Reviewerly/exHarmony huggingface dataset:
Identifying suspicious addresses in Bitcoin thefts

Bitcoin as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in the bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. When analyzing transaction flows, FABT exploits the notion of “bitcoin fluid” to track where the bitcoins passed through given addresses (called dyeing addresses) have flown and determine the extent to which each of the other addresses is related to the dyeing addresses. The splitting, merging, and dyeing operators are used to capture the distribution of coins throughout transaction flows. FABT also applies visualization techniques for further analysis of the suspicious addresses. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals.
Artificial Intelligence Integrated Blockchain For Training Autonomous Cars

Artificial Intelligence integrated Blockchain proposes an integration of Artificial Intelligence and Blockchain Technology, the two most cutting-edge technologies of recent days, which in turn is capable of opening a wide spectrum of opportunities that can be termed as revolutionary in the field of technology. This idea of an artificially intelligent blockchain can be used to simplify the learning process of autonomous, driverless, self-learning cars significantly by utilizing blockchain for imparting the learned information. Autonomous cars learn using reinforcement learning. Take training a driverless car to apply break when it comes close to a wall for instance. At first, the car keeps driving until it hits the wall for a number of times. These experiences get stored in its memory and after a few collisions, the car learns from its experience that it needs to stop when it reaches too close to a wall. In current industry, each car is trained individually until each of them learns when to stop automatically to avoid collision. This paper proposes that each car should be connected to a shared public ledger where they can share their experience so that all the cars possessing that shared ledger can learn when to stop from the experience of one single car thereby eliminating the cumbersome task of training each car separately. This collective learning can be carried out using Blockchain Technology.
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

In this article, we propose a novel object detection algorithm named ”Deep Regionlets” by integrating deep neural networks and a conventional detection schema for accurate generic object detection. Motivated by the effectiveness of regionlets for modeling object deformations and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select sub-regions from which features can be learned from. An object proposal typically contains three – 16 sub-regions. The regionlet learning module focuses on local feature selection and transformations to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a “gating network” within the regionlet leaning module to enable instance dependent soft feature selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present ablation studies and extensive experiments on the PASCAL VOC dataset and the Microsoft COCO dataset. The proposed method yields competitive performance over state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.
Privacy-Preserving Parametric Inference: A Case for Robust Statistics

Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric inference with differential privacy guarantees. We first obtain differentially private estimators based on bounded influence M-estimators by leveraging their gross-error sensitivity in the calibration of a noise term added to them in order to ensure privacy. We then show how a similar construction can also be applied to construct differentially private test statistics analogous to the Wald, score and likelihood ratio tests. We provide statistical guarantees for all our proposals via an asymptotic analysis. An interesting consequence of our results is to further clarify the connection between differential privacy and robust statistics. In particular, we demonstrate that differential privacy is a weaker stability requirement than infinitesimal robustness, and show that robust M-estimators can be easily randomized in order to guarantee both differential privacy and robustness towards the presence of contaminated data. We illustrate our results both on simulated and real data.
An Empirical Study of Abbreviations and Expansions in Software Artifacts

Expanding abbreviations is an important text normalization technique used for the purpose of either increasing developer comprehension or supporting the application of natural-language-based tools for source code identifiers. This paper closely studies abbreviations and where their expansions occur in different software artifacts. Without abbreviation expansion, developers will spend more time in comprehending the code they need to update, and tools analyzing software may obtain weak or non-generalizable results. There are numerous techniques for expanding abbreviations, most of which struggle to reach an average expansion accuracy of 59-62% on general source code identifiers. In this paper, we reveal some characteristics of abbreviations and their expansions through an empirical study of 861 abbreviation-expansion pairs extracted from 5 open-source systems in addition to analyzing previous literature. We use these characteristics to identify how current approaches may be complementary and how their results should be reported in the future to help maximize both our understanding of how they compare with other expansion techniques and their reproducibility.
An analytical framework for high-speed hardware particle swarm optimization

Engineering optimization techniques are computationally intensive and can challenge implementations on tightly-constrained embedded systems. Particle Swarm Optimization (PSO) is a well-known bio-inspired algorithm that is adopted in various applications, such as, transportation, robotics, energy, etc. In this paper, a high-speed PSO hardware processor is developed with focus on outperforming similar state-of-the-art implementations. In addition, the investigation comprises the development of an analytical framework that captures wide characteristics of optimization algorithm implementations, in hardware and software, using key simple and combined heterogeneous indicators. The framework proposes a combined Optimization Fitness Indicator that can classify the performance of PSO implementations when targeting different evaluation functions. The two targeted processing systems are Field Programmable Gate Arrays for hardware implementations and a high-end multi-core computer for software implementations. The investigation confirms the successful development of a PSO processor with appealing performance characteristics that outperforms recently presented implementations. The proposed hardware implementation attains 23,300 improvement ratio of execution times with an elliptic evaluation function. In addition, a speedup of 1777 times is achieved with a Shifted Schwefels function. Indeed, the developed framework successfully classifies PSO implementations according to multiple and heterogeneous properties for a variety of benchmark functions.
Authentication and Copyright Protection of Videos Under Transmitting Specifications

A lot of video content shared over the Internet have some problems, among which are the authentication and multimedia protection. The chapter provides a detailed overview of the transmitting specifications of video content, such as H.264/AVC, H.264/SVC, and H.265/HEVC in the sense of a watermarking scheme. Possible attacks against video sequence or single frames are classified as the intentional or accidental attacks. We study a selection of the relevant regions in the semi I-frames under formulated restrictions and conditions caused by the transmitted specifications. We simulate a sequence of I-frames in the original video sequence by a criterion of significant motion between three neighboring frames. The relevant regions are selected through a creation of the motion map, saliency map, textural map, blue component map, and joint map. Invariance to global Rotation, Scaling, Translation (RST) attacks is provided by feature-based approach. The novelty is in that we do not embed watermark in these stable regions, but coordinates of descriptors as they were in the host frame. This allows us to avoid the corresponding matches between descriptors in the host and watermarked frames. In order to provide invariance to RST attacks in the stable regions, we use exponential moments calculated on a unit circle. After compensation of the global RST attacks, we check other pairs of corresponding feature points consequently in order to find the possible local geometric distortions.
No-reference stereoscopic image quality assessment using a multi-task CNN and registered distortion representation

Scene discrepancy between the left and right views presents more challenges for image quality assessment (IQA) of stereoscopic images as opposed to monocular ones. Existing no-reference stereoscopic IQA (NR-SIQA) metrics cannot achieve a good performance on asymmetrically distorted stereoscopic images. In this paper, we propose an NR-SIQA index that first addresses scene discrepancy by means of image registration. It then uses a registered distortion representation based on the left and registered right views to represent the distortion in the stereoscopic image. Because different distortion types influence image quality differently, a multi-task convolutional neural network (CNN) is employed to learn image quality prediction and distortion-type identification simultaneously. We first design a one-column multi-task CNN model, that learns from the registered distortion representation. Then, we extend the one-column model to a three-column model, which also learns from the left and right views. Our experimental results validate the effectiveness of the proposed registered distortion representation and multi-task CNN architecture. The proposed one- and three-column models outperform the state-of-the-art NR-SIQA metrics, especially for asymmetrically distorted stereoscopic images.
Contextualizing Rename Decisions using Refactorings and Commit Messages

Identifier names are the atoms of comprehension; weak identifier names decrease productivity by increasing the chance that developers make mistakes and increasing the time taken to understand chunks of code. Therefore, it is vital to support developers in naming, and renaming, identifiers. In this paper, we study how terms in an identifier change during the application of rename refactorings and contextualize these changes using co-occurring refactorings and commit messages. The goal of this work is to understand how different development activities affect the type of changes applied to names during a rename. Results of this study can help researchers understand more about developers' naming habits and support developers in determining when to rename and what words to use.
Toward Interface Defaults for Vague Modifiers in Natural Language Interfaces for Visual Analysis

Natural language interfaces for data visualizations tools are growing in importance, but little research has been done on how a system should respond to questions that contain vague modifiers like "high" and "expensive." This paper makes a first step toward design guidelines for this problem, based on existing research from cognitive linguistics and the results of a new empirical study with 274 crowdsourcing participants. A comparison of four bar chart-based views finds that highlighting the top items according to distribution-sensitive values is preferred in most cases and is a good starting point as a design guideline.
Greasing the Turbines? Corruption and access to electricity in Africa

Using survey data from the Afrobarometer, we document that respondents who live in areas where more people report having paid a bribe to access household services are more likely to have access to electricity. The existing literature suggests three mechanisms that could explain why more corruption increases access to electricity. Corruption could incentivize firms to operate in the shadow economy and facilitate them doing so. Corruption could reduce enforcement of the law for electricity theft. Finally, corruption could lower firms' costs by allowing them to reduce the burden of red tape and or ignore standards. The data do not support the first two of these mechanisms suggesting that corruption is "greasing the wheels" for firms in the electricity sector. Supporting this conclusion is the fact that this relationship between corruption and access is only statistically significant in countries where the ease of getting electricity is low and there is private participation in the electricity sector.
The Fundamental Problem of Logical Omniscience

Abstract We propose a solution to the problem of logical omniscience in what we take to be its fundamental version: as concerning arbitrary agents and the knowledge attitude per se. Our logic of knowledge is a spin-off from a general theory of thick content, whereby the content of a sentence has two components: (i) an intension, taking care of truth conditions; and (ii) a topic, taking care of subject matter. We present a list of plausible logical validities and invalidities for the logic of knowledge per se for arbitrary agents, and isolate three explanatory factors for them: (1) the topic-sensitivity of content; (2) the fragmentation of knowledge states; (3) the defeasibility of knowledge acquisition. We then present a novel dynamic epistemic logic that yields precisely the desired validities and invalidities, for which we provide expressivity and completeness results. We contrast this with related systems and address possible objections.
Insulator Faults Detection Based on Deep Learning

Electrical insulators are mainly used in transmission system for electrical insulation and mechanical support purpose. Since the insulators are exposed to environment, they can be victim of stresses (electrical, mechanical, environmental) which can lead to bunch-drop of insulators. Bunch-drop of insulators can occur due to aging, overloading, corrosion and so on. So, in this paper we propose a new method which can detect bunch-drop of insulators. We propose an object detection algorithm (YOLO, you only look once) in order to perform insulator localization and its bunch-drop detection. YOLO is the stat-of-the-art object detection method which is based on deep learning. YOLO adopts supervised learning mode. It takes as input labeled dataset. So, we trained YOLOv3 on insulator dataset of 2000 images with corresponding labels for two classes, insulator and the defect. Features are extracted by those images using several convolutional layers (53 layers). Then logistic regression is used for performing classes probabilities and labels predictions. The advantage of YOLO over other object detection method is based on its speed; thus, it is fast and can process 45 frames per second. The experiment results show that the proposed method can effectively localize insulator and detect its bunch-drop.
Adversarial Robustness by One Bit Double Quantization for Visual Classification

In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images are injected with adversarial noise. The proposed system can remove the adversarial noise completely on this particular attack scenario. First, we analyze the potential sources of adversarial noise and classify adversarial examples into three classes. We then propose a novel effective solution, dquant, to target a specific class of adversarial examples. The first quantization is 1-bit dithering applied to both training and test images. The second one is linear quantization, which is applied to test images just before being inputted to a model to remove any adversarial noise. The linear quantizer guarantees that original 1-bit test images will be restored regardless of adversarial noise distance, and, therefore, dquant maintains identical accuracy whether or not the model is under attack. The results show that dquant achieves comparable accuracy, 85.28% on the CIFAR-10 and 94.99% on the Oxford-IIIT Pet datasets against three state-of-the-art adversaries with even a previously untested maximum adversarial distance of 64.
Revisit of Certificateless Signature Scheme Used to Remote Authentication Schemes for Wireless Body Area Networks

The Internet of Things (IoT), recognized as one of the major technological revolutions in the century, is deployed and used today sociality. The related security issues are taken into account by the academia and industry. Recently, an online/offline certificateless signature scheme (OO-CLS) proposed by Saeed et al. is used to construct a heterogeneous remote anonymous authentication protocol (HRAAP) in wireless body area networks based on the IoT. However, in this article, we show that the scheme is vulnerable to the forgery attack which is not necessary to know any information except public system parameters. Furthermore, we show that the sensor node can generate the partial private keys and secret values of other sensor nodes after it obtains its partial private key. This causes that the HRAAP is also insecure. Finally, we improve the OO-CLS and analyze the security of our improved scheme.
Emerging Frontiers in Smart Environment and Healthcare – A Vision

The second half of the twentieth century has witnessed unprecedented development and growth in information technology. What began as a foundational technology in exploring science and engineering has evolved into ground-breaking tools in most fields of endeavor. Today, rapidly expanding applications of informatics are being developed in diverse fields such as business management, transportation, environmental monitoring, building management, various aspects of healthcare, and even in fine arts. Driven by new developments in data analytics, Big Data, Artificial Intelligence and Deep Learning neural networks, enormous opportunities are opening up in therapeutic application of IT well beyond clinical diagnostic aids and computational biology. Augmentation of human mental faculties particularly with respect to multivariate analysis will drive major areas of research in the coming years. A number of potential research areas are presented in the paper.
Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification

In recent years, deep learning (DL) has been successfully and widely applied in the person reidentification (Re-ID). However, the DL-based person Re-ID methods face a bottleneck that the scales of most existing person Re-ID databases are not large enough for training very deep models. To address this problem, a body symmetry and part-locality-guided direct nonparametric deep feature enhancement (DNDFE) method is proposed in this article. Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons, the proposed method designs two nonparametric layers, namely, the body symmetry average pooling and local normalization layers, to construct a DNDFE module to well explore the body symmetry and part locality properties. The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance. The experimental results have shown that the proposed DNDFE method is superior to multiple state-of-the-art person Re-ID methods in terms of accuracy and efficiency.
Convolutional Neural Networks for Noise Classification and Denoising of Images

The goal of this paper is to find whether a convolutional neural network (CNN) performs better than the existing blind algorithms for image denoising, and, if yes, whether the noise statistics has an effect on the performance gap. For automatic identification of noise distribution, we used two different convolutional neural networks, VGG-16 and Inception-v3, and it was found that Inception-v3 identifies the noise distribution more accurately over a set of nine possible distributions, namely, Gaussian, log-normal, uniform, exponential, Poisson, salt and pepper, Rayleigh, speckle and Erlang. Next, for each of these noisy image sets, we compared the performance of FFDNet, a CNN based denoising method, with noise clinic, a blind denoising algorithm. It was found that CNN based denoising outperforms blind denoising in general, with an average improvement of 16% in peak signal to noise ratio (PSNR). The improvement is however very prominent for salt and pepper type noise with a PSNR difference of 72%, whereas for noise distributions such as Gaussian, FFDNet could achieve only a 2% improvement over noise clinic. The results indicate that for developing a CNN based optimum denoising platform, consideration of noise distribution is necessary.
Virtual Learning Environment for an Industrial Assembly Task

In this paper, we present a virtual learning environment for an industrial assembly task, which combines an easyto-use interaction with an intuitive user experience.It is shown that such a virtual environment can be used for initial training to introduce tasks to new employees, but also experts may benefit from advanced training in case of new products, or new assembly routines.Consequently, this application was validated twice.First, in a lab pilot study showing that the simple interactions and helpful instructions were appreciated by the participants.Second, professionals from industry were asked to perform the task and to evaluate the usefulness of the virtual learning environment considering its industrial applicability.Based on the achieved scores in common evaluative questionnaires and the post-study interviews, both, the lab pilot and the industrial study, have performed well and will be further developed in close collaboration with the industrial partner.