Reviewerly/exHarmony huggingface dataset:
Detecting Incorrect Build Rules

Automated build systems are routinely used by software engineers to minimize the number of objects that need to be recompiled after incremental changes to the source files of a project. In order to achieve efficient and correct builds, developers must provide the build tools with dependency information between the files and modules of a project, usually expressed in a macro language specific to each build tool. In order to guarantee correctness, the authors of these tools are responsible for enumerating all the files whose contents an output depends on. Unfortunately, this is a tedious process and not all dependencies are captured in practice, which leads to incorrect builds. We automatically uncover such missing dependencies through a novel method that we call build fuzzing. The correctness of build definitions is verified by modifying files in a project, triggering incremental builds and comparing the set of changed files to the set of expected changes. These sets are determined using a dependency graph inferred by tracing the system calls executed during a clean build. We evaluate our method by exhaustively testing build rules of open-source projects, uncovering issues leading to race conditions and faulty builds in 31 of them. We provide a discussion of the bugs we detect, identifying anti-patterns in the use of the macro languages. We fix some of the issues in projects where the features of build systems allow a clean solution.
Quantum algorithm for the computation of the reactant conversion rate in homogeneous turbulence

The rapid developments that have occurred in quantum computing platforms over the past few years raise important questions about the potential for applications of this new type of technology to fluid dynamics and combustion problems, and the timescales on which such applications might be possible.As a concrete example, here a quantum algorithm is developed and employed for predicting the rate of reactant conversion in the binary reaction of F + rO → (1 + r) P roduct in non-premixed homogeneous turbulence.These relations are obtained by means of a transported probability density function equation.The quantum algorithm is developed to solve this equation and is shown to yield the rate of the reactants' conversion much more efficiently than current classical methods, achieving a quadratic quantum speedup, in line with expectations for speedups arising from quantum metrology techniques more broadly.This provides an important example of a quantum algorithm with a real engineering application, which can build a connection to present work in turbulent combustion modelling and form the basis for further development of quantum computing platforms and their applications to fluid dynamics.
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction

We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world's languages.We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages.The key idea is to use Byte Pair Encodings (BPEs) as basic units for multilingual embeddings.Through zero-shot transfer from English sentiment, we learn a seed lexicon for each language in the domain of PBC+.Through domain adaptation, we then generalize the domain-specific lexicon to a general one.We show -across typologically diverse languages in PBC+ -good quality of seed and general-domain sentiment lexicons by intrinsic and extrinsic and by automatic and human evaluation.We make freely available our code, seed sentiment lexicons for all 1593 languages and induced general-domain sentiment lexicons for 200 languages. 1
Learning one‐to‐many stylised Chinese character transformation and generation by generative adversarial networks

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this article, the authors propose a novel method of Chinese character fonts transformation and generation based on generative adversarial networks. The authors’ model is able to generate multiple fonts at once through font style-specifying mechanism and it can generate a new font at the same time if the authors combine the characteristics of existing fonts.
SpotWeb

Many cloud providers offer servers with transient availability at a reduced cost. These servers can be unilaterally revoked by the provider, usually after a warning period to the user. Until recently, it has been thought that these servers are not suitable to run latency-sensitive workloads due to their transient availability. In this paper, we introduce SpotWeb, a framework for running latency-sensitive web workloads on transient computing platforms while maintaining the Quality-of-Service (QoS) of the running applications. SpotWeb is based on three novel concepts; using multi-period optimization---a novel approach developed in finance---for server selection; transiency-aware load-balancing; and using intelligent capacity over-provisioning. We implement SpotWeb and evaluate its performance in both simulations and testbed experiments. Our results show that SpotWeb reduces costs by up to 50% compared to state-of-the-art solutions while being scalable to hundreds of cloud server configurations.
Multi Authority Access Control in a Cloud EHR System with MA-ABE

With the rapid adoption of Cloud-based Electronic Health Record (EHR) systems, health providers are particularly concerned about managing data privacy on the cloud.Existing approaches have either a scalability bottleneck by requiring that patients approve each sharing of their medical data or a trust bottleneck by having a single authority control every access thereby creating the problem of a single point of attack.To address both these bottlenecks, we have developed a novel framework that enables policy based multi-authority access authorization to EHR systems accessed by multiple care providers from different locations or organizations.This framework, which resides on the Edge, has been built using the Multi-Authority Attribute Based Encryption (MA-ABE) and Semantic Web technologies to provide a secure, semantically rich approach to facilitate secure data sharing among organizations who manage different attributes of end users using a shared dataset.In this paper, we describe our novel approach along with the proof of concept prototype that we created to evaluate our framework.
Fog-based local and remote policy enforcement for preserving data privacy in the Internet of Things

The pervasive nature of the Internet of Things has resulted in generating a huge amount of data about the lives of IoT users. This data includes Personally Identifiable Information (PII) that reflects people's behaviors, interests, lifestyles, and everyday routines. Protecting PII from privacy violations is a challenge since IoT data need to be handled by public networks, servers, and clouds, which are untrusted parties for data owners. In this paper, a solution called Policy Enforcement Fog Module (PEFM) is proposed for protecting sensitive IoT data whenever they are accessed throughout their entire lifecycle. PEFM uses the power of policy enforcement in the edge-fog infrastructures for protecting data accessed within users’ local domains. For data that need to be sent to remote domains, PEFM uses Active Data Bundle (ADB); an executable and self-protecting construct that can run on any visited host and enforces privacy policies automatically for data accessed by these hosts. To test the feasibility of PEFM in realistic IoT systems, a framework of using PEFM as a privacy control for Foscam home security system is simulated. The experimental results show that PEFM assures data privacy via data minimization due to selective data disclosures. Better privacy controls with minimal overhead can be achieved if most PEFM processes are executed by the local fog nodes. Migrating parts of PEFM processes to remote fog nodes or the cloud incurs more overhead than using strictly local fog nodes. This overhead is the cost for a higher level of privacy regarding lifecycle data protection.
C-Sprite

Many domains, such as the Internet of Things and Social Media, demand to combine data streams with background knowledge to enable meaningful analysis in real-time. When background knowledge takes the form of taxonomies and class hierarchies, Semantic Web technologies are valuable tools and their extension to data streams, namely RDF Stream processing (RSP), offers the opportunity to integrate the background knowledge with RDF streams. In particular, RSP Engines can continuously answer SPARQL queries while performing reasoning. However, current RSP engines are at risk of failing to perform reasoning at the required throughput. In this paper, we formalize continuous hierarchical reasoning. We propose an optimized algorithm, namely C-Sprite, that operates in constant time and scales linearly in the number of continuous queries (to be evaluated in parallel). We present two implementations of C-Sprite: one exploits a language feature often found in existing Stream Processing engines while the other is an optimized implementation. The empirical evaluation shows that the proposed solution is at least twice as fast as current approaches.
Linear fuzzy rule base interpolation using fuzzy geometry

Fuzzy Rule Interpolation (FRI) provides an interpretable decision in sparse fuzzy rule based system. The objective of this work is to establish a mathematical demonstration of the pattern of existing fuzzy rule base using fuzzy geometry. Though several authors contributed on fuzzy rule base interpolation but there is a need to generate closed mathematical form of interpolating pattern. The present work is an initiative to demonstrate the same. First part of this paper presents some spatial geometrical transformation of a fuzzy point. In the second part of this paper, a new FRI scheme is suggested using fuzzy geometry with above mentioned transformation. The proposed method operates in two different steps. In the first step, all the fuzzy rules are converted into fuzzy sets or mostly fuzzy points in higher dimension by using mathematical operator on the individual of antecedent and consequent parts. All rules or fuzzy points are then joined with a class of fuzzy line segments (FLS). Second step considers the identification of mathematical pattern of the interpolated piecewise linear fuzzy polynomial which is able to compute the desired conclusion of a given observation. The presented method not only associates the FRI technique to classical interpolation technique, but also promises to provide the geometrical visualization of the behaviour of fuzzy sets during the interpolation process.
An Accelerated Sequential PCP-Based Method for Ground-Moving Objects Detection From Aerial Videos

The detection of ground-moving objects in aerial videos has evolved over the years to handle more challenges such as large camera motion, the small size of the objects, and occlusion. Recently, aerial detection has been attempted using principal component pursuit (PCP) due to its superiority in detecting small moving objects. However, PCP-based detection methods generally suffer from high-false detections as well as high-computational loads. This paper presents a novel PCP-based detection method called kinematic regularization with local null space pursuit (KRLNSP) that drastically reduces false detections and the computational loads. KRLNSP models the background in an aerial video as a subspace that spans a low-dimension subspace while it models the moving objects as moving sparse. Accordingly, the detection is achieved by using multiple local null spaces and enhanced kinematic regularization. The multiple local null spaces allow real-time execution to nullify the background while preserving the moving objects unchanged. The kinematic regularization penalizes these moving objects to filter out false detections. The extensive evaluation of KRLNSP and relevant current state-of-the-art methods prove that the KRLNSP outperforms these methods (the true positive rate of KRLNSP is 98% and its false positive rate is 0.4%) and significantly reduces the computational loads (KRLNSP execution time is 0.3 s/frame).
Updatable Block-Level Message-Locked Encryption

Deduplication is widely used for reducing the storage requirement for storage service providers. Nevertheless, it is unclear how to support deduplication of encrypted data securely until the study of Bellare et al. on message-locked encryption (MLE, Eurocrypt 2013). While updating (shared) files is natural, existing MLE solutions do not allow efficient update of encrypted files stored remotely. Even modifying a single bit requires the expensive way of downloading and decrypting a large ciphertext (then re-uploading). This paper initiates the study of updatable block-level MLE, a new primitive in incremental cryptography and cloud cryptography. Our proposed provably-secure construction is updatable with computation cost logarithmic in the file size. It naturally supports block-level deduplication. It also supports proof-of-ownership which protects storage providers from being abused as a free content distribution network. Our experiments show its practical performance relative to the original MLE and existing non-updatable block-level MLE.
Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots

Summary This paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.
Modeling the Interaction of Computer Errors by Four-Valued Contaminating Logics

Logics based on weak Kleene algebra (WKA) and related structures have been recently proposed as a tool for reasoning about flaws in computer programs. The key element of this proposal is the presence, in WKA and related structures, of a non-classical truth-value that is "contaminating" in the sense that whenever the value is assigned to a formula $$\phi $$, any complex formula in which $$\phi $$ appears is assigned that value as well. Under such interpretations, the contaminating states represent occurrences of a flaw. However, since different programs and machines can interact with (or be nested into) one another, we need to account for different kind of errors, and this calls for an evaluation of systems with multiple contaminating values. In this paper, we make steps toward these evaluation systems by considering two logics, $$\mathsf {HYB}_{1}$$ and $$\mathsf {HYB}_{2}$$, whose semantic interpretations account for two contaminating values beside classical values 0 and 1. In particular, we provide two main formal contributions. First, we give a characterization of their relations of (multiple-conclusion) logical consequence—that is, necessary and sufficient conditions for a set $$\varDelta $$ of formulas to logically follow from a set $$\varGamma $$ of formulas in $$\mathsf {HYB}_{1}$$ or $$\mathsf {HYB}_{2}$$. Second, we provide sound and complete sequent calculi for the two logics.
RCDR: A Recommender Based Method for Computational Drug Repurposing

Computational Drug repurposing is the problem of finding new uses for known drugs. To achieve this goal, a significant number of computational methods have been proposed, which can be categorized as Network-based and Non-network-based methods. Since network-based methods have a lot of advantages, this problem can be modelled as a network-based recommendation system. In this paper, we propose an effective approach, RCDR (Recommender Based Computational Drug Repurposing), to prioritize candidate drugs for diseases. Initially, we use drug and disease similarities to build a new drug-disease score matrix. Then, we adopt a collaborative filtering model to recommend which disease can be treated by the new drug. The experiment results show that RCDR proposes well performance compared with other state-of-the-art approaches.
Edge Detection Based on Quantum Canny Enhancement for Medical Imaging

Abstract The cancer or object detection on image Computed Tomography, X-Ray, and Mammography often use segmentation methods. The most common segmentation method used is based on edge detection. The edge detection method which is proposed in this study is Quantum based with Canny operator. The quality improvement of edge detection results is emphasized on the method to generate the gradient magnitude value which is done by observing the strength of neighbour pixel values. The strength of this pixel is obtained by calculating the pixel change value and the distance of neighbour pixels to the centre pixel. The results of the implementation show that the proposed method produces a better PSNR value than the original Canny, Sobel, Robert, and Prewitt Operator.
Development and Usability Analysis of a Mixed Reality GPS Navigation Application for the Microsoft HoloLens

Navigation in real environments is arguably one of the primary applications for the mixed reality (MR) interaction paradigm. We propose a wearable MR system based on off-the-shelf devices as an alternative to the widespread handheld-based GPS navigation paradigm. Our system uses virtual holograms placed on the terrain instead of the usual heads-up display approach where the augmentations follow the line of sight. In a user experiment, we assessed performance and usability. We monitored user attention through EEG while performing a navigation task using either the MR or the handheld interface. Results show that users deemed our solution to offer a higher visibility to both the oncoming traffic and the suggested route. EEG readings also exposed a significantly less demanding focus level for our prototype. An easiness to learn and use was also indicated for the MR system.
Accident Alert System Application Using a Privacy-Preserving Blockchain-Based Incentive Mechanism

In this era of rapid growth of vehicles, the ratio of road accident increases day by day. Nowadays, Traffic incidents are persistent problems in both developed and developing countries which result in huge loss of life and property. No one in this world is ready to gaze what's happening around them. Nobody cares even when an accident occurs. This paper provides an innovative solution by developing an Accident Alert Message System using an Android Smartphone Application that can be used from the accident zone. The application uses GPS technology for location mapping and sends an alert and notification of an accident. The generated accident alert message is endorsed by the nearby registered users who also witness the accident to ensure the increased reputation of the message. Based on the endorsement of the message, the system will instantly transmit the location of the accident to the nearby emergency services. In this case, users usually lack the enthusiasm to generate or endorse alert messages because they might fear that their privacy will be breached. At the same time, users do not benefit from generating or endorsing alert messages which also makes them lack the enthusiasm or motivation to respond to messages. In order to provide a solution to resolve these issues, this paper presents a novel privacy-preserving Blockchain - Based Incentive Mechanism for Accident Alert Message System. The main objective of the paper is to encourage the users to generate and endorse accident alert messages from the accident zone without revealing the user's identity. Also, some incentives to the users are paid to the message generators and endorsers and the transactions get stored based on the Blockchain technology; hence the privacy of the user is preserved. Our proposed system ensures the reliability of alert messages without revealing the privacy of the user and is reliable and efficient in the non-fully-trusted environment.
Worker Assistance in Smart Production Environments Using Pervasive Technologies

Recent trends towards digitization in the industrial domain are also driving profound socio-technical changes. On the one hand, these technologies enable shorter product lifecycles and servitization, but on the other hand, the increasing technical complexity of the equipment makes its operation and maintenance a challenge for workers. Assistance systems using pervasive technologies can bridge the gap between the abilities of the workers and the demands of handling technical complexity by enriching workplace activities with relevant, context-dependent information. In this paper, we present an application that replaces a conventional, paper-based maintenance manual with digital, Augmented Reality based instructions that are delivered at the appropriate place and time.
RFAcc

Random forest (RF) is a widely adopted machine learning method for solving classification and regression problems. Training a random forest demands a large number of relational comparison and data movement operations, which take long time when using modern CPUs. Accelerating random forest training using either GPUs or FPGAs achieves only modest speedups.
LSTM and HMM Comparison for Home Activity Anomaly Detection

Behavioral changes in daily home activities may be linked with health problems. Therefore, anomaly detection on sequence pattern of home activities is important for healthcare monitoring. In this paper, an anomaly detection method based on Long Short-Term Memory (LSTM) neural network is proposed to detect anomalies on sequence pattern of home activities. A comparison study of LSTM and Hidden Markov Model (HMM) was conducted to evaluate their performance under different training set size and model's hyperparameters. The experimental results demonstrated that LSTM is comparable to HMM in detecting anomalies on sequence pattern of home activities. The test accuracies of the best LSTM and HMM models are both 87.50%.