Reviewerly/exHarmony huggingface dataset:
Relevance analysis using revision identifier in MS word

Abstract Electronic documents often contain personal or confidential information, which can be used as valuable evidence in criminal investigations. In the digital investigation, special techniques are required for grouping and screening electronic documents, because it is challenging to analyze relationships between numerous documents in storage devices manually. To this end, although techniques such as keyword search, similarity search, topic modeling, metadata analysis, and document clustering are continually being studied, there are still limitations for revealing the relevance of documents. Specifically, metadata used in previous research are not always values present in the documents, and clustering methods with specific keywords may be incomplete because text‐based contents (including metadata) can be easily modified or deleted by users. In this work, we propose a novel method to efficiently group Microsoft Office Word 2007+ (MS Word) files by using revision identifier (RSID). Through a thorough understanding of the RSID, examiners can predict organizations to which a specific user belongs, and further, it is likely to discover unexpected interpersonal relationships. An experiment with a public dataset (GovDocs) provides that it is possible to categorize documents more effectively by combining our proposal with previously studied methods. Furthermore, we introduce a new document tracking method to understand the editing history and movement of a file, and then demonstrate its usefulness through an experiment with documents from a real case.
Formation of Students' Professional Identity as a Tool for Intensifying Labor Capital: Career Guidance Activities

The paper examines the phenomenon of profes-sional identity as a tool for intensifying labor capital. Professional identity implies the orientation of an individual to the constant development of profes-sional qualities, which over time provides the econ-omy with highly qualified personnel. However, in modern Russian realities, many factors complicate the formation of professional identity, including those at the stage of acquiring higher professional education. The results of the sociological study of the state of professional identity of university stu-dents in Ufa revealed some problems in the for-mation of ideas about the profession (specialty) and the motivation for its choice. So, at the time of enter-ing a university, young people often have a superfi-cial idea of the chosen specialty, they do not have a process of professional determination. The motiva-tion for the professional choice of students is equal-ly rational and irrational, with every fourth student choosing a specialty under the influence of random factors. The study also showed that the role of the institution of secondary general education in the process of professional self-determination is not of prevailing importance, while in developed countries the school is the primary element of the vocational guidance system. Based on the analysis of the data presented, it is necessary to make managerial deci-sions in the field of career guidance in Russia, and measures of career guidance work with students at school are proposed.
Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

Semantic segmentation is one of the key problems in the field of computer vision, as it enables computer image understanding. However, most research and applications of semantic segmentation focus on addressing unique segmentation problems, where there is only one gold standard segmentation result for every input image. This may not be true in some problems, e.g., medical applications. We may have non-unique segmentation annotations as different surgeons may perform successful surgeries for the same patient in slightly different ways. To comprehensively learn non-unique segmentation tasks, we propose the reward-penalty Dice loss (RPDL) function as the optimization objective for deep convolutional neural networks (DCNN). RPDL is capable of helping DCNN learn non-unique segmentation by enhancing common regions and penalizing outside ones. Experimental results show that RPDL improves the performance of DCNN models by up to 18.4% compared with other loss functions on our collected surgical dataset.
Auditor Evaluation and Reporting on Cybersecurity Risks

Tremendous improvements in information networking capabilities have brought with them increased security risks resulting from the deterioration of the ability of a physical layer of computer security to protect an organization's information system. As a result, audit committees have had to deal with new security issues as well as the need to understand the cyber perpetrator and ensure the proper training of employees to consider cybersecurity risks. Standard setters including the Institute of Internal Auditors and the American Institute of Certified Public Accountants have issued guidance about lines of defense and reporting on an entity's cybersecurity risk management program and controls, respectively. Each of these topics is considered along with how cybersecurity guidance from COBIT, the National Institute of Standards and Technology, and the Center for Internet Security can be mapped into five cyber infrastructure domains to provide an approach to evaluate a system of cybersecurity.
A Novel Ensemble Representation Framework for Sentiment Classification

Text representation has a critical impact on the accuracy of text classifiers which is imperative to be strengthened. On the other hand, the question of how the state-of-the-art embeddings outperform previous approaches cannot be well explained. To advance text representation and better understand the internal mechanism, we propose a novel end-to-end framework named Ensemble Framework for Text Embedding (EFTE), which weightedly combines diverse embeddings and simultaneously represents sentences' and tokens' features in a more reasonable way. According to the experimental results in sentiment classification, our proposed embedding apparently improves the effectiveness compared to six single embeddings. Moreover, the importance of each embedding in terms of EFTE integration and how different embeddings influence the results by classification are discussed.
Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments

Clustering based on Adaptive Resonance Theory (ART) has been actively studied. In previous studies, ART-based clustering algorithms with a topological structure have been proposed and showed their superior self-organizing ability. However, this method deteriorates the clustering performance at high noise ratios. In this paper, we propose a multilayer clustering algorithm based on a topological ART-based clustering for improving a noise reduction ability. Simulation experiments show that the proposed algorithm achieves excellent clustering performance on a 2D synthetic dataset in high noise environments.
Determination of Loop Currents by Loop Elimination Method

A loop elimination method for finding the loop currents in a circuit is introduced. It eliminates solving the number of equations simultaneously.
Detection and Localization of Embedded Subtitles in a Video Stream

Videos with superimposed external subtitles constitute the major part of modern video content. However, there are quite a lot of diverse videos with embedded subtitles as well. In this regard, the problem arises of extracting and converting embedded subtitles into modern formats of external subtitles. Important steps in solving this problem are detection, localization, and binding of these subtitles to frames of the video stream. This paper proposes a method for detection and localization of embedded subtitles in video stream. The method is based on the search for static regions in the frames of the video stream and subsequent analysis of the connected areas inside them. Based on the results of this analysis, we determine whether a region belongs to the area of subtitles and localize text strings in the detected subtitles. The proposed method does not require large computational costs and can work in real time.
Understanding the Role of Artificial Neural Networks in the Prediction of Mental Health Diseases

The healthcare organizations and life sciences are increasingly leveraging machine learning techniques such as Artificial Neural Networks (ANN) due to their potentiality. A considerable issue being faced by the healthcare organizations is to provide quality services at costs that can be easily afforded. Throughout the entire course of health care, many patients develop serious problems that can lead to severe discomfort, expensive treatment, disabilities and more. Early prediction of disease is one of the most important requirements in medical field. It allows the healthcare professionals to undertake preventive measures thereby lending a helping hand in improving patient safety, quality of care as well as reducing the medical costs too. Many prognostic methods focus on ANN to provide new and deep insights into the patient’s health condition. This paper provides the description of working and learning of predictive machine learning technique ANN, for solving many real-world decision modelling problems in context to health care. It throws light on the factors responsible for different mental diseases which are then perceived and mathematically modelled.
Design and Application of Regional Multi-energy Complementary Distributed Energy Management and Control Platform Based on Big Data

This paper builds a distributed energy management and control platform based on big data for distributed energy variety, large capacity, the current situation of distribution and massive amounts of data. The platform uses big data and cloud computing technology to collect grid integration business systems and third-party internet platform of multi-source data, then it carries out data analysis and data mining. It has the functions of comprehensively analyzing the regional distributed energy operation and output, collecting and sending the operation, analyzing the development trend and maintenance information of the abnormal power station. It improves the maintenance efficiency and reduces the impact of the output change on the power grid.
Micro-controllers: Promoting Structurally Flexible Controllers in Self-Aware Computing Systems

To promote structurally flexible controllers in systems such as self-aware computing systems, this paper proposes the use of micro-controllers. Instead of generic monolithic controllers, like Rainbow, we advocate the use of service-specific micro-controllers which can be based on microservices. Although traditional generic controllers can be configured parametrically according to system needs, their use and reuse are nevertheless restrictive because of the wide range of services expected from the different stages of the feedback control loop. The solution being advocated is to have structurally flexible controllers that can be composed from micro-controllers. Controlling the architectural configuration of these micro-controllers is a meta-controller that is able to conFigure the controller according to the services required for controlling the target system. The feasibility of the proposed approach is demonstrated using the PhoneAdapter case study in which micro-controllers are configured at run-time depending on changes affecting the system or its environment.
A Novel Clustering-Based Filter Pruning Method for Efficient Deep Neural Networks

Deep neural networks have achieved great success in various applications, accompanied by a significant increase in the computational operations and storage costs. It is difficult to deploy this model on embedded systems. Therefore, model compress is a popular solution to reduce the above overheads. In this paper, a new filter pruning method based on the clustering algorithm is proposed to compress network models. First, we perform clustering with features of filters and select one for each category as a representative. Next, we rank all filters according to their impacts on the result to select configurable amounts of top features. Finally, we prune the redundant connections that are not selected. We empirically demonstrate the effectiveness of our approach with several network models, including VGG and ResNet. Experimental results show that on CIFAR-10, our method reduces inference costs for VGG-16 by up to 44% and ResNet-32 by up to 50%, while the accuracy can regain close to the original level.
Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms

Pedestrian tracking has long been considered an important problem, especially in security applications. Previously, many approaches have been proposed with various types of sensors. One popular method is Pedestrian Dead Reckoning (PDR) [1] which is based on the inertial measurement unit (IMU) sensor. However PDR is an integration and threshold based method, which suffers from accumulation errors and low accuracy. In this paper, we propose a novel method in which the sensor data is fed into a deep learning model to predict the displacements and orientations of the pedestrian. We also devise a new apparatus to collect and construct databases containing synchronized IMU sensor data and precise locations measured by a LIDAR. The preliminary results are promising, and we plan to push this forward by collecting more data and adapting the deep learning model for all general pedestrian motions.
Extracting categories with prototypes in artificial cognitive agents

In this paper a general strategy of determining categories with prototypes is presented and its variations are discussed. It is assumed that the task is carried out by an artificial agent which autonomously develops and maintains its private ontological knowledge base. The computation of a category and its prototype is based on a learning set consisting of messages obtained by the agent from other participants of external communication processes who are considered teachers and treated as sources of new meanings. The teachers communicate their beliefs related to an inclusion of particular objects to a category which the listening agent is trying to learn. Potential categories are defined over a related cognitive space defined with respect to a particular distance or similarity measure, both available to the artificial agent along with computational mechanisms for determining central objects in learning sets. Simplified computational examples of calculations performed within the proposed strategy are presented.
Linear Scalability from Sharding and PoS

Scalability is one of the most important problems in blockchain and has been the focus of both industry practitioners and academic researchers since Bitcoin was born. The blockchain has insufficient ability in handling large-scale concurrent transactions. The more transactions that are processed in the network, the more scalability problems appear in the network. Compared to the transaction throughput achieved by the electronic payment channels of mature development, the limitation is magnified. In this paper, we propose a novel consensus mechanism from sharding and Proof-of-Stake (PoS)—a scalable blockchain model that supports high concurrency, and achieve the linear expansion of transaction processing scale while ensuring security in order to prove the feasibility of the proposal. The proposal views the blocks in the network as two levels, namely, intermediate transition blocks (i.e., middle blocks) and final confirmation blocks (i.e., final blocks), and takes epoch as the basic unit of the consensus mechanism operation cycle. An epoch is a recursive process for each cycle of the consensus mechanism operation. Each epoch is equipped with four types of interactions, namely, node sharding, transaction sharding, internal consensus, and final block generation, and thereby determines the network status via main chain and shard chains. Given n nodes in the network, we construct one Validity group and \(p-1\) Regular groups (each one contains n/p nodes). The regular groups create transition blocks according to the transaction pool; the validity group extracts information from the transition blocks created by the regular groups, generates and sends final confirmation blocks to the main chain. PoS consensus is exploited to ensure that adversaries are not able to launch attacks on specific shards (neither transaction nor node shards). We also describe how to re-group the nodes in and add new node to the network. We provide the security analysis under several standard attack models.
Incremental Temporal Frequent Pattern Mining Based on Spark Streaming

As we are mining streaming data using frequent pattern, the accumulation of time is a huge factor in influencing the relationship between data items. In this case, how to keep track of the historical information of streaming data efficiently and design a temporal frequent measurement which considers time accumulation are two critical problems of frequent pattern for mining streaming data. As a result, we bring out a scheme of incremental temporal frequent pattern mining based on spark streaming framework. More specifically, as for the time property of streaming data, we design a calculating approach of temporal frequency which could decay and increase along with time. Simultaneously, as for the property of information accumulation, we put forward the ITFP (Incremental Temporal Frequent Pattern mining) algorithm. In order to minimize the spatial cost of recording historical information, this algorithm draws into TFP-tree (Time Frequent Pattern tree) to save historical frequent patterns, and reconstructs TFP-tree to achieve incremental mining. Additionally, a temporal sub-frequent pattern is proposed as backtracking window of historical information to reduce the deviation of frequent pattern. Finally, we test and verify above mentioned approaches through using public paper data set. And the experimental results show that the ITFP mining based on spark streaming has better performance of accuracy and validity, and even better extendibility under the distributed circumstance.
Detailed Usability Heuristics: A Breakdown of Usability Heuristics to Enhance Comprehension for Novice Evaluators

Heuristic evaluation (HE) is one of the most commonly used usability evaluation methods. In HE, 3–5 evaluators evaluate a certain system guided by a list of usability heuristics with the goal of detecting usability issues. Although HE is popular in the usability field, it heavily depends on the expertise of the evaluator, meaning that there is a large gap in HE performance between expert evaluators and novice evaluators. One of the factors contributing to this gap is the difficulty of thoroughly understanding usability heuristics. Usability heuristics are abstract and require simplification to be better understood by novice evaluators. In this work, our goal was to simplify Nielsen’s heuristics to make them easier to understand since they are one of the most popular sets of usability heuristics. We interviewed 15 usability experts with at least 4 years of experience in the field from both academia and industry and asked them to explain Nielsen’s heuristics in detail. We analyzed their responses, and we produced a modified list that is more detailed than Nielsen’s original list.
Accelerating Sparse Convolutional Neural Networks Based on Dataflow Architecture

Convolutional Neural Networks (CNNs) achieve state-of-the art performance in a wide range of applications including image recognition, speech recognition, and natural language processing. Large-scale CNNs generally have encountered limitations in computing and storage resources, but sparse CNNs have emerged as an effective solution to reduce the amount of computation and memory required. Though existing neural networks accelerators are able to efficiently process sparse networks, the strong coupling of algorithms and structures makes them inflexible. Dataflow architecture can implement different neural network applications through flexible instruction scheduling. The dataflow architecture needs to be initialized at execution time to load instructions into the computing array. Running a dense convolutional layer only needs to be initialized once due to regular calculations. However, running a sparse convolutional layer requires multiple initializations, which takes a long time to fetch instructions from memory, resulting in the computing array being idle and degrading performance. In this paper, we propose an instruction sharing strategy based on the field content in the instruction, which can reduce initialization time and improve performance. Moreover, we use an extended instruction sharing strategy based on the static nature of filters to remove filters-related instructions, further reducing initialization time. Experiments show that our strategies achieve 1.69x (Alexnet), 1.45x (VGG-16) speedup and 37.2\(\%\) (Alexnet), 34.26\(\%\) (VGG-16) energy reduction compared with dense networks. Also, they achieve on average 2.34x (Alexnet), 2.12x (VGG-16) and 1.75x (Alexnet), 1.49x (VGG-16) speedup over Titan Xp GPU and Cambricon-X for our benchmarks.
Lightweight Image Super-Resolution Reconstruction With Hierarchical Feature-Driven Network

Deep convolutional neural networks (CNNs) have emerged as powerful tool for single image super-resolution (SISR). However, enormous parameters hinder their real-world applications. To address this issue, we propose a lightweight hierarchical feature-driven network (HFDN) that can fully explore local and global hierarchical feature information. Specifically, we devise a hierarchical fuse module (HFM), which contains an adaptive dense unit (ADU) and an enhancement unit (EU), to effectively utilize local hierarchical information and encourage layer-wise feature reuse. Further, we introduce the channel and spatial attention mechanisms to emphasize informative details. In addition, we propose the multi-supervised reconstruction (MSR) strategy, which amplifies feature maps at different levels of network to exploit global hierarchical information and recover high-quality image. Experimental results on the benchmark datasets prove that the proposed network performs superior to the state-of-the-art methods both quantitatively and qualitatively.
Language Abstractions and Techniques for Developing Collective Adaptive Systems Using Context-oriented Programming

Collective Adaptive Systems (CAS) are increasingly used as a model for building and managing largescale cyber-physical systems, highlighting the need for a flexible approach for their definition and engineering. CAS consist of multiple individual context-aware self-adaptive systems, which need to adapt to their own environment, but also interact with each other, resulting in the global CAS behavior. Interactions can emerge as complementary or conflicting, and needs to be managed to assure a correct global behavior. Context-oriented Programming (COP) is one of the programming paradigms that has risen as a suitable approach to developing self-adaptive systems, due to the natural mapping of its programming constructs to self-adaptive systems taxonomy. Recently, multiple extensions to the COP model have been developed, which manage interactions between adaptive components (or systems), enabling its suitability for developing CAS as well. In this paper, we map out the COP language abstractions to the characteristics of CAS, demonstrating COP applicability, its formalization, verification tools, and adaptation composition techniques to foster CAS. Our discussion is illustrated using city-wide bus transport management system, as an example of a CAS.