Reviewerly/exHarmony huggingface dataset:
A new Ensemble approach for Software Fault Prediction

Software Fault prediction is about prediction of faulty modules in the software systems. This prediction is done using different metrics such as lines of code, depth of inheritance tree etc. The fault prediction is carried out during early stages of development, as it decreases the maintenance costs and improve the software quality. Though software engineering research community have employed certain approaches to build an effective fault prediction model by using machine learning techniques, but the resampling techniques are used as an explicit activity in their studies. There is no study which reports the impact of discriminative power of resampling technique with classifiers. The main objective is to utilize the best combination of classifier with resampling technique in the model averaging method. In this study, we have investigated and compared the effect of different ensemble methods in improving the performance of prediction model. We have benchmarked the model averaging method using existing ensemble methods such as voting and stacking. The class imbalance problem is tackled with the use of different resampling techniques such as SMOTE, random under sampling and random over sampling. Five classifiers have been employed in this study due to their wide acceptance in fault prediction context which includes decision trees, logistic regression, Naive bayes, multinomial naive bayes and K nearest neighbor. The model is tested on 4 publicly available datasets from the PROMISE repository. In order to evaluate the performance of fault prediction model, F-measure is used. In order to avoid the sample randomness and biasness, the results are cross validated using k-fold ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=10$</tex> ) cross validation. The experimental results showed that model averaging method outperformed when compared with other ensembles
SLA-aware resource scheduling algorithm for cloud storage

Abstract Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts.
Deep Learning Method for Content-Based Retrieval of Focal Liver Lesions Using Multiphase Contrast-Enhanced Computer Tomography Images

A content-based image retrieval (CBIR) system can support radiologists in making clinical diagnosis through image analysis. Multiphase contrast-enhanced computer tomography (CT) images are more effective than single contrast-enhanced CT images in detecting and characterizing focal liver lesions (FLLs). This study proposes a deep learning method for the CBIR of FLLs using multiphase contrast-enhanced CT images. We use deep convolutional neural networks (DCNNs) to extract the temporal– spatial features from multiphase CT images. Compared with the conventional low- and mid-level features, the high-level features extracted by the DCNN can significantly improve the retrieval accuracy. The effectiveness of the proposed method was demonstrated through experiments with our multiphase FLL CT dataset, which is called as MPCT-FLL dataset. The mean average precision (mAP) was improved from 0.76 to 0.84.
Robust path control for an autonomous ground vehicle in rough terrain

Typically, it is not trivial for an autonomous ground vehicle (AGV) to navigate in rough terrain due to lots of unexpectable uncertainties. Because the performance of the autonomous navigation can be degraded by the uncertainties, the AGV requires a robust control system against them. To handle the necessity, this study proposes the robust path controller by utilizing a disturbance observer (DOB)-based control method. The proposed controller is developed by two steps: the first step is to design a controller for handling a nominal system of the AGV, and the other is to add the DOB to the nominal controller. Then, the determined control input is distributed into tire forces because the tire forces are final resource enabling the AGV to move. The tire force distribution is defined as an optimization problem with some constraints, and the solution is obtained by the CVXGEN. Finally, numerical simulations and field tests with a skid-type ground vehicle, developed by the authors’ laboratory, are conducted to validate the performance of the proposed method.
Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine

The fiber Bragg grating (FBG) sensor calibration process is critical for optimizing performance. Real-time dynamic calibration is essential to improve the measured accuracy of the sensor. In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations and improve the predictive speed. Polynomial fitting, a back propagation (BP) network, and a radial basis function (RBF) network were compared, and the results showed the dynamic method not only had a better generalization performance but also had a faster learning process. The dynamic calibration enabled the real-time measured data of the FBG sensor to input calibration models as online learning samples continuously, and could solve the insufficient coverage problem of static calibration training samples, so as to improve the long-term stability, accuracy of prediction, and generalization ability of the FBG sensor.
Attentive Relational State Representation in Decentralized Multiagent Reinforcement Learning

In multiagent reinforcement learning (MARL), it is crucial for each agent to model the relation with its neighbors. Existing approaches usually resort to concatenate the features of multiple neighbors, fixing the size and the identity of the inputs. But these settings are inflexible and unscalable. In this article, we propose an attentive relational encoder (ARE), which is a novel scalable feedforward neural module, to attentionally aggregate an arbitrary-sized neighboring feature set for state representation in the decentralized MARL. The ARE actively selects the relevant information from the neighboring agents and is permutation invariant, computationally efficient, and flexible to interactive multiagent systems. Our method consistently outperforms the latest competing decentralized MARL methods in several multiagent tasks. In particular, it shows strong cooperative performance in challenging StarCraft micromanagement tasks and achieves over a 96% winning rate against the most difficult noncheating built-in artificial intelligence bots.
Approximation of Quantum States Using Decision Diagrams

The computational power of quantum computers poses major challenges to new design tools since representing pure quantum states typically requires exponentially large memory. As shown previously, decision diagrams can reduce these memory requirements by exploiting redundancies. In this work, we demonstrate further reductions by allowing for small inaccuracies in the quantum state representation. Such inaccuracies are legitimate since quantum computers themselves experience gate and measurement errors and since quantum algorithms are somewhat resistant to errors (even without error correction). We develop four dedicated schemes that exploit these observations and effectively approximate quantum states represented by decision diagrams. We empirically show that the proposed schemes reduce the size of decision diagrams by up to several orders of magnitude while controlling the fidelity of approximate quantum state representations.
Understanding Video Streaming Algorithms in the Wild

While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player’s response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fits-all solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.
Voting-Based Document Image Skew Detection

Optical Character Recognition (OCR) is an indispensable tool for technology users nowadays, as our natural language is presented through text. We live under the need of having information at hand in every circumstance and, at the same time, having machines understand visual content and thus enable the user to be able to search through large quantities of text. To detect textual information and page layout in an image page, the latter must be properly oriented. This is the problem of the so-called document deskew, i.e., finding the skew angle and rotating by its opposite. This paper presents an original approach which combines various algorithms that solve the skew detection problem, with the purpose of always having at least one to compensate for the others’ shortcomings, so that any type of input document can be processed with good precision and solid confidence in the output result. The tests performed proved that the proposed solution is very robust and accurate, thus being suitable for large scale digitization projects.
Consistency analysis and flow secure enforcement of SELinux policies

SELinux policies used in practice contain tens of thousands of rules, making it hard to comprehend their impact on the security and to verify whether they actually meet the intended security goals. In this paper, we describe an approach for reasoning about the consistency of a given SELinux policy by analyzing the information flows caused by it. For this purpose, we model SELinux policy rules using the Readers-Writers Flow Model (RWFM). We have used this approach to implement a static policy analysis tool as well as a run-time monitor. The static policy analysis tool identifies all the possible indirect flows in a given policy and then filters out those indirect flows that pose a high threat. Given an indirect flow, the tool can also identify the sequences of accesses that cause the indirect flow. The tool also ranks the rules and domains based on the number of policy violations they cause. Thus, the static analysis tool is useful for policy writers to develop flow secure policies. The run-time monitor, on the other hand, keeps track of the information flows in an SELinux system and detects indirect flows dynamically. This helps in ensuring flow secure enforcement of a given SELinux policy as per the specification. The efficiency and efficacy of our implementations are demonstrated through experimental analysis on large, real-life policies.
Further Steps Towards a Logic of Polarization in Social Networks

In this paper we look at different ways of modally defining properties related to the concept of balance in signed social networks where relations can be either positive or negative. The motivation is to be able to formally reason about the social phenomenon of group polarization, for which balance theory forms a network-theoretical underpinning. The starting point is a recently developed basic modal logic that axiomatizes the class of social networks that are balanced up to a certain degree. This property is not modally definable but can be captured using a deduction rule. In this paper we examine different possibilities for extending this basic language, in order to, first, be able to define frame properties such as balance and related properties such as non-overlapping positive and negative relations and collective connectedness as axioms, and, second, be able to define the property of full balance rather than balanced-up-to-a-degree. We consider extensions with both static modalities such as the universal and the difference modality, the intersection modality, and nominals known from hybrid logic, as well as dynamic global bridge modalities known from sabotage logic. Along the way we provide axioms for weak balance. Finally, to explore measures of how far a network is from polarization, we consider and compare variations of distance measures between models in relation to balance.
Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy

With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.
Lightweight Image Super-Resolution With Mobile Share-Source Network

Within the development of the deep convolutional neural network, the great achievements had been made in the single-image super-resolution (SISR) task. However, the higher performance always comes with the deeper layers which also brings larger numbers of network operations and parameters that make it hard to implement in practice. In our work, a lightly super-resolution, named Mobile Share- Source Network (MSSN), is purposed to address these practical issues. In MSSN, a high-efficiency block, the mobile adaptive weighted residual unit, is designed to fulfill the need for the reduction in both parameters and the Mult-Adds while maintaining the performance with importing the deep separable convolution. Moreover, it brings into the Adaptive Weighted Share-Source Skip Connection, getting abundant information from the shallow layer which helps reconstruct better images. The experimental results show that our network has fewer numbers of parameters and operations than the state-of-the-art lightweight network while maintaining high reconstruction quality comparing with many state-of-the-art super-resolution methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM).
A benchmark suite for designing combinational logic circuits via metaheuristics

The evolvable hardware literature reports several methods for the evolution of digital circuits. However, there is a large variability in the set of problems and the appropriate metrics used for the evaluation of the results. In this paper, we propose a set of representative problems to comparatively evaluate metaheuristics when designing Combinational Logic Circuits (CLCs). We also define a set of performance measurements and descriptive statistics to analyze the results found by the search techniques. As a case study, we compare Cartesian Genetic Programming variants applied to this domain. The results highlight the benefits of the proposed heterogeneous benchmark suite in the analysis of metaheuristics when designing CLCs.
A Study on Music Education Based on Artificial Intelligence

Abstract Artificial intelligence has developed rapidly and is widely used in various fields. Music robots with specific neural networks can understand, analyze, and create music. In this paper, based on the artificial intelligence technology, human music intelligence is analyzed through big data to explore and discuss the construction of various new interactive teaching music intelligent systems in the field of professional music education with music artificial intelligence technology as the platform, seeking a new mode of music perception, cognition, creation, and education.
Generating Posets Beyond N

We introduce iposets—posets with interfaces—equipped with a novel gluing composition along interfaces and the standard parallel composition. We study their basic algebraic properties as well as the hierarchy of gluing-parallel posets generated from singletons by finitary applications of the two compositions. We show that not only series-parallel posets, but also interval orders, which seem more interesting for modelling concurrent and distributed systems, can be generated, but not all posets. Generating posets is also important for constructing free algebras for concurrent semirings and Kleene algebras that allow compositional reasoning about such systems.
Clustering Glossary Terms Extracted from Large-Sized Software Requirements using FastText

Specialized terms used in the requirements document should be defined in a glossary. We propose a technique for automated extraction and clustering of glossary terms from large-sized requirements documents. We use text chunking combined with WordNet removal to extract candidate glossary terms. Next, we apply a state-of-the art neural word embeddings model for clustering glossary terms based on semantic similarity measures. Word embeddings are capable of capturing the context of a word and compute its semantic similarity relation with other words used in a document. Its use for clustering ensures that terms that are used in similar ways belong to the same cluster. We apply our technique to the CrowdRE dataset, which is a large-sized dataset with around 3000 crowd-generated requirements for smart home applications. To measure the effectiveness of our extraction and clustering technique we manually extract and cluster the glossary terms from CrowdRE dataset and use it for computing precision, recall and coverage. Results indicate that our approach can be very useful for extracting and clustering of glossary terms from a large body of requirements.
An ensemble-based approach to the security-oriented classification of low-level log traces

Traditionally, Expert Systems have found a natural application in the behavioral analysis of processes. In fact, they have proved effective in the tasks of interpreting the data collected during the process executions and of analyzing these data with the aim of diagnosing/detecting anomalies. In this context, we focus on log data generated by executions of business processes, and consider the issue of detecting “insecure” process instances, involving some kind of security breach (e.g. attacks, frauds). We propose a hybrid framework for accomplishing a security-oriented classification of activity-unawaretraces, i.e., traces consisting of “low-level” events with no explicit reference to the “high-level” activities the analysts are typically familiar with. The framework integrates two classification approaches traditionally used as alternative ways to decide on the “secureness” of process traces: (i) a model-driven approach, using knowledge of behavioral models expressed at the abstraction level of the activities, and (ii) an example driven approach, exploiting the availability of event sequences labeled by experts as symptomatic of “secure” or “insecure” behavior. The core of our solution is a meta-classifier combining (i) and (ii) thanks to a probabilistic Montecarlo mechanism that allows the traces to be simultaneously viewed as sequences of low-level events and of high-level activities. The framework has been empirically proved effective in jointly exploiting the two aforementioned forms of knowledge/expertise, typically coming from different experts, and in acting as a sort of “super-expert” classification tool. Its accuracy and efficiency make it a solid basis for implementing a novel kind of expert system for the security-oriented monitoring/analysis of business processes.
Measuring Domain Portability and Error Propagation in Biomedical QA

In this work we present Google’s submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). The core of our systems are based on BERT QA models, specifically the model of [1]. In this report, and via our submissions, we aimed to investigate two research questions. We start by studying how domain portable are QA systems that have been pre-trained and fine-tuned on general texts, e.g., Wikipedia. We measure this via two submissions. The first is a non-adapted model that uses a public pre-trained BERT model and is fine-tuned on the Natural Questions data set [4]. The second system takes this non-adapted model and fine-tunes it with the BioASQ training data. Next, we study the impact of error propagation in end-to-end retrieval and QA systems. Again we test this via two submissions. The first uses human annotated relevant documents and snippets as input to the model and the second predicted documents and snippets. Our main findings are that domain specific fine-tuning can benefit Biomedical QA. However, the biggest quality bottleneck is at the retrieval stage, where we see large drops in metrics – over 10pts absolute – when using non gold inputs to the QA model.
Dynamic Hand Gesture Based Sign Word Recognition Using Convolutional Neural Network with Feature Fusion

Gesture-based sign language recognition systems play an important role in human-computer interaction to develop communication between deaf communities and other people. Where the deaf community, hard of hearing, and deaf family members express their feelings and communicate with others. In this case, hand gestures have been a promising subject and applied to the very practical application of sign language recognition (SLR). SLR is highly influenced by the recognition of hand, as the sign word is a form of communicative gesture. However, the diversity and complexities of the gestures of the hand can greatly affect reliability and recognition rates. To solve this problem, this paper introduces an effective sign word recognition system using a deep learning technique, including feature fusion convolutional neural network. In the proposed system, the input image is captured from the live video using a low cost device, such as a webcam and preprocessed hand gesture image. The pre-processing is accomplished with the conversion of YCbCr, binarization, erosion and finally hole fillings. Two channels of CNN are used to extract the features from preprocessed images. The feature fusion is performed at the fully connected layer and this feature is used for gesture classification by the softmax classifier. An experimental setup established in our laboratory environment and the user can recognize the signs of fifteen common words in real-time. The experimental results show high recognition accuracy in gesture-based sign word recognition compared with the state-of-art systems.