Reviewerly/exHarmony huggingface dataset:
An ontology automation construction scheme for Chinese e‐government thesaurus optimizing

Abstract To optimize the term hierarchy in the manual e‐government thesaurus, we combine the mainstream knowledge organization technology to form a complete set of ontology automation construction scheme. We build an e‐government knowledge base by using subject words in the Comprehensive E‐government Thesaurus as the term set and encyclopedia text as the corpus. The specific work includes the extraction of semantic features from the bag‐of‐words model, determination of the number of clusters by linear and nonlinear dimensionality reduction, division of terms by spectral clustering, social network analysis to determine the class label, and storing knowledge ontology via OWL. The recall rate of term hierarchy in the ontology is excellent, indicating the ontology has good knowledge extensibility, and also proving the efficiency of the scheme proposed in this work. Besides, the application model of a term hierarchy in information retrieval can show a richer semantic relation than the original thesaurus to guide the retrieval extension of government information resources.
Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base stations called eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. Each eNodeB generates a large number of key performance indicators (KPIs). Hundreds of thousands of eNodeBs are typically deployed to cover a nation-wide service area. Operators need to handle hundreds of millions of KPIs to cover the areas. It is impractical to handle manually such a huge amount of KPI data, and automation of data processing is therefore desired. To improve network operation efficiency, a suitable machine learning technique is used to learn and classify individual eNodeBs into different states based on multiple performance metrics during a specific time window. However, an issue with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network that consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.
Scale Invariant Fast PHT based Copy-Move Forgery Detection

Copy-Move forgery is a type of image forgery wherein a patch from the image is copied and pasted on the same image either to increase the occurrence of a particular object or to conceal some important detail in the image. This paper addresses the issue of copy-move forgery using the block-based method of feature extraction. In block-based methods of feature extraction, PHT is one of the competing solutions, but it is not much robust to scaling. This paper proposes Scale-Invariant Fast PHT (SIFPHT) algorithm to detect the copy-move forgery which uses Fast PHT [1] for extracting the features from the blocks. Fast PHT has a higher convergence rate than the traditional PHT, and the results prove that the speed-up of almost 4 is attained for detecting the forgery. Moreover, the Fast PHT features so obtained from the blocks are normalized before comparison due to which the scaled forged segments are also identified. Further, Fast K-Means clustering is used to estimate the similarity in the blocks and hence detect the copy-move forgery.
Performance Evaluation of Some Machine Learning Algorithms in NASA Defect Prediction Data Sets

The main purpose of machine learning is to model the systems making predictions by using some mathematical and operational features on the data with computers [1]. Today, there are many studies on machine learning in all areas of the software world. Software Defect Prediction is a sub-branch that progresses rapidly in machine learning. In this study, five of machine learning classification algorithms were conducted on with PYTHON programming language on defect prediction data sets which are JM1, KC1, CM1, PC1 in the PROMISE repository. These data sets are created within the scope of the publicly available NASA institution's Metric Data Program. The accuracy, recall, precision and F-measure and support values of the algorithms on the data are compared. When the results are examined in terms of the accuracy of machine learning algorithms, the accuracy rates of the algorithms are quite high in all 4 data sets. The highest success rates were obtained from the classification algorithms applied in 4 data sets in CMl and PCI data sets. In 4 data sets, the highest success rates were seen with Random Forest algorithm.
Towards Efficient Retrieval of Top-k Entities in Systems of Engagement

Next-generation enterprise management systems are beginning to be developed based on the Systems of Engagement (SOE) model. We visualize an SOE as a set of entities. Each entity is modeled by a single parent document with dynamic embedded links (i.e., child documents) that contain multi-modal information about the entity from various networks. We address the problem of efficiently retrieving the top-k entities in an SOE for keyword-based queries. In particular, we propose an efficient bitmap-based approach for quickly identifying the candidate set of entities, whose parent documents contain all queried keywords. Moreover, we propose the two-tier HI-tree index, which uses both hashing and inverted indexes, for efficient document relevance score lookups. Our performance evaluation with both real and synthetic datasets demonstrates the overall effectiveness of our proposed schemes.
Data Augmentation for Sentiment Analysis in English – The Online Approach

This paper investigates a change of approach to textual data augmentation for sentiment classification, by switching from offline to online data modification. In other words, from changing the data before the training is started to using transformed samples during the training process. This allows utilizing the information about the current loss of the classifier. We try training with examples that maximize, minimize the loss, or are randomly sampled. We observe that the maximizing variant performs best in most cases. We use 2 neural network architectures, 3 data augmentation methods, and test them on 4 different datasets. Our experiments indicate that the switch to the online data augmentation improves the results for recurrent neural networks in all cases and for convolutional networks in some cases. The improvement reaches 2.3% above the baseline in terms of accuracy, averaged over all datasets, and 2.25% on one of the datasets, but averaged over dataset sizes.
Deep learning-based drone detection in infrared imagery with limited training data

The increased availability and capabilities of drones in the consumer market has lead to increased risk in air traffic control and other public safety concerns. Automated drone detection systems can help to generate alerts and increase reaction time by security forces. Recently proposed approaches and systems are usually based on a combination of sensors and machine learning to carry out the detection of drones. While electro-optical imagery is the most prevalent modality, infrared sensors can complement it by providing better visibility in certain situations with cluttered background or low light conditions. A key limitation when using infrared data is the limited availability of data for training machine learning methods. In this work, we specifically focus on the task of drone detection in infrared imagery. Our main focus lies on investigating how the small amount of available infrared data can be compensated for. We approach this problem through three different types of experiments. First, we compare a detector resulting from training on limited infrared data with a detector trained on more diverse optical data. We then propose and evaluate several methods for pre-processing optical data in such a way that it better resembles the characteristics of infrared data. Finally, we train detectors on a combination of infrared and pre-processed optical data and evaluate the trade-off between amount of available infrared data and achieved accuracy of the resulting detector. We evaluate all detectors on our own set of diverse infrared recordings. Our results show that suitable pre-processing of optical data can significantly improve the resulting accuracy and performs much better than training solely on limited infrared data.
Analysis of Learning Effect using a SQL Learning Support System in the Class

This study presents analytical results of the relationship between student grade in a database class and frequency of SQL execution using our SQL learning support system. In the class of “database system”, students are required to learn SQL, which is a standard query language for the relational database system, as the fundamental knowledge of this subject. Through the SQL learning, students can learn the practical database skills for design and operation of database system. Therefore, in the database class, it is clearly important to lead students to correct understanding of SQL for nurturing skilled engineer of the database system. For this purpose, we have developed a web-based learning system for supporting SQL practice. In this study, using this SQL learning support system, we recorded and analyzed learning histories of students in the class conducted in 2015. The analytical results showed that there was a strong correlation between the frequency of SQL execution and the grade of final examination.
An Adaptive Embedding Framework for Heterogeneous Information Networks

Heterogeneous information networks (HINs) have been ubiquitous in the real-world. HIN embeddings, which encode various information of the networks into low-dimensional vectors, can facilitate a wide range of applications on graph-structured data. Existing HIN embedding methods include random walk based methods that may not fully utilize the edge semantics and knowledge graph embedding methods that restrict the expression ability of topological information. In this paper, we propose a novel adaptive embedding framework, which integrates these two kinds of methods to preserve both topological information and relational information. By incorporating an assistant knowledge graph embedding model, the proposed framework performs efficient biased random walk under the guidance of edge semantics.
Path-Based Academic Paper Recommendation

The overabundance of academic papers makes it difficult for researchers to find relevant or interested papers. To address the problem, existing studies have developed many approaches to recommend academic papers effectively. Most of them mainly utilize content-based filtering or citation analysis to measure similarity or relatedness of two papers and recommend relevant papers to the given query. However, these recommended papers are usually discrete from each other, i.e., the relationship between recommended papers are omitted, which disables researchers from having an sight into the time-oriented development of the topic they are interested in. To overcome the drawbacks of existing work, we propose a novel academic paper recommendation method called PAPR (Path-based Academic Paper Recommendation). Our method aims to recommend an ordered path of relevant papers, which are of great benefit in helping researchers understand the development of a specific topic. During process, we take both content and network structure into account to learn the representation of a paper. Next, the similarity between papers are measured based on the representation. The experimental results based on real data show that the proposed method outperforms the state-of-art methods.
Technology as a source of complexity and challenge for special victims unit (SVU) investigators

Although there has been significant public and academic interest in the ability of police to harness new technologies in order to solve crimes, there has been significantly less focus on how the proliferation of new technologies has impacted police workloads. In this exploratory study, we begin the process of rectifying this oversight by exploring some of the challenges mobile technologies pose to investigators working in a special investigations unit. Our work is informed by an analysis of data collected through in-depth interviews with police investigators to address the following research question: “To what extent has the complexity of special victims (sex crimes) investigations changed over time?”. Our findings indicate that technology is the most prominent factor leading to increased complexity of investigations. Specifically, technology adds to the volume of evidence that must be examined and managed, rapid advances in technology require additional training and expertise, and despite technological advances to assist in investigations, the process remains largely manual.
Real-Time Abnormal Event Detection in the Compressed Domain of CCTV Systems by LDA Model

For storage and displaying, huge amounts of data are produced by the CCTV (Close Circuit TeleVision) systems. The automatic detection of abnormal events in the compressed domain of these systems makes it possible to extract these events to alert and possibly store the sequences in a compressed format to optimize the capacity of storage and transfer of data. This paper describes a solution for a real-time abnormal event detection. The proposed method is based on the LDA model for classifying events in the compressed domain in CCTV systems. Experimental results, demonstrating reliable real-time extractions and storage, shows that the classification of events with the LDA model allows the extraction of abnormal events in the compressed domain at very high compression rate with an accuracy of 95% for two standardized datasets considered.
Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

ABSTRACT A formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.
Discovering Meaningful Labelings for RTS Game Replays via Replay Embeddings

Real-Time Strategy (RTS) games are an interesting environment to study challenging AI problems, such as real-time adversarial planning and opponent modeling. In this paper we focus on approaches that make use of replay data, which usually encode domain expert knowledge of gameplay. Some of these approaches use supervised learning to learn player/agent strategy models and thus rely on these replays being annotated with specific strategies or other labels. However, replays do not usually contain labels for these strategies. The problem we address in this paper is the automatic discovery of meaningful labeling of replays in RTS games. We address this problem by learning action and replay embeddings via recursive neural network models such as LSTMs. These embedded replays can then be clustered to discover labelings by using the clusters as the labels. We show that we can learn embeddings and discover labelings for replays that are correlated with meaningful information from those replays.
Rinascimento: using event-value functions for playing Splendor

In the realm of games research, Artificial General Intelligence algorithms often use score as main reward signal for learning or playing actions. However this has shown its severe limitations when the point rewards are very rare or absent until the end of the game. This paper proposes a new approach based on event logging: the game state triggers an event every time one of its features changes. These events are processed by an Event-value Function (EF) that assigns a value to a single action or a sequence. The experiments have shown that such approach can mitigate the problem of scarce point rewards and improve the AI performance. Furthermore this represents a step forward in controlling the strategy adopted by the artificial agent, by describing a much richer and controllable behavioural space through the EF. Tuned EF are able to neatly synthesise the relevance of the events in the game. Agents using an EF show more robust when playing games with several opponents.
Image Restoration on Residual Aggregation Network in Poor Weather Condition

Image restoration in poor weather conditions can assist military combatants to efficiently and accurately perform object detection, object recognition and object tracking. Moreover, in security systems, traffic navigation, etc. it also has high application value. Aiming at the problem of image distortion caused by different poor weather conditions like dust, rain, snow, fog, haze, etc. this paper proposes a new deep neural network based image restoration technology, a residual aggregation module is constructed for extracting the detailed features. Furthermore, dense connection is applied to combine low-dimensional features and generate high-dimensional features. The experimental results show that the network achieves superior results in image de-raining(IDR) compared with Deep Detail Network(DDN) and Dual Convolutional Neural Network(DualCNN) while obtaining favorable performances in image de-noising, image de-hazing, image de-blurring, image de-raindrops and other tasks.
Diverse Enumeration of Maximal Cliques

Maximal clique enumeration is a well-studied problem due to its many applications. We present a new algorithm for this problem that enumerates maximal cliques in a diverse ordering. The main idea behind our approach is to adapt the classic Bron-Kerbosch (BK) algorithm by, conceptually, jumping between different nodes in the execution tree. Special care is taken to ensure that (1) each maximal clique is created precisely once, (2) the theoretical runtime remains the same as in the BK algorithm and (3) memory requirements remain reasonable. Experimental results show that we indeed achieve our goals, and moreover, that the cliques are enumerated in a diverse order.
Ethical Considerations in Case Studies

High-level ethical principles have been introduced in software engineering. However, ethical concerns in empirical case studies require further investigation. This study aims to investigate ethical considerations in case studies of software engineering and evaluate the identified ethical concerns through a multi-case study in industry. 12 papers were selected and reviewed to extract ethical considerations, and an evidence-based analysis through coding is performed. As a result, 21 ethical considerations in total have been identified and mapped to case study design steps. 21 recommended actions have been proposed to mitigate ethical considerations based on the experience while adopting case studies in four software development companies, which could be used as a guideline for practitioners to conduct empirical studies in practice from the ethical perspective.
Did the Children Behave?

This work investigates the interplay between Child-Computer Interaction and attachment, a psychological construct that accounts for how children perceive their parents to be. In particular, the article makes use of a multimodal approach to test whether children with different attachment conditions tend to use differently the same interactive system. The experiments show that the accuracy in predicting usage behaviour changes, to a statistically significant extent, according to the attachment conditions of the 52 experiment participants (age-range 5 to 9). Such a result suggests that attachment-relevant processes are actually at work when people interact with technology, at least when it comes to children.
Fast and Scalable Outlier Detection with Sorted Hypercubes

Outlier detection is the task responsible for finding novel or rare phenomena that provide valuable insights in many areas of the industry. The neighborhood-based algorithms are largely used to tackle this problem due to the intuitive interpretation and wide applicability in different domains. Their major drawback is the intensive neighborhood search that takes hours or even days to complete in large data, thus being impractical in many real-world scenarios. This paper proposes HySortOD -- a novel algorithm that uses an efficient hypercube-ordering-and-searching strategy for fast outlier detection. Its main focus is the analysis of data with many instances and a low-to-moderate number of dimensions. We performed comprehensive experiments using real data with up to ~500k instances and ~120 dimensions, where our new algorithm outperformed 7 state-of-the-art competitors in runtime, being up to 4 orders of magnitude faster in large data. Specifically, 12 well-known benchmark datasets were deeply investigated and one case study in the crucial task of breast cancer detection was also performed to demonstrate that our approach can be successfully used as an out-of-the-box solution for real-world, non-benchmark problems. Based on our experiments, we also identified default parameter values that allow us to be parameter-free and yet report high-quality results.
A survey about industries that blockchain can transform

Blockchain become, because of its security, decentralized architecture and flexibility, one of the most important technologies in our days. Blockchain is a very good alternative for traditional system architectures. Researchers in almost all domains tried to adapt this new technology in their area and develop new solutions. The blockchain community has grown significantly and the proposed solutions become more and more complex. Problems like fraud, data integrity and data manipulation, distributed authority, information privacy and security are solved through blockchain. The technology can be successfully combined with traditional centralized architectures creating in this way hybrid systems with high complexity and security. The propose of the current paper is to make a review over multiple domains of solutions developed using blockchain, to compare their architectures, and to emphasize the initial requirements and constrains and how this technology was applied.
Efficient Outside Computation

Weighted deduction systems provide a framework for describing parsing algorithms that can be used with a variety of operations for combining the values of partial derivations. For some operations, inside values can be computed efficiently, but outside values cannot. We view out-side values as functions from inside values to the total value of all derivations, and we analyze outside computation in terms of function composition. This viewpoint helps explain why efficient outside computation is possible in many settings, despite the lack of a general outside algorithm for semiring operations.