Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Evolutionary Algorithms for Data Classification Using KEEL Tool

Evolutionary algorithms are inspired by the biological model of evolution and natural selection and are used to solve computationally hard problems, also known as NP-hard problems. The main motive to use these algorithms is their robust and adaptive nature to provide best search techniques for complex problems. This paper presents a comparative analysis of classification of algorithm's family instead of algorithms comparison using KEEL tool. This work compares SSMA-C, DROP3PSO-C, FURIA-C, GFS-MaxLogitBoost-Cand CPSO-C algorithms. Further, these selected evolutionary algorithms are compared against two statistical classifiers using the Wilcoxon signed rank test and Friedman test on following datasets—bupa, ecoli, glass, haberman, iris, monks, vehicle, and wine—to calculate classification efficiencies of these algorithms. Experimental results reveal some differences among these algorithms. Visualization module in the model has been used to give overall results as a summary while statistical test using Clas-Wilcoxin-ST compared the algorithms in a pair-wise fashion to conclude experimental findings.
From recursive to dynamic: An algorithm for dealing with a problem

Abstract One of the problems in data science is related to data management, especially big data. It is indirectly related to algorithms in computer science. Therefore, the meeting between big data and the appropriate algorithm to produce a good output requires characterization and explanation. This paper reveals one of them, by involving the recursive and dynamic principles of programming, and by comparison giving choices as answers to problems.
The Yahoo Breaches of 2013 and 2014

In 2016, Yahoo disclosed to the public that it had been breached in 2014. Yahoo’s 2014 breach exposed the names, email addresses, telephone numbers, birthdates, “hashed” passwords, and, in some cases, security questions of over 500 million users. While investigating the breach of 2014, Yahoo discovered that the company had been separately breached in 2013. Yahoo initially reported that the 2013 breach affected over one billion users while it was in the process of getting acquired by Verizon. In October 2017, after its acquisition by Verizon was complete, Yahoo reported that the 2013 breach affected all three billion users. Figure 7-1 shows a timeline of these breaches and the major events that occurred after the breaches. Yahoo was questioned and criticized for disclosing the breaches two to three years after they occurred. During a Senate hearing that took place in the aftermath of the breaches, frustrated Senator Thune of South Dakota asked former Yahoo CEO Marissa Mayer, “Why the delay in disclosing it? I mean it took from 2013, three years.”
RIIT: Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning.

In recent years, Multi-Agent Deep Reinforcement Learning (MADRL) has been successfully applied to various complex scenarios such as computer games and robot swarms. We investigate the impact of implementation tricks of state-of-the-art (SOTA) QMIX-based algorithms. Firstly, we find that such tricks, described as auxiliary details to the core algorithm, seemingly of secondary importance, have a major impact. Our finding demonstrates that, after minimal tuning, QMIX attains extraordinarily high win rates and achieves SOTA in the StarCraft Multi-Agent Challenge (SMAC). Furthermore, we find QMIX's monotonicity condition improves sample efficiency in some cooperative tasks. We propose a new policy-based algorithm, called RIIT, to verify the importance of the monotonicity condition. RIIT also achieves SOTA in policy-based algorithms. At last, we prove theoretically that the Purely Cooperative Tasks can be represented by the monotonic mixing networks. We open-sourced the code at \url{this https URL}.
A Method for Identifying Origin of Digital Images Using a Convolutional Neural Network

The rapid development of deep learning techniques has created new challenges in identifying the origin of digital images because generative adversarial networks and variational autoencoders can create plausible digital images whose contents are not present in natural scenes. In this paper, we consider the origin that can be broken down into three categories: natural photographic image (NPI), computer generated graphic (CGG), and deep network generated image (DGI). A method is presented for effectively identifying the origin of digital images that is based on a convolutional neural network (CNN) and uses a local-to-global framework to reduce training complexity. By feeding labeled data, the CNN is trained to predict the origin of local patches cropped from an image. The origin of the full-size image is then determined by majority voting. Unlike previous forensic methods, the CNN takes the raw pixels as input without the aid of “residual map”. Experimental results revealed that not only the high-frequency components but also the middle-frequency ones contribute to origin identification. The proposed method achieved up to 95.21% identification accuracy and behaved robustly against several common post-processing operations including JPEG compression, scaling, geometric transformation, and contrast stretching. The quantitative results demonstrate that the proposed method is more effective than handcrafted feature-based methods.
Metaphysics of Being of Information

In article the metaphysics of being of information is considered and her cat­egorical status. The concept of information plays an especially important role in cognitive disciplines – neurology, cognitive psychology, sociology and arti­ficial intelligence theories altogether forming the scientific basis of new epi­stemology. Information is understood as the objective reality perceived by the subject by means of touch bodies which it fixes and comprehends by means of various semantic pro-positions, logical-mathematical operations and calculations, using it in various communicative practices for achievement of the purposes. On the basis of philosophical traditions of interpretation of cat­egory of life and various interpretations of this abstract concept of modern nat­ural sciences the new understanding of category of life as a basic metaphysical concept of science is offered. The philosophical bases of the theory of creative participation of the person in events of the Universe the famous American physicist-theorist J.A. Wheeler who put forward the thesis “all from Bit” (It from Bit), the information paradigm of the universe developed by the prom­inent modern philosopher L. Floridi. It is demonstrated that the origination and evolution of all objects takes place due to the existence of specific information programs that express the fundamental creativity of the nature. The statement that the category of life in modern interpretation both in natural-science, and in sociocultural aspects expresses life of information, or the information life presented in unity of three spheres of reality – matter, lives and consciousness (reason) is proved.
Is Sharing Datasets the Answer to the New Challenges of Reproductive Biology Research?

Data sharing increases the speed of research and saves time and resources while ensuring transparency and reproducibility. We have analyzed this behavior through the reproductive biology community. Our study revealed that Q1 (44%) and Q2 (36%) JCR reproductive biology journals are the most active journals in data sharing.
Intelligent Vehicle Automatic Lane Changing Lateral Control Method based on Deep Learning

The steering wheel control accuracy of traditional vehicle automatic lane changing lateral control method is poor, so a new lateral control method of intelligent vehicle automatic lane changing based on deep learning is proposed. Build an autonomous driving platform for smart cars, focus on the design of the automatic steering system, modify the steering wheel, calculate the resistance of the road steering wheel, use millimeter wave radar to complete detection and sensing; design the vehicle's lateral controller, which mainly uses fuzzy algorithm combination The sliding mode variable structure control method reduces the chattering problem of the sliding mode control method and enhances the control stability; optimizes the lane line recognition algorithm, uses the Sobel operator template to solve the pixel point gradient in the horizontal and vertical directions, and completes the lane line recognition. The simulation results show that the control angle of the steering wheel is closer to that of the target.
Local Seismicity in the Eastern Alps From GPU-Based Template Matching

&amp;lt;p&amp;gt;The AlpArray programme &amp;quot;Mountain Building Processes in 4D&amp;quot; is an interdisciplinary project aimed to image the structure of the Alps and understand their formation. The goal is to be able to model the entire crust-mantle system in three dimensions, and investigate its evolution through time. Seismicity can reveal spatial and temporal patterns of faulting and thereby help to understand the current tectonic structure and motions in the Earth's crust. The south-eastern Alps are of special interest as they include the current plate boundary between Adria and Eurasia, but their undelying structure is poorly resolved and seismicity seems to be scarce. Being able to detect the smallest earthquakes is therefore of key importance.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Swath-D was an AlpArray complementary experiment in which approximately 150 broadband seismic stations were deployed in the Eastern Alps from late 2017 to late 2019. With a station spacing of around 15 km, it is much denser than the AlpArray Backbone network. In this work, data from these stations, combined with publicly available broadband data from the region, were used to detect, localize, and characterize microseismic events. A combination of energy-based detection and template matching was applied to both discover previously unidentified seismic activity and yield a high number of detections. An efficient GPU-based implementation was of critical importance to handle computationally demanding detection methods and the large data volume. Here, we present our methods and workflow, and a new map of seismicity in the south-eastern Alps.&amp;lt;/p&amp;gt;
Coronavirus as a burning glass for digital risks?

The digital space represents a global interaction and communication sphere for all people. In this sphere children connect with young people and adults across almost all countries, cultures and age groups. The constant improvement and implementation of automatic translation programs, for example in social media, also means that language barriers in the digital space are becoming increasingly blurred. This evolution, which is actually to be welcomed, promises at best the emergence of a kind of Internet-based global society, but it also presents a dark side. Criminology assumes that many forms of crime - regardless of the respective national criminal law - typically arise from interaction between people. There are few arguments to argue against the fact that this is not the case on the Internet - which actually makes it the first truly widespread and global crime scene in the world. Children, too, are a natural part of this educated global scene (or space) and are thus exposed to the digital forms of crime that go hand in hand with it. At the European level, children aged 9-16 spend an average of 167 minutes each day online (Smahel et al. 2019). In Germany alone, 97 percent of minors aged 12 and over use the Internet several times a week (Feierabend et al. 2020). Children are also intrinsically confronted with the digital risks of this area. These risks range from cybergrooming and sexual harassment in social media and online games, to the confrontation with extremism and violent content, to risks such as cyberbullying and stalking (cf. Bötticher 2020; Gabriel 2020; Rüdiger 2020). Almost 25 percent of children in the European Union who use the Internet have already reported having had at least one negative online experience in the past year (Smahel et al. 2019). The U-25 study from Germany even speaks of minors perceiving the Internet as a culture of injustice (Borchard et al. 2018).
A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students

&lt;span id="docs-internal-guid-5a78994c-7fff-41c1-c57f-91661e44674c"&gt;&lt;span&gt;The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.&lt;/span&gt;&lt;/span&gt;
An improved mammography malignancy model with self-supervised learning

As deep learning greatly accelerates the field of computer vision, there has been growing interest in applying deep learning models for the purpose of predicting the presence of cancer in mammography images. However, unlike in conventional object recognition where one can leverage very large diverse datasets such as ImageNet, datasets for identifying cancer with mammography images are typically small and potentially non-representative due to the high cost of acquiring medical data and labels. This makes the training and assessment of such models challenging and raises reliability as well as generalizability concerns. In this work, we propose using the jigsaw task1 as a self-supervised method to pre-train models in the case where unlabeled data is available. We show that models that are pre-trained with this task outperform randomly initialized models even when they are only trained on a half or a quarter of the train set for the malignancy prediction task. In particular, we find that when using only a quarter of the labeled data, model trained using randomly initialized weights has an area under the receiver operating characteristic curve (AUC) of 0.944. On the other hand, the model that was pre-trained with the jigsaw task achieved an AUC of 0.958 when fine-tuned on the same quarter of the training set for the malignancy prediction task, outperforming even the model that was trained on all of the labeled data starting from randomized weights (0.954 AUC). Furthermore, we propose using performance on the jigsaw task as a way to measure confidence in our model’s predictions to enable the option to abstain from making a prediction when the model is not confident. We tested multiple strategies to filter out samples on which the jigsaw model perform poorly and measured the AUC in the remaining pool of samples. We show that the best filtering strategy improves malignancy prediction performance from an AUC of 0.890 on a completely unfiltered, off-site test set from a different country to an AUC of 0.913 on the filtered set.
Membangkitkan Budaya Literasi Melalui Pendekatan Think Pair Share Berbasis Saintifik Dalam Pembelajaran Bahasa Indonesia Menuju Siswa Berkarakter

Indonesian language learning is a means of delivering knowledge. Through learning Indonesian especially reading and writing, students can master other subjects. Student learning success is strongly influenced by their ability in language. Understanding literacy through a scientific-based think-pair-share approach in learning Indonesian is more directed at information skills. The ability of information refers to several activities, namely thinking, pairing, and sharing in which there are activities to observe, ask questions, try, associate, and communicate. The process of observing includes finding information, seeing, listening and listening. The questioning activity is carried out as a process of building students' knowledge in the form of concepts in group discussions. The activity tries to be useful to increase student curiosity which includes planning, designing, and obtaining information. Associating activities can be done by analyzing and predicting, while communicating activities are a means to convey the results of conceptualization in oral or written form.
The effect of regulatory performance on economic growth

The tendency to fall into a middle-income trap position due to weak regulatory performance remains a longstanding issue faced by the majority of upper-middle-income economies (UMIE). Although regulatory performance is considered as one of the most vital factors that determine the financial progression and stability of a nation, this variable is often overlooked in previous economic growth literature. Hence, this study aims to examine the important role of regulatory performance in accelerating the economic growth of 30 upper-middle-income economies countries within the timeframe of 2000 to 2018. Using the two-step system Generalised Method of Moments (GMM), the findings reported that regulatory performance is indeed essential in stimulating economic growth. The results also indicated that lengthy procedures and waiting period involved in opening and registering new businesses are a common predicament for start-up firms. This proves to be a demotivating factor for new investors, indirectly slowing down the economic performance of the UMIE. As this study leads to various policy implications, future studies can consider advancing research in this area by further examining the link between regulatory performances and economic growth for individual countries based on a time series analysis.
VA-RED2: Video Adaptive Redundancy Reduction

Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both. The type of redundant features depends on the dynamics and type of events in the video: static videos have more temporal redundancy while videos focusing on objects tend to have more channel redundancy. Here we present a redundancy reduction framework, termed VA-RED2, which is input-dependent. Specifically, our VA-RED2 framework uses an input-dependent policy to decide how many features need to be computed for temporal and channel dimensions. To keep the capacity of the original model, after fully computing the necessary features, we reconstruct the remaining redundant features from those using cheap linear operations. We learn the adaptive policy jointly with the network weights in a differentiable way with a shared-weight mechanism, making it highly efficient. Extensive experiments on multiple video datasets and different visual tasks show that our framework achieves 20%−40% reduction in computation (FLOPs) when compared to state-of-the-art methods without any performance loss. Project page: http://people.csail.mit.edu/bpan/va-red/.
Research on the Operation Management Mode of Intelligent Hydroelectric Power Plant

Based on the actual situation of Gezhouba Hydroelectric Power Plant, this paper studies the operation management mode from four aspects: Operation on duty mode, patrol inspection mode, performance appraisal system and work modularization mechanism. And based on the research results, we carry out practical activities in the process of exploration. In the practice of reform, the operation on duty mode of “Centralized Duty, Regional Emergency and Work Support” and the management idea of “digitalization, informatization and modularization” are adopted to sort out and explore the operation and management mode of intelligent hydroelectric power plant, which not only realizes the coordination between human and equipment but also has accumulated important experience for realizing the excellent goal of “unattended operation and integrated regulation and control”. Finally, through the analysis of the workload statistics in 2016 and 2017, the paper proves that the reform of operation and management mode has achieved remarkable results, injecting new vitality into the development concept of “innovation, coordination, green, open and sharing”.
How Has Deep Learning Revolutionized Human Language Technology?

Communication by voice and text, which we refer to as natural language, is a skill that separates humans from all other species. Only humans possess the complete linguistic package. Since the first computer was invented, it's been our dream to interact with computers using spoken and written words. For decades, machine learning approaches in automatic speech recognition (ASR) and natural language processing (NLP) have been based on shallow models such as Gaussian Mixture Models (GMMs) and hidden Markov models (HMM). These approaches use hand-crafted features and models that attempt to integrate knowledge of speech production, perception and linguistics. We often refer to this knowledge as subject matter expertise. Integration of such knowledge has been a cornerstone of signal processing research for decades.
Design of a Cognitive Rehabilitation System Based on Gesture Recognition

Abstract Computer-assisted cognitive training is an effective intervention for patients with mild cognitive impairment (MCI), which can avoid the disadvantages of traditional cognitive training that consumes a lot of medical resources and is difficult to be standardized. However, many computer-assisted cognitive training systems have unfriendly human-computer interaction, for not considering that most MCI patients have certain difficulties in using computers. In this paper, we design a cognitive training system which allows patients to implement human-computer interaction through gestures. First, a gesture recognition algorithm is proposed, in which we implement gesture segmentation based on YCbCr color space and Otsu algorithm, extract Fourier Descriptors of gesture contour as feature vectors and use SVM algorithm to train a classifier to recognize gestures. Then, the graphical user interface (GUI) of the system is designed to realize the task requirement of cognitive training for the MCI patients. Finally, the results of tests show the accuracy of the algorithm and the feasibility of the GUI. With the above computer-assisted cognitive training system, patients can achieve human-computer interaction only through gestures without the need to use keyboard, mouse, etc., greatly reducing the burden of patients during training.