Reviewerly/exHarmony huggingface dataset:
Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System

A suitable treatment for breast cancer patients may be developed via proper proliferation rate quantification, which can determine the prognosis by emphasizing the tumor growth statistics. A common analysis involves pathologists investigating the microscopic images from the breast tissue biopsy slide stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and ineffectiveness, especially because the diagnosis depends on the facility and the pathologists’ insights and experiences. To leverage clinicians’ confidence when diagnosing cancerous grades, a convolutional neural network is considered feasible as a computer-based observer. Therefore, this study proposes a novel scheme for grading breast cancer malignancy from invasive ductal carcinoma. Our proposed classifiers were designed by implementing multistage transfer learning incorporating domain and histopathological transformations. Domain adaptation using the pre-trained model and weights of well-known architectures, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify 40 × magnification BreaKHis dataset into eight classes. Then, the best models, in our case, InceptionV3 and Xception, containing the domain and histopathology pre-trained weights, were used to categorize the Databiox database into grades 1, 2, or 3. To provide comprehensive reports, this study offers a patchless automated grading system for magnification-dependent and -independent classifications. Our classifiers achieved outstanding performance by presenting an overall accuracy of 90.17% ± 3.08%–97.67% ± 1.09% and an 𝐹1-score of 0.9013–0.9760 for magnification-dependent classification. Thus, the results may provide an applicable methodology for breast cancer grading systems in clinical settings.
Can Cryptocurrency Be a New Safe-Haven?

In the digital era, there are many investable assets and make profit from it. Because investments always have risk, the investors then decrease their risk by finding assets that are expected to retain or increase in their values during times of market turbulence. Herein the assets which can retain or increase in their values will be called as safe-haven assets. Gold has been known as the safe-haven asset, however cryptocurrency is a class of digital assets which is claimed as the new gold in the digital era. Consequently, this study aims to answer a question “Can cryptocurrency be a new safe-haven?”.
Approach

The third chapter describes our vision towards integrating modern tools and methodologies in a full DevOps cycle. To assure the relevance for the aviation industry, various connections to the rigorous safety-related objectives are drawn. Furthermore, we reason about our choice regarding tools and methodologies.
Mlanet: Multilevel Aggregation Network for Binocular Eye-Fixation Prediction

Saliency prediction is an underexplored but fundamental task in computer vision, especially for binocular images. For binocular images, saliency prediction aims to find the most visually distinctive parts to imitate the operation of the human visual system. Existing saliency prediction models do not utilize the extracted multilevel features adequately. In this paper, we introduce a novel saliency prediction framework called multilevel aggregation network (MLANet) to explicitly model the multilevel features of binocular images. We split the multilevel features into shallow and deep features using the ResNet-34 backbone. First, we introduce the aggregation module to integrate adjacent shallow and deep features. Thereafter, we utilize shallow aggregation and multiscale-aware modules to locate objects of different sizes with features from adjacent levels. For better reconstruction, we integrate deep- and shallow-level features with the help of feature guidance and deploy dual-attention modules to select the discriminative enhanced characteristics. Experimental results on two benchmark datasets, NCTU and S3D, for binocular eye-fixation prediction show that the proposed MLANet outperforms the other compared methods.
A Popularity Prediction Framework for Online Streaming Videos Using User Engagement and Implicit Feedback

Streaming content can engage people from all walks of life and in recent years people tend to spend a significant amount of time interacting with videos, television shows, and podcasts. User engagement plays a vital role when dealing with such content on online streaming platforms. However, quantifying user engagement with the streaming content is a challenging task. Towards this, there are different parameters through which user engagement can be represented, such as the number of views, likes, dislikes, and the number of comments. In general, the popularity or user engagement of a video can not be directly established using simple indices such as view count. Herein, we have combined implicit and explicit feedback (and call this metric ranking score) from viewers to deduce the popularity of the video content. Further, a new metric: comparative watch percentage, is proposed which is computed by using views and watch duration during a specified period. Particularly, we propose a method for ranking score calculation, where we combine the results of implicit (watch duration and watch percentage, and relative watch percentage) and explicit (number of likes and dislikes) feedback. By using multiple linear regression we predict the ranking score for a video with a root mean squared error of 0.446. We further propose a search mechanism in which we identify similar videos based on title similarity with the user query. Our predicted ranking score is used for sorting the search results for video recommendation systems. Hence, through this search mechanism we attempt to solve the cold start problem in video recommendation systems. attempt to solve the cold start problem in video recommendation systems. attempt to solve the cold start problem in video recommendation systems. to solve the cold start problem in video recommendation systems.
Evaluación del uso de Wolfram Alpha en el Aprendizaje de Algebra Lineal en Teleducación

The use of ICTs in mathematics learning has the potential to improve academic performance in an interactive environment. Teleducation in COVID times demands the active use of technological tools, thus generating the need to determine which tools are suitable for the different branches of mathematics. This work evaluates the use of the Wolfram Alpha tool in the teaching of Linear Algebra in teleducation in COVID times. This tool was selected because of its ease of use, detailed answers and because it does not require software installation. The research was applied to second semester engineering students, dividing the students into a control group and an experimental group. Pre- and post-treatment test results were statistically analyzed to detect whether there was an increase in academic performance. Students using Wolfram Alpha completed a survey and interview to assess their perception of the tool. The experimental group performed better than the control group. The use of Wolfram Alpha was well accepted by the students suggesting its future use in Linear Algebra courses for tele-education and even face-to-face education in post-pandemic times.
Application of vectorized test artifacts

This chapter provides comprehensive guidelines for conducting several industrial case studies at various levels of software testing. Several pipelines are provided for employing artificial intelligence and machine learning techniques. A step-by-step guide is provided for empirical evaluation of the obtained results. Several industrial test optimization applications for the proposed solutions in this book are provided in this chapter. All the mentioned concepts in this book are exemplified by several industrial use cases in this chapter.
MABUSE: A margin optimization based feature subset selection algorithm using boosting principles

Feature subset selection is one of the most common procedures in machine learning tasks. In a broad sense, feature selection methods can be classified into three major groups, embedded, filter and wrapper methods. Although wrappers might attain superior classification performances, they suffer from scalability issues as they are more computationally expensive than the other methods. Filters are typically faster, and sometimes they are the only applicable methods when the datasets are large. In the field of classification, margin optimization has been proven to be an efficient approach for improving the generalization performance of many classification models. Although margins have been used as criteria for feature selection, in most cases, the most advanced methods are wrappers, which suffer from high computational costs and do not outperform the faster algorithms. In this paper, we propose MABUSE, which is a feature selection method that optimizes margins from a filter perspective. We consider a nearest-neighbor margin definition and, borrowing from the strategy of classifier ensemble construction using boosting, we develop a new method that uses a simple heuristic search. Extensive experimental validation demonstrates that our proposed approach outperforms the state-of-the-art algorithms in both classification and reduction, and has a computational cost that is similar to previous algorithms.
DASH Streaming traffic influence over energy efficient ethernet to improve energy savings

Dynamic Streaming over HTTP (DASH) is the main standard used in online video streaming services, given that more than 1.2 billion pay subscribers around the world use this standard. This fact entails billions of streaming connections between video servers and client displays. These devices involved in the streaming connection use an Ethernet Interface Card that consumes energy. In order to reduce the energy consumption, IEEE proposed the 802.3az Energy Efficient Ethernet standard, with a mechanism to make the network card change to a low power consumption mode when it is not in transmission mode. This behavior will be beneficial for services where traffic is sent in bursts, for instance video packet bursts like in video streaming over Real Time Transport Protocol in IPTV or the widely used DASH standard. Therefore, in this study the Ethernet traffic pattern when transmitting online video content using DASH is characterized in order to analyze the efficiency of the IEEE 802.3az standard under this video streaming scenario, and to verify the convenience of activating this energy saving alternative at the network interface of billions of client devices. The experiments have been conducted using a test-bed consisting of a full DASH streaming architecture, comparing different video segment sizes and changing the available bandwidth during the experiments in different scenarios in order to analyze the effect of the DASH content segment size on the Ethernet traffic pattern to identify the trade-off between energy efficiency, the energy savings, and the impact on the performance of the dynamic adaptation on the video streaming and reproduction.
The Comparing of Korean Learner’s Perception of Teaching Presence in Online and Offline Korean Language Classes

The purpose of this study is to improve the quality of online and offline Korean classes in order to increase the demand for Korean and to globalize Korean language education.&lt;br/&gt; In this study, online and offline Korean language classes were conducted for learners to compare the Korean learner’s perception of Teaching Presence. The participants of the study were 116 students from Korean language class who belonged to two different groups. One group studied in an online learning environment and the other group studied in an offline environment. Data analysis was completed using descriptive statistic analysis, correlation analysis, and hierarchical regression analysis with SPSS 21.0. In addition, in order to examine the mediating effects of the perception of Teaching Presence, the major findings from the study were as follows:&lt;br/&gt; The perception of Teaching Presence in the offline class proved to be more effective than in the online class. The perception of Teaching Presence is a sensation that people experience when they work with complete focus and has been validated in numerous studies as a critical component of learning. This study suggests that further development of specific teaching and learning methods should be developed in order to effectively conduct classes online. Also, in order to develop high-qualified online courses in Korean language education, we suggest the providing of training for Korean educators, and the development of software specializing in language education.
SIGURNOST ČUVANjA I PRENOSA PODATAKA I INFORMACIJA KOD ELEKTRONSKE TRGOVINE I ELEKTRONSKOG (INTERNET) BANKARSTVA

Securing transaction and providing secure storage and transmission of information at electronic commerce and electronic banking is a very complex and expensive process. They are based on the establishment of a protected system and a protocol that will be able to identify possible fraud and analyze the risk and loss that can occur. The protection system demands installation of a predefined security mechanism such as access controll, user authentication, encryption of information and establishing of security protocol. Each system which is collecting and storing data and information belongs to the group of threatened system and needs protection. Generally, each person, object or event, which can potentially leads to security breach, can be considered as threat. Such threat may be accidental (unwanted file deletion) or intentionall (malicious modification of sensitive data or hardware system). However, those threats must be identified and prevented, and if they have already succeeded, an appropriatte procedure should be taken for the disposal and minimization of damages.
Racism Toward Chinese Ethnic Group in Indonesian Social Media: Hate Speeches Analysis from Forensic Linguistic Perspective

Anti-Chinese sentiments are still deeply ingrained in the Indonesian public discourse. The issue of their identity and position triggers to many discriminatory actions toward them. The purpose of this study is to analyze hate speech against Chinese ethnic group expressed in Indonesian social media which has officially evaluated as a legal offense by the authorities. The theories of speech acts from Austin (1962) and Searle (1969) for a forensic linguistic study are employed to examine the hate speech as utterances and evaluate them in the context of Indonesian legal regulations. The data were obtained from Direktori Putusan Mahkamah Agung from 2019 - 2021. The results of the analysis reveal that hate speech portrays hurtful feelings and harmful attitudes. It is reflected by malice (expressive speech act), insult, or accuse (assertive speech act), persuasions so that others commit crimes (directive speech act), and crime plans in the future (commissive speech act). Then, there are ten forms of pejorative labels for the Chinese ethnic group. Race-based hate speeches expressed in the online media are prohibited according to Article 28 paragraph (2) of The ITE Law Article Number 19 of 2016. They are prejudiced action, violate human rights and cannot be considered as freedom of opinion and democracy because of their dangerous perlocutionary effects. The effects can provoke discriminatory action and even crime. Keywords: anti-Chinese, chinese ethnic group forensic linguistics, hate speech, racism.
Deep Reinforcement Learning Based Controller for Modified Claus Process

The modified Claus process is used to recover sulfur from acid gases containing high concentrations of H2S. The downstream process, a tail gas treatment system, is required to reduce atmospheric emission of sulfur compounds to the level required by air pollution control regulations. In this study, a deep reinforcement learning (DRL) based controller was developed to minimize the concentration variations of H2S and SO2 in the tail gas from the modified Claus process. In addition, the sequence to sequence (Seq2Seq) networks were trained by the plant data to capture the dynamic information between the manipulated and controlled variables. Thereafter, the optimal operating policy can be found through that the advantage actor-critic (A2C) algorithm was implemented to the DRL agent by interacting with the environment constructed by Seq2Seq. The results show that the variations of H2S and SO2 can be reduced 40 % and 36 %, respectively, compared with that of applying the traditional control strategy.
Practical Human Interface System for Transition Guidance in Chemical Plants using Reinforcement Learning

In chemical plants, transition operations, such as changing the production load from 100% to 80%, are commonly performed to satisfy production needs. As plant models used in conventional automatic control methods (e.g. step response models) cannot predict non-steady states, these transition operations warrant manual control. Previously, we proposed an automatic optimal control method using dynamic simulators and reinforcement learning, a machine learning method in artificial intelligence (AI), for transition operations. We implemented this existing AI system in an actual industrial plant and determined that further improvements were required in the interaction between the system and human operators for reliable and acceptable guidance. In this paper, we propose a human interface system for realising optimal transition operations by enabling AI to cooperate with human operators. To validate and authorise the AI-proposed manipulations performed by human operators, the interface system presented the entire procedure and sensors influencing the AI decision for online disturbance rejection prior to actual manipulations. The interface, coupled with the control method, was evaluated experimentally in an actual chemical plant. The proposed system demonstrated optimised transition operations for producing purity changes under abrupt heavy rain disturbance in terms of guidance.
Semantic Relations of Sub-models in an Enterprise Model

Enterprise modeling is a set of tools, methods and practices for an aligned development of business, functional, organizational and technical aspects of an enterprise. Therefore, an enterprise model is always a set of sub-models of different semantics. In order to form a consistent enterprise model, its sub-models should be aligned to each other. The practice of modeling shows the difficulties in design of an aligned set of sub-models of an enterprise model. In this paper we present a review of enterprise modeling approaches aiming to find the reasons of difficulties. Our review shows that enterprise modeling approaches not sufficiently use the semantic relations of sub-models for building an enterprise model. This paper identifies and formalizes the semantic relations of sub-models and suggests to use them as constraints directing the design of aligned sub-models. The constraints imposed by sub-models of the enterprise model to each other are illustrated with a case study in ArchiMate.
Belief, Knowledge and Possibility

By applying the extended system of modal logic developed in [32], we present a logical analysis of the Gettier problem. Based on the result, in particular, we clarify the following point. In the literature on the Gettier problem, most authors seem to share the opinion that in general ‘belief’ precedes ‘knowledge’: we believe a proposition while we do not necessarily believe ourselves to have known it to be true. Interestingly, our analysis suggests that the story is the other way round in a sense: in general, for some proposition p, we believe that we have known that p, then we believe that p. Accordingly, even if given some reason for wanting to believe that p, we usually do not have the bare belief that p. Instead, in such a situation, more deliberately we think that it might be that p, or it can be hypothesized that p. Then, we can say, what we have at the start is not the bare belief that p, but “might possibility” or “hypothetical possibility” that p, which presumably involves abductive reasoning.
Compression Models from Elementary Incremental Laws

Five compression laws were formulated using simple incremental relationships involving fundamental index properties of porous media such as porosity and void ratio. Models were presented in terms of void ratio-effective stress relationship and porosity-depth trends. All theoretical expressions were derived by assuming suitable incremental compression laws. Comparison with experimental data indicates that the models can describe a number of features of the behavior of sand and sedimentary rocks.
Ocean Modeling Analysis and Modeling Based on Deep Learning

The ocean comprises an uninterrupted body of salt water confined within a vast basin on the earth’s surface. The ocean is the largest ecosystem on earth with rich and diverse biological resources. Organisms that reside in salty water are referred to as “marine life.” Plants, animals, and microorganisms including archaea and bacteria are examples of these. The existence of marine life is not only a biological resource but also an economic source. Toys and other industries that imitate marine life have emerged in the market. A different modeling design of marine life has improved with the passage of time and the concept of modeling aesthetics has been incorporated. The identification of marine life images is challenging due to the complexity of the maritime environment, and there are several flaws in marine life models. The rise of deep learning has brought some new ideas for the weaknesses in marine life modeling, and the advantages of convolutional neural networks have contributed to some of the concepts based on deep learning. This research analyses marine modeling by using the benefits of convolutional neural networks, so that people can better understand marine life modeling. The experimental results indicate that the proposed approach has achieved good results in marine life detection, and the modeling effect of ocean modeling analysis based on deep learning is good.
Research on Enterprise Cooperation Management Strategy Analysis System Based on Knowledge Transfer Model

The cooperative management strategy plays an important role in the development direction of enterprises. However, there are still many problems in the cooperation management strategy, the long-term development strategy is lacking in the management, which seriously hindered the development of enterprises. Knowledge transfer is an important research field and practice field of knowledge management, the qualitative analysis of knowledge transfer can be applied to the analysis of enterprise cooperation management strategy, which can solve the problems of enterprise management strategy analysis more effectively. In this paper, an enterprise cooperation management strategy analysis system based on the knowledge transfer model is built, in which the EAI algorithm is applied to the analysis of enterprise cooperation management strategy. Finally, the performance of the system is tested in many ways, the results show that the EAI algorithm can not only reduce the computational complexity but also can reduce the degradation phenomenon of the original evolutionary algorithm. The system can withstand the access of a large number of users at the same time, which can meet the needs of users.