Reviewerly/exHarmony huggingface dataset:
Diagram Image Retrieval and Analysis: Challenges and Opportunities

Deep learning has achieved significant advances for tasks such as image classification, segmentation, and retrieval; this advance has not yet been realized on scientific and technical drawing images. Research for technical diagram image analysis and retrieval retain much less well developed compared to natural images; one major reason is that the dominant features in scientific diagram images are shape and topology, no color and intensity features, which are essential in retrieval and analysis of natural images. One important purpose of this review, along with some challenges and opportunities, is to draw the attention of researchers and practitioners in the Computer Vision community to the strong needs of advancing research for diagram image retrieval and analysis, beyond the current focus on natural images, in order to move machine vision closer to artificial general intelligence. This paper investigates recent research on diagram image retrieval and analysis, with an emphasis on methods using content-based image retrieval (CBIR), textures, shapes, topology and geometry. Based on our systematic review of key research on diagram image retrieval and analysis, we then demonstrate and discuss some of the main technical challenges to be overcome for diagram image retrieval and analysis, and point out future research opportunities from technical and application perspectives.
Fast-Track to Research Data Management in Experimental Material Science–Setting the Ground for Research Group Level Materials Digitalization

Research data management is a major necessity for the digital transformation in material science. Material science is multifaceted and experimental data, especially, is highly diverse. We demonstrate an adjustable approach to a group level data management based on a customizable document management software. Our solution is to continuously transform data management workflows from generalized to specialized data management. We start up fast with a relatively unregulated base setting and adapt continuously over the period of use to transform more and more data procedures into specialized data management workflows. By continuous adaptation and integration of analysis workflows and metadata schemes, the amount and the quality of the data improves. As an example of this process, in a period of 36 months, data on over 1800 samples, mainly materials libraries with hundreds of individual samples, were collected. The research data management system now contains over 1700 deposition processes and more than 4000 characterization documents. From initially mainly user-defined data input, an increased number of specialized data processing workflows was developed allowing the collection of more specialized, quality-assured data sets.
Overview of Digital Forensics and Anti-Forensics Techniques

Digital forensics is very essential in any investigation where data is involved after a security breach. Data contents might be personal, business-related, or strictly confidential. The aim of digital forensics is to legally acquire and analyze the examined data, while Anti-forensics techniques aim to hide, manipulate, and even wipe the data, or to target the credibility of the acquired evidence. This paper presents the current antiforensics techniques, the methods applied, and the available countermeasures.
Validating the Adoption of Heterogeneous Internet of Things with Blockchain

Emerging technologies such as Internet of Things (IoT) and blockchain have affected the digital transformation. Blockchain, on the one hand, was initially developed for the purpose of financial trading due to its robustness especially for fault tolerance and cryptographic security in addition to its decentralized architecture. IoT, on the other hand, is an open interconnected network of smart devices able to communicate simultaneously. This arises a challenge in privacy and security, specifically for the data being exchanged. To overcome this, studies have focused on the blockchain to resolve the security and privacy issues of IoT. Indeed, limited studies have proposed to assess blockchain’s viability for IoT and the associated challenges. In this paper, a conceptual model has proposed to identify the crucial factors affecting the adoption of blockchain in IoT. The model consists of four dimensions of factors that we assume will affect the adoption of the two technologies. The dimensions are: attitude-related factors, social influence related factors, data-related factors, and security-related factors. This model is validated through a survey that was distributed between professionals in blockchain and IoT. The findings show a significant impact of data-related factors on the adoption of blockchain in IoT and the intention to use them. The model can play an important role in the development of strategies, standards, and performance assessment.
Conceptual knowledge and sensitization on Asperger's syndrome based on the constructivist approach through virtual reality

The immense capabilities provided by virtual reality for enhancing empathy and altering social behaviors, make it possible to use virtual reality for the promotion of the understanding between individuals. A social issue that needs to be addressed is the discrimination and marginalization of people with Asperger's syndrome (AS) from the working force, stemming from the lack of recognition and justification of their oddities. In this paper, the effectiveness of a virtual reality simulation, raising awareness on this specific issue, is evaluated. Awareness is a term used in this research to refer to the combination of conceptual knowledge and sensitization on a specific issue. An experiment with between-group design has been carried out, where half of the participants used the simulation via a VR headset device and the other half was given a transcript of relevant medical articles instead. The comparison between the two groups indicates that the group which used the simulation gained more knowledge on AS than the group which used the transcript. Moreover, the parameter of sensitization indicates positive correlations with place illusion in the VR group.
A Throughput-Latency Co-Optimised Cascade of Convolutional Neural Network Classifiers

Convolutional Neural Networks constitute a prominent AI model for classification tasks, serving a broad span of diverse application domains. To enable their efficient deployment in real-world tasks, the inherent redundancy of CNNs is frequently exploited to eliminate unnecessary computational costs. Driven by the fact that not all inputs require the same amount of computation to drive a confident prediction, multi-precision cascade classifiers have been recently introduced. FPGAs comprise a promising platform for the deployment of such input-dependent computation models, due to their enhanced customisation capabilities. Current literature, however, is limited to throughput-optimised cascade implementations, employing large batching at the expense of a substantial latency aggravation prohibiting their deployment on real-time scenarios. In this work, we introduce a novel methodology for throughput-latency co-optimised cascaded CNN classification, deployed on a custom FPGA architecture tailored to the target application and deployment platform, with respect to a set of user-specified requirements on accuracy and performance. Our experiments indicate that the proposed approach achieves comparable throughput gains with related state-of-the-art works, under substantially reduced overhead in latency, enabling its deployment on latency-sensitive applications.
NEVA: Visual Analytics to Identify Fraudulent Networks

Trust-ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well-defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill-defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false-negative and false-positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance-enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.
Drowsiness Detection Based on Facial Landmark and Uniform Local Binary Pattern

Abstract When driving a vehicle, it is often challenging for someone to force his condition to keep driving even though in sleepy condition, thus causing a traffic accident. One of the characteristics of drowsy drivers is the eyes are closed for a certain period. This research proposes a system to detect drowsiness, thus can alert the drowsy driver. The first step is to detect the face using a Funnel-structured cascade algorithm. And then extract the facial landmark features on the face to get the eyes location. The features of eyes are extracted by using a Uniform Local Binary Pattern (ULBP) and the Eyes Aspect Ratio (EAR). EAR is the distance between points at eye landmarks. After the features have been extracted, the system classifies the eyes, whether closed or open by using Support Vector Machine (SVM) method. The system calculates the percentage of eye closure (PERCLOS) to detect drowsiness. Based on the experimental results, the proposed method yields the best accuracy of 95.5% and the optimal value of PERCLOS in drowsiness detection is greater than or equal to 60% with a period of 20 frames.
GPS-based Mobile Cross-platform Cargo Tracking System with Web-based Application

Cross-platform development is becoming widely used by developers, and writing for separate platforms is being replaced by developing a single code base that will work across multiple platforms simultaneously, while reducing cost and time. The purpose of this paper is to demonstrate cross-platform development by creating a cargo tracking system that will work on multiple platforms with web application by tracking cargo using Global Positioning System (GPS), since the transport business has played a vital role in the evolution of human civilization. In this system, Google Flutter technology is used to create a mobile application that works on both Android and iOS platforms at the same time, by providing maps to clients showing their cargo location using Google Map API, as well as providing a web-based application.
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In this paper, to improve the performance of capped L1-norm twin support vector machine (CTSVM), we first propose a new robust twin bounded support vector machine (RTBSVM) by introducing the regularization term. The significant advantage of our RTBSVM over CTSVM is that the structural risk minimization principle is implemented. This embodies the marrow of statistical learning theory, so this modification can improve the performance of classification. Furthermore, to accelerate the computation of RTBSVM and simultaneously inherit the merit of robustness, we construct a least squares version of RTBSVM (called RTBSVM). This formulation leads to a simple and fast algorithm for binary classifiers by solving just two systems of linear equations. Finally, we derive two simple and effective iterative optimization algorithms for solving RTBSVM and FRTBSVM, respectively. Simultaneously, we theoretically rigorously analyze and prove the computational complexity, local optimality and convergence of the algorithms. Experimental results on one synthetic dataset and nine UCI datasets demonstrate that our methods are competitive with other methods. Additionally, the FRTBSVM is directly applied to recognize the purity of hybrid maize seeds using near-infrared spectral data. Experiments show that our method achieves better performance than the traditional methods in most spectral regions.
Activity-specific caloric expenditure estimation from kinetic energy harvesting in wearable devices

Accurate and efficient estimation of caloric expenditure during daily activities is desirable in tracking personal activity and health. Kinetic energy harvesting (KEH) has created an opportunity for wearable devices with limited battery power to realize long-term human health monitoring. We postulate to utilize KEH device for calorie estimation instead of accelerometer considering that the kinetic energy is produced when the user expends some calories by doing the bodily movement. In this paper, we propose to use the output voltage of KEH for activity intensity classification and develop activity-specific regression models combined with anthropometric characteristics for caloric expenditure estimation by random forest. To validate our approach, we build a KEH hardware platform and collect the dataset of seven different activities in free-living conditions from ten participants. Experiment results show that our approach achieves an accuracy of 95. 7% to classify intensity of activity and yields a R2 coefficient of 0.93 for regression model and a root mean square error (RMSE) of 1.09 kcal/min to estimate calorie expenditure by only using the output voltage of KEH. The comparison experiments validate the effectiveness of KEH device for caloric expenditure estimation act as a substitute for accelerometer in terms of estimation accuracy and energy saving.
Benchmarking Combinations of Learning and Testing Algorithms for Active Automata Learning

Active automata learning comprises techniques for learning automata models of black-box systems by testing such systems. While this form of learning enables model-based analysis and verification, it may also require a substantial amount of interactions with considered systems to learn adequate models, which capture the systems' behaviour. The test cases executed during learning can be divided into two categories: (1) test cases to gain knowledge about a system and (2) test cases to falsify a learned hypothesis automaton. The former are selected by learning algorithms, whereas the latter are selected by conformance-testing algorithms. There exist various options for both types of algorithms and there are dependencies between them. In this paper, we investigate the performance of combinations of four different learning algorithms and seven different testing algorithms. For this purpose, we perform learning experiments using 39 benchmark models. Based on experimental results, we discuss insights regarding the performance of different configurations for various types of systems. These insights may serve as guidance for future users of active automata learning.
Connecting digital elements with physical learning contexts: an educational escape-the-room game for supporting learning in young children

(2020). Connecting digital elements with physical learning contexts: an educational escape-the-room game for supporting learning in young children. Technology, Pedagogy and Education: Vol. 29, No. 4, pp. 425-444.
PCM: Precision-Controlled Memory System for Energy Efficient Deep Neural Network Training

Deep neural network (DNN) training suffers from the significant energy consumption in memory system, and most existing energy reduction techniques for memory system have focused on introducing low precision that is compatible with computing unit (e.g., FP16, FP8). These researches have shown that even in learning the networks with FP16 data precision, it is possible to provide training accuracy as good as FP32, de facto standard of the DNN training. However, our extensive experiments show that we can further reduce the data precision while maintaining the training accuracy of DNNs, which can be obtained by truncating some least significant bits (LSBs) of FP16, named as hard approximation. Nevertheless, the existing hard-ware structures for DNN training cannot efficiently support such low precision. In this work, we propose a novel memory system architecture for GPUs, named as precision-controlled memory system (PCM), which allows for flexible management at the level of hard approximation. PCM provides high DRAM bandwidth by distributing each precision to different channels with as transposed data mapping on DRAM. In addition, PCM supports fine-grained hard approximation in the L1 data cache using software-controlled registers, which can reduce data movement and thereby improve energy saving and system performance. Furthermore, PCM facilitates the reduction of data maintenance energy, which accounts for a considerable portion of memory energy consumption, by controlling refresh period of DRAM. The experimental results show that in training CIFAR-100 dataset on Resnet-20 with precision tuning, PCM achieves energy saving and performance enhancement by 66% and 20%, respectively, without loss of accuracy.
Gradient descent evolved imbalanced data gravitation classification with an application on Internet video traffic identification

In the last decade, the increasing video traffic, especially illegal videos brought big challenges for Internet management. Generally, abnormal videos, such as illegal videos only account for a small percentage which makes the detection of such videos to be a typical imbalanced classification problem. In this study, we propose a new imbalanced learning method, namely, the imbalanced data gravitation classification model based the gradient descent (IDGC-GD), to handle imbalanced problems. In IDGC-GD model, we use the gradient descent algorithm to optimize feature weights of the imbalanced data gravitation classification (IDGC) model. Then, we try to build an accurate video traffic identification solution using IDGC-GD. We conduct a set of comparing experiments between IDGC-GD and seven imbalanced learning algorithms using 21 open data sets and four video traffic data sets collected from the real application. Experimental results show that our method is promising for solving imbalanced problems, including Internet video traffic identification.
A Self Learning Chat-Bot From User Interactions and Preferences

A chatbot is an interactive AI tool that tries to imitate human behavior that interprets information provided and responds accordingly in either textual or audio format. Nowadays chatbots are used to efficiently carry out digital communication. Our concept introduces a new field of research in chatbot communication. Nowadays most chatbots efficiently complete the required task, but there is one thing to notice. Many of the conversations between a human and a chatbot are repetitive. Technology has always been reducing human effort and hence a multi-agent system is proposed that has a chatbot as a middleware between the user and the outside world. This chatbot has something unique, i.e. it understands the user and their requirements. It is like having an assistant who understands the user & their requirements. Now since it understands the user, it has the capability of making decisions on behalf of the user, thereby reducing the efforts of a user to carry out a task. Our concept of considering one task that is done with the help of a chatbot is demonstrated. The proposed system adapts and acts accordingly on behalf of the user.
Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Abstract Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.
BIoTA: A Buildout IoT Application Language

Domain-Specific Languages (DSLs) serve a wide variety of purposes in the development of complex applications.The Internet of Things (IoT) brings additional complexity to software development due to its inherent distribution and inclusion of a massive number of heterogeneous devices (sensors and actuators).The process of developing software architectures for IoT involves the interaction of several components that play distinct roles.In this paper, we propose a language called BIoTA (Buildout IoT Application Language), a DSL whose objective is to assist and streamline the building of software architectures for IoT.The specification and implementation of the BIoTA language involve a grammar and a compiler, responsible for syntax and semantic analysis, as well as code generation.They facilitate the formalization of software architectures using automata, which would otherwise be created in an informal and ad-hoc manner to address specific IoT scenarios.We developed an IDE (Integrated Development Environment) capable of reading and creating software architectures using the BIoTA language.With the BIoTA IDE, we demonstrate three examples of software architectures for IoT smart applications in public buildings, irrigation, and parking.The IDE also makes it possible to export architectures to a software distribution package pattern based on containers (Docker Compose) for future deployment.
JomDataMining: Learning Behavior Affecting Their Academic Performance, Really?

This research aims to study the relationship between university's students learning behavior affecting their educational academic performance by using the educational data mining method. The objective of this study is to identify the learning behavior of the university students through the data mining method. It is also to allow the academician to evaluate the university student's educational academic performance and to predict the university student's learning behavior affecting their educational academic performance in their university. Tinto's model is the main theoretical framework that studies the learning behavior of the student, together with emotional behavior factors to formulate a prediction of the student academic performance. It enables academician to analyze the characteristics of students enrolling into the universities such as their family background, their goals, priorities enrolling into universities and their experience they obtained. Positive result of this study has depicted the relationship between university student's behaviors affecting their academic performance. There is a significant positive correlation between student's learning behavior and their educational academic performance.