Reviewerly/exHarmony huggingface dataset:
MOAManager: A tool to support data stream experiments

Summary This short communication describes Massive Online Analysis Manager Hypertext Preprocessor framework (MOAManager), an open‐source web tool that assists in the creation, execution, extraction, and formatting of experiments in online environments using the MOA framework. MOAManager also brings together a set of features for data analysis, including the calculation of metrics and statistical tests for performance evaluation. The purpose of the tool is to provide a structure that automates several repetitive experimental steps, helping the users to customize, run, and analyze the experiments quickly and reliably, enabling gains in productivity.
DELFI: Mislabelled Human Context Detection Using Multi-Feature Similarity Linking

Context Aware (CA) systems that adapt to user behaviors have many real-world uses. CA systems require accurately labeled training data to learn models of users' context behavior. Unfortunately, it is difficult to gather sufficient realistic context data in controlled environments where reliable labels can be gathered. Therefore, recent works have used in-the-wild study designs, where data is gathered through passive sensing devices such as smartphones while users periodically supply corresponding context labels. However, labels gathered this way can be unreliable as users may provide incomplete or inaccurate labels which makes it difficult to build robust CA models. We propose DELFI (Detecting Erroneous Labels using Feature-linking Insights), a visual analytics approach to discover and clean unlabeled or mislabeled context data. Visualizations enable highlighting of similar data to find patterns and anomalies in behaviors. However, this is challenging when working with erroneous human-labelled data as linking similar context labels is flawed since the labels themselves are in question. DELFI identifies probably-mislabeled instances by color-coding them based on an anomaly score. Additionally, DELFI links similar instances based on a novel concept called Multi-Feature Similarity Linking, which facilitates the identification of probably true labels of mislabeled and unlabeled data. We demonstrate the utility of our approach with use cases and evaluation from domain experts.
$N$-Qubit State Protection Against Amplitude Damping by Quantum Feed-Forward Control and its Reversal

We propose a state protection scheme of quantum feed-forward control and its reversal (QFFCR) by quantum weak measurement and quantum control to protect the arbitrary N-qubit state in amplitude-damping channel (ADC). Before the state enters the ADC, a weak measurement and an offsetting operation are made to make the state almost immune to the effect of ADC. After the state passes the ADC, the reversed offsetting operation and a correction rotation are applied to recover the state. Unlike most previous state protection schemes, ours does not use weak measurement reversal, but a correction rotation to bring back the state to its initial amount. Our protection scheme can also protect entangled states even for heavy damping probabilities effectively. Fidelity and success probability are used to evaluate the effect of the proposed scheme. The general formula for complete recovery and the total success probability of N-qubit system are derived. The effectiveness of our control scheme is demonstrated by simulation results.
Collaborative Graph Walk for Semi-Supervised Multi-label Node Classification

In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named Multi-Label-Graph-Walk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multi-label node classification task as simultaneous graph walks conducted by multiple label-specific agents. Furthermore, policies of the label-wise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple label-specific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multi-label classification performance than the state-of-the-art approaches and conduct more efficient graph exploration.
Spear Phishing Simulation in Critical Sector: Telecommunication and Defense Sub-sector

Phishing is an attack that uses social engineering techniques to steal users' confidential information like passwords and banking information. It happens when cyber criminals disguised as a trusted entity and deceived users to click on fake links in e-mail received by the user. Cyber criminals also act to target phishing attacks from individuals to organizations that are specific to the country's critical sector, and this is known as a spear phishing. In fact, the telecommunication sector is one of the main targets of cyber criminals using spear phishing attacks to obtain user-sensitive information. The main objective of this work is to identify the level of cyber security in the organization under the telecommunication sector and defense sub-sector by using existing general simulation procedure. The procedure is adapted and modified according to the organization's working environment. The first simulation was conducted on June 4, 2018 involving 39 employees. Findings showed that all respondents did not respond to the spear phishing e-mails received. In fact, the results of the questionnaire conducted after the end of the simulation found that all respondents were able to identify all indicators on spear phishing e-mails quickly and easily. This proves that the level of awareness and knowledge of cyber security of the population is high. The second simulation was conducted in stages, from October 29 to November 15, 2018 using a different approach. Of the 39 e-mails sent, 12 respondents (31%) responded to the received e-mail by clicking on the link in the e-mail content. Based on the results of this second simulation, this spear phishing attack was successfully implemented and proved that the new simulation procedure can be used in the telecommunication sector and defense sub-sector.
Sparse multiple instance learning with non-convex penalty

Multiple instance learning (MIL) usually plays an important role in weakly labeled learning. It is able to reduce cost noted by accurate annotations and remove noisy samples with useless labels in large scale databases. However, in many real applications, the training data always contains many redundant features. In order to remove those residual features and improve the computational efficiency, some convex and nonconvex regularization terms are applied. In this paper we apply the MCP function to propose an efficient sparse modle SyMIL, and then use the proximal stochastic subgradient method (PSSG) to solve it. The convergence analysis of the algorithm is given in details. Extensive experiments show that our model can reduce redundant features effectively while ensuring the accuracy. Quantitative comparisons against several regularization terms demonstrate the advantages of the MCP regularizer in this model. Moreover, PSSG can be observed to have slightly faster convergence rate than some other methods.
Directed Acyclic Graph as Tangle: an IoT Alternative to Blockchains

Blockchains are established as the most widely used P2P distributed application for Distributed Ledger Technologies. Nevertheless, their successful existence, especially known due to the cryptocurrency of Bitcoin, gets more and more competitors. One of those competitors is IOTA, which is based on a tangle or a Directed Acyclic Graph architecture. This paper stresses potentials of such architecture,especially in future IoT applications. In particular, few of the most import security issues are analyzed.
Optimized Polynomial Multiplier Over Commutative Rings on FPGAs: A Case Study on BIKE

In this paper, we present two constant-time FPGA-based polynomial multipliers for post-quantum secure key encapsulation mechanisms based on quasi-cyclic codes, which are among round 2 candidates in the NIST PQC standardization process. The pipelined hardware architecture for polynomial multiplications proposed in this work are fully parameterized in terms of the size of the polynomial, and can be further tuned flexibly to achieve a trade-off between time and area depending on individual needs. We also present a case study on the BIKE key generators which use these two polynomial multiplier architectures as building blocks. Compared with the state-of-the-art hardware implementation of BIKE, the design proposed in this work is around 9× faster in terms of run-time while maintaining an over 6× smaller time-area product.
Integer Programming as a General Solution Methodology for Path-Based Optimization in Robotics: Principles, Best Practices, and Applications

Integer programming (IP) has proven to be highly effective in solving many path-based optimization problems in robotics. However, the applications of IP are generally done in an ad-hoc, problem-specific manner. In this work, after examined a wide range of path-based optimization problems, we describe an IP solution methodology for these problems that is both easy to apply (in two simple steps) and high-performance in terms of the computation time and the achieved optimality. We demonstrate the generality of our approach through the application to three challenging path-based optimization problems: multi-robot path planning(MPP), minimum constraint removal(MCR), and reward collection problems(RCPs). Associated experiments show that the approach can efficiently produce (near-)optimal solutions for problems with large state spaces, complex constraints, and complicated objective functions. In conjunction with the proposition of the IP methodology, we introduce two new and practical robotics problems: multi-robot minimum constraint removal(MMCR) and multi-robot path planning(MPP) with partial solutions, which can be quickly and effectively solved using our proposed IP solution pipeline.
A+ Tuning: Architecture+Application Auto-Tuning for In-Memory Data-Processing Frameworks

Processing big data eventually leads to an upsurge in datacenters' power consumption, which is one of the pivotal concerns to be addressed. Many of the existing works focus on optimizing either power or performance, which is not the best parameter to consider for achieving high energy efficiency with low operational costs. Furthermore, the existing works require profiling of big data applications exhaustively and only consider tuning of either architectural or software parameters, often leading to sub-optimal settings. To cope up with the above-mentioned drawbacks of the existing works, we propose a system, A+ Tuning (Architecture + Application Auto-tuning) which enables us to determine a close to optimal settings by simultaneously optimizing for Energy Delay Product(EDP), representing energy efficiency. The proposed A+ Tuning involves a) profile the incoming unknown applications to different types (compute-bound, memory-bound and etc.) based on known applications classification result; b) co-locate the applications, and c) employs a machine learning-based model to determine the optimal settings and tune from both architectural and application settings for the co-located applications. By applying the proposed A+Tuning system, datacenters achieve up to 4×EDP improvement compared to fairshare methodology and 2.5×compared with recent works such as BestConfig.
Pairwise local Fisher and naive Bayes: Improving two standard discriminants

The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the naive Bayes, which is based on marginals only. In this paper we extend both discriminants by modeling dependence between pairs of variables. In the continuous case this is done by local Gaussian versions of the Fisher discriminant. In the discrete case the naive Bayes is extended by taking geometric averages of pairwise joint probabilities. We also indicate how the two approaches can be combined for mixed continuous and discrete data. The new discriminants show promising results in a number of simulation experiments and real data illustrations.
Enhanced key establishment technique for secure data access in cloud

Today the IT environment concentrates on Cloud computing where the usage of cloud based applications poses various security threats. Many possible cloud applications fall victim to a tragedy of the generic approach such that a shared cloud service is becoming unstable and insecure based on the commercial demands by IT and other business organization. Companies should develop proper security guidelines for public and private cloud use and utilize an adequate cloud access model. Based on set of features in the three common cloud services, a set of security capabilities is identified which are needed to exercise those characteristic and the cryptographic dealing they entail. Random Number Generators can be used in cloud hosts especially for virtualized machines. Several purpose of Pseudo Random Number Generator (PRNG) exists in cloud which mainly can be used to authenticate users to the information stored on cloud, salts for the passwords or for statistical sampling. ANSI X9.31 is one standard pseudo random number generator used in many standard cryptographic algorithms like AES, RSA that are managed in securing data access in cloud applications. As ANSI X9.31 seed values are not secure they are directly used for generation. With brute force attack the seed values are exposed. On retrieval of seed values the key can be predicted which leads to insecurity of the information stored on the cloud. The proposed obfuscation technique protects from intruders. The proposed technique takes considerable minimum amount of time when compared with the existing method.
Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.
Adaptive Deep Path: Efficient Coverage of a Known Environment under Various Configurations

Coverage path planning of a known environment sees a variety of applications, including cleaning, surveillance, agriculture and 3D printing. Most approaches employ hard-coded heuristics or other application-specific requirements, making them hard to extend to other problem scenarios or “configurations,” such as different motion strategies or robot size. This work presents a unifying, general, and adaptive framework, called adaptive deep path (AD Path), for coverage path planning problems under a variety of configurations. It can improve path efficiency with respect to both path length and number of turns, and can flexibly accommodate different problem configuration options. We evaluate AD Path against a state-of-the-art baseline in four complex environments with different configurations. We show that our approach can produce efficient paths; our experimental results show that AD Path can reduce the path length by 21.8% and the number turns by 38.6% on average compared with the baseline.
Toward Secure, Privacy-Preserving, and Interoperable Medical Data Sharing via Blockchain

In the era of cloud computing and big data analysis, how to efficiently share and utilize medical information scattered across various care providers has become a critical problem. This paper proposes a new framework for sharing medical data in a secure and privacy-preserving way. This framework holistically integrates multi-authority attribute based encryption, blockchain and smart contract, as well as software defined networking to define and enforce sharing policies. Specifically in our framework, patients' medical records are encrypted and stored in hospital databases, where strict access controls are enforced with attribute based encryption coupled with privacy level classification. Our framework leverages blockchain technology to connect scattered private databases from participating hospitals for efficient and secure data provision, smart contracts to enable the business logic of clinical data usage, and software defined networking to revoke sharing privileges. The performance evaluation of our prototype demonstrates that the associated computation costs are reasonable in practice.
Blockchain Framework for Peer-to-Peer Energy Trading with Credit Rating

A peer-to-peer (P2P) energy trading market allows participants to trade their energy with each other directly. In this market, a pricing mechanism can regulate the trading price between traders and a scoring criterion could encourage participants good behaviors. This paper proposes a P2P energy trading model, including a blockchain framework to secure traders information and execute transactions, a two-level pricing mechanism to determine the trading price for each transaction and a credit rating system to improve the quality of the P2P market. The design of these methods to achieve their respective functions and the creation of blockchain are discussed in this paper. The results of a case study demonstrate the effectiveness of the proposed P2P trading model.
A Novel Integrity Authentication Algorithm Based on Perceptual Speech Hash and Learned Dictionaries

Perceptual speech hash and robust watermarking have been widely investigated to solve the problems of authenticating speech integrity. The former generates a watermark and the latter embeds the watermark into the speech signal to implement speech integrity authentication. In this paper, we propose a perceptual speech hash algorithm and a robust watermarking algorithm for speech integrity authentication. To obtain perceptual speech hash values, we propose a gammatone filter model of the speech signal to extract sensitive auditory features (denoted by gammatone features). A random Gaussian matrix is used to reduce the dimensionality of the features of the gammatone to generate perceptual speech hash values. For the watermarking algorithm, we construct learned dictionaries to obtain the robust sparse feature of coefficients of the stationary wavelet transforms, and embed a watermark (perceptual speech hash values) into the sparse feature by patchwork and quantization index modulation. We illustrate the good imperceptibility of the authentication scheme in terms of the signal-to-noise ratio, objective difference grade, and subjective difference grade, and verify its robustness against common signal processing operations while maintaining imperceptibility. Moreover, our proposed method is sensitive to the malicious modification of the watermarked speech. Compared with state-of-the-art algorithms, the proposed algorithm can obtain better comprehensive performance in the detection and localization of tampering with the content of speech.
Spam Detection in Social Networks based on Peer Acceptance

Online Social Networks (OSNs) have become immensely popular for spammers spreading malicious content and links. Using the nature of OSNs, spammers frequently change their behavior to avoid detection. The current approaches for spam detection are mainly based on classification techniques. Classification techniques have known issues such as “data labelling”, “spam drift”, “imbalanced datasets” and “data fabrication” which hinder the performance and the accuracy of spam detection. In this paper we propose a fully unsupervised approach using a user's peer acceptance within the OSN to distinguish spammers from legitimate users. Peer acceptance can be derived based on common shared interests over multiple shared topics. The contribution of this research is an unsupervised method to detect spammers based on users’ peer acceptance generated from users’ post content. While not as accurate as traditional supervised classification techniques, our unsupervised techniques are able to achieve 94.1% accuracy on some datasets without the need for labelling.
Data Clustering Using Hybrid Genetic Algorithm with k-Means and k-Medoids Algorithms

Clustering methods separate a set of data points into groups or clusters, where data points of each cluster have the similar properties and are dissimilar from those of other clusters. In general k-means and k-medoids methods are used for data clustering. These clustering methods are heuristic and may stuck in a local optima. To avoid this problem, we propose a hybrid Genetic Algorithm (HGA) for data clustering. For this purpose, we propose a genetic encoding of the clustering problem, where data points are separated into k clusters. The cluster centers of the generated clusters are determined using the techniques of both k-means and k-medoids methods. The fitness of the clustering is calculated using the sum of Euclidean distances of each data point from its cluster center. We experiment with Iris, Seeds, and Ionosphere datasets. Experimental results show that the proposed HGA generates 2.67% to 28.68% higher clustering accuracies than the clustering accuracies previously reported in the literature.
Inferring implications in semantic maps via the Apriori algorithm

Semantic maps are powerful tools for analyzing cross-language variations with implications between semantic functions to construct the relevant conceptual space. However, as existing semantic maps cannot illustrate the imbalance of implications between functions, a further discussion of inferring implications is highly demanded. The problem of inferring implications and the imbalance of implications between functions above is similar to the well-known problem of generating all significant association rules between items purchased by customers, and the Apriori algorithm offers an effective solution to relieve such issue. Here, alternative schematic diagrams based on the Apriori algorithm are employed to supplement semantic maps, which is justified by reproducing the same results as Cysouw obtained on person marking using his datasets. Furthermore, in our study, an implication in number from singular to plural is observed in person marking. We have solved the issue of imbalance and obtained credible implication rules between primitives in groups. We can mine practicable directed implication rules and reveal rules hard to notice before, such as active primitives between groups.