Reviewerly/exHarmony huggingface dataset:
Full coverage of a reader's interests in context‐based information filtering

Abstract We present a collection of algorithms to filter a stream of documents in such a way that the filtered documents will cover as well as possible the interest of a person, keeping in mind that, at any given time, the offered documents should not only be relevant, but should also be diversified, in the sense of covering all the interests of the person. We use a modification of the WEBSOM algorithm to create a user model based on a self‐organizing network trained using a collection of documents representative of the person's interests. We introduce the concepts of freshness and coverage . A document is fresh if it belongs to a semantic area of interest to a person for which no documents were seen in the recent past; a group of documents has coverage to the extent to which it is a good representation of all the interests of a person. Our tests show that these algorithms can effectively increase the coverage of the documents that are shown to the user without overly affecting precision.
Data activism and meta-documentary in six films by Forensic Architecture

Data activism –or data-centered campaigning, mobilization, and research– is a hybrid, shifting endeavor. Data activist organizations are currently exploring new tools and languages to communicate findings and influence judicial and political processes. However, it is the method of turning data into a film that sets Forensic Architecture (FA) apart from other data activist endeavors. This article employs ideas from social movement and documentary studies to examine six films produced and disseminated by FA. These documentaries expose official corruption relating to abuse and killings in Burundi, Israel and Palestine, Syria, and the Mediterranean. The analysis employs the lenses of data activism and the meta-documentary to think about how FA uses participatory strategies, involves victims and human rights organizations, places science and technology at the center of its narratives, generates counter-stories implicating new data agents and methods, and uses a new fora to influence court cases and change the status quo. Ultimately, it illustrates the potential for impact offered by hybrid forms of data activism.
Real-time, High-resolution Depth Upsampling on Embedded Accelerators

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1
A Deep Learning-Based Framework for the Classification of Non-functional Requirements

State-of-the-art solutions to the classification of Non-functional requirements are mostly based on supervised machine learning models, requiring a reasonable amount of time in feature engineering. Deep learning, on the other hand, does not need to define features explicitly. This research aims to design and develop an automatic system to classify Non-functional requirements in multiple classes based on deep learning techniques. Specifically, we investigate the design and application of four neural network models; Artificial Neural Network, Convolutional Neural Network, Long Short-term Memory, and Gated Recurrent Unit to classify Non-functional requirements into five classes: reliability, usability efficiency, maintainability, and portability. However, these models require a large, annotated corpus and prone to overfitting. To address this, we proposed a novel framework for text augmentation. This technique uses a sort and concatenates approach to merge two sentences belonging to the same class to generate a two-time increase in data size yet preserving the domain vocabulary. We have compared our results with the state-of-the-art Easy data augmentation approach. Our findings indicate that the NFRs classification model improved when trained with fine-tuned word embedding and CUSTOM augmentation approach. Interestingly Convolutional Neural Network turned out to be an outstanding learner with a jump in accuracy from 60% to 96% compared to the first approach. This simple text augmentation approach can add value for tasks where domain-specific terminologies play an essential role.
Multi-span Extractive Reading Comprehension Without Multi-span Supervision

This study focuses on multi-span reading comprehension (RC), which requires answering questions with multiple text spans. Existing approaches for extracting multiple answers require an elaborate dataset that contains questions requiring multiple answers. We propose a method for rewriting single-span answers extracted using several different models to detect single/multiple answer(s). With this approach, only a simple dataset and models for single-span RC are required. We consider multi-span RC with zero-shot learning. Experimental results using the DROP and QUOREF datasets demonstrate that the proposed method improves the exact match (EM) and F1 scores by a large margin on multi-span RC, compared to the baseline models. We further analyzed the effectiveness of combining different models and a strategy for such combinations when applied to multi-span RC.
TermInteract: An Online Tool for Terminologists Aimed at Providing Terminology Quality Metrics

This position paper describes the results of our meetings with terminologists aimed at capturing missing functionalities concerning term extraction. These experts lack an online tool to allow them the evaluation, modification and creation of terminologies from corpora in different languages. The result is an open-access online website where terminologists can extract automatically terminologies and can upload terminologies created by third parties. The tool also allows them to polish collaboratively the terminologies. Additionally, this tool provides a quality metrics based on the changes made to the terminology, as well as a way to compare two terminologies for the same corpus but created with different methods. We have tested the tool with terminologies in English and Spanish, with corpora from several domains: bio (covid19), legal and scientific papers. Our preliminary results point out the utility of this tool for terminologists.
LGCCF: A Linear Graph Convolutional Collaborative Filtering with Social Influence

Collaborative filtering (CF) is the dominant technique in personalized recommendation. It models user-item interactions to select the relevant items for a user, and it is widely applied in real recommender systems. Recently, graph convolutional network (GCN) has been incorporated into CF, and it achieves better performance in many recommendation scenarios. However, existing works usually suffer from limited performance due to data sparsity and high computational costs in large user-item graphs. In this paper, we propose a linear graph convolutional CF (LGCCF) framework that incorporates the social influence as side information to help improve recommendation and address the aforementioned issues. Specifically, LGCCF integrates the user-item interactions and the social influence into a unified GCN model to alleviate data sparsity. Furthermore, in the graph convolutional operations of LGCCF, we remove the nonlinear transformations and replace them with linear embedding propagations to overcome training difficulty and improve the recommendation performance. Finally, extensive experiments conducted on two real datasets show that LGCCF consistently outperforms the state-of-the-art recommendation methods.
Comprehensive Learning Particle Swarm Optimizer for Intelligent Visual Tracking

Visual object tracking remains an accessible and demanding challenge in computer vision, despite recent breakthroughs in the area. Modern applications need trackers, even on embedded systems, to be accurate and very fast. This paper proposed a robust solution based on object tracking derived from the Bhattacharyya coefficient as a measure of similarity dependent on Comprehensive learning particle swarm optimization (CLPSO). To reveal the performance of proposed algorithm's capacity, existing Mean-Shift based object tracking system is compared with the proposed system.
Design Thinking at Scale: A Multi Team Design Thinking Approach

Design Thinking has become an important and widely accepted innovation approach in many companies worldwide. However, it is mostly used in small innovation or research projects, or is outsourced to innovation labs or design agencies. Furthermore, there is little published data on how to leverage Design Thinking for complex applications. Multiple, collaborating development teams are required to construct sophisticated software products for different stakeholder groups. Therefore, similarly to scaled agile development, several Design Thinking teams should be employed in complex software projects to take advantage of parallel work capabilities and to investigate needs and demands of several customers, user and stakeholder groups. However, currently no approaches to scale Design Thinking to multiple teams exist. With this chapter, we aim to tackle this challenge and present a scaled Design Thinking approach, in which multiple teams conceive ideas in a Design Thinking phase and implement the resulting ideas in follow-up projects ultimately converging into one project and product. This approach is validated in an educational context with a master-level seminar and in the resulting follow-up projects in and outside of the curriculum. We discuss our experiences from the case study as well as the benefits and challenges of our approach. With this study we provide first insights on how to scale Design Thinking to multiple teams and how to leverage its capabilities for complex software products.
Comparing consecutive letter counts in multiple context-free languages

Context-free grammars are not able to capture cross-serial dependencies occurring in some natural languages. To overcome this issue, Seki et al. introduced a generalization called m-multiple context-free grammars (m-MCFGs), which deal with m-tuples of strings. We show that m-MCFGs are capable of comparing the number of consecutive occurrences of at most 2m different letters. In particular, the language {a1n1a2n2⋯a2m+1n2m+1|n1≥n2≥…≥n2m+1≥0} is (m+1)-multiple context-free, but not m-multiple context-free.
The Study of Synchronization in Quantum Key Distribution System

The paper considers the main methods of synchronization in quantum cryptography systems. A generalized structure of a quantum key distribution system with phase coding of photon states is described. The results of experimental studies of the synchronization process in a two-pass quantum communication system are presented and the features of optical pulse detection are shown. Analytical expressions are given for calculating the error of detecting the length of a quantum channel during synchronization. The estimation of the security of the synchronization process using a multiphoton pulse is given. An optical signal detection algorithm that reduces the probability of an attack on the quantum key distribution system during synchronization is proposed.
Design and implementation of digital library at Sharia and Law Faculty UIN Sunan Gunung Djati Bandung

Abstract The development of communication and media information technology makes an impact on how people can access information through various media, including libraries that store a collection of information stored and electronically accessible through a digital library. The Faculty of Sharia and the law of UIN Sunan Gunung Djati as a higher education institution answered the challenge with the development of this information and communication technology to provide this information and communication-based service by developing digital libraries geared towards improving the library service process with good quality and speed, as well as improving the effectiveness and efficiency of the library’s governance to facilitate the teaching and learning process for students. Open Source Software (OSS) is digital library web based at the Faculty of Sharia and Law is designed and implemented to fulfil the library automation in which small or big scale used SliMS.
A Traceable and Authenticated IoTs Trigger Event of Private Security Record Based on Blockchain

Recently, private security services have become increasingly needed by the public. The proposed scheme involves blockchain technology with a smart contract. When a private security company signs a contract with a client, they install an Internet of Things (IoTs) device in the client’s house and connect it with the IoT main controller; then, the IoT main controller connects to the security control center (SCC). Once there is an event triggered (e.g., a break-in or fire incident) by the IoTs device, the controller sends a message to the SCC. The SCC allocates a security guard (SG) to the incident scene immediately. After the task is accomplished, the SG sends a message to the SCC. All of these record the messages and events chained in the blockchain center. The proposed scheme makes security event records have the following characteristics: authenticated, traceable, and integral. The proposed scheme is proved by a security analysis with mutual authentication, traceability, integrity, and non-repudiation. The known attacks (e.g., man-in-the-middle attack, replay attack, forgery attack) are avoided by message encryption and a signing mechanism. Threat models in the communication phase can also be avoided. Finally, computation cost, communication performance, and comparison with related works are also discussed to prove its applicability. We also provide an arbitration mechanism, so that the proposed scheme can reduce disputes between private security companies and the client.
Enhancing Lattice-Based Motion Planning With Introspective Learning and Reasoning

Lattice-based motion planning is a hybrid planning method where a plan made up of discrete actions simultaneously is a physically feasible trajectory. The planning takes both discrete and continuous aspects into account, for example action pre-conditions and collision-free action-duration in the configuration space. Safe motion planing rely on well-calibrated safety-margins for collision checking. The trajectory tracking controller must further be able to reliably execute the motions within this safety margin for the execution to be safe. In this work we are concerned with introspective learning and reasoning about controller performance over time. Normal controller execution of the different actions is learned using reliable and uncertainty-aware machine learning techniques. By correcting for execution bias we manage to substantially reduce the safety margin of motion actions. Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner by the use of more accurate execution predictions with a smaller safety margin. The presented approach allows for explicit awareness of controller performance under normal circumstances, and timely detection of incorrect performance in abnormal circumstances. Evaluation is made on the nonlinear dynamics of a quadcopter in 3D using simulation. Video: https://youtu.be/STmZduvSUMM
An offline parallel architecture for forensic multimedia classification

Abstract Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Machine Learning (ML) techniques. In digital forensics domain, ML algorithms have been applied for cybercrime investigation such as child abuse investigations, malware classification, and image forensics. This paper addresses this issues and deals with forensic analysis of digital images and videos. In particular, this work aims at proposing a multimedia classification tool with a parallel software architecture for a fast inspection, which is easy to use (to be used by officers during a search), requires limited hardware resources and it is built on an open-source software to limit its costs. Moreover, this tool must be able to quickly inspect multiple devices at a time. When positives are found in a device, such device will be seized for a deeper analysis later in the lab. It will not be seized otherwise, reducing the inconvenience for the suspect as well as the time required for the next analysis phase. As a case study, we focus on the identification of child pornography images. Experimental results show that the proposed architecture is capable of guaranteeing a high recall, a fast process and high performances in real scenarios.
Vector Base Approach Filter for One Channel Noise Removal in A Color Digital Image

Abstract The digitization process can cause misinformation in the digital image. The distortion of information in a digital image is called noise, this condition causes visual damage to the image. Therefore, the damaged image needs to be repaired to enhance the image quality. In this paper, new image filtering method with vector approach is proposed. This method compares the intensity level of each pixel in every part of image. The experimental results show that the proposed method works well and restores the visual of the image as it is in the real condition. This is because the process of determining new pixel value on the proposed method considers the pixels value in image globally.
Semi-Automatic Reliable Explanations for Prediction in Graphs

We discuss reliability for local explanations for prediction in graphs. Meaningfully explaining predictions of machine learning models is an open and important research question particularly in relation to human judgement. Objectively evaluating explanation is strongly required to make Artificial Intelligence trusted. Model-agnostic local explanation methods such as LIME have recently emerged and are being widely used for commonly used types of data: tables, texts, and images. A locally linear regression model is constructed using perturbed data generated in the vicinity of the instance to be explained with LIME. However, the creation of adequate perturbed data is not necessarily easy depending on the task and dataset leading to less explainability and instability of explanations. It is more problematic to apply such local explanation methods to graph data because there are difficulties unique to graphs such as the complexity of the structure and the variety of definitions of the distance among them. We propose a local explanation method for graphs originated from LIME and for fundamental synthetic datasets experimentally investigate the characteristics of perturbed data concerning explainability, e.g., “what perturbed data can increase explainability?” or “is there a criterion to determine reliable explanations regarding perturbed data?”. The effect of the following various factors in the explanation process are investigated: a generation method for perturbed data, distance function, dataset, prediction model, and data augmentation due to noise in training. Although the effect of these factors is quite complex, the ratio of the perturbed data that has a different class than the instance has is the most important index for higher explanations independently of the complex effect. We also propose a practical method to semi-automatically determine a reliable explanation with minimum human support using this index. We also evaluate a model-agnostic manner in our proposed method for graph classification tasks and prediction models such as graph convolutional networks (GCNs) or support vector machines (SVM) with graph kernels. The results indicate that the locally linear regression model can work well under specific situations. Moreover, the possibility to obtain better explainability than with the state-of-the-art graph explanation method GNNExplainer is shown as a reference.
A Heuristic Algorithm for Deadline-Based Resource Allocation in Cloud Using Modified Fish Swarm Algorithm

Virtualization plays an indispensable role in improving the efficacy and agility of cloud computing. This process involves in assigning resources to the cloud application users based on the requirements, where most of the resources are being virtual in nature. These resources are utilized by the users for implementing tasks for a certain time period. Virtualization assists in effective usage of hardware resources. Based on the application, the users may require definite amount of resources to be utilized in a definite time period. Thus, a deadline that is a start time and end time for every resource needs to be considered. Deadline specifically relates to time limit for the implementation of tasks in the workflow. In this paper, the resource allocation is optimized based on deadline as the optimization parameter using modified fish swarm algorithm (FSA).
The Formation of the University Reputation Capital under the Digital Transformation of the Environment

Abstract In modern conditions, the importance of digitalization for the university success in the education market is growing rapidly. Competitiveness, and ultimately survival of the educational institution in the information environment depends on how timely it will be included in this process. This article is devoted to considering the features of the digital transformation process and its impact on the university reputation. The development trends of distance education in Russia are analyzed, main missions of a modern university are examined in detail, and it is proved to maintain its competitiveness in the context of digitalization, the university must implement not only basic - educational and scientific missions, but also new, entrepreneurial and innovative ones. Only those universities that succeed in efficiently operating in all directions will be able to strengthen their positions in the market. The features of monitoring the reputation of the university are revealed, the target audiences, to which communication efforts should be directed, are considered. Particular attention is paid to methods of reputation analysis. One way to maintain a reputation is publicity. Publicity monitoring during the formation of a university’s reputation in the context of convergence and digitalization allows one to be more attentive to promotion tools.
THE TRAINING APPLICATION BASED ON VR INTERACTION SCENARIOS – WITH EXAMPLES FOR LOGISTICS

The main goal of the article is to share some experimental view onto VR training applications, that allows for adaptation of the directed training scenario to a user being trained. The resulted application allows to the three scenarios being selected in any combination of ones, as well as the tasks modification through the input files, before a test starts. The program was developed in the Unity engine, using the modern UnityXR framework providing extensive support for leading virtual reality hardware. The resulting solution presents the possibility of an efficient training in Virtual Reality while modifying the course of training without any needs to recompile the program, and also it shows some positive values of using VR technology as a didactic solution.