Reviewerly/exHarmony huggingface dataset:
Generating Purpose-Driven Explanations: The Case of Process Predictive Model Inspection

Explainable AI is an emerging branch of data science that focuses on demystifying the complex computation logic of machine learning with an aim to improve the transparency, validity and trust in automated decisions. While existing research focuses on building methods and techniques to explain ‘black-box’ models, much attention has not been paid to generating model explanations. Effective model explanations are often driven by the purpose of explanation in a given problem context. In this paper, we propose a framework to support generating model explanations for the purpose of model inspection in the context of predictive process analytics. We build a visual explanation platform as an implementation of the proposed framework for inspecting and analysing a process predictive model, and demonstrate the applicability of the framework using a real-life case study on a loan application process.
Distributed On-Policy Actor-Critic Reinforcement Learning

In this paper, a novel distributed on-policy Actor-Critic algorithm for multiagent reinforcement learning is proposed.The algorithm consists of the temporal difference scheme with function approximation at the Critic stage, and a policy gradient algorithm at the Actor stage, derived starting from a global objective.At both stages, decentralized agreement among the agents is achieved using the linear dynamic consensus strategy.Compared to the existing schemes, the algorithm has improved convergence rate and noise immunity, and a possibility to achieve multi-task global optimization.
Investigating the Medical Coverage of a Translation System into Pictographs for Patients with an Intellectual Disability

Communication between physician and patients can lead to misunderstandings, especially for disabled people.An automatic system that translates natural language into a pictographic language is one of the solutions that could help to overcome this issue.In this preliminary study, we present the French version of a translation system using the Arasaac pictographs and we investigate the strategies used by speech therapists to translate into pictographs.We also evaluate the medical coverage of this tool for translating physician questions and patient instructions.
English Learning Patterns for Mobile Users Based on Multiple Data Mining Algorithms

The analysis of online learning models is of great significance for the configuration and development of a distance learning platform with a better learning experience, and the development of more effective and accurate online learning evaluation and guidance. The purpose of this article is to study the extraction and recognition of English mobile user learning patterns based on multiple data mining algorithms. First, the research foundation is explained, and on this basis, the importance of using data mining technology to analyze the learning model is put forward. Subsequently, the research status and related theories and technologies of online learning behavior analysis and data mining technology at home and abroad are introduced. Taking the mobile users of the “Smart English” platform as the main research object, the relevant data mining algorithms are used to extract and analyze the characteristics of their learning methods. Experimental results show that there are significant differences in behavior in the study of the number of active days and forum posts of mobile users with different education levels (P < 0.05).
Improvement of Clarity for Foggy/Dusty Weather Images Using Triple Threshold Method

The visual quality of collected photographs can be greatly reduced by inclement dusty weather, which makes it hard to notice crucial image details. Image capture in such conditions frequently results in unwanted artifacts such as weak contrast, insufficient hues, or color cast. As a result, several strategies for processing such undesirable events and recovering lucid outcomes using appropriate colors have been developed. Because of the differences in the processing ideas used, these methods range from basic to complex. This paper introduces a new technique for processing poor quality photos acquired in adverse dusty weather that utilizes customized fuzzy intensification operators. Several tests were conducted to assess the proposed techniques processing ability, with the results demonstrating its ability to filter diverse degraded images. It was particularly good at delivering acceptable and revealing fine details in the processed photographs.
Consensus Scalability in Blockchain Network

The consensus mechanisms are used in a blockchain to achieve the essential agreement on a single value among various multiagent and distributed processes. The consensus algorithm is categorized into two networks, i.e., permissionless and permissioned networks of blockchain. The permissionless network consists of Proof of Work (PoW), Proof of Stake (PoS), Bitcoin-NG, and others. The permissioned network contains PBFT-based consensus mechanisms. This mechanism performs the roles in a leader selection and in a mining procedure to enhance the system’s transaction throughput. First, we identify the issues of PoW consensus and find out the problems such as scalability in terms of transaction throughput and consensus finality. These two parameters have been required to enhance the consensus mechanism so that it can improve the system’s performance. Then, this chapter described the bitcoin-NG, which improves the scalability of the PoW consensus mechanism. We also highlight steps of scaling the byzantine to improve the transaction throughput and performance of the system. Finally, we consider various scenarios of leader and process to solve the issue of consensus scalability with different PBFT-based consensus mechanisms. All mechanism runs various transactions within a specific time and avoids the forks that occurred during the insertion process of the new block. Thus, the chapter presents multiple research directions to improve the scalability of the consensus by identifying various issues in the current consensus mechanism.
Climatology of Bosnia and Herzegovina

The general climatic characteristics of Bosnia and Herzegovina are observed from the aspect of the most important climatic factors and elements. The emphasis is on the influence of the relief structure through the exposure of the northern parts to the impact of cold continental air coming from the north and northeast and the limitation of the influence of the Adriatic Sea to the mountains of the Dinaric direction in the south. Also, an important aspect is the barometric depressions, i.e., the distribution and directions of air mass movement through the territory of Bosnia and Herzegovina. These most important elements influenced the characteristics of basic meteorological elements; i.e., temperature and precipitation, for which spatial models for Bosnia and Herzegovina (with longitudinal and transversal profiles) were developed using qualitative methodological kriging analyzers and presented with a large number of analytical thematic maps and graphs. Based on the previous contents, in the following part, the climatic regionalization of Bosnia and Herzegovina, according to Köppen's classification, is elaborated. A detailed division into climate classes, types, subtypes, and variants was made, which were interpreted in great detail from the aspect of spatial coverage and general climatic characteristics and their share in the area of Bosnia and Herzegovina. Thematic graphic and cartographic representations accompany the description of hierarchical units classified in the climate distribution.
Hand Gesture Recognition by Hand Landmark Classification

These years, computer vision technology has been rapidly advanced. On the other hand, customer satisfaction is essential for the industry to improve its service but use traditional methods ineffectively. Using technology such as computer vision, we can now collect the information we are looking for directly from a human. Humans can use many kinds of modalities to interact with computers. Hands are perhaps the largest source of body language information after the face. To understand the gesture's meaning, we can use MediaPipe Hands developed at Google LLC as a method to track and recognize human hands. However, if we want to understand some kinds of hand gestures using MediaPipe Hands, we need to create a condition ourselves by using if-else conditions. This research tried to collect the many varieties of each hand gesture using the 21 key points in x, y, and z coordinates as a feature. As classifiers, we chose the support vector machine (SVM) and the artificial neural network (ANN). This research found that SVM using a polynomial kernel is the best among all of the methods we used as a classifier method for the 3D value of 21 key points from the hand skeleton. The accuracy and F1-score from SVM using a polynomial kernel were 86.26% accuracy and 82% F1-score, respectively, representing the best performance for each class of all the methods we used in this research.
To the biography of S. P. Shevyrev — Professor of Moscow University: attribution, dating and publication of the letter “To an Unknown Addressee” (RGALI)

One of the unpublished letters of S. P. Shevyrev is stored in the RGALI with a note — “to an unidentified person with the address ‘Mikhail Nikolaevich’ dated January 11, b/g”. References and mentions in the letter allow us to name the addressee and the date of the address to him with absolute accuracy: Shevyrev wrote to Mikhail Nikolaevich Pokhvisnev, censor of the Moscow Censorship Committee, on January 11, 1855. In turn, the real circumstances of its origin disclosed in the commentary to the document shed light on several not quite clear episodes from the history of Russian culture in the mid-19th century, including preparations for the centennial of Moscow University, and also provide important details of the last decade of Shevyrev's life.
Crime reporting and police controlling: Mobile and web-based approach for information-sharing in Iraq

Abstract Crimes are increasing in our society as a serious worldwide issue. Fast reporting of crimes is a significantly important area in anticrime. This problem is visible in Iraq as people avoid information-sharing due to the lack of trust in the security system despite some contact lines between citizens and police in Iraq. Furthermore, there has been a little empirical study in this field. We proposed a multi-approach for crime reporting and police control to address these issues. First, this study has two goals: (1) investigating the adopted method in reporting crimes to police sectors to identify the gap and, (2) developing a mobile application for crime reporting and keeping it undisclosed and exclusive for crime witnesses to report. The approach utilised 200 participants to develop the proposed app. Results have shown that the proposed system can quickly monitor and track criminals based on a cloud-based online database. In addition, the application user will specify certain details to be sent, such as location, case type and time. Other information will be sent directly by the system following the designed algorithm.
New Approaches Towards the Delivery of Service Information Using Semantic Correlation Rules

Knowledge about products and their applications, as well as how to use them, is often distributed in different places within companies. In the past, this has been an internally and externally accepted situation leading to time-consuming searches for required data. Nowadays, expectations of in-time delivery of information and the general view of knowledge processes as well as on associated data are changing radically. Traditional methods and approaches for storing, distributing and accessing information are therefore in many cases inappropriate and are questioned on industry-side. For example, in the domain of customer service of B2B products in machinery industry, it was common to communicate by phone with experts and help-desk-organizations, who provided the required information by experience or by informal information exchange networks. Under the pressure of digitalization and the constant increase in efficiency, as well as the demand of customers to obtain requested information more quickly, new ways of providing and connecting information are becoming increasingly relevant. It is still unrealistic to assume that all relevant information can be prepared specific to each target group and to each application within a short period of time. Rather, the challenge is to create and store information in the future in a structured way with meaningful semantic and consistent metadata and, on the other hand, to relate this new information to existing information in such a way that the user can be helped quickly and easily. Several preconditions are crucial to build up a new information infrastructure: First, management has to be convinced of the benefits of storage and intelligent search environments and to get support from relevant stakeholders. Subsequently, new information architectures require smart approaches for content authoring and metadata enrichment supporting information delivery via different media channels for relevant customer groups. Time and effort for setting up this new architectures has to be argued and justified..
Book Structure

Despite the successful application of crowdsourced testing as we introduced in previous chapters, there are still challenges faced to fully take advantage of this new testing paradigm. For example, previous chapters mentioned there are large number of duplicate reports submitted by diversified crowd workers who conduct the testing in parallel. It is extremely valuable to automatically detect those duplicate reports and aggregate them together for a summarized view, which can facilitate the crowdsourced testing managers in their following bug resolving activities. Another example is that the previous industrial case study has also revealed that some crowd workers can detect bugs in a crowdsourced testing task, while others may fail to detect bugs in the same task. If there is automatic support for recommending a set of crowd workers to a specific crowdsourced testing task, more bugs can be detected, and the bug detection activity would be more efficient.
Summary and Outlook

Visual question answering is a significant topic in current AI research and has been linked to many applications such as AI assistant and dialog systems. As a cross-disciplinary task, this topic has attracted considerable attention from researchers in different communities, such as computer vision and natural language processing. VQA is a typical cross-modal task since it requires machines to simultaneously understand visual content (images and videos) and natural language, and, in certain cases, common sense knowledge. Nevertheless, certain challenges must be addressed to realize artificial general intelligence.
Real-Time Application with Data Mining and Machine Learning

Data mining and machine learning are the most expressive research and application domain. All real-time application directly or indirectly depends on data mining and machine learning. There are many relevant fields, like data analysis in finance, retail, telecommunications sector, analyzing biological data, other scientific uses, and intrusion detection. The most expressive research and application domain is data mining and machine learning. Data mining and machine learning are used in all real-time applications, whether directly or indirectly. Data analysis in finance, retail, telecommunications, biological data analysis, extra scientific applications, and intrusion detection are just a few examples where it can be used. Because it captures a lot of data from sales, client purchase histories, product transportation, consumption, and services, DM has a lot of applications in the retail industry. It's only logical that the amount of data collected will continue to climb as the Internet's accessibility, cost, and popularity increase. In the retail industry, DM assists in the detection of customer buying behaviors and trends, resulting in improved customer service and increased customer retention and satisfaction.
Effective Non-visual Access to Diagrams via an Augmented Natural Language Interface

Abstract This paper describes the design and validation of a number of HCI techniques that enable more effective non-visual access to diagrammatically displayed data through an adapted Natural Language Interface (NLI). These techniques have been incorporated into an accessible web-based NLI to diagrams prototype and qualitatively evaluated with blind persons for solving disparate analytical and navigational tasks supported by diagrams. Each technique is described and validation results discussed.
Illustration Art Design System Based on VR Virtual Reality Technology

Illustration has a long history of development. With the progress of society and the improvement of science and technology, media carriers are constantly evolving. Illustration in the virtual reality environment has broken away from the bondage of paper media, presented to the public in a variety of display ways, and integrated into our lives. In order to achieve the requirements, this paper adopts the idea of layered architecture to design the system architecture, functional modules and database. Through the performance test of the system, we can know that the system developed in this paper has good reliability and availability, and can be put into practical use.
Similar Feature Extraction Network for Occluded Person Re-identification

Occluded person re-identification (Re-ID) is a challenging task in real-world scenarios due to the extensive conditions that persons are occluded by various obstacles. Although state-of-the-art methods with additional cues such as pose estimation and segmentation have achieved great success, they did not overcome data bias and the dependency on the accuracy of other detectors. In this paper, we propose a novel similar feature extraction network (SFE-Net) for occluded person Re-ID to address these issues. Firstly, we introduce the adaptive convolution method to separate the features of occluded and non-occluded regions, where local and global features are sufficiently used. We then apply adaptive aggregating parameters to find a better weighting strategy automatically. Finally, the transformer encoder architecture is utilized for generating discriminative features. Extensive experiments show SFE-Net outperforms state-of-the-art methods on both occluded and holistic datasets.
Designing a professional e-portfolio to promote 3D printing-based entrepreneurial thinking as a case study: A technical writing project

The purpose of this project is to design a professional e-portfolio in the form of an interactive website that could be potentially accessed by budding entrepreneurs looking to get information to start a 3D-printing based business locally in the Aizu/Fukushima area. 3D printing here is used as a case study to understand how such technical content could be positioned most optimally in a professional context and with maximum impact. This paper outlines a process that leads towards the final design of the website and will focus on the technical writing, design, and usability principles. Adoption of such principles is expected to make the website more interactive and usable in the target situation. This website is geared towards helping anyone who wants to start a new 3D printing business. In this research, we will limit our consideration to help people living in Aizu to revitalise the Aizu area for the 3D printing business. The content in the e-portfolio includes types of 3D printers, 3D printing technologies, sharing of 3D models, with the web design based on various types of concept mapping schemas, faceted search, SWOT analysis, etc. From this information, users can search for and browse what they want towards customising their preferences. Users can also contact the administrator for business consultations and website requests for start-ups. This is a work-in-progress paper, as the design of the e-portfolio has been left at a preliminary level, with more focus on content placement and organisation, but without any usability testing.
GWDGA: An Effective Adversarial DGA

Domain name generation algorithm (DGA) is a technique that can help botnets improve their crypticity. To discover botnets, the DGA detection methods based on deep learning have been proposed, which significantly reduce the usability of DGAs. However, due to the security problems of deep learning itself, this type of DGA detection method also faces the threat of adversarial attacks. Adversarial DGAs can generate anti-detection domain names to deceive the DGA classifier so that it can help researchers discover the shortcomings of existing DGA detection methods. Meanwhile, the existing adversarial DGAs generally only generate domain names based on characters without considering word-level elements. In this paper, a new type of adversarial DGA named GWDGA was proposed. It is implemented based on the statistical language model, the gated convolutional network, and the variational auto-encoder. The core point of GWDGA is to treat domain names as a combination of commonly used n-grams and high-frequency words so that it can learn their intrinsic arrangements and generate new domain names. GWDGA was evaluated on three well-known DGA classifiers (Endgame, Invincea, MIT). The AUCs of GWDGA under the three detection methods are 0.43, 0.42, and 0.43, respectively. The result shows that GWDGA has stronger anti-detection capabilities than similar adversarial DGA. Its performance is stable under the detection of DGA classifiers of different architectures. In addition, the ability of the classifiers to resist GWDGA can be improved by adding the adversarial domain names generated by GWDGA to the training sets of the classifiers.
Transmission optimisation technology based on edge-network

With the development of network infrastructure, user data has shown an exponential growth. Improving the transmission quality of user network is essential for the current requirements, however, user network environment is generally quite complicated and there exists all kinds of problems, consequently, the traditional central server architecture cannot solve these issues out because it cannot support the high throughput in the network. According to these drawbacks, in this paper, we propose a new architecture, which is made of the edge nodes as points of presence (POP). This architecture improves significantly the user experience including connection and transmission by using network detection, data encryption, and smart access point selection. In particular, this improvement becomes more obvious to the user who experiences the slow network speed in network disk. Furthermore, this technology can be applied on services that require low latency and high throughput.
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing

Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases.Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance.However, current attention-based approaches can only model such alignments at the token level and have unsatisfactory generalization capability.In this paper, we propose a new approach to leveraging explicit lexico-logical alignments.It first identifies possible phrase-level alignments and injects them as additional contexts to guide the parsing procedure.Experimental results on SQUALL show that our approach can make better use of such alignments and obtains an absolute improvement of 3.4% compared with the current state-of-the-art.