Reviewerly/exHarmony huggingface dataset:
Characterizing and Understanding HPC Job Failures Over The 2K-Day Life of IBM BlueGene/Q System

An in-depth understanding of the failure features of HPC jobs in a supercomputer is critical to the large-scale system maintenance and improvement of the service quality for users. In this paper, we investigate the features of hundreds of thousands of jobs in one of the most powerful supercomputers, the IBM Blue Gene/Q Mira, based on 2001 days of observations with a total of over 32.44 billion core-hours. We study the impact of the system's events on the jobs' execution in order to understand the system's reliability from the perspective of jobs and users. The characterization involves a joint analysis based on multiple data sources, including the reliability, availability, and serviceability (RAS) log; job scheduling log; the log regarding each job's physical execution tasks; and the I/O behavior log. We present 22 valuable takeaways based on our in-depth analysis. For instance, 99,245 job failures are reported in the job-scheduling log, a large majority (99.4%) of which are due to user behavior (such as bugs in code, wrong configuration, or misoperations). The job failures are correlated with multiple metrics and attributes, such as users/projects and job execution structure (number of tasks, scale, and core-hours). The best-fitting distributions of a failed job's execution length (or interruption interval) include Weibull, Pareto, inverse Gaussian, and Erlang/exponential, depending on the types of errors (i.e., exit codes). The RAS events affecting job executions exhibit a high correlation with users and core-hours and have a strong locality feature. In terms of the failed jobs, our similarity-based event-filtering analysis indicates that the mean time to interruption is about 3.5 days.
Blockchain Technology for Smartphones and Constrained IoT Devices: A Future Perspective and Implementation

The blockchain technology is currently penetrating different sides of modern ICT community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect. At the same time, the use of wearable and mobile devices may also become a part of blockchain operation, especially during the charging time. The paper considers the possibility of using a large number of constrained devices supporting the operation of the blockchain. The utilization of such devices is expected to improve the efficiency of the system and also to attract a more substantial number of users. Authors propose a novel consensus algorithm based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS). The paper first overviews the existing strategies and further describes the developed cryptographic primitives used to build a blockchain involving mobile devices. A brief numerical evaluation of the designed system is also provided in the paper.
Analysis of the Average Confirmation Time of Transactions in a Blockchain System

Blockchain is well known as a database technology supporting digital currencies, such as Bitcoin, Ether, and Ripple. Regarding the mining process as a vacation, and the block-verification process as a service, we establish a type of non-exhaustive queueing model with a limited batch service and a possible zero-transaction service. By selecting the beginning instant of a block-verification process as a regeneration point and using the method of a generating function, we obtain the stationary probability distribution for the number of transactions in the system at the regeneration points. Then, we derive the average number of transactions and the average confirmation time of transactions in the blockchain system. Finally, we provide numerical results with analysis and simulation to demonstrate how the average number of transactions and the average confirmation time of transactions in the blockchain system change with the mining parameter.
Distributed Subgraph Matching on Big Knowledge Graphs Using Pregel

With RDF becoming the de facto standard for representing knowledge graphs, it is indispensable to develop scalable subgraph matching algorithms over big RDF graphs stored in distributed clusters.In this paper, we propose a novel distributed subgraph matching method SP-Tree, using the Pregel model, to answer subgraph matching queries on big RDF graphs.In our method, the query graph is transformed to a variant spanning tree based on the shortest paths.Two optimization techniques are proposed to improve the efficiency of our algorithms.One employs RDF shapes to filter out local computations and messages passed, the other postpones the Cartesian product operations in the matching process to reduce intermediate results.The extensive experiments on both synthetic and real-world datasets show that our SP-Tree subgraph matching method outperforms the state-of-the-art methods by an order of magnitude.
Anti Dependency Distance Minimization in Short Sequences. A Graph Theoretic Approach

Dependency distance minimization (DDm) is a word order principle favouring the placement of syntactically related words close to each other in sentences. Massive evidence of the principle has been reported for more than a decade with the help of syntactic dependency treebanks where long sentences abound. However, it has been predicted theoretically that the principle is more likely to be beaten in short sequences by the principle of surprisal minimization (predictability maximization). Here we introduce a simple binomial test to verify such a hypothesis. In short sentences, we find anti-DDm for some languages from different families. Our analysis of the syntactic dependency structures suggests that anti-DDm is produced by star trees.
A multiscale dilated dense convolutional network for saliency prediction with instance-level attention competition

Data-driven saliency estimation attracts increasing interests in recent years because of the establishment of large-scale annotated datasets and the evolution of deep convolutional neural networks (CNN). Although CNN-based models perform much better than traditional ones in saliency prediction, there is still a gap between computational models and human behavior. One reason is that existing approaches fail assigning correct saliency to different objects in scenes with multiple objects. In this paper, we propose a multiscale dilated dense convolutional network to handle instance-level attention competition for better saliency prediction. In the proposed architecture, dense connections encode inter- and intra-class features for instance-level attention competition, dilated convolution collects contextual information to enrich feature representations of instances, and shortcut connections provide multiscale features for attention competition across scales. According to evaluations on three challenging datasets, CAT2000, SALICON, and MIT1003, the proposed model achieves the state-of-the-art performance.
Design and Implementation of Intelligent Manhole Cover Monitoring System Based on NB-IoT

The problem of stolen and poorly managed gas well covers in cities has become increasingly prominent, and the existing urban manhole cover monitoring system has single monitoring parameters, immature technology and insufficient comprehensive analysis capabilities.To solve these problems, this paper designs an intelligent manhole cover monitoring system based on narrow band Internet of things (NB-IoT) technology.The system consists of perception layer, network layer and application layer.The sensing layer is based on the embedded ARM microprocessor and combines sensor technology to collect the manhole cover data.The network layer takes NB-IoT technology as the core and is responsible for the communication connection between the access device and the background.The application layer mainly completes the functions of data storage, display and alarm.The test results show that the system works stably and data transmission is reliable, which is helpful to realize intelligent management of smart city and create a harmonious and safe smart city environment.
Opportunities for software reuse in an uncertain world: From past to emerging trends

Abstract Much has been investigated about software reuse since the software crisis. The development of software reuse methods, implementation techniques, and cost models has resulted in a significant amount of research over years. Nevertheless, the increasing adoption of reuse techniques, many of them subsumed under higher level software engineering processes, and advanced programming techniques that ease the way to reuse software assets, have hidden somehow in the recent years new research trends on the practice of reuse and caused the disappearance of several reuse conferences. Also, new forms of reuse like open data and feature models have brought new opportunities for reuse beyond the traditional software components. From past to present, we summarize in this research the recent history of software reuse, and we report new research areas and forms of reuse according to current needs in industry and application domains, as well as promising research trends for the upcoming years.
Multi-view laplacian least squares for human emotion recognition

Human emotion recognition is an emerging and important area in the field of human–computer interaction and artificial intelligence, which has been more and more related with multi-view learning methods. Subspace learning is an important direction of multi-view learning. However, most existing subspace learning methods could not make full use of both category discriminant information and local neighborhood information. As a typical subspace learning method, partial least squares (PLS) performs better and more robustly than many other subspace learning methods, because PLS is optimized with iteration method. However, PLS suffers from linear relationship assumption and two-view limitation. In this paper, a new nonlinear multi-view laplacian least squares (MvLLS) is proposed. MvLLS constructs a global laplacian weighted graph (GLWP) to introduce category discriminant information as well as protects the local neighborhood information. Optimized with iteration method, MvLLS is a multi-view extension of PLS. The proposed method has great extendibility and robustness. To meet the requirements of large-scale applications, weighted local preserving embedding (WLPE) is proposed as the out-of-sample extension of MvLLS, basing on the idea of maintaining the manifold structures of original space. Finally, the proposed method is verified on three multi-view emotion recognition tasks, the experiment results validate the effectiveness and robustness of MvLLS.
Bi-Level Attention Model for Sentiment Analysis of Short Texts

Short text is an important form of information dissemination and opinion expression in various social media platforms.Sentiment analysis of short texts is beneficial for the understanding of customers' emotional state, obtaining customers' opinions and attitudes toward events, information and products, however, is difficult because the sparsity of the short-text data.Unlike the traditional methods using the external knowledge, this paper proposes a bi-level attention model for sentiment analysis of short texts, which does not rely on external knowledge to deal with the data sparsity.Specifically, at word level, our model improves the effect of word representation by introducing latent topic information into word-level semantic representation.Neural topic model is used to discover the latent topic of the text.A new topic-word attention mechanism is presented to explore the semantics of words from the perspective of topic-word association; At the sequence level, a secondary attention mechanism is used to capture the relationship between local and global sentiment expression.Experiments on the ChnSentiCorp-Htl-ba-10000 and NLPCC-ECGC datasets validate the effectiveness of the BAM model.
Malware Evasion Attack and Defense

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.
Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network

High-resolution optical remote sensing data can be utilized to investigate the human behavior and the activities of artificial targets, for example ship detection on the sea. Recently, the deep convolutional neural network (DCNN) in the field of deep learning is widely used in image processing, especially in target detection tasks. Therefore, a complete processing system called the broad area target search (BATS) is proposed based on DCNN in this paper, which contains data import, processing and storage steps. In this system, aiming at the problem of onshore false alarms, a method named as Mask-Faster R-CNN is proposed to differentiate the target and non-target areas by introducing a semantic segmentation sub network into the Faster R-CNN. In addition, we propose a DCNN framework named as Saliency-Faster R-CNN to deal with the problem of multi-scale ships detection, which solves the problem of missing detection caused by the inconsistency between large-scale targets and training samples. Based on these DCNN-based methods, the BATS system is tested to verify that our system can integrate different ship detection methods to effectively solve the problems that existed in the ship detection task. Furthermore, our system provides an interface for users, as a data-driven learning, to optimize the DCNN-based methods.
VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.
A metaheuristic-driven approach to fine-tune Deep Boltzmann Machines

Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings of these techniques involves the choice of their hyperparameters, since they have a significant impact on the final results. This work addresses the issue of fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic optimization techniques with different backgrounds, such as swarm intelligence, memory- and evolutionary-based approaches. Experiments conducted in three public datasets for binary image reconstruction showed that metaheuristic techniques can obtain reasonable results.
Towards Tax Compliance by Design: A Decentralized Validation of Tax Processes Using Blockchain Technology

Tax compliance requires businesses to implement compliant processes but also documenting and reporting mechanisms to proof a compliant process execution. Non-compliance has far-reaching consequences ranging from financial penalties to criminal investigations, thus threatening the competitive position of a business. Therefore, the Organisation for Economic Co-operation and Development (OECD) investigates means, among others the blockchain technology, to ease the tax compliance efforts of businesses. However, ensuring tax compliance in business processes is a complex and challenging task, which is why tax compliance management is often implemented as an independent area of responsibility within a business. Nevertheless, an integration of tax compliance into the business processes is necessary to enable compliance by design, resulting in efficient processes. In this paper, we investigate whether blockchain technology can contribute to tax compliance by design in business processes. To meet this end, we provide a conceptual design and a prototype for compliant process execution in the context of value-added taxes.
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks

Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challenge for the deployment of ML-based systems in safety-and security-critical environments like autonomous driving, disease detection or unmanned aerial vehicles. In the past years we have seen an impressive amount of publications presenting more and more new adversarial attacks. However, the attack research seems to be rather unstructured and new attacks often appear to be random selections from the unlimited set of possible adversarial attacks. With this publication, we present a structured analysis of the adversarial attack creation process. By detecting different building blocks of adversarial attacks, we outline the road to new sets of adversarial attacks. We call this the "attack generator". In the pursuit of this objective, we summarize and extend existing adversarial perturbation taxonomies. The resulting taxonomy is then linked to the application context of computer vision systems for autonomous vehicles, i.e. semantic segmentation and object detection. Finally, in order to prove the usefulness of the attack generator, we investigate existing semantic segmentation attacks with respect to the detected defining components of adversarial attacks.
Exploiting Memory Corruption Vulnerabilities in Connman for IoT Devices

In the recent past, there has been a rapid increase in attacks on consumer Internet-of-Things (IoT) devices. Several attacks currently focus on easy targets for exploitation, such as weak configurations (weak default passwords). However, with governments, industries, and organizations proposing new laws and regulations to reduce and prevent such easy targets in the IoT space, attackers will move to more subtle exploits in these devices. Memory corruption vulnerabilities are a significant class of vulnerabilities in software security through which attackers can gain control of the entire system. Numerous memory corruption vulnerabilities have been found in IoT firmware already deployed in the consumer market. This paper presents an approach for exploiting stack-based buffer-overflow attacks in IoT firmware, to hijack the device remotely. To show the feasibility of this approach, we demonstrate exploiting a common network software application, Connman, used widely in IoT firmware such as Samsung smart TVs. A series of experiments are reported on, including: crashing and executing arbitrary code in the targeted software application in a controlled environment, adopting the attacks in uncontrolled environments (with standard software defenses such as W⊕X and ASLR enabled), and installing publicly available IoT firmware that uses this software application on a Raspberry Pi. The presented exploits demonstrate the ease in which an adversary can control IoT devices.
Intra-hour energy potential forecasting in a central solar power plant receiver combining Meteosat images and atmospheric extinction

In the search for techniques that control the final energy reaching the receivers of central solar power plants, this work presents a novel combination of several methodologies for establishing a complex and valuable system within the user operating system. To establish the final energy level in the central receiver, the direct normal irradiance (DNI) was predicted at ground level using METEOSAT images combined with a novel system involving two digital cameras that determine atmospheric Extinction in the lower atmospheric layer. The predicted values were used as inputs for a solar plant model (Fiat-Lux) to trace the path of the sunlight according to the mirror features. The flux simulations presented a normalized root-mean square error (nRMSE) below 6% and correlation coefficients (R) above 0.94 when compared to real and predicted values. Therefore, it would be possible to quantify the losses produced in the path between the heliostats and the central receiver by integrating different technologies.
Lattice Based Cryptography : Its Applications, Areas of Interest &amp; Future Scope

With a recent increase in the advancement of the technology, computer system and it’s sensitive data are getting exhibited to unauthorised users, with steadily corroding the fundamentals of computer security. This, in fact, demanded fundamental innovations that require several cryptographic paradigms and security protocol. Previously it was thought that asymmetric cryptographic key like RSA, Diffie-Hellman, are very hard to decrypt by classical computation, but with the implementation of quantum computation, it is proved that this kind of cryptographic algorithm is very easy to decrypt and hackers can steal important Data [3], [6]. After the release of Ajtai’s research paper " Generating Hard Instances of Lattice Problem" [2], a million reasons arose in the minds of the researchers to develop on methods based on lattice-based cryptography to improve their security needs. Firstly, it provided much stronger security, and the average-case of several problems in lattice-based cryptography seems equivalent to the worst-case problem of these problems [2]. Moreover, lattices have the potential to persuade the cryptanalytic attacks created by any quantum computers i.e Quantum Computational Secure [3]. In this paper, we will discuss lattice-based cryptosystem, it’s security dimensions, a general brief on how it works, future scope, applications and areas of Interest.
Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions

This paper describes an experimental test campaign while using an Unmanned Aerial Vehicle (UAV) and measuring the obtained UAV positions during different flight tasks and in different operative conditions. A new test procedure has been presented and tested for different devices in various weather conditions. This paper describes and analyses the measurements of the flight trajectory of the UAV that was performed with the use of a robotic total station (RTS), as compared to the design data and the data recorded in the internal memory of the UAV. Five different test tasks have been conducted. The obtained results have allowed for the assessment of the correctness of task performance as compared to the design and to determine the flying accuracy of the entire UAV set. The proposed set of tasks can be successfully utilised to control the correctness of operation of various types of UAVs and it may be implemented as a universal test to verify the algorithms optimising take-offs and landings, test flights of the objects, as well as flight planning in various terrain and weather conditions, which will increase the safety of the flights while using UAVs.