Reviewerly/exHarmony huggingface dataset:
POSEIDON: A Graphical Editor for Item Selection Rules Within Feature Combination Rule Contexts

A car line can offer more than $$10^{100}$$ variants, and for each customer order a concrete selection of features and parts needs to be done. The respective selection rules are interconnected and are subject to constraints imposed by different car lines. We address the problem of finding and fixing logical errors in these interconnected selection rules within different contexts of allowed feature combinations. Previous work has focused on text-based or matrix-like representations which presented challenges regarding cognitive complexity, size of the representation, and usability. We present an integrated visualization of the combined practical effects of item selection and feature combination rules. The implemented tool detects logical errors and supports user workflows to fix the data visually.
Enhancing the defectiveness prediction of methods and classes via JIT

Abstract Context Defect prediction can help at prioritizing testing tasks by, for instance, ranking a list of items (methods and classes) according to their likelihood to be defective. While many studies investigated how to predict the defectiveness of commits, methods, or classes separately, no study investigated how these predictions differ or benefit each other. Specifically, at the end of a release, before the code is shipped to production, testing can be aided by ranking methods or classes, and we do not know which of the two approaches is more accurate. Moreover, every commit touches one or more methods in one or more classes; hence, the likelihood of a method and a class being defective can be associated with the likelihood of the touching commits being defective. Thus, it is reasonable to assume that the accuracy of methods-defectiveness-predictions (MDP) and the class-defectiveness-predictions (CDP) are increased by leveraging commits-defectiveness-predictions (aka JIT). Objective The contribution of this paper is fourfold: (i) We compare methods and classes in terms of defectiveness and (ii) of accuracy in defectiveness prediction, (iii) we propose and evaluate a first and simple approach that leverages JIT to increase MDP accuracy and (iv) CDP accuracy. Method We analyse accuracy using two types of metrics (threshold-independent and effort-aware). We also use feature selection metrics, nine machine learning defect prediction classifiers, more than 2.000 defects related to 38 releases of nine open source projects from the Apache ecosystem. Our results are based on a ground truth with a total of 285,139 data points and 46 features among commits, methods and classes. Results Our results show that leveraging JIT by using a simple median approach increases the accuracy of MDP by an average of 17% AUC and 46% PofB10 while it increases the accuracy of CDP by an average of 31% AUC and 38% PofB20. Conclusions From a practitioner’s perspective, it is better to predict and rank defective methods than defective classes. From a researcher’s perspective, there is a high potential for leveraging statement-defectiveness-prediction (SDP) to aid MDP and CDP.
<scp>ForensicNet</scp>: Modern convolutional neural network‐based image forgery detection network

Abstract The advancements in Image editing techniques produce realistic‐looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth‐wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth‐wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state‐of‐the‐art methods by a large margin.
Multi-auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation

Recommending appropriate tags to items can facilitate content organization, retrieval, consumption, and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations. In this article, we propose a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for tag recommendation, which couples item collaborative information and item multi-auxiliary information, i.e., content and social graph, by defining a generative process. Specifically, the model learns deep latent embeddings from different item auxiliary information using variational auto-encoders (VAE), which could form a generative distribution over each auxiliary information by introducing a latent variable parameterized by deep neural network. Moreover, to recommend tags for new items, item multi-auxiliary latent embeddings are utilized as a surrogate through the item decoder for predicting recommendation probabilities of each tag, where reconstruction losses are added in the training phase to constrain the generation for feedback predictions via different auxiliary embeddings. In addition, an inductive variational graph auto-encoder is designed to infer latent embeddings of new items in the test phase, such that item social information could be exploited for new items. Extensive experiments on MovieLens and citeulike datasets demonstrate the effectiveness of our method.
An Axiomatic System for Concessive Conditionals

Abstract According to the analysis of concessive conditionals suggested by Crupi and Iacona, a concessive conditional $$p{{\,\mathrm{\hookrightarrow }\,}}q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mrow> <mml:mspace /> <mml:mo>↪</mml:mo> <mml:mspace /> </mml:mrow> <mml:mi>q</mml:mi> </mml:mrow> </mml:math> is adequately formalized as a conjunction of conditionals. This paper presents a sound and complete axiomatic system for concessive conditionals so understood. The soundness and completeness proofs that will be provided rely on a method that has been employed by Raidl, Iacona, and Crupi to prove the soundness and completeness of an analogous system for evidential conditionals.
A Dynamic Live Streaming Service Architecture Integrated Sensing and Control

The live streaming of Over-The-Top (OTT) hot events will cause the tidal traffic of the network. As viewership grows, this high network load can lead to network blocking, poor quality of experience for users, high transmission costs for operators, and increased energy consumption. Most OTT lives content providers offer video services with unicast. To save bandwidth, there are some transmission solutions that also use multicast. Transmitting OTT video using the existing Internet Protocol television (IPTV) network is a win-win situation for telecom operators and OTT content providers. However, so far as we know, there is no dynamic service system for live streaming content that is easy to control over the existing IPTV network. As a result, it is more difficult to provide OTT live content on the existing IPTV network. In this paper, we propose a dynamic system architecture for live streaming services with integrated sensing and control. This architecture can leverage existing IPTV networks for real-time smart sensing and control of media services. The client does not need to make modifications and still uses the unicast protocol for video content delivery. We use a MA as a bridge between multicast and unicast. It receives client requests, converts multicast to unicast, and provides services to users. In addition, we design a unicast-multicast convergence method, which first provides unicast services to viewers and then dynamically switches to multicast service, reducing bandwidth and also improving the viewer experience.
A Trustworthy Content Moderation Scheme Based on Permissioned Blockchain

Content moderation is one of the most important issues in media industry. With the increasing of media content, most service providers (SPs) have been challenged with huge content moderation workload and tight deadline. We are concerned about the repeated moderation of popular content caused by ‘head’ effect, and find the lack of trust among SPs is the basic reason. To deal with this, we propose a novel content moderation scheme based on permissioned blockchain, which provides trustworthy moderation data sharing operation. We also design the content moderation mechanism based on video feature, which is used to identify the same video with different formats. Compared with the traditional approach, the proposed scheme can both ensure the integrity of the moderation data and reduce the total computation overhead.
PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem

Abstract Background As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud. Results Our main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data. Conclusion The proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.
Linguistic and Vocal Markers of Microbehaviors Between Team Members During Analog Space Exploration Missions

We used machine learning classifiers and dialog state tracking models, combined with natural language processing techniques relying on lexicon-based methods and data-driven methods, to automatically detect positive and negative microbehaviors between team members in nine, four-person teams in a simulated space habitat. Our findings indicate that the psycholinguistic markers extracted using the linguistic inquiry and word count, STRESSnet dictionaries, and acoustic features can achieve an f1-score up to 54.87% in a three-class classification problem. Our findings also suggest that modeling turns between the sender and target of microbehaviors is significantly more effective in detecting microbehavior than only modeling the sender’s information. Finally, we demonstrate the effect of introducing context for detection purposes. Dialog state tracking approaches that model the linguistic interaction between team members and incorporate contextual information about the task and sentiment of the conversation can further yield improved performance, depicting an f1-score of 57.73%.
Shielding Graph for eXact Analytics With SGX

Graphs nicely capture data from various domains, allowing the computations of many analytic tasks via graph queries. Graphs of real-world data are often large, albeit useful, and the involved computation can be too heavyweight for commodity computers. For secure outsourcing, we propose (SGX) <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , a forward-secure structured encryption scheme for graph data, which uses lightweight cryptographic techniques with a trusted execution environment such as SGX. To process million-scale graphs by the limited memory of SGX, we load data on-demand using Dijkstra's algorithm and Fibonacci heap. Compared with most prior graph encryption schemes, (SGX) <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> supports exact shortest-distance queries instead of approximation and can be easily extended to other graph-based analytics.
Developing and Evaluating an Artificial Intelligence Model for Malicious URL Detection

Today, the increased use of the internet has become important in our lives and new communication technologies, social networks, e-commerce, online banking, and among other applications have a significant impact on the promotion and growth of business. In our study, we aimed to work with a large dataset and to achieve the best results in detecting malicious URL addresses using an artificial intelligence model. A 7-layer RNN model was used in the study, and two similar national and international datasets were combined and merged to create a big new dataset consisting of 579,112 URL addresses. Then, this new data set is divided into training and test sets. first data set was trained at the model and then the second data set was processed test. When this data set was processed in our model, we achieved a success rate of over 91%. This rate is a very good result of detecting malicious url addresses. Your contribution with this work is significant in developing more effective methods for detecting harmful sites as internet usage increases, parallel use of artificial intelligence models makes detection of such sites more effective and potentially assist users in protecting from various types of cyber-attacks is targeted.
Multisensor Cross-Media Data Mining Method Assisted by Expert System

The information generated by the multifaceted and multilevel processing of the data of multiple sensors is more meaningful than the information obtained by a single sensor and provides accurate information and decision-making basis for various application systems. How to apply data mining theory to the multisensor cross-media field has become a research hotspot. Through the analysis of multisensor cross-media data, it is of great significance to dig out the important rules, information, or knowledge hidden in it and use it for cross-media retrieval engine. The rule acquisition is the “bottleneck” problem of the expert system. This paper adopts the data mining method based on the rough set to acquire the rules and improves the basic algorithm of attribute reduction. Using the attribute reduction algorithm and the heuristic value reduction algorithm, the calculation is simplified and the reduction efficiency is improved. In the presentation, according to the characteristics of cross-media and the application requirements of expert systems, this paper takes the case representation based on features as the basis and classifies cases according to feature attributes. In case retrieval, according to the hierarchical structure of case features, the entire case database is organized into a multilevel hierarchical index structure. In this paper, a cross-media retrieval engine is constructed from the perspective of classifier design, and the Euclidean distance is used as the similarity matching model of image content. The mutual retrieval of images and audios preliminarily forms the design process of retrieval from one media type to another and establishes a corresponding cross-media index. The experimental results show that the algorithm has better processing effect and higher accuracy than other algorithms. Different k-nearest neighbor values were selected in the experiment, and it reached about 96% in the test environment of libsvm toolbox, which is better than the processing results of LE and LLE algorithms.
“We Share All Data with Each Other”: Data-Sharing in Peer-to-Peer Relationships

Abstract Although the topic of data-sharing has boomed in the past few years, practices of datasharing have attracted only scant attention within working groups and scientific cooperation (peer-to-peer data-sharing). To understand these practices, the author draws on Max Weber’s concept of social relationship, conceptualizing data-sharing as social action that takes place within a social relationship. The empirical material consists of interviews with 34 researchers representing five disciplines—linguistics, biology, psychology, computer sciences, and neurosciences. The analysis identifies three social forms of data-sharing in peer-to-peer relationships: (a) closed communal sharing, which is based on a feeling of belonging together; (b) closed associative sharing, in which the participants act on the basis of an agreement; and (c) open associative sharing, which is oriented to “institutional imperatives” (Merton) and to formal regulations. The study shows that far more data-sharing is occurring in scientific practice than seems to be apparent from a concept of open data alone. If the main goal of open-data policy programs is to encourage researchers to increase access to their data, it could be instructive to study the three forms of data-sharing to improve the understanding of why and how scientists make their data accessible to other researchers.
A Robust Approach to Detecting Non-Equivalent Quantum Circuits Using Specially Designed Stimuli

As several compilation and optimization techniques have been proposed, equivalence checking for quantum circuits has become essential in design flows. The state-of-the-art to this problem observed that even small errors substantially affect the entire quantum system. As a result, it exploited random simulations to prove the non-equivalence of two quantum circuits. However, when errors occurred close to outputs, it was hard for the work to prove the non-equivalence of some non-equivalent quantum circuits under a limited number of simulations. In this work, we propose a novel simulation-based approach using a set of specially designed stimuli. The simulation runs of the proposed approach is linear rather than exponential to the number of quantum bits of a circuit. According to the experimental results, the success rate of our approach is 100% (100%) under a simulation run (execution time) constraint for a set of benchmarks, while that of the state-of-the-art is only 69% (74%) on average. Our approach also achieves a speedup of 26 on average.
Accelerating Convolutional Neural Networks in Frequency Domain via Kernel-Sharing Approach

Convolutional neural networks (CNNs) are typically computationally heavy. Fast algorithms such as fast Fourier transforms (FFTs), are promising in significantly reducing computation complexity by replacing convolutions with frequency-domain element-wise multiplication. However, the increased high memory access overhead of complex weights counteracts the computing benefit, because frequency-domain convolutions not only pad weights to the same size as input maps, but also have no sharable complex kernel weights. In this work, we propose an FFT-based kernel-sharing technique called FS-Conv to reduce memory access. Based on FS-Conv, we derive the sharable complex weights in frequency-domain convolutions, which has never been solved. FS-Conv includes a hybrid padding approach, which utilizes the inherent periodic characteristic of FFT transformation to provide sharable complex weights for different blocks of complex input maps. We in addition build a frequency-domain inference accelerator (called Yixin) that can utilize the sharable complex weights for CNN accelerations. Evaluation results demonstrate the significant performance and energy efficiency benefits compared with the state-of-the-art baseline.
EO-Shield

Smart devices, especially Internet-connected devices, typically incorporate security protocols and cryptographic algorithms to ensure the control flow integrity and information security. However, there are various invasive and non-invasive attacks trying to tamper with these devices. Chip-level active shield has been proved to be an effective countermeasure against invasive attacks, but existing active shields cannot be utilized to counter side-channel attacks (SCAs). In this paper, we propose a multi-function protection scheme and an active shield prototype to against invasive and non-invasive attacks simultaneously. The protection scheme has a complex active shield implemented using the top metal layer of the chip and an information leakage obfuscation module underneath. The leakage obfuscation module generates its protection patterns based on the operating conditions of the circuit that needs to be protected, thus reducing the correlation between electromagnetic (EM) emanations and cryptographic data. We implement the protection scheme on one Advanced Encryption Standard (AES) circuit to demonstrate the effectiveness of the method. Experiment results demonstrate that the information leakage obfuscation module decreases SNR below 0.6 and reduces the success rate of SCAs. Compared to existing single-function protection methods against physical attacks, the proposed scheme provides good performance against both invasive and non-invasive attacks.
Quantization through Search

Quantization has become an essential technique in compressing deep neural networks for deployment onto resource-constrained hardware. It is noticed that, the hardware efficiency of implementing quantized networks is highly coupled with the actual values to be quantized into, and therefore, with given bit widths, we can smartly choose a value space to further boost the hardware efficiency. For example, using weights of only integer powers of two, multiplication can be fulfilled by bit operations. Under such circumstances, however, existing quantization-aware training methods are either not suitable to apply or unable to unleash the expressiveness of very low bit-widths. For the best hardware efficiency, we revisit the quantization of convolutional neural networks and propose to address the training process from a weight-searching angle, as opposed to optimizing the quantizer functions as in existing works. Extensive experiments on CIFAR10 and ImageNet classification tasks are examined with implementations onto well-established CNN architectures, such as ResNet, VGG, and MobileNet, etc. It is shown the proposed method can achieve a lower accuracy loss than the state of arts, and/or improving implementation efficiency by using hardware-friendly weight values at the same time.