Reviewerly/exHarmony huggingface dataset:
Flush+Reload Attacks on SEED

Abstract Flush+Reload is a powerful access-driven cache attack in which the attacker leverages a security weakness in the X86 processor architecture to extract the private data of the victim. This attack can be mounted in a cross-core setting, where the memory deduplication is enabled and several users are sharing the same physical machine. In this paper, for the first time, we demonstrate that SEED implementation of OpenSSL 1.1.0 running inside the victim VM is vulnerable against the Flush+Reload attacks and the attacker can recover the keys of this encryption. SEED is a standard encryption algorithm that was developed by the Korea Information Security Agency (KISA) and has been used for confidential services in the Republic of Korea. Our work demonstrates that the attacker can retrieve the secret keys of SEED in 3 min in the native setup and 4 min in the cross-VM setup by performing the Flush+Reload technique. Our experimental results show that common implementation of this standard cipher is vulnerable to Flush+Reload attack in both native and cross-VM settings. To the best of our knowledge, this paper presents the first cache-based attack on a SEED block cipher.
A secure blockchain-based housing rental platform

In the housing rental market, some intermediaries often publish malicious rental prices or provide false housing information, taking advantage of the asymmetry and opacity of the rental market, to harm the interests of tenants. Blockchain has the characteristics of decentralization, immutability, and trustworthiness, which can well address the problems faced in the housing rental market. In this paper, we establish a secure transaction platform for housing rental based on blockchain. First of all, we replace the trusted third party in the traditional house rental platform using the blockchain technology to reduce the trust cost of the platform. Then, we adopt an identity access mechanism based on zero-knowledge proof to ensure the identity legality of users in the housing rental platform. Finally, homomorphic encryption algorithms are employed to protect the private information of users in the process of housing transactions. The evaluation results show that the proposed scheme can well solve the drawbacks of the traditional housing rental platform and ensure the safety of transactions between the rental parties on the platform.
Decision-Tree Based Root Cause Localization for Anomalies in Smart IoT Systems

With the rapid growth of Internet of Things (IoTs), Internet-connected devices and home appliances gain popularity on the consumer electronic market. New home IoT products with built-in network connections and intelligent functionalities are quickly rolled out to the market. As predicted by Gartner, there will be more than 500 IoT devices deployed in a typical household by 2022. The easy device integration and advanced automation logic also brings new challenges with regard to security and privacy. IoT devices have been reported as unreliable because of the constraints in costs and resources. Anomalies of IoT devices include malfunctions of the physical part or the cyber part of an IoT device, as well as abnormal behaviors due to malicious attacks. Abnormal IoT devices could cause severe consequences, because they reside in the home environment and have critical functions that can change the physical world, such as door (smart lock) opening, smart oven burning (which could cause fire), or smart water valve opening (which could cause flooding). In this paper, we study the important issue of localizing the root cause of anomalies in a smart environment (e.g., smart homes and smart offices). We propose to use decision trees for efficient and effective anomaly root cause localization. We construct decision trees from automation rules that control the operations of smart IoT devices in a smart environment. Our performance evaluation on data collected from real smart homes demonstrate the effectiveness of our proposed approach.
Object detection based on self feature distillation

Abstract For object detection, traditional feature distillation methods need to select features from the teacher model to supervise student model training. But what kind of teacher model to choose and what kind of feature transformation to do are two significant problems. In this work, we put forward a self-supervised target detection approach, a simple and effective teacher-free feature distillation method. Specifically, we utilize more semantic features in-depth to distill shallow features, and in the channel dimension, we utilize salient features to monitor the remaining features. We use L 2 loss to achieve feature minimization, with complex feature transformation and loss definition, because there is a smaller semantic gap between features from the same model and features from the same model. We prove theoretically that such self feature distillation is equivalent to adding feature perturbation and self feature of different subnetworks. For the first time, Self Feature Distillation (SFD) extends feature distillation from teacher model avatar into a general feature regularization method. Our experiments show that SFD is superior to the current feature regularization method. At the same time, self achieves 1.5 times of the training acceleration of SOTA’s teacher-based distillation method because it does not need additional training parameters as the teacher model.
Patterned fabric image retrieval using relevant feedback via geometric similarity

Due to the potential value in many areas, such as e-commerce and inventory management, fabric image retrieval, which is a special case of content-based image retrieval, has recently become a research hotspot. As a major category of textile fabrics, patterned fabrics have a diverse and complex appearance, making the retrieval task more challenging. To address this situation, this paper proposes a novel approach for patterned fabric based on the non-subsampled contourlet transform (NSCT) feature descriptor and relevance feedback technique. To integrate the color information into the NSCT feature descriptor, we extract the feature of patterned fabric images in HSV color space. An outlier rejection-based parametric relevance feedback algorithm is employed to adjust the similarity matrix to improve the retrieval results. The experimental results not only show the effectiveness of the proposed approach but also demonstrate that it can significantly improve the performance of the retrieval system compared to other state-of-the-art algorithms.
Evaluating an Augmented Reality-Based Partially Assisted Approach to Remote Assistance in Heterogeneous Robotic Applications

Among the countless applications of Augmented Reality (AR) in the industry, remote assistance represents one of the most prominent and widely studied use cases. Recently, the way in which assistance can be delivered started to evolve, unleashing the full potential of such technology. New methodologies have been proposed able to foster operators' autonomy and reduce under-utilization of skilled human resources. This paper studies the effectiveness of a recently proposed approach to AR-based remote assistance, referred to as partially assisted, which differs from the traditional step-by-step guidance in the way the AR hints are conveyed by the expert to the operator. The suitability of this approach has been proved already for a number of simple industrial tasks, but a comprehensive study has yet to be performed for validating its effectiveness in complex use cases. This paper addresses this lack by considering as a case study the mastering of a robotic manipulator, a procedure involving a number of heterogeneous operations. The performance of the partially assisted approach is compared with step-by-step guidance based on both objective and subjective metrics. Results showed that the former approach could be particularly effective in reducing the time investment for the expert, allowing the operator to autonomously complete the assigned task in a time comparable to traditional assistance with a negligible need for further support.
Research productivity: trend and comparative analyses by regions and continents

Using data for the period 2000 to 2019, the aim of this paper is to: (i) profile and compare research publications in regions and continents worldwide namely Africa, Western Europe, Eastern Europe, Northern America, Latin America, the Asiatic region, the Pacific region and the Middle East; (ii) assess factors associated with research productivity and (iii) verify if African countries are closing the deep gap of research production and by extension, detect factors on which to improve and boost the catch-up process. The empirical evidence is based on the Poisson regression model, quantile regression for counts data and panel negative binomial regression. The findings can be summarised as follows: (i) continuous and linear increasing trends in the production of knowledge are noted in developing regions specifically in Africa even if the contribution of the continent to global research is marginal; (ii) in countries with least production, ‘internet users’ is not significant but schooling modulates its effect on research production contrarily to countries in the upper part of the distribution and (iii) in Africa, if the number of schooling years increases by one, the number of documents or published works produced is expected to increase by a factor of 1.147.
Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation-maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good ( >0.75 ), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer's disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient's distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients' z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.
Recommender Systems for Software Project Managers

The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.
Research on evaluation method of stereo vision measurement system based on parameter-driven

Stereo vision measurement system is widely used in aerospace manufacturing, intelligent robot and other fields because of its unique advantages. However, when using the stereo vision measurement system for measurement, there is no guidance for the parameter configuration of the measurement system for the scene and task constraints, resulting in the stereo vision measurement system cannot play the maximum performance. In this paper, from two aspects of local measurement of camera parameters and global measurement of system parameters, the relevant model is established to analyze the measurement performance of a single independent camera and the performance of the whole stereo vision measurement system. Firstly, the relationship between single camera calibration parameters and measurement accuracy is analyzed; secondly, the relationship between stereo measurement system parameters and measurement accuracy is analyzed; finally, the stereo vision measurement error function is established to guide the design of measurement system parameters. According to the actual measurement experiment, the correctness of the analysis and the accuracy of the established function are verified, it is efficiently used for guiding the parameter design and camera selection of the measurement system in different measurement scene and task.
3D Hand Pose Estimation via aligned latent space injection and kinematic losses

In this paper, we propose a novel multi-stage deep learning methodology to accurately tackle the problem of hand pose estimation. More specifically, we initially propose a disentanglement stage to differentiate the significant pose-specific information from the irrelevant background noise and illumination variations of RGB images. Then, we introduce a variational alignment stage to accurately align the latent spaces of the pose-specific and the true hand pose information, effectively improving the discrimination ability of the proposed methodology. Finally, we propose the use of two loss terms to impose physiological and geometrical kinematic constraints to the predicted hand poses, empowering the proposed methodology to avoid non-plausible poses. During all stages, a novel injection decoder is introduced, improving significantly the decoding accuracy of the latent space. Extensive experimentation on two well-known datasets (i.e., RHD and STB) validate the ability of the proposed methodology to achieve accurate hand pose estimation results, overcoming current state-of-the-art methods.
Analysing Large Repositories of Medical Images

In clinical analysis, medical radiology is a widely used technique to make a noninvasive medical diagnosis that establishes the presence of an injury or disease without requiring invasive surgery. The purpose of computer-aided diagnosis (CAD) is to assist the clinician in interpreting the acquired data. In recent years, the application of machine learning techniques in this field has greatly increased, leading to increased accuracy or even complete replacement of manually created models. The main reason for the increased use of these techniques in medical image analysis is due to the fact that medical data has become increasingly available, the computational power of computers has increased, and significant advances have been made in machine learning, especially in machine vision applications. This development is a driving force behind major changes in the field of medicine, both in the laboratory and in the clinic. Unlike filtering techniques, machine learning can open up new methods for diagnosing diseases that were previously unthinkable. Moreover, the implementation of personalised medicine in the clinic, i.e. modelling specific conditions closely related to patient characteristics, requires the use of machine learning. In this paper, we give an overview of the field and present a set of guidelines that can be helpful in analysing large collections of medical images using data-driven techniques.
Investigating Differences in Gaze and Typing Behavior Across Writing Genres

Writing is one of the most common activities undertaken on a computer, and the activity of writing has been widely studied. Given that writing is an intensively cognitive process, it makes sense that the type of writing that is being produced would have an effect on the writer’s gaze and typing behaviors. However, only a few studies have explored this relationship. In this paper, we study the gaze-typing behaviors, specifically, the coordination between eye gaze and typing dynamics, of writers who are producing original articles in different genres: reminiscent, logical and creative. Our study focuses on Chinese typing, particularly via the Pinyin input method, which generates text via a two step method, and requires additional cognitive processes compared to typing in phonographic languages such as English. Our study involves 46 native Chinese speakers of varying ages from children to elderly. Our method deploys statistics- and sequence-based features to infer the mental state of the author during the writing process. The statistics-based features focus on modeling the overall gaze-typing behaviors during the process and the sequence-based features focus on the transition of the gaze-typing behaviors as the piece of writing progresses. Using a linear support-vector machine, we achieve an overall accuracy over 88% for the article-genre detection by using a leave-one-subject-out cross-validation evaluation.
Evaluating Extractive Summarization Techniques on News Articles

In recent years, due to the rise of deep learning and natural language processing, text summarization has become a huge topic among scholars. Text summarization derives a shorter coherent version of a longer document. There are two methods of summarization namely, abstractive and extractive. This paper focuses on extractive summarization using TextRank and BERT. These algorithms have been tested under various circumstances to determine the best and they all perform better on certain parameters. The goal of this paper is to determine which algorithm performs better as compared to human generated extractive summaries on news dataset. The same dataset was used for both these algorithms and the summaries were evaluated using ROUGE Score. The result showed that TextRank yielded a better ROUGE score as compared to BERT. TextRank showed higher F-measure and recall while BERT had higher precision.
Aspect Level Sentiment Classification with Semantic Distance Attention Networks

Aspect-Based Sentiment Analysis (ABSA) is a kind of sentiment analysis, which extracts opinions expressed for all aspects of the entity. Compared with document-level and sentence-level sentiment analysis tasks, aspect-level sentiment analysis tasks are more granular and more challenging. Aspect-based sentiment analysis is a fine-grained sentiment analysis task, its purpose is to analyze and predict the sentiment polarity corresponding to each aspect commented in the sentence. We proposed a semantic distance attention with BERT model (SDA-BERT), which uses BERT to obtain high-quality word vectors and semantic coding and calculate the SDA value and extract the aspect semantic features. Experiments have proved that SDA-BERT has achieved excellent results on all three public data sets, and achieving accuracy rates of 86.98%, 85.45% and 86.74%, respectively. Compared with various models of machine learning and attention mechanism, the experiment has achieved better results.
A Survey On Adaptive Bitrate Algorithms and Their Improvisations

Recent years have witnessed people engaging in video streaming for innumerable purposes varying from education, entertainment, research, surveillance and many other fields. There have been multiple convenient ways to access videos and people can watch videos from numerous devices with outspread geographical locations irrespective of their network speeds. To make the video streaming adapt to varying network conditions, the adaptive bitrate algorithms (ABR) were developed. According to network conditions and throughput, the best available quality video chunk is played resulting in a considerable cut-back in buffering time, with which quality of experience (QoE) of the user is increased and makes the streaming services smooth. This ensures that watch content is of apt resolution according to the network speed. The ABR algorithms are based on fixed rule based logic and are specific to the device environment on which they are supposed to be deployed. Our proposed paper discusses the existing ABR algorithms and provides a comparative study of their performance with respect to QoE improvisations.
Research on Topology and Security Behaviour Intelligent Analysis by Hybrid P2P Traffic

Abstract At present, most of the popular P2P topology sharing systems are based on unstructured P2P topology. This topology uses flooding method to spread query information, which has good stability, but the efficiency is very low. According to the changing characteristics of the communication attribute state of the network behavior which occupies a large network bandwidth and communicates more frequently, an attribute migration state-oriented network communication behavior analysis method is proposed. The research on the identification of P2P traffic is of great significance for the management of P2P network. In view of the shortcomings of the current P2P traffic identification methods, such as large error and unstable identification results, in order to improve the identification effect of P2P traffic, a P2P traffic identification method based on neural network is proposed. This paper proposes a network file sharing system Kapa, which is based on a hybrid hierarchical P2P topology, which combines the advantages of unstructured and structured P2P topology and has strong practicability. The recognition effect and reliability of this method for P2P applications are verified.
Enhancing of dye-sensitized solar cells efficiency using graphene - MWCNT nanocomposite as photoanode

TiO2/NC nanocomposite was prepared using hydrothermal method reaction for the effective distribution of TiO2 nanoparticles on carbonaceous materials. MWCNT was incorporated in a nanocomposite not only to prevent the restacking of graphene but also to increase the electron transfer from TiO2. The detailed characterization of the nanocomposite was performed using SEM and XRD due to the improved electron transfer and the higher adsorption for N719. It showed energy conversion efficiency (8.96%) which is good compared to pure TiO2 (8.147%). TiO2/NC nanocomposite was suitable as a photoanode in DSSC.