Reviewerly/exHarmony huggingface dataset:
Pull Requests or Commits? Which Method Should We Use to Study Contributors' Behavior?

Social coding environments have been consistently growing since the popularization of the contribution model known as pull-based. This model has facilitated how developers make their contributions; developers can easily place a few pull requests without further commitment. Developers without strong ties to a project, the so-called casual contributors, often make a single contribution before disappearing. Interestingly, some studies about the topic use the number of commits made to identify the casual contributors, while others use the number of merged pull requests. Does the method used influence the results? In this paper, we replicate a study about casual contributors that relied on commits to identify and analyze these contributors. To achieve this goal, we analyzed the same set of GitHub-hosted software repositories used in the original paper. By using pull requests, we found an average of 66% casual contributors (in comparison to 48.98% when using commits), who were responsible for 12.5% of the contributions accepted (1.73% when using commits). We used a sample of 442 developers to investigate the accuracy of the method. We found that 11.3% of the contributors identified using the pull requests were misclassified (26.2% using commits). We also evidenced that using pull requests is more precise for determining the number of contributions, given that GitHub projects mostly follow the pull-based process. Our results indicate that the method used for mining contributors' data has the potential to influence the results. With this replication, it may be possible to improve previous results and reduce future efforts for new researchers when conducting studies that rely on the number of contributions.
Explaining reasoning algorithms with persuasiveness

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users' behaviours.
A Blockchain Based Distributed Vehicular Network Architecture for Smart Cities

In this paper, we propose a blockchain-based data sharing mechanism for Vehicular Network. We introduce edge service providers placed near to ordinary vehicle nodes to fulfill their requests. Smart vehicles generate a huge amount of data which is stored in the Interplanetary File System (IPFS). IPFS is a distributed file storage system that overcomes the limitations of centralized architecture. Monetary incentive is given to edge vehicle nodes for providing services to ordinary nodes. Ordinary nodes give reviews against services provided by the edge nodes that are stored in a blockchain. A smart contract is used to automate system processes without third party involvement and checking reviews of the edge node. To optimize gas consumption, we used Proof of Authority (PoA) as a consensus mechanism for transaction validation. PoA enhances overall system performance and optimized gas consumption. The caching server is introduced to store frequently used services in memory and provided to ordinary vehicles upon request. Moreover, we have used symmetric key cryptographic mechanism which ensures data security and privacy. A trust management system is proposed, which ensures the reputation of nodes. The trust value is stored in a blockchain, which determines the authenticity of nodes involved in a network. From simulation results, it is shown that our proposed system is efficient for the vehicular network.
Face Recognition via Convolutional Neural Networks and Siamese Neural Networks

Deep convolutional neural networks is playing very important role to solve computer vision task in these decades. In this research has shown implementation state of art face recognition methods and compared them. Advantages and disadvantages of Convolutional and Siamese neural networks is explored for the face recognition task. The novel face recognition system accuracy is checked on the widely used Labelled Faces in the Wild (LFW) dataset. In the study has introduced concepts of triplet loss function. Experiment results can be useful for the developers who is going to create AI applications such as Security system, Self-learning, Visitor analysis system, Face recognition system, Face verification system and many more.
Ludic-HRI

People are inherently playful, and playfulness matters not only when engaging in actual play but also in all other activities. Based on this, I propose using a ludic design approach as a means to broaden the design space for Human-Robot Interaction (HRI). In this paper, I discuss the application of ludic design in HRI and explore how ludic activities can act as a mechanism for achieving new understandings from people during their interactions with robots. Two projects, BubbleBot and Sketching Robot, are presented as cases of designing ludic activities with robots. In my work, I have investigated how people perceived robots by applying exploratory research through design methods in creating the ludic experiences. I am continuously learning from my design and exploring potential areas for designing robots while identifying new values and goals for having robots in our lives.
Rate-Distortion-Preserving Forensic Watermarking Using Quantization Parameter Variation

Watermarking enables the identification of digital pirates that illegally redistribute copyright-protected videos. One of the main challenges is for the watermark to be imperceptible, while not increasing the video bit rate. Additionally, the system should be robust to attacks that attempt to remove the watermark. Therefore, this paper proposes a robust watermarking technique that does not degrade the video quality nor negatively affect the video bit rate. In other words, it preserves the video encoder's compression efficiency or rate-distortion performance. For watermark embedding, the quantization parameters are varied during video compression. As a result, different compression artifacts are introduced, although they do not distort the video more than those that occur during ordinary video compression. The collection of artifacts represents the watermark and is used for watermark detection. The experimental results prove that the proposed approach retains the rate-distortion performance better than state-of-the-art techniques. Furthermore, the watermarks are robust to recompression and noise attacks. In conclusion, the proposed method enables content providers to perform forensic watermarking without affecting the compression efficiency.
Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed roadmap can encode inherent properties of the environment which are useful when agents have to avoid each other in that same environment. Like Probabilistic Roadmaps (PRMs), ODRM's first step is generating samples from C-space. In a second step ODRM optimizes vertex positions and edge directions by Stochastic Gradient Descent (SGD). This leads to emergent properties like edges parallel to walls and patterns similar to two-lane streets or roundabouts. Agents can then navigate on this graph by searching their path independently and solving occurring agent-agent collisions at run-time. Using the graphs generated by ODRM compared to an non-optimized graph significantly fewer agent-agent collisions happen.
Automatic baseball commentary generation using deep learning

Video captioning is known as a method to summarize or explain a video. However, it is very difficult to create sports commentaries, which are running scene-by-scene descriptions, for sports videos by using conventional video captioning. This is the case because sports commentary requires not only specific and varied information about every scene, such as player action descriptions in baseball, but also background knowledge and dynamic at-bats statistics that are not found in the video. We propose a new system to automatically generate commentary for baseball games. In our system, given real-time baseball videos, suitable descriptions are relayed using four deep-learning models (i.e., a scene classifier, player detector, motion recognizer, and pitching result recognizer) integrated with domain ontology. Using these four deep-learning models, pieces of information about "who is doing what in which area of the field" and "what results are expected" are obtained. This approach is used to select an appropriate template, which is combined with baseball ontology knowledge for the generation of commentary. We evaluate our system using baseball games from the KBO (Korea Baseball Organization) League's 2018 season. As a result of the evaluation, we identify that our system1 can act as a commentator for baseball games.
Some new results on dimension and Bose distance for various classes of BCH codes

In this paper, we give the dimension and the minimum distance of two subclasses of narrow-sense primitive BCH codes over Fq with designed distance δ=aqm−1−1(resp. δ=aqm−1q−1) for all 1≤a≤q−1, where q is a prime power and m>1 is a positive integer. As a consequence, we obtain an affirmative answer to two conjectures proposed by C. Ding in 2015. Furthermore, using the previous part, we extend some results of Yue and Hu [16], and we give the dimension and, in some cases, the Bose distance for a large designed distance in the range [aqm−1q−1,aqm−1q−1+T] for 0≤a≤q−2, where T=qm+12−1 if m is odd, and T=2qm2−1 if m is even.
The application of convolutional neural networks to the automation of a meteor detection pipeline

A new detection pipeline has been developed to automatically detect meteors in the Canadian Automated Meteor Observatory's influx camera data. The new pipeline removes a major bottleneck from the previous implementation: the need for human involvement in the verification of events. Filtering is performed by a convolutional neural network (CNN), trained to remove false positives from real meteors. The pipeline contains a new detection algorithm designed to identify regions of interest (samples) for the CNN to filter. Once filtered, valid detections are stitched together and extracted by the last part of the pipeline, since detections are performed by searching for meteors in every frame. Having trained on 50745 manually labelled samples, the best of 18 tested CNNs was able to filter 5485 unseen samples with an accuracy of 99.8%. This was achieved with the use of a custom-built application, designed to aid in creating CNNs and visualizing their outputs.
A novel quality prediction model for component based software system using ACO–NM optimized extreme learning machine

Component-based software engineering is currently a development strategy used to improve complex embedded systems. The engineers have to deal with a large number of quality requirements (e.g. safety, security, availability, reliability, maintainability, portability, performance, and temporal correctness requirements), hence the development of complex embedded systems is becoming a challenging task. Enhancement of the quality prediction in component-based software engineering systems using soft computing techniques is the foremost intention of the research. Therefore, this paper proposes an extreme learning machine (ELM) classifier with the ant colony optimization algorithm and Nelder–Mead (ACO–NM) soft computing approach for component quality prediction. To promote efficient software systems and the ability of the software to work under several computer configurations maintainability, independence, and portability are taken as three core software components metrics for measuring the quality prediction. The ELM uses AC–NM for updating its weight to transform the quality constraints into objective functions for providing a global optimum quality prediction. The experimental results have shown that the proposed work gives an improved performance in terms of Sensitivity, Precision, Specificity, Accuracy, Mathews correlation coefficient, false positive rate, negative predictive value, false discovery rate, and rate of convergence.
Towards a formal model for composable container systems

In modern cloud-based architectures, containers play a central role: they provide powerful isolation mechanisms such that developers can focus on the logic and dependencies of applications while system administrators can focus on deployment and management issue. We propose a formal model for container-based systems, using the framework of Bigraphical Reactive Systems (BRSs). These graphs can be analysed and manipulated using techniques from graph theory: properties about containers can be formalized as properties of the corresponding bigraphic representations. Moreover, it turns out that the composition of containers as performed by e.g. docker-compose, corresponds precisely to the composition of the corresponding bigraphs inside an "environment bigraph" which in turn is obtained directly from the YAML file used to define the composition of containers.
Robonomics

Can or should a robot ever engage in a financial transaction with a human? If so, how? How about an enforceable agreement? Blockchain technology has enabled the development of cryptocurrencies, smart contracts, and unlocked a plethora of other disruptive technologies. But, beyond its use case in cryptocurrencies, and in network coordination, blockchain technology may have serious sociotechnical implications in the future co-existence of robots and humans. Motivated by the recent explosion of interest around blockchains, and our extensive work on open-source blockchain technology and its integration into robotics - this paper addresses these questions and provides insights into how blockchains and other decentralized technologies can impact our interactions with robots and enable the social integration of robots into human society.
The Best Parameters to Select Instagram Account for Endorsement using Web Scraping

As one of the popular social media, Instagram is also used as a media for promotion and marketing of products. One of the most efficient ways to market a product is by using endorsements. However, at this time, many Instagram accounts have many fake followers or often called fakes followers. Based on these problems, there needs to be a system that can support decisions in choosing a good endorse account on Instagram. In this study, researchers used the Simple Additive Weighting (SAW) method for processing parameters and web scraping to retrieve data from accounts on Instagram automatically. Instagram account parameters used in this study include the number of followers, number of likes, number of comets, and posts that are always updated. Determination of the best parameters for the selection of Endorse accounts on Instagram has been successfully carried out, which is shown from the results of the system accuracy of 75%. It proves that the parameters chosen are the best parameters to select Instagram account for endorsement.
An Efficient Adaptive Attention Neural Network for Social Recommendation

Traditional recommendation algorithms based on collaborative filtering suffer from a data sparsity problem.The emergence of online social network has enriched the user's information, realizing a new way to solve recommendation tasks.Social-aware recommendation algorithms can effectively alleviate the data sparsity problem and improve the performance of recommendation systems.Despite the success of these algorithms, they have some common limitations.Most algorithms assume that social networks are homogeneous, with similar preferences among connected users.However, users may only share similar preferences in some aspects.Besides, different friends affect the user's preference in different levels.And this influence of friends on users' preference should be adaptive.Even close friends may have different influences in different decision-making processes.For example, a user may trust a friend in ''travel'' but distrust this friend in ''music'' because this friend had more travel experiences.Motivated by the above limitations, we designed a neural network model called adaptive attention neural network for social recommendation (ANSR) to study the interaction between a user and his or her social friends as well as infer the complex influence of the user's social relationships on the user's preferences.By utilizing the co-attention mechanism, we can not only extract the user's special attention to certain aspects of their friends but also determine the adaptive influences of different friends on the user.When the user interacts with different items, different attention weights will be assigned to the user and his or her friends, respectively.In addition, we also utilize network embedding to learn more efficient features of each user and incorporate these features into the ANSR to enhance the recommendation results.Moreover, we also conduct extensive experiments on four different real-world datasets and demonstrate that our proposed method performs better on all datasets compared with the state-of-the-art baseline methods.
Approximate Filtering Methods in Continuous-Time Stochastic Systems

The goal of this chapter is to consider algorithms based on the particle method for solving the optimal filtering problem for nonlinear continuous-time stochastic observation systems not only by the minimum mean squared error estimate, but also by the maximum a posteriori estimate. Particle filters are proposed on the basis of Duncan–Mortensen–Zakai equation, as well as, on the basis of robust Duncan–Mortensen–Zakai equation. To find the mode of the conditional distribution approximately, Edgeworth series is used for the conditional probability density expansion. This approach allows to reduce significantly the computation time in contrast to finding the mode by estimating the conditional probability density, for example, the histogram or kernel estimations.
Hiding data with simplified diffractive imaging based hybrid method

The diffractive imaging-based encryption (DIBE) is vulnerable to chosen plaintext attack (CPA) because of the direct relation between plaintext and ciphertext. In this paper, a HYBRID method, which consists of combining simplified diffractive imaging-based encryption (SDIBE) and data hiding method, has been developed for safe data transmission. While the method has basic features of DIBE due to the SDIBE, it has higher security thanks to data hiding step which hides CCD pattern to a random noisy carrier with large size. As a result of the hiding, CPA has become unavailable anymore by preventing the direct connection between plaintext and ciphertext. The large size of the carrier and additional keys provide a huge key space which protects the method to brute force attacks. Moreover, resistance of the hybrid method against known-plaintext attack (KPA) and ciphertext only attack (COA) is high due to the nonlinearity of the SDIBE and using of public and private keys. In addition, while the hybrid method brings a different approach to DIBE, it also provides that more than one different data transmission can be made in a single carrier. Numerical simulations are presented to test the security and feasibility.
Deep Learning Based Identification of Suspicious Return Statements

Identifiers in source code are composed of terms in natural languages. Such terms, as well as phrases composed of such terms, convey rich semantics that could be exploited for program analysis and comprehension. To this end, in this paper we propose a deep learning based approach, called MLDetector, to identifying suspicious return statements by leveraging semantics conveyed by the natural language phrases that are used as identifiers in the source code. We specially design a deep neural network to tell whether a given return statement matches its corresponding method signature. The rationale is that both method signature and return value should explicitly specify the output of the method, and thus a significant mismatch between method signature and return value may suggest a suspicious return statement. To address the challenge of lacking negative training data, i.e., incorrect return statements, we generate negative training data automatically by transforming real-world correct return statements. To feed code into neural network, we convert them into vectors by Word2Vec, an unsupervised neural network based learning algorithm. We evaluate the proposed approach in two parts. In the first part, we evaluate it on 500 open-source applications by automatically generating labeled training data. Results suggest that the precision of the proposed approach varies from 83% to 90%. In the second part, we conduct a case study on 100 real-world applications. Evaluation results suggest that 42 out of 65 real-world incorrect return statements are detected (with precision of 59%).