Reviewerly/exHarmony huggingface dataset:
Out of the Niche: Using Direct Search Methods to Find Multiple Global Optima

Multimodal optimization deals with problems where multiple feasible global solutions coexist. Despite sharing a common objective function value, some global optima may be preferred to others for various reasons. In such cases, it is paramount to devise methods that are able to find as many global optima as possible within an affordable computational budget. Niching strategies have received an overwhelming attention in recent years as the most suitable technique to tackle these kinds of problems. In this paper we explore a different approach, based on a systematic yet versatile use of traditional direct search methods. When tested over reference benchmark functions, our proposal, despite its apparent simplicity, noticeably resists the comparison with state-of-the-art niching methods in most cases, both in the number of global optima found and in the number of function evaluations required. However, rather than trying to outperform niching methods—far more elaborated—our aim is to enrich them with the knowledge gained from exploiting the distinctive features of direct search methods. To that end, we propose two new performance measures that can be used to evaluate, compare and monitor the progress of optimization algorithms of (possibly) very different nature in their effort to find as many global optima of a given multimodal objective function as possible. We believe that adopting these metrics as reference criteria could lead to more sophisticated and computationally-efficient algorithms, which could benefit from the brute force of derivative-free local search methods.
The social side of cryptocurrency: Exploring the investors’ ideological realities from Romanian Facebook groups

The complexity of blockchain technologies is reflected in the various ways in which social actors operate with crypto transactions, and this is seen in the wide range of discourses and practices adopted on the crypto market. Using social constructivism, this study highlights the fact that social practices in the crypto area respect certain thematic ideologies, through which individual discourses reflect different pragmatic expectations in relation to virtually encrypted transactions. Drawing on inductive thematic analysis based on 38 semi-structured interviews with Romanian investors, four major themes emerge: (1) unbreakable power of code encompasses rather libertarian discourses on potential state policies, (2) key role of peers explains the investments in the crypto market as a form of community membership, (3) assuming plenty of risks highlights the discourses focused on substantial and unpredictable gains, and (4) preferring safety above all encompasses discourses on financial stability through rather conservative behaviors.
Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

This study derives a learning algorithm for a quaternion neural network using the steepest descent method extended to quaternion numbers. This applies the generalised Hamiltonian–Real calculus to obtain derivatives of a real–valued cost function concerning quaternion variables and designs a feedback–feedforward controller as a control system application using such a network. The quaternion neural network is trained in real-time by introducing a feedback error learning framework to the controller. Thus, the quaternion neural network-based controller functions as an adaptive-type controller. The designed controller is applied to the control problem of a three-link robot manipulator, with the control task of making the robot manipulator’s end effector follow a desired trajectory in the Cartesian space. Computational experiments are conducted to investigate the learning capability and the characteristics of the quaternion neural network used in the controller. The experimental results confirm the feasibility of using the derived learning algorithm based on the generalised Hamiltonian–Real calculus to train the quaternion neural network and the availability of such a network for a control systems application.
Understanding and predicting bugs fixed by API-migrations

Bug tracking systems are standard repositories that preserve a large number of uncovered bugs. Once a bug is reported in these repositories, developers search for appropriate changes to fix the bug. However, discovering the changes that can fix the bugs has a negative influence on the schedule and cost of projects. Mainly, fixing bugs could be done by performing some other maintenance changes. In this work, we study and examine the role of adaptive maintenance in the context of API-migration during bug fixing activities through a case study on KOffice, Extragear/graphics, and Open Scene Graph projects. Our goal is to direct developers towards potential bugs early in development which are more likely to be fixed by performing adaptive changes as opposed to other maintenance tasks. We examined the reports of fixed bugs from the bug tracking systems of the studied projects, then we explored several factors related to variant dimensions of the reports and their relevant version history commits, in order to evaluate their effectiveness to decide whether a bug is likely to be fixed by adaptive changes. Our case study results show that bug residency time, textual contents of the report, the component that the bug was found in, and reporter/commenter experience show significant differences between the bugs that are fixed by adaptive changes and other fixed bugs.
The Dilemma and Countermeasures of Personal Privacy Protection in the Era of Big Data

Personal privacy in the era of big data involves information privacy, communication privacy, space privacy, and portrait privacy. Currently, personal privacy leaks happen frequently, affecting people's daily life and property safety. In response to the dilemma faced by personal privacy protection, we should actively improve relevant laws and regulations, improve key technologies for personal privacy protection, and enhance individual privacy protection awareness.
Cycle-autoencoder based block-sparse joint representation for single sample face recognition

Single sample face recognition (FR) uses only one image per person for training. It is a challenging task due to insufficient training samples and dramatic variance of unseen images in real applications. In this paper, we propose a cycle-autoencoder model to generate facial variation from the single training sample and remove the variation in the testing set. Our approach adopts a cycle consistency scheme to formulate the generation and removal models in one framework. Considering the prior structure of the images produced from the generation and removal models, we further propose a block-sparse joint representation method, which integrates the representation procedure of all testing samples and obtains all the coefficients simultaneously. The experimental results on AR, Extended Yale B and CUFS datasets not only demonstrate the effectiveness of our proposed method, but also represent its robustness to complex facial variations. Experiments on the CUFS dataset also show that our approach is appropriate for sketch face recognition with single sample per person.
Brazilian Portuguese Hate Speech Classification using BERTimbau

Hate speech is a language that attacks or denigrates a specific group based on their characteristics, such as their race, ethnicity, or sexual orientation. Hate speech became widespread and spread through social networks, blogs, videos, and other communication channels. With anonymity and a sense of impunity, people feel encouraged to spread their hatred on the internet. In this work, we used the BERT model for the Portuguese language called BERTimbau to classify hate speech in three datasets in Portuguese, available in the literature: OFFCOMBR-2, OFFCOMBR-3, and Fortuna et. al. (2019) dataset. Still, we performed some preprocessing and an oversampling technique on the datasets. Finally, we compared the results obtained with results obtained by works available in the literature. Experiments with BERTimbau, using preprocessing and oversampling obtained better results than other classification techniques.
Performance Comparison of Support Vector Regression, Random Forest and Multiple Linear Regression to Forecast the Power of Photovoltaic Panels

With the significant development and expansion of renewable energies, production sources have varied and the network has become more difficult to manage. Therefore, predicting the electricity generated by renewable sources has become critical. In this perspective, machine learning, as part of artificial intelligence, appears to be one of the best ways to achieve this aim. Machine learning techniques can control the variations in renewable energy output and therefore, facilitate their integration into the energy mix. Thus, one of the major goals of this research is to perform a comprehensive comparison of three popular machine learning techniques, including multiple linear regression, support vector regression and random forest, for the hourly prediction of the power produced by photovoltaic solar panels. Residual analysis is performed to visually test the investigated regression models. The results revealed that random forest achieved the best prediction accuracy in the testing phase with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =96% and RMSE=0.39 KW.
Encrypted Live Streaming Channel Identification With Time-Sync Comments

The time-sync comments have been prevalent in modern live streaming systems to provide a real-time interaction experience for viewers. Whereas, the time-sync comments traffic can also act as a delicate fingerprint of encrypted live channels, leading to potential risks of privacy leakage. Most of previous video channel identification strategies with video bitrate-based fingerprint presume strict requirements on the implementation environments, which often assume that there is no interference from irrelevant traffic flows or network conditions. However, the time-sync comments sessions are distinct from other irrelevant traffic flows, and the traffic pattern is resilient to various network conditions, e.g., bandwidth limitation and transmission delay. In this paper, we design a system for encrypted live channel identification with time-sync comments traffic analysis. Specifically, both the inter-application and inner-application traffic filters are proposed to eliminate the irrelevant traffic flows, respectively. Further, a comment rate estimation method is developed through investigation of relationship between comment number, comment length and packet length. Finally, the dynamic time warping(DTW) algorithm is improved for similarity matching in delay tolerant environment. In order to evaluate the system performance, we setup the prototype system with AWS EC2 server and utilize the real world trace data from Youtube and BiliBili. The experimental results show that the accuracy of the filter can reach 93.2&#x0025;, and the accuracy of the comment rate estimation method can reach up to 91&#x0025;. The match accuracy between fingerprint and comment rate can reach 92.1&#x0025; within 200 seconds eavesdropping, which is 2&#x0025; higher than using bitrate fingerprint and traffic pattern in the latest research, and can be increased to 98.2&#x0025; when the eavesdropping time extends to 500 seconds.
A Two-Stage Framework for Diverse Traffic Light Recognition Based on Individual Signal Detection

The detection of traffic lights is an indispensable part of advanced driver assistance systems. This paper presents a new two-stage detection framework which is able to recognize various types of traffic signals. Different from the techniques based on the direct detection of traffic lights, we consider the individual signal bulbs as targets for detection and recognition. In our two-stage approach, the first detection stage aims to achieve a very low miss rate on the traffic signal bulbs with possibly high false positives. It is then followed by the second recognition stage to single out the correct traffic signals using a classification network. The proposed method overcomes the diverse traffic light detection problem due to various arrangements of signal bulbs in different countries. It is also capable of simultaneously detecting individual traffic signals including the arrow lights. Thus, the categories of traffic light states for classification and training can be reduced. The experiments carried out using our road scene images and public datasets have demonstrated the feasibility of the proposed technique.
SURVEY ON GUJARAT’S DEAF AND MUTE SCHOOL’S SPECIAL EDUCATORS TO UNDERSTAND THE EDUCATION OF DEAF COMMUNITY IN THIS PANDEMIC

Purpose: The purpose of this study to understand the deaf community’s education in this pandemic situation. : This pandemic evolves digitization process all over the world. The schools are also come with a remote and online learning-teaching process. This online learning is a turning point of a deaf students.This study includes how schools are handling the deaf students and teach them online in this pandemic. It will helps to facilitate the education in a better way to a deaf community. Method: The Survey of special educators by taking one on one discussion and interviews. The Questionnaire was prepared and taken the data of deaf student’s education. Due to the pandemic situation google meet and telephonic conversion helps a lot. I visited to 2 deaf-mute schools of Gujarat region to understand the teaching methodology, teaching materials and the pedagogy of teaching. Result:Gujarati Sign Language is need to become a one standard form of language, as it differs with Indian Sign Language. The sequence of sentence is changed in Gujarati Sign Language and it differs of grammar. For e.g.you – only one sign but in Gujarati તમે, તારા, તારી, તમારાall has a different signs. As well as they different signs for series alphabets. Any language with grammar provide a complete meaning of that language.It will be helpful to all the students in writing as well as understanding the meaning of that sentence. Conclusion:There were many challenges to teach deaf-mute students on online platform, as there is a huge loss in this community in entire pandemic. As they were lack of resources, lack of support and lack of understanding did not give an expected output.The purpose of this paper to understand deaf education system in Gujarat region in this pandemic. The Survey helps to provide a better solution of flowing the education in deaf communityand development of them. Technology helps to decrease the problems of deaf-mute community, if digitalization of sign language dictionary will take a place.Non-challengeable person speaks and write the sentence or word and that will be converted into sign and played by 3D avatar.When student will see the content many times with signs that will help into their education. That will be helpful to understand the message from non-challengeable person to deaf person.
A Componentwise Approach to Weakly Supervised Semantic Segmentation Using Dual-Feedback Network

Recent weakly supervised semantic segmentation methods generate pseudolabels to recover the lost position information in weak labels for training the segmentation network. Unfortunately, those pseudolabels often contain mislabeled regions and inaccurate boundaries due to the incomplete recovery of position information. It turns out that the result of semantic segmentation becomes determinate to a certain degree. In this article, we decompose the position information into two components: high-level semantic information and low-level physical information, and develop a componentwise approach to recover each component independently. Specifically, we propose a simple yet effective pseudolabels updating mechanism to iteratively correct mislabeled regions inside objects to precisely refine high-level semantic information. To reconstruct low-level physical information, we utilize a customized superpixel-based random walk mechanism to trim the boundaries. Finally, we design a novel network architecture, namely, a dual-feedback network (DFN), to integrate the two mechanisms into a unified model. Experiments on benchmark datasets show that DFN outperforms the existing state-of-the-art methods in terms of intersection-over-union (mIoU).
Decision tree-based blending method using deep-learning for network management

Network traffic classification is a key component for network management, Quality-of-Service management, as well as for network security. Therefore, developing machine learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. However, among the classification methods applied to network traffic classification so far, there is no one method that outperforms all the others. They all have advantages and inconveniences depending on the application domain. Therefore,this paper proposes an intelligent traffic management model by using deep learning (DL) that incorporates multiple Decision Tree-based models. Our model deploys a blending ensemble learning method to combine tree-based classifiers in order to maximize generalization accuracy. Using two datasets, we show that our proposed ensemble model is efficient for network traffic classification. Furthermore, the proposed approach is also compared against other representative machine-learning and deep-learning models and the results demonstrate that our approach provides better performance compared to others.
Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types

The study and treatment of cancer is traditionally specialized to the cancer's site of origin. However, certain phenotypes are shared across cancer types and have important implications for clinical care. To date, automating the identification of these characteristics from routine clinical data - irrespective of the type of cancer - is impaired by tissue-specific variability and limited labeled data. Whole-genome doubling is one such phenotype; whole-genome doubling events occur in nearly every type of cancer and have significant prognostic implications. Using digitized histopathology slide images of primary tumor biopsies, we train a deep neural network end-to-end to accurately generalize few-shot classification of whole-genome doubling across 17 cancer types. By taking a meta-learning approach, cancer types are treated as separate but jointly-learned tasks. This approach outperforms a traditional neural network classifier and quickly generalizes to both held-out cancer types and batch effects. These results demonstrate the unrealized potential for meta-learning to not only account for between-cancer type variability but also remedy technical variability, enabling real-time identification of cancer phenotypes that are too often costly and inefficient to obtain.
Intelligent Decision Support for Cybersecurity Incident Response Teams: Autonomic Architecture and Mitigation Search

Critical infrastructures must be able to mitigate, at runtime, suspected ongoing cyberattacks that have eluded preventive security measures. To tackle this issue, we first propose an autonomic computing architecture for a Cyber-Security Incident Response Team Intelligent Decision Support System (CSIRT-IDSS) with a precise set of technologies for each of its components. We then zoom in on the component responsible for proposing to the CSIRT, automatically ranked sets of runtime actions to mitigate suspected ongoing cyber-attacks. We formalize its task as a Constraint Optimization Problem (COP). We then propose to implement it by a Constraint Object-Oriented Logic Program (COOLP) deployed as a containerized web service through the integration of three orthogonal extensions of Logic Programming (LP): Web Service Oriented LP (WSOLP), Constraint LP (CLP) and Object-Oriented LP (OOLP). This integration supports seamlessly reusing platform and task independent cybersecurity ontological knowledge to dynamically build a mitigation action search COP that is customized to an input suspected cyberattack action set. This customization then allows the COP, to be solved by a generic CLP engine efficiently enough to propose mitigation actions to the CSIRT team while they can still be effective. To validate this approach, we implemented a prototype called CARMAS (Cyber Attack Runtime Mitigation Action Search) and ran scalability tests on simulated attacks with various COP construction strategies.
The effect of career path planning on organisational performance

Researchers consider career path planning an important subject. It has attracted the attention of human resource managers in specific and organisational administrations in general, because of its effective role in developing and managing occupations, and its positive direct impact on the organisational performance when employed effectively. The study attempted to recognise the effect of career path planning on organisational performance of Jordan pharmaceutical companies. The researchers employed a descriptive analytical methodology to gather and analyse data. The population of the study included all Jordan pharmaceutical companies (No. 15). The researchers applied a random sampling method in selecting four companies for studying. Employees in the four companies holding supervisory positions participated in the study. The career path planning had affected organisational performance in the pharmaceutical companies. The researchers recommend Jordan pharmaceutical companies to adopt the culture of career path planning and recommend other researchers to increase academic research on it.
Optical random micro-phase-shift DropConnect in a diffractive deep neural network

The formulation and training of unitary neural networks is the basis of an active modulation diffractive deep neural network. In this Letter, an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference. Optically, the random micro-phase-shift acts like a random phase sparse griddle with respect to values and positions, and is operated in the optical path of a projective imaging system. We investigate the performance of the full-drilling and part-drilling phenomena. In general, random micro-phase-shift part-drilling outperforms its full-drilling counterpart both in the training and inference since there are more possible recombinations of geometrical ray deflections induced by random phase DropConnect.
Social trade networks and compromise solution in multi-agent interaction

The online trade network model is considered in this paper. In some nodes of such network consumers are located. Each consumer purchases a particular good at cost due to the price of goods and transport costs. In some nodes of the network online-retailers seek to allocate the points of goods delivery. The points of goods delivery are placed according to the optimality principle. The compromise solution is chosen as optimality principle in the model. The algorithm for finding the optimal placement of goods delivery points is proposed in this paper.