Reviewerly/exHarmony huggingface dataset:
On the Evolutionary Relationship between Change Coupling and Fix-Inducing Changes

Change Coupling (CC) is the implicit relation formed between two or more changing software artifacts (e.g. source code). These artifacts are found to have design issues and code smells. Existing research has revealed the relationship between the change coupled relation of a class with the number of bugs in bug repositories. However, this ignored their true relation at the creation time of bugs or erroneous changes known as Fix-Inducing Changes (FIC). This paper tries to find the actual relationship between FIC and change coupled relations with respect to considering recent and all commits. This is done by traversing the entire history of a repository with a commit window of 100 commits and collecting data about FICs and metrics related to change coupling and object oriented system. It is found from the analysis that recent CC relations at the time of error are more correlated with new errors. Besides, it is found that explanatory power for predicting future erroneous change is more in recent CC relation than the one formed by considering all commits starting from the 1st commit.
CALIBRATING PHOTOGRAMMETRIC AIRBORNE CAMERA SYSTEMS WITH DIFFRACTIVE OPTICAL ELEMENTS

Abstract. This paper presents a laboratory approach for geometric calibration of airborne camera systems. The setup uses an incoming laser beam, which is split by Diffractive Optical Elements (DOE) into a number of beams with precisely-known propagation directions. Each point of the diffraction pattern represents a point at infinity and is invariant against translation. A single image is sufficient to allow a complete camera calibration in accordance with classical camera calibration methods using the pinhole camera model and a distortion model. The presented method is time saving, since complex bundle adjustment procedures with several images are not necessary. It is well suited for the use with frame camera systems, but it works in principle also for pushbroom scanners. In order to prove the reliability, a conventional test field calibration is compared against the presented approach, showing that all estimated camera parameters are just insignificantly different. Furthermore a test flight over the Zeche Zollern reference target has been conducted. The aerotriangulation results shows that calibrating an airborne camera system with DOE is a feasible solution.
Anomaly Detection for Bivariate Signals

The anomaly detection problem for univariate or multivariate time series is a critical question in many practical applications as industrial processes control, biological measures, engine monitoring, supervision of all kinds of behavior. In this paper we propose an empirical approach to detect anomalies in the behavior of multivariate time series. The approach is based on the empirical estimation of conditional quantiles. The method is tested on artificial data and its effectiveness is proven in the real framework of aircraft-engines monitoring.
Facial morphology prediction after complete denture restoration based on principal component analysis

In developing treatment plan before complete denture restoration, doctors need to help the patient regain chewing ability while considering facial shape reconstruction after the surgery. At present, facial deformation prediction depends on the subjective judgment and experience of doctors; thus, an accurate basis for scientific quantitative analysis is lacking. With the development of computer technology, this paper proposed new facial morphology prediction method based on principal component analysis. Firstly, the curvature feature template with few feature points is constructed to replace the deformed areas of facial models. Secondly, the principal component analysis method is used to construct an elastic deformation prediction model for complex skin tissue. Finally, the Laplacian deformation technology is used to reconstruct the facial model and to obtain an intuitive digital 3D model. This method can adjust the facial deformation amplitude interactively by controlling shape parameters and predict the effect in consideration of different doctors' varied needs and habits. The experiments show that this method can predict the facial models interactively and the average deviation between the prediction models and the post-treatment facial models is between -2.102 and 2.102 mm by adjusting the shape parameters.
Teacher Support, Reading Strategy and Reading Literacy: A Two-Level Mediation Model

This study used a large-scale random sampling survey data called Regional Assessment of Education Quality (RAEQ) organized by Collaborative Innovation Center of Assessment toward Basic Education Quality in 2014. The school bullying phenomenon is described from the perspective of middle school students who were bullied. Regression is used to estimate the effects of academic achievement, self-esteem, interpersonal relationship and pro-social behavior on the different types of school bullying. And the differences between urban and rural areas among the influence factors were also compared. The conclusions are as follows. First, the average frequency of verbal bullying is the highest, relational bullying is in the middle level, and physical bullying is the lowest. Second, boys are more likely to suffer school bullying weighted more in verbal and physical bullying. Third, those who are single-parent, migrant, left behind and rural origin are more vulnerable to various types of bullying. Interestingly, students from single-parent families are more likely to suffer relational bullying, while migrant and rural children are more likely to get verbal bullying. Fourth, the level of self-esteem has a U-shaped relationship with the frequencies of all kinds of school bullying, and it is more significant and clear in the relational bullying. Fifth, interpersonal relationship, including parent-child, peer-peer, and teacher-student relationship can affect the school bullying significantly, and the peer relationship has a bigger effect on the relational bullying for urban students compared with rural ones. Therefore, parents, teachers and schools should pay more attention to and even interfere with the potentially bullied students to prevent and control school bullying.
Adaptive cascade single-shot detector on wireless sensor networks

The target detection model based on convolutional neural networks has recently achieved a series of exciting results in the target detection tasks of the PASCAL VOC and MS COCO data sets. However, limited by the data set for a particular scenario, some techniques or models applied to the actual environment are often not satisfactory. Based on cluster analysis and deep neural network, this paper proposed a new Statistic Experience-based Adaptive One-shot Network (SENet). The whole model solved the following practical problems. (1) By clustering the existing image classification dataset ImageNet, a common set of target detection datasets is formed, and a data set named ImageNet iLOC is formed to solve the object detection. The problem of single and insufficient quantities in the task. (2) We use cluster analysis on the size and shape of objects in each sample, which solves the problem of inaccurate manual selection of suggested areas during object detection. (3) In the multi-resolution training and prediction process, we reasonably allocate the size and shape of the suggested frame at different resolutions, greatly improve the utilization rate of the proposed frame, reduce the calculation amount of the model, and further improve the real-time performance of the model. The experimental results show that the model has a breakthrough in accuracy and speed (FPS reaches 54 in the case of a 3.4% increase in mAP).
RANCANG BANGUN VENDING MACHINE MENGGUNAKAN QR CODE BERBASIS MIKROKONTROLER

One of the technologies that continues to be developed today is automated system technology. An automatic system that has the potential to continue to develop in this modern era is the vending machine. Along with the development of technology, vending machines have begun to be developed using tags / RFID cards (Radio Frequency Identification) in lieu of paper money. The disadvantage of this system is the dependence of machine activation on an RFID tag / card that still has security risks. Therefore, a vending machine will be developed using QR (Quick Response) Code and integrated with an Android-based smartphone instead of RFID. The microcontroller based vending machine system uses a QR Code to work to serve drinks automatically after processing serial data sent by a smartphone through an application that has been designed with Bluetooth communication. Users can register an account on the application and fill in the balance to buy canned drinks at the vending machine.
Impacts of Linnik Flight Usage Patterns on Cuckoo Search for Real-Parameter Global Optimization Problems

Several contemporary algorithms, including cuckoo search (CS), were applied to the CEC 2017 problem set, which includes a wide variety of 120 very difficult subproblems.We found that the algorithms were ineffective, especially when the number of dimensions was high.We configured several usage patterns of Linnik flight with the inverse of the golden ratio (1/ ) to replace Lévy flight in CS, resulting in a new search mechanism that increased the efficiency of the CS algorithm.The impacts of each Linnik flight usage pattern were evaluated using the CEC 2017.The experimental results showed that: 1) CS variants that used Linnik flight were more capable than CS variants that used Lévy flight and 2) CS variants that used a mixture of Linnik flight and quantum-behaved mechanisms were even more capable.The primary effect of Linnik flight is the strengthening of the ranking, while that of the quantum-behaved mechanisms is a decreased error.A chaotic-initialized quantum-Linnik flight CS (CQLCS) algorithm is proposed.Among the 66 competitive methods applied to the CEC 2017, CQLCS ranked first and won the contemporary competitive algorithms section, which also included several advanced variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms.The CQLCS could potentially be improved further by adjusting the probability of the occurrence of Linnik flight.The processes for building improved variants were analyzed to discover how, or if, this improvement could be achieved.Finally, the CQLCS algorithm required fewer lines of code to run than did the DE and PSO variants.
Multitask Deep Neural Networks for Tele-Wide Stereo Matching

In this article, we propose deep learning solutions for the estimation of the real world depth of elements in a scene captured by two cameras with different field of views. We consider a realistic smart-phone scenario, where the first field of view (FOV) is a wide FOV with $1 \times $ the optical zoom, and the second FOV is contained in the first FOV captured by a tele zoom lens with $2 \times $ the optical zoom. We refer to the problem of estimating the depth for all elements in the union of the FOVs which corresponds to the Wide FOV as `tele-wide stereo matching'. Traditional approaches can only estimate the disparity or depth in the overlapped FOV, corresponding to the Tele FOV, using stereo matching algorithms. To benchmark this novel problem, we introduce a single-image inverse-depth estimation (SIDE) solution to estimate the disparity from the image corresponding to the union Wide FOV only. We also design a novel multitask tele-wide stereo matching deep neural network (MT-TW-SMNet), which is the first to combine the stereo matching and the single image depth tasks in one network. Moreover, we propose multiple methods for the fusion between the above networks. For example, we have input feature fusion, that utilizes the disparity estimated by stereo-matching as an additional input feature for SIDE. We also designed networks for decision fusion, that deploys a stacked hour glass (SHG) network for fusion and refinement of the disparity maps from both the SIDE network and the MT-TW-SMNet. These fusion schemes significantly improve the accuracy. Experimental results on KITTI and SceneFlow datasets demonstrate that our proposed approaches provide a reasonable solution to the tele-wide stereo matching problem. We demonstrate the effectiveness of our solutions in generating the Bokeh effect on the full Wide FOV.
The Applicability of Artificial Intelligence in International Law

Law reacts to the progression of scientific technology in the end.Though conservative, changes are beginning to take place due to Artificial Intelligence (AI).AI is automating conventional legal works, creating a new industry namely Legal-Tech.This paper investigates the characteristics and flow of legal-AI and computational law while focusing on the applicability of AI to international law.Mainly, the paper reviews three critical areas: dispute resolution, trial prediction, and machine translation, respectively.International law has different characteristics than the domestic law applied in each country.Unlike domestic law, international law has not been aggregated from a pandect, and it is a still daunting task to draw any meaningful insights for further analysis due mainly to limited data (i.e., trial cases and precedents).Nevertheless, AI is already penetrating the legal ecology system, and international law would eventually accept the influx of such changes exhibiting greater force.
Revisiting design principles of Salsa and ChaCha

Salsa and ChaCha are well known names in the family of stream ciphers. In this paper, we first revisit the existing attacks on these ciphers. We first perform an accurate computation of the attack complexities of the existing technique instead of the estimation used in previous works. This improves the complexity by some margin. The differential attacks using probabilistic neutral bits against ChaCha and Salsa involve two probability biases: forward probability bias ($ \epsilon_d $) and backward probability bias ($ \epsilon_a $). In the second part of the paper, we suggest a method to increase the backward probability bias, which helps reduce the attack complexity. Finally, we focus on the design principle of ChaCha. We suggest a slight modification in the design of this cipher as a countermeasure of the differential attacks against it. We show that the key recovery attacks proposed against ChaCha will not be effective on this modified version.
Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user's location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker's goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the "Residence" category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users' population that determines the complexity of the identification task, rather than the individual behaviour.
Generalized binomial state: Nonclassical features observed through various witnesses and a quantifier of nonclassicality

Experimental realization of various quantum states of interest has become possible in the recent past due to the rapid developments in the field of quantum state engineering. Nonclassical properties of such states have led to various exciting applications, specifically in the area of quantum information processing. The present article aims to study lower- and higher-order nonclassical features of such an engineered quantum state (a generalized binomial state based on Abel's formula). Present study has revealed that the state studied here is highly nonclassical. Specifically, higher-order nonclassical properties of this state are reported using a set of witnesses, like higher-order antibunching, higher-order sub-Poissonian photon statistics, higher-order squeezing (both Hong Mandel type and Hillery type). A set of other witnesses for lower- and higher-order nonclassicality (e.g., Vogel's criterion and Agarwal's A parameter) have also been explored. Further, an analytic expression for the Wigner function of the generalized binomial state is reported and the same is used to witness nonclassicality and to quantify the amount of nonclassicality present in the system by computing the nonclassical volume (volume of the negative part of the Wigner function). Optical tomogram of the generalized binomial state is also computed for various conditions as Wigner function cannot be measured directly in an experiment in general, but the same can be obtained from the optical tomogram with the help of Radon transform.
Create by Doing – Action Sequencing in VR

In every virtual reality application, there are actions to perform, often in a logical order. This logical ordering can be a predefined sequence of actions, enriched with the representation of different possibilities, which we refer to as a scenario. Authoring such a scenario for virtual reality is still a difficult task, as it needs both the expertise from the domain expert and the developer. We propose to let the domain expert create in virtual reality the scenario by herself without coding, through the paradigm of creating by doing. The domain expert can run an application, record the sequence of actions as a scenario, and then reuse this scenario for other purposes, such as an automatic replay of the scenario by a virtual actor to check the obtained scenario, the injection of this scenario as a constraint or a guide for a trainee, or the monitoring of the scenario unfolding during a procedure.
Learning Discriminative Features for Image Retrieval

Discriminative local features obtained from activations of convolutional neural networks have proven to be essential for image retrieval. To improve retrieval performance, many recent works aim to obtain more powerful and discriminative features. In this work, we propose a new attention layer to assess the importance of local features and assign higher weights to those more discriminative. Furthermore, we present a scale and mask module to filter out the meaningless local features and scale the major components. This module not only reduces the impact of the various scales of the major components in images by scaling them on the feature maps, but also filters out the redundant and confusing features with the MAX-Mask. Finally, the features are aggregated into the image representation. Experimental evaluations demonstrate that the proposed method outperforms the state-of-the-art methods on standard image retrieval datasets.
Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass

Mobile devices are increasingly sophisticated while smartphones continue to make the latest generation that immerses the supporting tools needed in everyday life such as cameras, GPS, Microphones, and various sensors such as light sensors, a direction sensor, acceleration sensor (i.e., accelerometer) and the gyroscope sensor.This study aims to classify human activities from the accelerometer and gyroscope sensors on a Sony z3+ smartphone.To implement our system, we collect labeled accelerometer and gyroscope data from eight users when they carry out daily activity.Every activity was recorded for 22 seconds, total data that we use every activity is 2000 data with the total amount of data is 16000 data.This data we classify using the Multiclass Support Vector Machine (SVM) method reaches 97.40% accuracy using a 70% ratio as training data and 30% as test data, the classification process takes 5 seconds to classify the data.
THE DIFFICULTIES OF WRITING SCIENTIFIC WORK AT THE ENGLISH EDUCATION STUDENTS

The purpose of this study was to determine the students’ difficulties in writing scientific papers at the English Language Education, Universitas Muslim Indonesia and to find out the needs and learning objectives of the students. The sample was consisted of 44 English Education students registered at the UMI Faculty of Literature during the 2018/2019 academic year. To examine this problem, the study was conducted in a descriptive quantitative design. The instrument used in this study was questionnaires. Research questions consist of: 1) what are the difficulties faced by English Education major students in academic / scientific writing? 2) what are the common errors of English Education students in writing scientific papers? 3) what teaching approach can facilitate success in scientific writing? Based on the results of the study, the difficulties faced in the process of writing scientific papers were related to aspects of grammar (31%), scientific writing style / rules (24%), vocabulary, spelling and coherence (11%), writing arrangement (7%) and punctuation (5%); Three common errors that occur in writing scientific papers namely spelling (29.2%), use of capital letters (17, 75%), and punctuation (15.27%). The approaches needed by the teachers to strengthen and help students to improve their scientific writing referred to reviewing writing such as the level of words, sentences, paragraphs, and grammar, making outlines, applying three steps in the writing process such as planning, writing and revision, and understanding the formal and informal styles as well as oral and written forms so as to be able to avoid words and phrases that were not needed.
An Achieving Data Exchange Cross-Chain Alliance Protocol

This paper provides a method and system for establishing and communicating cross-chain alliances between blockchains. The method for establishing the cross-chain alliance includes: obtaining communication requirements of each communication node in multiple blockchains, each node establishing a cross-chain alliance and configuring identity certificates and transaction certificates and establishing cross-chain smart contracts.The blockchain cross-chain communication method includes: obtaining a communication request by using each blockchain in the cross-chain alliance established above, and establishing a node smart contract for each node; if a communication request is initiated, performing an endorsement policy verification on the communication request, and then performing the full sequence signature verification and data consistency verification, when the communication request is verified, the communication between the nodes is implemented according to the cross-chain smart contract.