Reviewerly/exHarmony huggingface dataset:
Development of ‘say’-derived constructions

Abstract Totemo in contemporary Japanese is a degree adverb (intensifier). Previous studies have reported that totemo derived from the adverb totemo kakutemo , which means ‘either way’ (and hence is a bipolar item) in classical Japanese. These studies also reported that totemo became a negative polarity item (an adverb modifying words for negative evaluation), but then shifted to a positive polarity item (an adverb modifying words for positive evaluation), and that counter-expectation factors played an important role in this shift. It is reported that the ‘say’-derived complementizer develops into a hearsay evidential marker, counter-expectation marker, and in some cases an intensifier (e.g. Wang et al. 2003 ) in some languages. Tote in classical Japanese is known as a ‘say’-derived complementizer, but it does not grammaticalize into an intensifier. This study maintains that the intensifier totemo also derived from the verb ‘say’ and the entire process of grammaticalization of totemo may be chronologized as follows: concessive use of quotative &gt; concessive use of hearsay evidential &gt; counter-expectation marker &gt; intensifier. Thus, this study reveals the language-specific development of grammaticalization of the intensifier totemo . We also reveal that a reanalysis of the concessive subordinator and the elision of the complement clause preceding totemo as a sentence initial counter-expectation marker further gave rise to the sentence-medial parenthetical phrase (intensifier) totemo .
A Novel Method of 2D Computation and Reconstruction

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and handwriting identification by using the set of key points. Nodes are treated as characteristic points of signature or handwriting for modeling and writer recognition. Identification of handwritten letters or symbols need modeling and the model of each individual symbol or character is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter γ as probability distribution function enables curve parameterization and interpolation for each specific letter or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.
A Probabilistic Forward Search Value Iteration Algorithm for POMDP

Point-based value iteration methods are a class of practical algorithms for solving the POMDP model. The critical process of these methods is the exploration of the belief point set B. Forward search value iteration(FSVI) can reduce the complexity and improve efficiency significantly by using the optimal strategy of the underlying MDP. However, it does not utilize the observations of the model, making it not so efficient in the large-scale POMDP problems. A probabilistic forwardsearching value iteration algorithm (PFSVI) is presented in the paper to make up the shortage of FSVI. During the exploration, PFSVI uses the alias method to sample the action a* based on weighted Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDP</sup> function and sample the state based on b and the transition function. Then, PFSVI selects the observation z, which lets the successor point b <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a*,z</sup> farthest from B. PFSVI can improve the effect by sampling according to the environment and reaching more vast space than FSVI. Experiment results of four benchmarks show that PFSVI can achieve better global optimal solutions than FSVI and PBVI, especially in large-scale problems.
A Comparison of Touchscreen and Mouse for Real-World and Abstract Tasks with Older Adults

Computer technology is increasingly being used to facilitate the timely identification of cognitive impairment in older adults. Our Cognitive Testing on Computer (C-TOC) project aims to develop a self-administered online test for older adults to take at their home. Due to the freedom of devices they can use, it is important to investigate whether different input devices can impact test performance. We compared touchscreen and mouse input on both abstract and real-world pointing and dragging tasks: classic Fitts’s Law tasks and tasks drawn from C-TOC. The abstract and real-world tasks were designed to require equivalent motor skills. Our research goals were to determine (1) if performance on computerized cognitive tasks are affected by input device, and (2) if performance differences due to input device can be explained by those observed on Fitts’s Law tasks. Sixteen older adults completed both types of tasks using a touchscreen and a mouse. We found that input device affected speed on three out of four cognitive tasks while only affecting accuracy on one task. Secondarily, our results suggest that Fitts’s Law results of differences in mouse and touch cannot be used to predict device differences in the performance on C-TOC tests. As an additional research goal, we looked into the movement patterns of one real-world dragging task—the C-TOC Pattern Construction task—to see if they could provide richer performance measures, beyond speed and accuracy. Such measures could compensate for the lack of a clinician observer who is typically present in comparable paper-based cognitive tests. We found that older adults naturally adopted different movement patterns on the two devices: they tended to make shorter moves and a greater number of moves on a touchscreen than with a mouse. Altogether, our results suggest that careful device-based performance calibration will be needed in computerized tests.
Identification of Outliers in Gene Expression Data

Identification of outliers is a big challenge in big data although it has drawn a great deal of attention in recent years. Among all big data problems, the detection of outliers in gene expression data warrants extra attention because of its inherent complexity. Although a variety of outlier detection methods are available in the literature, Tomlins et al. (Tomlins et al. Science 310:644–648, 2005) argued that traditional analytical methods, for example, a two-sample t-statistic, which search for common activation of genes across a class of cancer samples, will fail to detect cancer genes, which show differential expression in a subset of cancer samples or cancer outliers. They developed the cancer outlier profile analysis (COPA) method to detect cancer genes and outliers. Inspired by the COPA statistic, some authors have proposed other methods for detecting cancer-related genes with cancer outlier profiles in the framework of multiple testing (Tibshirani and Hastie Tibshirani and Hastie Biostatistics 8:2–8, 2007; Wu Wu Biostatistics 8:566–575, 2007; Lian Lian Biostatistics 9:411–418, 2008; Wang and Rekaya Wang and Rekaya Biomarker Insights 5:69–78, 2010). Such cancer outlier analyses are affected by many problems especially if there is an outlier in the dataset then classical measures of location and scale are seriously affected. So the test statistic using these parameters might not be appropriate to detect outliers. In this study, we try to robustify one existing method. We propose a new technique called expressed robust t-statistic (ERT) for the identification of outliers. The usefulness of the proposed methods is then investigated through a Monte Carlo simulation.
Genelleştirilmiş Doğrusal Modeller Kullanılarak Satış Geçmişine Dayalı Talep Tahminlemesi

It's vital for commercial enterprises to accurately predict demand by utilizing the existing sales data. Such predictive analytics is a crucial part of their decision support systems to increase the profitability of the company.In predictive data analytics, the branch of regression modeling is used to predict a numerical response variable like sale amount. In this category, linear models are simple and easy to interpret yet they permit generalization to very powerful and flexible families of models which are called Generalized linear models (GLM). The generalization potential over simple linear regression can be explained twofold: First, GLM relax the assumption of normally distributed error terms. Moreover, the relationship of the set of predictor variables and the response variable could be represented by a set of link functions rather than the sole choice of the identity function.This work models the sales amount prediction problem through the use of GLM. Unique company sales data are explored and the response variable, sale amount is fitted to the Gamma distribution. Then, inverse link function, which is the canonical one in the case of gamma-distributed response variable is used. The experimental results are compared with the other regression models and the classification algorithms. The model selection is performed via the use of MSE and AIC metrics respectively. The results show that GLM is better than the linear regression. As for the classification algorithms, Random Forest and GLM are the top performers. Moreover, categorization on the predictor variables improves model fitting results significantly.
“It Has Been Quite a Journey”

Zimbabwe's health information system has developed over the years from a paper-based system using T-tally systems which had their fair share of successes and challenges. These challenges prompted the then Ministry of Health and Child Welfare, now, the Ministry of Health and Child Care and its partners to implement electronic health information management systems. This chapter therefore describes the challenges faced in Zimbabwe's health information management and how the Ministry of Health and Child Care and its partners responded to these challenges. The study also highlights success stories in health information management in the country. The purpose of the study is achieved through a literature review covering health information management in Zimbabwe.
Towards Resilient Cyber-Physical eHealth Systems

Current eHealth systems are cyber-physical systems (CPS) making safety-critical decisions based on information from other systems not known during development. To achieve the trust of users, measures of safety have to be taken into consideration in accordance with the ""privacy by design"" approach. This requires secure storage of information and guaranteeing safe exchange of data preventing unauthorized access, loss of data and cyber-attacks. From citizens’ point of view, eHealth is wholeness in which sectors of information security (availability/confidentiality/integrity) hold true. Present procedures emphasize confidentiality at the expense of integrity and availability, and regulations/instructions are used as an excuse not to change even vital information. The mental-picture of cyber security should turn from “threat, crime, attack” to “trust”. Creating confidence in safe digital future is truly needed in the integration of the digital and physical world’s leading to a new digital revolution. The precondition for the exchange of information “trust” must be systematically built at every CPS’ level (cyber, platform, and people).
Computer Scientists with Serious Music Avocations Share Their Thinking

This article presents the cognitive capacities and tendencies in the lives of computer scientists who play classical music as a serious avocation. Using qualitative research and thematic analysis methods, narratives of seven participants are constructed through open-ended interviews, from which the essence of their thinking skills is extracted in the form of four primary themes: being in the zone, assuming an engineering/scientific mindset, aesthetic thinking and joyous thinking. With such a rich thinking toolkit, this study motivates educators to support individuals with interdisciplinary interests and calls for such individuals not to leave behind their musical passions despite pragmatic career considerations.
Strategies To Attract Information Technology Students: An Extrapolation Of Worker Experiences

The predicted dramatic increase in demand for IT workers in the next decade suggests an opportunity for IT educators to establish plans to ramp up to meet these needs.The purpose of this paper is to help educators understand factors that are important to IT workers and that can be influenced by the educational process.The work reported here is part of an NSF grant that explored current IT worker experiences and motivations.Data for the study was collected using a forty-four-item questionnaire sent to currently employed IT workers in the Boston area.Stepwise multiple linear regression was used to relate certain outcome variables to attributes and opinions of the respondents as represented by question item responses.Relationships have been discovered that will help educators craft academic programs and identify potentially successful students in IT.For example, the analysis indicates questionnaire items associated with the challenge presented by an IT profession and family educational backgrounds in technical areas are important outcome predictors, while gender and age are less important.
A Case Study for User Rating Prediction on Automobile Recommendation System Using MapReduce

Recommender systems have been widely used in contemporary industry and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. Whats more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.
Assisted Coherence Distillation of Certain Mixed States

We study the assisted coherence distillation of two special classes of mixed states including the states with vanished basis-dependent discord in the incoherent basis of the second party and Werner two-qubit states via the sets of operations such as local incoherent operations and classical communication (LICC), local quantum-incoherent operations and classical communication (LQICC), separable incoherent operations (SI), and separable quantum-incoherent operations (SQI). We show that the assisted distillable coherence of the former can reach the upper bound, namely quantum-incoherent (QI) relative entropy via all the sets of operations considered above. In contrast, for the Werner two-qubit states the assisted distillable coherence is strictly smaller than the quantum-incoherent (QI) relative entropy. Our results also mean that the optimal rate of assisted coherence distillation of Werner two-qubit states can be achieved even in one-copy scenarios without the complex joint quantum operations on many copies. Finally, we discuss the coherence location in the multipartite system.
Evaluation of Basic Data Compression Algorithms in a Distributed Environment

Data compression methods aim at reducing the data size in order to make data transfer more efficient. To accomplish data compression, the basic algorithms such as Huffman, Lempel-Ziv (LZ), Shannon-Fano (SF) and Run-Length Encoding (RLE) are widely used. Most of the applications incorporate different variants of these algorithms. This paper analyzes the execution times, compression ratio and efficiency of compression methods in a client-server distributed environment. The data from a client is distributed to multiple processors/servers, subsequently compressed by the servers at remote locations, and sent back to the client. Our experimentation has been carried out using Simgrid Framework. Our results show that the LZ algorithm attains better efficiency/scalability and compression ratio, however, it works slower than other algorithms.
Coordinated Movement of Multiple Robots in Outdoor Environment with Obstacles Pages

This article mainly focuses on the proposal of the program that coordinates the movement of multiple robots in outdoor environment. The emphasis of the proposed algorithm is on collision free movement. Simulation with four robots that are controlled in an environment with obstacles is the result of these experiments. The formation of robots consists of a leader and three following robots, which are trying to copy leader¢‚¬„¢s movement. Robots use GPS, laser rangefinder, as well as bumpers sensors for the environment scanning. GPS is used as positioning sensor and other sensors are used for reactive navigation. Besides that, simulation uses virtual reality from Microsoft Robotics Studio 4 (MRS). In addition, programming language used for this simulation is called Visual Programming Language. Advantage of MRS usage is that the final code can be simply transferred into real robots.
Theory of Relative Dependency:&amp;#xD; Higher Coupling Concentration in Smaller Modules and its Implications for Software Refactoring and Quality

Our recent studies have repeatedly found that smaller modules are proportionally more defect-prone. In this paper, we formulated and tested a hypothesis stating that smaller modules are proportionally more coupled given that dependencies caused by coupling has been consistently associated with defect-proneness. We found strong evidence supporting this hypothesis. Further, we found that refactoring practices exacerbate this effect. Based on the highly consistent results obtained in this study, we state an empirically-based theory for software modules called the theory of relative dependency: In large scale software systems, smaller modules will be proportionally more dependent compared to larger ones. The implications of our findings for practice are twofold: (1) we now have an empirically supported mechanism explaining the observations that defect concentration is higher in smaller modules, which can be used by practitioners as an evidence while seeking resources and support to revise or amend the existing quality assurance and quality control practices in their organizations; (2) for projects that refactor extensively, such as those using agile methods, focusing defect detection and correction activities on smaller modules will lead to even more effective defect detection.
Information Utilization Ratio in Heuristic Optimization Algorithms

Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how sophisticated an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.
Cybercartography: A multimodal approach

Cartography is primarily a visual discipline. The concept of Cybercartography, however, helps move cartography beyond the visual modality with the ultimate goal of using all sensory modalities. To this end, Cybercartography permits us to migrate from a unimodal visual approach to a multimodal approach capitalizing upon combinations of sensory modalities, such as visual, auditory, and touch. The main reasons for developing multimodal interfaces are to help users achieve more efficient, natural, usable and easy to learn ways of interacting with computer applications, such as electronic maps. Furthermore, by offering users more extensive computing capabilities, multimodal interfaces permit more empowering applications relative to traditional interfaces. Such empowerment would be exemplified by a dynamic electronic map system, which could be interacted with using auditory, touch, and visual senses. Research is less advanced concerning the relationships between the map symbols and their meaning for nonvisual modalities (e.g., sound and haptic) than for the visual modality. This chapter examines the potential contribution of four nonvisual modalities to enhanced cartographic visualization. Specifically, these modalities are: speech, gesture, sound, and haptic (i.e., touch). Each of these four modalities is discussed in terms of their: (1) relative advantages and limits; (2) unimodal application; (3) multimodal application; and (4) cognitive workload implications.
LANGUAGE CAMP IN MALAYSIA: ASNAF STUDENTS’ NEEDS AND PERCEPTION

Purpose of the study: This study aims to identify Asnaf students’ needs in language learning and their perception of the implementation of the language camp module that had been developed by researchers using the ADDIE model&#x0D; Methodology: In-depth analysis of focus group interviews with Asnaf students was conducted, and the data obtained were analysed thematically according to several categories&#x0D; Main Findings and Novelty: The findings of this study revealed that students believed that their anxiety was lowered when they were given the opportunity to cooperate with their friends in a fully immersive and fun English language learning context&#x0D; Applications of this study: The modules and the findings of this study could be used by any English language educators to conduct English language camps especially to Asnaf children who stay in charity homes.
Improved PCA Face Recognition Algorithm

Face recognition technology, as a biometric recognition technology, is very mature and has many applications. It achieves in-depth applications in smart campus systems, such as classroom attendance, classroom behavior analysis, and smart restaurants. Using human faces as the face data foundation, computer vision and image processing technologies are applied to research and implement face recognition. Based on the principal component analysis (PCA) theory, this paper analyzed the characteristics of face data, studied the face recognition algorithm. Considering the LBP and SVM algorithm, an improved PCA face recognition algorithm was proposed. Through comparative experiments, the results show that the proposed algorithm can improve the accuracy of face recognition.