Reviewerly/exHarmony huggingface dataset:
Well-calibrated and specialized probability estimation trees

In many predictive modeling scenarios, the production set inputs that later will be used for the actual prediction is available and could be utilized in the modeling process. In fact, many predictive models are generated with an existing production set in mind. Despite this, few approaches utilize this information in order to produce models optimized on the production set at hand. If these models need to be comprehensible, the oracle coaching framework can be applied, often resulting in interpretable models, e.g., decision trees and rule sets, with accuracies on par with opaque models like neural networks and ensembles, on the specific production set. In oracle coaching, a strong but opaque predictive model is used to label instances, including the production set, which are later learned by a weaker but interpretable model. In this paper, oracle coaching is, for the first time, used for improving the calibration of probabilistic predictors. More specifically, setups where oracle coaching are combined with the techniques Platt scaling, isotonic regression and Venn-Abers are suggested and evaluated for calibrating probability estimation trees (PETs). A key contribution is the setup designs ensuring that the oracle-coached PETs, that per definition utilize knowledge about production data, remain well-calibrated. In the experimentation, using 23 publicly available data sets, it is shown that oracle-coached models are not only more accurate, but also significantly better calibrated, compared to standard induction. Interestingly enough, this holds both for the uncalibrated PETs, and for all calibration techniques evaluated, i.e., Platt scaling, isotonic regression and Venn-Abers. As expected, all three external techniques significantly improved the calibration of the original PETs. Finally, an outright comparison between the three external calibration techniques showed that Venn-Abers significantly outperformed the alternatives in most setups.
Automatic labelling of brain tissues in MR images through spatial indexes based hybrid atlas forest

The multi-atlas-based methods are widely applied in the automatic labelling in magnetic resonance (MR) images. However, most multi-atlas-based methods require that all atlases be registered to the target image accurately to have a correct label propagation. In this study, the authors introduce the term spatial indexes and construct a hybrid atlas forest model to gather the labelling information from all atlases without propagating labels from every single atlas. Furthermore, a new automatic labelling method using the hybrid atlas forest model based on spatial indexes is proposed. In the proposed framework, an atlas is chosen arbitrarily as a reference image and the spatial indexes are constructed on this image space. Then, the samples are selected from all atlases in the dataset based on the spatial indexes to construct a samples pool. Finally, the hybrid atlas forest model will be trained on the samples pool and used to predict the labelling of the target. Experiments are conducted on two public datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method reduces the requirement of strong dependence on precise registration and improve the accuracy of labelling.
DDM: A Demand-Based Dynamic Mitigation for SMT Transient Channels

In this paper, we define a Simultaneous Multi-Threading(SMT) Transient Channel, stemmed from the sharing mechanism between multi threads running on the same core, which can be used to steal information by attackers. In order to alleviate this problem, we design a demand-based dynamic mitigation (DDM). The DDM writes the processes' security requirements to the CPU register sets, and then the operating system invokes the HLT(an assembly language instruction in X86 computer architecture) to dynamically turn hyper threading on or off according to the values of registers. We modify the Linux kernel and use MSR register groups of Intel processor to implement scheme. The evaluation results show that DDM can significantly protect key information against the SMT transient side-channel such as PortsMash that rely on SMT, and the performance loss of DDM is less than 8%.
Mobile Multimedia Education for Language Disorders

This article describes the importance of new multimedia tools that provide multi-sensory education for language learning, especially for students who need special education. These contemporary appliances stimulate the senses, enter the motiva-tion and reinforce the memory. In particular, these tools advance language skills such as comprehension, oral language and vocabulary, speaking expressions, reading capacities and writing abilities. Also, the evaluation for students with lan-guage disorders lies on cognitive and metacognitive capabilities, being created by these interactive media devices.
New Assumptions and Efficient Cryptosystems from the e-th Power Residue Symbol

The e-th power residue symbol $$\left( \frac{\alpha }{\mathfrak {p}}\right) _e$$ is a useful mathematical tool in cryptography, where $$\alpha $$ is an integer, $$\mathfrak {p}$$ is a prime ideal in the prime factorization of $$p\mathbb {Z}[\zeta _e]$$ with a large prime p satisfying $$e | p-1$$ , and $$\zeta _e$$ is an e-th primitive root of unity. One famous case of the e-th power symbol is the first semantic secure public key cryptosystem due to Goldwasser and Micali (at STOC 1982). In this paper, we revisit the e-th power residue symbol and its applications. In particular, we prove that computing the e-th power residue symbol is equivalent to solving the discrete logarithm problem. By this result, we give a natural extension of the Goldwasser-Micali cryptosystem, where e is an integer only containing small prime factors. Compared to another extension of the Goldwasser-Micali cryptosystem due to Joye and Libert (at EUROCRYPT 2013), our proposal is more efficient in terms of bandwidth utilization and decryption cost. With a new hardness assumption naturally extended from the one used in the Goldwasser-Micali cryptosystem, our proposal is provable IND-CPA secure. Furthermore, we show that our results on the e-th power residue symbol can also be used to construct lossy trapdoor functions and circular and leakage resilient public key encryptions with more efficiency and better bandwidth utilization.
Log-convex set of Lindblad semigroups acting on <i>N</i>-level system

We analyze the set ${\cal A}_N^Q$ of mixed unitary channels represented in the Weyl basis and accessible by a Lindblad semigroup acting on an $N$-level quantum system. General necessary and sufficient conditions for a mixed Weyl quantum channel of an arbitrary dimension to be accessible by a semigroup are established. The set ${\cal A}_N^Q$ is shown to be log--convex and star-shaped with respect to the completely depolarizing channel. A decoherence supermap acting in the space of Lindblad operators transforms them into the space of Kolmogorov generators of classical semigroups. We show that for mixed Weyl channels the hyper-decoherence commutes with the dynamics, so that decohering a quantum accessible channel we obtain a bistochastic matrix form the set ${\cal A}_N^C$ of classical maps accessible by a semigroup. Focusing on $3$-level systems we investigate the geometry of the sets of quantum accessible maps, its classical counterpart and the support of their spectra. We demonstrate that the set ${\cal A}_3^Q$ is not included in the set ${\cal U}^Q_3$ of quantum unistochastic channels, although an analogous relation holds for $N=2$. The set of transition matrices obtained by hyper-decoherence of unistochastic channels of order $N\ge 3$ is shown to be larger than the set of unistochastic matrices of this order, and yields a motivation to introduce the larger sets of $k$-unistochastic matrices.
Effects of computer-assisted instruction on students’ achievement in atomic and nuclear physics in Senior Secondary Schools in Rivers State

The study examined the Effects of computer-Assisted Instruction (CAI) on students’ achievement in Atomic and Nuclear Physics in Rivers state. The objective of the study was to determine the difference in the academic achievement Physics students when taught atomic and nuclear physics with CAI and lecture method. The study adopted pretest-posttest design of quasi-experimental research. The study was carried out in Rivers state senior secondary schools. The population of the study comprised of all senior secondary school 3 physics students in Rivers state. The sample size of the study was drawn using purposive random sampling. The total sample size of the study was one hundred and twenty (120). The sample size was grouped to control (60) and experimental groups (60). The instrument used for the study was an achievement test which was tagged “Atomic and Nuclear Physic Achievement Test (ANPAT)”. The instrument consisted 40 Multiple Choice Question (MCQ). The instrument was sectioned into four parts, each section measured students’ knowledge on the models of atom, energy quantization, photoelectric emission and thermionic emission. The reliability of the instrument was tested using test-retest method which yielded 0.88 reliability coefficient. Mean and standard deviation was used for data analysis while t-test was used to test the hypotheses at 0.05 level of significance. It was found that students that were taught atomic and nuclear physics with CAI (60.5) have higher mean achievement scores than those taught with lecture method (44.1). This implies that students taught atomic and nuclear physics using computer assisted instruction performed better than those taught with lecture method. The t-test analysis also showed that the differences that exist in the mean are statistically significant at 0.05. Thus, the study recommended that government should assist the schools to make adequate computers available for teaching and learning physics in secondary schools.
A Real-Time Fault Location Mechanism Combining CGP Code and Deep Learning

The rapid increase in the scale and complexity of the circuit system has led to serious problems in safety and reliability. Therefore, fault tolerance was proposed. Fault location as part of fault tolerance is indispensable. However, fault location methods are mostly limited to small data volume and high system complexity. How to achieve the fault location of the circuit system has always been a focus question. This paper proposes a Hierarchical Multi-module Fault Location Mechanism (HMFLM). Cartesian Genetic Programming (CGP) is exploited to generate circuits and random injects faults into it. The model matching library is used to store the training model of the layering module circuit and detect circuit faults in real time. The recovery priority of the fault circuits utilize Fault Analysis Tree (FAT) to determine, therefore, we can effectively facilitate fault recovery. The results show HMFLM can effectively locate multiple faults and improves the real-time and reliability of fault diagnosis.
Generating and Exploiting Cost Predictions in Heuristic State-Space Planning

This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema.
On the Popularity of Emoticons

Emoticons are now ubiquitous in people’s daily communication in China. The popularity of this new kind of expression is attributed to complex interplay of environment, culture, economy and life style. The functions of emoticons in communication center on the social aspect, to both the sender and the receiver. The aim of the study is to describe, analyze and summarize the usage of emoticons and the social functions in communication.
W-Trans: A Weighted Transition Matrix Learning Algorithm for the Sensor-Based Human Activity Recognition

The sensor-based human activity recognition has been wildly applied in behavior tracking, health monitoring, indoor localization etc. Using activity continuity to assist activity recognition is an important research issue, in which the activity transition matrix which describes the activity transformation in real scenarios is the most important parameter. Aiming at the problem that the current classic transition matrix learning algorithm cannot fuse weights of sample classification results, a weighted transition matrix learning algorithm is proposed in this paper. First, the basic definitions of an improved Hidden Markov Model (HMM) which fuses weights of classification results are given. Then, the recursive formula of transition matrix learning is derived, and the learning algorithm W-Trans is put forward. Finally, the proposed algorithm is simulated with the public data sets. The evaluation results show that the proposed algorithm outperforms the classical Baum-Welch algorithm under evaluation metrics of both the cosine similarity and the euler distance. By applying W-Trans to current activity recognition post-process methods, the advantage of our method is verified.
A blockchain‐based traceable group loan system

Summary Difficulties in financing and low utilization of funds are main financial problems that plague the development of small and medium‐sized enterprises. The key to solving this problem lies in opening up the social data circulation between enterprises. It is a good solution for enterprises with frequent data interactions to form groups. Using group loans, the borrowing enterprises could solve the funding difficulties and the loan enterprises could improve the utilization rate of funds. In this article, we construct a group loan system based on blockchain technology, which can promote the free flow of funds among enterprises in the group. We combine the blockchain with the trusted execution environment to realize the automatic determination of loan conditions and realize the automatic execution of smart contracts. We also use the linkable group signature technology to ensure the traceability of loan users while protecting the anonymity. In addition, we use homomorphic encryption technology to make the statement confidential and computable.
The Impact of Authentic Leadership in the OCB and Employees Commitment in the Jordanian Companies

The study aimed to measure the impact of authentic leadership in organizational citizenship behavior and organizational commitment in Jordanian Companies.Six companies operating in different sectors were selected.Sample unit consist 31 employees.Questionnaire designed to collect data with 5 Likert Scale, Statistical analysis was performed descriptive using the arithmetic mean, Standard deviation.The results show high levels of organizational citizenship behavior and medium levels of authentic leadership and organizational commitment.The results of the Simple regression analysis were existence the impact statistically significant of Authentic leadership in organizational citizenship behavior and organizational commitment among workers in companies.The impact statistically significant authentic leadership in organizational citizenship behavior greatest degree than organizational commitment, and the Impact statistically significant Authentic leadership in organizational commitment.This research recommends the companies to be more attention in the authentic leadership and organizational commitment and strengthening the organizational citizenship behavior.
Implementation of Task-Centered Design in a Web-Based Catalogue

E-catalogue is an optional way to facilitate the process of marketing in a company and increase its revenue.Its digital form allows a company to perform regular updates quickly and easily at zero cost, ensuring the accuracy of product data when it is presented to customer.Unlike conventional catalogs, e-catalogs facilitate a direct relationship between the company and its customers.The benefit of the e-catalog should be felt by both company and customers.Unfortunately, designing an ideal e-catalog that can give mutual benefit for company and customers is relatively tricky.It's easy for e-catalog product data to become messy and inconsistent.It's tedious and time-consuming to continuously update and manage the data.This study aims to design a standardized web-based catalog using task-centered design to give more benefits for the company and customers.By using a well-structured questionnaire, data were obtained from both company and customers.The questionnaire was developed using a modified-Nielsen's usability testing model.This model considers several factors that influence the usability of a system namely learnability, efficiency, memorability, errors, and satisfaction.Of the overall indicators, the average number of user satisfaction is 86.72% (minimum threshold = 80%) which means the web-based catalog is very feasible.Therefore, it can be concluded that the e-catalog can be well-received by the company and its customers.
Automated Exam Question Set Generator Using Utility Based Agent and Learning Agent

Exam is an evaluation tool to measure teaching and learning outcomes of educators and their learners respectively.Nowadays, an automated exam question set generator is a must have to reduce educator's time on preparation of exam question set and increase the quality of exam question set.This paper proposes an Automated Exam Question Set Generator (AEQSG) using Utility Based Agent (UBA) and Learning Agent (LA).Furthermore, AEQSG applies Bloom Taxonomy (BT) scaling to automate Bloom's Taxonomy (BT) difficulty level distribution and Genetic Algorithm (GA) to optimize the generation of exam question set and generate high quality exam question set that follow educational institution's guide-lines.The purpose of utility based agent in AEQSG is to give the user an option to choose actions based on a user's preference (utility) for each generation state.Meanwhile, the purpose of learning agent in AEQSG is to learn from its past exam results (past generation experiences).
Chemical disease relation extraction task using genetic algorithm with two novelvoting methods for classifier subset selection

Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system (MCS) for the extraction of chemical-induced disease relations. A genetic algorithm (GA) is employed to select classifier ensembles from a pool of base classifiers. Moreover, the voting method used for combining the members of each of the ensembles is also selected during evolution in the GA framework. The performances of the MCSs are determined by the algorithms used for selecting the classifiers, the diversity among the selected classifiers, and the voting method used in the classifier combination. The base classifiers are represented in the form of chromosomes, where each chromosome contains all information on the ensemble it represents: the subset of classifiers voting and the voting method. The chromosomes are evolved using a variety of genetic selection, mating, and mutation techniques in order to find an optimal solution. The aim of the proposed system is to select the subset of classifiers with diverse abilities while maximizing the strengths of the best classifiers in the classifier ensemble for a given voting method. Two main contributions of this work are the evolution of the voting bit as part of the GA and the novel approach of using two different decision-making under uncertainty techniques as voting methods. Furthermore, two different selection algorithms and crossover operators are employed as ways of increasing variations during evolution. We validated our proposed method on nine different experimental settings and they produced good results comparable to the state-of-the-art systems, thereby justifying our approach.
Recurrent Neural Networks for Meteorological Time Series Imputation

The aim of the work presented in this paper is to analyze the effectiveness of recurrent neural networks in imputation processes of meteorological time series, for this six different models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are implemented and it is experimented with hourly meteorological time series such as temperature, wind direction and wind velocity.The implemented models have architectures of 2, 3 and 4 sequential layers and their results are compared with each other, as well as with other imputation techniques for univariate time series mainly based on moving averages.The results show that for temperature time series on average the recurrent neural network achieve better results than the imputation techniques based on moving averages; in the case of wind direction time series, on average only one model based on RNN manages to exceed the models based on moving averages; and finally, for wind velocity time series on average, no RNNbased model manages to exceed the results achieved by moving averages-based models.
Towards Real-Time Monitoring of the Hajj

An automated approach to explore the fundamental properties of high-density pedestrian traffic is outlined. The framework operates on video or time lapse images captured from surveillance cameras. For pedestrian velocity extraction, the framework incorporates cross-correlation based Particle Image Velocimetry (PIV) techniques. For pedestrian density estimation, the framework relies on the Machine Learning technique of the Boosted Regression Trees. The information collected from images in pixel coordinates are transformed to world coordinates with a pin-hole camera based projective transformation technique. The framework has been tested with high density crowd images acquired during the Muslim religious event, the Hajj. Accuracy and performance of the framework are reported.
Improving The Level Of Life Safety In The University

The article deals with modern challenges of personal security. The purpose of the article is to reveal the potential of youth self-government in educational institutions of higher education. The author identifies a complex task of educational organizations, which includes not only educational functions, but also educational aspects of the entire process of preparing young people for adulthood, in particular the task of forming a culture of safety for employees and students of educational organizations, based on spiritual and moral positions. The role and place of complex security of educational organizations are shown, the points of view on its organization are analyzed. The essence of social security of the educational environment, which determines the conditions for the development of safety culture of higher education students, is revealed. Thus, the article shows that student self-government can be a key factor in the involvement of students in the social life of the higher education institution, which will contribute to the formation of barriers to the development and spread of destructive ideologies. To implement these measures, it is necessary to create a unified educational, scientific, spiritual, moral and cultural-leisure environment where each student can prove himself in various spheres of life and realize his best personal qualities.