Reviewerly/exHarmony huggingface dataset:
Security in the Presence of Key Reuse: Context-Separable Interfaces and Their Applications

Key separation is often difficult to enforce in practice. While key reuse can be catastrophic for security, we know of a number of cryptographic schemes for which it is provably safe. But existing formal models, such as the notions of joint security (Haber-Pinkas, CCS '01) and agility (Acar et al., EUROCRYPT '10), do not address the full range of key-reuse attacks—in particular, those that break the abstraction of the scheme, or exploit protocol interactions at a higher level of abstraction. This work attends to these vectors by focusing on two key elements: the game that codifies the scheme under attack, as well as its intended adversarial model; and the underlying interface that exposes secret key operations for use by the game. Our main security experiment considers the implications of using an interface (in practice, the API of a software library or a hardware platform such as TPM) to realize the scheme specified by the game when the interface is shared with other unspecified, insecure, or even malicious applications. After building up a definitional framework, we apply it to the analysis of two real-world schemes: the EdDSA signature algorithm and the Noise protocol framework. Both provide some degree of context separability, a design pattern for interfaces and their applications that aids in the deployment of secure protocols.
Topology Verification of Low Voltage Distribution Network Based on k-means Clustering Algorithm

Abstract Aiming at the problem of topology connection error existing in GIS system of low voltage distribution network, a topology verification algorithm using AMI voltage measurement data combined with k-means clustering algorithm is proposed. Firstly, the voltage data of the consumers in the low-voltage substation area is obtained by the AMI measurement system. Then k-means clustering algorithm is used by the similarity to cluster the voltage curves to identify and verify the users connected on the incorrectly transformer stations. An improved method of noise processing using data density set is proposed to solve the problem of initial cluster centre selection in k-means. For the problem of k value selection, an automatic correction of optimal k value is proposed. The feasibility and effectiveness of the improved k-means clustering algorithm in topology verification are verified by a practical example.
A Study for Selecting the Best One-Stage Detector for Autonomous Driving

A development of deep learning has accelerated research into autonomous driving. Especially, deep-learning based object detection has been actively studied and has become an essential technology for autonomous driving. In this paper, the representative one-stage detectors are evaluated and compared using the autonomous driving dataset, and the best algorithm is proposed in terms of trade-off between detection accuracy and processing speed. In addition, the effect of input size in utilizing this algorithm for autonomous driving application is analyzed through various experiments, and finally the most suitable input size for autonomous driving is proposed.
A Deep Learning Framework to Predict Rating for Cold Start Item Using Item Metadata

Recommender systems improve browsing experience of users for large amount of items by assisting selection and classification of items utilizing item metadata. The performance of recommender system usually deteriorates when implicit data is used with limited user interaction history also regarded as cold start (CS) problem. This paper proposes a model to address cold start problem using content based technique where user or item metadata is used to break this ice barrier. The proposed method utilizes the feature extraction techniques (such as term frequencyInverse document frequency(TF-IDF)) and word embedding technique (Word2Vec). These content features are then used to predict the ratings for CS items by constructing user profiles using stacked auto-encoder. Experiments performed on largest real world dataset provided by Movielens 20M shows that proposed model outperforms the state-of-the-art approaches in CS item scenario.
Reducing the Inter-speaker Variance of CNN Acoustic Models Using Unsupervised Adversarial Multi-task Training

Although the Deep Neural Network (DNN) technology has brought significant improvements in automatic speech recognition, the technology is still vulnerable to changing environmental conditions. The adversarial multi-task training method was recently proposed to increase the domain and noise robustness of DNN acoustic models. Here, we apply this method to reduce the inter-speaker variance of a convolutional neural network-based speech recognition system. One drawback of the baseline method is that it requires speaker labels for the training dataset. Hence, we propose two modifications which allow the application of the method in the unsupervised scenarios; that is, when speaker annotation is not available. Our approach applies unsupervised speaker clustering, which is based on a standard feature set in the first case, while in the second case we modify the network structure to perform speaker discrimination in the manner of a Siamese DNN. In the supervised scenario we report a relative error rate reduction of 4%. The two unsupervised approaches achieve smaller, but consistent improvements of about 3% on average.
Evaluation of the Influence of the Dispersion Properties of a Fiber-Optic Line on the Efficiency of an Algorithm for Single-Photon Synchronization of Quantum Key Distribution System

The relevance of the development and modernization of synchronization algorithms, which reduce the synchronization time of the quantum key distribution (QKD) system, while maintaining enhanced security against unauthorized access, is noted. The synchronization algorithm is described, where, to increase security from unauthorized access, quantum pulses are used as synchronizing signals. A quantitative assessment of the influence of the dispersion properties of a fiber-optic line on the efficiency of the two-stage single-photon synchronization algorithm of the autocompensation QKD system is given.
Image Enhancement using Chaotic Maps and Bio-inspired Multi-objective Optimization

Image enhancement is a preprocessing step required in most image processing and computer vision applications because a high quality image leads to better and accurate processing results. Usually the image quality is subjectively associated to its contrast, but, by increasing the contrast, other distortions can be created. In this paper is proposed a context-free image enhancement method based on the multi-objective Particle Swarm Optimization algorithm combined with chaotic sequences. As objective functions, the expected contrast gain and tone distortion are used. The experiments revealed that for the particle swarm algorithm the results are better when the chaotic maps are used instead of the standard random evolution. The results are also superior to those obtained by the traditional histogram equalization method.
Early Virtual Reality User Experience and Usability Assessment of a Surgical Shape Memory Alloy Aspiration/Irrigation Instrument

Performing user experience (UX) and usability studies in VR to assess early product prototypes is common in the automotive and other sectors but has not yet been applied for the development of surgical instruments. For the development of an aspiration/irrigation instrument using a shape memory alloy (SMA) tube, we wanted to get an early user feedback of our prototype from medical staff. We conducted a study with 22 participants with a medical background and considered a potential presence bias of the UX and usability ratings. The results confirm the novelty and usefulness of a SMA tube and gave us valuable feedback for optimizing the shapeshifting mechanism.
Advancing Reproducibility Through Shared Data: Bridging Archival and Library Practice

At the Children's Hospital of Philadelphia (CHOP), a team of librarians and archivists is implementing a new biomedical research data archives and a data discovery catalog as part of Arcus, a multi-year strategic initiative of the CHOP Research Institute dedicated to making research data more broadly available within the institution. Arcus presents the Library Science team with the opportunity to develop new methods and solutions for addressing a number of important issues. This paper serves to highlight three areas of focus. First, archival appraisal methods will be used for CHOP's large-scale biomedical research data, which presents issues for quality data management and preservation. The Archives will employ expanded appraisal workflows to encompass strong selection and prioritization criteria before a collection is ingested, as opposed to current archival research data frameworks, which situate appraisal after a collection is acquired. Second, CHOP research data efforts will be organized as archival collections because they offer a framework for encapsulating research and mapping the relationships between a dataset, software, protocols and other contextual information critical to data reproducibility. Third, a custom descriptive metadata schema is being developed because archival item-level descriptive metadata doesn't address the complex discovery needs of diverse biomedical data. This will require addressing tension between archival arrangement and the item-level descriptive metadata required for discovery.
Social web video clustering based on multi-modal and clustering ensemble

Web videos are rich resources for people to satisfy their information and entertainment needs. Previous studies have applied clustering methods by using textual information of videos tagged by the up-loaders to perform web video categorization, which helps users easily find videos that they really want and then increase the user’s satisfaction. However, web video categorization remains a challenging task due to the difficulties in accuracy measuring the semantic relation between terms in videos. In this paper, a novel framework for social web video clustering is proposed by improving the similarity calculation method of web videos and using the clustering ensemble. It consists of the following steps: 1) A new semantic based on Vector Space Model (VSM) is defined by considering the semantic relation of terms obtained from the lexical reference system (WordNet). 2) Word2vec is used to capture the continuous vectors as semantic information in the form of vector set of terms in a document. 3) The comprehensive extension of Semantic VSM by utilizing the Normalized Google Distance is presented. 4) The linear combining function is embodied to combine the similarity based on the optimal values of the parameter to control the weights of models before applying them to clustering paradigms and the Clustering Ensemble is employed to integrate the results of each clustering with Must-Link constraint. Experimental evaluations on real-world social web video datasets demonstrate that the proposed method effectively facilitates the clustering and achieves promising performance.
Simplifying the Classification of App Reviews Using Only Lexical Features

User reviews submitted to app marketplaces contain information that falls into different categories, e.g., feature evaluation, feature request, and bug report. This information is valuable for developers to improve the quality of mobile applications. However, due to the large volume of reviews received every day, manual classification of user reviews into these categories is not feasible. Therefore, developing automatic classification methods using machine learning approaches is desirable. In this study, we address the problem of automatic classification of app review sentences (as opposed to full reviews) into different categories. We compare the simplest textual machine learning classifier using only lexical features – the so-called Bag-of-Words (BoW) approach – with more complex models used in previous work adopting rich linguistic features. We find that the performance of the simple BoW model is very competitive and has the advantage of not requiring any external linguistic tools to extract the features. Moreover, we experiment with deep learning based Convolutional Neural Network (CNN) models that have recently achieved state-of-the-art results in many classification tasks. We find that, on average, the CNN models do not perform significantly better than the simple BoW model. Finally, the manual analysis of misclassification errors and data annotations suggests that classifying review sentences in isolation does not always contain enough information to make a correct prediction. Thus, we suggest that adopting neural models to incorporate additional contextual knowledge might improve the classification performance.
Experimental Evaluation of a Secure and Ubiquitous Architecture for Electronic Health Records Retrieval

Electronic health record (EHR) systems play a relevant role in hospital daily activities. They support the access of patient data, such as medication delivery and history of hospitalizations. Currently, it is essential to provide easy and secure access to medical records by hospital staff. Pervasive and ubiquitous computing can lead to a better way to manage EHR systems. In this scenario, one of the greatest research challenges is to ensure device authentication avoiding its impersonation. The main contribution of this work is to carry out an extended experimental evaluation of a secure architecture based on ubiquitous computing for medical records retrieval and maintenance to demonstrate its feasibility through of experiments in laboratory and hospital scenarios. For evaluation, this article implements a prototype that performs the medication delivery procedure. Experimental results indicate the system functionality, usability with a decreasing user learning curve, and no significant overhead in terms of communication delay.
A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer

Cartesian Genetic Programming (CGP) is a powerful and popular tool for automatic generation of computer programs to solve user defined tasks. This paper proposes a Co-evolutionary CGP (named Co-CGP) which can automatically gain high-order knowledge to accelerate the search. In the Co-CGP, two modules are working in cooperation to solve a given problem. One module focuses on solving a series of small scale problems of the same type to generate the building blocks. Simultaneously, the second module focuses on combing the available building blocks to construct the final solution. Besides, an adaptive control strategy is introduced to automatically evaluate the effectiveness of the building blocks and adjust the search behaviour adaptively so as to improve search efficiency. The proposed Co-CGP is tested on eight problems with different complexities. Experimental results show that the Co-CGP can significantly improve the performance of CGP, in terms of both search efficiency and accuracy.
Building a Pronunciation Dictionary for the Kabyle Language

This paper presents the methods used and results obtained for the creation of a Festival-compatible pronunciation dictionary of above 10k words for the kabyle language. Kabyle is a berber dialect spoken in Northern Algeria. This dictionary will be useful in the design of text-to-speech and automatic speech recognition systems for the kabyle language. It was built using a bootstrapping method in which we incrementally build rules to predict word pronunciations while correcting wrong predictions. We thus obtain a large pronunciation dictionary as well as a set of rules to predict pronunciations for unknown words. The rules are embedded in Classification and Regression Trees and achieve 91,62% of correct prediction rate for entire words and 97,85% for phonemes.
Off-topic Detection Model based on Biterm-LDA and Doc2vec

Chinese writing in primary and secondary schools occupies an extremely important position in Chinese education. With the advent of natural language processing, the automatic e ssay review system has gradually matured, which has greatly promoted the development of composition writing. Especially the off-topic detection plays a key role in the automatic essay review system. We propose effective methods for off-topic detection. Firstly, we use Biterm-LDA combined with Doc2vec to inspect the topic and semantics of composition. Secondly, we propose a threshold calculation method based on the topic composition class center under different topic compositions. Finally, the ROC curve is employed to find the optimal threshold for each type of topic composition, then according to the optimal threshold, the off topic essay is judged. Experiments of the five types of topic composition show the average F1-score value of the off-topic detection reach about 65%.
Enabling Joins over Cassandra NoSQL Databases

Over the last few years, we witness an explosion on the development of data management solutions for big data applications. To this direction, NoSQL databases provide new opportunities by enabling elastic scaling, fault tolerance, high availability and schema flexibility. Despite these benefits, their limitations in the flexibility of query mechanisms impose a real barrier for any application that has not predetermined access use-cases. One of the main reasons for this bottleneck is that NoSQL databases do not directly support joins. In this paper, we propose a data management solution, designed initially for eHealth environments, that relies on NoSQL Cassandra databases and efficiently supports joins, requiring no set-up time. More specifically, we present a query optimization and execution module, that can be placed, at runtime, on top of any Cassandra cluster, efficiently combining information from different column-families. Our optimizer rewrites input queries to queries for individual column-families and considers two join algorithms implemented for the efficient execution of the requested joins. Our evaluation demonstrates the feasibility of our solution and the advantages gained, compared to the only solution currently available by DataStax. To the best of our knowledge, our approach is the first and the only available open source solution allowing joins over NoSQL Cassandra databases.
Methods of identifying talent in the field of engineering and natural sciences

Abstract The distinctive characteristics of giftedness in the field of engineering and natural sciences are highlighted. The problems of intellectual giftedness are considered. The application of the projective method of diagnosing divergent thinking to determine endowments in the field of engineering and natural sciences, taking into account its adaptation, is substantiated.
Three-Dimensional (3D) Polygon Mesh Authentication Using Sequential Bit Substitution Strategy

Accelerated growth in higher dimensional media processingBorah, Sagarika has promoted the usage of 3D representation of objectsBorah, Bhogeswar and also are easily distributed over network for various reasons. To ensure the authenticity of such susceptible 3D models, authentication-based watermarking techniques are often used. This paper discusses a semi-fragile, blind watermarking algorithm that is able to detect a vertex-level tamper in the mesh and also detects topological manipulations. The watermark generation is carried out by self-embedding pattern. A low-complexity sequential bit substitution strategy is proposed to embed the model geometry information as verification bits in a specific pattern. To make the mesh invariant to vertex-reordering operation, vertices are indexed with a topology-oriented mesh traversal strategy. The performance of the approach is depicted with the proper tamper detection and localization achievement and minimal distortion to the surface of the model.
Environmental pollution monitoring using a Web-based GIS in Surakarta

Abstract The case of environmental pollution is usually not all exposed onmedia. As such, environmental pollution monitoring needs to be done, hence the government can take legal measures against it. Monitoring environmental pollution will be more effective when involving community. The development of geospatial technologies such as Web-based GIS is helpful for monitoring pollution inenvironment. This is due to the Web-based GIS has several advantages including easy tooperate, easily accessible and able to present data (spatial and non-spatial) in real time. The purpose of this research is to develop a web-based GIS for monitoring environmental pollution. The software was developed using Rapid Application Development (RAD). Software development using technology based on opensource (CodeIgniter, Bootstrap, OpenLayers, and GML). The results show that the utilization of web-based GIS technology for environmental pollution monitoring is easily accessible and operated by user. Furthermore, the trial results show that the utilization of developed web-based GIS technology can operate properly and present data interactively.
Retpoline Technique for Mitigating Spectre Attack

The Speculative execution (Spectre) is a chip attempting to predict the future to improve the system performance which involves multiple logical branches. It will start working out the math for all those branches before the program even has to decide between them. Normally, it works together with caching which is to speed up the memory access by filled with the data that will need some and often the output of the speculative execution is stored here. The speculative execution and caching in the operating system improve the overall system and operating system performance through the prediction of data or resources to be used and cache memory for quick access to data or resources. The problem arises where this function could allow potential attackers to get access to data they should not have access to by exploiting Spectre attack which also known as side-channel attack and Meltdown. Spectre attack and Meltdown open up the possibilities for dangerous attacks which involved the breach of security and confidentiality of the user. Various techniques and patches have been introduced to mitigating the Spectre attack and Meltdown. In this project, we study the technique used to mitigate the Spectre attack variant 2 introduced by Google called Retpoline technique in the networking virtualised environment.
Identifying Technical Debt through a Code Comment Mining Tool

Context: The software industry often has to deal with several challenges to deliver and maintain products, such as providing useful software with high quality, on time, and on the budget. This challenge is difficult, if not impossible, to overcome, and software engineers end up developing immature artifacts that cause unexpected delays and make the whole system difficult to maintain and evolve in the future. That is what the Software Engineering (SE) community now calls Technical Debts. Objective: The main goal of this paper is to propose an approach to support and automate the identification of different types of TD through code comment analysis, as well as to propose and evaluate the eXcomment. Method: We carry out a proof-of-concept study in two Open Source Projects: ArgoUML and JFreeChart. Results: Our findings indicate that the eXcomment make it possible to select a list of suitable comments to support TD identification automatically. The study provided new evidence on how software engineers can use code comments to detect and classify TD items automatically. Conclusion: This work contributes to bridge the gap between the TD identification area and code comment analysis, successfully using code comments to detect several types of TD.