Reviewerly/exHarmony huggingface dataset:
Underwater Acoustic Image Enhancement by Using Fast Super-Resolution with Generative Adversarial Networks

Acoustic sensors play a fundamental role in underwater applications. They are used to perform a wide variety of tasks: from the perception of the surrounding environment to the support of inertial sensors in navigation strategies. The quality of the acquired images deeply affects the obtained results and, consequently, image enhancement approaches need to be developed and tested. Super-Resolution (SR) techniques are employed to reconstruct one high-resolution image by composing a sequence of low-resolution ones. By applying these strategies, the information content of an image can be considerably increased, but the required computational time is incompatible for real-time employment. Due to this limitation, an SR Generative Adversarial Network (SRGAN) approach has been developed in the presented work, where the SR images are used during the training phase of the GAN framework. The proposed approach, which has been developed for images provided by a Forward-Looking Sonar (FLS), can guarantee a solid trade-off between the quality of the generated high-resolution image and the run-time execution.
Energy-Efficient VM Management in OpenStack-Based Private Cloud

The increase in usage of cloud data centers in recent years has led to high utilization of resources. Allocating of new virtual machines (VMs), disabling of existing hosts, and existing VM being removed are the reasons due to which the resource utilization in data centers vary over time. For efficient resource utilization, detection of the host's load using prediction techniques is an important issue. Furthermore, host load detection enhances the scheduling which results in higher utilization of the compute, network, and storage resources. Default scheduler in OpenStack uses a worst fit algorithm for VM allocation which leaves large fragments of RAM in compute nodes. In this work, VMs are scheduled based on user request using modified best fit algorithm depending on the prediction results that minimize the unnecessary large fragments of RAM in compute nodes. The prediction of host load is carried out using machine learning and statistical models. Furthermore, as a part of continuous resource monitoring and server consolidation, load balancing is performed based on the prediction result to balance the load on all servers. This helps in the energy-efficient consolidation to optimize energy consumption of hosts. We conduct the experimentation of proposed work using OpenStack based cloud testbed in a multi-node environment. Experimental results show that machine learning model LSTM and the statistical ARIMA model give comparatively good results for PlanetLab CPU trace data set. Also, the results reveal that the load among the servers is fairly distributed, and there is a significant improvement in energy saving.
Blockchain-based Automated Formal Model for Smart Market System

The blockchain technology is creating a great impact on the stakeholders of different departments. It resolves data security and centralized database issues. In this work, blockchain-based smart market system is presented to ensure quality of products and to increase tax ratio. In smart market system, we have defined customer, distributors, stores and distributor departments as operation in VDM-SL in which their details are managed and stored in blockchain. Furthermore, product sale, purchase and quality details are saved as transactions in the blockchain. All the details are viewed by relevant departments such as quality assurance authority, distribution departments, distributors and customers using mobile application, which are also described as operations in the system specification. Due to lack of space, we have not mentioned customer and distributor departments in detail. Unified Modeling Language (UML) is used to describe blockchain-based smart market diagrammatically. We have automated our model using Non-deterministic Finite Automata (NFA) to realize behavior of the system. Vienna Development Method-Specification Language (VDM-SL) is used to formally describe and analyze the system properties and invariants. VDM-SL toolbox is used to ensure system correctness and efficiency before implementation phase.
HisNet: A Polarity Lexicon based on WordNet for Emotion Analysis

Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in size as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.
MoRES: A Mobile App to Help Elderly People Grasp and Comply with COVID-19 Restrictions

This paper introduces MoRES: a monitoring framework aimed at helping elderly people to understand and comply with the new mobility restrictions associated to the COVID-19 pandemic. The MoRES framework provides elderly people with a mobile app allowing them to visualise how crowded a particular area is and which are the allowed and forbidden places, according to the restrictions imposed by the authorities at a national, regional and local level. Additional functionalities supporting politicians, health authorities and public security forces are also supported by the MoRES framework.
Don't fool yourself with Forward Privacy, Your queries STILL belong to us!

Dynamic Searchable Symmetric Encryption (DSSE) enables a user to perform encrypted search queries on encrypted data stored on a server. Recently, a notion of Forward Privacy (FP) was introduced to guarantee that a newly added document cannot be linked to previous queries, and to thwart relative attacks and lessen information leakage and its consequences. However, in this paper we show that the forward-private schemes have no advantage (in preventing the related attacks) compared to traditional approaches, and previous attacks are still applicable on FP schemes. In FP approaches, access pattern leakage is still possible and can be employed to uncover the search pattern which can be used by passive and adaptive attacks. To address this issue, we construct a new parallelizable DSSE approach to obfuscate the access and search pattern. Our cost-efficient scheme supports both updates and searches. Our security proof and performance analysis demonstrate the practicality, efficiency, and security of our approach.
Weakly Supervised Domain Adaptation for Aspect Extraction via Multilevel Interaction Transfer

Fine-grained aspect term extraction is an essential subtask in aspect-based opinion analysis. It aims to identify the aspect terms (also known as opinion targets) of a product or service in each sentence. To learn a good aspect extraction model, an expensive annotation process is usually involved to acquire sufficient token-level labels for each domain, which is not realistic. To address this limitation, some previous works propose domain adaptation strategies to transfer knowledge from a sufficiently labeled source domain to unlabeled target domains. However, due to both the difficulty of fine-grained prediction problems and the large domain gap between different domains, the performance is still far from satisfactory. In this work, we conduct a pioneer study on leveraging sentence-level aspect category labels that can be usually available in commercial services, such as review sites or social media to promote token-level transfer for extraction purpose. Specifically, the aspect category information can be used to construct pivot knowledge for transfer with the assumption that the interactions between the sentence-level aspect category and the token-level aspect terms are invariant across domains. To this end, we propose a novel multilevel reconstruction mechanism that aligns both the fine- and coarse-grained information in multiple levels of abstractions. Comprehensive experiments over several benchmark data sets clearly demonstrate that our approach can fully utilize the sentence-level aspect category labels to improve cross-domain aspect term extraction with a large performance gain.
Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. Our study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.
Blockchain-Based Smart and Secure Healthcare System

The healthcare data today is scrappy and not stored properly at a single location. However, some of the records are not even made digital that leads to diffident researches. The number of substandard medicines in the market is increasing, so is the deceitful insurance claims. Moreover, the patient-centric models are in high demand, because the patients are becoming more aware of all the aspects and requesting an infrastructure that gives most of the controls to patients themselves. Such systems would reduce the need for intermediaries, and also keep the confidentiality and anonymity of the patient data intact. The desired framework is supposed to safely handle the Electronic Health Records (EHRs), insurance claims and regulatory policies, promote researches and medicinal discoveries, etc. The healthcare data is big and heterogeneous that is usually stored in the cloud environment. However, the number of attackers on the cloud is increasing; therefore, a framework is required where the data privacy is maintained. In this chapter, a smart healthcare system based on blockchain technology is discussed, which would have the ability to provide real-time data access in a transparent, traceable, and trustful manner. The blockchain has transformed several industries including finance, manufacturing, e-commerce, education, etc. and is making its way into the healthcare industry as well. The blockchain is a distributed technology based on peer-to-peer decentralized network, which does not have any central authority controlling the system. The data within the blockchain network is represented in the forms of blocks. Cryptographic algorithms are also applied to maintain the integrity of the data. Thus, making it the appropriate choice for healthcare applications.
Smart Contract Deployment in Ethereum Learning Made Easy

Information technology has seen quite a few marvels of invention and new discoveries in terms of technology. Until now this was a revolutionary era in which change was something which is not accepted easily. Blockchain is the example of that change, which no one has thought of a technology that keeps and upholds the long-term record keeping. However, the adoption of technology is quite risky and requires a lot of analysis. The chapter covers Blockchain’s brief summary in terms of its evolution from its origin, why we should opt for blockchain as a technology, a brief introduction to blockchain, types of blockchain Platform, how blockchain is driven by consensus mechanism along with its listed objectives which play an important role. There are several consensus mechanisms listed and explained with the conceptual point of view. The chapter enlists the smart contract, which is required to drive the business based upon some business rules. Alongside, blockchain implementation with its types is explained with their brief steps followed. Blockchain implementation is explained with the help of Ethereum in-depth with dedicated steps with deeply explained.
Analysis of graduation prediction on time based on student academic performance using the Naïve Bayes Algorithm with data mining implementation (Case study: Department of Industrial Engineering USU)

Abstract One indicator of higher education performance is student graduation presentation on time with a passing percentage of more than or equal to 50%. The abundance of data in the Higher Education Academic Information System (SIA) is not used in measuring the performance of a College, especially the Study Program. The Industrial Engineering Department summarizes student data in the SIA as a database and processes it according to what is needed. During this time, the data were abundant in SIA has not been utilized to assess student performance. When the Study Program conducts a student academic evaluation, the Study Program evaluates it based on the variable number of credits, GPA, and constraints. In the Study Program, an appropriate classification is needed to make it easier to evaluate student performance. The method that is used is the Naïve Bayes algorithm in classifying students’ academic performance USU Department of Industrial Engineering. Attributes are used, among other NIM, Name, Gender, Region of Origin, Origin School, Entrance, and GPA. From the results of testing 173 sample data using the Naïve Bayes algorithm, the pattern formed has a match accuracy of 70.83%, which means that it is useful in predicting student graduation.
THE MAIN FEATURES OF THE INTRODUCTION OF DIGITAL INNOVATIVE METHODS IN EDUCATIONAL ACTIVITIES AND THE SIGNIFICANCE OF THEIR APPLICATION FOR TEACHING FOREIGN STUDENTS

In the article, the authors consider the main features of the introduction of digital innovative methods in educational activities, as well as the significance of their application for teaching foreign students. The transition to a digital society in which information and knowledge are the main social values means radical changes in the educational system. Study of education issues in a digital society has shown a direct relationship between international cooperation and continuous education system, because in the context of the study features of education in the information world the idea of education throughout life, continuing education, needs to obtain a new vector of development. The article highlights the main problems in the development of academic mobility programs for students and solutions, as well as criteria for the effectiveness and profitability of an educational program for international students. The main features of the introduction of digital innovative methods in educational activities are also analyzed. It is noted that modern educational technologies based on artificial intelligence are increasingly being implemented in universities and other higher education institutions, complementing existing educational programs and transforming them into innovative digital ones. The article describes the forms of introduction and use of artificial intelligence and its functions in higher education. The transformation of educational relations is largely associated with the transformation and use of new digital educational technologies. The article describes the traditional and new digital educational technologies.
Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out

Large-scale factual knowledge graphs (KGs) such as DBpedia and Wikidata are essential to many popular downstream tasks and are also widely used by various research communities as training and/or benchmarking data. Despite their immense success and utility, these KGs are surprisingly noisy. In this study, we investigate the quality of these KGs, where the typing error rate is estimated to be 27% for coarse-grained types on average, and even 73% for certain fine-grained types. In pursuit of solutions, we propose an active typing error detection algorithm that maximizes the utilization of both gold and noisy labels. We also comprehensively discuss and compare the state-of-the-art in unsupervised, semi-supervised, and supervised paradigms to deal with typing errors in factual KGs. The outcomes of this study provide guidelines for researchers to use noisy factual KGs. To help practitioners deploy the techniques and conduct further research, we published our code and data 1.
Efficient Object Detection based on Deep Feature Fusion Network

Abstract Real-time object detection is crucial for many applications, such as automatic driving, security monitoring. It is vital for these application to perform accurate detection while keeping real-time performance. This paper propose an efficient object detection method based on deep feature fusing strategy. The feature extraction network employs deep convolution neural network to fuse multi-channel input, including colored image, infrared image and motion image. Multiple sources of images can provide complementary information, which is beneficial to accurate object detection. The backbone network is based on darknet-53 while integrating with feature aggression modules to capture features of shallow and deep activation maps. Our network is first trained on large-scale IMAGENET and COCO datasets, and then fine-tuned on small-scale datasets collected in real world. A series of quantitatively and qualitatively experiments are conducted to show superiority and efficiency of our method.
Sentiment Analysis of Movie Reviews: A Comparative Study between the Naive-Bayes Classifier and a Rule-based Approach

Movie reviews are vital in telling the viewer whether a movie is worth watching or not. They can be classified into textual and non-textual movie reviews. While non-textual movie reviews (stars) give the user information as to how the movie fairs, textual movie reviews give the user a more detailed picture on the positive and negative aspects of the movie. Sentiment Analysis is the use of natural language processing, text analysis, biometrics and computational linguistics to identify, quantify, extract and effectively study states and subjective information given in textual format. This paper aims to conduct sentiment analysis of reviews of movies by using the Naive-Bayes algorithm and compare the results to that of a Rule-Based Approach using the AFINN-111 sentiment dictionary.
Learning to Select Instance: Simultaneous Transfer Learning and Clustering

Transfer learning leverages knowledge from a source domain with rich data to a target domain with sparse data. However, the difference between the source and target data distribution weakens the transferability. To bridge this gap, we focus on selecting source instances that are closely related to and have the same distribution as the target domain. In this paper, we propose a novel Adaptive Clustering Transfer Learning (ACTL) method to improve transferability. Specifically, we simultaneously train the instance selector and the transfer learning model. The selector adaptively conducts clustering on the training data and learns the weights for source instances. The weight will activate or inhibit the contribution of the corresponding source instance during transfer learning. Meanwhile, the transfer learning model guides the selector to learn the weight appropriately according to the objective function. To evaluate the effectiveness of our method, we conduct experiments on two different tasks including recommender system and text matching. Experimental results show that our method consistently outperforms competing methods and the selected source instances share a similar data distribution with the target domain.
Comparison of Average Linkage and K-Means Methods in Clustering Indonesia’s Provinces Based on Welfare Indicators

Abstract This study compares two clustering methods, i.e. average linkage and K-means, in grouping Indonesia’s provinces based on welfare indicators in education, health, and income. Data from Statistics Indonesia (BPS) covering Indonesia’s 34 provinces are used. Welfare variables exercised in this study are population, rate of population with government-assisted health covers, morbidity rate, human development index, expense rate per capita, and rate of the population aged 15 or over who graduated from junior high school (completed Year 9). Results show that the average linkage method generates three clusters; the first cluster of which consists of 32 provinces, while the second and third clusters each consist of only one province. On the other hand, the K-means method is set to generate equally three clusters. Unlike the first method, K-means’s first cluster, in this case, consists of 14 provinces, while its second and thirds clusters consist of 13 and 7 provinces, respectively. Performances of both methods are measured using the variance ratio. The average linkage and k-means cluster methods yield variance ratios of 0.08275 and 0.28881, respectively. Based on these criteria, the average linkage method is shown to exercise a better performance due to its smaller variance ratio.
Understanding Synonymous Referring Expressions via Contrastive Features

Abstract Referring expression comprehension aims to localize objects identified by natural language descriptions. This is a challenging task as it requires understanding of both visual and language domains. One nature is that each object can be described by synonymous sentences with paraphrases, and such varieties in languages have critical impact on learning a comprehension model. While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences. To this end, we develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels, where features extracted from synonymous sentences to describe the same object should be closer to each other after mapping to the visual domain. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets, and demonstrate that our method performs favorably against the state-of-the-art approaches. Furthermore, since the varieties in expressions become larger across datasets when they describe objects in different ways, we present the cross-dataset and transfer learning settings to validate the ability of our learned transferable features.