Reviewerly/exHarmony huggingface dataset:
Research on Face Matching Algorithms for Smart Classrooms

Face matching technology is a key part in the applications of face recognition technology. It can be used for classroom behavior analysis, and then improve the teaching effect. The paper introduces the algorithm structure and loss function of face detection algorithm based on MTCNN, and realizes face comparison based on FaceNet model. According to the different tasks of training data set and algorithm in different convolution layer, the diversity of face samples in training set is adjusted. Through MTCNN's sub network training, the weight coefficient of loss function and various thresholds are changed to improve the training effect and enhance the robustness of face detection. Considering the problems of multi person detection, face recognition illumination robustness, algorithm implementation cost and application real-time, this paper integrates MTCNN detection algorithm, FaceNet model and image similarity comparison algorithm, and implements the algorithm on LFW data set. Then, under the condition of specific face segmentation, the selection method of the best matching threshold is analyzed, and the results of Euclidean distance comparison of different faces under the best threshold are given. At last, it gets the recognition accuracy improved by 0.21% on the LFW dataset, which lays a technical foundation for the realization of classroom behavior analysis of students' location attendance.
A Conceptual Model of Military Blockchain for Repair Parts Supply Chain Management

In meeting customer demand, Supply Chain Management (SCM) consists of all levels involved either directly or indirectly. However, SCM has few limitations such as counterfeit parts, uncertainty risks and data breach. Supplying a genuine spare part is important for all organizations especially military. Therefore, this study is proposing a conceptual model of military blockchain. This military blockchain will assist military depot to trace spare parts from its origin and to improve the supply chain performance. Changes on the transaction will be notified to all because the blockchain is using cryptographic protected system. As a result, this conceptual model of military blockchain is an alternative system to improve military logistics SCM.
Emerging Paradigms and Practices in Cloud Resource Management

Cloud computing is one of the hottest technology paradigms started in 2006 with the advent of access to information and communication technology (ICT) resources as the hosted services via the Internet, and Amazon was deliberated as the pioneer. This paradigm shift started with two popularly known features (pay-per-use and shared ownership) and gathered enormous popularity and adoption in Small- and Medium-scale Enterprises. Today, the world has entered into the industry 4.0 era. The conceptualization journey of cloud started with the idea of utility computing, which was first coined by John McCarthy in 1961. Initially, it was perceived that “computing” will be viewed as a public utility in futuristic technology paradigms. Today, this perception has been transformed into visible reality and exponentially growing in computing, communication, and collaboration of systems. This chapter is a broad overview that outlines the important concerns pertaining to the foundation of cloud computing, core concepts that make the cloud a revolutionary technology, and brief evolution with the responses to why the cloud is so sticky technology. Since resource allocation and management is one of the key objective of cloud computing, therefore, this chapter proposes to cover the effective resource management techniques with efficient allocation or reallocation of resources. The cloud contains a large variety of off-site or onsite shareable resources, and therefore effective and efficient resource management is constantly a critical issue to maintain trust, transparency, and availability of services. From the cloud service providers (CSPs) viewpoint, cloud resources must be distributed in a transparent manner with trust and judicious compliances to customers. Also, the promised quality attributes should be maintained in the cloud computing environment. The organization complains about trust and transparency in billings of cloud services. This chapter covers a case study toward the mitigation of trust and transparency issues in consumed cloud resources and their monitoring and metering management in alignment with cloud governance policies and management frameworks over intercloud and intraclouds. The core concepts, definitions, functions, processes of resource management, and contextualized frameworks are covered with challenges to make this chapter a complete knowledge set.
A Generic Model of a Social Collective

We propose a generic model of an intelligent collective that emulates social communication. Its main elements are interchangeable, which allows fast building of specific collective models. The key point is the asynchronous communication between agents. The model allows changes in knowledge representation, modes of communication, algorithms used to receive new knowledge and memory algorithms dealing with forgetting older knowledge. We discuss multiple variants of all those elements, including communication based on Twitter and Facebook social networks. We test examples of specific collectives built using the generic model.
The Teaching Practice of Acquiring Skill by 3D CAD and Technology of NC Machines Using 3D Printer

The Tokyo Metropolitan vocational skills development center provides knowledge, techniques, and skills necessary for job search for people who found a new job, search for a job, or hope to change jobs. 3D CAD・CAM processing course is to provide knowledge and skill with a wide range of manufacturing technology. We proposed a new lesson for drafting digital data by 3D CAD and processing manufacturing techniques. This lesson is creative and original theme using 3D printer and trial products by 3D printer for producing by NC machines. As a result, it was made clear that this lesson had the good effects for the students to acquire drafting and machining skills.
Aspect-based sentiment analysis via relation-aware collaborative learning

Aspect-based sentiment analysis (ABSA) contains three subtasks, namely aspect term extraction, opinion term extraction and aspect-level sentiment classification. In order to make full use of the relationship between the three subtasks, some recent studies have successfully tried to use a unified framework to solve the problem of aspect-based sentiment analysis. However, these studies have not yet integrated domain knowledge into the model. Inspired by the post-training task, we propose a joint model (RACL-BERT-PT). This model combines the pre-training model BERT-PT with domain knowledge and the unified joint training framework RACL. The experimental results show that our model has achieved better results than previous experiments on three public data.
Development of a thermal-visible video surveillance system based on fractional order tv-model

Abstract Detection and tracking of moving objects in video are an active and challenging research area of computer vision devoted to the applications of video surveillance. The several environmental conditions such as dark, foggy, snowing and rainy, and open-ended goals of the problem motivate to develop a robust surveillance system that based on the thermal-visible video spectrum fusion. However, visible-visible and thermal-thermal spectrum based surveillance model show insufficiency under such conditions. The detection of moving objects is performed by means of optical flow. This paper presents a novel fractional order total variation (TV) model in the estimation of optical flow. In particular, the presented fractional order TV-model is designed by generalizing an integer order total variation model formed by using a combination of total variation and quadratic regularization terms. However, it is challenging to solve such complex minimization problem due to the non-differentiability nature of fractional order TV-term. The fractional order derivative is discretized using the Grünwald-Letnikov derivative. The primal-dual algorithm is used as an iterative scheme to solve the resulting formulation. Finally, a number of experimental results on a fusion of visible-visible, thermal-thermal and visible-thermal video spectrum demonstrate the effectiveness of the model.
Competition-Based Adaptive Caching for Out-of-core Graph Processing

A graph engine should possess adaptability to ensure efficient processing despite a variety of graph data and algorithms. In terms of out-of-core graph engines, which exploit a hierarchical memory structure, an adaptive caching scheme is necessary to sustain effectiveness of memory usage. A caching policy selectively stores data likely to be used in the upper-layer memory based on its own expectation about the future workload. However, the graph workload contains a complexity of memory access according to graph data, algorithm, and configurations. This makes it difficult for a static caching policy to respond to the changes in workload. In this paper, we propose a graph-adaptive caching scheme which ensures consistent effectiveness under the changing workloads. Our caching scheme employs an adaptive policy that responds to changes in real-time workloads. To detect the changes, we adopt the competition procedures between two contrasting properties-locality and regularity-that appear in graph workloads. In addition, we combine two window adjustment techniques to alleviate the overhead from competition procedures. The proposed caching scheme is applicable to different types of graph engines, achieving better efficiency in memory usage. Our experimental results prove that our scheme improves the performance of graph processing by up to 65% compared to existing schemes.
The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.
Extracting functional programs from Coq, in Coq

Abstract We implement extraction of Coq programs to functional languages based on MetaCoq’s certified erasure. We extend the MetaCoq erasure output language with typing information and use it as an intermediate representation, which we call ${\lambda^T_\square}$ . We complement the extraction functionality with a full pipeline that includes several standard transformations (e.g. eta-expansion and inlining) implemented in a proof-generating manner along with a verified optimisation pass removing unused arguments. We prove the pass correct wrt. a conventional call-by-value operational semantics of functional languages. From the optimised ${\lambda^T_\square}$ representation, we obtain code in two functional smart contract languages, Liquidity and CameLIGO, the functional language Elm, and a subset of the multi-paradigm language for systems programming Rust. Rust is currently gaining popularity as a language for smart contracts, and we demonstrate how our extraction can be used to extract smart contract code for the Concordium network. The development is done in the context of the ConCert framework that enables smart contract verification. We contribute with two verified real-world smart contracts (boardroom voting and escrow), which we use, among other examples, to exemplify the applicability of the pipeline. In addition, we develop a verified web application and extract it to fully functional Elm code. In total, this gives us a way to write dependently typed programs in Coq, verify, and then extract them to several target languages while retaining a small trusted computing base of only MetaCoq and the pretty-printers into these languages.
An Efficient Real-Time Vehicle Re-Identification Scheme Using Urban Surveillance Videos

With the explosive use of surveillance and onboard cameras, vision-based Vehicle Re-identification (VReID) has attracted widespread attention. The goal of VReID is to search and identify the target vehicle from a large number of images. An efficient VReID model can help the police make fast decisions and improve regional security. The major challenge of the VReID is to distinguish the subtle visual difference between different vehicles. In this work, we propose a Compact Attention Unit (CAU) that relies on a single attention map to extract the discriminative local features of the vehicle. We add two CAUs to the truncated ResNet to construct a small but efficient VReID model, ResNetT-CAU. The feature representation of the vehicle image is the concatenation of the extracted global and local features. Compared with the original ResNet, the model size of ResNetT-CAU is reduced by 60% and has excellent VReID performance. We conduct experiments on two benchmark datasets, VeRi and VehicleID. The results show the proposed model stably achieves excellent VReID performance with very fast processing speed.
Nonlinear Adaptive Control of Unmanned Aerial Vehicle Using Echo State Neural Networks

This paper presents the use of Echo State neural Networks (ESN) for nonlinear adaptive control of a fixed-wing unmanned aerial vehicle (UAV). Both offline and online trained ESNs are investigated. Flight test data and simulated data are used to train the ESNs offline. The first offline trained ESN is used to estimate the inverse transformation function required for feedback linearization, specifically for the lateral-directional case. The second ESN is trained to estimate error in the output of the first ESN. The estimated error from the second ESN is used to make real time controller corrections as well as update the weighting parameters of the output phase of the first ESN, and thus allowing the controller to adapt in flight. Simulated results show that including online adaptive control improves performance in the presence of noise and disturbances.
Appearance Based Feature Extraction and Selection Methods for Facial Expression Recognition

Faces contain a plethora of information such as identity of a person, facial expressions and other properties like race etc. Human face conveys the wealth of their opinions, emotions in the form of facial expressions. The problem of automatic facial expression is both interesting and quite challenging with a strong impact on many application areas such as animation and human-computer interaction (HCI). The field has shown a tremendous growth over past years with its benchmarking efforts and progress. In this paper, the study of facial expression recognition based feature extraction and selection techniques is presented. The paper offers an insight into the work of researchers who have considered appearance based feature extraction techniques for facial expression recognition. In addition, a discussion is made by specifying some of the open question related to facial expression recognition.
Cybersecurity Metrics for Human-Robot Collaborative Automotive Manufacturing

A human-robot collaborative system was developed where a human co-worker and a collaborative robot collaborated to perform assembly tasks. We assumed such assembly tasks as the representative assembly tasks in the automotive manufacturing industry. We assumed that the collaborative assembly system would be a part of an industrial control system (ICS) in actual industrial environment. We recruited 20 human subjects to perform the assembly tasks in collaboration with the robot. We trained the subjects to enable them to perform the collaborative assembly tasks, and evaluate cybersecurity conditions (status) of the collaborative system. Each subject performed the collaborative assembly separately with the robot. At the end of the assembly task, each subject was asked to brainstorm, write down the cybersecurity assessment criteria as best as he/she could perceive or realize through his/her experiences with the collaborative system, and rate the cybersecurity status of the collaborative system for each of the criteria (i.e., how capable the collaborative system was in fulfilling the cybersecurity requirements for each criterion) using a five-point subjective rating scale (a Likert scale), where 1 indicated the least and 5 indicated the most secured system. We then analyzed the responses and determined a list of the tentative cybersecurity assessment criteria with their relative importance (the total frequency of each criterion proposed by the subjects was to indicate the importance level for that criterion). We also proposed how machine learning and data analytics could be applied to analyze the cybersecurity metrics and enhance cybersecurity in the collaborative system. The proposed cybersecurity metrics can serve as the preliminary effort towards developing comprehensive cybersecurity metrics and methods for human-robot collaborative assembly in automotive manufacturing in particular and for human-robot collaborative systems in general.
Bayesian Pruned Random Rule Foams for XAI

A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.
‘A mobile phone is like a friendship. It depends from person to person how it is used': mobile phone relationships among low-income women in urban Bangladesh

This article presents ethnographic accounts of the mobile phone mediated experiences of connection and intimacy among young low-income women in Dhaka, Bangladesh. It argues that mobile phones are a ‘pathway to empowerment’ insofar as the ever-evolving social practices of ‘wrong-number friendships’, long-distance courtship, and the management and manipulation of new contacts on mobile phones broaden the range of social interactions, enable expressions of aspirational mobility, and contribute to the evolution of a sense of self. It also explores how the affective power of mobile phone communication in intimate relationships alters perceptions of distance and creates forms of ‘immobile mobility’. Thus, this article builds on research that argues that the material affordances of a mobile phone challenge social and cultural gender norms and power dynamics. Moreover, by drawing out how mobility, gender and class intersect with the social and emotional experiences of low-income women in Bangladesh, it goes beyond existing literature and offers a more nuanced perspective on the notion that access to technology among marginalised and disempowered groups leads to poverty alleviation and greater equality.
Neural Network Based Handwritten Character Recognition

In this paper, an endeavor is made to perceive handwritten characters for English letters in order. The principle point of this task is to plan a master framework for, "HCR(English) utilizing Neural Network". that can viably perceive a specific character of type design utilizing the Artificial Neural Network approach. The handwritten character acknowledgment issue has become the most well-known issue in AI. Handwritten character acknowledgment has been a difficult space of examination, with the execution of Machine Learning we propose a Neural Network based methodology. Acknowledgment, precision rate, execution and execution time are a significant model that will be met by the technique being utilized.
Testing Self-Adaptive Software With Probabilistic Guarantees on Performance Metrics: Extended and Comparative Results

This paper discusses methods to test the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approach to overcome the mentioned difficulties and provide probabilistic guarantees on the software performance. We describe the set up needed for the application of a probabilistic approach. We then discuss the traditional tools from statistics that could be applied to analyse the results, highlighting their limitations and motivating why they are unsuitable for the given problem. We propose the use of a novel tool – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the Scenario Theory</i> – to overcome said limitations. We conclude the paper with a thorough empirical evaluation of the proposed approach, using three adaptive software applications: the Tele-Assistance Service, the Self-Adaptive Video Encoder, and the Traffic Reconfiguration via Adaptive Participatory Planning. With the first, we empirically expose the trade-off between data collection and confidence in the testing campaign. With the second, we demonstrate how to compare different adaptation strategies. With the third, we discuss the role of the randomisation in the selection of test inputs. In the evaluation, we apply the scenario theory and also classical statistical tools: Monte Carlo and Extreme Value Theory. We provide a complete evaluation and a thorough comparison of the confidence and guarantees that can be given with all the approaches.