Reviewerly/exHarmony huggingface dataset:
Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

Defects such as the duality and the incompleteness in natural language software requirements specification have a significant impact on the success of software projects. By now, many approaches have been proposed to assist requirements analysts to identify these defects. Different from these approaches, this paper focuses on the requirements incompleteness implied by the conditional statements, and proposes a sentence embedding- and antonym-based approach for detecting the requirements incompleteness. The basic idea is that when one condition is stated, its opposite condition should also be there. Otherwise, the requirements specification is incomplete. Based on the state-of-the-art machine learning and natural language processing techniques, the proposed approach first extracts the conditional sentences from the requirements specification, and elicits the conditional statements which contain one or more conditional expressions. Then, the conditional statements are clustered using the sentence embedding technique. The conditional statements in each cluster are further analyzed to detect the potential incompleteness by using negative particles and antonyms. A benchmark dataset from an aerospace requirements specification has been used to validate the proposed approach. The experimental results have shown that the recall of the proposed approach reaches 68.75%, and the F1-measure (F1) 52.38%.
Quantum Algorithms and Oracles with the Scalable ZX-calculus

The ZX-calculus was introduced as a graphical language able to represent specific quantum primitives in an intuitive way. The recent completeness results have shown the theoretical possibility of a purely graphical description of quantum processes. However, in practice, such approaches are limited by the intrinsic low level nature of ZX calculus. The scalable notations have been proposed as an attempt to recover an higher level point of view while maintaining the topological rewriting rules of a graphical language. We demonstrate that the scalable ZX-calculus provides a formal, intuitive, and compact framework to describe and prove quantum algorithms. As a proof of concept, we consider the standard oracle-based quantum algorithms: Deutsch-Jozsa, Bernstein-Vazirani, Simon, and Grover algorithms, and we show they can be described and proved graphically.
Zero Trust Access Control with Context-Aware and Behavior-Based Continuous Authentication for Smart Homes

Generally, approaches to build the security of Smart Home Systems (SHS) require big amount of data to implement Access Control and Intrusion Detection Systems, with storage in cloud, for instance, being a vulnerability to inhabitants privacy. Besides, most works rely on cloud computing or resources in the cloud to perform security tasks, what can be exploited by attackers. This work presents the ZASH (Zero-Aware Smart Home System), an Access Control for SHS. ZASH uses Continuous Authentication with Zero Trust, supported by real-time context and activity information, enabled by Edge Computing and Markov Chain, to prevent and mitigate impersonation attacks that aim to invade inhabitants privacy. An experimental evaluation demonstrated the system capability to dynamically adapt to new inhabitants behaviors withal blocking impersonation attacks.
Analysis of Different Approach Used in Particle Swarm Optimization Variants: A Comparative Study

For every type of system transformation data/information plays a very important role. Everywhere artificial intelligence system is being deployed to reduce human efforts, as well as to improve system performance, accuracy, effectiveness and throughput of system. These artificial intelligent system works only on information feed to the system as an input. Swarm Intelligence is a most effective way of transmitting information between two communication parties. Most of the Artificial Intelligence system is based on Swarm Intelligence technique where the hardware system (Robots) and software is connected to each other for performing task with robots. This paper represents the survey and comparative analysis of various swarm optimization technique based on their working parameter.
Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

The smoothing effect of data interpolation could cause useful information loss in geochemical mapping, and the uncertainty assessment of geochemical anomaly could help to extract reasonable anomalies. In this paper, multiple-point geostatistical simulation and local singularity analysis (LSA) are proposed to identify regional geochemical anomalies and potential mineral resources areas. Taking Cu geochemical data in the Mila Mountain Region, southern Tibet, as an example, several conclusions were obtained: (1) geochemical mapping based on the direct sampling (DS) algorithm of multiple-point geostatistics can avoid the smoothing effect through geochemical pattern simulation; (2) 200 realizations generated by the direct sampling simulation reflect the uncertainty of an unsampled value, and the geochemical anomaly of each realization can be extracted by local singularity analysis, which shows geochemical anomaly uncertainty; (3) the singularity-quantile (S-Q) analysis method was used to determine the separation thresholds of E-type α, and uncertainty analysis was carried out on the copper anomaly to obtain the anomaly probability map, which should be more reasonable than the interpolation-based geochemical map for geochemical anomaly identification. According to the anomaly probability and favorable geological conditions in the study area, several potential mineral resource targets were preliminarily delineated to provide direction for subsequent mineral exploration.
The 15th Edition of the Multi-Agent Programming Contest - The GOAL-DTU Team

We provide an overview of the GOAL-DTU system for the Multi-Agent Programming Contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the GOAL programming language. We evaluate the performance of our agents in the contest and, finally, we discuss how to improve the system based on an analysis of its strengths and weaknesses.
Toward a quantum future for South Africa

South Africa has a long history in quantum research with an explosion of activity in the recent decade.Bolstered by this momentum, in March 2021, the South African Quantum Technologies Initiative (SA QuTI) was formally launched by the South African Department of Science and Innovation with the first seed funding released in September 2021.SA QuTI puts quantum technology firmly on
HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks

Internet users have suffered from phishing attacks for a long time. Attackers deceive users through malicious constructed phishing websites to steal sensitive information, such as bank account numbers, website usernames, and passwords. In recent years, many phishing detection solutions have been proposed, which mainly leverage whitelists or blacklists, website content, or side channel-based techniques. However, with the continuous improvement of phishing technology, current methods have difficulty in achieving effective detection. Hence, in this paper, we propose an effective phishing website detection approach, which we call HinPhish. HinPhish extracts various link relationships from webpages and uses domains and resource objects to construct a heterogeneous information network. HinPhish applies a modified algorithm to leverage the characteristics of different link types in order to calculate the phish-score of the target domain on the webpage. Moreover, HinPhish not only improves the accuracy of detection, but also can increase the phishing cost for attackers. Extensive experimental results demonstrate that HinPhish can achieve an accuracy of 0.9856 and F1-score of 0.9858.
DWSA: An Intelligent Document Structural Analysis Model for Information Extraction and Data Mining

The structure of a document contains rich information such as logical relations in context, hierarchy, affiliation, dependence, and applicability. It will greatly affect the accuracy of document information processing, particularly of legal documents and business contracts. Therefore, intelligent document structural analysis is important to information extraction and data mining. However, unlike the well-studied field of text semantic analysis, current work in document structural analysis is still scarce. In this paper, we propose an intelligent document structural analysis framework through data pre-processing, feature engineering, and structural classification with a dynamic sample weighting algorithm. As a typical application, we collect more than 11,000 insurance document content samples and carry out the machine learning experiments to check the efficiency of our framework. Meanwhile, to address the sample imbalance problem in the hierarchy classification task, a dynamic sample weighting algorithm is incorporated into our Dynamic Weighting Structural Analysis (DWSA) framework, in which the weights of different category tags according to the structural levels are iterated dynamically in training. Our results show that the DWSA has significantly improved the comprehensive accuracy and the classification F1-score of each category. The comprehensive accuracy is as high as 94.68% (3.36% absolute improvement) and the Macro F1-score is 88.29% (5.1% absolute improvement).
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision

Sequence labeling aims to predict a finegrained sequence of labels for the text.However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data.This is exacerbated when we meet a diverse range of languages.In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages.Specifically, we propose a Meta Teacher-Student (MetaTS) Network, a novel meta learning method to alleviate data scarcity by leveraging large multilingual unlabeled data.Prior teacher-student frameworks of self-training rely on rigid teaching strategies, which may hardly produce high-quality pseudo-labels for consecutive and interdependent tokens.On the contrary, MetaTS allows the teacher to dynamically adapt its pseudoannotation strategies by the student's feedback on the generated pseudo-labeled data of each language and thus mitigate error propagation from noisy pseudo-labels.Extensive experiments on both public and real-world multilingual sequence labeling datasets empirically demonstrate the effectiveness of MetaTS 1 .
Enhanced densely dehazing network for single image haze removal under railway scenes

Purpose This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog. Design/methodology/approach It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper. Findings The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail. Originality/value This study provides a new method for image enhancing in the railway monitoring system.
SpEYEders: Adults’ and children’s affective responses during immersive playful gaze interactions transforming virtual spiders

Specific phobias like spider phobia represent a frequent mental health problem in children and adolescents, demanding innovative prevention and treatment approaches. We therefore develop an eye tracking supported Virtual Reality serious game for school-aged children, realizing gaze interactions to promote attention towards, and positive experiences during exposure to spiders. Within pilot studies in adults (n=30) and children (n=14) without fear of spiders, we assessed positive and negative affect during prototype gaze feedback through five different variants: If gazed for few seconds, the virtual spider changed into a shrunk, a rainbow coloured, or dying spider, or morphed into a smileyball, or speaks friendly. We found the highest positive affect for the rainbow and smileyball variant, followed by the shrunk and friendly speaking variant. In contrast, the dying variant was excluded due to the possible induction of negative affect. Findings indicate eligible variants for the further development of the VR serious game.
Accelerating multi-objective neural architecture search by random-weight evaluation

Abstract For the goal of automated design of high-performance deep convolutional neural networks (CNNs), neural architecture search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments. To address this issue, we first introduce a new performance estimation metric, named random-weight evaluation (RWE) to quantify the quality of CNNs in a cost-efficient manner. Instead of fully training the entire CNN, the RWE only trains its last layer and leaves the remainders with randomly initialized weights, which results in a single network evaluation in seconds. Second, a complexity metric is adopted for multi-objective NAS to balance the model size and performance. Overall, our proposed method obtains a set of efficient models with state-of-the-art performance in two real-world search spaces. Then the results obtained on the CIFAR-10 dataset are transferred to the ImageNet dataset to validate the practicality of the proposed algorithm. Moreover, ablation studies on NAS-Bench-301 datasets reveal the effectiveness of the proposed RWE in estimating the performance compared to existing methods.
A real-time related key attack on the WG-16 stream cipher for securing 4G-LTE networks

The WG-16 stream cipher proposed in 2013 is an efficient variant of the well-known WG stream cipher family. WG-16 inherits good randomness properties of the WG stream cipher family and is intended for use in confidentiality and integrity algorithms in mobile communications, such as 4G-LTE networks. This paper finds that there exist related Key-IV pairs for the WG-16 stream cipher that can generate keystreams which are exact shifts of each other throughout the keystream generation. By exploiting this slide property, a real-time related key attack on WG-16 is proposed, which recovers all 128 key bits with time complexity of about 235.81, requiring 235.81 chosen IVs and 6 related keys. We verify this attack on a common PC, which shows that our attack can recover all 128 key bits of WG-16 within four days. The experimental results indicate that WG-16 can be broken in real time in the multiple related key setting and is not secure as claimed by the designers. This is the first attack that defeats the practical security of WG-16.
The implementation of smartphones in the instructional process in the views of the female students at Hail University

This study aims to examine the implications of implementing smartphones in the educational process in the views of students at the University of Hail. The descriptive survey method was used in the research. To achieve the goal of the research, a questionnaire was prepared regarding the degree of use of smartphones in the learning process which is consisted of 20 paragraphs. The research sample was the female students at the University of Hail. It is found that the overall degree is medium as the arithmetic average is 3.34 with a standard deviation of 0.28. It was concluded that students are prodigiously interested in reviewing the university sites. Moreover, it was found that students are immensely skilled in using smartphones for the exchange of electronic messages and the exchange of information. However, they are less interested in submitting electronic assignments. Few students are watching educational presentations on smartphones because of their small size screens and high drainage from the batteries. Therefore, it is highly recommended to conduct experimental studies showing the importance of using the smartphone.
On Type-2 Fuzzy Sets and Type-2 Fuzzy Systems

One of the advantages of systems based on fuzzy logic (fuzzy systems) is the possibility of a soft switch from one set of values of input parameters of the system to another, when different conclusions are drawn for different sets of these values. A fuzzy set of type 2 is a direct generalization of an ordinary fuzzy set. In this paper, we review some branches of the theory of type-2 fuzzy sets and the theory of type-2 fuzzy systems. We discuss operations on type-2 fuzzy sets, type-2 fuzzy relations, and the centroids of type-2 fuzzy sets and describe type-2 functional fuzzy systems and type-2 relational fuzzy systems.
Nonlinear physical modeling sound synthesis of cymbals involving dynamics of washers and sticks/mallets

This paper is concerned with the extension of the existing nonlinear physical modeling sound synthesis of cymbals to that involving the dynamics of washers supporting the center of cymbals and sticks/mallets striking the cymbals. The body of a cymbal is physically modeled as a shallow spherical shell and its governing equation is discretized in space using the finite difference method, as was implemented in existing research. In addition, a washer, related to the support conditions of the cymbal, is modeled as a single-degree-of-freedom vibration system and involved in the physical model of the cymbal. Furthermore, a stick/mallet striking the cymbal is also involved by modeling it as a multi-degree-of-freedom system using the finite element method and coupled with the cymbal vibration. The time-domain differential of the total system is discretized using implicit finite difference schemes. Trial numerical calculations demonstrate that the developed method is effective in the sound synthesis of the cymbal, grasping the change of timbre due to the dynamics of washers and sticks/mallets.
A New Speech Recognition Model in a Human-Robot Interaction Scenario Using NAO Robot: Proposal and Preliminary Model

There are several terms for speech recognition. Auto speech recognition (ASR), speech-to-text, and computer speech recognition are all terms used to describe speech recognition. A single user's voice it is necessary to distinguish between speech recognition and voice recognition. The first is to translate speech into text, such as, the audible voice and concept (human speech), and the second is to define only sound, such as, animal sound, car, etc. There is no algorithm that is specifically designed for this field; instead, techniques such as N-grams and neural networks are used to explain and treat this type. Natural Language Processing (NLP), Hidden Markov Model (HMM), and Speaker Diarization (SD). The last type would be addressed in my work. Natural language processing is a computational technique that can be used and applied to various levels of linguistic analysis (dare, deep analysis) to represent natural language in a useful or more representation. It is still possible to improve current recognition and identification systems in order to achieve greater accuracy. A new approach has been proposed that distinguishes speech in four stages: speech recognition, tokenization, extracting features of speech from texts, and part speech: The three patterns of Name Entity Recognition (NER), followed by the possibility of implementing the proposed model It achieved more accurate and applied results in an educational environment by using a NAO-robot.
A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.
Learning Affordances: Theoretical Considerations for Design of Immersive Virtual Reality in Training and Education

Technological advances have led to a rapid increase in the implementation of virtual reality (VR) across multiple sectors of society. Further, we are seeing more researchers explore how the technology can be used to promote learning and training in a variety of domains. But there is a problematic gap between development of VR for training and education and learning theory to ensure its efficacy. We address this need by providing a theoretical lens through which to evaluate existing research in VR. We consider technology developments that have made VR more sophisticated and draw from research in the learning and cognitive sciences to evaluate their utility as learning affordances. With this, we examine existing research as a way to illustrate the practical value of theoretical evaluation. We conclude with a discussion how this theoretical framing can point the way for both better designed studies to accelerate learning and training as well as for more innovative adaptations for accelerating learning in immersive virtual reality.
e-Commerce Online Intelligent Customer Service System Based on Fuzzy Control

In order to improve the intelligence of the e-commerce online intelligent customer service system, this paper proposes a deep rejection recognition algorithm based on the maximum interval squared hinge loss and combines the actual needs of the e-commerce online customer service system to build an intelligent customer service system with the support of the fuzzy control system. Moreover, this article chooses to build a domain ontology library for structured storage of domain knowledge needed by customer service chatbots. In addition, this article analyzes the dialogue structure based on the speech act model and combines the semantic vector model of the question sentence on the basis of the dialogue structure to understand the question sentence, which helps to improve the accuracy of the answer feedback of the Internet shopping customer service robot. Finally, this article designs experiments to verify the performance of the online customer service system constructed in this article and analyzes the experimental results through statistical methods. The experimental results show that the online intelligent customer service system constructed in this paper has certain practical effects.