Reviewerly/exHarmony huggingface dataset:
SUBSTITUTION CHARACTERISTICS OF FACTORIAL SETS AND CRITERIA FOR CHOOSING THE SINGLE SUBSTITUTIONS

The substitution and permutation function, which are presented in the article as elements of a number of factorial sets, are the key functions of cryptographic systems that provide diffusion and mixing of information. A new scale of notation is proposed while analyzing this problem. This is the notation scale of a number of factorial sets. This scale of notation helps to index the elements of a number of factorial sets and establish a one-to-one correspondence between the number and a specific type of substitution. This allows analyzing substitutions characteristics systematically. This paper presents the basic concepts of a number of the factorial sets. It is noted that the permutations of the factorial sets form symmetric permutation groups, and specific permutations (when raised to a power) form cyclic groups. The group axioms are fulfilled for the permutations of a number of factorial sets. Also, the definition domain, the group operation of multiplication, and identical and inverse substitutions are given for them. The number of independent cycles, decrement, inverse, parity and sign are common characteristics of the substitutions of a number of factorial sets. The criteria for choosing single substitutions with the best characteristics are proposed.
Modification of Hargreaves Equation Coefficient to Estimate Reference Evapotranspiration in Gangwondo

Reference evapotranspiration (ET 0 ) can be estimated using Hargreaves equation with temperature data alone.The equation has a tendency to overestimate for regions of high relative humidity and to underestimate for regions of wind speed greater than 3 m s -1 .The study was conducted to determine Hargreaves equation coefficient in 11 regions in Gangwondo by comparing ET 0 determined by modified Hargreaves equation with ET 0 by the FAO Penman-Monteith equation.The Penman-Monteith ET 0 values were determined using the weather data including solar duration, air temperature, humidity, and wind speed for 11 weather stations in Gangwondo from 2007 to 2017.The modified Hargreaves coefficients for inland regions such as Chuncheon, Hongcheon, Cheorwon, Wonju, Inje, Yeongwol, Daegwallyeong, and Taebaek ranged from 0.00181 to 0.00199, less than the default value 0.0023.The modified coefficients for coastal regions such as Gangneung, Sokcho, and Donghae ranged from 0.00275 to 0.00302, greater than the default value.Modified Hargreaves ET 0 using modified coefficients reduced root mean square error (RMSE) from 0.84 mm d -1 to 0.69 mm d -1 and increased Nash-Sutcliffe coefficient (NSC) from 0.69 to 0.78 compared with original Hargreaves ET 0 .Therefore the modified Hargreaves coefficients obtained from the study can provide more accurate ET 0 estimates than using original default value by considering regional climate characteristics in Gangwondo.
A Novel Recommendation System for Housing Search: An MCDM Approach

Recommendation system is the part and parcel of modern days’ information retrieval system. It takes input from the users and comes up with a personalized list of choices or alternatives to the user. This process makes the selection easy for the users. Recommendation system is helpful where the number of available choices is too large. In this paper, we have proposed a rank-based flat recommendation system. We took importance factors against the housing attributes from the users along with some preference values. These were processed through PROMETHEE II method to generate a rank-based recommendation system.
A New Approach for Estimating Monthly Global Solar Irradiation Based on Empirical and Artificial Neural Networks Models: A Case Study of Al-Hoceima Province, in Morocco North Region

Knowledge of the monthly global solar irradiation is crucial for the planning, design, and dimensioning of solar photovoltaic systems. However, global horizontal solar irradiation is not readily available at all sites. In this regard, the authors proposed a hybrid model that combines an empirical model with an artificial neural network technique to estimate the monthly global solar irradiation for the case study of the city of Al-Hoceima, Morocco. To this end, 17 empirical models are calibrated and evaluated in order to obtain the best empirical model, where the output of this empirical model is combined with one or more parameters such as sunshine duration (S), extraterrestrial radiation (Ho), mean, maximum and minimum temperatures (Tmean), (Tmax), (Tmin), the ratio (Tmin/Tmax), and the difference (Tmax-Tmin). These combinations of parameters are used as inputs to different MLP models that were developed and evaluated in order to find the model with the highest overall performance. The simulation results demonstrated that the proposed hybrid approach provides the best results in terms of RMSE, R2, and MAE and outperforms MLP or empirical models used individually. Also, the results indicated that the proposed model gives higher performance than five machine learning algorithms including SVR, Random Forest, Xgboost, decision tree, and K-nearest neighbors.
Teaching Cases Analysis of Integration of Electromechanical Control of Linkage Mechanism

Abstract In the teaching design of electromechanics integration for mechanism, three designs (machinery, motor and control) work in parallel. Before the mechanical team completes the design, the motor and control team needs to obtain information about the machinery in advance. The integrated teaching of the three elements of machinery, motor and control of the planar linkage mechanism is discussed in this article, creating an enhanced environment for electromechanics integration in teaching mechanism combining the virtual simulation and physical production, integrating the five aspects of design, simulation, control, manufacturing and testing, therefore establishing an innovation model of mechanical principles of research and exploratory type teaching, problem-oriented learning-type teaching.
Bidirectional LSTM-based Network for Fall Prediction in a Humanoid

Fall prediction for humanoid robots is a challenging problem. Increasing interest in the introduction of biped humanoids for diverse applications, e.g., search and rescue in a disaster scenario, necessitates endowing them with robust ways of handling environmental disturbances. We begin the paper with a brief review of previously proposed methods to predict an impending fall event for a biped perturbed by an unknown external force. Next, to improve on prior works, we consider a bidirectional long short-term memory (BLSTM) network, which makes use of historical measurements of system states as inputs, to effectively predict fall probability in real time. Through extensive simulation experiments, which utilize external forces with random magnitudes, directions, locations, and times of application, we demonstrate that the proposed BLSTM network can robustly predict fall events. In contrast to learning-based methods in prior literature, the technique of this paper is shown to reduce false positive rate (FPR) for fall prediction while achieving nearly the same level of lead time in predicting an approaching fall. To perform the sensitive tradeoff between the FPR and lead time, a tuning parameter α is included in our training process to permit a user control over the resulting prediction model. With these contributions, a biped can take advantage of the proposed BLSTM prediction model and tailor its effectiveness to the task at hand.
Utilizing GPGPUs for simulating quantum circuit

Abstract State vector describing the state of an n-qubit quantum system requires 2 n complex numbers, which means the memory usage of simulating a quantum computer grows exponentially with respect to the number of qubits. For the memory of a single hardware is limited, distributed computation is necessary for simulating quantum systems with large number of qubits. In our proposal, we developed a multi-GPU solution. State vector is divided into several chunks and allocated to different GPU device. Making it possible to simulate quantum circuits of any size on appropriate GPU cluster. We analyse the theoretical computation resource and come to the conclusion that memory copy bandwidth between GPUs is the bottleneck of time consumption. Total execution time is O(logDN/DN) if peer-to-peer memory copy is supported, otherwise time elapsed is O(logDN) where DN is number of GPU devices. Memory consumption on one GPU is O(1/DN).
Training of math teachers on development of test materials for assessment of students’ academic performance

The subject of this research is the question of advanced training of math teachers on organization of tests for assessment of students&amp;rsquo; academic performance. The author determines the problems associated with ineptitude of the teachers to develop materials for testing academic achievements, insufficiency of advanced training courses dedicated to diagnostic and assessment of academic performance of students, lack of theoretical knowledge for conducting diagnostic procedures. Special attention is given to the requirements to professional competences of teachers in the area of assessment of students&amp;rsquo; academic performance, formulated in the occupational standards of pedagogues, Federal State Educational Standard of public education, and Concept of Mathematical Education of the Russian Federation. The definitions of pedagogical diagnostic testing formulated by K. Ingenkamp, I. P. Podlasy, V. M. Polonsky, A. B. Vorontsov are presented; the stages of diagnostic testing activity are outlined. As one of the solutions, the author describes the training experience of math teachers of Novosibirsk in the classes of consulting center and creative team, arranged by the Municipal Center of Education Development on the premises of Gymnasium No.10. The scientific novelty consists in elaboration of training course for the teachers (thematic content is presented in the article) and its implementation by the author. Activity of the center and creative team received the awards of city and regional contests &amp;ldquo;Secret of Success&amp;rdquo;, &amp;ldquo;Innovations in Education&amp;rdquo;, and &amp;ldquo;Educational Siberia&amp;rdquo;. In the beginning of training, the teacher were offered to fill in the questionnaire for defining their goals from taking these classes. Some of the survey results are provided in the article. Although the training of teachers is still in progress, their feedback and awards of pedagogical contests hold out a hope that its is the right path.
Building A New Blueprint for Operating Workflow Efficiently

Business processes are indicating how to handle and deal with different business situations. Companies focus on the flexibility of information technology architecture as their main business strategy for decision making. Their systems should satisfy the adaptability criteria. In this paper, we propose a framework to measure the quality of the existing workflow by using the quality of services criteria. Based on the results of quality of services measurements, an enhancement of the framework should be obtained using the concepts of graph theory such as min-cut and max-cut algorithms and some re-factoring techniques. To reduce the bugs that could occur and to reach the long lifespan for an organization workflow, we proposed, designed, and implemented a new mainframe that takes a workflow as an input to check whether it meets the specific quality of services measurements. If the workflow is not qualified, it will be enhanced automatically using our proposed algorithms to meet the quality of services rules and check for the consistency within the system. The main frameworks components are the quality measurement, determination, parsing, and code adaptation. We presented and discussed a real case study to help in understanding, illustrating and analyzing the proposed framework behavior and its approach applicability, in addition to, analyzing different related scenarios.
Serendipitous Page Recommendation on Web IndeX System with Potential Preferences

Most recommendation systems excessively pursue the recommendation accuracy and give rise to over-specialization. However, the existing recommendation systems research has not studied serendipity much. Hence, the serendipitous item recommendation has received more attention in recent years. The serendipitous recommendation of our research is not included in the area that the user predict easily but recommends the keywords that match the potential preferences. Potential preferences are those that are present in the user profile, which the user may not know. In this research, we recommend keywords that can express serendipity by intersecting the relation between keywords mainly. Furthermore, we propose the related page recommendation method on Web IndeX System for recommending linked pages related to these serendipitous keywords based on the user's potential preferences.
An Evolutionary Algorithm Inspired by Evolution of Birdsong Grammars to Quickly Obtain Practical Solutions to Large-scaled Problems

We propose an evolutionary algorithm inspired by evolution of birdsong grammars. The proposed algorithm is designed to quickly obtain practical solutions to large-scaled problems. The main mechanism for obtaining practical solutions quickly is to represent solution candidates in a compact way using grammars, that is to say, to make the search space smaller than the original one using grammars. Also, we apply the algorithm to a large-scaled OneMax problem and its variants. The simulation results show that the algorithm tends to quickly produce solution candidates with simple bit patterns and with not so bad fitness values for large-scaled problems as a bit pattern of the global optimum becomes more random. That can be considered as that practical solutions to large-scaled problems are obtained quickly. It is also shown that when the problem size is smaller, the algorithm tends to produce a precise bit pattern of the global optimum.
An Acceleration Framework for Super-Resolution Network via Region Difficulty Self-adaption

With the development of deep neural network (DNN), many DNN-based super-resolution (SR) models have achieved state-of-the-art (SOTA) performance. But the applications of these SOTA models are limited by the high computational and memory costs. We observed that different image regions have different difficulties in the process of SR reconstruction. However, current DNN-based SR models process different types of regions equally, and thus involve much redundant computation in the regions with low SR difficulty. To address this limitation, this paper proposes a general acceleration framework for SR networks, which first distinguishes the SR difficulty of image regions, and then applies large model and light model for difficult regions and easy regions respectively. Experimental results demonstrated that the proposed acceleration framework can accelerate SOTA SR networks with 2–4 times without reducing of quality performance.
Should the Introduction of Futures Be Responsible for the Crash of Bitcoin?

The price of Bitcoin reached its peak a mere few days after the introduction of Bitcoin futures and suffered an 80% loss in the following year. In this paper, we find a significant and negative relationship between the introduction of Bitcoin futures and Bitcoin returns, and an insignificant or positive relationship for other 7 major non-Bitcoin cryptocurrencies. Within the first 45 days after the futures launch, Bitcoin suffered a -26.50% loss, while other cryptocurrencies could still provide positive returns. We presume that the launch of Bitcoin futures was to an extent responsible for the crash of Bitcoin.
Detection of Threshold Fall Angle of Elderly Patients for Protective Suit Purposes

Falls are the second leading global cause for unintentional injury and deaths as per the world health organization [WHO]. Adults over 65 years undergo the highest number of fatal falls. This has led to the development many types of automatic fall detection systems. Here, a simple threshold angle detection method is proposed and is calculated by recording a person's falling movement. The falling movement is recorded with the help of a simple video recorder. By using the software, the time and distance of a person falling movement is calculated from its initial standing position to the ground. The result is obtained, and a 15° angle is considered as a threshold angle for elderly patients based on the detailed study of different BMI. Based on this approach, it will be feasible to establish the reliability of the development of the fall prevention system for the elderly and to be the basis for future growth.
Research on Power Network Data Management Based on Convolutional Neural Network

Abstract Faced with huge business data, collecting and statistics of these data can be easily achieved through the system. However, during data analysis and result output, there is a large amount of data that cannot be intuitively accepted at a glance, resulting in disorder and inefficient work. Therefore, the grid load thermal map is scaled with cubic convolution interpolation in the paper, which requires the offset distance determination of the floating point in the horizontal and vertical directions. Moreover, based on the operating data of transformer and line, the analysis and verification of the load thermal map in power grid is realized. Besides, problem equipment can be clearly displayed on the map, which is convenient for staffs to identify and analyze.
Embodied Presence, COVID-19 and the Transcendence of ITopian Fear

The arrival of the COVID-19 virus has led to an acceleration of four trends in our modern, technology-focused societies: the development of ITopia (a dystopian future where technology undermines our humanity) with its imbalanced computational rationality;the collapse of sensemaking;the spread of a culture of pessimism and misanthropy;and the denial of death This article makes the case for the practice of embodied presence (mindfulness) and cognitive responsibility as means to return to a more grounded experience of life, consciousness and self amongst modern populations, and thus to help resolve these four problems
Income Levels, Governance and Inclusive Human Development in Sub-Saharan Africa

This study examines how income-driven governance affects inclusive human development in Sub-Saharan Africa with data for the period 2000-2012. The empirical evidence is based on the Generalised Method of Moments (GMM) and Tobit regressions. Nine bundled and unbundled concepts of governance are used: political (voice & accountability and political stability/no violence), economic (government effectiveness and regulation quality) and institutional (corruption-control and the rule of law) governances. The main finding is that ‘middle income’-driven governance has a higher effect on inclusive human development than ‘low income’-driven governance. Policy implications are discussed in the light of: (i) the contemporary relevance of findings; (ii) the pivotal role of a higher income level in the post-2015 sustainable development agenda; and (iii) inconsistent strands in the literature and in foreign aid policies.
Development of an Intelligent Control System for Grounding Blades and Operational Blocking of Disconnectors

This work is devoted to the study of digital substation technology and analysis of the possibilities of automation of the process of control of switching devices. The scientific novelty of the research consists in the use of an ontological model for describing a single-line substation circuit, generating substation objects and modeling switching switches, as well as applying a multi-agent approach to solving the task.
Research on Pet Dog Species Identification Based on Convolution Neural Network

At present, the related research of image recognition is getting more and more popular, but in the process of research, the recognition effect of the model is not good enough and it is easy to misrecognize. This paper proposes an improvement solution for the above problems on the selection and construction of the model structure and the adjustment and optimization methods in the model training process. The final result achieves 96% recognition accuracy on the data composed of 9092 pet dog images. It is proved that the model by choosing deep-level network model and adopts regularization method to adjusting and optimizing the model, which can effectively improve model for image recognition effect.
Saliency Detection in Text Documents using Policy-Driven Reinforcement Learning Methodologies

Abstract As the amount of information grows, it is challenging to find concise information. Thus it is necessary to build a system that could present human quality summaries. Saliency detection is a tool that provides abstracts or keywords of a given document. In this paper, three different approaches have been implemented for saliency detection. In all these three approaches, sentences are represented as a feature vector. In the first approach, features like root words, vocabulary intersections, words, and inclusion of numerical data use. This model is trained by using general Algorithms, Like Porter’s Stemmer, Spell check. In the second approach, apart from the features used in the first approach, TF-IDF scores, Mean, Standard Deviation, and a Threshold value of a word is also used as features. In the third approach, Maximal Marginal Relevance (MMR) algorithm is used to generate a summary.
Quantum Chaos and the Spectrum of Factoring

Abstract The factorization ensemble is a set to which integer factorable numbers , having the same trivial factorization complexity, belong. Hence, the Rivest‐Shamir‐Adleman (RSA) cryptographic moduli pertain to this set. A function can be defined therein which will be associated to the energy of a system of ions in a Penning trap. This is the quantum factoring simulator hypothesis connecting quantum mechanics with number theory. Here, a possible setup of the simulator from the magnetron energies of a Coulomb crystal in a cylindrical trap is described. Then, quantum mechanically, these energies may have only discrete values. To test the validity of the simulator hypothesis, evidence of this kind of discreteness from the statistics of the of a large random sample of RSA moduli is reported; indeed, their unfolded distance probability distribution fits to a Gaussian Unitary Ensemble, exactly as required if they actually correspond to the quantum energy levels spacing of a magnetically confined system that exhibits chaos. The confirmation of these predictions is consistent with the quantum simulator hypothesis and, thereby, it points to the existence of a liaison between quantum mechanics and number theory.
RMPD: Method for Enhancing the Robustness of Recommendations With Attack Environments

Personalized item recommendation has become a hot topic research among academic and industry community. But lots of purposeful fraudsters maybe perform different attacks on the recommender system to insert fake ratings, which could reduce the authenticity and reliability of recommendations. For a recommender system with fraudsters, it is crucial to detect malicious ratings and reduce the proportion of fraudster's ratings. This paper presents a method Prediction and Detection of Rating Matrix(RMPD) combining rating prediction and attack detection. The detection results of the attackers are applied to the rating prediction, thereby controlling the contribution and proportion of attackers to the rating prediction component both in training and learning, and then implementing more accurate item rating projections. The method will also solve the problem of data sparsity in the recommender system to some extent. The superiority of the proposed method in predicting recommendation performance compared with other baseline methods is demonstrated on real-world datasets. The ablation experiment proves the necessity of the components.