Reviewerly/exHarmony huggingface dataset:
Detecting Semantic Conflicts via Automated Behavior Change Detection

Branching and merging are common practices in collaborative software development. They increase developer productivity by fostering teamwork, allowing developers to independently contribute to a software project. Despite such benefits, branching and merging comes at a cost-the need to merge software and to resolve merge conflicts, which often occur in practice. While modern merge techniques, such as 3-way or structured merge, can resolve many such conflicts automatically, they fail when the conflict arises not at the syntactic, but the semantic level. Detecting such conflicts requires understanding the behavior of the software, which is beyond the capabilities of most existing merge tools. As such, semantic conflicts can only be identified and fixed with significant effort and knowledge of the changes to be merged. While semantic merge tools have been proposed, they are usually heavyweight, based on static analysis, and need explicit specifications of program behavior. In this work, we take a different route and explore the automated creation of unit tests as partial specifications to detect unwanted behavior changes (conflicts) when merging software.We systematically explore the detection of semantic conflicts through unit-test generation. Relying on a ground-truth dataset of 38 software merge scenarios, which we extracted from GitHub, we manually analyzed them and investigated whether semantic conflicts exist. Next, we apply test-generation tools to study their detection rates. We propose improvements (code transformations) and study their effectiveness, as well as we qualitatively analyze the detection results and propose future improvements. For example, we analyze the generated test suites for false-negative cases to understand why the conflict was not detected. Our results evidence the feasibility of using test-case generation to detect semantic conflicts as a method that is versatile and requires only limited deployment effort in practice, as well as it does not require explicit behavior specifications.
Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services

Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software, emerges as a preferred technology. However, there are a large number of cloud service providers and they offer different pricing strategies for various applications in various regions. Therefore, it is of great importance for them that incoming service requests are assigned to the appropriate cloud services at minimum cost and provide maximum user satisfaction [quality of service (QoS) attributes]. Due to the issues, such as multiple cloud providers, different QoS requirements, different service level agreements and uncertainties in demand, price and availability, the optimization of resource allocation present further challenges. The objective of our study is to optimize the cost and performance of video on demand applications using cloud content delivery networks, storage and transcoders based on the QoS requirements of users. To solve the NP-hard problem, Particle Swarm Optimization (PSO) technique is used due to the easiness in its concept and coding, less sensitive to the nature of the objective function, limited number of parameters and generating high quality solution within a short time. We propose a new method in which the optimum solution is affected not only by the best solution of the particle and global best solution but also by the best solution of the neighborhood particles in that iteration. This ternary approach is implemented into the well-known discrete and constrained PSO, achieving the minimum cost with user satisfaction for allocation of video requests to cloud resources. Although the proposed method yields better results in terms of accuracy, execution time of the algorithm is not reasonable. To overcome this inefficiency; ternary approach is embedded into multi-swarm PSO and it is parallelized and combined with greedy heuristic algorithms. The results of the comparison with the benchmarking algorithms show that our proposed method yields better results from the standpoint of both accuracy and execution time.
Industrial Use Cases for Augmented Reality Application

This paper presents the application of the augmented reality technology (AR) in the industrial field. Based on industrial use cases the implementation of two AR applications and their potential to digitize the product lifecycle processes will be discussed. The use cases were developed within cooperation projects between the University of Applied Sciences Karlsruhe and a firefighting equipment and fire truck manufacturer and have the following topics: maintenance engineering, and product configuration management. The objective behind the realization of the use cases is the investigation of the AR application in the industry and its capability as digital transformation technology along the product lifecycle. The presented use cases deliver experiences and suggestions in terms of praxis-oriented development and deployment of the AR technology in the engineering field, which can be used for prospective AR research works.
General 3D Room Layout from a Single View by Render-and-Compare

We present a novel method to reconstruct the 3D layout of a room—walls, floors, ceilings—from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to find the set of 3D polygons that constitute the layout. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate. As no dataset was available to evaluate our method quantitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts.
Forecasting of renewable generation for applications in smart grid power systems

This chapter focuses on the growth, development, and future of some renewable energy technologies that are the future of distributed generation. The main focus, however, is on the probabilistic forecasting of intermittent types of renewable energy generation due to their need in different smart grid applications. A comprehensive idea is developed about the different kinds of forecasting techniques with evaluation metrics. Various forecasting approaches are discussed with the primary focus on quantile-based probabilistic forecasting. Using the case of photovoltaic generation, four probabilistic forecasting models are simulated, and their forecasting capabilities are analyzed. A complete section is dedicated to discussion and analysis of the obtained results. The use of real-world datasets for the simulations helps in studying the actual cases, thus providing motivation for applications in planning, management, and smart grid operations.
Multi-Domain Image-to-Image Translation via a Unified Circular Framework

The image-to-image translation aims to learn the corresponding information between the source and target domains. Several state-of-the-art works have made significant progress based on generative adversarial networks (GANs). However, most existing one-to-one translation methods ignore the correlations among different domain pairs. We argue that there is common information among different domain pairs and it is vital to multiple domain pairs translation. In this paper, we propose a unified circular framework for multiple domain pairs translation, leveraging a shared knowledge module across numerous domains. One selected translation pair can benefit from the complementary information from other pairs, and the sharing knowledge is conducive to mutual learning between domains. Moreover, absolute consistency loss is proposed and applied in the corresponding feature maps to ensure intra-domain consistency. Furthermore, our model can be trained in an end-to-end manner. Extensive experiments demonstrate the effectiveness of our approach on several complex translation scenarios, such as Thermal IR switching, weather changing, and semantic transfer tasks.
eNNclave

Outsourcing machine learning inference creates a confidentiality dilemma: either the client has to trust the server with potentially sensitive input data, or the server has to share his commercially valuable model. Known remedies include homomorphic encryption, multi-party computation, or placing the entire model in a trusted enclave. None of these are suitable for large models. For two relevant use cases, we show that it is possible to keep all confidential model parameters in the last (dense) layers of deep neural networks. This allows us to split the model such that the confidential parts fit into a trusted enclave on the client side. We present the eNNclave toolchain to cut TensorFlow models at any layer, splitting them into public and enclaved layers. This preserves TensorFlow's performance optimizations and hardware support for public layers, while keeping the parameters of the enclaved layers private. Evaluations on several machine learning tasks spanning multiple domains show that fast inference is possible while keeping the sensitive model parameters confidential. Accuracy results are close to the baseline where all layers carry sensitive information and confirm our approach is practical.
Rootless Containers with Podman for HPC

Containers have become popular in HPC environments to improve the mobility of applications and the delivery of user-supplied code. In this paper we evaluate Podman, an enterprise container engine that supports rootless containers, in combination with runc and crun as container runtimes using a real-world workload with LS-DYNA, and the industry-standard benchmarks sysbench and STREAM. The results suggest that Podman with crun only introduces a similar low overhead as HPC-focused container technologies.
A New Sequential Coding Method for Secure Data Communication

As everyone is aware that security of data communication is essential in modern scenario. There is a continuous fight between cryptographers and cryptanalysists. Cryptographers want to develop new methods and protocols to safeguard the valuable information but on the other side, cryptanalysists find various methods to extract the information. So a new sequential coding based method for securing data communication is proposed here. The proposed method requires an encryption key along with other ingredients and it can be implementable very easily in various situations. The security analysis is also provided to prove the validity. The claim is that the proposed method also satisfies Kerchoff's law of secrecy. We want to keep the method simple enough so that it can be used on mobile phones as well.
Secret key distillation over satellite-to-satellite free-space optics channel with a limited-sized aperture eavesdropper in the same plane of the legitimate receiver

Conventionally, unconditional information security has been studied by quantum cryptography although the assumption of an omnipotent eavesdropper is too strict for some realistic implementations. In this paper, we study the realistic secret key distillation over a satellite-to-satellite free space optics channel where we assume a limited-sized aperture eavesdropper (Eve) in the same plane of the legitimate receiver (Bob) and determine the secret key rate lower bounds correspondingly. We first study the input power dependency without assumptions on Bob's detection scheme before optimizing the input power to determine lower bounds as functions of transmission distances, center frequency or Eve aperture radius. Then we calculate analytical expressions regarding the SKR lower bound and upper bound as transmission distance goes to infinity. We also incorporate specific discrete variable (DV) and continuous variable (CV) protocols for comparison. We demonstrate that significantly higher SKR lower bounds can be achieved compared to traditional unrestricted Eve scenario.
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning

Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input. The model provides an additional integration of user intent along with text generation, trained with multi-task learning paradigm along with an additional regularization technique to penalize generating the wrong entity as output. The model further incorporates a Knowledge Graph entity lookup during inference to guarantee the generated output is state-full based on the local knowledge graph provided. We finally evaluated the model using the BLEU score, empirical evaluation depicts that our proposed architecture can aid in the betterment of task-oriented dialogue system’s performance.
Experimental Quantification of the Entanglement of Noisy Twin Beams

Gradual loss of the entanglement of a twin beam containing around 25 photon pairs with the increasing external noise is experimentally investigated. The entanglement is quantified by the non-classicality depths and the non-classicality counting parameters related to several non-classicality criteria. The reduction of intensity moments of the analyzed multi-mode twin beams to single-mode ones allows to determine the negativity as another quantifier of the entanglement. Both the raw photocount histograms and the reconstructed photon-number distributions are analyzed in parallel.
Machine learning corrected quantum dynamics calculations

Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is often difficult to assess because they depend on the Hamiltonian parameters and the particular observable under study. Here, we illustrate a general, system and approximation-independent, approach to improve the accuracy of quantum dynamics approximations. The method is based on a Bayesian machine learning (BML) algorithm that is trained by a small number of rigorous results and a large number of approximate calculations, resulting in ML models that accurately capture the dependence of the dynamics results on the quantum dynamics parameters. Most importantly, the present work demonstrates that the BML models can generalize quantum results to different dynamical processes. Thus, a ML model trained by a combination of approximate and rigorous results for a certain inelastic transition can make accurate predictions for different transitions without rigorous calculations. This opens the possibility of improving the accuracy of approximate calculations for quantum transitions that are out of reach of rigorous scattering calculations.
Proximal policy optimization via enhanced exploration efficiency

Proximal policy optimization (PPO) algorithm is a deep reinforcement learning algorithm with outstanding performance, especially in continuous control tasks. But the performance of this method is still affected by its exploration ability. Based on continuous control tasks, this paper analyzes the original Gaussian action exploration mechanism in PPO algorithm, and clarifies the influence of exploration ability on performance. Afterward, aiming at the problem of exploration, an exploration enhancement mechanism based on uncertainty estimation is designed in this paper. Then, we apply exploration enhancement theory to PPO algorithm and propose the proximal policy optimization algorithm with intrinsic exploration module (IEM-PPO). In the experimental parts, we evaluate our method on multiple tasks in MuJoCo phsysical simulator, and compare IEM-PPO algorithm with PPO and PPO with intrinsic curiosity module (ICM-PPO). The experimental results demonstrate that IEM-PPO algorithm performs better in terms of sample efficiency and cumulative reward, and has stability and robustness.
Open Multiparty Sessions

Multiparty sessions are systems of concurrent processes, which allow several participants to communicate by sending and receiving messages.Their overall behaviour can be described by means of global types.Typable multiparty session enjoy lock-freedom.We look at multiparty sessions as open systems by a suitable definition of connection transforming compatible processes into gateways (forwarders).A relation resembling the standard subtyping relation for session types is used to formalise compatibility.We show that the session obtained by connection can be typed by manipulating the global types of the starting sessions.This allows us to prove that lock-freedom is preserved by connection.
Method-level bug localization using hybrid multi-objective search

Context: One of the time-consuming maintenance tasks is the localization of bugs especially in large software systems. Developers have to follow a tedious process to reproduce the abnormal behavior then inspect a large number of files. While several studies have been proposed for bugs localization, the majority of them are recommending classes/files as outputs which may still require high inspection effort. Furthermore, there is a significant difference between the natural language used in bug reports and the programming language which limits the efficiency of existing approaches since most of them are mainly based on lexical similarity. Objective: In this paper, we propose an automated approach to find and rank the potential methods in order to localize the source of a bug based on a bug report description. Method: Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a hybrid multi-objective optimization algorithm combining local and global search. The relevance of the recommended code fragments is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. Our approach operates on two main steps. The first step is to find the best set of classes satisfying the two conflicting criteria of relevance and the number of classes to recommend using a global search based on NSGA-II. The second step is to locate the most appropriate methods to inspect, using a local multi-objective search based on Simulated Annealing (MOSA) from the list of classes recommended by the first step. Results: We evaluated our system on 6 open source Java projects, using the version of the project before fixing the bug of many bug reports. Our hybrid multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 78% of the bug reports leading to a significant reduction of developers’ effort comparing to class-level bug localization techniques. Conclusion: The experimental results show that the search-based approach significantly outperforms four state-of-the-art methods in recommending relevant files for bug reports.
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transparent. The lack of interpretability hinders the applicability of RL in safety-critical scenarios. While several methods have attempted to interpret vision-based RL, most come without detailed explanation for the agent's behavior. In this paper, we propose a self-supervised interpretable framework, which can discover interpretable features to enable easy understanding of RL agents even for non-experts. Specifically, a self-supervised interpretable network (SSINet) is employed to produce fine-grained attention masks for highlighting task-relevant information, which constitutes most evidence for the agent's decisions. We verify and evaluate our method on several Atari 2600 games as well as Duckietown, which is a challenging self-driving car simulator environment. The results show that our method renders empirical evidences about how the agent makes decisions and why the agent performs well or badly, especially when transferred to novel scenes. Overall, our method provides valuable insight into the internal decision-making process of vision-based RL. In addition, our method does not use any external labelled data, and thus demonstrates the possibility to learn high-quality mask through a self-supervised manner, which may shed light on new paradigms for label-free vision learning such as self-supervised segmentation and detection.
A systematic approach to deriving incremental type checkers

Static typing can guide programmers if feedback is immediate. Therefore, all major IDEs incrementalize type checking in some way. However, prior approaches to incremental type checking are often specialized and hard to transfer to new type systems. In this paper, we propose a systematic approach for deriving incremental type checkers from textbook-style type system specifications. Our approach is based on compiling inference rules to Datalog, a carefully limited logic programming language for which incremental solvers exist. The key contribution of this paper is to discover an encoding of the infinite typing relation as a finite Datalog relation in a way that yields efficient incremental updates. We implemented the compiler as part of a type system DSL and show that it supports simple types, some local type inference, operator overloading, universal types, and iso-recursive types.
Deep Learning model-based Multimedia forgery detection

Images and videos can be spread very conveniently using social media platforms like WhatsApp and Facebook. The authenticity of this information cannot be verified easily but it spreads swiftly. Fake images or videos are a new threat for people as they spread false information and rumors. Advances in technology have given rise to several techniques that can easily generate fake images or videos. Deepfakes and spliced images are some of the results of such advances. They pose a great menace to the internet Tackling and detecting such an entity is a tricky task. Our paper portrays a technique to detect such entities. It will assist people in detecting bogus content and have confidence on the legitimacy of the content on the internet We present a description of CNN based approach and evaluate its results. The drawbacks of the traditional approach have been minimized using Inception Residual Network architecture based CNNs.