Reviewerly/exHarmony huggingface dataset:
Interpretability of Neural Networks with Probability Density Functions

Abstract It is an interesting topic to interpret artificial neural networks (ANNs) by considering some change various approaches. This paper explores the relationship between the input and output units of the simplest ANN, a single layer perceptron for the binary classification problem, from the probability point of view. If the feature variables of datasets follow independent normal distribution and outputs are activated by sigmoid function or smooth Relu function, we advocate that the probability density function (pdf) of the output variable is an exponential family distribution. Furthermore, by introducing an intermediate variable, the pdf of the output variable can be written as a linear combination of three normal distributions with same spread but different centers. Based on these results, the probability of the predicted class label can be written as a standard normal cumulative distribution function (cdf). The originality of this paper comes with interesting theoretical results to provide ANNs with a new description of the relationship between input variables to output variables, which can enable ANNs to be understood from a new perspective. Extensive experiments based on one artificial synthesized dataset and ten real‐world benchmark datasets validate the reasonability of those results.
Efficient Hardware-Agnostic DBMS Operator Implementation Using SYCL

Heterogeneous hardware requires tedious optimization of DBMS algorithms for each platform it supports when implemented with vendor-specific toolchains. Such an approach inevitably leads to specialization and maintainability issues. In this paper we evaluate several hash-joins implemented with a high-level language SYCL and compare the data across different execution devices. We provide a roof-line performance estimations for algorithms and show these implementations are on par with existing hardware specific implementations.
Statistical Analysis of Refactoring Bug Reports in Eclipse Bugzilla

Eclipse Java Development Tool (JDT) is one of the most popular Java Integrated Development Environments which offers frequently used refactorings including rename, move and extract. However, JDT refactorings are flawed by a number of known bugs. We discovered that 5,045 bugs related to JDT refactorings have been reported on Eclipse's bug report website (Eclipse Bugzilla) as of January 2021. Many of these bugs are fixed after being reported, but others remain unfixed or even forgotten. Our analysis of submitted refactoring bugs shows that 18.4% will not be fixed. We also found that a refactoring bug takes 674 days on average to receive its final resolution. This paper explains the results and findings of our statistical analysis of JDT refactoring bugs in Eclipse Bugzilla.
A Resource-Blockchain Framework for Safeguarding IoT

Privacy concerning resource discovery and selection on the Internet of Things (IoT) platform has emerged as a crucial challenge due to constrained network environment, poor throughput, and access control system in the Resource Directory (RD), which could potentially lead to information breaches among intelligent devices. Blockchain is indeed the latest technological model for global information management, point-to-point exchange, consensus process, asymmetric authentication, cognitive agreement, and perhaps other computing innovations to address the challenge. Considering these advantages, a resource-blockchain framework for safeguarding IoT is proposed in this article. The framework facilitates efficient access of information among devices, gathers information about the constrained environment, accepts data from heterogeneous resources and stores them on CoAP-based RD. Moreover, it provides secure transactions and records authorised user’s information via Blockchain-based centralised server which acts as the frontend of CoAP based REST, dispatcher or repository and proxy server. The study demonstrates that Blockchain technologies help in strengthening privacy capabilities and safeguarding intelligent devices.
A Decision Support Systemfor Commercial Lending

Decision processes are activities mostly periodic, repetitiveand time consuming. This brings experts to refer to their knowledge of past experiences. Developing a knowledge base clearly facilitates the similar subsequent recommendations actions and reduces greatly the time necessary to make a decision, especially in constrained tine applications. In this paper, we propose a knowledge-based decision support system called BL-KBDSS where rules and procedures used by the experts in past experience are stored in a knowledge base and managed by a case-based reasoner. We are experimenting with our approach in financial field, more precisely, to commercial lending, a semi-structured. BL-KBDSS will support experts to make decisions and compensate for the lack of experience of young bank officers. II will also disseminate the experience available on various bankingagencies.
Guided Digital Tours and Covid-19. Comparison of Cases in Europe and Definition of Models Grounded on Data

What is a virtual tour? Is a virtual tour the same as an online guided tour? When museums throughout Europe were closed during the lockdown they tried to offer their public digital experiences. This paper will analyse four cases (in Belgium, UK and Italy) and will attempt to identify and describe the different communication strategies and user experience models they adopted. The paper is based on notes collected using qualitative methods (case study, digital ethnography, narrative inquiry, grounded theory). Focus will be placed on the guided tours organized by Gallerie Estensi (Italy). The paper is also an attempt to define what an online guided tour is and the key elements of its design.
Object Recognition System for Autonomous Vehicles Based on PCA and 1D-CNN

Object recognition system is an automobile safety system designed for the safety of the autonomous vehicle and other traffic participants and reduces collision risk. Road accidents have long been a significant issue involving loss of life and property. So recent years have seen rapid developments in autonomous and semi-autonomous vehicles. Autonomous vehicles are a comprehensive solution built for safety and comfort on the roads. This solution has many challenges. One of these challenges is to spot and recognize obstacles while navigating. As humans do, the only way to discover and recognize these obstacles is to see them. Therefore, vision systems are an essential part of this type of vehicle. This paper proposed a vision-based system for autonomous vehicles to recognize objects and traffic lights on the road. The proposed system contains three phases: image pre-processing, feature extraction, and classification. In the first phase, some image pre-processing techniques are applied to prepare and improve the input images, consisting of three stages: convert color images to grayscale, histogram equalization, and image resize. In the second phase, extraction of the features from images using Principal Component Analysis (PCA). In the third phase, the extracted features are fed as input to the proposed One-dimensional Convolutional Neural Network (1D-CNN) model for object classification and recognition. The results show that the proposed CNN model achieved a high recognition rate where the classification precision rate reached 100%, and the error rate is 0%. The low number of false alarms and the high precision rate proves that the proposed system performs very well in recognizing the objects.
Quantifying over Boolean announcements

Various extensions of public announcement logic have been proposed with quantification over announcements. The best-known extension is called arbitrary public announcement logic, APAL. It contains a primitive language construct Box phi intuitively expressing that "after every public announcement of a formula, formula phi is true". The logic APAL is undecidable and it has an infinitary axiomatization. Now consider restricting the APAL quantification to public announcements of Boolean formulas only, such that Box phi intuitively expresses that "after every public announcement of a Boolean formula, formula phi is true". This logic can therefore called Boolean arbitrary public announcement logic, BAPAL. The logic BAPAL is the subject of this work. Unlike APAL it has a finitary axiomatization. Also, BAPAL is not at least as expressive as APAL. A further claim that BAPAL is decidable is deferred to a companion paper.
A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

The current deep convolution features based on retrieval methods cannot fully use the characteristics of the salient image regions.Also, they cannot effectively suppress the background noises, so it is a challenging task to retrieve objects in cluttered scenarios.To solve the problem, we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features.The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.After that, the feature aggregation mechanism aggregates the keypoints to a compact vector representation according to the scores evaluated by the attention mechanism.The core of the aggregation mechanism is to allow features with high scores to participate in residual operations of all cluster centers.Finally, we get the improved image representation by fusing aggregated feature descriptor and global feature of the input image.To effectively evaluate the proposed method, we have carried out a series of experiments on large-scale image datasets and compared them with other state-of-the-art methods.Experiments show that this method greatly improves the precision of image retrieval and computational efficiency.
Power rank: An interactive web page ranking algorithm

In the present era, one of the most important assets, information may be availed through different medium out of which the web has the status of most popular medium. Nowadays, several businesses, public domain enterprises and government organizations uses web to float and fetch information, supervision, and communications with external bodies. The importance of web pages thus becomes an inherently subjective matter whose dependency is on the reader's interests and understanding. But there exists much more than this that can be said impartially about the solitary web pages importance. In this book chapter, a new iterative web page ranking algorithm is proposed named as Power Rank. It uses the web graph where every outgoing edge of a page is weight on the basis of in-links and out-links as a power of a number to calculate the rank of web page in website. The objective of the proposed algorithm is to maximize the power of a path to select the page, indicating the page with high power. The experimental result gains the significant difference in terms of performance measures and proves that proposed algorithm outperforms over state-of-art algorithms like Page Rank, HITS and Weighted Page Rank in comparative analysis.
The Trap of Sisyphean Work in Differential Evolution and How to Avoid It

A good balance between the exploration and exploitation phases steers an optimization process of a metaheuristic algorithm toward the optimum(s). The exploration phase investigates different regions, and the exploitation phase finds solutions within the current region of the search space. During an optimization process, switches between these phases occur frequently, and consequently, the process may re-enter the already investigated area of the search space. While re-entering might be necessary, the re-evaluation of searched solutions, so-called duplicates, is unsatisfactory which also often occur when the process is trapped in the exploitation phase around the optima. This Sisyphean work could affect an optimization process’s cost and/or performance greatly, especially when the number of duplicates is huge or a single evaluation is expensive. Lack of awareness of the duplicate problems may lead to the situation that most of the optimization time is spent on evaluation of duplicates. In this chapter, we will take a holistic approach to study the problem of the occurrence of duplicates during the execution of eleven well-known differential evolution algorithms and the artificial bee colony algorithm using CEC 2015 benchmark functions and the static economic load dispatch problem. Through these experiments, we share our findings and suggested guidelines for the future directions of duplicate studies.
An Unorthodox Security Framework using Adapted Blockchain Architecture for Internet of Drones

The evolution of drones from military applications to the consumer world has seen many technological developments. COVID-19 pandemic also witnessed the deployment of drones in a large number of citizen-centric applications. The increasing number of drones and limited operational arena should have a well-suited ecosystem for the success of collaborative operations. As a result, the Internet of Drones (IoD) emerged as a modular framework employing algorithmic coordination of drones. Although these developments have been welcomed they bring along a wide range of security challenges. Drones are highly mobile and they rely on wireless links to communicate among themselves and with the infrastructure. Moreover, they carry a lot of data related to the application where they are deployed. Thus, this brings attention to the security aspects involved in the IoD ecosystem, including various challenges and the mechanisms available to address those challenges. Though several conventional security techniques (like encryption, encoding, and hashing) are already available they are not fully optimized for the IoD environment. This mechanism involves an associated overhead and trade-offs when deployed in the IoD scenario due to limited computational capability of drones. Hence, this brings the necessity to explore alternative security mechanisms like blockchain. However, the conventional blockchain has its limitation of heavy computational primitives (like mining) and may not be useful for the IoD. Hence, we have proposed an unorthodox security framework using an adapted blockchain architecture for IoD. This unorthodox mechanism was validated in a simulated environment and proved to be suitable for the IoD ecosystem.
Historical and Informational Potential of the University Libraries Websites

Historically, the scientific library has always been an important component of the structure of higher educational institution in Russia. Development of new technologies, creation of electronic information environment and availability of universities websites imposes on libraries certain responsibilities, associated not only with the provision of electronic resources and services to users, but also with the reflection on websites of various information about the libraries themselves, in particular, regarding their history. This information not only affects the formation of libraryʼs image, but can also become the basis for conducting modern scientific research in the field of library science. The presented research is devoted to studying the reflection of the history of libraries in the web space and determining the main trends in the development of information resources based on information on the history of librarianship. The authors highlight the results of the analysis of content of the libraries websites of the universities subordinate to the Ministry of Science and Higher Education of the Russian Federation. The paper presents the data obtained upon the study of websites (web pages) of 252 universities and their libraries. The study was conducted from February to April 2021. There authors identified five options for the representation of materials on the history of librarianship: historical reference, historical essay, comprehensive information product, presentation in Microsoft PowerPoint, multimedia video presentation. Historical reference is the most common of them is. Based on the dates of establishment of universities and libraries compared in the course of study, it was possible to present in dynamics the general picture of their development.
A Double-Layer Subsumption Mechanism for Enforcing Sequential Behaviors in a Cognitive Architecture

This paper describes the design and implementation of a double-layer subsumption mechanism, used to integrate a plan-based subsystem with a subsumption architecture, within the MECA Cognitive Architecture. This mechanism enables MECA to benefit both from the flexibility of behavior, which is characteristic from Subsumption Architectures, and the regularity and previsibility of executing specific sequences of behavior, generated by a planning subsystem, in the cases when this is required. We provide a brief overview of the original design of MECA, explaining why such a scheme is an improvement in the original design, and what are its potential benefits regarding the architecture capabilities. In the sequence, we provide a detailed explanation of the new design, splitting the old architecture Behavioral Codelets into two new categories: Behavioral Codelets (with the same name, but a different functionality) and Activity Codelets, together with a new Activity Tracking Codelet for tracking plan execution. Finally, we consider the improved benefits of the new scheme.
Machine Learning: A Novel Tool for Archaeology

The nature of archaeology is complex, and is not confined to the humanistic world, but can easily spread to scientific one. Nowadays, it is not uncommon to find archaeologists using new technologies in order to gather, analyze, and interpret data and disseminate the results. It is clear that every aspect of the work of the archaeologists is now actively revolutionized by modern technologies. In this framework, the use of machine learning techniques proves to be extremely interesting. New opportunities for the classification of artifacts, the prediction of site location, and the standardization of remains analysis can now be offered to archaeology. Although the introduction of machine learning in archaeology dates back to the 1970s, an extensive use in day-to-day work is still far away. Probably, the greatest obstacle is the need for a trained archaeologist expert in machine learning. While the improvements that machine learning could introduce in archaeology are clear, the role of the archaeologist in machine learning applications is not. This limitation should be removed increasing the knowledge in the archaeological community. This work means to offer an answer to this need providing descriptions of the main algorithms as well as their applications in the archaeological community.
Research on Position Recommendation System Based on Convolutional Neural Network

Abstract It is tough to find the perfect position that matches job hunters’ skills and salary levels in many positions. The traditional job recommendation systems are usually based on the keywords entered by the job seeker to get a job list. However, it is difficult for job-seekers to find their favorite positions the fastest among the vast number of positions. The advent of recommender systems makes this tedious process simple. In this article, we propose a new algorithm that combines the emerging TextCNN algorithm with collaborative filtering. Our method can more fully consider the skills and realization of job seekers and is better than just using TextCNN recommendation or just collaborative filtering. Our method can help job seekers quickly find a job that perfectly matches their skills after inputting their skills.
An Eye Tracking Perspective on How Developers Rate Source Code Readability Rules

Writing readable source code is generally considered good practice because it reduces comprehension time for both the original developer and others that have to read and maintain it. We conducted a code readability rating study using eye tracking equipment as part of a larger project where we compared pairs of Java methods side by side. The methods were written such that one followed a readability rule and the other did not. The participants were tasked with rating which method they considered to be more readable. An explanation of the rating was also optionally provided. Eye tracking data was collected and analyzed during the rating process. We found that developers rated the snippet in the pair of methods that avoided nested if statements as more readable on average. There was no clear preference in the use of do-while statements. In addition, more developer fixation attention was on the snippet that avoided do while loops and the snippet pairs relating to nested if statements had more equal fixation attention across the snippets.
Model-Based Kalman Filter Design on an FPGA

This paper presents a design and real-time implementation of Kalman Filter on hardware considering a model-based approach. The filter is designed with different data types in the MATLAB Simulink environment using the Model Composer Blocks Library. After automatic code generation with Model Composer, the designed filter is exported to Vivado Design Suite as Intellectual Property (IP). Verification of the design is carried out on Field Programmable Gate Array (FPGA). By comparing the reference MATLAB model and realization on hardware, the design is verified for data accuracy. Moreover, performance outcomes for real-time applications like timing and utilization analyses are examined.
Nonlinear Optimization of Light Field Point Cloud

The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorithm with per-layer disparity filtering and consistency-based holes filling. Together with that we reformulate the reconstruction result to a form of point cloud from different light field viewpoints and propose a non-linear optimization of it. The capability of our method to reconstruct scenes with acceptable quality was verified by evaluation on a publicly available dataset.
A constant-time sampling algorithm for binary Gaussian distribution over the integers

Discrete Gaussian sampling over the integers is one of fundamental operations in lattice-based cryptography. The binary Gaussian distribution DZ+,σ2 is a special discrete Gaussian distribution with σ2=1/(2ln⁡2) and μ=0 over the set of non-negative integers Z+, and a sampling algorithm for DZ+,σ2 can be used as the base sampler in a generic algorithm based on rejection sampling for any discrete Gaussian distribution over the integers. We present a constant-time algorithm for sampling from the binary Gaussian distribution DZ+,σ2. It requires no precomputation storage and mainly relies on bitwise operations, which could be more hardware-friendly. Its computational complexity is lower than that of the algorithm based on the full-tree Knuth-Yao method, and its entropy consumption is smaller than that of the full-table access algorithm based on a cumulative distribution table. The Rényi-divergence based security analysis of our constant-time algorithm can also be simplified.