Reviewerly/exHarmony huggingface dataset:
Drill the Cork of Information Bottleneck by Inputting the Most Important Data

Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The widely used minibatch stochastic gradient descent (SGD) still needs to be accelerated. As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase. Based on this principle, we further study typicality sampling, an efficient data selection method, and propose a new explanation of how it helps accelerate the training process of the deep networks. We show that the fitting phase depicted in the IB theory will be boosted with a high signal-to-noise ratio of gradient approximation if the typicality sampling is appropriately adopted. Furthermore, this finding also implies that the prior information of the training set is critical to the optimization process, and the better use of the most important data can help the information flow through the bottleneck faster. Both theoretical analysis and experimental results on synthetic and real-world datasets demonstrate our conclusions.
House Price Prediction With Statistical Analysis in Support Vector Machine Learning for Regression Estimation

Support vector learning machines are supervised models that analyze data for classification analysis. However, with modifications these models can be extended for regression estimation. In this paper, we use Dean De Cock’s Iowa house price dataset, composed of quantitative and qualitative variables, to build a predictive house pricing model. We employ a systematic approach to preparing the data and processing the features using statistical analysis. To reduce model over-fitting, we use stratified k-fold cross-validation. We also use various metrics for measuring model performance including r-squared ( R 2 ) and mean absolute error (MAE). Moreover, we explore various kernels for hyper-dimensional feature mapping to account for data that is not linearly separable. We compare the kernel performance and find that there is a trade-off between model complexity and performance. Furthermore, there also exists a trade-off between maximum accuracy achievable and general model performance.
Transforming Higher Education Systems Architectures Through Adoption of Secure Overlay Blockchain Technologies

The adoption of Distributed Ledger Technology (DLT) has been growing tremendously in recent years following the introduction of Bitcoin in 2009. However, the usefulness of DLT is not limited to the financial sector, and this paper investigates the viability of DLT architectures for use in Higher Education (HE). This sector faces challenging financial constraints, and one way to address this problem is to adopt emerging DLT technologies as architectures for HE systems. This article presents the ASTER Open Source system, a hybrid DLT integration within the context of a student submission system for assignment grading purposes. ASTER addresses many concerns of traditional system architectures such as centralisation, system downtime, and decoupling; all of which are mitigated through the use of blockchain technology. The advantages and drawbacks of such a new approach are discussed, including the aspect of security concerns relating to student work being submitted to a public ledger.
The Impact of Software Engineering Methods for Cloud Computing Models – A Survey

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Cyber Security in the Global Village and Challenges for Bangladesh: An Overview on Legal Context

With the rapid penetration of the Internet and other information and communication technology worldwide, cyber-crime is emerging as a threat to personal data stored in computers and likely to affect the entire data systems. Even the United States, one of the most technologically advanced countries, is also subjected to such crimes. Bangladesh, being a less developed country, is also at the risk of cyber-crimes that might jeopardize the country’s national security. As the incumbent government eyes to ensure internet connectivity at all government institutions by 2021 upholding the motto of ‘Digital Bangladesh’, more and more national and multinational companies are offering online services to their services through the internet following the government’s agenda. From shopping to Banking, all are just a click away with the higher rate of internet penetration. However, criminals are also using the online platform where they are committing various sorts of criminal activities including phishing, hacking, and stealing personal data. Hence, the state-owned, as well as private organizations, might fall prey to cyber-attacks which might affect the lives of the entire population. Moreover, the country’s 90% of software is unlicensed that also intensifies the risk of cybercrimes thanks to their compromised security issue. In addition, the recent tug of wars between Bangladeshi and Indian hackers impacted the diplomatic relations between the two nations. More importantly, there have been scores of media reports saying that terror groups use online platforms for financing and maintaining intra-group communications. In this context, the existing laws and government moves against cyber-crimes are apparently very scanty to combat the burgeoning threat. The study attempts to shed light upon the threat posed by cyber-crimes in the context of the global village with an emphasis on the perspective of Bangladesh.
Tractability Frontiers in Probabilistic Team Semantics and Existential Second-Order Logic over the Reals

Probabilistic team semantics is a framework for logical analysis of probabilistic dependencies. Our focus is on the complexity and expressivity of probabilistic inclusion logic and its extensions. We identify a natural fragment of existential second-order logic with additive real arithmetic that captures exactly the expressivity of probabilistic inclusion logic. We furthermore relate these formalisms to linear programming, and doing so obtain PTIME data complexity for the logics. Moreover, on finite structures, we show that the full existential second-order logic with additive real arithmetic can only express NP properties.
The infinite limit of separable permutations

Abstract Let denote the uniform probability measure on the set of separable permutations in S n . Let with an appropriate metric and denote by the compact metric space consisting of functions from to which are injections when restricted to ; that is, if , i ≠ j , then . Extending permutations by defining , for j &gt; n , we have . We show that converges weakly on to a limiting distribution of regenerative type, which we calculate explicitly.
An Android Application for Explaining Form Deviations Using 3D Models

The research aims to develop a mobile learning engineering application, presenting the form deviations according to ISO 1101. This application aims to improve the quality of tolerances and dimensional control and 3D Modelling courses for engineering students, covering the following lines of studies: design, robotics, industrial and mechanical engineering. The previous studies focused on mobile learning's impact on student achievement show that the method could be one of the promising educational technologies. The educational applications used on mobile devices develop a friendly environment and generate a positive effect on learning. The authors conducted a detailed analysis of the educational applications presenting geometrical tolerances available in Google Play. This paper also presents the advantages and disadvantages of the published applications. In 2018 we published the educational android application ISO Checker. They are using this mobile application integrated into the engineering courses students transmitted positive feedback. Students are hi-tech learners, and they are in trend with current technological innovations in education.
Another Parallelism Technique of GLCM Implementation Using CUDA Programming

In pattern recognition and image processing feature extraction is one of essential parts as it facilitates the subsequent learning and generalization steps. Feature extraction aims to get the special characteristics of an object that can be applied to produce the desired information. One of commonly used algorithm for feature extraction is Gray Level Co-occurrence Matrix (GLCM). Though this algorithm produces a good result, the twelve Haralick texture features requires high computation if CPUs are used. The emergence of the Graphics Processing Unit (GPU) helps cope with all that requires high computation and running GLCM calculations in GPU is just a matter of seconds. To benefit greatly from thousands of CUDA cores, one should be able to appropriately implement GLCM algorithm. GPU for GLCM had been done in several studies and each offered various improvement in computation times. In this research, we propose another technique of implementing GLCM algorithm and it shows that the computation time of the twelve Haralick texture features is 450 times faster than using CPU.
Preserved Extra-Foveal Processing of Object Semantics in Alzheimer’s Disease

Alzheimer's disease (AD) patients underperform on a range of tasks requiring semantic processing, but it is unclear whether this impairment is due to a generalised loss of semantic knowledge or to issues in accessing and selecting such information from memory. The objective of this eye-tracking visual search study was to determine whether semantic expectancy mechanisms known to support object recognition in healthy adults are preserved in AD patients. Furthermore, as AD patients are often reported to be impaired in accessing information in extra-foveal vision, we investigated whether that was also the case in our study. Twenty AD patients and 20 age-matched controls searched for a target object among an array of distractors presented extra-foveally. The distractors were either semantically related or unrelated to the target (e.g., a car in an array with other vehicles or kitchen items). Results showed that semantically related objects were detected with more difficulty than semantically unrelated objects by both groups, but more markedly by the AD group. Participants looked earlier and for longer at the critical objects when these were semantically unrelated to the distractors. Our findings show that AD patients can process the semantics of objects and access it in extra-foveal vision. This suggests that their impairments in semantic processing may reflect difficulties in accessing semantic information rather than a generalised loss of semantic memory.
Automation of control the state of graphic collections and of content selection for design tasks in the printing

Abstract Results of pilot studies of use of average color for problems of support of graphic collections in current state and selection of graphics for the color parameters set by the user – search of duplicates of images, selection of graphics due to data entry of color in the RGB format and also search of illustrations in the set color range or the specific image are provided. Based on the conducted researches it was established that under certain conditions the indicator of average color of the image rather precisely finds duplicates of illustrations and can be applied to selection of the same images. The research showed that the JPG format, despite the low opportunities in transfer of color, can be considered the optimum format for search of duplicates of digital images. Also, it was defined that BMP, TIFF, PNG behave equally in tasks of finding of duplicates, optimum deviations from average color for the solution of this task were revealed.
A digital educational tool for learning the Aymara language in the region of Ayacucho, Peru

This paper presents the design, development, and validation of a mobile educational tool that is contextualized in order to overcome and adapt to the limitations of current basic services, such as education, in the indigenous populations of the Peruvian Amazon in the context of the COVID 19 pandemic. The Aymara language has a significant number of speakers, but for many years the services offered by the Peruvian State were not oriented or adapted to the cultural context of the Aymara people, which became even more evident during the pandemic. Tests were carried out with the use of the tool on primary school children in the city of Ayacucho, and it was found that the ease of use of the tool, and the friendly design with the interaction of sounds and images, help and favor the rapid learning of the children.
Reducing Thickness Deviation of W-Shaped Structures in Manufacturing DRAM Products Using RSM and ANN_GA

Thickness deviation on a W-shaped structure of word lines in dynamic random access memories (DRAMs) is detrimental to the DRAM manufacturing's yield rate. The case company has suffered from a low yield rate in their DRAM manufacturing due to large thickness deviations on the W-shaped structures. This article proposed a new approach to find an appropriate setting of manufacturing control factors' values to decrease the thickness deviation. In the proposed method, we first used the fractional factorial design to select important control factors related to the thickness deviation. We then used the gradient descent method to find a region of control factors that contained a second-order solution to the problem and adopted the response surface method (RSM) to find the second-order solution. However, the second-order RSM was limited by its second-order relationship between the input control factors and the output thickness deviation. To further reduce the thickness deviation, we used an artificial neural network (ANN) to predict the thickness deviation given a set of control factors' input values. We then used a genetic algorithm (GA) to find a better solution to the problem with the predicted thickness deviations by the ANN. The confirmation experiment showed that the GA had found a better solution than the RSM method. With the proposed GA method, the case company has successfully reduced the thickness deviation from 45.0 to 12.9 Å, saving U.S. $205 000 per year on their DRAM manufacturing cost.
A Usage-Based Insurance Policy Bidding and Support Platform Using Internet of Vehicles Infrastructure and Blockchain Technology

This paper presents a usage-based insurance (UBI) platform that incorporates Internet of Vehicles (IoV) and blockchain technologies, discussing the potential stakeholders, business models, and interaction modes involved in this platform. Existing UBI products mostly use data on the driver’s mileage, driving period, or driving region for more accurate insurance calculations. Automobile UBI encourages customers to continue improving their ability to drive safety and provides a means to smoothly, transparently, and rationally calculate insurance pricing and payout. This paper proposes blockchain architecture to remedy management problems in a UBI environment. A bidding mechanism suitable for the blockchain-based UBI platform was designed to close the information gap between the insurance company and consumer, thus increasing consumer trust in the platform.
The Impact of Strategic Planning on Organizational Performance: The Case of Public Organization in Qatar: أثر التخطيط الاستراتيجي على الأداء المؤسسي: دراسة حالة منظمة حكومية بدولة قطر

The main objective of the research is to investigate the degree of effects of strategic planning and its dimensions (vision and mission, strategic objectives and values, projects and operational plans, and follow-ups and control) on organizational performance. Data was collected by using a closed-ended survey from simple random sample of employees. Data were analyzed by using suitable descriptive and analytical statistical methods available at SPSS. Results of data analysis revealed that there is strong and positive relationship between strategic planning and organizational performance. It was found that organizational performance is greatly influenced by strategic planning. Based on that, it was recommended to support strategic planning in public organizations.
Dual-Term Loss Function For Shape-Aware Medical Image Segmentation

Besides network architecture, researchers have recently focused their attention on the loss function for the Convolutional Neural Network-based medical image segmentation. The loss function is used to evaluate the pixel-wise similarity in the whole image to reflect the correctness of the segmentation, which lacks awareness of the object boundaries. In this work, we propose a novel dual-term loss function with a region-based term and a shape-aware term, which forces the network to learn more boundary details by maximizing the probability difference between the two sides of the boundary. Our method adjusts the output probability appropriately according to the shape. We evaluated our approach on three datasets, and the results show that the proposed loss function outperforms other mainstream loss functions.
An Efficient Approach to Find Frequent Item Sets in Large Database

In recent years, Data Mining [1] [2] is a constitutive technique for finding useful knowledge from large transactional dataset. Association rule mining [3] is one of the resuscitative data mining techniques. It finds the interesting patterns from large datasets to maximize the profit of the future business. Several algorithms are available to find frequent patterns. Apriori and FP-Tree [4] [5] algorithms are most common techniques for discovering frequent item sets. The Apriori algorithm uses a breadth-first search approach to find all significant frequent patterns. This is performed by candidate generation method which takes several number of database scans. The FP-Tree algorithm scans the whole database twice to discover significant frequent patterns without generation of candidate. So the main motive behind this proposed approach to discover frequent patterns from transactional database in minimum execution time. The proposed TR-FC-GCM (Transaction Reduction - Frequency Count - Generate Combination Method) finds all significant frequent patterns by generating all possible combinations of an item with single database scan and also works better for null and full datasets. The comparative results of TR-FC-GCM, Apriori and FP-Tree algorithms with different transactions and thresholds, it clearly shows that TR-FC-GCM algorithm outperforms than Apriori and FP-Tree algorithms.
QOS of Data Networks Analyzing Based on the Fuzzy Knowledge Base

With the rapid growth of data traffic from various information sources and the complexity of services increasing there is a current new trend in the ICT field that has been called the Big Data processing trend. There are a lot of up-to-day intelligent technics and systems used for overcoming this trend but the computational and data processing complexity under real-time requirements continues to be one of the important disadvantages for many engineering fields. The paper deals with an approach of Big Data stream structuring into fuzzy logic rules for fuzzy knowledge base development that has no large data processing complexity. To guarantee the correctness of the fuzzy knowledge base the metagraph theory apparatus is used based on control conflicting and duplicate rules under consideration of their logical inter-connections. Usage of the fuzzy knowledge base during Big Data stream processing helps to decrease data processing time for decision-making systems in real engineering fields.
Development of Stability Control Mechanisms in Neural Network Forecasting Systems

Abstract The problem of ensuring the stable functioning of time series forecasting systems based on streaming recurrent neural networks with controlled elements is considered. The mechanisms necessary and sufficient for its maintenance are derived, which involve maintaining the balance of the learning history and modifying the synapse learning rules in order to establish a balance between positive and negative potential. The results of experiments to assess the accuracy of forecasting are presented.
Homophily at a glance: visual homophily estimation in network graphs is robust under time constraints

Abstract Network graphs are used for high-stake decision making in medical and other contexts. For instance, graph drawings conveying relatedness can be relevant in the context of spreading diseases. Node-link diagrams can be used to visually assess the degree of homophily in a network—a condition where a presence of the link is more likely when nodes are similar. In an online experiment ( N = 531), we tested how robustly laypeople can judge homophily from node-link diagrams and how variation of time constraints and layout of the diagrams affect judgments. The results showed that participants were able to give appropriate judgments. While granting more time led to better performance, the effects were small. Rather, the first seconds account for most of the information an individual can extract from the graphs. Furthermore, we showed a difference in performance between two types of layouts (bipartite and polarized). Results have consequences for communicating the degree of homophily in network graphs to the public.
Numerals in Financial Narratives

Abstract Numerals are more common in financial narratives than in documents from other domains, which makes understanding numerals very important when analyzing financial documents. In this chapter, we summarize our work on numerals in financial narratives and share findings from the FinNum shared task series in the 14th and 15th NTCIR Conferences. In Sect. 5.1, we discuss how to understand the meaning of a given numeral, and in Sect. 5.2, we discuss numeral attachment, where we link numerals and named entities. In Sect. 5.3, we show experimental results from downstream tasks that demonstrate the importance of numeral understanding in financial narratives. We conclude by proposing future research directions in Sect. 5.4.
Information technologies in the management of technical systems - development of the engineering education

Abstract The current level of production development requires the training of qualified specialists capable of solving complex scientific and technical problems in the field of technical systems management using rapidly changing information technologies. Consequently, the training of professional personnel should provide for the needs of developers of advanced control systems for complex dynamic objects. This problem is successfully solved at the Department of Automation and Control Processes (ACP department) in ETU “LETI”. Students training is carried out in two complementary directions “Management in technical systems” and “Information systems and technologies”. Long-term experience proves the advisability of choosing such an approach in the context of rapidly developing technologies. The report contains studies based on the application of data analysis methods and models that dynamically integrate the information necessary for adaptive management of the process of advanced specialists training. The dataset includes: actual employers’ data, production needs, on the one hand, data obtained during the educational process, on the other hand. At the same time, the data set includes data that enable students to assess the effectiveness of the educational services provision, the level of professional training in the areas of specialty. Comprehensive analysis of data integrated in a unified environment, based on the methods of intelligent systems, analytics and knowledge engineering, proves the validity of choosing this approach in the learning process.