Reviewerly/exHarmony huggingface dataset:
A Multi-scale Progressive Method of Image Super-Resolution

In recent year, researchers have gradually focused on single image super-resolution for large scale factors. Single image contains scarce high-frequency details, which is insufficient to reconstruct high-resolution image. To address this problem, we propose a multi-scale progressive image super-resolution reconstruction network (MSPN) based on the asymmetric Laplacian pyramid structure. Our proposed network allows us to separate the difficult problem into several subproblems for better performance. Specially, we propose an improved multi-scale feature extraction block (MSFB) to widen our proposed network and achieve deeper and more effective feature information exploitation. Moreover, weight normalization is applied into MSFB to tackle the gradient vanishing and gradient exploding problem, and to accelerate the convergence speed of training. In addition, we introduce pyramid pooling layer into the upsampling module to further enhance the image reconstruction performance by aggregating local and global context information. Extensive evaluations on benchmark datasets show that our proposed algorithm gains great performance against the state-of-the-art methods in terms of accuracy and visual effect.
Protection Safeguarding Public Examining For Secure Distributed Storage

Cloud computing is internet computing that is based empowers sharing of administrations. Numerous users place their data when you take a gander at the cloud. In any case, the truth that users at this point don't have actual belonging in regards to the conceivably enormous estimations of redistributed data helps make the data honesty security in cloud computing a somewhat testing and undertaking that is possibly considerable especially for users with compelled computing resources and capacities. So rightness of data and security is a worry that is prime.
The Situation of Bibliographic Control in Vietnamese Libraries

The role of bibliographic control in Vietnamese libraries is analyzed from various perspectives such as the function, objectives, and direction of standardization in cataloging. In Vietnam, the unified description cataloging and bibliographic control in libraries still faces many difficulties due to the inconsistent application of the MARC21, AACR2, DDC, ISBD, and other emerging cataloging standards in the Vietnamese library system. This chapter analyzes the limitations of applying standards through the Cataloguing in Publication (CIP) tool and offers suggestions for improving the situation for better bibliographic control and better information for users.
A framework for designing space-efficient dictionaries for parameterized and order-preserving matching

Let P be a collection of d patterns {P1,P2,…,Pd} of total length n characters, which are chosen from an alphabet Σ of size σ. Given a text T (over Σ), the dictionary indexing problem is to create a data structure using which we can report all positions j (called occurrences) where at least one of the patterns Pi∈P is a match with the same-length substring of T that starts at j. We consider this problem under the following definitions of matching. Parameterized Matching: The characters of Σ are partitioned into static characters and parameterized characters. Two equal length strings S and S′ are a parameterized match iff the static characters match exactly, and there exists a one-to-one function which renames the parameterized characters in S to those in S′. Order-Preserving Matching: The alphabet Σ is ordered. Two equal length strings S and S′ are an order-preserving match iff for any two integers i,j∈[1,|S|], S[i]≺S[j]⇔S′[i]≺S′[j], where ≺ denotes the precedence order in Σ.
Data Scraping, Ingestation, and Modeling: Bringing Data from cars.com into the Intro Stats Class

New tools have made it much easier for students to develop skills to work with interesting data sets as they begin to extract meaning from data. To fully appreciate the statistical analysis cycle, students benefit from repeated experiences collecting, ingesting, wrangling, analyzing data and communicating results. How can we bring such opportunities into the classroom? We describe a classroom activity, originally developed by Danny Kaplan (Macalester College), in which students can expand upon statistical problem solving by hand-scraping data from cars.com, ingesting these data into R, then carrying out analyses of the relationships between price, mileage, and model year for a selected type of car.
Transfer Learning with Sparse Associative Memories

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design deep neural networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.
A Research of Distributed Security and QoS Testing Framework

Since the birth of the Internet, the quality of network service has been a widespread concerned problem. With the continuous development of communication and information technology, people gradually realized that the contradiction between the limited resources and the business requirements of network cannot be fundamentally solved. In this paper, we design and develop a distributed security quality of service testing framework called AweQoS(AwesomeQoS), to adapt to the current complex network environment. This paper puts forward the necessity that some security tests should be closely combined with quality of service testing, and further discusses the basic methods of distributed denial of service attack and defense. We introduce the design idea and working process of AweQoS in detail, and introduce a bandwidth test method based on user datagram protocol. Experimental results show that this new test method has better test performance and potential under the AweQoS framework.
Concept for Human and Computer to Determine Reason Based Scene Location

The motivation of human computer interaction (HCI) is to improve how humans use computers. One approach can be developing technology that allows the human and computer to collaborate. In some cases creating this type of collaboration or joint approach to for example solve a problem or identify an important piece of information. While some researchers investigate ways that do not include the human, HCI focuses on keeping the technology linked to the human. At this stage in our research, the human is the collaborator utilizing the computer as a teammate to accomplish the task. The task for this work falls under the area of ongoing research in scene understanding. We present a concept that allows the location of an image to be determined based on object detection performed by the computer and the human using reasoning to generate possible candidates for the location that an image can represent. The human may use his or her own knowledge to reason about the options or again working with the computer to glean from reasoning engines that include knowledge. The paper will present this idea and the work that has started.
Potential Legal Challenges for Blockchain Technology in Competition Law

Abstract Since decentralized organizations such as blockchain are not recognized as legal persons, questions arise regarding the ability to detect anti-competitive practices and their perpetrators. Under certain circumstances, if a competitor is unreasonably refused in access to technology, it may well be interpreted as creating an obstacle to his/her entry into the market, which may constitute a violation of the legislation of a country on the protection of economic competition. The exchange of information between players of the same market can present antitrust risks if it helps to fix prices for their products/services or to establish other forms of coordination between such players. The purpose of this study is to provide a description of current EU anticompetitive practices in the field of blockchain-technologies application, as well as to identify challenges in the EU antitrust law related to the emergence of blockchain. This article highlights the challenges blockchain poses for analyzing unilateral anti-competitive practices. This study suggests that EU competition law has a lack of operational and measurement tools to map competitive interactions taking place outside the relevant market, which could lead to rather short-sighted competition law enforcement focusing only on horizontal competition restrictions on relevant markets. The relevance of the topic is associated with the fact that the increased popularity of the use of blockchain technology requires an answer to the question of its legal nature and inclusion in the legal field in order to balance the interests of all parties to economic and legal relations.
Market and Technology Monitoring Driven by Knowledge Graphs

In this paper, we describe an ongoing research project that aims to detect and trace trends for markets and technologies, hidden behind the vast amount of diverse information populated through the whole world. Our goal is to detect and follow upcoming and ongoing trends in a domain-agnostic and automatized fashion. In this paper we describe our experiences from the initial project steps and our approach using a continuously growing Knowledge Graph. We use a general model that allows us to capture identified mentions and relationships and resolve them into a number of entity and fact classes. Based on two business use cases we present first results where we already gained new insights into various technological developments without the intervention of human domain experts.
Smart Accident Recognition and Alerting System for Edge Devices

The rate of road accidents has been rising over the years and the high fatalities in these accidents is a matter of great concern. In accidents like these, every second matters. Many times the medical services are unable to make it in time resulting in an unfortunate loss of life, making road accident deaths an integral issue. Also, chances of accidents are equally high during late hours. In an emergency at odd hours, one cannot guarantee if anyone will be present around to inform hospitals, or the police. This paper proposes an End-to-End Deep Learning solution to automate accident recognition and send real-time alerts to emergency services, that is the nearest hospitals and police stations. The proposed system is aimed to be deployed on edge devices attached to roadside CCTV cameras. For this purpose, an optimization step is performed on the deep learning models using the Intel's OpenVINO toolkit which improves performance on edge devices.
Relation Extraction using Language Model Based on Knowledge Graph

Abstract Relation extraction is an important task in natural language processing (NLP). The existing methods generally pay more attention on extracting textual semantic information from text, but ignore the relation contextual information from existed relations in datasets, which is very important for the performance of relation extraction task. In this paper, we represent each individual entity as a embedding based on entities and relations knowledge graph, which encodes the relation contextual information between the given entity pairs and relations. Besides, inspired by the impressive performance of language models recently, we used the language model to leverage word semantic information, in which word semantic information can be better captured than word embedding. The experimental results on SemEval2010 Task 8 dataset showed that the F1-score of our proposed method improved nearly 3% compared with the previous methods.
Comparison in Power-Forecasting Methods for Geographically Distributed PV Power Systems Using Their Previous Datasets

Power generation from photovoltaic (PV) power systems is increasing from the perspective of environmental sustainability. However, the amount of PV power generated is unstable because it depends on weather conditions. For this reason, accurate prediction of PV power is required by electric power suppliers, and various forecast methods have been proposed, such as methods using machine learning. This study uses another method that predicts the short-term output of geographically distributed PV systems from the optical flow which is one of image processing methods of normalized output power distribution by applying motion compensation prediction, which is a technology for estimation of motion images. In this paper, we compare the proposed method with the persistence forecast, and evaluate the effectiveness of the proposed method. As a result, the average MAPE of the proposed method was 4.62 %, whereas that of persistence forecast was 6.4 %, which indicates that the proposed method is an effective approach for PV power forecast.
All in the Family: Cataloging the Alice Hoffman Collection

The acquisition and cataloging of the Alice Hoffman Collection at University Archives and Special Collections (UASC) of the Adelphi University Libraries allowed for a successful collaboration between the Catalog and Metadata Strategies Department and UASC, and a novel approach to the methodology of applying metadata when cataloging a unified bibliographic family. This case study provides detailed steps taken in cataloging the novels, foreign language translations, and special editions of the Alice Hoffman Collection, both from the standpoint of the creation of workflows and a working team, the creation of RDA MARC 21 bibliographic records informed by the theory of Functional Requirements for Bibliographic Records (FRBR), its Work-Expression-Manifestation-Item (WEMI) model, and the bibliographic relationships inherent in a unified bibliographic family. As an exercise in practical cataloging, this article provides ideas and a roadmap to cataloging successfully a unique collection of titles for the purposes of ease of cataloging, the collocation of resources, and the benefit of the end-user or researcher.
Dual-Learning-Based Neural Machine Translation Using Undirected Sequence Model for Document Translation

Document-level machine translation remains challenging owing to the high time complexity of existing models. In this paper, we propose a dual-learning-based neural machine translation (NMT) using undirected neural sequence model for document-level translation. Dual-learning mechanism can enable an NMT system to automatically learn from corpora through a reinforcement learning process. Undirected neural sequence models such as Bidirectional Encoder Representations from Transformers (BERT) have achieved success on several natural language processing (NLP) tasks. Inspired by a BERT-like machine translation model, we employ a constant-time decoding strategy in our model. In addition, we utilize a two-step training strategy. The experimental results show that our approach has much faster decoding speed than a previous document-level NMT model on several document-level translation tasks while the loss of our approach’s translation quality is acceptable.
The role of blended learning in the process of profilization of the system of secondary general education

The article focuses on the incompleteness of the process of building a system of profilization in the Russian general education school, as well as on the increasing role of specialized education as a global demand. It is argued that a factor contributing to quality profilization is the introduction and reliance on blended learning. The author gives various classifications of blended learning models of domestic and foreign researchers. Presented are the results of several surveys aimed at identifying the attitude of students, parents and teachers to blended learning and technologies of blended learning, if they were used in the educational process in the framework of the profile school.
Leveraging Knowledge Context Information to Enhance Personalized Recommendation

Knowledge graphs (KGs) have proven to be effective to improve the performance of recommendation. However, with the tremendous increase of users and items, existing methods still face several challenging problems: (1) path-based methods rely heavily on manually designed meta-path; (2) embedding-based methods lack sufficient considerations of user personality. To overcome the shortcomings of previous works, we propose a novel model, named KCER, short for leveraging Knowledge Context to Enhance Recommendation. Firstly, KCER generates the representation of knowledge context associating with specific user-item pairs. Then to obtain enriched user representations, we leverage a gated attention network to extracted meaningful information from the associated knowledge context and user dedicated ID embedding. We conduct extensive experiments on three real-world datasets to evaluate the model. The experimental results show the superiority of KCER compared with other state-of-the-art methods.
Probabilistic Generation of Sequences Under Constraints

There is growing interest in the ability to generate natural and meaningful sequences (e.g., in domains such as language or music). Many existing sequence generation models, including Markov and neural algorithms, capture local coherence, but have no mechanism for applying the structural constraints that are so often essential for the development of meaning. We describe a novel solution to this problem which combines hidden Markov models with constraints, allowing sequences which obey user-defined constraints to be generated according to data-driven probability distributions. Compared to other constrained probabilistic solutions, our Constrained Hidden Markov Process (CHiMP) has significantly greater expressivity, allowing the user to generate constrained sequences that are longer and which have more numerous structural constraints.
Robust communication strategy for federated learning by incorporating self-attention

Federated learning is an emerging machine learning setting, which can train a shared model on large amounts of decentralized data while protecting data privacy. However, the communication cost of federated learning is heavy, especially for mobile devices with higher latency and lower throughput. Although several algorithms have been proposed to reduce the communication cost, they are extremely sensitive to data distribution, even inapplicable to the real client Non-IID data. In this paper, we propose an effective communication strategy for federated learning called FedSAA, which increases the testing performance on Non-IID data by introducing self- attention mechanism. Two major innovations of our paper are presented here. Firstly, we utilize self-attention mechanism to optimize both the server-to-client and the client-to-client parameter divergence during the model aggregation process so as to improve the model robustness for Non-IID data. Secondly, we adopt the sign compression operator to help data transmission between nodes. The experimental results demonstrate that the model accuracy of our communication-efficient strategy for federated learning with Non-IID data is superior to other communication-efficient algorithms.
Event detection without trigger words on movie scripts

Event extraction is a challenging task in text mining. Current research on event extraction is often based on the sentence level, while the research based on the document level is less investigated. In this paper, we propose a deep learning model, termed BLMA, which combines Bi-LSTM and Multi-head weighted Attention to detect multiple event types. Unlike traditional methods, BLMA can directly detect events in texts without trigger words. We regard the event detection task of the chapter as a multi-label classification problem and apply the BLMA onto the application of detecting events in movie scripts. Experiment results show that BLMA performs well on an automatically tagged screenplay dataset containing 11 event types. F1 score of event detection using BLMA reaches 91.6% for a chapter with a single event, and 93.9% with multiple events, which outperforms other models. Therefore, our deep learning approach can effectively extract events from a chapter, which can be used for event detection on movie scripts. In addition, we explore the contribution of each component of the BLMA to the results, and find that multi-head attention has a significant impact on the performance. Keywords: Event Detection, Bi-LSTM, Attention.
SIM Boxing Problem: ALMADAR ALJADID Case Study

Telecommunication fraud is considered as a significant problem that costs mobile network service providers billions of dollars annually. Although adventing new technologies expected to reduce fraud, it has provided fraudsters new techniques to commit fraud. SIM Box fraud is considered one of developed fraud that has emerged with the use of VoIP technologies. Fraudsters have used this technology to bypass their calls the international/national gateway switch and standard termination fees. Efficient fraud detection systems as well as data analysis systems can be used to enable telecom operators to stop fraud and save a lot of money. In this work, SIM Boxing problem is studied and identifying SIM Box fraud subscriber scenario is proposed. More precisely, after examining the attributes of the Call Detail Records (CDRs) of mobile subscribers, an algorithm to identify the data set of normal and fraudulent behavior has been implemented in this work. We applied this study on ALMADAR Company’s CDRs and we found that only 2% to 9% of behaviors (depending on data length) might show important tendency for fraudulent use and need to be monitored and examined accurately.
Coverage-driven deep prediction intervals method

Prediction Intervals (PIs) provide a method to quantify the uncertainty of deep neural networks' point forecasts. High-quality PIs should be as narrow as possible while covering a designated percentage of data points, which is an axiomatic theory. Lower Upper Bound Estimation (LUBE) method is the first to incorporate this axiom into the neural networks loss function, however it pays much attention to the interval width, and thus the coverage probability does not achieve the desired result. Consequently, the PIs are unreliable and their practical application risks are elevated. In this paper, prioritizing coverage probability, we propose a coverage-driven approach that is generalized to any neural networks model, combining bootstrap method with improved LUBE method. We show that PIs constructed by our method are more reliable, and model uncertainty is quantified using bootstrap. Moreover, compared with two novel PI methods, benchmark experiments show our method is able to reduce the mean PI width by more than 7.5% while obtaining the better results in coverage probability.
Utilization of Lessons Learned in Product Development

New product development is the engine of competitiveness in the automotive industry. Beyond the technical content, the success of a project is significantly influenced by management performance, procedures, and teamwork. Lessons learned within a project and between projects can reduce efforts, development time, and costs. This knowledge sharing contributes to company-level sustainability of success by preventing the reoccurrence of mistakes and errors. 105 experts filled a voluntary online survey about their opinion about lessons learned. Two-third of the respondents use any form of lessons learned. Shared files are evaluated as the most useful way of managing the information flow, followed by targeted databases. Cluster analysis explored three utilization patterns of lessons learned. A minority of the experts found that managing lessons learned are problematic to feasible, but they are the source of information. Next to them, an active and inactive user group is identified.