Reviewerly/exHarmony huggingface dataset:
A Comparative Study on Improvement of Image Compression Method using Hybrid DCT-DWT Techniques with Huffman Encoding for Wireless Sensor Network Application

Nowadays, the demands on the usage of wireless network are increasing rapidly from year to year. Wireless network is a large scale of area where many nodes are connecting to each other to communicate using a device. Primarily, wireless network also tend to be as a link to transmit and receive any multimedia such as image, sound, video, document and etc. In order to receive the transmitted media correctly, most type of media must be compressed before being transmitted and decompressed after being received by the device or else the device used must have the ability to read the media in a compressed way. In this paper, a hybrid compression of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with Huffman encoding technique are proposed for Wireless Sensor Network (WSN) application. Data compression is very useful to remove the redundant data and reduce the size of image. After conducting a comprehensive observation, it is found that hybrid compression is suitable due to the process consist of the combination of multiple compression techniques which suits for Wireless Sensor Network’s application focusing on ZigBee platform.
A Smart Public Security Strategy: The New Taipei City Technology Defense Plan

New Taipei City is one of six special municipalities in Taiwan and the country’s most populous city. As part of the New Taipei City Government, the New Taipei City Police Department has jurisdiction over police administration and affairs. Because a smart defense system that improves work flow and increases efficiency would benefit a police department undergoing a police shortage, this study proposes the New Taipei City Technology Defense Plan. Five services—a policing service, a traffic e-service, an emergency call/command service, a surveillance service, and an enhanced investigation service—are integrated through human-centered design into a technology defense shield so that a police department can provide full functionality while stretching to cover a lack of human resources. The plan has been successfully deployed and is assisting law enforcement.
Product Classification Using Microdata Annotations

Markup languages such as RDFa and Microdata have been widely used by e-shops to embed structured product data, as evidence has shown that they improve click-through rates for e-shops and potentially increases their sales. While e-shops often embed certain categorisation information in their product data in order to improve their products’ visibility to product search and aggregator services, such site-specific product category labels are highly inconsistent and unusable across websites. This work studies the task of automatically classifying products into a universal categorisation taxonomy, using their markup data published on the Web. Using three new neural network models adapted based on previous work, we analyse the effect of different kinds of product markup data on this task, and show that: (1) despite the highly heterogeneous nature of the site-specific categories, they can be used as very effective features - even only by themselves - for the classification task; and (2) our best performing model can significantly improve state of the art on this task by up to 9.6% points in macro-average F1.
Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings

Abstract Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with taxonomic information from taxonomies like Word-Net. This taxonomic enrichment can be done by combining natural-corpus embeddings with taxonomic embeddings (e.g. those trained on a random-walk of WordNet’s structure). This paper conducts a deep analysis of this assumption and shows that both the size of the natural corpus and of the random-walk coverage of the WordNet structure play a crucial role in the performance of combined (enriched) vectors in both similarity tasks. Specifically, we show that embeddings trained on medium-sized natural corpora benefit the most from taxonomic enrichment whilst embeddings trained on large natural corpora only benefit from this enrichment when evaluated on taxonomic similarity tasks. The implication of this is that care has to be taken in controlling the size of the natural corpus and the size of the random-walk used to train vectors. In addition, we find that, whilst the WordNet structure is finite and it is possible to fully traverse it in a single pass, the repetition of well-connected WordNet concepts in extended random-walks effectively reinforces taxonomic relations in the learned embeddings.
Designing Self-presence in Immersive Virtual Reality to Improve Cognitive Performance—A Research Proposal

With the increasing availability of immersive virtual reality (IVR) technologies, new opportunities to change individuals’ behavior become possible. Notably, recent research showed that by creating a full-body ownership illusion of a virtual avatar looking similar to Einstein, users’ cognitive performance can be enhanced. However, although research is quite consistent in reporting that visuomotor synchrony in IVR achieved with body tracking suffices to elicit body ownership illusions that change behavior, it is still unclear whether strengthening these visuomotor illusions with additional technological design elements, such as visuotactile feedback, can contribute to increase desired outcomes even more. In this research in progress paper, we aim to conduct a 2 (physical feedback: low vs. high) × 2 (avatar design: normal vs. high intelligence) between-subjects experiment in IVR to test this assumption. In addition to subjective measures, we use heart rate and electrodermal activity to assess the strength of self-presence induced through the illusions.
Exercises Recommendation in Adaptive Learning System

The adaptive learning system develops gradually, but most attention is paid to the construction of student model and domain model. In this paper, a recommendation algorithm based on students' current knowledge level is proposed to match suitable exercises and avoid homogenization of learning content for all students, for the purpose of achieving so-called "adaptative". It is worth noting that the learning system recommendation is different from the general recommendation. Not only the method, the evaluation standard of recommendation result is also different. We should not simply recommend to students the exercises they must or must not mastered, but recommend to them the learning resources they should have within the range of their abilities according to the theory of proximal development zone. We also use the bayesian knowledge tracing model to judge students' mastery of knowledge as the evaluation standard of this algorithm.
A double-crystal scheme with full compensation of the Migdall effect

The double-crystal scheme for polarization-entangled photon pairs generation is presented. The negative impact of the Migdall effect on the polarization entanglement is fully compensated in the scheme. The theory for quantifying of entanglement decrease as the action of the Migdall effect is formulated.
Exploring Content-based Video Relevance for Video Click-Through Rate Prediction

This paper describes our solution for the Hulu Challenge. To answer the challenge, we introduce two content-based models, namely, Cascading Mapping Network (CMN) and Relevant-Enhanced Deep Interest Network (REDIN). CMN predicts video Click-Through Rate (CTR) by predicting content-based video relevance. REDIN mainly improves the popular Deep Interest Network by adding explicit video relevance constraint, which provides guidance for low-level video feature learning thus helpful for CTR prediction. Based on the two models, our solution obtains Area Under Curve (AUC) score of 0.6022 and 0.6155 on the TV-shows and Movie track respectively. What is more, we are one of the only two teams giving scores of over 0.6 on both tracks. The results justify the effectiveness and stability of our proposed solution.
Matching Keys and Encrypted Manuscripts

Historical cryptology is the study of historical encrypted messages aiming at their decryption by analyzing the mathematical, linguistic and other coding patterns and their historical context. In libraries and archives we can find quite a lot of ciphers, as well as keys describing the method used to transform the plaintext message into a ciphertext. In this paper, we present work on automatically mapping keys to ciphers to reconstruct the original plaintext message, and use language models generated from historical texts to guess the underlying plaintext language.
Enhanced Sketchnoting Through Semantic Integration of Learning Material

Within the transition from pen-and-paper to digital solutions, many tablet-based note-taking apps have been developed. In addition, sketchnoting, a highly visual form of note-taking, has gained in popularity, especially in teaching and learning. While current solutions already reduce the burden of organizing and searching through stacks of paper, they provide little support in finding additional information related to the notes, and search capabilities are mostly limited to textual queries and content. In this paper, we present a novel solution for digital sketchnoting aimed at enhancing the learning experience of students. In addition to common capabilities such as handwriting recognition, our note-taking app integrates semantic annotation and drawing recognition. Handwritten notes are recognized and passed through concept recognition and entity linking tools to enable a knowledge graph-based integration of contextually relevant learning resources. We extend traditional search capabilities by including the semantic metadata from the related material as well as enabling visual queries to find recognized sketches. Finally, resembling the exchange of paper notes among students, our app allows to share the semantically enhanced notes with other devices.
Identifying and Linking Entities of Multimedia Franchise on Manga, Anime and Video Game from Wikipedia

In Japan, manga, anime and video game (MAG) are viewed as popular culture. Many MAG works are published across media, i.e. shared characters, story, universe, etc. Entities and relationships describing multimedia franchise are helpful for general audiences to search MAG items. However, multimedia franchises are established by the cognition of audiences, it has been difficult for memory institutions to create authorized datasets of MAG multimedia franchise. In this paper, we propose a method to link institutional data and multimedia franchise entities from Wikipedia. Articles about MAG works in Wikipedia are created and edited by audiences. Many MAG articles are organized based on the structure of multimedia franchise. The proposed method is to identify of multimedia franchise entities automatically from patterns of relationships between these articles. This paper shows the result of the extraction and linking them to the bibliographic records of Media Arts Database (MADB) produced by the culture ministries of Japan.
INFORMATION AND COMMUNICATION TECHNOLOGY ADOPTION AND ITS INFLUENCING FACTORS: A STUDY OF INDIAN SMEs

Purpose: The current study intends to estimate empirically the factors, which more likely influence the ICT adoption by Indian SMEs.&#x0D; Methodology: Exploratory research has been applied and the current study uses a logistic regression model to estimate the data. The Database was selected as a DV to estimate the model.&#x0D; Main Findings: The summary result of all the five models show that there is one significant variable that explains the ICT adoption among SMEs, i.e. complexity. This shows that the adoption of the innovative process is complex for small enterprises since these innovative processes are beyond the scope of these enterprises. The other independent variables are also significant except Trialability, Observability, Owner IS, Employee IS, Management Support, Image, etc. &#x0D; Implications / Applications: The role of SMEs has become more crucial in developing nations because they have the ability to significantly improving income distribution, creation of employment, poverty eradication, as well as export growth. Technology up-gradation is the most significant factor in capacity building for enhancing competitiveness in SMEs. The main aim of innovation policy is to promote new ideas, such as products, processes, and organization techniques.&#x0D; Novelty/Originality of this study: Numerous factors like social, economic, political, human and other factors that influence ICT adoption have been examined in previous literature. In addition to this, there are some other factors like technological, organizational and environmental also impact ICT adoption. In this aspect, none of the studies in previous was well-studied in Indian SMEs context. Therefore the present paper bridged this gap by examining the factors, which more likely influence the ICT adoption by Indian SMEs. Understanding this importance would enable suitable policy reviews and intervention measures that help ICT adoption.
The role of geochemistry in understanding mineral systems

SummaryThe mineral system concept is a valuable framework to use for mineral exploration because it allows the user to interpret their data with more flexibility than for traditional ore deposit models. Better context is provided by a well constructed mineral deposit model because fundamental processes can be adapted to a broad range of systems.Geochemistry adds much needed detail to any mineral deposit model in three main areas: (1) immobile element geochemistry to better constrain lithological units and define a more detailed stratigraphy, (2) alteration geochemistry to quantify mass balance leading to a better understanding of hydrothermal fluid flow and potential trap sites, and (3) metal enrichment signatures and how they vary across a mineral system so that exploration can be focussed on those parts of the mineral system that have the highest likelihood of exploration success.
SAR Ship Detection Method Based on Convolutional Neural Network and Multi-layer Feature Fusion

SAR ship detection plays an important role in marine traffic monitoring. Traditional SAR target detection methods are mostly based on intensity differences between target and clutter, which is limited especially in complex scenes, for instance coastal areas. In order to improve the detection performance in complex scenes, a SAR ship detection method based on convolutional neural network named LCMF is proposed in this paper. Firstly, a base network with low complexity is employed to extract features. Secondly, the ‘top-down’ approach is adopted to gradually fuse the semantically strong features, which is helpful for reducing false alarms, with the low-level high-resolution features to improve the detection performance on small targets. Finally, small-scaled anchor is designed to obtain region proposals, and these proposals are further fed to classification and regression network, which outputs the final detection results. Experiments on the sentinel-1A dataset demonstrate that the proposed method can detect ship targets in SAR images of complex scenes with high speed and accuracy.
Extracting Literal Assertions for DBpedia from Wikipedia Abstracts

Knowledge Graph completion deals with the addition of missing facts to knowledge graphs. While quite a few approaches exist for type and link prediction in knowledge graphs, the addition of literal values (also called instance or entity attributes) is not very well covered in the literature. In this paper, we present an approach for extracting numerical and date literal values from Wikipedia abstracts. We show that our approach can add 643k additional literal values to DBpedia at a precision of about 95%.
Hyperspectral imaging of sedimentary iron ores – beyond borders

SummaryBanded iron formations (BIFs) and granular iron formations (GIFs) are the two primary types of sediment hosted iron ore deposits and provide the primary source for iron, globally. Although both deposit types are comprised of bedded chemical sediments and chert layers, these deposits show notable differences in mineralogy and texture which can affect grade and processing behaviour.Hyperspectral imaging technology is able to map key iron ore minerals as well as the textural relationships between these minerals. This paper presents a case study comparing the mineralogy and texture of two distinct iron ore regions across the globe from one another: the Biwabik Iron Formation in Minnesota, U.S.A. (GIF) and the Pilbara Region in Western Australia (BIF). Using hyperspectral imaging data, the mineralogy and texture of these two distinct regions are compared to one another and to traditional iron ore characterisation from core logging. The results show that hyperspectral imaging technology is a useful tool in mapping the mineralogical, textural, and grade characteristics of both BIFs and GIFs. The application of this technology in the iron ore industry would provide rapid, accurate, and cost-effective ore characterisation to meet the ever-increasing production demand.
Security Certification in Payment Card Industry

The massive payment card industry (PCI) involves various entities such as merchants, issuer banks, acquirer banks, and card brands. Ensuring security for all entities that process payment card information is a challenging task. The PCI Security Standards Council requires all entities to be compliant with the PCI Data Security Standard (DSS), which specifies a series of security requirements. However, little is known regarding how well PCI DSS is enforced in practice. In this paper, we take a measurement approach to systematically evaluate the PCI DSS certification process for e-commerce websites. We develop an e-commerce web application testbed, BuggyCart, which can flexibly add or remove 35 PCI DSS related vulnerabilities. Then we use the testbed to examine the capability and limitations of PCI scanners and the rigor of the certification process. We find that there is an alarming gap between the security standard and its real-world enforcement. None of the 6 PCI scanners we tested are fully compliant with the PCI scanning guidelines, issuing certificates to merchants that still have major vulnerabilities. To further examine the compliance status of real-world e-commerce websites, we build a new lightweight scanning tool named PciCheckerLite and scan 1,203 e-commerce websites across various business sectors. The results confirm that 86% of the websites have at least one PCI DSS violation that should have disqualified them as non-compliant. Our in-depth accuracy analysis also shows that PciCheckerLite's output is more precise than w3af. We reached out to the PCI Security Council to share our research results to improve the enforcement in practice.
The Mapping and Optimization Method of Quantum Circuits for Clifford + T Gate

In order to solve the fault tolerance and reliability problems of quantum circuit, a series of structural equivalence rules and optimization operation strategies of quantum circuit are proposed to minimize the number of T gates, increase T gate depth, minimize circuit level, reduce fault tolerance implementation costs and increase circuit reliability.In order to satisfy the nearest neighbor constraints of some quantum systems, a LNN (linear nearest neighbor) arrangement algorithm based on Clifford + T gate quantum circuit is presented.Experiments are done on some benchmarks of RevLib, the results show that the optimization rate of most functions and the running time of the algorithm are better than those of the existing literature.
Development of the text analysis software agent (chat bot) for the library based on the question and answer system TWIN

The development of a text analysis software agent is presented for a library based on the TWIN question-response system. A review of modern platforms for creating chat bots. The results of experiments with a trained text analysis software agent are described. The trained agent fully provided correct information during the experiments.
Multi-level Graph Drawing Using Infomap Clustering

Infomap clustering finds the community structures that minimize the expected description length of a random walk trajectory; algorithms for infomap clustering run fast in practice for large graphs. In this paper we leverage the effectiveness of Infomap clustering combined with the multi-level graph drawing paradigm. Experiments show that our new Infomap based multi-level algorithm produces good visualization of large and complex networks, with significant improvement in quality metrics.
Intelligent Detection of Large-Scale KPI Streams Anomaly Based on Transfer Learning

In the complex and variable SDDC (Software Defined Data Center) environment, in order to ensure that applications and services are undisturbed, it needs to closely monitor various KPI (Key Performance Indicators, such as CPU utilization, number of online users, request response delays, etc.) streams of resources and services. However, the frequent launch and update of applications, which produces new KPIs or change the data characteristics of KPIs monitored, so that the original anomaly detection model may become unavailable. So we propose ADT-SHL (Anomaly Detection through Transferring the Shared-hidden-layers model). It first clusters historical KPIs by similarity, and then all KPIs in each cluster are through Shared-hidden-layers method to train VAE (Variational Auto-Encoder) anomaly detection model so as to reconstruct KPIs with corresponding characteristics. When a new KPI is generated, ADT-SHL classify it into similar cluster, and finally model of the cluster is transferred and fine-tuned to detect the new KPI anomalies. This process is rapid, accurate and without manual tuning or labeling for new KPI. and its F-scores range 0.69 to 0.959 for the studied KPIs from two cloud vendors and others, ADT-SHL is only lower a state-of-art supervised method under all labelings by 3.05% and greatly outperforming a state-of-art unsupervised method by 67.46%, and compared with them, ADT-SHL reduce model training time by 94% on average.