Reviewerly/exHarmony huggingface dataset:
Deep compact polyhedral conic classifier for open and closed set recognition

In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function. The proposed method has one loss term that allows the margin maximization to maximize the inter-class separation and another loss term that controls the compactness of the class acceptance regions. Our proposed method has a nice geometric interpretation using polyhedral conic function geometry. We tested the proposed method on various visual classification problems including closed/open set recognition and anomaly detection. The experimental results show that the proposed method typically outperforms other state-of-the-art methods, and becomes a better choice compared to other tested methods especially for open set recognition type problems. The source code of the proposed method is available at https://github.com/bdrhn9/dc-epcc.
The Current Status and progress of Adversarial Examples Attacks

Applications based on deep learning are prevailing in many real-world scenarios. However, due to the interpretability of deep learning and the emergence of adversarial examples attacks, their reliability has been put into doubt. As for Natural Language Processing, adversarial samples that can be semantically similar to the original ones can also be generated so that NLP classification can be fooled without human observers' attention. In this paper, we give an overview of the existing method in implementing adversarial examples attacks. Firstly, we introduce the implementation of these methods and the damage they impose on the security, integrity, and robustness of NLP systems. Secondly, we discuss problems like the limited attention on the logic of defense, the curse of dimensionality, and the difficulty of implementing white-box adversaries. Finally, to present our opinion on ML security's structure, our discussion upon the standardization of both attack and defense is also included.
A fuzzy CMAC learning approach to image based visual servoing system

This study presents a fuzzy robotic joint controller using a cerebellar model articulation controller (CMAC) integrating a Takagi-Sugeno (T-S) framework with an online compensator for an articulated manipulator. The proposed controller is applied to image-based visual servoing (IBVS), including closed-loop feedback control and the kinematic Jacobian calculation. This approach learns a mapping from image feature errors for each joint's velocity instead of the classical kinematics, thereby reducing the computational complexity and improving the self-regulation ability of the control system. These connecting weights of the cerebellar model learn offline, and an online compensator that uses reinforcement learning is developed to resolve system noise and uncertainties in an unknown environment. Compared with the classical inverse kinematics model, this approach does not need an excessive computational expense so that this proportional controller can be implemented in general scenarios with an eye-in-hand configuration. Experimental results show the proposed method can outperform the classical IBVS controller.
An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach

Abstract Examining the general impact of Controlled Language (CL) rules in the context of Machine Translation (MT) has been an area of research for many years. The present study focuses on the following question: how do CL rules impact MT output individually ? By analysing a German corpus-based test suite of technical texts that have been translated into English by different MT systems, this study endeavours to answer this question at different levels: the general impact of CL rules (rule- and system-independent), their impact at rule level (system-independent) as well as at rule and system level. The results of five MT systems are analysed and contrasted: a rule-based system, a statistical system, two differently constructed hybrid systems, and a neural system. For this, a mixed-methods triangulation approach that includes error annotation, human evaluation, and automatic evaluation was applied. The data was analysed both qualitatively and quantitatively in terms of CL influence on the following parameters: number and type of MT errors, style and content quality, and scores of two automatic evaluation metrics. In line with many studies, the results show a general positive impact of the applied CL rules on the MT output. However, at rule level, only four rules proved to have positive effects on the aforementioned parameters; three rules had negative effects on the parameters; and two rules did not show any significant impact. At rule and system level, the rules affected the MT systems differently, as expected. Rules that had a positive impact on earlier MT approaches did not show the same impact on the neural MT approach. Furthermore, neural MT delivered distinctly better results than earlier MT approaches, namely the highest error-free, style and content quality rates both before and after applying the rules, which indicates that neural MT offers a promising solution that no longer requires CL rules for improving the MT output.
Exploring human factors of the agile software tester

Abstract Although extensive research has been conducted on the characteristics of the agile developer, little attention has been given to the features of the software-testing role. This paper explores the human factors of the software testers working in agile projects through a qualitative study focusing on how these factors are perceived. We interviewed 22 agile software practitioners working in three international companies: 14 testers, five developers, and three designers. Additionally, we observed 11 meetings and daily work of 13 participants in one of the companies. Our findings show that the views on the human factors shaping the agile software tester’s role were crystallized into seven traits, which the agile team members saw as central for the software-testing role: the ability to see the whole picture, good communication skills, detail-orientation, structuredness, creativeness, curiosity, and adaptability. The testers spent half their day communicating and learned how to mitigate the fact that they had to bring bad news to other project members. They also facilitated communication between the business side and development. Based on our results, we propose the seven traits as dimensions to consider for organizations recruiting agile software testers, as well as a reference for IT and non-IT professionals considering a software-testing career.
Deep Learning-Driven Gaussian Modeling and Improved Motion Detection Algorithm of the Three-Frame Difference Method

To enhance the effect of motion detection, a Gaussian modeling algorithm is proposed to fix holes and breaks caused by the conventional frame difference method. The proposed algorithm uses an improved three-frame difference method. A three-frame image sequence with one frame interval is selected for pairwise difference calculation. The logical “OR” operation is used to achieve fast motion detection and to reduce voids and fractures. The Gaussian algorithm establishes an adaptive learning model to make the size and contour of the motion detection more accurate. The motion extracted by the improved three-frame difference method and Gaussian model is logically summed to obtain the final motion foreground picture. Moreover, a moving target detection method, based on the U-Net deep learning network, is proposed to reduce the dependency of deep learning on the number of training datasets. It helps the algorithm to train models on small datasets. Next, it calculates the ratio of the number of positive and negative samples in the dataset and uses the reciprocal of the ratio as the sample weight to deal with the imbalance of positive and negative samples. Finally, a threshold is set to predict the results for obtaining the moving object detection accuracy. Experimental results show that the algorithm can suppress the generation and rupture of holes and reduce the noise. Also, it can quickly and accurately detect movement to meet the design requirements.
Mining consistent correspondences using co-occurrence statistics

In this paper, we propose a mismatch removal method, which mines consistent image feature correspondences using co-occurrence statistics. The proposed method relies on a co-occurrence matrix that counts the number of pixel value pairs co-occurring within the images. Specifically, we propose to integrate the co-occurrence statistics with local spatial information, to preserve the consensus of neighborhood elements. Then, a new measure based on co-occurrence statistics is defined for correspondence similarity, to preserve the consensus of neighborhood topology. After that, with the consensus of neighborhood elements and neighborhood topology, the mismatch removal problem is formulated into a mathematical model, which has a closed-form solution. Extensive experiments show that the proposed method is able to achieve superior or competitive performance on matching accuracy over several state-of-the-art competing methods. In addition, we further exploit the consensus of neighborhood elements and neighborhood topology to propose a novel guided sampling method, which can significantly improve the quality of sampling minimal subsets over state-of-the-arts for two-view geometric model fitting.
A framework for uncertainty-aware visual analytics of proteins

Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate from various sources and can have a significant effect on algorithms and decisions based upon such models. Incorporating uncertainty in state-of-the-art visualization approaches for molecular data is an important issue to ensure that scientists analyzing the data are aware of the inherent uncertainty present in the representation of the molecular data. In this work, we introduce a framework that allows biochemists to explore molecular data in a familiar environment while including uncertainty information within the visualizations. Our framework is based on an anisotropic description of proteins that can be propagated along with required computations, providing multiple views that extend prominent visualization approaches to visually encode uncertainty of atom positions, allowing interactive exploration. We show the effectiveness of our approach by applying it to multiple real-world datasets and gathering user feedback.
GraphCrypto: Next generation data security approach towards sustainable smart city building

Data confidentiality and integrity are essential security goals in the data communication in the smart city or similar applications. Despite significant changes in modern data communication systems and devices, the cryptosystems are still the primary option to achieve data security. Several cryptosystems have been developed, implemented and standardized to date. However, there is always a continuous demand for a new cryptosystem for providing security during data communication. In this paper, a novel symmetric cryptosystem is presented for achieving data confidentiality. The proposed cryptosystem is a graph-based system, where an algebraic structure of directed graphs along with a novel operation is defined to provide the base of the cryptosystem. Here, a directed graph represents a plaintext, a ciphertext, and a secret key as well. The defined operation is used for encrypting and decrypting plaintext and ciphertext graphs. The proposed cryptosystem provides a simplified transformation process and representation of plaintext to plaintext graph. The correctness of the proposed cryptosystem is validated, and also shown that the conventional brute-force approach to obtain the key from either plaintext or ciphertext is exponential and this computational time is significantly higher than AES. This implies that the proposed cryptosystem is highly secured against this attack. The proposed cryptosystem is implemented and compared with AES and the results infer that it incurs a marginally higher execution time than the AES for larger plaintext due to the involvement of matrix operations.
The Development of Fire Safety Appliances Inspection Training using Virtual Reality (VR) Technology

Abstract Nowadays, inspection training is still in a conventional way, and the inspector should provide their time and effort to go on-site to conduct an inspection and take a risk or by face to face learning. Considering the not efficient and what is at stake, it is crucial to seek a reliable inspector. Creating competencies and skill for the professional inspector is need many experiences and training. For Conducting inspector training, an environmental simulation based on the conditions of what needs to be inspected is required. Expensive to make environmental simulations and reasons for limited space and time spent on conducting inspection training for surveyors. Virtual reality technology can be a solution to this problem because virtual reality does not become risky and do not takes many costs invested. This research will start with problem identification, data collecting, analysis phase, creating a virtual object, and Trial Phase. The deliverable of this research would be a remotely controllable virtual reality application based android operation system which is the model and design based on the container ship. The application was design with two main modes, which is Ship Tour Mode that can bring the user to explore the ship environment and see the explanation of fire fighting appliances and Inspection Mode that bring the user to finish a 15 minutes mission to find all the fire fighting appliances in the entire ship and fulfill the inspection checklist. Virtual Reality is beneficial for any sector because it can increase competencies, experience, easy access and easy to use. Based on the objective of this application, it can be concluded that the application can develop and simulate fire safety appliances inspection by making a model of the real condition of the fire safety appliances into 3D modelling and install into a virtual reality application maker. And based on the user questionnaire result, this application in the excellent category because the total value is 364 which is between 360 and 450 (maximum), which is the excellent category.
Abnormal flow detection in industrial control network based on deep reinforcement learning

Industrial control systems are the brain and central nervous system of a country's vital infrastructure. Once the control system collapses, the consequences are unimaginable. Therefore, the safety of industrial control system has become the top priority in the field of safety. Aiming at the problem that the traditional abnormal flow detection model in the industrial control system is not accurate in identifying abnormalities, we combine the perception ability of deep learning with the decision-making ability of reinforcement learning, and propose an abnormal flow detection model based on deep reinforcement learning. The neural network is used to extract the features of the preprocessed dataset, and then the learning strategy can be adjusted according to the special advantages of strengthening the decision-making ability of learning and feedback. The experimental results show that the model based on deep reinforcement learning can achieve 98.06% accuracy in abnormal flow detection.Compared with various methods proposed by peers in current literature, this method is superior to other technologies in four evaluation indexes including accuracy rate, accuracy rate, recall rate and F1 score, among which the accuracy is increased by 2 percentage points.
Does augmented reality augment user affordance? The effect of technological characteristics on game behaviour

This study conceptualises a framework of technological affordances for augmented reality games (ARG) relative to the affordances of internalised and embodied experiences by users. This study examines players’ affordances and investigate how they influence user experience in ARG. It explores how affordances are perceived and enacted by users in an augmented environment to maximise user experience of ARG. A multimethod research approach was utilised that integrated ethnographic and statistical methods. Qualitative study confirmed the general structure of affordance framework, while also revealing relational structures with other variables to explore. Based on the affordance factors identified from the ethnographic methods, a survey questionnaire was created to map and investigate the effects of affordance on the user’s cognitive processes and the influence of affordance on the gameplay process. The results show that technological properties of the ARG system affect opportunities for action available in the environment (affordances). The heuristic role of immersion and presence affordances through underlying cues appear to trigger a player’s sensory representations of affective affordances.
A Survey of Privacy Solutions using Blockchain for Recommender Systems: Current Status, Classification and Open Issues

Abstract Due to the rapid growth of Internet, E-commerce and Internet of Things, people use Web based services for most of their needs including buying items, reading books, watching online shows etc. Several companies are using recommender systems to influence people’s choices based on their likings, behaviours etc. Hence, people fear that their privacy is violated. Also, some of the online applications are not safe and secure. One way to overcome the privacy related issues is using secured solutions such as incorporating blockchain technologies for privacy-based applications. The decentralized nature of blockchain technologies have resolved several security, and authentication problems of Internet of Things systems. In this paper, we conduct a comprehensive survey on the privacy solutions for recommender systems emphasising current status, classification and open issues. We also discuss blockchain technology, including its structure as well as applications of blockchain technology for privacy solutions of recommender systems. Furthermore, we discuss the limitations and delve into future trends that blockchain technology can be adapted for privacy-base applications in the years to come.
Generating UML Use Case and Activity Diagrams Using NLP Techniques and Heuristics Rules

The process of generating Unified Modeling Language (UML) Diagrams from Natural Language (NL) requirements is considered a complex and challenging task. Software requirements specification is often written in NL format, which causes potential problems. Requirement's analysts analyze and process natural language requirements manually to extract the UML elements. The manual analysis takes a lot of time and effort, which justifies the need for automated support. This paper proposes an approach to facilitate the NL requirements analysis process and UML diagrams extraction from NL textual requirements using natural language processing (NLP) techniques and heuristics rules. This approach focuses on generating use-case and activity diagrams. The approach has been applied to a case study and evaluated through an experimental. The evaluation of the approach will be conducted through a comparative study. The experimental results prove that the proposed approach is considerably improved as compared to the other approaches.
Short Video Streaming With Data Wastage Awareness

Fueled by emerging short video applications (e.g., TikTok), streaming short videos is now ubiquitous among mobile users. A major problem in short video streaming is data wastage, i.e., the downloaded video data is not watched but discarded due to video switching, so the bandwidth consumed in video transferring is wasted. Our measurements show that 45.2% of video data is wasted in practice, which is a significant proportion that can lead to tremendous financial loss. To tackle the problem, this work develops a novel algorithm called Wastage Aware Streaming (WAS) which learns viewing behaviors and network conditions to reduce data wastage while keeping Quality-of-Experience (QoE) intact. Extensive evaluations show that WAS can substantially reduce data wastage, e.g., 70%, without any adverse impact on QoE, thus it offers a practical solution for data wastage in short video services.
Fastest rates for stochastic mirror descent methods

Relative smoothness—a notion introduced in Birnbaum et al. (Proceedings of the 12th ACM conference on electronic commerce, ACM, pp 127–136, 2011) and recently rediscovered in Bauschke et al. (Math Oper Res 330–348, 2016) and Lu et al. (Relatively-smooth convex optimization by first-order methods, and applications, arXiv:1610.05708 , 2016)—generalizes the standard notion of smoothness typically used in the analysis of gradient type methods. In this work we are taking ideas from well studied field of stochastic convex optimization and using them in order to obtain faster algorithms for minimizing relatively smooth functions. We propose and analyze two new algorithms: Relative Randomized Coordinate Descent (relRCD) and Relative Stochastic Gradient Descent (relSGD), both generalizing famous algorithms in the standard smooth setting. The methods we propose can be in fact seen as particular instances of stochastic mirror descent algorithms, which has been usually analyzed under stronger assumptions: Lipschitzness of the objective and strong convexity of the reference function. As a consequence, one of the proposed methods, relRCD corresponds to the first stochastic variant of mirror descent algorithm with linear convergence rate.
Face Identity for Face Mask Recognition System

Facial recognition system analyzes and compares patterns based on the facial information of a person. The technology is widely implemented as a security mechanism in the area such as public surveillance and mobile communication to identify the identity of a person. Today, wearing a face mask is mandatory during COVID-19 pandemic. However, it has become challenging for facial recognition technology to recognize the identity of a person wearing a face mask. This preliminary study set out to develop and measure the effectiveness of a face recognition system to identify the identity of a person with a face mask. The system uses a deep learning model for face mask detection and Procrustes analysis to measure the face identity similarity. A combination of standard face recognition performance measures was used to measure the effectiveness of the system. A self-obtained dataset consists of 35 images of persons with a face mask from different identity background was investigated. The results indicate that the system effectively recognizes the identity of a person with a face mask up to 97.14% accuracy. Despite its exploratory nature, this study offers some insight into face masked identity recognition. A natural progression of this work is to explore other types of face mask detection and similarity methods.