Reviewerly/exHarmony huggingface dataset:
Research on User-Oriented Quality Management of Network Operation and Maintenance

The transformation of operators' traffic management, the demand for refined management, and the improvement of users' satisfaction have put forward higher requirements for network maintenance. In order to better network fine operation and maintenance management around user perception, this paper analyzes the influence of quality of service (QoS) and quality of experience (QoE) on users' subjective feelings, establishes an evaluation model to quantify user experience quality, and lists a practical application scenario using this model.
Solar Climatic Atlas of Daily Usability by Variable Sky in Algeria

The purpose of this paper is the estimation of solar radiation conversion systems profitability thresholds installed in several climatic zones of Algeria. For this, a numerical simulation has been developed to estimate the profitability thresholds of solar flat plate collectors by zone as well as the effects of input temperature variation, optical efficiency as well as overall losses on the usability function. Finally, the solar atlases of the average monthly usability have been plotted and the zones favorable to the installation of the solar flat plat collectors are identified.
Naturalness-Preserving Image Stitching Based on Optimal Seam Estimation Considering Parallax

Image stitching that produces a panoramic image from multiple images is one of the fundamental techniques in computer vision. Conventional image stitching methods have a problem that they yield a visually implausible result when parallax, i.e. the visual difference between the same objects in multiple images because of the differences of camera locations and angles, exists. To solve this problem, this paper proposes an image stitching method based on optimal seam estimation considering parallax. Compared with existing methods, the novelty of this work can be found in the use of optical flow and color information for estimating the optimal seam. Concretely, parallax is represented as the movement of the same object between the input two images. Then, the movement is estimated by using optical flow. Furthermore, by newly using color information as well, the proposed method can accurately estimate the movement even for the region with small gradients. Experimental results show that the proposed method can produce a naturalness-preserving stitching result.
Comprehensive quantitative evaluation of effect of lightning protection project based on risk governance and prospect of the application context

Prevention of the potential risks to power grid operation is an important way to protect the security and improve the equipment operation level. Scientific quantitative evaluation of the effect of lightning protection investment is a foundation to optimization of investment decision, enhance investment efficiency and effects, and to promote investment accuracy. Therefore, this paper presents a comprehensive quantitative evaluation of investment effects. First, by analyzing the mechanism of risk prevention of different lightning protection measures, a comprehensive effect quantitative evaluation index system is established which contains various effects and reflects the characteristics of risk prevention. Secondly, the paper realizes the quantitative evaluation, constructs the investment effects quantitative evaluation model, and normalized coefficient for the first time. Finally, apply quantitative evaluation model to evaluate actual investment effects and discuss the practical application contexts.
DeepFaFM :A Field-array Factorization Machine based Neural Network for CTR Prediction

CTR prediction is an important research direction in the field of recommendation system. In recent years, DeepFM has been concerned because it can learn the low- and high-order feature interactions end-to-end, and it has become one of the best models for CTR prediction. However, due to the inadequate feature interactions in the FM component of DeepFM model, the prediction accuracy is reduced. Compared with DeepFFM, although the FFM component of DeepFFM model attributes the same feature to the same filed to alleviates the inadequate feature interactions in FM model, its binomial parameter number is far greater than DeepFM, which increases the pressure of recommendation system. In this paper, we propose a new model called DeepFaFM. It provides a scalable coding matrix with adjustable factors, which can dynamically balance the operation pressure according to the actual production situation. Compared with DeepFM, which is one of the latest classification models, it has the advantages of DeepFM, improves the inadequate feature interactions in the operation of DeepFM, and improves the accuracy of the model. Experiments show that DeepFaFM is better than DeepFM in accuracy, and is equivalent to DeepFFM but its operation efficiency is significantly higher than DeepFFM. In particular, DeepFaFM has a scalable variable, which can dynamically balance the accuracy and calculation pressure according to the actual production situation, improving the flexibility of the model.
Non-destructive studies of prehistoric lithic material culture in the search for sources of geologic raw materials: an overview of techniques and issues

Geological raw materials are an important component of the archaeological record in the Prehistory (namely due to their persistence under buried conditions) and pinpointing their original position on the geological context could give important information about activities of ancient human groups. However, archaeological lithic artefacts impose strict restrictions on analytical procedures due to the need to preserve features with cultural meaning, which might affect the effectiveness of the analytical procedure. We attempt here a critical overview of non-destructive techniques for the search of the source of geological raw materials used to make prehistoric lithic artefacts, highlighting issues involved in the interactions between techniques and study objects. It is emphasized that the success of provenance studies will strongly depend on the sensitivity of the analyses undertaken to the characteristics of the sample, as well as the geological information available.
Fundamental Limits of Approximate Gradient Coding

In the distributed graident coding problem, it has been established that, to exactly recover the gradient under s slow machines, the mmimum computation load (number of stored data partitions) of each worker is at least linear (s + 1), which incurs a large overhead when s is large [13]. In this paper, we focus on approximate gradient coding that aims to recover the gradient with bounded error ε. Theoretically, our main contributions are three-fold: (i) we analyze the structure of optimal gradient codes, and derive the information-theoretical lower bound of minimum computation load: O(log(n)/log(n/s)) for ε = 0 and d ≥ O(log(1/ε)/log(n/s)) for ε &gt; 0, where d is the computation load, and ε is the error in the gradient computation; (ii) we design two approximate gradient coding schemes that exactly match such lower bounds based on random edge removal process; (iii) we implement our schemes and demonstrate the advantage of the approaches over the current fastest gradient coding strategies. The proposed schemes provide order-wise improvement over the state of the art in terms of computation load, and are also optimal in terms of both computation load and latency.
A Knowledge-Enriched Model for Emotional Conversation Generation

In this poster, we propose a knowledge-enriched emotional conversation generation model (KE-EGM) that can ensure high quality content and focus on the impact of emotional factors during the conversation. First, we apply a multi-embedding fusion layer to provide this model with the token-level and sentence-level understanding. Then, the emotion flow attention mechanism combines flow emotion state and attention mechanism to learn and capture emotional information during the conversation dynamically. Finally, the multi-objective optimization mechanism is introduced to detect and generate fine-grained emotional responses. The experimental results show that KE-EGM outperforms several baselines not only in the content aspect but also in the emotional aspect.
Adaptive Data Fusion in Inertial Sensors and Data Quality Analysis of Sensor Networks

This paper investigates microelectromechanical systems (MEMS) technology sensors. The device- and system-level analyses are performed. An investigation is focused on multi-degree-of-freedom (MDOF) microsystem-technology inertial sensors which are widely used in mobile devices, surveillance and reconnaissance platforms, wearable electronics, guidance and navigation systems, etc. Open problems of data quality (DQ) in sensor networks, evaluation of sensor data, data fusion, DQ improvement, and data homogeneity in MDOF sensors are addressed. The DQ metrics design and their use are reported. Our research and algorithmic developments are focused on data assertion, adaptive signal processing, asynchronous data fusion and data management from heterogeneous multiple sensors and sensor networks. Using a serial protocol I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C interface, considered MDOF sensors can fuse the directly measured data, as well as output the post-processed data. The integrated sensors (triaxial accelerometers, gyroscopes and magnetometers) ensure physics-consistent data-intensive processing, advance modalities, interoperability, integration, compliance and redundancy. Alternative solutions in adaptive filtering and near-real-time reconfigurable signal processing are considered to enable dynamic range, improve precision and accuracy, ensure robustness, minimize uncertainties, etc. The experimental results are reported.
A Hybrid "Quantum and Classical" Method for Outlier Detection

In this paper, we present a novel method for outlier detection based on a hybrid approach involving both quantum and classical computing. The proposed method proceeds according to two steps: The first step uses classical computing by preparing and initializing components for the second step involving quantum computing. The latter uses adapted versions of some well-referenced quantum algorithms such as the quantum calculation of Hamming distance, and the minimum finding of Durr-Hoyer. The proposed method is based on the calculation of the distance between the instance to be tested and its Kth nearest neighbor. The test instance is thus considered an outlier if the calculated distance is greater than a given threshold T. At the end of this paper, we present an experimentation of the method and a performance analysis showing a quadratic improvement in terms of computational complexity compared to classical methods of outlier detection.
Tool For Review Analysis Of Product

Today people buy products online rather than manual shopping. These e-commerce websites let their customers write product feedback and review in the form of rating. The review given by these customers helps the company personnel of the product understand where their product stands in the market. At the same time help other fellow customers decide whether this product is suitable for them to buy. Reviews can be fraudulent in a way that they demote the product or advertising different products which might misdirect the customer about the quality of the product. Hence, we have worked towards developing a tool which will classify the reviews as fake or genuine and provide it to the user. The proposed tool will operate on the principle of the Weighted Ensemble Classifier. The said ensemble classifier uses Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifiers. Experimental results show that the proposed ensemble classifier is efficient in the fake review detection task.
Adopting the Discovery Method to Develop Linguistic Competence Among International Students: Experience from Russia and Belarus

The article addresses linguistic competence and the importance of its formation for academic and professional purposes. It also posits that linguistic competence is an important part of the communicative one.The Discovery Method is proposed as a privileged tool when teaching international students to apply the norms and rules established in a language. In this article the authors give a number of reasons why this method should be used, and how to apply it during English teaching process in a class.The research is based on the data collected during English and Russian classes and is conducted in three universities by means of observation, discussion, interview, and questionnaire.The article proposes a set of 3 tables of descriptors to assess the student’s linguistic competence in three types of class activities: translation, writing, and reading. It also introduces a framework of exercises for teachers who wish to use the Discovery Method.Theoretical ground has been set for further research, focusing on the results of using the Discovery Method for linguistic competence formation.
High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests

Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto et al.'s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds.
Research on an Automatic Conversion Method of Power Flow Input Data from PSD-BPA to Pandapower

With the popularity of Python and its wide application in the scientific computing field, Pandapower, which is an open source power system analysis tool based on Python, is also recognized and appreciated. It is perfect for research and education which based on power flow calculation since it has advantages of easy programming, convenient operation, accurate calculation and intuitive result. Nevertheless, direct application of Pandapower is faced with a problem of lacking input data since power flow data is mainly in PSD-BPA data format in China. In order to solve quickly power flow in Pandapower, a novel method for converting PSD-BPA power flow input data to Pandapower data is presented in this paper, followed by a conversion program is implemented by Python. Furthermore, two actual grid models are used for data conversion. The corresponding calculation results of power flow show the accuracy of the proposed method. The work in the paper is helpful to promote the widespread use of Pandapower in the field of power system simulation research and education.
A Multi-factor Biometric-Based User Authentication Protocol for IoT Networks

User identity has become important in the growing environment of the Internet of things (IoT). Many technologies and resources have emerged with IoT in areas such as control, health care, and safety. The services offered by the client can be requested from anywhere, via smart device applications at any time. This renders IoT key to security and privacy. Security in IoT is therefore of vital importance, enabling reliable access to the services; authentication of dual factors could provide high security. This chapter suggests a mobile user authentication focused on flexible biometric authentication and a key management framework for protect IoT service entry.
Knowledge Graph from Informal Text: Architecture, Components, Algorithms and Applications

Graphs are formal structures that are used to represent various categories of relationships between different entities of complex systems. The underlying assumption is that given the parameters characterizing the entities and the relationships between such entities and graphs effectively provide a hierarchical representation of the entities. Building an industrial domain knowledge graph (KG) is feasible and important as the corpus is rich (a plethora of modules, signals, states, etc.). It is a great aid to someone who is not a domain expert as it provides a granular view of the domain. It provisions context-specific domain knowledge that can be easily mined with minimal human effort.
User Authentication using Secret Key Sharing

Due to the growth of internet usage by individuals, the security of personal information has become a prime concern, so that unauthorized person does not get access to the confidential information. Authentication of user is the solution to the problem. Secret sharing is the method for increasing security of data by distributing the secret among a group of participants. In secret sharing, only when a desired number of participants come together only then the secret can be recovered. In this paper, we have analyzed different secret sharing schemes and classified them based on their characteristics.Many mechanisms have been designed for authenticating user in wireless mobile environment. But none of these mechanisms concern the security of user’s secret key and other sensitive data stored in mobile phone in case the device comes in hands of attacker. We propose a system that performs two factor authentication (i.e. two mobile devices are required) with server for getting access to banking system. The authentication process cannot be successfully completed by the mobile devices alone and during the process reconstruction of the key is not needed so that neither device can hold a complete key. The proposed key sharing scheme using authentication protocol analyzes the security to check that in the practical applications all known security requirements are fulfilled, particularly the key exposure attack resistance.
A Dynamic Illustration Approach For Arabic Text

In this paper, we present an approach to dynamically transform simple Modern Standard Arabic children's stories scripts to the best representative images that can illustrate efficiently the meaning of words and word senses. We connect formally multiple datasets involved in our framework. We then apply several techniques to find the images and associate them with related word senses. First, we apply natural language processing techniques to analyze the text in stories and we build a semantic representation of main characters and events in each paragraph. Second, we apply various query formulation techniques as a brief scenario to enhance image web search which showed better accuracy as per preliminary results. Third, most significant queries are chosen to retrieve a list of possible candidate images from our multimedia database and search engines (i.e., Google and Bing). Instructors can then select and validate the ranked contextual images to compose the final visualization for each paragraph.
Comparative Analysis of Machine Learning Techniques in Health System

Medical diagnosis is a complicated task and plays a vital role in saving human lives so it needs to be executed accurately and efficiently. An appropriate and accurate computer based automated decision support system is required to reduce cost for achieving clinical tests. Machine learning (ML) techniques have become important to support decision making. In this paper, we present an evaluation and comparison of various machine learning techniques which has emerged in recent years. The result shows descriptive statistics of frequency of various machine learning techniques. Of all the ML techniques under the review, Random Forest (RF) has the highest of frequency of usage.
Metapath-Guided Credit Allocation for Identifying Representative Works

The ability to identify the representative works of a researcher has important implications in a wide range of areas, including hiring, funding, and promotion systems. In this paper, we propose a metapath-guided credit allocation method (MGCA) for identifying the representative works of individual researchers. MGCA utilizes metapath-guided neighbours to exploit rich semantic information in heterogeneous information network for locating a researcher’s field of expertise, and explicitly captures the importance of a paper, its relevance to other papers, and the unequally distributed contribution of each citation via a two-step credit allocation. We validate MGCA by applying it on the American Physical Society dataset in the scenario of identifying the Nobel prize winning papers of the Nobel laureates. Experiments demonstrate that the proposed method can significantly outperform the existing approaches.
DCSim: Cooling Energy Aware VM Allocation Framework for a Cloud Data Center

Explosion of digital content has resulted in large amounts of resources being provisioned and managed for various applications in cloud Data Centers. Energy consumption in these large Cloud Data Centers is a rising concern, accounting for 1.3% of the worlds electricity consumption [1]. Data Center cooling accounts for 40% of this energy consumption [2]. Of the various mechanisms available for studying the energy consumption in Data Centers, a simulation based approach is quite popular. In this paper, we propose DCSim, a configurable extension to CloudSim, a popularly used cloud infrastructure and simulation framework. CloudSim provides coarse power models to calculate total energy consumption in a Data Center for a given workload, but has no provision to factor in the Data Center topology and current cooled area into this power model. This makes building intelligent cooling energy aware allocation policies in CloudSim difficult. In DCSim, we introduce a novel Data Center model that addresses the shortcomings of CloudSim by encapsulating concepts of Racks, Aisles, Sectors and Zones (collectively referred to as DCObjects). We also provide the capability to model the cooling of these DCObjects. This makes the study of cooling aware resource provisioning for workloads easier. The DCObjects and the Data Center model presented are designed to be fully extensible to support future developments in this area. In this work we also implement a Cooling aware VM allocation policy, and demonstrate using multiple algorithms, that this VM allocation policy will effectively reduce the total Data Center power consumption by 18.18% over an algorithm which does not factor in the cooled DCObjects.