Reviewerly/exHarmony huggingface dataset:
Prediction of Investment Realization Value Using Support Vector Regression (SVR) Methods

Investment realization is one of the factors that support economic growth. Increasing investment realization has a substantially positive impact, including reducing the number of unemployed, reducing poor people, and increasing state income. For this reason, it is essential to have a forecasting method to predict the value of the realizable investment that can be used as a reference to decide on making a policy to increase investment realization value. Because the data pattern of the realizable value of the Investment in Magelang City, Central Java Province, has a nonlinear data pattern, so it needs a forecasting method to overcome this problem, which in this research uses the SVR method. The SVR method uses the RBF kernel function to overcome the nonlinear data patterns and uses the grid search method to obtain the optimal model parameters. Prediction results show the use of Polynomial kernel and RBF kernel has an accurate level of accuracy with MAPE values for RBF kernels of 7.24% and Polynomial kernels of 8.17%.
Load Balancing in Specific IOT Systems

Load balancing is done to distribute network traffic across different servers efficiently using specific techniques, ensuring maximum speed, resource utilization and preventing overloading of any particular server. As in case of Data Streaming IOT systems, we can determine the frequency of traffic coming to our servers beforehand, static algorithms are good for such systems but they suffer from few drawbacks like uneven distribution of load among servers and requirement of dedicated hardware for load balancing. Here we have proposed a new hybrid approach for balancing the load on IOT Applications where we are already aware of the incoming traffic to our system from a specific device in a specified interval. We have found that static algorithms work better than dynamic algorithms for certain applications, so we have tried to combine features of dynamic load balancing with static to achieve optimum performance on various parameters. We propose this approach because it is easy to implement and its performance is very comparable to dynamic algorithms considering desirable features of both have been used to create the algorithm and minimize the drawbacks.
Self-Protection Approach for Cloud Computing

In the preceding years, there has been speedy growth in Cloud Computing. The volume of cloud storage is also increasing very fast, due to which security concerns are also gathering significant attention, including data protection, application integrity, network security, virtualization security, and identity management. Data protection is one of the most remarkable security issues since associations will not move their information to remote machines if there is no ensured data protection from the cloud service providers. To secure the data of the organization’s or customers on cloud storage lots of techniques has been used but it is still in threat. However, the Ministry of Electronics and Information Technology (MeitY) has declared a policy to provide strategic direction for acceptance of cloud services by the Government is “MeghRaj Policy.” It provides some legal guidelines for the procurement of cloud services. These issues such as privacy, network security, and so on, will give rise to cybercrime. In India, there is no devoted regulatory framework, lack of privacy laws, inadequate data security, and so on, to maintain the same. The author will discuss (1) concept of cloud security; (2) different types of security available in the cloud; (3) issues; (4) challenges; (5) guidelines provided by the government to cloud service providers; (6) concept security techniques and types; (7) applicability of Indian cyberlaw; and (8) conclusion.
Application of XML and TCP communication on Dynamic customization of the smart home system

Abstract Aiming at the problem of personalized customization of a smart home system, a scheme of personalized customization of the smart home system based on parsing XML authorization files is proposed. First, develop an XML authorization file generator, and use it to generate personalized XML authorization files; secondly, use the developed app to parse the XML authorization file to realize the legal authorization of the app and the dynamic generation of the home control interface; finally, click buttons on the control interface to trigger the TCP communication between the app and ESP8266, and then with the help of esp8266 to realize the switch of electrical appliances. Experimental results show that dynamic customization of the smart home system based on XML can implement dynamic customization of control interfaces for various home layouts, and can effectively control the home furnishings.
An Extended HTN Language for Onboard Planning and Acting Applied to a Goal-Based Autonomous Satellite

Nowadays, there is an increasing demand to design space systems with a higher level of onboard autonomy. One way to achieve this is to equip the satellite flight software with an autonomous decision system, such as model-based planning techniques. This article addresses an extended Hierarchical Task Network (HTN) description language for satellite onboard mission planning and acting. Our proposal seeks a lightweight and practical approach to model the space system and at the same time meets the current demands (scalability, intelligibility, and performance) for an autonomous satellite. To promote a clear understanding of the proposed language, we provide a grammar for the hierarchical domain and onboard problem representation. A realistic case study focused on a Brazilian remote-sensing satellite illustrates the effectiveness and practicability of our language. An onboard HTN planning approach is also implemented to verify in practice the usability of the model. We carry out experimental scenarios on a typical setup of satellite onboard computer using a space-qualified processor. The results obtained show that our proposal could overcome the main setbacks of the temporal planning model, which is the prevailing approach in the space domain until today.
Does face restoration improve face verification?

Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases.
Free-space confocal magneto-optical spectroscopies at milliKelvin temperatures

We describe the operation of a free-space confocal optical microscope operated in a dilution refrigerator. The microscope is installed on a cold insertable probe to enable fast sample exchange while the refrigerator is held at low temperatures. A vector magnet provides a 6 T field normal to the sample and 1 T fields at arbitrary angles. A variety of optical microscopies and spectroscopies, including photoluminescence, Raman, magneto-optical Kerr effect, and spin relaxometry measurements are described, and some of the challenges associated with performing these measurements at milliKelvin temperatures are explored.
Research on Time-frequency Feature Fusion Method based on Dynamic Gesture Identity Authentication

In this paper, three kinds of time-frequency feature fusion methods based on dynamic gesture sensor are analyzed. First, the time-domain feature of sensor data is extracted; second, the cepstrum feature, the power spectrum feature of Fourier Transform (FT) and the wavelet transform (WT) feature of gesture data are extracted and trained using SVM classifier model. The influence of feature extraction methods based on the fusion of time-domain features and three frequency-domain features on dynamic gesture authentication is compared, the experimental results show that the fusion of time domain features and frequency domain features of wavelet transform with fewer feature points has the best effect, which improves the accuracy of gesture authentication, So the method is worth promoting.
Selection of temperature-induced fringe displacements to evaluate stress field with the thermal transient stepping photoelasticity method

To simplify the implementation of photoelasticity studies, the recently introduced Thermal Transient Stepping (TTS) method produces a stress field, from images with fringe displacements induced by temperature. These images are acquired without using mechanically-induced load variations, nor rotating optical devices. However, TTS produces stress fields with unwrapping errors, due to the lack of a strategy to select adequately the fringe displacements. We addressed this limitation by evaluating different thermal stimulations, and their effects in the performance of TTS. This allows us to achieve stress fields with higher fringe orders.
Detecting Number of Passengers in a Moving Vehicle with Publicly Available Data

Detecting and counting people inside vehicles with little to no human input has many applications today. From assisting rescue authorities to smart transportation systems; from automatic crash response to law enforcement of High Occupancy Vehicle and High Occupancy Toll lanes. We propose a framework for counting passengers in moving vehicles based on various image features from various objects and contextual information. Each feature can be computed with state-of-the-art techniques, like Fisher Vectors, before being consolidated for a final detection score. Images from publicly available surveillance road cameras were taken to create a real-world data set, before training the convolutional neural network YOLOv3. Preliminary results show good prospective for this approach with the potential for improvements in each object detected in scene, thus improving the overall results. Future work can explore image enhancement with generative adversarial networks.
Chesskell: a two-player game at the type level

Extensions to Haskell's type system, as implemented in GHC, have given developers more tools to express the domain-specific rules and invariants of their programs in types. For these extensions to see mainstream adoption, their use in complex applications has to be practical. We present Chesskell, an EDSL for describing Chess games where a type-level model of the full FIDE ruleset prevents us from expressing games with invalid moves. Our work highlights current limitations when using GHC to express such complex rules due to the resulting memory usage and compile times, which we report on. We further present some approaches for working around those limitations.
전시기 일본 국정 국어교과서 제재의 특징 분석 - 초국가주의ㆍ군국주의 사상의 침윤 과정을 중심으로 -

This study was directed to study how the ultra-national and militaristic ideologies demanded of the unusual wartime in Japan were carried out in Japanese government-published reading textbook used for the compulsory education state. To this end, the process of reorganizing the topics used in reading textbooks and introducing new topics was examined, and the guidelines of the teacher's guidebook were analyzed with focus. To bring the banner for Japan-first ideology to realize Five Races under One Union and to guide the Greater East Asia, Japan used materials such as Japanese, Mount Fuji, and Japanese flag and tried to shape people’s perceptions that Greater East Asia was the center of the world and Japan guided Greater East Asia. It also tried to enable children to acquire the spirit of national defense by reflecting the times of the war. For the purpose, imitative plays like military promenades or military games were performed by connecting children’s activity to life, and children were performed the role that the nation needed and guided to take their sacrifices through scientific, industrial, and cultural topics with military and nationalism elements melted naturally. In other words, Japanese reading textbook based on ultranationalism and militarism ideals in wartime slowly infiltrated children by utilizing the topics the needs of the times. It was designed systematically by politicians, military personnel, educators, and children’s writers as emerged in the textbook compilation process. Ultimately, the Japanese reading textbooks in wartime was used as a useful tool to support and to promote the ideological system of the Greater East Asia.
Concepts, Potentials, and Requirements

Understanding the embodiment of robotic devices and using this knowledge to improve human-robot interaction touches a variety of open research questions. A considerable body of research outlines the complexity and plasticity of human bodily experience. When examining robotic devices, which can be seen as “intelligent” tools, this challenge is getting even tougher due to the interaction of the involved agents. Beyond this, recent studies point out how the investigation of psychological fundamentals can benefit from using robotic devices for human-in-the-loop evaluation in return. This chapter conveys and discusses fundamental concepts and terminology of human body experience, aiming at accessibility for all concerned disciplines. Concepts of different body representations and the presence in virtual environments are presented along with fundamentals of human motor control and haptic perception. Based on those, perspective potentials in human-robot interaction are outlined with respect to control, sensing, feedback, and assessment. To support the design and application of human-in-the-loop approaches in fundamental research and engineering, the related literature is analyzed to determine and assess crucial design requirements.
Short-term Photovoltaic Plant Output Prediction based on the Case-based Reasoning

Establish an accurate prediction model of photovoltaic power generation output is of great significance for the planning and operation of grid-connected PV systems. Due to the low predicting capability of current meteorological information, the introduction of meteorological data will limit improvement in prediction accuracy. To solve this problem, this paper proposes a short-term photovoltaic plant output prediction model based on the Case-Based Reasoning (CBR). Take the actual power output data of the PV power plant in Golmud area as the basic data to make simulation. The results show that compared with the traditional fuzzy grey prediction model, the prediction model proposed in this paper have higher prediction accuracy and prediction efficiency.
PREPARATION OF FUTURE TEACHERS OF NATURAL SCIENCES FOR RESEARCH ACTIVITIES

The article describes the scientific basis for the formation of research competence of future teachers of natural sciences, substantiates the need for research to understand the essence of social, economic, and environmental phenomena, innovative solutions to professional problems. It is substantiated that research activity in a higher pedagogical educational institution is an essential objective condition for the holistic professional competence formation of a science teacher, which ensures the development of the research competence as an important quality of a modern specialist.&#x0D; The research of Ukrainian scientists on the formation of research competence of future teachers is analyzed, and the requirements of the National Qualifications Framework are highlighted, on the basis of which the category ‘research competence of future teachers of natural sciences’ is defined as an integrative quality of personality that characterizes their readiness to solve research (search, design, consulting, organizational and managerial) tasks by applying methods of scientific knowledge, application of the diagnostic approach in educational, scientific, natural and managerial activities and which is expressed in the unity of value-motivational, cognitive and operational components.&#x0D; It is proposed to understand the process of systematic accumulation of positive quantitative and qualitative changes under the formation of research competence of future teachers of natural sciences, which allow applicants for higher education to effectively carry out educational, natural science research. The peculiarities of preparation of future teachers for research activities are considered, taking into account the following principles: scientific, systematic, a connection of theory with practice, continuity of the educational process, humanism and provide two main interrelated areas: teaching elements of research work, formation of research experience; actual research, which is conducted under the guidance of teachers. The system of preparation for research activities of future teachers of natural sciences is characterized, which is characterized by openness, dynamism, manageability, flexibility and provides three main components: theoretical, organizational, and methodological, and practical.
An Adaptive Differential Evolution Algorithm Utilizing Failure Information and Success Information

Differential Evolution (DE) has been successfully applied to a variety of optimization problems. The performance of DE is affected by two algorithm parameters of the scaling factor and the crossover rate. Much research has been done in order to adaptively control the parameters. One of the most successful studies on adaptive parameter control is JADE, where the two parameter values are generated according to two probability distributions which are tuned by the parameter values in success cases. In this paper, we propose a new method that utilizes not only success information but also failure information. A measure, which indicates how much the pair of generated parameter values will lead to failure, is defined. The pair is rejected probabilistically according to the measure and a new pair is regenerated until a good pair is generated. The effect of the proposed method is shown by solving various benchmark problems.
<i>A&amp;I</i>, a Suite for Piano, Android Device and Vibration Speaker

Abstract As part of an ongoing work with speakers that use uncommon technologies to project sound, the author considers here the vibration speaker, a kind of transducer that coaxes a surface to vibrate by direct contact. Presenting pros and cons of vibration speakers, the author illustrates a piano suite that uses an application expressly built for Android as an interface between the sound produced in, on, or with the piano and the speaker: The sound is captured by the Android device, processed by the application, sent via Bluetooth to the speaker sitting on the piano soundboard, and eventually thereby amplified. This relatively simple system allows for a large array of timbral possibilities and unconventional ways of playing the piano.
A Combined Feature Selection Techniques and Distance Measures based Approach for Author Profiling

Author Profiling is a task of determining the demographic characteristics like Gender, Age, location, Nativity language, Personality traits etc. about the author of a document. Author profiling (AP) is used in various applications such as marketing, forensic science, security and linguistic profile. The researchers proposed different types of solutions to author profiling based on stylistic features, content based features and deep learning techniques. The content based features proved their significance to improve the performance of author profiles prediction. Several approaches faced a problem of high dimensionality of features when experimented with content based features. In this work, the experiment conducted with various feature selection techniques to avoid the high dimensionality problem as well as determining the most relevant features to distinguish the writing styles of the authors. Different feature selection techniques like information gain, gini index, chi square, mutual information and relative discriminative criterion are used in this work to identify relevant features and remove redundant features. The documents are represented as vectors by using identified features and forwarded to different machine learning algorithms. Various machine learning algorithms such as support vector machine, naive bayes, decision tree, random forest, k-nearest neighbor and logistic regression are used to evaluate the proposed feature selection technique based approach. The experiment continued with six distance measures such as Euclidean Distance, Manhattan Distance, Minkowski Distance, Cosine, Jaccard and Dice to compute the similarity between training and test documents. The PAN competition 2014 reviews dataset is used for gender and age prediction of the author. The experimental results attained in this work are good when compared with several popular solutions to author profiling.
Analysis of Autonomous Learning Ability of English Learners Based on Online Teaching Modes

With the rapid development of information technology in recent years, learning and teaching approaches are facing profound changes. Online teaching is a kind of teaching means that makes use of network means and resources to make teachers and students can realize remote communication and achieve teaching effect. It has the advantages of abundant teaching resources, flexible forms and breaking through the limitations of time and space which is becoming a widely used teaching means in the educational circle. More and more people are using Tencent Classroom, Dingtalk, Xuexitong et al. and other relatively perfect network teaching platform to achieve network teaching. This paper investigated in vocational college English learners' autonomous learning ability, found that autonomous learning abilities have a direct impact on online learning effect, and vocational colleges English learners' autonomous learning ability is in a medium level, or it can be understood as a medium level is bottleneck in the development of their autonomous learning ability. The main reason for this result is that learners' ability to use English learning strategies is relatively weak. This paper also puts forward some countermeasures in order to improve learners' autonomous learning ability. Meanwhile, suggestions are put forward for English teachers in vocational colleges on how to cultivate students' autonomous learning ability.