Reviewerly/exHarmony huggingface dataset:
Research on Automatic Vulnerability Mining Model Based on Knowledge Graph

In the information extraction, information sources can be screened according to the characteristics of the target network at the present stage, and the knowledge graph generated thereby can play a role in assisting the security analysis of the general network or power grid control network, mobile Internet and other special networks. In the method proposed in this paper, knowledge reasoning is mainly based on the attack conditions and attack methods to reason about the success rate and return of the attack. Through the obtained quality information, map construction information extraction and reasoning are performed to realize the correlation analysis of the information, and the information processing results are stored in the graphic structure. When analyzing the alerts generated by IDS, it is necessary to solve the multi-source alarm format generated by various devices produced by different suppliers. The attack diagram constructs the attack mode to guide the defense side to take targeted defense measures, and the attack success rate is used to judge the defense priority of all network nodes. After completing the construction of the graph, the attack graph is generated for the specific network environment under the guidance of the knowledge graph. In the process of attack graph generation, attack method and attack condition of attack instance can be used to guide the match of pre-condition and post-condition, so as to find the attack path. Attack success rate and attack profit attribute can be used to assist subsequent risk analysis. After simulation tests, the timeliness and availability of the system are verified, and this makes a contribution to the grid network management.
Interdisplinary Physical Music: A Blind Spot in Education on Acoustics

In attaining a degree in music, education on musical acoustics is multidisciplinary. Musical acoustics contains specialized knowledge, history, methodology, and practices, yet music itself is interdisciplinary. Ideally, acousticians recognize and validate that music is scientific and so, turn a blind eye or pay little attention to interdisciplinary physical music (i.e., migrating from music to musical acoustics). This paper examines the critical fault line erupting beneath the structural foundations of music through musical acoustics to navigate questions seldomly asked. These questions include–who should teach interdisciplinary physical music, e.g., music and mathematics, musical acoustics, psychoacoustics, what are the sets of curriculum standards and evaluation procedures suitable for the inter-multidisciplinary physical music. Finally, the paper proposes the reason why these paths are seldom crossed and provides a solution: a psychoacoustic approach to mathematical music theory as conduit to the intersection of fields with concepts traversing many disciplines.
Relational Synthesis for Pattern Matching

We present a completely declarative approach to synthesizing pattern matching construct implementations based on application of relational programming, a specific form of constraint logic programming. Our approach is based on relational representations of both the high-level semantics of pattern matching and the semantics of an intermediate-level implementation language. This choice makes our approach, in principle, very scalable as we only need to modify the high-level semantics in order to synthesize the implementation of a pattern matching new feature. Our evaluation on a set of small samples, partially taken from existing literature shows, that our framework is capable of synthesizing optimal implementations quickly. Our in-depth stress evaluation on a number of artificial benchmarks, however, has shown the need for future improvements.
Research on the Application of Artificial Intelligence Natural Language Processing Technology in Japanese Teaching

Abstract In the context of artificial intelligence, natural language processing technology has matured and it is a key technology for foreign language teaching and research direction. The application of artificial intelligence natural language processing technology to Japanese teaching is essentially a language processing technology that combines computer science and artificial intelligence. Based on this, this article will analyze the application of artificial intelligence natural language processing technology in Japanese teaching, hoping to have a certain reference significance for Japanese teachers’ educational technology research.
Certified Semantics for Relational Programming

We present a formal study of semantics for the relational programming language miniKanren. First, we formulate a denotational semantics which corresponds to the minimal Herbrand model for definite logic programs. Second, we present operational semantics which models interleaving, the distinctive feature of miniKanren implementation, and prove its soundness and completeness w.r.t. the denotational semantics. Our development is supported by a Coq specification, from which a reference interpreter can be extracted. We also derive from our main result a certified semantics (and a reference interpreter) for SLD resolution with cut and prove its soundness.
Recommendations for the probity of computer evidence

There exists widespread misunderstanding about the nature of computers and how and why they are liable to fail. The present approach to the disclosure or discovery and evaluation of evidence produced by computers in legal proceedings is unsatisfactory. The central problem is the evidential presumption that computers are reliable. This presumption is not warranted. To this end, recommendations are proposed to rectify this problem with the aim of increasing the probability of a fair trial.&#x0D; Index words: electronic evidence; computer systems; disclosure; discovery; recommendations for judges; fairness of legal proceedings
Are You Being Rhetorical? A Description of Rhetorical Move Annotation Tools and Open Corpus of Sample Machine-Annotated Rhetorical Moves

Writing analytics has emerged as a sub-field of learning analytics, with applications including the provision of formative feedback to students in developing their writing capacities. Rhetorical markers in writing have become a key feature in this feedback, with a number of tools being developed across research and teaching contexts. However, there is no shared corpus of texts annotated by these tools, nor is it clear how the tool annotations compare. Thus, resources are scarce for comparing tools for both tool development and pedagogic purposes. In this paper, we conduct such a comparison and introduce a sample corpus of texts representative of the particular genres, a subset of which has been annotated using three rhetorical analysis tools (one of which has two versions). This paper aims to provide both a description of the tools and a shared dataset in order to support extensions of existing analyses and tool design in support of writing skill development. We intend the description of these tools, which share a focus on rhetorical structures, alongside the corpus, to be a preliminary step to enable further research, with regard to both tool development and tool interaction
Design Fuzzy Expert System And Certainty Factor In Early Detection Of Stroke Disease

With the rapid development of the world, science and technology have progressed a lot, where all require computerized work, including technology in the health sector. Health problems are one of the social issues that must be carried out, with the cost of medical treatment and doctor consultation is very expensive. It is difficult to get information and treat the disease properly and then to prevent strokes. To see someone who is at risk of strokes, of course, requires a diagnosis from a doctor or an expert in stroke disease. But unfortunately, not all of the society can support themselves by doctors or experts seeing that many strokes sufferers have to help quickly. Based on this problem, an expert system needs to diagnose or make a decision based on expert sources in a stroke to be more effective, accurate, and efficient. The system design uses the Certainty Factor Method to measure the certainty of 20 stroke symptoms and the Fuzzy Logic as logical logic in processing nine patient's medical history. In this study's discussion, getting the expert system's F-measure (F1 score) was 0.857, which indicates a good performance expert system.
Federated Multi-Task Learning for Competing Constraints.

In addition to accuracy, fairness and robustness are two critical concerns for federated learning systems. In this work, we first identify that robustness to adversarial training-time attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these constraints, we propose employing a simple, general multi-task learning objective, and analyze the ability of the objective to achieve a favorable tradeoff between fairness and robustness. We develop a scalable solver for the objective and show that multi-task learning can enable more accurate, robust, and fair models relative to state-of-the-art baselines across a suite of federated datasets.
Evaluation of the Effectiveness of Different Image Skeletonization Methods in Biometric Security Systems

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide a better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.
Impact Of Employee Engagement On Organisational Performance: Systematic Review of 2014-2019

Firm’s performance has been well affected by engagement of the employees with their work. An employee who is engaged with his work is one who is taking interest in the work of the organization and showing some sort of initiatives in the benefits of the organization. There are total 17 articles were published from 2014 to 2019 and all of these are presented in this paper for systematic review on “impact of employee engagement on Organizational performance”. These articles revealed the existence of positive linkage between employee engagement with work and Organizational performance. As the total organizational outcomes while individual well-being is, doing the welfare of individual employee can be defined as Organizational Performance. Review of 17 articles from Google scholar interpret that interest in the topic of employee engagement with their work and the impact of it has increased potentially during the last few years and remains a productive area for the academic research in the coming years. We have found 17 articles purely related to our variables from Google Scholar. Systematic review of articles shows methodology of the articles, journals name in which the articles are published also survey type of the articles. Study of the articles could help in providing a proper guideline to implement the practical application in regard to increase employee engagement.
The English Camps as Method of Promoting Fun English at Elementary School Level in Indonesia

This article aimed to promote English in elementary school students through a fun learning method, called the Fun English Camp. Several studies had been conducted to encounter the best solution to handle this issue. The researchers used PRISMA Protocol as an instrument to collect the data that has been widely used in the process of selecting relevant articles. The researchers reviewed twenty five scientific publications, related to Fun English Camp that has become an English learning approach for beginner students. Through a review of twenty five scientific publications, for instance book and journal, the researchers got scientific evidence that introduction of a learning method with the term Fun English camp has an impact on promoting language learning for elementary school children in Indonesia. Thus, the fun English camp method can be an interesting method to be applied by elementary school curriculum design in Indonesia.&#x0D; Keywords: English Camps, Learning Method, Fun English Learning
Optical Nonlinear Activation Functions Based on MZI-Structure for Optical Neural Networks

We experimentally demonstrated an on-chip optical nonlinear activation function circuit for optical neural networks based on a conventional linear transformer, MZI-mesh. The proposed circuit is reconfigurable to perform multiple types of nonlinear activation functions.
Improving Relation Extraction with Relational Paraphrase Sentences

Supervised models for Relation Extraction (RE) typically require human-annotated training data.Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE.To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS).However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises.In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.Based on an existing labeled data, we first automatically build a task-specific paraphrase data.Then, we propose a novel model to learn the information of diverse relation expressions.In our model, we try to capture this information on the paraphrases via a joint learning framework.Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.
AirSim Drone Racing Lab

Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics. Our framework enables generation of racing tracks in multiple photo-realistic environments, orchestration of drone races, comes with a suite of gate assets, allows for multiple sensor modalities (monocular, depth, neuromorphic events, optical flow), different camera models, and benchmarking of planning, control, computer vision, and learning-based algorithms. We used our framework to host a simulation based drone racing competition at NeurIPS 2019. The competition binaries are available at our github repository.
A Practical Solution Against Business Email Compromise (BEC) Attack using Invoice Checksum

This research presents a practical countermeasure against the problem of the bogus invoice scheme, one of the most threatening BEC attacks in modern business. We introduce a straightforward yet highly practical method of creating a checksum from the invoice and shared secret information. Using the generated checksum allows the recipient to confirm the authenticity and integrity of the invoice before proceeding with the actual payment. In this work, generating and verifying of checksum information are done via a smartphone application. Also, the predetermined secret information is stored inside the smartphone to provide better protection against data theft. Lastly, an Android-based application for checksum generation and verification, supporting both manual input and QR code scan, is implemented to demonstrate the use case scenario and practicability of the proposed method.
Scale Adaptive Feature Pyramid Networks for 2D Object Detection

Object detection is one of the core tasks in computer vision. Object detection algorithms often have difficulty detecting objects with diverse scales, especially those with smaller scales. To cope with this issue, Lin et al. proposed feature pyramid networks (FPNs), which aim for a feature pyramid with higher semantic content at every scale level. The FPN consists of a bottom-up pyramid and a top-down pyramid. The bottom-up pyramid is induced by a convolutional neural network as its layers of feature maps. The top-down pyramid is formed by progressive up-sampling of a highly semantic yet low-resolution feature map at the top of the bottom-up pyramid. At each up-sampling step, feature maps of the bottom-up pyramid are fused with the top-down pyramid to produce highly semantic yet high-resolution feature maps in the top-down pyramid. Despite significant improvement, the FPN still misses small-scale objects. To further improve the detection of small-scale objects, this paper proposes scale adaptive feature pyramid networks (SAFPNs). The SAFPN employs weights chosen adaptively to each input image in fusing feature maps of the bottom-up pyramid and top-down pyramid. Scale adaptive weights are computed by using a scale attention module built into the feature map fusion computation. The scale attention module is trained end-to-end to adapt to the scale of objects contained in images of the training dataset. Experimental evaluation, using both the 2-stage detector faster R-CNN and 1-stage detector RetinaNet, demonstrated the proposed approach’s effectiveness.
The Impact of Differential Privacy on Model Fairness in Federated Learning

Federated learning is a machine learning framework where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the clients. Thus, federated learning could raise “unfairness” according to some fairness metrics. Differential privacy is a privacy model used to protect privacy in federated learning with bounded leakage about the presence of a specific point in the training data. Previous work showed that a reduction in accuracy induced by deep private models disproportionately impacts underrepresented groups. This motivates us to analyze the impact of differential privacy on model fairness in federated learning. In this work, we conduct extensive experiments to evaluate the impact of differential privacy on model fairness in federated learning. Experiments show that, with a proper choice of parameters, differential privacy might improve fairness with an ignoble reduction on accuracy.
Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.
The new distributed digital technology world trade and MNEs: another step in the inventive process

Major innovations are about reducing transactions costs. This has been true since the beginning of the Industrial Revolution and it continues along the inventive process in the last 220 years. The innovative technology of blockchains and cryptocurrencies with smart contracts (distributed digital technology) has a potential for substantial reduction of transactions costs that are rooted in human nature. Like earlier transformational innovative technologies, such as the steam locomotive and the internet, the new distributed digital technology may create disruptions in the market today, but if successful it will increase welfare by transforming the way that people transact in the market in the future. The major feature of the new digital technology is that it makes it possible for individuals to match their supply and demand without intervention of organisations like firms, financial intermediaries and regulating agencies. The new digital technology gets us closer to the neoclassical perfect market model. Distributed digital technology progresses in a step-by-step fashion. The first step was the Bitcoin: electronic peer-to-peer cash transfers without the need for central regulations. A number of services, particularly those that can be traded digitally, are next in line. Two potential applications of distributed digital applications are global trade in services and to MNEs.
Sentiment Analysis for Troll Detection on Weibo

The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter and Facebook. In China, the micro-blogging service provider, Sina Weibo, is the most popular such service. To influence public opinion, Weibo trolls—the so-called Water Army—can be hired to post deceptive comments. In this chapter, we focus on troll detection via sentiment analysis and other user activity data on the Sina Weibo platform. We implement techniques for Chinese sentence segmentation, word embedding, and sentiment score calculation. In recent years, troll detection and sentiment analysis have been studied, but we are not aware of previous research that considers troll detection based on sentiment analysis. We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies. Experimental results are generated and analyzed. A Chrome extension is presented that implements our proposed technique, which enables real-time troll detection when a user browses Sina Weibo.