Reviewerly/exHarmony huggingface dataset:
Relationship between Owner-Managers' Creative Self-Efficacy, Career Involvement and Career Subjective Well-Being: Evidence from Marine-Related Enterprises in Coastal City

Yu, C., 2020. Relationship between owner-managers' creative self-efficacy, career involvement and career subjective well-being: Evidence from marine-related enterprises in coastal city. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 442-445. Coconut Creek (Florida), ISSN 0749-0208.The study aimed to investigate relationship between entrepreneurs' creative self-efficacy, career involvement and career subjective well-being, career involvement mediated the relationship between creative self-efficacy and career subjective well-being. Reviews the literature relating to the effects of innovative behavior is presented to provide the basis for the research hypotheses. We surveyed a sample of 130 marine-related enterprises in coastal city (Shenzhen), in China, a wide range of location, regarding their creative self-efficacy, career involvement and career subjective well-being. Variables were measured by subjective and self-described questionnaires. Regression analyses, correlate analyses and so on were used to get the conclusion. The results showed that (1) Creative self-efficacy predicted career subjective well-being. (2) Career involvement as a mediator of the relationship between creative self-efficacy and career subjective well-being. An understanding of the antecedents of subjective well-being could provide decision-making advice for coastal cities to develop innovative behavior of marine-related enterprises. The results of this study had implications for the economic development of coastal cities and the owner-manager innovation behavior and career management of marine-related enterprises. This paper examined the relations among creative self-efficacy and subjective well-being in career field for the first time, and made a valuable contribution to the study of the owner-manager career and innovation literature of marine-related enterprises. This article also had certain reference value to the policy makers who intend to improve the innovation behavior and subjective well-being of the owners-managers of the enterprises in the coastal cities.
Leveraging Multilingual Resources for Language Invariant Sentiment Analysis.

Sentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages. However, these methods rely heavily on large amounts of labeled data or sentiment weighted language-specific lexical resources that are unavailable for low-resource languages. Our work attempts to tackle this data scarcity issue by introducing a neural architecture for language invariant sentiment analysis capable of leveraging various monolingual datasets for training without any kind of cross-lingual supervision. The proposed architecture attempts to learn language agnostic sentiment features via adversarial training on multiple resource-rich languages which can then be leveraged for inferring sentiment information at a sentence level on a low resource language. Our model outperforms the current state-of-the-art methods on the Multilingual Amazon Review Text Classification dataset [REF] and achieves significant performance gains over prior work on the low resource Sentiraama corpus [REF]. A detailed analysis of our research highlights the ability of our architecture to perform significantly well in the presence of minimal amounts of training data for low resource languages.
Evaluation methodology for mission-centric cyber situational awareness capabilities

The emerging need for cyber defence capabilities able to bring closer cyberspace supremacy in joint military operations has led defence practitioners to begin a cyber race where academy, researchers, industry and military organizations work together. Because of the higher maturity of civilian technologies for cybersecurity, this often involves adapting capabilities not initially intended for military use to new dual-use requirements, where concepts like operations, missions or Courses of Action (CoAs) shall be presented and aligned with the military doctrine. One of the main 'battle horses' in this transformation is to develop supporting systems able to facilitate the mission-centric acquisition of Cyber Situational Awareness (CSA), where the observations in the cyberspace shall be properly correlated, propagated and understood in the scope of planned/ongoing mission. But despite these CSA needs, there is a wide methodological gap in the lack of suitable validation and verification frameworks, which not only relies on the raising need for capabilities able to verify if the existing solutions meet the requirements to operate on military actions, but also to support the thorough development life-cycle of brand new cyber defence technologies. In these grounds, this research introduces a novel evaluation framework able to guide the evaluation of CSA related tools, for which three core validation concepts are discussed: software, operational and application tests. They cover from the day-to-day implementation of the new capabilities, to their ability of facilitating that human decision-makers acquire a joint operational picture.
Experiments on Cross-Language Information Retrieval Using Comparable Corpora of Chinese, Japanese, and Korean Languages

Kishida, Kazuaki Chen, Kuang-huaThis paper describes research activities for exploring techniques of cross-language information retrieval (CLIR) during the NACSIS Test Collection for Information Retrieval/NII Testbeds and Community for Information access Research (NTCIR)-1 to NTCIR-6 evaluation cycles, which mainly focused on Chinese, Japanese, and Korean (CJK) languages. First, general procedures and techniques of CLIR are briefly reviewed. Second, document collections that were used for the research tasks and test collection construction for retrieval experiments are explained. Specifically, CLIR tasks from NTCIR-3 to NTCIR-6 utilized multilingual corpora consisting of newspaper articles that were published in Taiwan, Japan, and Korea during the same time periods. A set of articles can be considered a “pseudo” comparable corpus because many events or affairs are commonly covered across languages in the articles. Such comparable corpora are helpful for comparing the performance of CLIR between pairs of CJK and English. This comparison leads to deeper insights into CLIR techniques. NTCIR CLIR tasks have been built on the basis of test collections that incorporate such comparable corpora. We summarize the technical advances observed in these CLIR tasks at the end of the paper.
Spectral Image Recognition Using Artificial Dynamic Neural Network in Information Resonance Mode

This paper shows a new type of artificial neural network with dynamic (oscillatory) neurons that have natural frequencies. Artificial neural network in the mode of information resonance implements a new method of recognition of multispectral images. A computer experiment was performed to recognize multispectral images by a dynamic neural network based on the resonance effect.
Research and Practice of Flip Classroom Based on WeChat Platform Combined with Formative Evaluation in Teaching

Objective: This paper constructs a joint teaching mode based on the WeChat flip class and formative evaluation, which is applied in the teaching of Analytical Chemistry, and explores its teaching effect.Methods: We adopted the new teaching mode with the students of Pharmacy in our university as the teaching objects, and then investigated on the learning effect and experience of the teaching objects via questionnaire after the teaching.Results: The research objects were satisfied with the learning effect and experience brought about by the new teaching mode, indicating that the teaching effect was good.Conclusion: The teaching mode based on WeChat flip classroom and formative evaluation can construct a relatively fair evaluation system, trigger students' interest in learning, promote their independent learning, effectively overcome their weariness of analytical chemistry, and effectively improve the teaching effect of analytical chemistry, which is worthy of continuous optimization and promotion in teaching.
CUDA Accelerated 2-OPT Local Search for the Traveling Salesman Problem

This research involves the development of a compute unified device architecture (CUDA) accelerated 2-opt local search algorithm for the traveling salesman problem (TSP). As one of the fundamental mathematical approaches to solving the TSP problem, the time complexity has generally reduced its efficiency, especially for large problem instances. Graphic processing unit (GPU) programming, especially CUDA has become more mainstream in high-performance computing (HPC) approaches and has made many intractable problems at least reasonably solvable in acceptable time. This chapter describes two CUDA accelerated 2-opt algorithms developed to solve the asymmetric TSP problem. Three separate hardware configurations were used to test the developed algorithms, and the results validate that the execution time decreased significantly, especially for the large problem instances when deployed on the GPU.
Diversification of Serbian-French-English-Spanish Parallel Corpus ParCoLab with Spoken Language Data

In this paper we present the efforts to diversify Serbian-French-English-Spanish corpus ParCoLab. ParCoLab is the project led by CLLE research unit (UMR 5263 CNRS) at the University of Toulouse, France, and the Romance Department at the University of Belgrade, Serbia. The main goal of the project is to create a freely searchable and widely applicable multilingual resource with Serbian as the pivot language. Initially, the majority of the corpus texts represented written language. Since diversity of text types contributes to the usefulness and applicability of a parallel corpus, a great deal of effort has been made to include spoken language data in the ParCoLab database. Transcripts and translations of TED talks, films and cartoons have been included so far, along with transcripts of original Serbian films. Thus, the 17.6M-word database of mainly literary texts has been extended with spoken language data and it now contains 32.9M words.
On a Temporal Logic of Prefixes and Infixes.

A classic result by Stockmeyer [Stockmeyer, 1974] gives a non-elementary lower bound to the emptiness problem for star-free generalized regular expressions. This result is intimately connected to the satisfiability problem for interval temporal logic, notably for formulas that make use of the so-called chop operator. Such an operator can indeed be interpreted as the inverse of the concatenation operation on regular languages, and this correspondence enables reductions between non-emptiness of star-free generalized regular expressions and satisfiability of formulas of the interval temporal logic of the chop operator under the homogeneity assumption [Halpern et al., 1983]. In this paper, we study the complexity of the satisfiability problem for a suitable weakening of the chop interval temporal logic, that can be equivalently viewed as a fragment of Halpern and Shoham interval logic featuring the operators B, for begins, corresponding to the prefix relation on pairs of intervals, and D, for during, corresponding to the infix relation. The homogeneous models of the considered logic naturally correspond to languages defined by restricted forms of regular expressions, that use union, complementation, and the inverses of the prefix and infix relations.
Efficient Computation of Expectations under Spanning Tree Distributions

We give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms. These algorithms are easy to implement with or without automatic differentiation software. We motivate the development of our framework with several \emph{cautionary tales} of previous research, which has developed numerous inefficient algorithms for computing expectations and their gradients. We demonstrate how our framework efficiently computes several quantities with known algorithms, including the expected attachment score, entropy, and generalized expectation criteria. As a bonus, we give algorithms for quantities that are missing in the literature, including the KL divergence. In all cases, our approach matches the efficiency of existing algorithms and, in several cases, reduces the runtime complexity by a factor of the sentence length. We validate the implementation of our framework through runtime experiments. We find our algorithms are up to 15 and 9 times faster than previous algorithms for computing the Shannon entropy and the gradient of the generalized expectation objective, respectively.
ANALYSIS OF THE CLIMATIC FEATURES OF THE REGIONS OF THE PRIMARY APPLICATION OF THE SYSTEMS FOR PRODUCING WATER FROM THE ATMOSPHERIC AIR

The object of research is the thermal and humidity processes occurring during artificial cooling of atmospheric air to the dew point temperature in the evaporators of refrigeration machines of the compression and absorption type. This study solves the problem of finding energy efficient heat and humidity modes for cooling atmospheric air to the dew point temperature in regions with a shortage of water resources and high solar installations. It is shown that practically in all considered climatic zones with a shortage of water resources, the process of obtaining water from atmospheric air is most energetically expended in the winter period of the year, and the most energetically efficient - in the summer period. In the summer period of the year, the specific energy consumption is numerically comparable in the cooling temperature range from 5 °C to 15 °C. It is also shown that the application of the technology of night radiation will create a reserve of natural cold for additional cooling of condensers of refrigerating machines of various types during the entire period of operation. The area of practical use of the research results: compression and absorption refrigeration machines operating in systems for obtaining water from atmospheric air and taking into account seasonal changes in the thermal and humidity parameters of atmospheric air. An innovative technological product: energy-efficient systems for obtaining water from atmospheric air based on refrigeration machines of the compression and absorption type, which also work with the help of solar radiation. Scope of application of the innovative technological product: systems for supplying the population with drinking and process water, mainly in areas with a shortage of water resources.
P-Aevol: An OpenMP Parallelization of a Biological Evolution Simulator, Through Decomposition in Multiple Loops

This paper presents how we have achieved the parallelization of Aevol, a biological evolution simulator, on multi-core architecture using the OpenMP standard. While it looks like a simple for-loop problem with independent iterations, the stochastic nature of Aevol makes the duration of the iterations unpredictable and it conveys a high irregularity. Classical scheduling algorithms of OpenMP runtimes turn out to be inefficient. By analysing the origin of this irregularity, this paper present how to transform the highly irregular Aevol for-loop to a sequence composed by a small duration irregular for-loop followed by work intensive for-loop easy to schedule using classical LPT algorithm. This method leads to a gain up to 27% from the best OpenMP loop schedule.
Too good to be true? Predicting author profiles from abusive language

The problem of online threats and abuse could potentially be mitigated with a computational approach, where sources of abuse are better understood or identified through author profiling. However, abusive language constitutes a specific domain of language for which it has not yet been tested whether differences emerge based on a text author's personality, age, or gender. This study examines statistical relationships between author demographics and abusive vs normal language, and performs prediction experiments for personality, age, and gender. Although some statistical relationships were established between author characteristics and language use, these patterns did not translate to high prediction performance. Personality traits were predicted within 15% of their actual value, age was predicted with an error margin of 10 years, and gender was classified correctly in 70% of the cases. These results are poor when compared to previous research on author profiling, therefore we urge caution in applying this within the context of abusive language and threat assessment.
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms

Evolutionary Neural Architecture Search (ENAS) can automatically design the architectures of Deep Neural Networks (DNNs) using evolutionary computation algorithms. However, most ENAS algorithms require intensive computational resource, which is not necessarily available to the users interested. Performance predictors are a type of regression models which can assist to accomplish the search, while without exerting much computational resource. Despite various performance predictors have been designed, they employ the same training protocol to build the regression models: 1) sampling a set of DNNs with performance as the training dataset, 2) training the model with the mean square error criterion, and 3) predicting the performance of DNNs newly generated during the ENAS. In this paper, we point out that the three steps constituting the training protocol are not well though-out through intuitive and illustrative examples. Furthermore, we propose a new training protocol to address these issues, consisting of designing a pairwise ranking indicator to construct the training target, proposing to use the logistic regression to fit the training samples, and developing a differential method to building the training instances. To verify the effectiveness of the proposed training protocol, four widely used regression models in the field of machine learning have been chosen to perform the comparisons on two benchmark datasets. The experimental results of all the comparisons demonstrate that the proposed training protocol can significantly improve the performance prediction accuracy against the traditional training protocols.
Monitoring dan Evaluasi Kinerja Karyawan menggunakan Algoritma Simple Additive Weighting dan Hungarian

Monitoring and evaluating the performance of Â employees of PT Akulaku Silvrr Indonesia still use microsoft excel application online, Â Â which has some weaknesses, which is an obstacle in monitoring is that Â adalah it is unable to give specific job ordersÂ Â Â and can not provide due date information,whereas Â in determining the provision of salary increase and employee benefits is done randomly and only based on the last education. Tohelp the company succeed in achieving its goals, a system can be needed that can help in making decisions on a large or small scale. TheÂ use of Simple Additive Weighting Â (SAW) method can be used to determine pay raises and incentivize precisely and accurately Â based on the specified criteria, while hungarian method is used to determine who will do the job at a difficult, easy and moderate level. So the rating for the salary increase for the support team is Andi Ansyah, Hangga Bagus, Chikal Aviv and Ray Awaludin. Determining the next task can be determined that Andi Ansyah can do the job easily and difficultly, Hangga Bagus can do the job easily and moderately, Chikal Aviv can do a medium and difficult job. While Ray Awaludin does not have the highest value for each job, so it is necessary to be given training.
Securing Data Communication of Internet of Things in 5G Using Network Steganography

Data transmission security is the foundation of reliable application of the Internet of Things. With the rapid development of the Internet of Things, big data, 5G technologies in recent years, the Internet of Things has been widely used in industrial production, healthy domain, smart environment domain, national security, and other fields. The problem that monitoring data can be easily eavesdropped and tampered in wireless channel transmission has been recognized gradually. Some confidential data with sensitive information of users, such as medical data, must be protected during transmission. The traditional method cannot meet the requirement of some privacy data transmission stage. In this paper, we proposed a novel approach based on network steganography to improve the security of data transmission in 5G IoT network, which could improve the security of private data with less communication overhead.
Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects

Rehabilitation and evaluation of motor function are important for motor disabled patients.In stride length estimation using an IMU attached to the foot, it is necessary to detect the time of the movement state, in which acceleration should be integrated.In our previous study, acceleration thresholds were used to determine the integration section, so it was necessary to adjust the threshold values for each subject.The purpose of this study was to develop a method for estimating stride length automatically using an artificial neural network (ANN).In this paper, a 4-layer ANN with feature extraction layers trained by autoencoder was tested.In addition, the methods of searching for the local minimum of acceleration or ANN output after detecting the movement state section by ANN were examined.The proposed method estimated the stride length for healthy subjects with error of -1.88 ± 2.36%, which was almost the same as the previous threshold based method (-0.97 ± 2.68%).The correlation coefficients between the estimated stride length and the reference value were 0.981 and 0.976 for the proposed and previous methods, respectively.The error ranges excluding outliers were between -7.03% and 3.23%, between -7.13% and 5.09% for the proposed and previous methods, respectively.The proposed method would be effective because the error range was smaller than the conventional method and no threshold adjustment was required.
Evaluation of Implementation of Public Elementary School Regrouping Policy in South Jakarta Region

This study aims to obtain factual information regarding the implementation of the policy of merging public elementary schools in the South Jakarta region which consists of SDN Rawajati 03, SDN Pondok Pinang 10, and SDN Pejaten Timur 11. The method used in this study is evaluative with data collection techniques using interviews, observation, and documentation. Subjects in the study are the Principal, Teachers, and School Operators. The research findings show that the school incorporation policy is very precisely implemented which is seen from three perspectives of school managers who are research subjects. Based on the principal's perspective, it is known that the school incorporation policy has an impact on school management that is more effective and efficient so that education can run smoothly. The school incorporation policy also makes teachers work together and discuss related learning strategies so that students become more effective in learning and teaching activities. Different results were found from the perspective of school operators, namely the implementation of the school merger policy resulting in the increasing workload of school operators.