Reviewerly/exHarmony huggingface dataset:
Multiple Sensor Track Fusion Algorithm Based on LSTM Network

Aiming at the problem of low accuracy of multisource sensor fusion caused by the difficult acquisition of prior information and weak compatibility of fusion data when tracking missiles in air combat, a track fusion algorithm based on the LSTM method is proposed. Since the track data is a kind of time-series information, the LSTM network is used to learn the relevant prior knowledge of the front and rear sequences, combined with the good nonlinear processing ability of deep learning, the network can learn the track information that is closer to the actual battlefield environment and realize track fusion. Experiments show that the effect of this method has significantly less error than the traditional method when there is common process noise, and compared with other neural networks, this method also shows a better fusion effect, indicating that the algorithm can effectively avoid the estimation error caused by the public a priori estimation and is feasible.
Malicious and Benign URL Dataset Generation Using Character-Level LSTM Models

As technologies advance, so do the attacks on them. Cybersecurity plays a significant role in society to protect everyone. Malicious URLs are links designed to promote scams, attacks, and frauds. Companies often have web filtering algorithms that will blacklist specific URLs as malicious; however, due to privacy concerns, they will not give outside entities access to their cybersecurity data. Unfortunately, this lack of data creates a dire need for more data in cybersecurity research and machine learning applications. This paper proposes using machine learning to generate new synthetic URLs characteristically indistinguishable from the data they replace. To do this two character-level long short-term memory (LSTM) models were trained, one to generate malicious URLs and one to generate benign URLs. To assess the quality of the synthetic data two tests were performed. (1) Classify the URLs into malicious and benign to ensure the characteristics of the original data were preserved. (2) Use the Levenstein ratio to check the similarity between the real and synthetic URLs to ensure sufficient anonymization. The results from the classification test show that the synthetic data classifier only slightly underperformed the real data classifier; however, with having accuracy, precision, recall, sensitivity, and specificity above 99%, it can be concluded that the characteristics of the malicious and benign URLs were preserved. The Levenstein ratio tests showed a mean of 67% and 79% similarity for the benign and malicious URLs, respectively. In the end, the character-level LSTM model successfully generated an anonymized, synthetic dataset, that was characteristically similar to the original, which could pave the way for the publication of many more datasets in this way.
Quadropong - Conditions for Mediating Collaborative Interaction in a Co-located Collaborative Digital Game using Multi-Display Composition

In this demo, we present Quadropong, a co-located collaborative digital game using multi-display composition. It consists of conditions in the form of ten game instances designed to mediate various levels of collaborative interaction in players. The conditions were designed based on variations in interdependence and in symmetry and asymmetry in skills and resources. The preliminary results from playtests indicate that it is possible to design conditions to mediate various levels of collaborative interaction.
Impact of Feedback Type on Explanatory Interactive Learning

Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario. Different user feedback types will have different impacts on user experience and the cost associated with collecting feedback since different feedback types involve different levels of image annotation. Although XIL has been used to improve classification performance in multiple domains, the impact of different user feedback types on model performance and explanation accuracy is not well studied. To guide future XIL work we compare the effectiveness of two different user feedback types in image classification tasks: (1) instructing an algorithm to ignore certain spurious image features, and (2) instructing an algorithm to focus on certain valid image features. We use explanations from a Gradient-weighted Class Activation Mapping (GradCAM) based XIL model to support both feedback types. We show that identifying and annotating spurious image features that a model finds salient results in superior classification and explanation accuracy than user feedback that tells a model to focus on valid image features.
Argument Schemes for Factor Ascription

Reasoning with legal cases by balancing factors (reasons to decide for and against the disputing parties) is a two stage process: first the factors must be ascribed and then these reasons for and against weighed to reach a decision. While the task of determining which set of reasons is stronger has received much attention, the task of factor ascription has not. Here we present a set of argument schemes for factor ascription, illustrated with a detailed example.
An Analysis of the Innovative Model of English Course Translation Teaching in Foreign Language Listening Teaching Relying on Interactive Digital Media

Traditional single English course translation education modes can no longer stimulate students’ interest in learning and require continuous improvement in the alignment of English learning needs with individual needs. The Ministry of Education of my country has explicitly advocated a new form of education, a model of university English education (CET) based on computers and classrooms. In a networked environment, CET is constantly optimizing and adjusting the ecological balance of students, teachers, and the environment by changing the concept of education, teaching methods, and modes of education. With the rise of SPOCS, MOOCs, and mixed education, the combination of face-to-face and online learning has become an important development trend in language education. SPOCS is a new educational model based on modern educational technology, with students as the center of educational activities and the recipients of knowledge. Through the new educational mode, students can complete their education independently. This becomes part of the internalization of classroom knowledge. Therefore, the new educational mode of CET education in foreign language listening education compensates for the shortcomings of traditional educational modes and enhances the enthusiasm of students studying CET courses. To facilitate interaction and interaction between classroom education and other countries’ education system, the new educational mode enhances students’ autonomous learning abilities and effectively promotes individual learning.
The Next Challenge for Data Protection Law

A considerable amount of literature has been published on autonomous vehicles, robotics in healthcare, and the danger of losing jobs and human control as a result of automation. However, there has been relatively little discussion about how AI might change scientific research. AI-assisted scientific research is already offering a significant boost to the process of scientific discovery. Not surprisingly, this radical change in scientific research will have significant consequences. First, if the research process becomes automated, it may be conducted by anyone, which puts citizen science in a new context. Just as developments in hardware and software made personal computers feasible for individual use, automated research may affect science in the future. Second, unlike researchers, AI and neural networks cannot explain their thinking yet. The fully automated research expands the ‘black box’, making oversight and ethical review problematic in systems opaque to outside scrutiny. Automated research raises many further questions about regulation, safety, funding and patentability. This chapter will focus on the issues connected with privacy and data protection, from the EU GDPR point of view.
Fusion of MFCC and IMFCC for Whispered Speech Recognition

Whisper is a low voice spoken in someone’s ear or in private. Whisper speech recognition has always been a hot research topic. Accurate recognition of whispered speech plays an important role in the digital model of speech generation. For example, in the login scene of mobile banking, “dynamic digital voiceprint + face recognition technology” is often used for voiceprint login and identity verification. However, there is no public corpus of Chinese string whispers. To this end, this paper first creates a Chinese digit string whisper corpus, which lays the foundation for the experiments in the paper and future research work; secondly, this paper tries two fusion methods, which are improved by 0.4% and 0.7% respectively on the basis of the baseline.
Managing Students' Learning Motivation in the Digitalization Context

In the modern world, digital technologies are not only a tool of the educational process, but also a living environment that opens up new educational opportunities. The system of “digital” education imposes certain requirements on students, the main ones of which are self-organization and self-motivation of educational activities. The article aims to solve the problem of increasing students' educational motivation in the modern conditions of total digitization. The authors conclude that the basis of self-education is internal motives, but they cannot be formed without external ones since internal needs appear only thanks to external influences. The creation of an external environment requires consideration of the peculiarities of young people's cognitive development. Modern students have a primarily visual thinking, a short-term memory with signs of transactional memory, an increased speed of receiving information under distracted attention, and an ability to study in a multitasking mode. When organizing education in a digital educational environment, taking into account the characteristics of representatives of the digital generation, one should turn to the structural elements of didactics: “teaching”, “learning”, “content”, “method”. In modern conditions, the elements themselves remain unchanged, but the relationships between them are modified. In the context of increasing educational motivation, the elements include change of the teacher's role; self-organization by a student of their educational activities, choice of their individual trajectory of study, forms and methods of educational activities; the use of interactive forms of teaching and assessment (blended learning, digital platform, virtual simulators, webinars, video lectures, joint sessions, etc.); group interaction in the form of collaborative learning with student-program-student, student-program-teacher relationships; the use of interactive teaching methods, typical group and team forms of work (collaborations, storytelling, gamification, project method, etc.). The results do not contradict the factors influencing the formation of sustainable motivation for learning activities stated by E.P. Ilyin and A.K. Markova, and are also confirmed by the answers to the survey among students.
KSG: Knowledge and Skill Graph

The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their relationships, traditional knowledge graphs are static and encyclopedic in nature. On this basis, event knowledge graph (Event KG) models the temporal and spatial dynamics by text processing to facilitate downstream applications, such as question-answering, recommendation and intelligent search. Existing KG research, on the other hand, mostly focuses on text processing and static facts, ignoring the vast quantity of dynamic behavioral information included in photos, movies, and pre-trained neural networks. In addition, no effort has been done to include behavioral intelligence information into the knowledge graph for deep reinforcement learning (DRL) and robot learning. In this paper, we propose a novel dynamic knowledge and skill graph (KSG), and then we develop a basic and specific KSG based on CN-DBpedia. The nodes are divided into entity and attribute nodes, with entity nodes containing the agent, environment, and skill (DRL policy or policy representation), and attribute nodes containing the entity description, pre-train network, and offline dataset. KSG can search for different agents' skills in various environments and provide transferable information for acquiring new skills. This is the first study that we are aware of that looks into dynamic KSG for skill retrieval and learning. Extensive experimental results on new skill learning show that KSG boosts new skill learning efficiency.
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models

Neural ranking models (NRMs) have achieved promising results in information retrieval. NRMs have also been shown to be vulnerable to adversarial examples. A typical Word Substitution Ranking Attack (WSRA) against NRMs was proposed recently, in which an attacker promotes a target document in rankings by adding human-imperceptible perturbations to its text. This raises concerns when deploying NRMs in real-world applications. Therefore, it is important to develop techniques that defend against such attacks for NRMs. In empirical defenses adversarial examples are found during training and used to augment the training set. However, such methods offer no theoretical guarantee on the models' robustness and may eventually be broken by other sophisticated WSRAs. To escape this arms race, rigorous and provable certified defense methods for NRMs are needed.
On D(n; q) quotients of large girth and hidden homomorphism based cryptographic protocols

Noncommutative cryptography is based on applications of algebraic structures like noncommutative groups, semigroups, and noncommutative rings.Its intersection with Multivariate cryptography contains studies of cryptographic applications of subsemigroups and subgroups of affine Cremona semigroups defined over finite commutative rings.Efficiently computed homomorphisms between stable subsemigroups of affine Cremona semigroups can be used in tame homomorphisms protocols schemes and their inverse versions.The implementation scheme with the sequence of subgroups of affine Cremona group that defines the projective limit was already suggested.We present the implementation of another scheme that uses two projective limits which define two different infinite groups and the homomorphism between them.The security of the corresponding algorithm is based on complexity of the decomposition problem for an element of affine Cremona semigroup into a product of given generators.These algorithms may be used in postquantum technologies.
PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN DIAGNOSA KERUSAKAN SEPEDA MOTOR MATIC HONDA BEAT MENGGUNAKAN METODE FORWARD CHAINING

An expert system is a system that seeks to adopt human knowledge to a computer that is designed to model problem solving abilities like an expert (Durkin, 1994). The three main components contained in any expert system are the knowledge base, the inference engine, and the user interface. Forward chaining is a strategy to predict or find a solution to a problem that starts with a set of known facts, then derives new facts based on rules whose premises match the known facts. This process is continued until the goal is reached or there are no more rules whose premise matches the known facts. The system maker application media uses PHP and MySQL as databases
Building the MSR Tool Kaiaulu: Design Principles and Experiences

Background: Since Alitheia Core was proposed and subsequently retired, tools that support empirical studies of software projects continue to be proposed, such as Codeface, Codeface4Smells, GrimoireLab and SmartSHARK, but they all make different design choices and provide overlapping functionality. Aims: We seek to understand the design decisions adopted by these tools–the good and the bad–along with their consequences, to understand why their authors reinvented functionality already present in other tools, and to help inform the design of future tools. Method: We used action research to evaluate the tools, and to determine a set of principles and anti-patterns to motivate a new tool design. Results: We identified 7 major design choices among the tools: 1) Abstraction Debt, 2) the use of Project Configuration Files, 3) the choice of Batch or Interactive Mode, 4) Minimal Paths to Data, 5) Familiar Software Abstractions, 6) Licensing and 7) the Perils of Code Reuse. Building on the observed good and bad design decisions, we created our own tool architecture and implemented it as an R package. Conclusions: Tools should not require onerous setup for users to obtain data. Authors should consider the conventions and abstractions used by their chosen language and build upon these instead of redefining them. Tools should encourage best practices in experiment reproducibility by leveraging self-contained and readable schemas that are used for tool automation, and reuse must be done with care to avoid depending on dead code.
Explicit attacks on the Bennett-Brassard 1984 protocol with partially distinguishable photons

The distinguishability of photons in nonoperational degrees of freedom compromises the unconditional security of quantum key distribution since an eavesdropper can improve attack strategies by exploiting this distinguishability. However, the optimal eavesdropping strategies in the presence of light-source side channels are not known. Here we provide several explicit attack strategies on the Bennett-Brassard 1984 (BB84) protocol with partially distinguishable photons. In particular, we consider the phase-covariant cloning attack, which is optimal in the absence of side channels, and show that there are even better strategies in the presence of side channels. The improved strategies exploit a measurement of the side-channel state followed by an attack on the signal photon. Our results explicitly demonstrate a reduction of the critical error rate and set an upper bound on the practical secret key rate.
A framework for conditional statement technical debt identification and description

Technical Debt occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35%, 59.36%, 31.78%, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct human evaluation to the SATD comments generated by SATDID. In 1-5 and 0-5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.
ADOPTING SMART BUILDING CONCEPT IN HISTORICAL BUILDING: CASE OF ABU JABER MUSEUM, JORDAN

Introduction: Heritage conservation attracts wide attention worldwide. Jordan has many heritage buildings that were preserved, rehabilitated, and adapted to new functions. However, these conservation efforts could not solve the energy consumption problem or maintain the economic and financial balance, thus reducing building efficiency, slowing down the conservation activity, and forcing us to consider new solutions, especially since Jordan has limited energy sources. Integrating smart technologies in historical buildings is widely effective in achieving the sustainability of their historical, symbolic architectural values. Purpose of the study: We aimed to explore the potential integration of smart technologies in cultural and heritage buildings. Methods: We suggested an alternative solution — adopting the smart building concept in the Abu Jaber Museum in Al-Salt (which recently got on the UNESCO World Heritage List since it has more than 1000 heritage buildings), using an automated lighting control system. Approach: We deployed the qualitative method and case study approach to investigate the potential of adopting the smart building concept in the historical building in Al-Salt, Jordan. The study sheds light on the possibilities of utilizing smart technologies in historical buildings in Al-Salt. The findings indicate that smart technologies can offer great opportunity in preserving the architectural heritage and raising the efficiency of heritage buildings. Novelty: The study provides a framework based mainly on automated lighting systems in historical buildings. For the first time, the focus was on historical buildings in Jordan and their performance.
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Virtual reality (VR) has shown great educational potential because it makes it possible to simulate any desired situation or event, thus playing an important role in addressing current educational challenges. Despite the unlimited learning possibilities that VR may offer, unless users are willing to apply virtual devices to education, the investment of time, money, and effort will be fruitless. It is therefore crucial to assess the educational interest of the first generation of VR users and to identify their current needs. To this end, in this study we designed an online questionnaire and applied it through the SaaS (Software as a service) of a private server. The sample consisted of 117 early VR adopters recruited via a main portal of communication and information technologies in Spain. In order to engage participants, we posted a thread in the main forum, which is dedicated to the advances and potential uses of VR. Once the responses were gathered, we analyzed the relationship between 12 variables (mean contrasts with Snedecor's F, and contingency analysis with chi-square and Sommer's d). The results showed that the current profile of a VR user is a male over 35 years old, with university studies, and who has purchased his viewer recently (<1 year). As for the learning and teaching applications that these users were interested in, only 13.7% of the participants in this study use VR for educational purposes, although 28.2% were interested, indicating that perhaps the lack of applications or learning experiences may be hampering the use of VR within education. Almost half of the early adopters surveyed would like to learn using VR technology and are somehow optimistic about the relationship between VR and education, particularly those who are younger.