Reviewerly/exHarmony huggingface dataset:
Progresos recientes en sistemas de gestión de la información científica

This piece summarises some of the most recent advances in the area of research information management and Current Research Information Systems (CRIS). With a focus on the increasing level of system interoperability that the implementation of the Cerif-XML Guidelines for CRIS Managers jointly issued by OpenAIRE and euroCRIS is making possible, the update lists a number of improvements that adding CRIS systems to the list of OpenAIRE data providers may deliver to the attempt to capture a more comprehensive snapshot of the research being conducted in Europe
O uso de ferramentas de voz android e ios para desenvolver inglês para fins acadêmicos (eap): habilidades de fala para falantes não nativos de inglês

ESAP refers to English for a specific discipline, which specificity includes technical or special vocabulary, varying uses of academic grammar such as passive voice and nominalization, and particular professional and academic genres which are produced by the discipline.Thinking in how to increase the English speaking confidence and competence of non-native speakers, this study has aims to analyze how the use of Android and iOS voice recording tools can develop students' speaking English skills in an English Letters graduation course in Brazil.The study pointed out that voice recording tools provide learners with opportunities to produce oral output, since they allow students to practice their speaking skills inside and outside the classroom, bringing up more confidence and courage to use ESAP in classroom.
Markerless Human Motion Tracking Using Microsoft Kinect SDK and Inverse Kinematics

Motion capture systems are used to gauge the kinematic features of the motion in numerous fields of research. Despite superb accuracy performance, the commercial systems are costly and difficult to use. To solve these issues, Kinect has been proposed as a low-priced markerless motion capture sensor, and its accuracy has been assessed using previous motion capture systems. However, in many of these studies, the anatomical joint angles captured using the Kinect are compared to the 3D rotation angles reported by the gold standard motion capture systems. These incompatibilities in the determination of the human joint angles can lead to higher error estimation. To accomplish a valid accuracy evaluation of the Kinect, we applied the inverse kinematics techniques in both Vicon and Kinect version 2 skeleton models to estimate lower body joint angles. The proposed method enabled us to capture the pelvic, hip, and knee joint angles using a single Kinect camera during gait. Moreover, the dependency of the proposed method to the position of the Kinect and the speed of the moving subject was investigated. In this study, the captured data of the Vicon motion capture system were used as ground-truth to assess the accuracy of the Kinect data. The results indicate the capability of Kinect in capturing human joint angles and also an affordable motion capture system applied in robotics and biomechanics applications.
Differential Evolution Enhanced with Multiple Dimensional Scaling

Differential evolution algorithm is a well-known intelligent optimization algorithm. In the search process, the algorithm tends to converge prematurely, making the population trapped in local optima. To solve this problem, this paper proposes a new differential evolution algorithm based on multiple dimensional scaling (MDS). We tested the performance of the algorithm on 11 benchmark functions. Experimental results show that the proposed algorithm can achieve higher accuracy when compared with some other evolutionary algorithms reported in the literatures.
A Low Power Hardware Implementation of Lifting based Reversible Watermarking for Medical Image

A lifting domain reversible data hiding is presented here. A content based watermark is produced from selective (4×4) sized coefficient blocks of particular sub-band. One covert key is utilized to secure the watermark for access management which is specified by user. The secure watermark is implanted within same coefficient block (4×4). In receiver side the retrieval of original image is done by the authentic user's secure key. For real time application the `very large scale integration' (VLSI) architecture of this proposed encoder is designed in hardware. Simulation of the encoder is done by `field programmable gate array' (FPGA) kit. The experimentation is performed over a range of benchmark images. The results justify the supremacy of the method. The encoder module consumes only 59.20mW power when operated at 120.135MHz frequency in case of real time implementation.
Hand detection and segmentation using smart path tracking fingers as features and expert system classifier

Nowadays, hand gesture recognition (HGR) is getting popular due to several applications such as remote based control using a hand, and security for access control. One of the major problems of HGR is the accuracy lacking hand detection and segmentation. In this paper, a new algorithm of hand detection will be presented, which works by tracking fingers smartly based on the planned path. The tracking operation is accomplished by assuming a point at the top middle of the image containing the object then this point slides few pixels down to be a reference point then branching into two slopes: left and right. On these slopes, fingers will be scanned to extract flip-numbers, which are considered as features to be classified accordingly by utilizing the expert system. Experiments were conducted using 100 images for 10-individual containing hand inside a cluttered background by using Dataset of Leap Motion and Microsoft Kinect hand acquisitions. The recorded accuracy is depended on the complexity of the Flip-Number setting, which is achieved 96%, 84% and 81% in case 6, 7 and 8 Flip_Numbers respectively, in which this result reflects a high level of finite accuracy in comparing with existing techniques.
A meta-analysis of sleep and work performance

The present study utilizes meta-analytic techniques to examine the extent literature on sleep and work performance in field research. In line with previous meta-analytic research, results indicate that sleep and work performance have a positive relationship. However, more importantly, results from moderator analyses reveal valuable insight into the nuances of the sleep-work performance relationship. Specifically, results indicate that the type of sleep measurement (sleep quantity, sleep quality, and sleepiness), work performance measurement (task performance, organizational citizenship behavior, and counterproductive work behavior), study design (between-person and within-person), source of sleep measurement (self-report and non self-report), and sleep recall window (daily, weekly/monthly, and more than monthly) differentially influence the strength of the sleep-work performance relationship. In general, results confirm that sleep is an important construct to consider when studying, or attempting to improve, performance in the organizational context. Furthermore, results highlight the importance of measurement and study design considerations when conducting sleep-work performance research in field settings. Research implications, practical implications, potential limitations, and future directions are also discussed.
Gender, Equity, and the Venture Funding Impact of Migrant Remittances in Developing Countries

International business and entrepreneurship research provides substantial insight on how women differ from men as new business (venture) founders, but little insight in on how women may differ from men as venture funders. We respond with theory and evidence explaining venture funding tendencies among migrant women remitting money to 48 developing countries from 2001-2010. Remittances increase home-country venture funding availability more when they come from diasporas with higher percentages of women. Gender-related remittance effects on venture-funding availability are magnified for diasporas from poorer developing countries and for diasporas from developing countries with less gender inequality in public and private sector employment. Migrant women likely remit for venture funding purposes more than migrant men, especially where home-country need and work opportunities for women are greater.
Blame Tracking and Type Error Debugging

In this work, we present an unexpected connection between gradual typing and type error debugging. Namely, we illustrate that gradual typing provides a natural way to defer type errors in statically ill-typed programs, providing more feedback than traditional approaches to deferring type errors. When evaluating expressions that lead to runtime type errors, the usefulness of the feedback depends on blame tracking, the defacto approach to locating the cause of such runtime type errors. Unfortunately, blame tracking suffers from the bias problem for type error localization in languages with type inference. We illustrate and formalize the bias problem for blame tracking, present ideas for adapting existing type error debugging techniques to combat this bias, and outline further challenges.
A hybrid predicting model for displacement of multifactor-triggered landslides

This paper presents a new hybrid model for land-slide distance prediction. In the model, the cumulative displacement are divided into three parts: the trend term, the period term, and the random noise obtained by the wavelet domain de-nosing method and Hodrick-Prescott (HP) filter. The trend term controlled by the geological conditions is generated using the double exponential smoothing (DES). The period term is predicted by the extreme learning machine (ELM) model, and the dynamic multi-swarm particle swarm optimizer (DMS-PSO) algorithm is applied to obtain optimal parameters of ELM. Case study involving real data collected from the Baishuihe landslide in China is used to verify that the hybrid approach enhances the ability to calculate the period term. Inputs of the proposed model include the period factors extracted from the seasonal triggers and displacement values which enhance excellently the robustness of the prediction model of the period displacement. Extensive experiments are carried out on the Baishuihe landslide dates. Comparing with the predictions obtained by the real original displacement, our model is efficient for predicting the landslide distance of multiple factors induced landslide.
Performance Analysis of Deskewing Techniques for Offline OCR

Optical Character Recognition (OCR) is a significantly important research domain of pattern recognition. Achieving a better accuracy of recognition along with acceptable execution efficiency is a key challenge in performing the recognition task. The stage of preprocessing which assist the step of feature extraction is responsible for performing a series of operation including Deskewing to retain and maintain only that part of the information which is essential for the operation of recognition. The paper presents detailed performance analysis different skew detection methods. The analysis presented in this paper will help in deciding which method performs well for any particular script. The analysis is helpful in deciding deskewing technique for handwritten & printed script.
The fully visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016

After the 2016 double dissolution election, the 45th Australian Parliament was formed. At the time of its swearing in, the Senate of the 45th Australian Parliament consisted of nine political parties, the largest number in the history of the Australian Parliament. Due to the breadth of the political spectrum that the Senate represented, the situation presented an interesting opportunity for the study of political interactions in the Australian context. Using publicly available Senate voting data in 2016, we quantitatively analyzed two aspects of the Senate. First, we analyzed the degree to which each of the non-government parties of the Senate is pro- or anti-government. Second, we analyzed the degree to which the votes of each of the non-government Senate parties are in concordance or discordance with one another. We utilized the fully visible Boltzmann machine (FVBM) model to conduct these analyses. The FVBM is an artificial neural network that can be viewed as a multivariate generalization of the Bernoulli distribution. Via a maximum pseudolikelihood estimation approach, we conducted parameter estimation and constructed hypothesis tests that revealed the interaction structures within the Australian Senate. The conclusions that we drew are well supported by external sources of information.
A cryptosystem based on chaotic maps and factoring problems

A cryptosystem allows a sender to send any confidential or private message using a receiver's public key and the receiver next confirms the integrity and validity of the received message using his own secret key. Cryptosystem algorithms can be categorised based on the type of security suppositions, for example discrete logarithm, factorisation, and elliptic curve hard problems, which are all currently believed to be unsolvable in a reasonable time of period. Recently, cryptosystems based on chaotic maps have been proposed. Due to some subtle and close relationship between the properties of traditional cryptosystems and chaotic-based systems, the idea of using chaotic in cryptography has received a great deal of attention from many cryptography's researchers. Therefore, to enhance system security, we explore the implementation of a cryptosystem algorithm based on both cryptographic and chaotic system characteristics. We also provide security against known cryptographic attacks and discuss the performance analysis of the developed system.
Development of the Multimedia Virtual Reality-Based Application for Physics Study Using the Leap Motion Controller

In this paper we suggest to use new gestures in controlling of the objects in virtual physical laboratory. These custom gestures are based on Leap Motion controller SDK and allows creating any of desirable hand motions. Such an approach allows creating the most intuitive and friendly interaction between human and software. In educational applications as in our case, this way of interaction provides the highest level of knowledge adoption because of native way of information purchasing.
A corpus Study on Narrative Texts in Turkish Language

This study aiming to determine the frequency of use of words in the narrative texts in which the best examples of standard language use are displayed ranks 268 stories compiled from the prominent names of Turkish story-writing. Frequency analyses were made on a corpus of approximately 450 thousand words and the frequency of use of words were tried to be listed according to their lexical categories. The words ranking at a certain percentage (about 2%-4%) were scored. It was observed that general words and grammatically functional words had higher frequencies, and the wordswere used with their antonyms. In terms of Turkish, categorization of words according to their positions in the sentence and their affixations, possible categorization of words in more than one lexical category, and variable meanings which homonyms gain in context were found as the most significant problems in frequency analysis and corpus formation.
An Improved Chicken Swarm Optimization for TSP

Chicken Swarm Optimization is a kind of swarm intelligence algorithm which simulates the hierarchy and behavior of chicken swarm. For Traveling Salesman Problem (TSP) of combinatorial optimization, a path optimization scheme based on improved chicken swarm optimization is proposed. Combining the strategy of exchange operator and exchange sequence, the chicken swarm optimization for solving continuous space problem is mapped to discrete path optimization. At the same time, in order to improve the moving ability of chicken swarm, a self-exploration strategy is added to the chicken swarm and simulated annealing operator is combined to improve the global search performance of the algorithm and prevent the chickens from falling into local optimum due to over quick convergence. Finally, through data testing and simulation of TSPLIB standard library, the experimental results show that the improved chicken swarm optimization has better optimization ability than other similar algorithms.
A COMBINED DATA STORAGE WITH ENCRYPTION AND KEYWORD BASED DATA RETRIEVAL USING SCDS-TM MODEL IN CLOUD

With the hasty growth of the Internet technologies and the need for increased computational methods, cloud computing has become a new paradigm in the business and IT endeavors. This technology facilitates the enterprises to move their confidential data into the cloud, where their data is stored in a remote data centers and accessed via the internet. These resources are controlled by third parties who may not be trustworthy and provide adequate data security. While this technology has many benefits, the security issues also increases. Various methods have been proposed for designing a secure cloud storage system.However many of the prior works suffers from security, confidentiality, and integrity issues that limit the functionalities of the storage system. Moreover, the existing schemes suffers delay due to the lack of online verification, and increasein the communication cost as data is retrieved back to the owner for every request made by the user. Hence it remains a challenge for constructing an efficient data storage and data retrieval. In this paper, we present the development of a secure and efficient cloud storage system, â€œSCDS-TMâ€ that supports data confidentiality, data integrity, and data retrieval functionalities. With the use of Elliptic Curve Cryptography and storage correctness proof, weovercame the data confidentiality and data integrity issues. In order to augment the data retrieval functionality, we exploited a keyword search method called â€œcoordinate matchingâ€ technique of capturing the related documents based upon the search query.The performance of the proposed method is analyzed based on various factors such as communication overhead,data transfer rate, query execution time, data security level, probability ratio and precision ratio.Thorough analysis of scrutinizing the privacy and efficiency allow our proposed method to be secure, efficient and offers a higher level of security with low communication overhead.
Hacking System Change

The literature on sociotechnical systems tends to focus either on market-based solutions or on coordinated governance as change drivers; thus neglecting the plurality of potential responses to situations in which information is limited, resources are scarce, time is short, stakes are high, and decision-making is urgent. This research develops the concept of System Hacking, after collecting and analysing qualitative data from 12 self-declared hackers and cybersecurity experts. Our analysis scrutinizes the definition of System Hacking and identifies 9 characteristics entailing this concept. Following its definition and characterization, we contrast this novel concept to other change drivers of sociotechnical systems; thereby unpacking possibilities for future research on how to steer change towards more desirable directions.
Multi-Cue Vehicle Detection for Semantic Video Compression in Georegistered Aerial Videos

Detection of moving objects such as vehicles in videos acquired from an airborne camera is very useful for video analytics applications. Using fast low power algorithms for onboard moving object detection would also provide region of interest-based semantic information for scene content aware image compression. This would enable more efficient and flexible communication link utilization in lowbandwidth airborne cloud computing networks. Despite recent advances in both UAV or drone platforms and imaging sensor technologies, vehicle detection from aerial video remains challenging due to small object sizes, platform motion and camera jitter, obscurations, scene complexity and degraded imaging conditions. This paper proposes an efficient moving vehicle detection pipeline which synergistically fuses both appearance and motion-based detections in a complementary manner using deep learning combined with flux tensor spatio-temporal filtering. Our proposed multi-cue pipeline is able to detect moving vehicles with high precision and recall, while filtering out false positives such as parked vehicles, through intelligent fusion. Experimental results show that incorporating contextual information of moving vehicles enables high semantic compression ratios of over 100:1 with high image fidelity, for better utilization of limited bandwidth air-to-ground network links.
Chinese Agricultural Entity Relation Extraction via Deep Learning

With the advent of Deep Learning (DL), Natural Language Processing (NLP) has progressed at a high speed in the past few decades. Some DL models have been established for Relation Extraction and outperform than the traditional Machine Learning (ML) methods. In this paper, we built four DL models: Piecewise Convolutional Neural Network (PCNN), Convolutional Neural Network (CNN), Recurrent Neural network (RNN) and Bidirectional Recurrent Neural Network (Bi-RNN) to tackle the task. Using PCNN, we outperform than other three models, and achieve Area under Curve (AUC) of 0.154 with just 24 epochs. And we use some selector mechanism to improve the model. Our experimental results show that: (1) Attention mechanism got the best compatibility with all models, but in some case, max pooling may perform better than it. (2) Using only word embeddings, the performance of the model will discount a lot.
Highly Succinct Dynamic Data Structures

It is attempted to elucidate a number of known dynamic data structures that come close to using the absolutely minimal amount of space, and their connections and ramifications are explored.
Opportunity Evaluation in Organizations: A Social Psychological Model

We develop a formal model of social psychological influences on collective opportunity evaluation in organizations. We use the model to analyze how group social and cognitive dynamics shape the ability to evaluate strategic opportunities accurately, in stable and dynamic environments and for organizations with different learning capabilities and processes. We find that the ability to choose good opportunities and forgo bad ones depends on the socio-cognitive position of the leaders of evaluation groups, relative to the knowledge of other organizational members and the external environment, and how they involve others in evaluation decisions. Surprisingly, we find that in dynamic environments using cognitively central members of the organization to aggregate individuals’ judgments is more effective than relying on experts and that homophily can be an effective way to collect others’ assessments. Overall, our analysis provides a more dynamic, networked and relational view of opportunity evaluation in organizations. We discuss implications for research on information aggregation in organizations and top management teams.