long short-term memory (LSTM) neural network and transformers are employed as building blocks for the local classifiers. The evaluation of the ATFusion model performance is demonstrated through experiments over the IEMOCAP dataset for the English language and the EYASE dataset for the Egyptian Arabic Dialect. The performance of the ATFusion Model compared to other state-of-the-art models achieved results of 76.213%, 75.146%, and 70.79%, 70.42%, unweighted accuracy and weighted accuracy over IEMOCAP and EYASE, respectively.
Dynamic Network Scheduling in TSN Networks Based on Improved Reinforcement Learning

With the continuous upgrading of communication networks and the development of artificial intelligence technology, improving the performance of communication networks by realizing their intelligence is a widely researched direction. For example, reinforcement learning is utilized for time-sensitive network (TSN) configuration to enhance its adaptability to dynamic environments. However, the training of DRL algorithms suffers from a time-consuming problem, which runs counter to the delay sensitivity of communication networks. Therefore, we utilize network prediction algorithms to assist reinforcement learning by proposing an improved DRL algorithm, called preDQN, which will be used to help agents explore the environment more efficiently and reduce the time they spend in the training process compared to the agent's blind exploration behavior in the DRL algorithm. Experimental validation has been carried out in a simulation environment and the experimental results show that the improved reinforcement learning algorithm preDQN can significantly improve its convergence.
Machine and Deep Learning Techniques to Classify Arousal Judgments in Dynamic Virtual Experience of Architecture

The architectural space impacts the emotional state of its inhabitants. Nevertheless, no studies have investigated, to date, how it influences the perception of others' affective states, possibly impacting our social behavior. This paper analyzes the eye-gaze data collected during a social scenario recreated after a promenade within virtual architectures. Immersive and dynamic virtual architectures were characterized by decreasing or increasing sidewall distance, ceiling height, windows height, and different colors. At the end of such an experience, participants judged the arousal level expressed by a virtual avatar. For the first time, we apply machine and deep learning techniques to the behavioral, environmental, and eye-gaze features extracted during the dynamic experience of virtual architectures. In order to verify the feasibility of automated classification of the final arousal judgment on the avatar emotional expression, we have considered both interpretable, i.e., decision trees, and black-box models, i.e., dense neural networks. The decision tree reached an accuracy rate of 66%, showing the importance of eye-gaze parameters to classify the participants' arousal judgment. The black-box dense neural network increased the accuracy up to 80%. Overall, our findings demonstrate the capability of artificial intelligence methodologies to classify and possibly predict the arousal judgment of body expressions at the end of a virtual promenade. Such knowledge will serve the design and evaluation of future spaces by combining virtual reality and artificial intelligence within the experience of architecture. In such a way, it will be possible to predict the influence of the surrounding architecture on human social behavior.
Effectiveness of Dynamic Analysis Tools and Honeypots for Inspecting Android Applications

Android applications are software components that are used to perform tasks such as messaging, calling, social networking, banking, trading, capturing photos and videos, etc on Android devices. Android users use a variety of such applications on their mobile devices as per the requirement. Android developers and security researchers can use certain tools to understand and analyse the dynamic behaviour of Android applications without installing them on real Android devices. This paper focuses on the detailed study of dynamic analysis tools and honeypots for inspecting the behaviour of Android applications either on a simulation environment or a real Android device. This paper will help Android developers and security researchers to select the appropriate tools based on the necessity of inspection.