Reviewerly/exHarmony huggingface dataset:
Seek for commonalities: Shared features extraction for multi-task reinforcement learning via adversarial training

Multi-task reinforcement learning is promising to alleviate the low sample efficiency and high computation cost of reinforcement learning algorithms. However, current methods mostly focus on unique features that are not conducive to the transfer between tasks. Moreover, they usually lack a balance mechanism among tasks, which often leads to the unnecessary occupation of training resources by tasks that have already been trained. To address the problems, a simple yet effective method referred to as Adaptive Experience buffer with Shared Features Multi-Task Reinforcement Learning (AESF-MTRL) is proposed. In AESF-MTRL, input observation of the environment is divided into shared features and unique features, which are extracted using different feature extractors. Unique features are extracted by simple gradient descent, while shared features are extracted using adversarial training, with an additional discriminator trained to ensure that the extracted features are indeed shared features. AESF-MTRL also maintains a reward stack to adjust the sampling ratio of trajectories from different tasks dynamically during the update period to balance the learning process of different tasks. Experiments on multiple robotics control environments demonstrate the effectiveness of the proposed method.
eat2pic

Given the complexity of human eating behaviors, developing interactions to change the way users eat or their choice of meals is challenging. In this study, we propose an interactive system called eat2pic designed to encourage healthy eating habits such as adopting a balanced diet and eating more slowly, by refraining the task of selecting meals into that of adding color to landscape pictures. The eat2pic system comprises a sensor-equipped chopstick (one of a pair) and two types of digital canvases. It provides fast feedback by recognizing a user's eating behavior in real time and displaying the result on a small canvas called "one-meal eat2pic." Moreover, it also provides slow feedback by displaying the number of colors of foods that the user consumed on a large canvas called "one-week eat2pic." The former was designed and implemented as a guide to help people eat more slowly, and the latter to encourage people to select more balanced menus. Through two user studies, we explored the experience of interaction with eat2pic, in which users' daily eating behavior was reflected in a series of "paintings," that is, images produced by the automated system. The experimental results suggest that eat2pic may provide an opportunity for reflection in meal selection and while eating, as well as assist users in becoming more aware of how they are eating and how balanced their daily meals are. We expect this system to inspire users' curiosity about different diets and ways of eating. This research also contributes to expanding the design space for products and services related to dietary support.
AN INTRODUCTION TO LINKED DATA AND THE SEMANTIC WEB

For a sustainable transformation of processes and services from metrology into a digital age, data needs to become more comprehensible to machines.Linked Data and tools from the Semantic Web are promising developments helping to reach this goal.We give an introduction to relevant concepts and tools that metrologists are being increasingly confronted with in the scope of digital transformation.
Attribute-Based Signatures for Range of Inner Product and Its Applications

In attribute-based signatures (ABS) for inner products, the digital signature analogue of attribute-based encryption for inner products (Katz et al., EuroCrypt’08), a signing-key (resp. signature) is labeled with an n-dimensional vector $$\textbf{x}\in \mathbb {Z}_p^n$$ (resp. $$\textbf{y}\in \mathbb {Z}_p^n$$ ) for a prime p, and the signing succeeds iff their inner product is zero, i.e., $$\langle \textbf{x}, \textbf{y} \rangle =0 \pmod p$$ . We generalize it to ABS for range of inner product (ARIP), requiring the inner product to be within an arbitrarily-chosen range [L, R]. As security notions, we define adaptive unforgeablity and perfect signer-privacy. The latter means that any signature reveals no more information about $$\textbf{x}$$ than $$\langle \textbf{x}, \textbf{y} \rangle \in [L,R]$$ . We propose two efficient schemes, secure under some Diffie-Hellman type assumptions in the standard model, based on non-interactive proof and linearly homomorphic signatures. The 2nd (resp. 1st) scheme is independent of the parameter n in secret-key size (resp. signature size and verification cost). We show that ARIP has many applications, e.g., ABS for range evaluation of polynomials/weighted averages, fuzzy identity-based signatures, time-specific signatures, ABS for range of Hamming/Euclidean distance and ABS for hyperellipsoid predicates.
Low-Light Image Enhancement Based on U-Net and Haar Wavelet Pooling

The inevitable environmental and technical limitations of image capturing has as a consequence that many images are frequently taken in inadequate and unbalanced lighting conditions. Low-light image enhancement has been very popular for improving the visual quality of image representations, while low-light images often require advanced techniques to improve the perception of information for a human viewer. One of the main objectives in increasing the lighting conditions is to retain patterns, texture, and style with minimal deviations from the considered image. To this direction, we propose a low-light image enhancement method with Haar wavelet-based pooling to preserve texture regions and increase their quality. The presented framework is based on the U-Net architecture to retain spatial information, with a multi-layer feature aggregation (MFA) method. The method obtains the details from the low-level layers in the stylization processing. The encoder is based on dense blocks, while the decoder is the reverse of the encoder, and extracts features that reconstruct the image. Experimental results show that the combination of the U-Net architecture with dense blocks and the wavelet-based pooling mechanism comprises an efficient approach in low-light image enhancement applications. Qualitative and quantitative evaluation demonstrates that the proposed framework reaches state-of-the-art accuracy but with less resources than LeGAN.
Automating Device Fingerprinting Attacks in 4G and 5G NSA Mobile Networks

Mobile networks are essential for modern societies. High bandwidth and low latency communications for massive numbers of subscribers have enabled various applications with much impact on life quality. However, the widespread of mobile network communications has also increased the interest in security and privacy issues. In this work, a known 4G and 5G NSA vulnerability, attach request fingerprinting, is examined in detail and a novel classification method for attach request fingerprinting is proposed and extensively evaluated. The novel method significantly increases the severity of the privacy attack.
Deep-Learning-Based Vulnerability Detection in Binary Executables

The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown how such detection can generally be enabled by deep learning methods, but appears to be very limited regarding the overall amount of detected vulnerabilities. We analyse to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardised LLVM Intermediate Representation. Te vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, our proposed technical approach and methodology enables an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
RadarSense: Accurate Recognition of Mid-air Hand Gestures with Radar Sensing and Few Training Examples

Microwave radars bring many benefits to mid-air gesture sensing due to their large field of view and independence from environmental conditions, such as ambient light and occlusion. However, radar signals are highly dimensional and usually require complex deep learning approaches. To understand this landscape, we report results from a systematic literature review of ( N =118) scientific papers on radar sensing, unveiling a large variety of radar technology of different operating frequencies and bandwidths and antenna configurations but also various gesture recognition techniques. Although highly accurate, these techniques require a large amount of training data that depend on the type of radar. Therefore, the training results cannot be easily transferred to other radars. To address this aspect, we introduce a new gesture recognition pipeline that implements advanced full-wave electromagnetic modeling and inversion to retrieve physical characteristics of gestures that are radar independent, i.e., independent of the source, antennas, and radar-hand interactions. Inversion of radar signals further reduces the size of the dataset by several orders of magnitude, while preserving the essential information. This approach is compatible with conventional gesture recognizers, such as those based on template matching, which only need a few training examples to deliver high recognition accuracy rates. To evaluate our gesture recognition pipeline, we conducted user-dependent and user-independent evaluations on a dataset of 16 gesture types collected with the Walabot, a low-cost off-the-shelf array radar. We contrast these results with those obtained for the same gesture types collected with an ultra-wideband radar made of a vector network analyzer with a single horn antenna and with a computer vision sensor, respectively. Based on our findings, we suggest some design implications to support future development in radar-based gesture recognition.
An Automated Face-mask Detection System using YOLOv5 for Preventing Spread of COVID-19

Object detection systems based on deep learning have been immensely successful incomplex object detection tasks images and have shown potential in a wide range of real-life applicationsincluding the COVID-19 pandemic. One of the key challenges in containing and mitigating the infectionamong the population is to ensure and enforce the proper use of face masks. The objective of this paperis to detect the proper use of facial masks among the urban population in a megacity. In this study, wetrained and validated a new dataset to detect images such as ‘with mask’, ‘without mask’, and ‘masknot in position’ using YOLOv5. The dataset is comprised of 6550 images with the three classes. Thedataset attained a commendable performance accuracy of 95% on mAP. This study can be implementedfor automated scanning for monitoring the proper use of face masks in different settings of public spaces.
The Influence of Different Weighting Methods on MADM Ranking Techniques and Its Impact on Network Selection for Handover in HetNet

Optimal radio access network selection for fast and efficient vertical handover decisions in a wireless heterogeneous network to provide seamless connectivity to ensure a wide range of services to multimode mobile terminals is one of the most challenging fields of research in mobility management in mobile communication. Various multiattribute decision-making-based network selection techniques have been proposed that use subjective, objective, and integrated weight computation methods. In this paper, we investigate the impact of various weighting methods on TOPSIS, VIKOR, and GRA ranking methods to determine how and what effects they have on the ping-pong handover of mobile nodes during roaming in heterogeneous wireless communication. The simulation results have shown that TOPSIS methods provide better ranking in association of entropy objective weighting method while VIKOR and GRA perform better in association with improved CRITIC. The TOPSIS + Entropy, VIKOR + Improved CRITIC, and GRA + Improved CRITIC methods provide enough difference between the currently servicing RAN and the selected RAN performance score to ensure the best network selection technique and reduce ping-pong handover.
Introduction to Natural Language Processing

Natural language processing (NLP) aims to make computers process human languages. NLP has a wide variety of applications today. You may already be using them when you are buying a book on Amazon, talking to Google Assistant or Siri when checking the weather, or talking to an automated chat agent when seeking customer service. In this chapter, we start with an overview of NLP and discuss NLP applications, key concepts, various NLP tasks, how to create models, the Python and R NLP libraries, and case studies.
Requirement or Not, That is the Question: A Case from the Railway Industry

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.
Entanglement generation and decoherence in a two-qubit system mediated by relativistic quantum field

Motivated by the Bose et al.-Matletto-Vedral (BMV) proposal for detecting quantum superposition of spacetime geometries, we study a toy model of a quantum entanglement generation between two spins (qubits) mediated by a relativistic free scalar field. After time evolution, spin correlation is generated through the interactions with the field. Because of the associated particle creation into an open system, the quantum state of spins is partially decohered. In this paper, we give a comprehensive study of the model based on the closed time path formalism, focusing on relativistic causality and quantum mechanical complementarity. We calculate various quantities such as spin correlations, entanglement entropies, mutual information and negativity, and study their behaviors in various limiting situations. In particular, we calculate the mutual information of the two spins and compare it with spin correlation functions. In some cases, its inequality relation improves the trade-off relation between the fringe visibility and the distinguishability (which-way information) by giving a stronger upper bound on the visibility. We also discuss why no quantum entanglement can be generated unless both spins are causally affected by one another while spin correlations are generated.
MTBC-BioNER: Multi-task Learning Using BioBERT and CharCNN for Biomedical Named Entity Recognition

In recent years, a large amount of biomedical text data has been generated with the rapid development of biomedical technology and computer technology. How to effectively process these data has become an important issue. Biomedical Named Entity Recognition (BioNER) is an important task in biomedical text information processing. It has an important impact on its downstream tasks, such as entity relation extraction, question answering systems, document classification, etc. However, existing works only relying on a single deep learning model are difficult to learn feature representations for biomedical text data. This paper proposes MTBC-BioNER model, which combines multi-task learning methods to train different related tasks at the same time, and combines the contextual word embedding vector generated by Biomedical Bidirectional Encoder Representation from Transformers (BioBERT) with the character embedding vector generated by Character-level Convolution Neural Network (CharCNN) to effectively represent the feature information of biomedical entities. This model regards each data set in 15 biomedical data sets as an independent task, and uses a specific module for different tasks. Through joint training of all data sets, the model can obtain common features among different tasks, so as to improve the universality of the model. Experiments on 15 biomedical datasets show the effectiveness of MTBC-BioNER.
Pose Estimation and Joint Angle Detection Using Mediapipe Machine Learning Solution

Health is one of the central aspects of life and innovative ways for its improvement are constantly being studied. Artificial intelligence has an extensive application and its contribution to health and medicine is widely recognized. In this paper, the application of machine learning algorithms in the field of health care is presented. A model for physical activity injury prevention based on the MediaPipe solution for body pose tracking has been developed. The solutions for pose estimation and detection of joint angles and angles relative to the horizontal are integrated into a comprehensive system that detects all key body landmarks and angles during the movement of the observed person. In addition, one of the goals of this research is to develop a flexible system with the ability to process a variety of inputs in terms of video content and format. The system is trained and tested on video inputs and can process front, left, and right perspectives. In the processing phase, a graph of posture and angle estimation is generated. The graph represents detected joints and the corresponding angles that vary depending on the observed perspective. The input is integrated with the graph and thus provides valuable information about body posture and alignment. The results provide support to professionals in physical activity monitoring and injury prevention.
Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcontroller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment.The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments.This work presents a practical and costeffective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation.The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it.The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot's motion accordingly.The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications.The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.
Special Education Teachers’ Perceptions Regarding the Operation of Immersive Content Experience Classrooms

The purpose of this study is to explore special education teachers’ perceptions regarding the operation of an immersive content experience classroom, as well as suggest efficient operational plans based on the results. For this, focus group interviews were conducted with seven special education teachers who work in a special school with an immersive content experience classroom and are in charge of the classroom operation. Collected data was analyzed using a constant comparative analysis method. As a result, four categories, 10 themes, and 36 sub-themes were derived. The results of this study are as follows. First, use of the immersive content experience classroom is still at the beginner level; it is mainly used for interests or experience, rather than for educational purposes. Also, it was found that students’ level of intelligence and physical manipulation as well as teachers’ ability to use this classroom setting had a great influence on classroom usage. Second, the limitations of using immersive content experience classrooms includes a lack of teachers’ ability to use them, students’ difficulty using the programs, a lack of programs suitable for various requirements, and insufficient classroom usage in connection with curriculum. Third, the effects of using immersive content experience classrooms include increasing students’ interest and participation in learning, a reduction in students’ challenging behaviors, providing realistic experience opportunities that redeem disabilities or environmental limitations, and providing opportunities to experience new technologies according to societal change. Last, for successful use of the immersive content experience classroom, it is required to develop high quality and easy-to-use programs that meet the educational needs of students with disabilities; it is also crucial to strengthen teachers’ competencies for efficient use of immersive contents, continuous program updates and device repairs. Based on these results, the implications for effective operational plans for the immersive content experience classroom were discussed.