Reviewerly/exHarmony huggingface dataset:
Interference-Aware Orchestration in Kubernetes

Nowadays, there is an increasing number of workloads, i.e. data serving, analytics, AI, HPC workloads, etc., executed on the Cloud. Although multi-tenancy has gained a lot of attention to optimize resource efficiency, current state-of-the-art resource orchestrators rely on typical metrics, such as CPU or memory utilization, for placing incoming workloads on the available pool of resources, thus, neglecting the interference effects from workload co-location. In this paper, we design an interference-aware cloud orchestrator, based on micro-architectural event monitoring. We integrate our solution with Kubernetes, one of the most widely used and commercially adopted cloud orchestration frameworks nowadays, and we show that we achieve higher performance, up to 32% compared to its default scheduler, for a variety of cloud representative workloads.
Combining One-vs-One Decomposition and Instance-Based Learning for Multi-Class Classification

Multi-class classification is one of the most important supervised learning problems and can be solved by either designing direct multi-class classifiers (direct strategy) or decomposing it into a set of binary classification problems (indirect strategy).Direct strategy only needs training one unified classifier while indirect strategy, especially the one-vs-one decomposition method, has shown its superiority and been utilized by some popular software packages.In this article, a first attempt towards bridging the gap between direct strategy and one-vs-one decomposition for multi-class classification is conducted, and accordingly a novel approach named CODIL is proposed.Specifically, CODIL firstly transforms the class vector into a ternary label matrix (only with {-1, 0, +1}) via one-vs-one rule, where each column of the label matrix corresponds to a pair of classes.Then, CODIL determines the binary label vector (only with {-1, +1}) for unseen instance by exploiting the manifold structure information residing in its k nearest neighbors, where each element of the label vector denotes the unseen instance's prediction for its corresponding class pair.Finally, the class for unseen instance is returned via majority voting based on the binary label vector.Extensive comparative studies are conducted between CODIL and six well-established multi-class approaches over seventeen benchmark multi-class data sets.The experimental results show the superiority of the proposed CODIL approach against the compared approaches.
A preliminary study on the adequacy of static analysis warnings with respect to code smell prediction

Code smells are poor implementation choices applied during software evolution that can affect source code maintainability. While several heuristic-based approaches have been proposed in the past, machine learning solutions have recently gained attention since they may potentially address some limitations of state-of-the-art approaches. Unfortunately, however, machine learning-based code smell detectors still suffer from low accuracy. In this paper, we aim at advancing the knowledge in the field by investigating the role of static analysis warnings as features of machine learning models for the detection of three code smell types. We first verify the potential contribution given by these features. Then, we build code smell prediction models exploiting the most relevant features coming from the first analysis. The main finding of the study reports that the warnings given by the considered tools lead the performance of code smell prediction models to drastically increase with respect to what reported by previous research in the field.
Multiple Sclerosis lesions detection by a hybrid Watershed-Clustering algorithm

<h2>Abstract</h2><h3>Background</h3> Computer Aided Diagnosis (CAD) systems have been developing in the last years with the aim of helping the diagnosis and monitoring of several diseases. We present a novel CAD system based on a hybrid Watershed-Clustering algorithm for the detection of lesions in Multiple Sclerosis. <h3>Methods</h3> Magnetic Resonance Imaging scans (FLAIR sequences without gadolinium) of 20 patients affected by Multiple Sclerosis with hyperintense lesions were studied. The CAD system consisted of the following automated processing steps: images recording, automated segmentation based on the Watershed algorithm, detection of lesions, extraction of both dynamic and morphological features, and classification of lesions by Cluster Analysis. <h3>Results</h3> The investigation was performed on 316 suspect regions including 255 lesion and 61 non-lesion cases. The Receiver Operating Characteristic analysis revealed a highly significant difference between lesions and non-lesions; the diagnostic accuracy was 87% (95% CI: 0.83–0.90), with an appropriate cut-off of 192.8; the sensitivity was 77% and the specificity was 87%. <h3>Conclusions</h3> In conclusion, we developed a CAD system by using a modified algorithm for automated image segmentation which may discriminate MS lesions from non-lesions. The proposed method generates a detection out-put that may be support the clinical evaluation.
Using static analysis tools to assist student project evaluation

Code review and static analysis tools are acknowledged as important instruments in software quality control and are used in the industry on a daily basis. In this exploratory study we examine how a well-known static analysis tool can be employed to assess the quality of student solutions to coding assignments. We examine all student solutions submitted to fulfill coding assignments required as part of an introductory programming course taught using Python. We show how teaching staff can evaluate the progress of individual students and how coding mistakes common to many students can be highlighted. We also show how teaching staff can improve their own understanding of perceived assignment complexity by evaluating the aggregate quality of student submitted source code.
Comparative Analysis of Bitcoin, Ethereum, and Libra

In recent years, with the popularity of Bitcoin and other cryptocurrencies, the blockchain technology behind it has gradually become a research focus. After the official launch of Facebook's cryptocurrency project Libra and the publication of the Libra white paper, Libra triggered extensive discussions around the world. Libra has aroused the public's awareness of open finance and is deeply impacting the traditional financial system. In this paper, we systematically review and discuss the blockchain technology and summarize Libra's innovations in consensus algorithm, performance, and application scenario through a comparative analysis of Libra, Bitcoin, and Ethereum. Finally, we put forward the challenges that Libra will face in the future.
Feature-Enhanced Nonequilibrium Bidirectional Long Short-Term Memory Model for Chinese Text Classification

This article proposes a model for Chinese text classification based on a feature-enhanced nonequilibrium bidirectional long short-term memory (Bi-LSTM) network that analyzes Chinese text information in depth and improves the accuracy of text classification.First, the bidirectional encoder representations from transformers model was used to vectorize the original Chinese corpus and extract preliminary semantic features.Then, a nonequilibrium Bi-LSTM network was applied to increase the weight of text information containing important semantics and further improve the effects of the key features in Chinese text classification.Simultaneously, a hierarchical attention mechanism was used to widen the gap between the important and unimportant data.Finally, the softmax function was used for classification.By comparing the classification performance of the proposed scheme with those of various other models, it was observed that the model substantially improved the precision of Chinese text classification and had a strong ability to recognize Chinese text features.The model achieved 97% precision on the experimental dataset.
On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective

We consider the discovery of future research collaborations as a link prediction problem applied on scientific knowledge graphs. Our approach integrates into a single knowledge graph both structured and unstructured textual data through a novel representation of multiple scientific documents. The Neo4j graph database is used for the representation of the proposed scientific knowledge graph. For the implementation of our approach, we use the Python programming language and the scikit-learn ML library. We benchmark our approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Our initial experimentations demonstrate a significant improvement of the accuracy of the future collaboration prediction task. The experimentations reported in this paper use the new COVID-19 Open Research Dataset.
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions

Motion estimation is one of the core challenges in computer vision. With traditional dual-frame approaches, occlusions and out-of-view motions are a limiting factor, especially in the context of environmental perception for vehicles due to the large (ego-) motion of objects. Our work pro-poses a novel data-driven approach for temporal fusion of scene flow estimates in a multi-frame setup to overcome the issue of occlusion. Contrary to most previous methods, we do not rely on a constant motion model, but instead learn a generic temporal relation of motion from data. In a second step, a neural network combines bi-directional scene flow estimates from a common reference frame, yielding a refined estimate and a natural byproduct of occlusion masks. This way, our approach provides a fast multi-frame extension for a variety of scene flow estimators, which outperforms the underlying dual-frame approaches.
A Novel Blockchain Based Smart Contract System for eReferral in Healthcare: HealthChain

The privacy of Electronic Health Records is facing a major issue while outsourcing data in the cloud or sharing the records among stakeholders which includes the leakage of private and sensitive information to unauthorized entities. This research mainly focuses on introducing an efficient referral mechanism employing advanced smart contracts for the effective sharing of healthcare records between stakeholders in healthcare industry. This referral system is designed on a patient-centric model and are limited to authorized providers in the healthdata network. This system is built by employing Hyperledger Fabric as the permissioned blockchain utilising Hyperledger composer which visualizes the couchDB and Interplanetary File System as decentralised data storage are combined for efficient and secure big data sharing in healthcare sector.
Understanding Player Patterns by Combining Knowledge-Based Data Abstraction with Interactive Visualization

Digital games are often created with large virtual worlds and a high number of degrees of freedom for players. For analyzing patterns of play, it is imperative to consider all features affecting the gameplay, which leads to an explosion in data space. To address this problem, we propose an approach for knowledge-based data abstraction -- inspired by applications in medicine -- that uses interactive visualizations based on game telemetry data to study player patterns. The approach involves iterative knowledge-based abstraction of elements of player action sequences to condense the data space to a level interpretable by game designers and user researchers. We developed this approach as part of developing a puzzle game using an existing interactive visualization tool, and demonstrate its value for understanding this game's player patterns. Based on the lessons learned from this study, we present a general set of guidelines for knowledge-based data abstraction for other game genres and interactive visualization systems.
WITHDRAWN: Integration of blockchain and IoT for data storage and management

Blockchain is a fast-evolving, encrypted, immutable and decentralized technology. It is used to store different data types such as transactions, at its core. But it's not about the cryptomonetary transactions. It can also be used to store many other items, such as properties, IoT or even multimedia content, such as songs, photos and videos. On HyperLedger Fabric Blockchain, we are managing IoT info. We collect and securely send data from IoT sensors to our HyperLedger Blockchain node using the MQTT protocol. We process the data and link it to our ledger after collecting data from the sensor. We also assess our network’s efficiency, taking into account different parameters such as timeout for batch, batch size and message count.
A comparison of classification methods across different data complexity scenarios and datasets

Recent research assessed the performance of classification methods mainly on concrete datasets whose statistical characteristics are unknown or unreported. The performance furthermore is often determined by only one performance measure, such as the area under the receiver operating characteristic curve. The performance of several classification methods in four different complexity scenarios and on datasets described by five data characteristics is compared in this paper. Synthetical datasets are used to control their statistical characteristics and real datasets are used to verify our findings. The performance of each classification method is determined by six measures. The investigation reveals that heterogeneous classifiers perform best on average, bagged CART is especially recommendable for datasets with low dimensionality and high sample size, kernel-based classification methods perform very well especially with a polynomial kernel, but require a rather long time for training and a nearest shrunken neighbor classifier is recommendable in case of unbalanced datasets. These findings help researchers and practitioners finding an appropriate method for their binary classification problems.
Hierarchical controlled quantum communication via the χ state under noisy environment

Wang et al. first studied hierarchical quantum information splitting of an arbitrary single-qubit state via the [Formula: see text] state as the entangled channel. There exists a hierarchy among the three receivers as far as the power to recover the teleported state is concerned. But the scheme is considered in ideal environment. In this paper, we reinvestigate the scheme in amplitude-damping and phase-damping noises. The fidelity and average fidelity are adopted to quantify the effect of noise. It is found that they are both dependent on the coefficients of the teleported state and the noise parameter. Moreover, we put forward a novel deterministic scheme to realize hierarchical controlled remote preparation of an arbitrary single-qubit state. Comparing with the previous scheme via the [Formula: see text] state, the sender does not need to perform information dividing due to the subtly constructed measurement basis. We also consider the proposed scheme under noisy environment.
Optimal Sequential Estimation for Asynchronous Sampling Discrete—Time Systems

The asynchronous optimal estimation problem is studied for linear discrete-time stochastic systems subject to a uniform state updating rate and random nonuniform measurement sampling rates. For the case where there are samples within a state updating period, a modified measurement equation from the original system is established for synchronization. Based on the established state-space model, an optimal sequential filter (SF) is developed in terms of the linear minimum variance (LMV) within a state updating period (SUP). It is noted that the SF is more suitable for dealing with the estimation problem of asynchronous sampling systems than other algorithms. Moreover, the proposed SF possesses the same estimation accuracy as that of the augmented batch filter (ABF), as well as a reduced computational cost. The equivalence of their estimation accuracy is strictly proven. Finally, the multisensor case is discussed. The effectiveness of the proposed algorithms is verified with simulation research.
Automatic Mass Classification in Breast Using Transfer Learning of Deep Convolutional Neural Network and Support Vector Machine

Mammography is the most widely used gold standard for screening breast cancer, where mass classification is a prominent step. Classification of mass in the breast is, however, an arduous problem as they usually have large variations in terms of shape, size, boundary, and texture. In this study, the process of mass classification is automated with the use of transfer learning of Deep Convolutional Neural Networks (DCNN) to extract features, the bagged decision tree for feature selection, and finally a Support Vector Machine (SVM) classifier for classifying the mass and non-mass tissue. Area Under ROC Curve (AUC) is chosen as the performance metric, which is then maximized for hyper-parameter tuning using a grid search. All experiments, in this paper, were conducted using the INbreast dataset. The best obtained AUC from the experimental results is O.994±0.003. Our results conclude that high-level distinctive features can be extracted from Mammograms by using the pre-trained DCNN, which can be used with the SVM classifier to robustly distinguish between the mass and non-mass presence in the breast.
TGSM: Towards trustworthy group-based service management for social IoT

The Social Internet of Things has been introduced as a social oriented approach to deal with IoT challenges. A service discovery process is also transformed by this paradigm. Since the Social Internet of Things includes millions of services across the network, the question arises that to what extent can we trust service providers? The appropriate selection of trustworthy service provider plays a key role in successful deployment of the SIoT. In this work, we address this issue by proposing a trustworthy group-based service management model (TGSM). The proposed discovery and selection process of an appropriate service provider relies on both trustworthiness measurement and performance assessment. In the trustworthiness measurement case, a punishment mechanism is adopted in trustworthiness evaluation to deal with objects’ selfish behavior. Likewise, we employed the HITS algorithm to propose a global dynamic reputation evaluation approach. We propose a service-based grouping approach as a novel architecture for SIoT network. We also consider the dynamic evolving of social relationships among the SIoT devices. Our SIoT paradigm, due to its intrinsically multilayered nature, is modeled upon a multiplex network. In the performance measurement case, some metrics are used to asses objects’ performance. To evaluate the proposed trust model, we extend the CRAWDAD dataset, and the objects in the dataset are categorized into five types. The simulation results show that the proportions of four selfish types of objects are dominated by completely trustworthy objects, which proves the effectiveness of our proposed TGSM model. Moreover, the results demonstrate HITS centrality measurement’s effectiveness, which leads the proportion of trustworthy service providers to nearly double in comparison with degree and eigenvector centrality in this context.
Jointly optimization for activity recognition in secure IoT-enabled elderly care applications

Elderly care is a significant livelihood project in the increasingly serious aging society. Nowadays, the wider application of the Internet of Things (IoT) technology on assistant means has making an importance contribution to elderly care in institutions and at home. On the broader data foundation collected by IoT devices, Human Activity Recognition (HAR) with its high demand in various elderly care applications also has grabbed considerable research attentions. However, the elderly’s sensitive data transmitted over the wireless communication channel need to address many security concerns, and the accuracy of activity recognition is susceptible to many influencing factors, especially, the employed feature selection method and classifier. In this paper, to ensure the confidentiality of the elderly’s sensitive data, a secure and efficient group-based key establishment and authentication framework is first proposed. Subsequently, activity recognition is investigated within the security data sensitive framework, where a feature reorganization based feature selection method is proposed with the demonstrated relationship between the recognition accuracy and strongly correlative features, and three classifiers are investigated based on classical ones. By contrast, an improved convolutional neural network (CNN) enabled classifier is singled out to conduct the activity recognition with the feature reorganization method. Finally, security proofs show that the proposed security framework can ensure data confidentiality and be resilient to possible attacks, and experimental analyses show investigations in activity recognition can achieve more cost-effective results.
A (distributional) semantic perspective on the processing of morphologically complex words

Abstract While morphemes are theoretically defined as linguistic units linking form and meaning, semantic effects in morphological processing are not reported consistently in the literature on derived and compound words. The lack of consistency in this line of research has often been attributed to methodological differences between studies or contextual effects. In this paper, we advance a different proposal where semantic effects emerge quite consistently if semantics is defined in a dynamic and flexible way, relying on distributional semantics approaches. In this light, we revisit morphological processing, taking a markedly cognitive perspective, as allowed by models that focus on morphology as systematic meaning transformation or that focus on the mapping between the orthographic form of words and their meanings.
Lattice Reduction with Approximate Enumeration Oracles

This work provides a systematic investigation of the use of approximate enumeration oracles in BKZ, building on recent technical progress on speeding-up lattice enumeration: relaxing (the search radius of) enumeration and extended preprocessing which preprocesses in a larger rank than the enumeration rank. First, we heuristically justify that relaxing enumeration with certain extreme pruning asymptotically achieves an exponential speed-up for reaching the same root Hermite factor (RHF). Second, we perform simulations/experiments to validate this and the performance for relaxed enumeration with numerically optimised pruning for both regular and extended preprocessing. Upgrading BKZ with such approximate enumeration oracles gives rise to our main result, namely a practical and faster (wrt. previous work) polynomial-space lattice reduction algorithm for reaching the same RHF in practical and cryptographic parameter ranges. We assess its concrete time/quality performance with extensive simulations and experiments.
Vulnerability Analysis and Security Research of Docker Container

Docker is an open source application container engine that enables users to pack applications and rely on packages to portable containers. However, Docker also has concerns about security. This paper starts from four aspects of Docker vulnerability, including file system isolation, process and communication isolation, device management and host resource constraints, network isolation and image transmission. Interact with the security module of Linux kernel to enhance the security of Docker, and take active and effective measures to enhance the security of Docker. This paper presents a general picture of current Docker security research, explores and looks into the development trend of Docker security research, and lays a good foundation for the better application of Docker in production.