Reviewerly/exHarmony huggingface dataset:
Preference in Abstract Argumentation

Consider an argument A that is attacked by an argument B, while A is preferred to B. Existing approaches will either ignore the attack or reverse it. In this paper we introduce a new reduction of preference and attack to defeat, based on the idea that in such a case, instead of ignoring the attack, the preference is ignored. We compare this new reduction with the two existing ones using a principle-based approach, for the four Dung semantics. The principle-based or axiomatic approach is a methodology to choose an argumentation semantics for a particular applica- tion, and to guide the search for new argumentation semantics. For this analysis, we also introduce a fourth reduction, and a semantics for preference-based argu- mentation based on extension selection. Our classification of twenty alternatives for preference-based abstract argumentation semantics using six principles suggests that our new reduction has some advantages over the existing ones, in the sense that if the set of preferences increases, the sets of accepted arguments increase as well.
Schemes to avoid entanglement sudden death of decohering two qubit system

We investigate the entanglement dynamics of two interacting qubits in a common vacuum environment. The inevitable environment interaction leads to entanglement sudden death (ESD) in a two qubit entangled system. The entanglement dynamics can be modified by the use of local unitary operations (quantum gates), applied on the system during its evolution. We show that these operations not only delays or avoids the ESD but also advances the entanglement revival with an enhanced entanglement depending on the time of operation. We have analytically found out different time windows for switching with different quantum gates so that the ESD can be completely avoided in the subsequent evolution of the system. Our result offers practical applications in the field of quantum information processing where the entanglement is a necessary resource.
Anxious anticipation prolongs emotional interference for rapid visual detection.

When we view emotionally arousing images, our perception of stimuli that follow soon afterward is transiently impaired-a phenomenon known as emotion-induced blindness. Previous studies have demonstrated that the magnitude and time course of this visual processing impairment is exaggerated by the presence of psychopathology and anxiety-related traits. Here, we tested whether emotional interference on a primary task can be modulated on a more dynamic basis, by the anticipation of unpredictable electric shock. We embedded naturalistic scenes in a 10-Hz rapid serial visual presentation (RSVP) stream, while varying the hedonic content of distractor images (aversive or neutral) and their temporal position (200, 400, and 700 ms) with respect to landscape targets. In Experiment 1, we found that, under typical conditions, aversive distractors induced a temporary visual performance decrement that exhibited a full rebound following a 400-ms distractor-target lag. In Experiment 2, subjects performed an identical RSVP task while under continuous threat of electric shock. We found that threat of unpredictable electric shock prolonged the duration of the emotional interference out to 400 ms and 700 ms, without affecting the overall magnitude of the performance impairment. In Experiment 3, the prolonged emotional interference under threat of unpredictable electric shock persisted at the 400-ms lag despite observed practice effects within subjects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Design and implementation of domain-specific cognitive system based on question similarity algorithm

The cognitive system is an artificial intelligence product that integrates natural language processing techniques. The biggest d on logical reasoning, which can help users improve exploration and accelerate discovery. On the basis of natural language processing and logical reasoning, the paper adopts similarity algorithm bas difference between big data analysis systems and cognitive systems is that cognitive systems can directly deal with the problems raised by natural language forms and provide predictions baseded on question sentences, designs a cognitive system for specific domains, and finally realizes it on the NAO robot platform.
An Efficient Privacy-Preserving Attribute-Based Encryption with Hidden Policy for Cloud Storage

Cloud storage is one of the most widely-used storage services, because it can provide users with unlimited, scalable, low-cost and convenient resource services. When data is outsourced to cloud for storage, data security and access control are the two essential issues that need to be addressed. Attribute-based encryption (ABE) scheme can provide sufficient data security and fine-grained access control for cloud data. As more and more attention is drawn to privacy protection, privacy preservation becomes another urgent issue for cloud storage. In ABE, since the access policies are generally stored in clear text, it will lead to the disclosure of users’ privacy. Some works sacrifice computational efficiency, key length or ciphertext size for privacy concerns. To solve these problems, this paper proposes an efficient privacy-preserving attribute-based encryption scheme with hidden policy for outsourced data. Using the idea of Boolean equivalent transformation, the proposed scheme achieves fast encryption and privacy protection for both data owner and legitimate visitors. In addition, the proposed scheme can satisfy constant secret key length and reasonable size of ciphertext requirements. We also conduct theoretical security analysis, and carry out experiments to prove that the proposed scheme has good performance in terms of computation, communication and storage overheads.
Notice of Retraction: CHN: an efficient algorithm for mining closed high utility itemsets with negative utility

Retracted.
Understanding Game-Based Privacy Proofs for Energy Consumption Aggregation Protocols

Despite the large number of privacy-preserving aggregation protocols in the smart grid, there is no common methodology for evaluating and comparing their privacy guarantees. Protocol discussion often lacks a formal evaluation of the proposed privacy guarantees. In order to transfer the well-established formal methodology of game-based proofs to the smart grid domain, in this paper we: 1) present a game-based privacy definition which addresses the privacy requirement to be captured in an aggregation protocol (the definition may be used or extended for other protocols); 2) exemplify our game-based proof technique for two aggregation protocols; and 3) provide a novel and compact way to visualize and easily compare the privacy guarantees of different protocols. We employ two sample protocols that reflect the basis of the most common approaches currently found in the energy aggregation literature. In summary, we contribute a guideline on how to conduct formal evaluations for protocol developers as well as an easy-to-understand way to assess the privacy guarantees of different aggregation protocols for non-experts.
An Instance Selection Algorithm Based on ReliefF

Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algorithm, based on the Jaccard index, the nearest instances of each class are found for each instance. Then, based on the nearest neighbor’s set, the weight of each instance is calculated. Finally, only instances with more weights are selected. This algorithm can reduce data at a specified rate and have the ability to run parallel on the instances. It can work on a variety of data sets with nominal and numeric data with missing values and is also suitable for working with imbalanced data sets. The proposed algorithm tests on three data sets. Results show that the proposed algorithm can reduce the volume of data, without a significant change in classification accuracy of these datasets.
Accurate Fe3+/Fe ratio from XAS spectra at the Fe K-edge

A new approach to extract accurate information on the Fe oxidation state has been tested on three different Fe-rich amphiboles annealed at 194–520 °C. The method is based on the analysis of the pre-edge structure of the X-ray absorption spectra at the Fe-K edge. The pre-edge band is deconvolved into four Gaussian components where the integrated intensities of the pairs of lower- and higher-energy bands are related to the ferrous to ferric ion ratio. These Fe3+/Fetot ratios have been compared with those obtained with independent techniques (X-ray diffraction and Mossbauer spectroscopy) on the same samples. The experimental data allowed establishing a calibration curve that is in good agreement with a recent statistical analysis based on spectroscopic data for the same type of minerals. The method could be extended to the analysis of other complex systems containing both Fe3+ and Fe2+ in octahedral coordination or to study the dynamics of iron under non-ambient conditions.
A Multichain Architecture for Distributed Supply Chain Design in Industry 4.0

The Fourth Industrial Revolution is centered around a self-organized production. Cyber-physical systems can be used to collect and share data for tracking, automation and control as well as evaluation and documentation. This has a wide-reaching impact on business models. Instead of a linear approach, concentrating on a single company, the focus needs to be on the complete production ecosystem represented by a dynamic and self-organized network, including the supply chain. The interconnectedness of the cyber-physical systems in these networks is rooted in the Internet of Things, but also shared business processes. Performance, stability, security as well as data integrity and access control hereby represent a major contributing factor, calling for a new concept of information processing systems. The blockchain is a distributed ledger combining cryptographic and game-theoretic concepts, which enable immutable transactions and automatic consensus of the parties involved about its state. Blockchains have evolved as a fast-developing technology, promising increased efficiency and security in many scenarios, especially use cases that primarily rely on all kinds of transactions. The paper follows a design science approach, examining the implications of blockchain and the industrial Internet of Things in Industry 4.0 (I40) on supply chain management. I40’s implications on supply chains are discussed and connected with favorable characteristics of blockchain technology. Based on this analysis, requirements for a decentralized enterprise information processing system are derived, resulting in a reference model for distributed supply chains of I40.
The Effect of Assistance on Learning and Affect in an Algebra Tutor

We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by having similar problem-example pairs in one version of the tutor (high-assistance condition) and reduced similarity problem-example pairs in the other version (reduced-assistance condition). The reduced-assistance condition resulted in significantly higher learning, without increasing negative affect like frustration.
A Blended Metric for Multi-label Optimisation and Evaluation

In multi-label classification, a large number of evaluation metrics exist, for example Hamming loss, exact match, and Jaccard similarity – but there are many more. In fact, there remains an apparent uncertainty in the multi-label literature about which metrics should be considered and when and how to optimise them. This has given rise to a proliferation of metrics, with some papers carrying out empirical evaluations under 10 or more different metrics in order to analyse method performance. We argue that further understanding of underlying mechanisms is necessary. In this paper we tackle the challenge of having a clearer view of evaluation strategies. We present a blended loss function. This function allows us to evaluate under the properties of several major loss functions with a single parameterisation. Furthermore we demonstrate the successful use of this metric as a surrogate loss for other metrics. We offer experimental investigation and theoretical backing to demonstrate that optimising this surrogate loss offers best results for several different metrics than optimising the metrics directly. It simplifies and provides insight to the task of evaluating multi-label prediction methodologies. Data related to this paper are available at: http://mulan.sourceforge.net/datasets-mlc.html , https://sourceforge.net/projects/meka/files/Datasets/ , http://www.ces.clemson.edu/~ahoover/stare/ .
Anomaly detection using control flow pattern and fuzzy regression in port container handling

Deviations in port container handling can be detected by many factors. One of them is anomalies in the process model. Several studies have proposed anomaly detection methods. However, these methods do not accommodate verbal judgments of experts. These methods treat instances with low deviation as containing anomalies while in reality not all instances with low deviation contain anomalies. Considering this, a method was developed for detecting anomalies in port container handling using fuzzy regression in order to accommodate verbal expert judgments on the rate of anomaly (ROA). First, a control flow pattern is built to form an anomaly pattern that will be used for detecting wrong patterns. Then, five anomaly attributes were declared, i.e. skip sequence, wrong throughput time (max), wrong throughput time (min), wrong patterns and wrong decisions. In the experiment, the rate of anomaly was found using three methods, namely fuzzy regression (FR), support vector regression (SVR) with radial basis function (RBF) kernel, and multiple linear regression (MLR). The results showed that fuzzy regression was better at detecting anomalies than multiple linear regression and support vector regression. The experimental validation showed that fuzzy regression combined with control flow pattern was able to reduce false positives and false negatives. The sensitivity, specificity and accuracy of the proposed method were 96%, 97% and 99%, respectively.
Unsupervised robust discriminative manifold embedding with self-expressiveness

Dimensionality reduction has obtained increasing attention in the machine learning and computer vision communities due to the curse of dimensionality. Many manifold embedding methods have been proposed for dimensionality reduction. Many of them are supervised and based on graph regularization whose weight affinity is determined by original noiseless data. When data are noisy, their performance may degrade. To address this issue, we present a novel unsupervised robust discriminative manifold embedding approach called URDME, which aims to offer a joint framework of dimensionality reduction, discriminative subspace learning , robust affinity representation and discriminative manifold embedding. The learned robust affinity not only captures the global geometry and intrinsic structure of underlying high-dimensional data, but also satisfies the self-expressiveness property. In addition, the learned projection matrix owns discriminative ability in the low-dimensional subspace. Experimental results on several public benchmark datasets corroborate the effectiveness of our approach and show its competitive performance compared with the related methods.
Applying the Positional Burrows–Wheeler Transform to all-pairs Hamming distance

Crochemore et al. gave in WABI 2017 an algorithm that from a set of input strings finds all pairs of strings that have Hamming distance at most a given threshold. The proposed algorithm first finds all long enough exact matches between the strings, and sorts these into pairs whose coordinates also match. Then the remaining pairs are verified for the Hamming distance threshold. The algorithm was shown to work in average linear time, under some constraints and assumptions. We show that one can use the Positional Burrows–Wheeler Transform (PBWT) by Durbin (Bioinformatics, 2014) to directly find all exact matches whose coordinates also match. The same structure also extends to verifying the pairs for the Hamming distance threshold. The same analysis as for the algorithm of Crochemore et al. applies. As a side result, we show how to extend PBWT for non-binary alphabets. The new operations provided by PBWT find other applications in similar tasks as those considered here.
Speeding up person finding using hidden Markov models

The ability of mobile service robots to efficiently search for a person is needed in a vast domain of applications. The search problem is especially challenging when the user is freely moving across the environment, the robot has only a constrained field of view, and visibility constraints arise from the environment. We propose in this article a novel approach that simulates the user’s presence at different locations in the environment based on a hidden Markov model (HMM). The HMM predicts the user’s motion and computes the observability likelihood at the different locations given the predictions. Our approach then selects effective search locations that maximize the user’s expected observability. The selection criterion hereby considers the visibility constraints along the robot’s path as well as the robot’s travel time to reach the search location. We performed both real-world and extensive simulated experiments to evaluate our method. In comparison to a greedy maximum coverage approach as well as to a greedy strategy that uses background information, we show that our framework leads to a significant reduction of the time needed to find the user.
Minerals of the Kabwe (“Broken Hill”) Mine, Central Province, Zambia

THE CITY OF KABWE, ZAMBIA'S SECOND LARGEST, is located some 120 kilometers north of Lusaka, the capital (fig. 1). The name Kabwe means “place of smelting” but refers to the pre-European smelting of...
Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence

Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitting to the uncertain knowledge, we propose to penalize confident fitting to the uncertain knowledge by the Maximum Entropy Regularizer (MER). Additionally, to reduce class imbalance and induce a self-paced curriculum on new classes, we exclude a few samples from the new classes in every mini-batch, which we call DropOut Sampling (DOS). We further rethink evaluation metrics for forgetting and intransigence in incremental learning by tracking each sample's confusion at the transition of a task since the existing metrics that compute the difference in accuracy are often misleading. We show that the proposed method, named 'MEDIC', outperforms the state-of-the-art incremental learning algorithms in accuracy, forgetting, and intransigence measured by both the existing and the proposed metrics by a large margin in extensive empirical validations on CIFAR100 and a popular subset of ImageNet dataset (TinyImageNet).
A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data

Abstract We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease.
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

This paper provides a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.
Teaching Digital Signal Processing With a Challenge on Image Forensics [SP Education]

With the advent of ubiquitous sensing and real-time data processing, the demand for engineers with solid signal processing skills has exceeded the supply by a large margin. However, even students in technical subjects often perceive signal processing as demanding and somewhat dry [4]. In many curricula, signal processing must compete with more attractive classes, such as hands-on courses on programming, robotics, or computer graphics. Hence, new directions in signal processing education are needed [1], [9], [10].
Detecting dementia in

Bai Li, Yi-Te Hsu, Frank Rudzicz. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.