Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and Electricity Wholesale Markets: Expert Insights on Potentials and Challenges for OTC Trading in Europe

In this manuscript, we investigate the adoption of blockchain for over-the-counter (OTC) electricity wholesale trading under the EU regulatory framework. Our analysis of the core legislation reveals six potential issues: (1) data immutability-related error correction, (2) personal data protection and immutability, (3) access to different data layers, (4) obligation and capacity to report, (5) identification of counterparties and (6) conflict of interest. These six issues were used as basis for a survey with experts in this field from industry and academia. The majority of our respondents indicated four major points: (i) reduction of transaction costs is the main expected benefit, (ii) the application of blockchain can be compliant with the current regulatory framework, (iii) a sandbox is the most welcome regulatory approach to reduce legal uncertainty, and (iv) the first use case to be commercially implemented is expected to be a P2P platform, ahead of a use case focused on post-trade processes. We believe that the results presented in this manuscript might serve as guidance for market participants aiming to enable the development of blockchain.
Fast and Accurate Hierarchical Clustering Based on Growing Multilayer Topology Training

Hierarchical clustering has been extensively applied for data analysis and knowledge discovery. However, the scalability of hierarchical clustering methods is generally limited due to their time complexity of O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where n is the size of the input data. To address this issue, we present a fast and accurate hierarchical clustering algorithm based on topology training. Specifically, a trained multilayer topological structure that fits the spatial distribution of the data is utilized to accelerate the similarity measurement, which dominates the computational cost in hierarchical clustering. Moreover, the topological structure also guides the merging steps in hierarchical clustering to form a meaningful and accurate clustering result. In addition, an incremental version of the proposed algorithm is further designed so that the proposed approach is applicable to the streaming data as well. Promising experimental results on various data sets demonstrate the efficiency and effectiveness of the proposed algorithms.
A comparison of machine learning techniques for file system forensics analysis

With the remarkable increase in computer crimes – particularly Internet related crimes – digital forensics become an urgent and a timely issue to study. Normally, digital forensics investigation aims to preserve any evidence in its most original form by identifying, collecting, and validating the digital information for the purpose of reconstructing past events. Most digital evidence is stored within the computer's file system. This research investigates and evaluates the applicability of several machine learning techniques in identifying incriminating evidence by tracing historical file system activities in order to determine how these files can be manipulated by different application programs. A dataset defined by a matrix/vector of features related to file system activity during a specific period of time has been collected. Such dataset has been used to train several machine learning techniques. Overall, the considered machine learning techniques show good results when they have been evaluated using a testing dataset containing unseen evidence. However, all algorithms encountered an essential obstacle that could be the main reason as why the experimental results were less than expectation that is the overlaps among the file system activities.
Performance Analysis of Cryptographic Pseudorandom Number Generators

Pseudorandom number generators (PRNGs) are important in cryptography and have been used in several security applications, such as authentication protocols, session keys, key distribution, and keystream for a one-time pad. Producing PRNGs is a critical issue because they should be uniform in distribution, unpredictable, and independent. Several PRNGs are available, but the problem lies in determining the one with the best performance. The performance of PRNGs is determined by passing a set of statistical tests’ set by the National Institute of Standards and Technology (NIST). The NIST also recommends random number generation using advanced encryption standard and triple data encryption standard algorithms with the counter mode of operation. Furthermore, several block cipher algorithms (BCAs) and alternates that are present may produce strong PRNG than NIST recommendations. Therefore, in this paper, different BCAs are explored and implemented, and their results are analyzed and addressed. This paper may help in producing a strong key generation, which is deemed necessary for cryptographic authentication and other security applications.
ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices

Deep learning solutions are being increasingly deployed in mobile applications, at least for the inference phase. Due to the large model size and computational requirements, model compression for deep neural networks (DNNs) becomes necessary, especially considering the real-time requirement in embedded systems. In this paper, we extend the prior work on systematic DNN weight pruning using ADMM (Alternating Direction Method of Multipliers). We integrate ADMM regularization with masked mapping/retraining, thereby guaranteeing solution feasibility and providing high solution quality. Besides superior performance on representative DNN benchmarks (e.g., AlexNet, ResNet), we focus on two new applications facial emotion detection and eye tracking, and develop a top-down framework of DNN training, model compression, and acceleration in mobile devices. Experimental results show that with negligible accuracy degradation, the proposed method can achieve significant storage/memory reduction and speedup in mobile devices.
Empirical Analysis of Bias in Voice-based Personal Assistants

Voice-based assistants are becoming increasingly widespread all over the world. However, the performance of these assistants in the interaction with users of languages and accents of developing countries is not clear yet. Eventual bias against specific language or accent of different groups of people in developing countries is maybe a factor to increase the digital gap in these countries. Our research aims at analysing the presence of bias in the interaction via audio. We carried out experiments to verify the quality of the recognition of phrases spoken by different groups of people. We evaluated the behaviour of Google Assistant and Siri for groups of people formed according to gender and regions that have different accents. Preliminary results indicate that accent and mispronunciation due to regional differences are not being properly considered by the assistants we have analyzed.
An Application of Ethereum smart contracts and IoT to logistics

Over the last few years, interest has emerged in blockchain, a decentralized ledger technology (DLT) created for use in cryptocurrencies, but with a great potential to be used in other application domains. One of them is supply chain management, tracking and tracing, whichare key processes to the logistics industry, made difficult due to lack of standards or trust between actors, miscommunication, fraud, bureaucratic delays, among other issues. In order to overcome some of these challenges, the solution presented in this article proposes a blockchain system application created with Ethereum smart contracts technology, to be used in supply chain and logistics for tracking and tracing products through the storage of valuabledata in a trustworthy and decentralized system. The technical solution presented here implements methods for automated tracking, certification and authentication, and integrates the communication of blockchain with IoT devices, which play a role in monitoring products and automating the tracking and clearance processes. We validate our approach by developing an application that highlights the benefits of these technologies applied to logistics, ultimatelygiving insight into the capabilities, qualities, but also limitations a system like this can have.
Measurement-device-independent quantum key distribution coexisting with classical communication

Abstract The possibility for quantum and classical communication to coexist on the same fiber is important for deployment and widespread adoption of quantum key distribution (QKD) and, more generally, a future quantum internet. While coexistence has been demonstrated for different QKD implementations, a comprehensive investigation for measurement-device independent (MDI) QKD—a recently proposed QKD protocol that cannot be broken by quantum hacking that targets vulnerabilities of single-photon detectors—is still missing. Here we experimentally demonstrate that MDI-QKD can operate simultaneously with at least five 10 Gbps bidirectional classical communication channels operating at around 1550 nm wavelength and over 40 km of spooled fiber, and we project communication rates in excess of 10 THz when moving the quantum channel from the third to the second telecommunication window. The similarity of MDI-QKD with quantum repeaters suggests that classical and generalized quantum networks can co-exist on the same fiber infrastructure.
Symbolic Execution for Attribution and Attack Synthesis in Neural Networks

This paper introduces DeepCheck, a new approach for validating Deep Neural Networks (DNNs) based on core ideas from program analysis, specifically from symbolic execution. DeepCheck implements techniques for lightweight symbolic analysis of DNNs and applies them in the context of image classification to address two challenging problems: 1) identification of important pixels (for attribution and adversarial generation); and 2) creation of adversarial attacks. Experimental results using the MNIST data-set show that DeepCheck's lightweight symbolic analysis provides a valuable tool for DNN validation.
Is This My Own Body? Changing the Perceptual and Affective Body Image Experience among College Students Using a New Virtual Reality Embodiment-Based Technique

Body image disturbances (BIDs) have been widely studied using virtual reality (VR) devices that induce a full body illusion (FBI) and allow manipulation of the individual's perceptual and affective experiences of the body. This study aimed to assess whether the induction of the FBI over a virtual body would produce changes in body-related anxiety and BIDs using a new whole-body visuo-tactile stimulation procedure. Fifty non-clinical participants were randomly assigned to synchronous or asynchronous visuo-tactile groups. During the pre-assessment, all participants filled in BIDs and body-anxiety questionnaires. Then, they were embodied into two virtual bodies (VBs): firstly, with their real measurements, and secondly, with a larger-size body. Body image disturbances, body anxiety, fear of gaining weight, and FBI levels were assessed after exposure to each avatar. All participants in both conditions showed higher levels of BIDs and body anxiety after owning the larger-size VB than after owning the real-size VB (p < 0.05). The synchronous visuo-tactile group had higher scores, although the differences did not reach statistical significance. This study provides evidence of the usefulness of this new embodiment-based technique to induce changes in BIDs or body anxiety in a non-clinical sample, being suitable for use in future body image interventions.
Keystone: A Framework for Architecting TEEs.

Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone -- the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and application features. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models.
Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median (rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.
Fully-Automatic Segmentation Of Kidneys In Clinical Ultrasound Images Using A Boundary Distance Regression Network

It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we developed a novel boundary distance regression deep neural network to segment the kidneys, informed by the fact that the kidney boundaries are relatively consistent across images in terms of their appearance. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from ultrasound images, then these feature maps are used as input to learn kidney boundary distance maps using a boundary distance regression network, and finally the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixel classification network in an end-to-end learning fashion. Experimental results have demonstrated that our method could effectively improve the performance of automatic kidney segmentation, significantly better than deep learning based pixel classification networks.
Sequential and Diverse Recommendation with Long Tail

Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction.
What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

While discriminative classifiers often yield strong predictive performance, missing feature values at prediction time can still be a challenge. Classifiers may not behave as expected under certain ways of substituting the missing values, since they inherently make assumptions about the data distribution they were trained on. In this paper, we propose a novel framework that classifies examples with missing features by computing the expected prediction with respect to a feature distribution. Moreover, we use geometric programming to learn a naive Bayes distribution that embeds a given logistic regression classifier and can efficiently take its expected predictions. Empirical evaluations show that our model achieves the same performance as the logistic regression with all features observed, and outperforms standard imputation techniques when features go missing during prediction time. Furthermore, we demonstrate that our method can be used to generate ``sufficient explanations'' of logistic regression classifications, by removing features that do not affect the classification.
Unsupervised method for normalization of hematoxylin-eosin stain in histological images

Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential limitation, normalization methods are useful for color correction. In this paper, a histological image color normalization is presented by considering the biological and hematoxylin-eosin properties. To this end, the stain representation of a reference image was applied in place of the original images representation, allowing the preservation of histological structures. This proposal was evaluated on histological images with great variations of contrast, and both visual and quantitative analyzes yielded promising results.
PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis.
Using multi-label classification to improve object detection

In this paper, a novel multi-task framework for object detection is proposed. The framework uses multi-label classification as an auxiliary task to improve object detection, and can be trained and tested end-to-end. The object detection branch adopts R-FCN methods to solve the object detection task. The multi-label branch uses attention mechanism to solve the multi-label classification task. The features, which are generated by the attention mechanism in the multi-label branch, contain rough localization information of the objects. Thus, the features can be useful for the object detection. Both the box-level features and the image-level features of multi-label are fused to improve the accuracy of the object detection. The proposed framework does not require any extra annotation, since the ground truth of the multi-label classification can be directly obtained from the bounding box annotations. This is different from other multi-task frameworks such as StuffNet and Mask R-CNN which need extra semantic segmentation and instance segmentation annotations. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO demonstrate the effectiveness of the proposed approach. Code has been made publicly available at: https://github.com/GT9505/MONet.
What does Neural Bring? Analysing Improvements in Morphosyntactic Annotation and Lemmatisation of Slovenian, Croatian and Serbian

We present experiments on Slovenian, Croatian and Serbian morphosyntactic annotation and lemmatisation between the former stateof-the-art for these three languages and one of the best performing systems at the CoNLL 2018 shared task, the Stanford NLP neural pipeline.Our experiments show significant improvements in morphosyntactic annotation, especially on categories where either semantic knowledge is needed, available through word embeddings, or where long-range dependencies have to be modelled.On the other hand, on the task of lemmatisation no improvements are obtained with the neural solution, mostly due to the heavy dependence of the task on the lookup in an external lexicon, but also due to obvious room for improvements in the Stanford NLP pipeline's lemmatisation.
Deep attention user-based collaborative filtering for recommendation

The user-based collaborative filtering (UCF) model has been widely used in industry for recommender systems. UCF predicts a user’s interest in an item based on rating information from similar user profiles. A neural network UCF model can learn effectively the high-order relations between users and items, but it cannot distinguish the importance of users in learning process. To mine the complex relationships between users and items, we incorporate a Deep+Shadow pattern to improve learning features effectively, namely as DeepUCF. Firstly, we define historical users, that is, users from the historical data of interactions with an item. The target user and historical users are calculated to capture complex process of user’s interacted item. Secondly, we integrate a shallow linear model to effectively solve single pair interaction problems. Finally, DeepUCF construct of a pair of user relations (interactive users with a history of items) for the input, and joint linear and nonlinear models to build relationships between users. More importantly, DeepUCF+a add an attention network to distinguish the historical user importance of items, which make DeepUCF more expressive. Experiments on real datasets show that DeepUCF and DeepUCF+a can effectively capture users’ complex high-order relationships, and achieve better performance.