Reviewerly/exHarmony huggingface dataset:
FPGA-Based Symmetric Re-Encryption Scheme to Secure Data Processing for Cloud-Integrated Internet of Things

A new scheme using field programmable gate arrays (FPGAs) to secure Internet of Things (IoT) data processing in public clouds against various attacks (including attacks from insiders) is proposed. The proposed scheme supports various business models involving multiple parties and allow the data owner to give temporary access to IoT data to specific clients at a public market place (the cloud). The scheme achieves perfect forward secrecy, provides FPGA authentication, a secure way to establish a symmetric session key between the on-cloud FPGA, the IoT device and the client, and allows user's configuration integrity check while running in the cloud FPGA. A symmetric proxy re-encryption (PRE) scheme is used to support the publish/subscribe mode of operation of IoT. A complete prototype has been implemented to show the feasibility of the proposed scheme. Formal verification of the proposed protocol verified that it does not have any vulnerabilities. Experimental results showed that an FPGA implementation of the proposed PRE was 6× faster than the SW implementation in transforming a ciphertext of size 1 GB.
Constructions of Linear Codes With One-Dimensional Hull

The hull of a linear code is defined to be the intersection of the code and its dual, and was originally introduced to classify finite projective planes. The hull plays an important role in determining the complexity of algorithms for checking permutation equivalence of two linear codes and computing the automorphism group of a linear code. It has been shown that these algorithms are very effective in general if the size of the hull is small. The objective of this paper is to present some sufficient and necessary conditions that linear codes and cyclic codes have one-dimensional hull. It is shown that there are no such binary or ternary cyclic codes. Based on these characterizations, some constructions of linear codes with one-dimensional hull were given by employing quadratic number fields, partial difference sets, and difference sets. We also construct cyclic codes with one-dimensional hull. Some optimal codes with one-dimensional hull are obtained.
Cognitive spammer: A Framework for PageRank analysis with Split by Over-sampling and Train by Under-fitting

From the past few years, there is an exponential increase in one of the most popular technologies of the modern era called as Internet of Things (IoT). In IoT, various objects perform the tasks of sensing, communication, and computation for providing uninterrupted services (e.g., e-health, e-transportation, security access, etc.) to the end users. In this era, Cognitive Internet of Things (CIoT) is an another paradigm of IoT developed to enhance the capabilities of intelligence in IoT objects where these objects can take independent decisions in any environment. IoT follows the service oriented architecture (SOA), in which the application layer is the topmost layer. It enables the IoT objects to interact with the other objects located across the globe. The power of learning, thinking, and understanding by these objects, can make the information access more accurate and reliable but Web spam is one of the challenges while accessing information from the web. It has been observed from the literature review that search engines are preferred mostly by the people for accessing information. The efficient ranking by the search engines can reduce the computational cost of information exchange by IoT objects. Search engines should be able to prevent the spam from being injected into the web. But, the existing techniques for this problem target in finding the spam after its occurrence in search engine result pages. So, in this proposal, we present an intelligent cognitive spammer framework, Cognitive spammer, which eliminates the spam pages during the web page rank score calculation by search engines. The framework update the Google’s ranking algorithm, PageRank in such a way that it automatically prevents link spam by considering the link structure of web for rank score computation. The updated PageRank algorithm provided the better ranking of web pages. The proposed framework is validated with the WEBSPAM-UK2007 dataset. Before processing, the dataset is preprocessed with a new technique, called as ‘Split by Over-sampling and Train by Under-fitting’ to remove the trade off between imbalanced instances of target class. After data cleaning, we applied machine learning techniques (Bagged model, Boosted linear model, etc) with the web page features to make accurate predictions. The detection classifiers only consider the link features of the web page irrespective of the page content. Out of the fifteen classifiers, best three are ensemble, which results in better performance with overall accuracy improvement. Ten-fold cross validation has also been applied with the resulted ensemble model, which results in getting the accuracy of 99.6% in the proposed scheme.
Prediction of partially observed human activity based on pre-trained deep representation

Prediction of complex human activities from a partially observed video is valuable in many practical applications but is a challenging problem. When a video is partially observed, maximizing the representational power of the given video is more important than modeling the temporal dynamics of the activity. In this paper, we propose a novel human activity descriptor for prediction, which can maximize the discriminative power of a system in a compact and efficient way using pre-trained deep networks. Specifically, the proposed descriptor can capture the potentially important pairwise relationships between objects without prior knowledge or preset attributes. The relationship information is automatically reflected during the descriptor construction procedure based on object’s participation ratios, local and global motion activations. Pre-trained Convolutional Neural Networks are utilized without additional model training procedure. From a practical point of view, the proposed method is more cost-effective when implementing a smart surveillance system. In the experiments, we evaluate the proposed methods in two cases: (1) prediction accuracy with different observation ratios, and (2) the effect of pre-trained network and layer selection. Experimental results from five public datasets verified the efficacy of the proposed method by outperforming competing methods with stable high-performance regardless of network selection.
Interval-valued data prediction via regularized artificial neural network

The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this paper, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Compared to existing hard-constrained methods, the RANN has the advantage that it does not necessarily reduce the prediction accuracy while preventing interval crossing. Extensive experiments are conducted based on both simulation and real-life datasets, with comparison to multiple traditional models, including the linear constrained center and range method, the least absolute shrinkage and selection operator-based interval-valued regression, the nonlinear interval kernel regression, the interval multi-layer perceptron and the multi-output support vector regression. Experimental results show that the proposed RANN model is an effective tool for interval-valued data prediction tasks with high prediction accuracy.
CERSE - Catalog for empirical research in software engineering: A Systematic mapping study

Context Empirical research in software engineering contributes towards developing scientific knowledge in this field, which in turn is relevant to inform decision-making in industry. A number of empirical studies have been carried out to date in software engineering, and the need for guidelines for conducting and evaluating such research has been stressed. Objective: The main goal of this mapping study is to identify and summarize the body of knowledge on research guidelines, assessment instruments and knowledge organization systems on how to conduct and evaluate empirical research in software engineering. Method: A systematic mapping study employing manual search and snowballing techniques was carried out to identify the suitable papers. To build up the catalog, we extracted and categorized information provided by the identified papers. Results: The mapping study comprises a list of 341 methodological papers, classified according to research methods, research phases covered, and type of instrument provided. Later, we derived a brief explanatory review of the instruments provided for each of the research methods. Conclusion: We provide: an aggregated body of knowledge on the state of the art relating to guidelines, assessment instruments and knowledge organization systems for carrying out empirical software engineering research; an exemplary usage scenario that can be used to guide those carrying out such studies is also provided. Finally, we discuss the catalog's implications for research practice and the needs for further research.
Knowledge graph embedding via reasoning over entities, relations, and text

Knowledge graph embedding has attracted significant research interest in the field of intelligent web, which aims to embed both entities and relations into a low-dimensional space. In particular, there are two fundamentally different kinds of models, latent feature models and graph feature models, to infer new predictions in the graph. Latent feature models are expert at using latent features of entities to explain triples and infer these features automatically from the data, while graph feature models are do well in extracting features from the observable graph patterns. Combining the strengths of these two fundamental models is a promising approach to increase the predictive performance of graph models. Thus, we propose a new combined model, named as Text-enhanced Knowledge Graph Embedding (TKGE), to perform inference over entities, relations, and text. The model is not only well-suited for modeling interactions of their latent features, but also well-suited for modeling paths between entities in the graph. Experimental results show that TKGE has significant improvements compared to baselines on two tasks: knowledge graph completion and triple classification.
Squirrel-Cage Local Binary Pattern and Its Application in Video Anomaly Detection

Local binary pattern (LBP) is one of the most successful feature descriptors. However, LBP and its variants have not been as successful as other feature descriptors in video anomaly detection (VAD). This is because LBP and its variants are mainly designed for spatial texture analysis. Although the volume LBP (VLBP) and the LBP-three orthogonal planes (LPB-TOP) have the capability of describing dynamic texture, they are seldom used as descriptors for VAD because 1) both VLBP and LBP-TOP are more suitable for natural scenes with rich dynamic textures, but sensitive to noise in the scenes with less dynamic textures, 2) the combination of motion and appearance not only limits their capability of motion characterizing but also brings the irrelevant appearance information such as background, and 3) high dimensionality is another drawback. In this paper, a new variant of the LBP called the squirrel-cage LBP (SCLBP) is proposed for VAD. By imitating the structure of squirrel cage rotor, the proposed SCLBP can be regarded as a stretched LBP in temporal direction and has two distinct features: 1) it is computed at vector-wise, rather than at pixel-wise (i.e., for the central vector with respect to its surrounding parallel vectors), and 2) the sign between two vectors is determined by the angle-based thresholding scheme. The SCLBP can effectively encode the motion information and is insensitive to noise and irrelevant disturbances caused by dynamic background and illumination change. To the best of our knowledge, The SCLBP is the first variant of the LBP specially designed for motion characterizing. The SCLBP has great flexibility, extendibility, and low dimensionality (only one-third of the LBP-TOP descriptor). The effectiveness of the proposed SCLBP descriptor is demonstrated on different public data sets and compared with the other dominant descriptors and state-of-the-art approaches in VAD.
Value-Flow-Based Demand-Driven Pointer Analysis for C and C++

We present SUPA, a value-flow-based demand-driven flow-and context-sensitive pointer analysis with strong updates for C and C++ programs.SUPA enables computing points-to information via value-flow refinement, in environments with small time and memory budgets.We formulate SUPA by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately.To answer a client query (a request for a variable's points-to set), SUPA reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program).In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted.We have implemented SUPA on top of LLVM (4.0.0) together with a comprehensive micro-benchmark suite after a years-long effort (consisting of around 400 test cases, including hand-written ones and the ones extracted from real programs).We have evaluated SUPA by choosing uninitialized pointer detection and C++ virtual table resolution as two major clients, using 24 real-world programs including 18 open-source C programs and 6 large CPU2000/2006 C++ benchmarks.For uninitialized pointer client, SUPA achieves improved precision as the analysis budget increases, with its flow-sensitive (context-insensitive) analysis reaching 97.4% of that achieved by whole-program Sparse Flow-Sensitive analysis (SFS) by consuming about 0.18 seconds and 65KB of memory per query, on average (with a budget of at most 10000 value-flow edges per query).With context-sensitivity also considered, SUPA becomes more precise for some programs but also incurs more analysis times.To further demonstrate the effectiveness of SUPA, we have also evaluated SUPA in resolving C++ virtual tables by querying the function pointers at every virtual callsite.Compared to analysis without strong updates for heap objects, SUPA's demand-driven context-sensitive strong update analysis reduces 7.35% spurious virtual table targets with only 0.4 secs per query, on average.
A Local Feature Descriptor Based on Combination of Structure and Texture Information for Multispectral Image Matching

Due to the significant nonlinear intensity changes of multispectral images, automatic image feature point matching is a challenging task. This letter addresses the problem and proposes a novel descriptor combining the structure and texture information to solve the nonlinear intensity variations of multispectral images. We first propose directional maps, i.e., the directional response maps (DMs) and the directional response binary maps (DBMs), which can capture the common structure and texture properties of multispectral images, respectively. We then use the spatial pooling pattern of the histogram of oriented gradients to separately describe the local region of each point of interest based on the DMs and DBMs. In order to speed up the calculation, we apply Gaussian filters to the DMs and average filters to the DBMs to construct the per-pixel histogram bins. Finally, we conjoin the normalized feature vectors corresponding to the structure description and texture description of each point of interest to obtain the histograms of directional maps (HoDMs). The proposed HoDM descriptor was evaluated using three data sets composed of images obtained in both visible light and infrared spectra. The experimental results confirm that the proposed HoDM descriptor is robust to the nonlinear intensity changes of multispectral images and has a superior matching performance as well as a much higher computational efficiency.
Hyperspectral Image Restoration Based on Low-Rank Recovery With a Local Neighborhood Weighted Spectral–Spatial Total Variation Model

Hyperspectral image (HSI) is often contaminated by mixed noise, which severely affects the visual quality and subsequent applications of the data. In this paper, HSI restoration based on low-rank recovery with a local neighborhood weighted spectral-spatial total variation (TV) model is proposed, which focuses on the preservation of spatial structure and spectral fidelity. The low-rank matrix model is adopted to exploit the spectral and spatial correlation information, and the l1-norm is used as a prior to remove the sparse noise. Furthermore, a local spatial neighborhood weighted spectral-spatial TV is utilized to jointly model the spectral-spatial prior information; specifically, the spectral and spatial differences are both considered in the TV term, and the weight is computed by considering the local neighborhood information in the spatial domain. Alternating direction method of multipliers optimization procedure is extended to solve the presented model. Experimental results demonstrate that the proposed method can remove the mixed noise, enhance the structural information simultaneously, and offer the best performance compared with several state-of-the-art HSI restoration methods.
A Stochastic Trust Region Algorithm Based on Careful Step Normalization

An algorithm is proposed for solving stochastic and finite-sum minimization problems. Based on a trust region methodology, the algorithm employs normalized steps, at least as long as the norms of the stochastic gradient estimates are within a specified interval. The complete algorithm—which dynamically chooses whether to employ normalized steps—is proved to have convergence guarantees that are similar to those possessed by a traditional stochastic gradient approach under various sets of conditions related to the accuracy of the stochastic gradient estimates and choice of step size sequence. The results of numerical experiments where the method is employed to minimize convex and nonconvex machine learning test problems are presented. These results illustrate that the method can outperform a traditional stochastic gradient approach.
The algorithm selection competitions 2015 and 2017

The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
ARMOR: A Secure Combinatorial Auction for Heterogeneous Spectrum

Dynamic spectrum allocation via auction is an effective solution to spectrum shortage. Combinatorial spectrum auction enables buyers to express diversified preferences towards different combinations of channels. Despite the effort to ensure truthfulness and maximize social welfare, spectrum auction also faces potential security risks. The leakage of sensitive information such as true valuation and location of bidders may incur severe economic damage. However, there is a lack of works that can provide sufficient protection against such security risks in combinatorial spectrum auction. In this paper, we propose ARMOR, to enable combinatorial auction for heterogeneous spectrum with privacy, which can preserve bidders' privacy while guaranteeing the economic-robustness of the combinatorial auction. We leverage the cryptographic methods, including homomorphic encryption, order-preserving encryption, and garbled circuits, to shield the bid and location information of buyers from the auctioneer. We design a novel location protection algorithm, which allows the auctioneer to exploit spectrum reuse opportunities without knowing the exact locations of buyers. Furthermore, we propose a verifiable payment scheme based on digital signature to prevent the auctioneer from forging the payment. The extensive experiments confirm that ARMOR maintains the good performance of the combinatorial spectrum auction, in terms of buyer satisfactory ratio and social welfare, and achieves privacy preservation with acceptable computation and communication costs.
Generalized Conditional Domain Adaptation: A Causal Perspective With Low-Rank Translators

Learning domain adaptive features aims to enhance the classification performance of the target domain by exploring the discriminant information from an auxiliary source set. Let X denote the feature and Y as the label. The most typical problem to be addressed is that P XY has a so large variation between different domains that classification in the target domain is difficult. In this paper, we study the generalized conditional domain adaptation (DA) problem, in which both P Y and P X|Y change across domains, in a causal perspective. We propose transforming the class conditional probability matching to the marginal probability matching problem, under a proper assumption. We build an intermediate domain by employing a regression model. In order to enforce the most relevant data to reconstruct the intermediate representations, a low-rank constraint is placed on the regression model for regularization. The low-rank constraint underlines a global algebraic structure between different domains, and stresses the group compactness in representing the samples. The new model is considered under the discriminant subspace framework, which is favorable in simultaneously extracting the classification information from the source domain and adaptation information across domains. The model can be solved by an alternative optimization manner of quadratic programming and the alternative Lagrange multiplier method. To the best of our knowledge, this paper is the first to exploit low-rank representation, from the source domain to the intermediate domain, to learn the domain adaptive features. Comprehensive experimental results validate that the proposed method provides better classification accuracies with DA, compared with well-established baselines.