Reviewerly/exHarmony huggingface dataset:
Adaptive Binary Coding for Scene Classification Based on Convolutional Networks

With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method is free of time to train or fine-tune the deep network and can effectively handle different applications. Experimental results on three public datasets, i.e., UIUC sports event dataset, MIT Indoor dataset, and UC Merced dataset in terms of three different classifiers, demonstrate that our method is superior to the state-of-the-art methods with large margins.
Outlier detection techniques for big data streams: focus on cyber security

In recent years, detecting outliers in big data streams has become a main challenge in several domains (e.g., medical monitoring, government security, information security, natural disasters, and online financial frauds). In fact, unlike regular static data, streams raise many issues like high multidimensionality, dynamic data distribution, unpredictable relationships, data sequences, uncertainty and transiency. Most of the proposed approaches can handle some of these issues but not all. In addition, they provide limited considerations with regard to scalability and performance. Real-world applications require high performance, resources optimisation and real-time responsiveness when detecting outliers. This is useful to extract knowledge, detect incidents and predict patterns changes. In this paper, we review and compare recent studies in detecting outliers for data streams. We investigate how researchers improved the outcome of different models and monitoring systems, especially in the context of cyber security.
Mobile Games and Academic Performance of University Students

The increasing number of students who are hooked on playing online mobile games (OMG) is alarming. As such, this study was realized to address the problem. This study assessed the gaming profile towards OMG and its relation to the academic performance of the engineering students of Eastern Visayas State University Tanauan Campus (EVSUTC). Specifically, the study investigated the correlation between student’s number of hours spent on playing OMG (at school and home), commonly played OMG (at school and home), reasons for playing OMG and attitudes on playing OMG with academic performance utilizing Eta and Pearson r correlation analyses. A random sample of 134 student respondents were selected through purposive sampling of those who are playing OMG using their mobile phones. Descriptive correlational research design was utilized and a validated survey instrument was employed to gather the needed information. The findings revealed that majority of the students played mobile legends and spent mostly 2 hours playing OMG for a reason of boredom. The overall attitudes of the students on playing OMG were interpreted as Less Favorable (M=2.58, SD=1.13). Out of the independent variables being set in the study, the number of hours spent on playing OMG at home (r=-0.188, p=0.039) and commonly played OMG at school (r=0.203, p=0.045) were found significantly correlated with student’s academic performance. Hence, the students’ time spent on playing OMG at home and the type of games that students played at school have significant bearing to their academic performance. As such, delimiting student’s usage of internet can be made to address the problem.
Spark

Functionalities of Hadoop (or other MapReduce frameworks) and Spark might seem duplicating, particularly from a high-level perspective. This is not surprising, considering that Spark is ultimately a type of MapReduce framework. However, Spark has gone a long way from a standard Hadoop stack of HDFS storage combined with the map and reduce routines.
Education as Fictitious Capital: The Crisis in the Social Importance of Knowledge

The article describes the system of mass higher education in terms of political economy. The author discusses how the social and economic results of the education system depend on the principles of its organization. In identifying the economic effect of education, the author emphasizes that in certain circumstances certificates of education may function in the market as fictitious capital, increasing the economic opportunities of certificate holders without any basis in an actual increase in training or professionalism. The analogy with fictitious capital is methodologically justified because the certificate itself brings about an increment in money received although it creates no new value. The social consequence of the formalization of education is the falsification of its cultural content and significance. This formalization of education and the decline in the role of education and knowledge in society may make the cultural capital to which education contributes fictitious as well because it is verified only by a certificate of qualification in place of an actual advanced level of education. The author describes the process of formalization of education as a bureaucratic attack on the autonomy of the academic environment. To overcome the formalization of education and to repel this bureaucratic attack on it, it is necessary to establish the proper conditions for academic and personal development while enlisting society at large in formulating standards and specific criteria for the development of education. The author sees the processes taking place in education as signs of a crisis of institutionalization as a principle for regulating human activity. This crisis is manifested today not only by numerous disruptions in the operation of the educational system but also in the crisis concerning its social significance, and more broadly in questioning the social significance of knowledge.
Water-Flow-Like Algorithm based Decision Support Model for Flood Control Operation System

Prediction of water level functioned by the local government Jakarta has only been tidal forecasts or predictions of water levels at sea.At present there is no open and close floodgate regulation system either through mathematical calculations or decision-making models that produce accurate numbers.The purpose of this research is to find out what parameters are involved in determining the floodgate opening using the water flow algorithm (WFA) method.It operated to model the river water flow to get the best decision for opening and closing floodgate in flood control of Manggarai floodgate in Jakarta.This research used quantitative data analysis method.The data reported was real data coming from the field.At the end of this study, the relationship between water level reported every hour and the amount of time needed for the water reservoir at the Manggarai floodgate to be fully alerted has been determined.WFA implemented academically in this study was functioned to predict the flow of water at the Manggarai floodgate, as a result of the flow of water coming from the Katulampa weir.Based on the prediction results of the water discharge, we are able to get the volume of water from the Manggarai floodgate, thus we are able to determine open decisions close at the Manggarai floodgate.
IMPLEMENTATION OF BLOCKCHAIN TECHNOLOGY IN THE SMART CITY

This document presents an attempt to determine guidelines for the implementation of the blockchain projects within smart city (SC) information systems (IS) to improve their security and sustainability through harnessing benefits of blockchain technology. Although available scientific sources are scarce a review of contemporary scientific literature and other available sources attempts to determine weaknesses of current IS within SC and to compare their vs benefits of the blockchain technology in order to show that blockchain technology can offer potential solution to weaknesses and improve SC IS and therefore the sustainability of the SC itself. However, this research only shows the need for such implementations and discuss what further research is required to provide a framework for the implementation of blockchain technology in smart cities.
A dynamic cluster job scheduling optimisation algorithm based on data irreversibility in sensor cloud

The optimisation algorithm based on irreversible data for the job scheduling of dynamic cluster is crucial to the improvement of the cluster rendering throughput and the cluster rendering efficiency. However, the dispatching imbalance of the cluster rendering task on the massive number of cluster rendering nodes will prolong the waiting time for the completion of job. Therefore, we propose dynamic cluster job scheduling optimisation algorithm based on data irreversibility (DCJS_DI). Firstly, we analyse the job scheduling target. Then, we exploit the frame independence with the clustering rendering and further establish the job scheduling model for the irreversible data dynamic cluster. Next, we elaborate on the job scheduling process of the irreversible data dynamic cluster and the dispatching process of the cluster rendering task. Finally, we investigate via simulation results the impacts of the job hunger and the resource fragmentation issue of the traditional job scheduling strategies on the system performance, the impacts of the multi-progress and multi-threading cluster rendering on the job completion time and the resource efficiency of the irreversible data dynamic cluster. We further study the extension of the cluster computation capability and the reliability issue of the cloud service.
Genetic Load Balancing Algorithms in Cloud Environment

Cloud services are broadly used in accomplishment, logistics, and computerized applications. It is not an easy technology, it consists of lots of issues like virtual machine management, scheduling of virtual machines, data security, providing resources (like hardware and software) and load balancing. The issue of load balancing arises in abundant applications but essentially they play an essential role in the application of cloud environment. Load balancing distributes a task into subtasks that can be performed together and mapping each of these programs to computational resources like a computer or a processor, the complete processor time will be decreased with upgrade processor usage. To solve the issue of load balancing various algorithms are proposed by authors in the recent past and one of them is genetic algorithms. The paper describes insight survey some genetic load balancing algorithms used in a cloud environment by taking into consideration different factors, further we have analyzed and correlated all these factors in order to do a comparative assessment based upon different parameters so as to identify the proficiency of different genetic algorithms
Data Mining

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
High Capacity Image Steganography using Pixel Value Differencing Method with Data Compression using Neural Network

The Digital Market Is Rapidly Growing Day By Day. So, Data Hiding Is Going To Increase Its Importance. Information Can Be Hidden In Different Embedding Mediums, Known As Carriers By Using Steganography Techniques. The Carriers Are Different Multimedia Medium Such As Images, Audio Files, Video Files, And Text Files .There Are Several Techniques Present To Achieve Data Hiding Like Least Significant Bit Insertion Method And Transform Domain Technique. The Data Hidden Capacity Inside The Cover Image Totally Depends On The Properties Of The Image Like Number Of Noisy Pixels. Data Compression Provides To Hide Large Amount Of Secret Data To Increase The Capacity And The Image Steganography Based On Any Neural Network Provides That The Size And Quality Of The Stego-Image Remains Unaltered After Data Embedding. In This Paper We Propose A New Method Combined With Data Compression Along With Data Embedding Technique And After Embedding To Maintain The Quality The Communication Channel Use The Neural Network. The Compression Technique Increase The Data Hiding Capacity And The Use Of Neural Network Maintain The Flow Of Data Processing Signal
Algorithm for Document Integrity Testing Pre Upload and Post Download from Cloud Storage

This era is of business and marketing, those who have no proper equipments, they can also proceed in this field. This is just because of cloud computing. Many organizations are still in ambivalence whether to adopt cloud computing or not. The biggest barrier before them is security of their sensitive data. This research paper is an attempt to encourage such organizations or individuals who are still thinking to adopt this fruitful as well as cheap and best service. In this paper we have proposed an integrity testing algorithm which tests whole documents at character level before uploading to cloud storage and after downloading from cloud storage. The idea of the algorithm resembles to the idea of ‘error detection and correction’. For more clearance of the idea refer future scope.
Empirical Research of Hot Topic Recognition and its Evolution Path Method for Scientific and Technological Literature

With the advent of big data era, the recognition of hot topics and the analysis of their evolution path in the frontier of a certain field of scientific and technological literature have received widespread attention from the academic community. It can not only reveal the development trend in a certain field of scientific and technological literature, but also discover the evolution law of topic content in different development stages of the field. However, there are still some problems in some current research methods, such as inaccurate recognition of hot topics and unclear evolution path, which seriously affect the comprehensiveness and accuracy of the analysis. To solve the above problems, this paper uses Latent Dirichlet Allocation (LDA) model to propose a hot topic recognition and evolution analysis method in scientific and technological literature field, which aims to reveal the evolution law of topic content level in different development stages of the field, such as inheritance, merging, division, and other topic evolution trends, so as to provide decision support for domain knowledge innovation services. Main research process is as follows. Firstly, LDA is used to extract global topics and stage topics. Secondly, similarity calculation algorithm is used to filter topics. Thirdly, novelty and support are used to identify hot topics. Fourthly, three paths of inheritance evolution, merging evolution and division evolution are formed for hot topics. Finally, the effectiveness of the method is verified by using 47,896 scientific and technological literature data in the field of intelligent algorithms in Web of Science as an empirical example.
Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes

COVID-19 severity spans an entire clinical spectrum from asymptomatic to fatal. Most patients who require in-hospital care are admitted to non-intensive wards, but their clinical conditions can deteriorate suddenly and some eventually die. Clinical data from patients’ case series have identified pre-hospital and in-hospital risk factors for adverse COVID-19 outcomes. However, most prior studies used static variables or dynamic changes of a few selected variables of interest. In this study, we aimed at integrating the analysis of time-varying multidimensional clinical-laboratory data to describe the pathways leading to COVID-19 outcomes among patients initially hospitalised in a non-intensive care setting. We collected the longitudinal retrospective data of 394 patients admitted to non-intensive care units at the University Hospital of Padova (Padova, Italy) due to COVID-19. We trained a dynamic Bayesian network (DBN) to encode the conditional probability relationships over time between death and all available demographics, pre-existing conditions, and clinical laboratory variables. We applied resampling, dynamic time warping, and prototyping to describe the typical trajectories of patients who died vs. those who survived. The DBN revealed that the trajectory linking demographics and pre-existing clinical conditions to death passed directly through kidney dysfunction or, more indirectly, through cardiac damage. As expected, admittance to the intensive care unit was linked to markers of respiratory function. Notably, death was linked to elevation in procalcitonin and D-dimer levels. Death was associated with persistently high levels of procalcitonin from admission and throughout the hospital stay, likely reflecting bacterial superinfection. A sudden raise in D-dimer levels 3–6 days after admission was also associated with subsequent death, possibly reflecting a worsening thrombotic microangiopathy. This innovative application of DBNs and prototyping to integrated data analysis enables visualising the patient's trajectories to COVID-19 outcomes and may instruct timely and appropriate clinical decisions.
Detection of malaria parasite in thick blood smears using deep learning

We're working on a deep learning algorithm which can distinguish between malaria parasites in blood smears in a video format which consists of several images. There are two processing steps in our method. Initially, we locate parasites by quickly inspecting a thick smear image and remove noise from that parasite. Then, using a customized Convolutional Neural Network (CNN), each image in a video is classified as one of two ways, whether it consists of parasite or not. We took a datafile of 1817 smear photographs from 148 victims from an openly accessible institute. This datafile was used to assess and trial our algorithm.
Improved Deep Unsupervised Hashing with Fine-grained Semantic Similarity Mining for Multi-Label Image Retrieval

In this paper, we study deep unsupervised hashing, a critical problem for approximate nearest neighbor research. Most recent methods solve this problem by semantic similarity reconstruction for guiding hashing network learning or contrastive learning of hash codes. However, in multi-label scenarios, these methods usually either generate an inaccurate similarity matrix without reflection of similarity ranking or suffer from the violation of the underlying assumption in contrastive learning, resulting in limited retrieval performance. To tackle this issue, we propose a novel method termed HAMAN, which explores semantics from a fine-grained view to enhance the ability of multi-label image retrieval. In particular, we reconstruct the pairwise similarity structure by matching fine-grained patch features generated by the pre-trained neural network, serving as reliable guidance for similarity preserving of hash codes. Moreover, a novel conditional contrastive learning on hash codes is proposed to adopt self-supervised learning in multi-label scenarios. According to extensive experiments on three multi-label datasets, the proposed method outperforms a broad range of state-of-the-art methods.
Port Contention Goes Portable

Microarchitectural side-channel attacks can derive secrets from the execution of vulnerable programs. Their implementation in web browsers represents a considerable extension of their attack surface, as a user simply browsing a malicious website, or even a malicious third-party advertisement in a benign cross-origin isolated website, can be a victim.
Formative Assessment of Students’ Academic Achievements in Primary General Education: Teacher Training

The aim of the study is to substantiate the need for assessment competence formation in future teachers. The paper identifies the issues of introducing formative assessment into schools and suggests a strategy for preparing future teachers to use these tools in their professional activities. Scientific novelty of the study lies in proving the connection between the formation of future teachers’ competence in the use of formative assessment techniques when conducting lessons at school and their research and reflexive independence in their own professional training at university. As a result, the researcher has proposed formative assessment procedures that can be implemented in professional teacher training, has provided insight into the methodology of their implementation, substantiated the techniques. Experimental teaching carried out in a number of universities has shown positive dynamics in the formation of future teachers’ competence in the use of formative assessment tools in their activities.
ACO:lossless quality score compression based on adaptive coding order

With the rapid development of high-throughput sequencing technology, the cost of whole genome sequencing drops rapidly, which leads to an exponential growth of genome data. How to efficiently compress the DNA data generated by large-scale genome projects has become an important factor restricting the further development of the DNA sequencing industry. Although the compression of DNA bases has achieved significant improvement in recent years, the compression of quality score is still challenging.In this paper, by reinvestigating the inherent correlations between the quality score and the sequencing process, we propose a novel lossless quality score compressor based on adaptive coding order (ACO). The main objective of ACO is to traverse the quality score adaptively in the most correlative trajectory according to the sequencing process. By cooperating with the adaptive arithmetic coding and an improved in-context strategy, ACO achieves the state-of-the-art quality score compression performances with moderate complexity for the next-generation sequencing (NGS) data.The competence enables ACO to serve as a candidate tool for quality score compression, ACO has been employed by AVS(Audio Video coding Standard Workgroup of China) and is freely available at https://github.com/Yoniming/ACO.