Reviewerly/exHarmony huggingface dataset:
Declarative Programming Approach for Fake Review Detection

Online reviews play an essential role in our daily life. Thus, approaches for detecting fake reviews are of high demand. This paper presents an approach to detect fake reviews incorporating the behavior of authors of reviews combined with properties derived from the content of their reviews. We aim to design a white-box approach which is becoming a major requirement nowadays in the industry. This is due to the fact that there are increasing social concerns about decisions made based on personal information. In other words, we seek to design a white-box model that can let users understand what is going on regarding their personal data. In contrast to blackbox models, such as deep-learning that are hard to be explained in general. Consequently, we propose a rule-based fake review detection system using Answer Set Programming (ASP) which is a powerful tool to declare malicious behavior patterns specified via a variety of constraints. This way we can create powerful models that combine, e.g., information about the number of reviews, the number of dislikes, the analysis of the points in time reviews have been written, qualitative properties of the content based on similarity measures and derived classification of reviews and products. Such models encode the problem phrased ”which reviews are to be considered genuine, fake, or need to be investigated further on” and can be used to compute an optimal solution by applying ASP techniques.
Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization

Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. To deal with these challenges, we propose a deep clustering framework consisting of a modified generative adversarial network (GAN) and an auxiliary classifier. The modification employs Sobel operations prior to the discriminator of the GAN to enhance the separability of the learned features. The discriminator is then leveraged to generate representations as the input to an auxiliary classifier. An adaptive objective function is utilised to train the auxiliary classifier for clustering the representations, aiming to increase the robustness by minimizing the divergence of multiple representations generated by the discriminator. The auxiliary classifier is implemented with a group of multiple cluster-heads, where a tolerance hyper-parameter is used to tackle imbalanced data. Our results indicate that the proposed method significantly outperforms state-of-the-art clustering methods on CIFAR-10 and CIFAR-100, and is competitive on the STL10 and MNIST datasets.
Bayesian wavelet shrinkage with beta priors

In wavelet shrinkage and thresholding, most of the standard techniques do not consider information that wavelet coefficients might be bounded, although information about bounded energy in signals can be readily available. To address this, we present a Bayesian approach for shrinkage of bounded wavelet coefficients in the context of non-parametric regression. We propose the use of a zero-contaminated beta distribution with a support symmetric around zero as the prior distribution for the location parameter in the wavelet domain in models with additive gaussian errors. The hyperparameters of the proposed model are closely related to the shrinkage level, which facilitates their elicitation and interpretation. For signals with a low signal-to-noise ratio, the associated Bayesian shrinkage rules provide significant improvement in performance in simulation studies when compared with standard techniques. Statistical properties such as bias, variance, classical and Bayesian risks of the associated shrinkage rules are presented and their performance is assessed in simulations studies involving standard test functions. Application to real neurological data set on spike sorting is also presented.
Nondestructive and rapid grading of tobacco leaves by use of a hand-held near-infrared spectrometer, based on a particle swarm optimization-extreme learning machine algorithm

A nondestructive and rapid method has been put forward to grade tobacco leaves in the paper. The method is based on a combination of a hand-held near-infrared spectrometer and a particle swarm optimization-extreme learning machine algorithm. Firstly, the spectral data of the training samples are collected directly from the tobacco leaves nondestructively by using a hand-held near infrared spectrometer without any pretreatment. Secondly, the training models of different classes are built using particle swarm optimization-extreme learning machine algorithm. Finally, the classes of test samples can be predicted by using the developed models. Besides, the classification results of particle swarm optimization-extreme learning machine algorithm are also compared with that of the traditional linear discriminant analysis, support vector machine, and extreme learning machine algorithms, respectively. The experimental results show the classification accuracy of the particle swarm optimization-extreme learning machine algorithm is comparable after the parameter optimization. It indicates that the interplay between the hand-held near-infrared spectroscopy technology and particle swarm optimization-extreme learning machine algorithm will provide a novel classification method for grading tobacco leaves in the purchasing process on the spot.
Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction

Semi-supervision is a promising paradigm for Bilingual Lexicon Induction (BLI) with limited annotations.However, previous semisupervised methods do not fully utilize the knowledge hidden in annotated and nonannotated data, which hinders further improvement of their performance.In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment.We design two message-passing mechanisms to transfer knowledge between annotated and non-annotated data, named prior optimal transport and bi-directional lexicon update respectively.Then, we perform semi-supervised learning based on a cyclic or a parallel parameter feeding routine to update our models.Our framework is a general framework that can incorporate any supervised and unsupervised BLI methods based on optimal transport.Experimental results on MUSE and VecMap datasets show significant improvement of our models.Ablation study also proves that the two-way interaction between the supervised signal and unsupervised alignment accounts for the gain of the overall performance.Results on distant language pairs further illustrate the advantage and robustness of our proposed method.
SAFER

Users of Internet of Things (IoT) devices are often unaware of their security risks and cannot sufficiently factor security considerations into their device selection. This puts networks, infrastructure and users at risk. We developed and evaluated SAFER, an IoT device risk assessment framework designed to improve users' ability to assess the security of connected devices. We deployed SAFER in a large multinational organization that permits use of private devices. To evaluate the framework, we conducted a mixed-method study with 20 employees. Our findings suggest that SAFER increases users' awareness of security issues. It provides valuable advice and impacts device selection. Based on our findings, we discuss implications for the design of device risk assessment tools, with particular regard to the relationship between risk communication and user perceptions of device complexity.
De-Anonymization of the Bitcoin Network Using Address Clustering

Bitcoin is the first cryptocurrency that was invented by a pseudonymous person called Satoshi Nakamoto in 2008. Users of bitcoin are not required to provide any of their personal information, and this pseudo-anonymity attracts people to exploit bitcoin for illegal transactions. A previous study tried to de-anonymize the bitcoin by using P2P network traffic and find out an IP address of each bitcoin address’ owner. However, this method could only obtain the small number of reliable mappings between a bitcoin address and its owner’s IP address. To improve this study, we added a bitcoin address clustering process so that we could suppose that all addresses in each cluster are owned by one entity. We then mapped each cluster to an IP address and showed that this change increased the percentages of reliable mappings that we could find. We also suggested some ways to obtain improvements of our method.
IIoT Digital Forensics and Major Security issues

the significant area in the growing field of internet security and IIoT connectivity is the way that forensic investigators will conduct investigation process with devices connected to industrial sensors. This part of process is known as IIoT digital forensics and investigation. The main research on IIoT digital forensic investigation has been done, but the current investigation process has revealed and identified major security issues need to be addressed. In parallel, major security issues faced by traditional forensic investigators dealing with IIoT connectivity and data security. This paper address the issues of the challenges and major security issues identified by review conducted in the prospective and emphasizes on the aforementioned security and challenges.
Improving requirements specification use by transferring attention with eye tracking data

Software requirements specifications are the main point of reference in traditional software projects. Especially in large projects, these documents get read by multiple people, multiple times. Several guidelines and templates already exist to support writing a good specification. However, not much research has been done in investigating how to support the use of specifications and help readers to find relevant information and navigate in the document more efficiently. We aim to ease the reading process of requirements specifications by making use of previously recorded attention data. Therefore, we created three different attention transfer features based on eye tracking data obtained from observing readers when using specifications. In a student experiment, we evaluated if these attention visualizations positively affect the roles software architect, UI-designer and tester when reading a specification for the first time. The results show that the attention visualizations did not decrease navigation effort, but helped to draw the readers’ attention towards highlighted parts and decreased the average time spent on pages. They were mostly perceived as valuable by the readers. We explored and evaluated the approach of visualizing other readers’ attention focus to help support new readers. Our results include interesting findings on what works well, what does not and what could be enhanced. We present several suggestions on how attention data could be used to fasten document navigation, direct reading and facilitate user-specific reading.
Kendall tau sequence distance: Extending Kendall tau from ranks to sequences

An edit distance is a measure of the minimum cost sequence of edit operations to transform one structure into another. Edit distance is most commonly encountered within the context of strings, where Wagner and Fischer's string edit distance is perhaps the most well-known. However, edit distance is not limited to strings. For example, there are several edit distance measures for permutations, including Wagner and Fischer's string edit distance since a permutation is a special case of a string. However, another edit distance for permutations is Kendall tau distance, which is the number of pairwise element inversions. On permutations, Kendall tau distance is equivalent to an edit distance with adjacent swap as the edit operation. A permutation is often used to represent a total ranking over a set of elements. There exist multiple extensions of Kendall tau distance from total rankings (permutations) to partial rankings (i.e., where multiple elements may have the same rank), but none of these are suitable for computing distance between sequences. We set out to explore extending Kendall tau distance in a different direction, namely from the special case of permutations to the more general case of strings or sequences of elements from some finite alphabet. We name our distance metric Kendall tau sequence distance, and define it as the minimum number of adjacent swaps necessary to transform one sequence into the other. We provide two $O(n \lg n)$ algorithms for computing it, and experimentally compare their relative performance. We also provide reference implementations of both algorithms in an open source Java library.
Comparative analysis of deep network models through transfer learning

Deep learning has had remarkable success in several applications such as classification, clustering, regression etc. Several assumptions are made during the learning process which may not be apt for all real-world applications due to change in the feature space. For the classification task, deep learning models are most appropriate if a large amount of data is used for training. Therefore, enhancement is made from deep learning to transfer learning by knowledge transfer from feature space. In this paper, the accuracy obtained, number of iterations, and time taken for classification of various pre-trained networks is compared through transfer learning. The results reveal that the accuracy is higher when the training data is large compared to that with a small dataset.
Learning ODE Models with Qualitative Structure Using Gaussian Processes

Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data. However, in many contexts explicit data collection is expensive and learning algorithms must be data-efficient to be feasible. This suggests using additional qualitative information about the system, which is often available from prior experiments or domain knowledge. We propose an approach to learning a vector field of differential equations using sparse Gaussian Processes that allows us to combine data and additional structural information, like Lie Group symmetries and fixed points. We show that this combination improves extrapolation performance and long-term behaviour significantly, while also reducing the computational cost.
Practical Quantum Key Distribution with Non-Phase-Randomized Coherent States

Quantum key distribution (QKD) based on coherent states is well known for its implementation simplicity, but it suffers from loss-dependent attacks based on optimal unambiguous state discrimination. Crucially, previous research has suggested that coherent-state QKD is limited to short distances, typically below 100 km assuming standard optical fiber loss and system parameters. In this work, we propose a six-coherent-state phase-encoding QKD protocol that is able to tolerate the total loss of up to 38 dB assuming realistic system parameters, and up to 56 dB loss assuming zero noise. The security of the protocol is calculated using a recently developed security proof technique based on semi-definite programming, which assumes only the inner-product information of the encoded coherent states, the expected statistics, and that the measurement is basis-independent. Our results thus suggest that coherent-state QKD could be a promising candidate for high-speed provably-secure QKD.
Aerobic Exercise with Superimposed Virtual Reality Improves Cognitive Flexibility and Selective Attention in Young Males

The literature to date is limited regarding the implantation of VR in healthy young individuals with a focus on cognitive function. Thirty healthy males aged between 22.8 and 24.3 years volunteered to participate in the study randomly and were assigned to one of two groups with alike exercises: an experimental group (GE, n = 15) that performed an exercise protocol with a VR game and a controlled group that performed the exercise protocol without the VR (CON, n = 15). A 128-card computerized version of the Wisconsin Card Sorting Task (WCST) and the Stroop test were completed before and after the exercise protocol. There was a significant interaction effect between time and condition for WCST preservation errors (F1,30 = 4.59, p = 0.041, η2p = 0.141) and a significant time effect for all WCST and Stroop outcomes in GE. Results of preliminary findings suggest that the use of a VR platform offers effective benefits with respect to cognitive flexibility and selective attention. In addition, participants can achieve additional benefits in cognitive flexibility by engaging in a traditional exercise protocol of a similar volume.
Machine Translation from Natural Language to Code Using Long-Short Term Memory

Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itself. To go a step further in this journey of removing human-computer language barrier, this paper proposes machine learning approach using Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to convert human language into programming language code. The programmer will write expressions for codes in layman's language, and the machine learning model will translate it to the targeted programming language. The proposed approach yields result with 74.40% accuracy. This can be further improved by incorporating additional techniques, which are also discussed in this paper.
Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise

In this paper, we describe how the hypergeometric test can be used to determine whether a given theme of interest occurs in a storyset at a frequency more than would be expected by chance. By a storyset we mean simply a list of stories defined according to a common attribute (e.g., author, movement, period). The test works roughly as follows: Given a background storyset and a sub-storyset of interest, the test determines whether a given theme is over-represented in the sub-storyset, based on comparing the proportions of stories in the sub-storyset and background storyset featuring the theme. A storyset is said to be "enriched" for a theme with respect to a particular background storyset, when the theme is identified as being significantly over-represented by the test. Furthermore, we introduce here a toy dataset consisting of 280 manually themed Star Trek television franchise episodes. As a proof of concept, we use the hypergeometric test to analyze the Star Trek stories for enriched themes. The hypergeometric testing approach to theme enrichment analysis is implemented for the Star Trek thematic dataset in the R package stoRy. A related R Shiny web application can be found at https://github.com/theme-ontology/shiny-apps.
A Recurrent Transformer Network for Novel View Action Synthesis

In this work, we address the problem of synthesizing human actions from novel views. Given an input video of an actor performing some action, we aim to synthesize a video with the same action performed from a novel view with the help of an appearance prior. We propose an end-to-end deep network to solve this problem. The proposed network utilizes the change in viewpoint to transform the action from the input view to the novel view in feature space. The transformed action is integrated with the target appearance using the proposed recurrent transformer network, which provides a transformed appearance for each time-step in the action sequence. The recurrent transformer network utilize action key-points which are determined in an unsupervised approach using the encoded action features. We also propose a hierarchical structure for the recurrent transformation which further improves the performance. We demonstrate the effectiveness of the proposed method through extensive experiments conducted on a large-scale multi-view action recognition NTU-RGB+D dataset. In addition, we show that the proposed method can transform the action to a novel viewpoint with an entirely different scene or actor. The code is publicly available at https://github.com/schatzkara/cross-view-video .
Detecting Brute-Force Attacks on Cryptocurrency Wallets

Blockchain is a distributed ledger, which is protected against malicious modifications by means of cryptographic tools, e.g. digital signatures and hash functions. One of the most prominent applications of blockchains is cryptocurrencies, such as Bitcoin. In this work, we consider a particular attack on wallets for collecting assets in a cryptocurrency network based on brute-force search attacks. Using Bitcoin as an example, we demonstrate that if the attack is implemented successfully, a legitimate user is able to prove that fact of this attack with a high probability. We also consider two options for modification of existing cryptocurrency protocols for dealing with this type of attacks. First, we discuss a modification that requires introducing changes in the Bitcoin protocol and allows diminishing the motivation to attack wallets. Second, an alternative option is the construction of special smart-contracts, which reward the users for providing evidence of the brute-force attack. The execution of this smart-contract can work as an automatic alarm that the employed cryptographic mechanisms, and (particularly) hash functions, have an evident vulnerability.
Generative View-Correlation Adaptation for Semi-supervised Multi-view Learning

Multi-view learning (MVL) explores the data extracted from multiple resources. It assumes that the complementary information between different views could be revealed to further improve the learning performance. There are two challenges. First, it is difficult to effectively combine the different view data while still fully preserve the view-specific information. Second, multi-view datasets are usually small, which means the model can be easily overfitted. To address the challenges, we propose a novel View-Correlation Adaptation (VCA) framework in semi-supervised fashion. A semi-supervised data augmentation me-thod is designed to generate extra features and labels based on both labeled and unlabeled samples. In addition, a cross-view adversarial training strategy is proposed to explore the structural information from one view and help the representation learning of the other view. Moreover, an effective and simple fusion network is proposed for the late fusion stage. In our model, all networks are jointly trained in an end-to-end fashion. Extensive experiments demonstrate that our approach is effective and stable compared with other state-of-the-art methods (Code is available on: https://github.com/wenwen0319/GVCA ).
Continuous experimentation on artificial intelligence software: a research agenda

Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an "unknown unknown" problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.