Reviewerly/exHarmony huggingface dataset:
Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles

Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model.
Recurrent Neural Network-Based Model for Named Entity Recognition with Improved Word Embeddings

Extraction of meaningful information from a huge amount of data available on the web is a quite challenging task. The challenges faced in information extraction can be overcome with the help of an efficient named entity recognition (NER) system. Named entities are the proper names that play an important role in searching important information of interest. In this study, an efficient deep learning-based NER technique has been proposed which recognizes the named entities belonging to the general domain from Hindi, Punjabi, and bilingual Hindi and Punjabi text. An important variant of recurrent neural network, namely bidirectional long short-term memory-based model using improved word embeddings has been developed. Improved word embeddings are the combination of character convolutional neural network embeddings and part of speech embeddings. The main findings of the study include the development of a NER system that can extract named entities not only from Hindi and Punjabi datasets individually but also from mixed Hindi and Punjabi text. Besides, improved word embeddings are the combination of character-level features and word-level features which we find as the novel work as per our knowledge. Improved word embeddings are found to be effective in achieving better results than the results obtained by earlier NER models with deep feature extraction tasks.
Artificial intelligence learning approach through total physical response embodiment teaching on French vocabulary learning retention

TPR (Total Physical Response) is a methodology for teaching foreign languages. In traditional TPR, teachers need to spend a considerable amount of time confirming the accuracy of students’ movements, which results in a low-efficiency teaching process and affects the fairness of student learning. A motion sensing system can assess the accuracy of body movements and guide students’ movements immediately after detecting them. Hence, in this study, an artificial intelligence (AI) motion sensing teaching system that combines gesture detection and French learning was written in Python. The system was then used to understand the effectiveness of different teaching methods among kinesthetic or non-kinesthetic students in French learning. Further, a delay test was conducted to assess learning retention. There were significant differences in the effects of the interaction between instructional methodologies and learning style tendency on learning retention. By connecting gestures, words, and sounds, students in the group that used the French TPRAI motion sensing teaching system were able to recall what they had learned through body movements on the delay test 14 days after the post-test to retain learning.
Robust Person Following Under Severe Indoor Illumination Changes for Mobile Robots: Online Color-Based Identification Update

Tracking a specific person in environments with non-uniform illumination is a difficult task for mobile robots. Image information such as color is essential to identify a target person. However, the information is not reliable under severe illumination changes unless the system can accommodate these changes over time. In this paper, we propose a robust identifier that has been combined with a deep learning technique to accommodate varying illumination in the ambient lighting of a scene. Moreover, an enhanced online update strategy for the person identification model is used to deal with the challenge of drifting the target person&#x0027;s appearance changes during tracking. Using the proposed method, the system achieves a successfully tracked rate above 90&#x0025; on real-world video sequences in which variations in illumination are dominant. We confirmed the effectiveness of the proposed method through target-following experiments using five different clothing colors in a real indoor environment where the lighting conditions change extremely.
Wearable Tactile Sensor Suit for Natural Body Dynamics Extraction: Case Study on Posture Prediction Based on Physical Reservoir Computing

We propose a wearable tactile sensor suit, which can be regarded as tactile sensor networks, for monitoring natural body dynamics to be exploited as a computational resource for estimating the posture of a human or robot that wears it. We emulated the periodic motions of a wearer (a human and an android robot) using a novel sensor suit with a 9-channel fabric tactile sensor on the left arm. The emulation was conducted by using a linear regression (LR) model of sensor states as readout modules that predict the next wearer's movement using the current sensor data. Our result shows that the LR performance is comparable with other recurrent neural network approaches, suggesting that a fabric tactile sensor network can monitor the natural body motions, and further, this natural body dynamics itself can be used as an effective computational resource.
Estimating Degradation of Machine Learning Data Assets

Large-scale adoption of Artificial Intelligence and Machine Learning (AI-ML) models fed by heterogeneous, possibly untrustworthy data sources has spurred interest in estimating degradation of such models due to spurious, adversarial, or low-quality data assets. We propose a quantitative estimate of the severity of classifiers’ training set degradation: an index expressing the deformation of the convex hulls of the classes computed on a held-out dataset generated via an unsupervised technique. We show that our index is computationally light, can be calculated incrementally and complements well existing ML data assets’ quality measures. As an experimentation, we present the computation of our index on a benchmark convolutional image classifier.
Naming Practices in Java Projects: An Empirical Study

Investigating the properties of identifier names and their role in software systems have been central to software engineering. Research in naming identifiers shows that informative names are crucial to improving the readability and maintainability of programs: essentially, intention-revealing names make code easier to understand and act as a basic form of documentation. Poorly named identifiers tend to hurt the comprehensibility and maintainability of software systems. However, most computer science curricula emphasize programming concepts and language syntax over naming guidelines and conventions. Consequently, programmers lack knowledge about naming practices. We carried out a study to explore the naming practices of Java programmers. We analyzed 1,421,607 identifier names (i.e., attributes, parameters, and variables names) from 40 open-source Java projects and categorized these names into eight naming practices. We examined the occurrence and prevalence of the categories across projects, and our results also highlight in which contexts identifiers following each naming practice tend to appear more regularly. The results can be helpful to enhance programmers’ awareness and contribute to improving educational materials and code review methods.
Design of Cost-Effective Auto-Encoder for Electric Motor Anomaly Detection in Resource Constrained Edge Device

The electric motor failure triggers the system paralyzation for various fields such as industry or transportation. Thus, continuous management is necessary. Recently, the automated anomaly detection system is adopted for reducing human exhaustion. Moreover, for improving the cost-effectiveness and monitoring stability, edge device computing is considered on system construction. For enabling edge computing, we need to achieve high performance with a low-complex anomaly detection model, considering the constrained resource. In this paper, we empirically evaluate various anomaly detection architectures from two perspectives for designing a cost-effective model. One of the perspectives is the feature aggregation method and the other one is whether to adopt the bottleneck structure or not for constructing autoencoder. The effectiveness and efficiency are improved by adopting linear feature aggregation and non-bottleneck structured auto-encoder. By combining the above two methods, the computational cost is reduced by 2 in 10k, while losing only 1.972% of the averaged anomaly detection performance.
Explainable Artificial Intelligence Approaches: Challenges and Perspectives

Artificial intelligence systems are used in many areas, for example, in finance and medicine. Every year, intelligent systems get for processing more and more data and make more and more decisions. All these decisions are exerting more and more influence on human destinies. Mistrust of fully non-human, autonomous artificial intelligence systems become the cornerstone. The key of mistrust is hidden in the lack of understanding as to why intelligent systems make this or that decision. To solve this problem, methods of explainable artificial intelligence are used. This article provides an overview of such methods and discusses the main trends in their development, as well as the implementation difficulties that one has to face during development and use.
Visualization-based improvement of neural machine translation

We introduce a novel visual-interactive approach for analyzing, understanding, and correcting neural machine translation. Our system supports users in automatically translating documents using neural machine translation and identifying and correcting possible erroneous translations. User corrections can then be used to fine-tune the neural machine translation model and automatically improve the whole document. While translation results of neural machine translation can be impressive, there are still many challenges such as over- and under-translation, domain-specific terminology, and handling long sentences, making it necessary for users to verify translation results. Our system aims at supporting users in this task. Our visual analytics approach combines several visualization techniques in an interactive system. A parallel coordinates plot with multiple metrics related to translation quality can be used to find, filter, and select translations that might contain errors. An interactive beam search visualization and graph- or matrix-based visualizations for attention weights can be used for post-editing and understanding machine-generated translations. The machine translation model is updated from user corrections to improve the translation quality of the whole document. We designed our approach for an LSTM-based translation model and extended it to also include the Transformer architecture. We show for representative examples possible mistranslations and how to use our system to deal with them. A user study revealed that many participants favor such a system over manual text-based translation, especially for translating large documents. Furthermore, we performed quantitative computer-based experiments that show that our system can be used to improve translation quality and reduce post-editing efforts for domain-specific documents.
Chaos in spin-torque oscillator with feedback circuit

Excitation of chaotic magnetization dynamics in nanomagnets is of great interest because it bridges the condensed matter physics and nonlinear science and has a potential to emerging technologies such as neuromorphic computing. However, it has been difficult to observe and identify chaos in spintronics devices because the excitation of chaos requires dynamics in a large-dimensional phase space, according to the Poincar\'e-Bendixson theorem. An efficient way to overcome this issue is using feedback, which enables the dynamical degrees of freedom to be increased even in a single device. Here, we experimentally demonstrate the excitation of chaos in a vortex spin-torque oscillator by utilizing a feedback circuit. The radio-frequency current emitted by the oscillator flows in the feedback circuit and is converted into an oscillating magnetic field. The oscillating field generates a torque acting on the vortex and modulates its dynamics, resulting in chaotic dynamics which can be tuned by electrical means.
How to take advantage of behavioral features for the early detection of grooming in online conversations

Detecting grooming behavior in online conversations has become a growing problem due to the large number of messaging platforms that children and young people use nowadays. The biggest drawback is the lack of tools focused on the automatic prevention of this risk. This paper proposes seven Behavioral Features (BFs) to be used for early grooming detection. A detailed study is conducted to understand the background that allows these features to contribute to tasks of early classification. Besides, we introduce the Behavioral Feature-Profile Specific Representation (BF-PSR) framework as an extension of the well-known Profile Specific Representation (PSR) framework to properly employ the proposed behavioral features. Experimental results reveal that our proposal outperforms all the concurrent methods and obtains state-of-the-art performance in the area of early grooming detection. Specifically, the new BF-PSR framework achieves a gain of more than 40% in effectiveness over five competitors when only 10% of the conversations’ content is available, thus it shows a substantial advantage to allow the early detection of grooming; besides, it maintains a similar gain in effectiveness as more data arrives. To the best of our knowledge, this is the first work to employ behavioral features for the early detection of grooming. Furthermore, we have assembled two new datasets called PJZ and PJZC to mitigate the lack of data in the grooming detection area. Both sets are publicly available for download aimed at fostering further researches. Additional experiments reveal that our BF-PSR framework outperforms all of the state-of-the-art methods when processing these new datasets.
A Microlearning path recommendation approach based on ant colony optimization

This paper presents the technical proposal of a novel approach based on Ant Colony Optimization (ACO) to recommend personalized microlearning paths considering the learning needs of the learner. In this study, the information of the learner was considered from a disciplinary ICT perspective, since the characteristics of our learner correspond to those of a professor with variable characteristics, such as the level of knowledge and their learning status. The recommendation problem is approached as an instance of the Traveling Salesman Problem (TSP), the educational pills represent the cities, the paths are the relationships between educational pills, the cost of going from one pill to another can be estimated by their degree of difficulty as well as the performance of the learner during the individual test. The results prove the approach proposal capacity to suggest microlearning path personalized recommendation according to the different levels of knowledge of the learners. The higher the number of learners, the behavior of the algorithm benefits in terms of stability.
LibBlock - Towards Decentralized Library System based on Blockchain and IPFS

In modern times, the definition and the library’s expected functionality did not change much as before. It is still a place for us to hold massive collections of information. Traditionally, libraries require physical storage space for writings and publications, but storing and managing costs can be tremendous. Although the aid of digital promises and computers allows a super high density of information storage, it does not lower the library’s complexity. As our main source of information is moving away from physical writings toward digital, the new digital library (i.e., state-run library) faces the challenges of records’ integrity and storage efficiency. Focused on this issue, we learn the demands from the Royal Library in Denmark and try to explore the use of blockchain technology. We introduce a system named LibBlock, by integrating with both smart contract and IPFS in order to provide a robust, decentralized, flexible, and adaptive e-Library, which enables the ease of scalability and rigid record keeping. In the evaluation, we investigate the initial performance of LibBlock with Ethereum and show its viability and efficiency.
A hybrid multi-population metaheuristic applied to load-sharing optimization of gas compressor stations

In this paper, a hybrid population-based, nature-inspired metaheuristic algorithm is developed and applied to solve the load-sharing optimization problem, which is a common problem that appears in many industrial applications. The proposed algorithm is a hybrid multi-population metaheuristic that combines the diversification capability of the Crow Search Algorithm (CSA) and the intensification capability of the Symbiotic Organisms Search (SOS). It uses two subpopulations. One subpopulation evolves according to the CSA algorithm and is responsible for finding promising regions in the search space. The second subpopulation evolves according to the SOS algorithm and is responsible for performing a refined search in the promising regions to find the best solution for the problem. The performance of the proposed algorithm is compared with the performance of five competing algorithms, including the stand-alone versions of CSA and SOS. The results obtained show that the proposed CSA-SOS outperformed all the competing algorithms in all experiments.
Dialogue Extraction and Translation from Stories on Thirukural Using Verb Cue Quote Content Source Identifiers

The term 'graphic novel' has been referring to a variety of narrative types published in the last twenty years. Recent researches in graphic novels focus on how the human brain processes the combination of images and text which as a result indicated that graphic novels are one among the excellent resources for advanced learners, dialogue analysis, conversion of image to graphical representation and many such related works. Thirukural (as always said to be as Ulaga Pothumurai (in Tamil), the common Veda), extracting content from the couplets and its existence in various articles and media elements is very complex. Researches in Thirukural include automatic couplet extraction from a database with keywords, bilingual language processing, rendering of couplets using different programming languages and works related to the same. Graphic novels are the most sought-after creative visualization tools that can be used to ensure a better way of approaching and teaching. In this work, quote content source identifier (QCSI) is used to automatically generate a storyboard containing seven panels with the constituents of a graphic novel in such a way that the moral behind a Thirukural is conveyed as a result of the sequence of dialogue exchanges between the characters. Couplets from Thirukural are chosen with stories on the same with dialogues between the characters are developed in the English language. Powerful natural language processing tools and libraries are used to extract the dialogues involved along with the source of each of these dialogues and to translate the extracted dialogues to Tamil. The techniques used upon evaluation of results are found to extract all the dialogues with appropriate characters involved in sublime translation results.
A Military Human Performance Management System Design using Machine Learning Algorithms

The area of human performance improvement has become a greater focus in recent military contexts as evidenced by Australian Defence Force projects. The aim of the design proposed in this paper is to develop a Performance Management System using Machine Learning (PMSML) to enhance the physical human performance of individual warfighters in combat situations through 1) early recognition and self-management of acute health events in the field; 2) forecasting of soldier (user) failure; and 3) proactive self-management of longer-term health outcomes during prolonged manoeuvres or combat situations. This paper proposes a high-level design and approach using machine learning algorithms to assess the feasibility of improving metrics such as health data accuracy and efficiency when transmitting data from sensors to the cloud in military networks. The significance of this design is to predict health conditions of users on a personalised basis for an individual&#x2019;s physical and mental health performance without compromising performance metrics using machine learning algorithms. Results show that machine learning algorithms outperformed other existing methods, which must compromise between certain metrics.
Exploring the Means and Benefits of Including Blockchain Smart Contracts to a Smart Manufacturing Environment: Water Bottling Plant Case Study

Blockchain smart contracts are increasingly becoming prominent with applications in all spheres of industry. The main advantages of blockchains that promulgate their use are the increased security, transparency, and traceability that it offers to users. This article explores how a blockchain smart contract can be incorporated into a smart manufacturing environment and what are its plausible benefits. This article is written based on research done on the case study of a Make-to-Order water bottling plant. The current challenge that necessitated this research stems from customer's need for transparency on the contents of the bottled water, date of filling, date of packaging, and elimination of third-party deliveries. The hypothesis of the research is that these challenges can be overcome by incorporating blockchain smart contracts into the water bottling plant.
ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics

This work describes a method for detecting JPEG compression as well as its grid origin.The JPEG algorithm performs a quantization of the DCT coefficients of non-overlapping 8×8 blocks of images, setting many of those coefficients to zero.The method described here exploits these facts and identifies the presence of a JPEG grid when a significant number of DCT zeros is observed for a given grid origin.This method can be applied globally to identify a JPEG compression, and also locally to identify image forgeries when misaligned or missing JPEG grids are found.The algorithm includes a statistical validation step according to Desolneux, Moisan and Morel's a contrario theory, which associates a number of false alarms (NFA) with each tampering detection.Detections are obtained by a threshold of the NFA, which renders the method fully automatic and endows it with a false alarm control mechanism.