Reviewerly/exHarmony huggingface dataset:
The Overlooked Benefits of 'Hate Speech': Not Just the Lesser of Two Evils

Many free-speech advocates view “hate speech” as deserving protection only because it is the lesser of two evils. This is unfortunate because the benefits of “hate speech” provide a valuable arrow in the quiver of free-speech advocacy. This essay provides a summary of the more commonly presented harms of censoring “hate speech,” the often overlooked benefits of allowing it, and an analysis of the strategic considerations for emphasizing the benefits.
Radar Signal Sorting in Small Sample Environment

Aiming at the problem of low accuracy of radar signal sorting in a small sample environment, a sample expansion method based on SMOTE algorithm is proposed. This method uses the Euclidean distance principle for data expansion, so that each sample is synthesized into a new sample, and the impact of the data imbalance on the classifier is reduced in terms of data, thereby improving the accuracy of radar signal sorting. Experimental results show that sample expansion based on SMOTE algorithm can effectively improve the accuracy of radar signal.
Constraint-Free Discretized Manifolds for Robotic Path Planning

Robotic path planning must avoid obstacles or singularities, and to check for such constraints periodically is computation and time intensive. This paper introduces Constraint-free Discretized Manifolds for robotic Path planning (CDMP) to formulate a constraint free space in the configuration and work spaces. Application based necessities such as obstacle and path visualization, or computational ease guides the choice of working in either space. The chosen constraint free manifold is then meshed using DistMesh, for use with path planning algorithms, which in this paper is A*. The merits offered by this solution are two fold- first, the formulated constraint free manifold is guaranteed to be singularity free irrespective of the start and end locations; second, the path chosen on this manifold will be the shortest path by virtue of using A*.
A Privacy Preserving System for Protecting Cloud Based Health Care Records Using Fine Grade Access Techniques

Abstract Protection of privacy has also received increasing attention in many fields, mainly in the sense of the Individual Health Record (IHR), as cloud computing has become an essential activity in the real world. Traditional encryption of the cypher text policy (CP-ABE) allows you to track the scrambled PHR information policy in a fine-grained fashion. Still, with the cypher text, the access policy is also sent. However, the entry policy does not allow users to be anonymous, as a considerable amount of sensitive information from legitimate users of the app is needed. It is now necessary to protect users [[CHECK_SINGLEQUOT_ENT]] privacy by hiding access methods. The vast majority of previous plans, however, have to address two problems: the first problem is that specific plans do not embrace a broad universe of attributes, so their common sense in PHR is dramatically reduced, and the second is that the decryption cost is very high because the entry mechanism is set up in the cypher paper. This dramatically reduces their usefulness as a result. To solve these issues, we are constructing a CP-ABE plot with sufficient decryption, where together the size of the available limits and the cost of decryption is endless. Also, using the dual system encoding technique, we show that the proposed conspiracies achieve optimum protection in the basic model.
A Drift Detection Method Based on Diversity Measure and McDiarmid’s Inequality in Data Streams

Mining data stream is an important research topic due to the development of network. The distribution of data in a dynamic environment changes over time, which is a challenging problem called concept drift. Concept drift need to be detected and handled as soon as possible because it can make previous models inaccurate. In this work, we propose a method, called Diversity Measure and McDiarmid Drift Detection Method (dmm-DDM), which combines diversity measure and inequality to detect drift detection. The dmm-DDM approach proceeds by sliding a window over diversity measure result, and associate window value with weights. The algorithm compares the weighted average inside the sliding window with the maximum weighted average. A huge difference between the two weighted averages, calculated by McDiarmin’s inequality, proves a concept drift. The experiment results show that the proposed method with less detection delay, less false position and less false negative than most of the compared methods.
MODELS OF TEACHERS TRAINING FUTURE TO ORGANIZE EXTRACURRICULAR ACTIVITIES

The process of socio-cultural modernization of the general education system primarily determined the structural and substantive development of the educational process of modern schools in the Russian Federation. One of these changes is the consolidation of extracurricular activities as a mandatory component of the main educational programs of general education. The process of staffing the implementation of extracurricular activities of schoolchildren should be based on the experience of preparing future teachers for this type of activity that has developed in the history of education. A periodization of the stages of the formation of the preparation of future teachers for the organization of extracurricular activities of schoolchildren in Russia is proposed: 1) the stage of the emergence of the system of training future teachers for the organization of extracurricular work, the features of which are associated with the formation of a system of training future teachers for the organization of extracurricular activities; 2) the stage of formation of the Soviet system of training future teachers for the organization of extracurricular, extracurricular activities, whose originality was due to the tendency towards the formation of a state model of extracurricular work with children; 3) the stage of evolution of the Soviet system of training students of pedagogical universities for the organization of extracurricular activities, associated with the formation of a practice-oriented approach; 4) the stage of systemic and structural transformations in the preparation of future teachers for the organization of extracurricular work, characterized by the focus of the system of training future teachers in the Russian Federation on the design and organization of the educational process; 5) the stage of modernization of the system of psychological and pedagogical training of students for the organization of extracurricular activities, aimed at the formation of a competence-based approach to the preparation of students of pedagogical universities in the Russian Federation; 6) the stage of standardization of the process of preparing future teachers for the organization of extracurricular activities, which is implemented as part of the formation of modern educational models.
Plagiarism and Forgery

The study of literary deception – which the US Library of Congress calls “literary forgeries and mystifications” in its catalog of subject headings – has plenty of attractions. We can treat the thief or the faker as an embodiment of the mythological trickster figure wreaking havoc on a staid establishment, or the crafty pícaro mischievously showing up his betters. Literary malefactors are adored for their chutzpah in embarrassing the self-important and revealing they are gullible fools and knaves.
How to Test the Extract Method Refactoring

Extract Method is probably the most commonly used refactoring. Although it may seem quite simple at a first glance, there are many cases that it can become fairly complex. Local variables and parameters can make this refactoring difficult to implement. The whole procedure can also become quite error-prone. The only way to be sure that we performed the refactoring without introducing bugs is by means of testing. Testing, however, seems to many application developers a very bothersome procedure. To facilitate their work we discuss patterns that customize existing testing techniques to the specificities of the Extract Method refactoring.
Multi-threshold Object Segmentation Algorithm on Low-contrast and Noisy Biomedical Images

Object detection and segmentation is an important direction in biological image processing. Traditional thresholding and labeling methods as well as machine learning methods are the two predominant ways to solve this problem. In this article, a multi-threshold algorithm doing cell segmentation is developed and applied on Fluo-N2DH-GOWT1 dataset[1-2], which contains low-contrast and noisy biomedical images. Besides, U-net is also utilized and the results of U-net as well as multi-threshold algorithm are compared to better illustrate the distinctions of the two algorithms.
Application of Modern Computer Information Technology in “Uyghur Language” Teaching

With the rapid development of computer teaching technology, it can not only improve the efficiency of classroom teaching and enhance the interaction between teachers and students, but also effectively activate the classroom atmosphere and increase students’ interest in learning. As an important development direction of computer technology in the future, its application prospects in classroom teaching are very broad. By enabling knowledge to be presented in front of students more three-dimensionally, creating a more realistic classroom teaching atmosphere for students, achieving visual and auditory double stimulation, so that students’ learning initiative and learning effects are comprehensively enhanced. This article analyzes the application in computer teaching, and then discusses computer-assisted teaching and cultivating students’ innovative thinking ability.
APPROACH TO MASS MEDIA NEWS INFORMATION WORKFLOW ANALYSIS IN VIEW OF ITS EMOTIONAL CHARACTERISTICS

Research of information environment features is a vital element of information and analytical activity that makes possible to form the situation view more complete and precise. It is an important benchmark to react events information. Development of approaches to the information workflow processing concerns different aspects. The approach to mass media news information workflow processing with regard to its emotional component analysis and its intensity measurement is presented in this article. The elements which form emotional characteristics of news texts are also included. The nature of mass media news is emotionally saturated, so it is important to identify the factors which pressurize emotional state, notice relative phrases and evaluate them in a proper way. Therefore, proper translation of each phrase is critical due to the presence of idioms next to social and political lexis in mass media, which are used by news texts authors to impart a message a brighter character. With this in view these phrases demand a proper translation. The name for lingual units that transmit emotions and emotional states is concepts. Each language disposes its own concepts system related inseparably to the national culture of the native speaker’s language. Concepts which transmit certain emotions in the text form its emotional coloring. It can be evaluated by quantitative and qualitative indexes by appropriation of respective weighting ratios to particular emotional expressions. These expressions are transmitted with conventional phrases or idioms. It is advisable to form basic vocabulary of the text corpora with corresponding characters set which is a part of emotional coloring rate assessment procedure. The intensity of information impact is assessed by the most significant criteria, which form news nature. Outcome is graphically performed at the user's display. This approach application enables examination of different aspects of mass media information stream and makes possible to estimate it by various characteristics. In this way the presented application extends practical opportunities of news information workflow processing with the view of national security interests.
Human Face Feature Extraction Based on a Partition Threshold Model

Image feature extraction technology has been applied to all aspects of our lives, such as science, education, aerospace, national defence, and industrial production. For example, a painting robot requires a face extraction method that meets human aesthetic standards. This paper mainly aims to solve the problem of conditional binary extraction of the human face against changeable and complex backgrounds, and under different lighting environments, in order to achieve better human face image feature extraction and higher image processing speeds - the average time to process one image is 3.3 seconds. Different from the traditional partition method, which only uses the pixel value information, our method combines a skin colour model, the edge structure of the face, and pixel information in the partition. The partition threshold model proposed in this paper is used to calculate the appropriate threshold value in each partition, so that each image partition gets the corresponding threshold value. Experimental results demonstrate the robustness of the scheme.
Enabling Text Translation Using the Suggestion Bar of a Virtual Keyboard

This work augments novel text translation features to the suggestion bar of a virtual keyboard to facilitate fast and easy translation on mobile devices. The method was evaluated in two user studies. In the first study, native Hindi and Mandarin speakers exchanged text messages in each other's language using the method. All participants found the method fast and easy, the quality and the flow of the conversation satisfactory, and wanted to use it frequently for multilingual and polyglot texting. In the second study, participants performed various translation tasks using the proposed method and the default Google keyboard's translation feature. Results revealed that the proposed method was significantly faster and required fewer actions for translation tasks. Further, most participants found the method more effective and user-friendly.
Syntactic complexity development in college students’ essay writing based on AWE

Syntactic complexity is considered to be an important device for assessing the quality of writing in a second language (L2), as it indicates the diversity and complexity of production units or grammatical structures. This paper studies the development of Chinese college students’ syntactic complexity in essay writing by using an Automatic Writing Evaluation (AWE) tool, the Pigai system (www.pigai.org, which has been most widely used in China in the last ten years). The data analysis showed that the students’ syntactic competences in their final drafts outperformed that in their first drafts in three aspects: length of production unit, amount of subordination, and amount of coordination.
Chaotic particle swarm optimization using a rotation transformation based on two best solutions

In this paper, we discuss the particle swarm optimization method (PSO) for global optimization, especially, a PSO using a perturbation-based chaotic updating system called PSO-SDPC. In this method, it is easy to select appropriate parameter values for effective search, and numerical experiments showed its good search ability. However, the search of the PSO-SDPC is not rotation-invariant because the perturbation terms of the chaotic updating system are added along the coordinate system of the standard basis, and the component-wise selection from the chaotic and the standard PSO updating systems for a particle's position deeply depends on the coordinate system. Therefore, in this paper, we improve the PSO-SDPC: the perturbations are added along a new coordinate system that is selected according to two best solutions, and all components of each particle's position are updated by the same system, which is selected from the two updating systems. Moreover, we show that the proposed method can be regarded as the rotation-invariant and keeps a high search ability for many problems through numerical experiments.
Active Object Searching Based on Deep Reinforcement Learning

Recently, computer vision has progressed a lot and object detection by robot has drawn interests of many researchers. By combining artificial intelligence and traditional industry together, computer vision and object detection has gained remarkable achievements. Though robot can detect the still complete images quite efficiently with a considerable accuracy, yet the active object detection and searching is a relatively new field. Since a robot is able to adjust its position to get a better view of the images, a good policy of the action selection should be payed attention to. In this paper, several DQN-like algorithms are introduced and compared. Then in a bid to weaken the estimation bias of them, a Maxmin DQN algorithm is implemented on a household dataset. After comparing the performance of these algorithms, the Maxmin DQN proves to be the best on average.
Lev Petrazhitsky and autodidactics at high school

We considered the formation and development of autodidactics in the Russian higher school at the beginning of the 20th century during the reform of the educational process, which introduced a subject system of education instead of the previously existing course. We revealed the role of L.I. Petrazhitsky in the development and implementation of the concept of autodidactics. We paid special attention to the organization of the educational process in autonomous universities. In humanitarian education, the personalist-axiological model of higher education is most effective. The traditional axiology of education determined the goals and methods of knowing scientific truth, while immersing students in the process of scientific research contributes to the development of their creative personalities. The need to be adequate to the level of achievements of the evolution of science involves the constant updating of the didactic complex: goals, content, forms and methods of learning. However, the customer of educational services forms a quasi-university focused on obtaining socio-cultural skills supposedly useful for the consumer of educational services. Such a model leads to the transformation of the scientific axiology of higher education and focuses on the criteria of utility and competence, which requires a different qualification, a different intellectual potential. There is a conflict of ambitendent innovative processes: autonomy opposes the authoritarian trend in education, while the higher school is technologizing the educational process aimed at forming narrow specialists.
Relative speed of writing

Writers can have many levels of writing skills and relative speed of writing at different levels. This enables them to write not only in their usual style of writing but also slower or more rapidly and at many different degrees of slant while writing easily and incorporating characteristics of another person's writing in their act of writing. Relative speed and writing skill are important factors for consideration.
Lightless Fields: Enhancement and Denoising of Light-Deficient Light Fields

Modern focused light field cameras are capable of capturing video at over 160 frames per second, but in so doing sacrifice shutter speed. Outside of laboratory environments, lighting can be problematic resulting in noisy light fields and poor depth reconstruction. To enhance and denoise modern focused light field cameras, we create a unique deep neural network that allows for the full light field to be processed at once, eliminates stitching artifacts, and takes advantage of feature redundancy between neighboring microlenses. We show that our double U-Net network, ENH-W, significantly outperforms several popular architectures and light field denoising methods in both visual and depth metrics.
A Study on Various Applications of Data Mining and Supervised Learning Techniques in Business Fraud Detection

Data mining technique helps us to extract useful data from a large dataset of any raw data. It is used to analyse and identify data patterns and to find anomalies and correlations within dataset to predict outcomes. Using a broad range of techniques, we can use this information to improve customer relationships and reduce risks. Data mining and supervised learning have applications in multiple fields of science and research. Machine learning looks at patterns of data and helps to predict future behaviour by learning from the patterns. Data mining is normally used as a source of information on which machine learning can be applied to solve some of problems in our daily life. Supervised learning is one type of machine learning method which uses labelled data consisting of input along with the label of inputs and generates one learned model (or classifier for classification type work) which can be used to label unknown data. Financial accounting fraud detection has become an emerging topic in the field of academic, research and industries.
A Random Forest Model for Personalized Learning in a Narrative Game

Her research interests include virtual reality and augmented reality, artificial intelligence, and modeling and scheduling of computer-integrated systems.Dr. Tang is very active in adapting and developing pedagogical methods and materials to enhance engineering education.Her most recent educational research includes the collaboration with Tennessee State University and local high schools to infuse cyberinfrastructure learning experience into the pre-engineering and technology-based classrooms, the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving, the integration of system-on-chip concepts across two
The role of industry-academia collaborative research in mineral exploration

ABSTRACT Many people may not be aware of the extent of Kurt Kyser's collaboration with mineral exploration companies through applied research and the development of innovative exploration technologies, starting at the University of Saskatchewan and continuing through the Queen's Facility for Isotope Research. Applied collaborative, geoscientific, industry-academia research and development programs can yield technological innovations that can improve the mineral exploration discovery rates of economic mineral deposits. Alliances between exploration geoscientists and geoscientific researchers can benefit both parties, contributing to the pure and applied geoscientific knowledge base and the development of innovations in mineral exploration technology. Through a collaboration that spanned over three decades, we gained insight into the potential for economic uranium deposits around the world in Canada, Australia, USA, Finland, Russia, Gabon, Namibia, Botswana, South Africa, and Guyana. Kurt, his research team, postdoctoral fellows, and students developed technological innovations related to holistic basin analysis for economic mineral potential, isotopes in mineral exploration, and biogeochemical exploration, among others. In this paper, the business of mineral exploration is briefly described, and some examples of industry-academic collaboration innovations brought forward through Kurt's research are identified. Kurt was a masterful and capable knowledge broker, which is a key criterion for bringing new technologies to application—a grand, curious, credible, patient, and attentive communicator—whether talking about science, business, or life and with first ministers, senior technocrats, peers, board members, first nation peoples, exploration geologists, investors, students, citizens, or friends.