Reviewerly/exHarmony huggingface dataset:
UMLCollab

In collaborative software modeling the two main types of collaboration still present problems, such as the constant interruptions that hinder the cognitive process in synchronous collaboration, and the complicated and costly stages of conflict resolution in asynchronous collaboration. For this, this paper proposes a technique called "UMLCollab". This technique combines aspects from synchronous and asynchronous collaboration. Through experiments, developers applied the proposed solution and they achieved to an intermediate productivity in relation to traditional collaboration methods. The results showed that the "UMLCollab" improved the correctness of the changed models, the notion of developer regarding to the resolution of conflicts, and enabled the parallel changes occurring while other collaborators are working on without degrade the software diagrams being modelled locally.
Improvement of a Monitoring System for Preventing Elderly Fall Down from a Bed

Elderly sometime falls down from the bed. And, elderly’s thighbone is broken. This accident makes it that it is decline that the quality of life of elderly. Therefore, to solve this problem, we proposed monitoring system. The proposed monitoring system is not able to adapt individual differences. To solve this problem, we proposed a new learning method. From the results of the previous researches, the new learning method is adapted to the proposed monitoring system. And ability of the proposed monitoring system is increase. From the experimental result, when the initial learning is completed, detection rate of the dangerous behavior is 79.8% (399/500) and detection rate of the safe behavior is 82.4% (412/500). After proposed learning method is executed, detection rate of the dangerous behavior is 84.0% (420/500) and detection rate of the safe behavior is 91.0% (455/500) From the experimental results, it is concluded that the predicts rate increase.
Trade-off between Performance and Security for Coding and Ring Learning With Errors-based Diffie-Hellman Cryptosystems

In this work, we provide a complexity analysis of the trade-off between performance and security for two post-quantum key exchange cryptosystems: a protocol based on coding theory (Ouroboros) and the lattice-based ring learning with errors key exchange (RLWE). Ouroboros is considered secure because, according to the general belief, the Quasi-Cyclic Syndrome Decoding (QCSD) problem is NP-hard. The RLWE cryptosystem is considered secure due to the hardness of the Learning With Errors problem, that is proven to be as hard as the Shortest Vector Problem (SVP). We analyze the tradeoff between performance and security for those cryptosystems in comparison with the Discrete Logarithm Problem (DLP) and the Integer Factorization Problem (IFP). Regarding key lengths, RLWE outperforms Ouroboros for desired security levels.
A Whirlwind of Change Data: How Libraries and Vendors Translate Data Into Discovery and Access

Libraries and vendors share change data to ensure successful discovery and access for users. Change data have increased dramatically and affected data communication, causing barriers to access. Serials data used to be simple: There were new, ceased, and title changes. Data were efficiently exchanged. Now data are complex and difficult to communicate in the digital age. This presentation will discuss the challenges of change data and the ways libraries and vendors are teaming up to calm the whirlwind of change.
The Psychological and Physiological Effects of Experimentally Induced Smiling During Physical and Social Pain

Author(s): Cross, Marie | Advisor(s): Pressman, Sarah D | Abstract: Smiling has been shown to be beneficial in a number of social situations. One context in which smiling has not been well researched, however, is within physical health and health-relevant outcomes. The small amount of research in this area has found that smiling may buffer the cardiovascular effects of acute pain. The goals of this dissertation were to 1) investigate smiling in the context of physical and social pain and 2) examine potential mediators (orbicularis oculi activation in Study 1 and positive affect in both studies) that may underlie the connections between smiling and acute pain. In Study 1, participants were randomly assigned to make Duchenne (genuine) smiles, grimaces, or neutral facial expressions during two acute physical pain tasks. Cardiovascular responses (heart rate, respiratory sinus arrhythmia, and pre-ejection period) were measured at baseline, during the tasks, and after the tasks. Pain threshold, pain tolerance, and self-reported pain were also measured. Results showed that trajectories across cardiovascular variables of interest did not significantly differ between the facial expression conditions. There were also no significant differences in pain tolerance or self-reported pain among the conditions. However, participants who made Duchenne smiles during the cold pressor had significantly higher pain threshold levels than participants who made grimaces, although there were no differences between either of these groups and the neutral facial expression condition. Furthermore, some evidence was found to support PA and orbicularis oculi activation as mediators of smiling and outcome variables. In Study 2, participants were randomly assigned to make Duchenne smiles or neutral facial expressions while they were included or excluded during Cyberball, a task that has been found to reliably elicit feelings of social exclusion. Results showed that participants who made Duchenne smiles while being excluded from Cyberball had significantly different heart rate trajectories than participants who were making neutral facial expressions. There were no differences in respiratory sinus arrhythmia, pre-ejection period, or self-reported pain between conditions. No evidence was found for positive affect as a mediator between smiling and outcome variables. Taken together, these studies provide some evidence that smiling during physically or socially painful situations might be beneficial.
LGM-PC

In this demonstration, we present LGM-PC, a QGIS plugin for automatic recommendation of categories for new POIs. LGM-PC allows users to train classification models on individual areas of POIs and, then, use these models in order to classify new POIs into categories. The tool produces ranked category recommendations, based solely on the name of the POI, its coordinates and properties of its surrounding POIs, which are already annotated with categories. The user is then required to validate the produced recommendations, by selecting the most fitting category. Being implemented as a QGIS plugin, LGM-PC allows the visualization of POIs on map layers, for further assisting the user in the final category selection task.
The development and application of computer aided software in college physical education

Abstract The development and application of computer aided software in college physical education has many benefits, such as, it is helpful for stimulating students’ subjective initiative and improving their innovative spirit and practical ability. The computer aided software can promote the physical education teaching in colleges and universities to be modernized, scientific and standardized. This paper first defines the meaning of computer aided software application, and then it analysis the frame design and ADDIR modeling. Finally, this paper briefly analysis the application of CAI and CAT to improve the overall teaching level of college physical education.
A Lightweight YOLOv2 Object Detector Using a Dilated Convolution

In recent years, object detection approaches such as you-only-look-once (YOLO) have been getting a special attention owing to the emerging trend of autonomous driving systems. However, memory and computation complexity are usually known as the bottlenecks in implementing a YOLOv2 in hardware design. This study proposes a simple yet effective variant of YOLOv2 by using dilated convolution to reduce its complexity. Experimental results show that the proposed method achieves up to 36% of memory access reduction and 9% of computation reduction on a network with a negligible performance loss of 1.75% on the well-known VOC dataset.
Performance Modeling and Assessment of Unified Video Surveillance System Based on Ubiquitous SG-eIoT

Video surveillance system is an important application system on the ubiquitous SG-eIoT. A comparative analysis of the traditional video surveillance scheme and the unified video surveillance solution in the eIoT environment is made. Network load and service latency parameters under the two schemes are theoretically modeled and simulated. Combined with the simulation results, the corresponding suggestions for the access of video terminals in the ubiquitous eIoT are given.
Processing automatic seismic event detections: an iterative sorting algorithm improving earthquake hypocentres using interevent cross-correlation

SUMMARY We present an iterative classification scheme using interevent cross-correlation to update an existing earthquake catalogue with similar events from a list of automatic seismic event detections. The algorithm automatically produces catalogue quality events, with improved hypocentres and reliable P- and S-arrival time information. Detected events are classified into four event categories with the purpose of using the top category, with the highest assessed event quality and highest true-to-false ratio, directly for local earthquake tomography without additional manual analysis. The remaining categories have varying proportions of lower quality events, quality being defined primarily by the number of observed phase onsets, and can be viewed as different priority groups for manual inspection to reduce the time spent by a seismic analyst. A list of 3348 event detections from the geothermally active volcanic region around Hengill, southwest Iceland, produced by our migration and stack detector (Wagner et al. 2017), was processed using a reference catalogue of 1108 manually picked events from the same area. P- and S-phase onset times were automatically determined for the detected events using correlation time lags with respect to manually picked phase arrivals from different multiple reference events at the same station. A significant improvement of the initial hypocentre estimates was achieved after relocating the detected events using the computed phase onset times. The differential time data set resulting from the correlation was successfully used for a double-difference relocation of the final updated catalogue. The routine can potentially be implemented in real-time seismic monitoring environments in combination with a variety of seismic event/phase detectors.
EFFECT OF ENTREPRENEURIAL ORIENTATION AND FINANCIAL RESOURCES ON THE PERFORMANCE OF WOMEN- OWNED ENTERPRISES IN NORTHERN NIGERIA

The objective of this study is to extend the Resource Based View (RBV) of the firm in investigating the effect of entrepreneurial orientation and financial resources on the performance of women owned enterprises in Northern Nigeria. The study is a cross-sectional survey which used structured questionnaire to collect data from 342women owner/ managers of micro and small businesses out of which 216 of the instrument was used for the analysis. The sample size was determined using Israel, (1992) formula for determining sample size. The hypothesized paths were examined with the use of Partial Least Squares Structural Equation Modeling (PLS-SEM). The result indicates that entrepreneurial orientation and financial resources have significant positive effects on the performance of women owned enterprises in the study area. The study recommends that women owner/ managers should adopt the managerial style that reflects their willingness to be innovative and proactive. Also women should intensify their efforts in sourcing for adequate financing from internal and external sources.
JPIAspectZ: A Formal Requirement Specification Language for Joint Point Interface AOP Applications

Aspect-oriented software development (AOSD) solves a few issues of the object-oriented software development (OOSD) approach and adds a few more concerning modules and their relationships. Join point interface (JPI) is an AOSD methodology that by the definition of the interface between advised artifacts and aspects solves associated AOSD issues to get software with a high modularity level. Looking for a JPI software development approach, this article proposes and exemplifies the use of JPIAspectZ, an extension of the formal aspect-oriented language AspectZ for the software JPI requirement specifications. Mainly, JPIAspectZ looks for a concept and model consistency in a JPI software development process. Since the main JPI characteristics are the joining point interfaces definitions, i. e. explicit associations definition between aspects and advised modules, thus, by JPI, classes are no longer oblivious of possible interaction with aspects, and aspects, for their action effectiveness, do not depend anymore on signatures of advisable module components. JPIAspectZ fully supports these JPI principles. As JPI application examples, this article shows the formal requirements specification for classic aspect-oriented and JPI examples, along with describing the advantages and disadvantages of this language.
Scrambled Body: A Method to Compare Full Body Illusion and Illusory Body Ownership of Body Parts

Humans can feel as if a fake body is their own body in the illusory body ownership. The illusion can be induced to a full body by a visual-tactile synchronicity or visual-motor synchronicity. In our previous study, illusory full body ownership of invisible body was elicited by synchronous movements of virtual gloves and socks. In this study, we aimed to investigate whether the spatial relationship is necessary for the full body illusion using a stimulus of scrambled body. In the scrambled body, positions of gloves and socks were scrambled. The results suggest that spatial relationship of body parts is necessary for the full body illusion.
Facial Expression Recognition System Based on Deep Residual Fusion Neural Network

Rich and varied facial expressions are the intuitive carriers for transmitting emotional information to each other. Due to the variety of facial expressions, the extraction of features is quite difficult. The traditional manual extraction method can neither achieve better recognition accuracy nor guarantee the recognition efficiency. This paper uses 18-layer residual neural network, and realizes permanent mapping by means of the short-circuit connection of residual modules to ensure the network capability of deep structures. At the same time, the CLBP texture features are extracted, and the two are innovatively combined to form a more representative description feature. The experimental results show that compared with the DCNN, DBN and other networks, the convergence time is shorter and the average recognition rate is 93.24%, which is nearly 5% higher.
Multi-target oriented UAV reconnaissance path planning algorithm

Aiming at the problem of path planning when unmanned aerial vehicle (UAV) reconnoitre multiple targets, a UAV path planning algorithm for multi-target reconnaissance is proposed. Firstly, the optimal k-means clustering algorithm is used to cluster a plurality of closely related targets into one target, which reduces the scale of the problem. Then, in order to reduce the path cost of the algorithm, the coding method and crossover operation of the genetic algorithm (GA) are optimized to solve the reconnaissance sequence under the minimum path cost. Finally, the reconnaissance path is generated based on the reconnaissance sequence. The simulation results show that the algorithm can effectively reduce the path cost by 17.5%.
HTTPS Keys in the Mediterranean

Internet banking, online shopping, and many other internet services use Hypertext Transfer Protocol Secure (HTTPS) with digital certificates in a public key infrastructure (PKI) to authenticate and guarantee security and privacy. This work presents an analysis of the cryptographic asymmetric algorithms used in the digital certificates. Indeed, we found only the RSA and ECC-based algorithms. With a large database populated for about 5 years, we were able to analyze asymmetric algorithms. For certificates using ECC, we found only curves recommended by NIST and SECG. For the certificates that use RSA, all modulus of the public key passed unbroken by the Fermat’s factorization method with 100 steps. However, we discovered 71 private keys of 1 024-bit size.
ENTREPRENEURIAL LEADERSHIP AND INNOVATION: THE MEDIATING ROLE OF EMPLOYEE VOICE BEHAVIOR

This study uses a systematic quantitative research approach to identify the relationship between entrepreneurial leadership (EL) and innovation through the moderating effect of employee voice behavior (EVB). This study focuses on employees of Pakistani medium and large enterprises and implies that there is a positive and significant relation between EL and innovation and similarly between EL and EVB. Current study concludes that EVB also partially mediate the relationship between EL and innovation, which means that there are other factors mediating their relationship about which further research needs to be done. It can be concluded from this paper that the firms having entrepreneurial leadership ability in their employees and having EVB in organizational culture are usually good at innovation.
KORELASI KETERAMPILAN MEMBACA PEMAHAMAN DAN KETERAMPILAN MENULIS TEKS HASIL OBSERVASI SISWA KELAS VII SMPN 16 TEBO

This study aims to determine the relationship of reading skills in the text of the observation report and the text writing skills of the observation results of the VII students of SMPN 16 Tebo. This type of research uses quantitative research. This research method is descriptive method with correlational design. The population is seventh grade students at SMPN 16 Tebo which are divided into 105 people into four classes. The sample of this study was 32 people. This study consisted of two variables, namely the independent variable (reading comprehension skills of the text from the seventh grade observation report of Tebo 16 Public High School) and (dependent variable (writing skill in the seventh grade of Tebo SMPN 16 observational report). observations, need to read comprehension skills from observation reports.
Blockchain and Payment Systems

Bitcoin—the first virtual currency based on blockchain technology—was born out of an anarcho-libertarian dream to create a monetary system that—by relying on ‘trustless trust’—would be completely independent of the state and established financial institutions. Today, there is no doubt that blockchain technology will transform payments, the financial industry, and many other areas. However, this chapter argues that in regard to payments, this transformation will be far from the libertarian ideal. Rather, blockchain (1) will enable the rise of new powerful intermediaries and (2) it will be embraced by established payment services providers, who will use blockchain to modernize their services. As a result, decentralized virtual currencies like Bitcoin will remain on the periphery of the mainstream payments landscape. Blockchain has focused too narrowly on providing a technological solution to the issue of scarcity and solving the double-spending problem. Yet, problems involved in monetary and payment systems are broader. In particular, payment systems provide for a broad range of mechanisms supporting circulation of money which, for the scale and complexity of a modern economy must be backed by the state. Money is a hybrid public–private institution and it seems naïve to think that technology alone could render the role of state institutions in monetary and payment systems obsolete.
Correction to: Word Sense Disambiguation of Malayalam Nouns

In the original version of the book, the following belated corrections are to be incorporated.
Analysis of Decision Tree Induction Algorithms

Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.
Karşılıklı Kimlik Doğrulaması Sağlayan Etkin Elektronik Çek Defteri Şeması

In 1988, Chaum et al. introduced the idea of electronic check. Then, Pasupathinathan et al. tried to come up with an electronic checkbook scheme. However, their scheme requires signature for each e-check and is not considered as an e-checkbook. Later, three e-checkbook propositions are made by T.H Chen et al., Chang et al., and C.L. Chen et al. based on the scheme of W.K. Chen et al. Recently, Sertkaya and Kalkar showed that these three e-checkbook schemes are susceptible to e-check forgery and/or e-check manipulation attacks. They also proposed fixes for these schemes. Nonetheless, fixed versions also carry out drawbacks of the original schemes, like heavy hashing computations, time-synchronization issues, and multiple communication rounds. This study offers an efficient and secure e-checkbook scheme with mutual authentication.
Sequential Anomaly Detection Using Nonparametric Density Estimators

In this paper, we introduce an online anomaly detection algorithm to detect the anomalies in the observed data with two step approach in an unsupervised framework. In the first step, we estimate the density of the sequentially observed data with a novel kernel based approach. To this end, we partition the observation space and use nonparametric Kernel Density Estimator (KDE) in each region on a partition such that we do not assume any underlying distribution for the data. Then, we compare the estimated density of the data with a threshold to decide whether it is anomalous. We also solve the bandwidth selection problem in kernel based approaches in an efficient way. For this, we assign a set of kernel bandwidth values to each region, and make each estimator to converge to the best bandwidth choice for the corresponding subspaces in time. In our experiments, we show that our algorithm significantly outperforms the anomaly detection algorithms, which are highly used in the literature.
Removing the Oddity in First Degree Entailment

I discuss an old problem with first degree entailment (FDE), namely the unintuitive way disjunctions and conjunctions between statements with values B and N are defined. I supply a solution to this problem that involves a modification of FDE that leaves the philosophical motivation for the logic unharmed. Furthermore, I argue that this modification allows us to incorporate the philosophical core idea of exactly true logic without leading to the unusual inferential behavior of that logic.
Paper Recommendation with Item-Level Collaborative Memory Network

The recommendation system can recommend information to users personally and efficiently, which satisfies the user’s demand for information in the information age, and has become a hot topic in the current era. In the recommendation system, users and items and the interaction of their own information has a crucial impact on the efficiency and accuracy of the recommendations. However, most of the existing recommendation systems usually design the systems as user-base only, considering the user’s influence on the item in the recommendation, which to some extent blurs the interaction between items and users at the item level, unknown and potential connections between items and users are not well considered. In this paper, we propose a collaborative memory network that can focus on the potential relation between items and users, and consider the impact of items’ characteristics on user behavior. Experiments have shown that our improvement is better than the original method and other baseline models.