Reviewerly/exHarmony huggingface dataset:
Byzantine Fault-Tolerant Parallelized Stochastic Gradient Descent for Linear Regression

This paper addresses the problem of Byzantine fault-tolerance in parallelized stochastic gradient descent (SGD) method solving for a linear regression problem. We consider a synchronous system comprising of a master and multiple workers, where up to a (known) constant number of workers are Byzantine faulty. Byzantine faulty workers may send incorrect information to the master during an execution of the parallelized SGD method. To mitigate the detrimental impact of Byzantine faulty workers, we replace the averaging of gradients in the traditional parallelized SGD method by a provably more robust gradient aggregation rule. The crux of the proposed gradient aggregation rule is a gradient-filter, named comparative gradient clipping(CGC) filter. We show that the resultant parallelized SGD method obtains a good estimate of the regression parameter even in presence of bounded fraction of Byzantine faulty workers. The upper bound derived for the asymptotic estimation error only grows linearly with the fraction of Byzantine faulty workers.
Chinese Text Classification Based on Neural Networks and Word2vec

Neural network models have proved capable of achieving remarkable performance in sentence and document modeling. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two mainstream architectures for such modeling tasks, adopt totally different ways of understanding natural languages. The classical CNN, despite its wide application in image classification, is rarely used for text classification. The RNN is capable of processing texts of variable lengths and can hence facilitate text classification. In this study, we aim to analyze the performance of both neural network models on Chinese text classification. On the basis of word2vec, a commonly adopted technology in language processing, we trained two neural network models, TextCNN and TextRNN, on the HUCNews dataset, and we compared their performance with the methods of THUCTC (Tsinghua University Chinese Text Classification) [1]. The experiment results show that the accuracy of Chinese text classification can be improved from 88:60% (THUCTC) to 96:36% (TextCNN) and 94:62% (TextRNN), respectively.
Using Research Literature to Generate Datasets of Implicit Feedback for Recommending Scientific Items

In an age of information overload, we are faced with seemingly endless options from which a small number of choices must be made.For applications such as search engines and online stores, Recommender Systems have long become the key tool for assisting users in their choices.Interestingly, the use of Recommender Systems for recommending scientific items remains a rarity.One difficulty is that the development of such systems depends on the availability of adequate datasets of users' feedback.While there are several datasets available with the ratings of the users for books, music, or films, there is a lack of similar datasets for scientific fields, such as Astronomy and Life and Health Sciences.To address this issue, we propose a methodology that explores scientific literature for generating utility matrices of implicit feedback.The proposed methodology consists in identifying a list of items, finding research articles related to them, extracting the authors from each article, and finally creating a dataset where users are unique authors from the collected articles, and the rating values are the number of articles a unique author wrote about an item.Considering that literature is available for every scientific field, the methodology is in principle applicable to Recommender Systems in any scientific field.The methodology, which we call LIBRETTI (LIterature Based RecommEndaTion of scienTific Items), was assessed in two distinct study cases, Astronomy and Chemistry.Several evaluation metrics for the datasets generated with LIBRETTI were compared to those derived from other available datasets using the same set of recommender algorithms.The results were found to be similar, which provides a solid indication that LIBRETTI is a promising approach for generating datasets of implicit feedback for recommending scientific items.
Similar Music Instrument Detection via Deep Convolution YOLO-Generative Adversarial Network

Object detection and image recognition are important research topics in machine learning and artificial intelligence. The major challenge of the computer vision image recognition is to detect and recognize a similar object. Generative Adversarial Network (GAN) based on Convolution Neural Network (CNN) is proposed to faces this challenge. The advantage of the GAN is represented by its architecture which consists of a generator and discriminator to detect real or fake image generated by the machine. In this paper, we adopt the advantage of GAN and combine with YOLO algorithm to identify similar music instruments. YOLO is fast Region based CNN with powerful computation. Using Deep Convolution YOLO-GAN will enhance the capability of YOLO detection process and outperform the original YOLO capability.
From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions

There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization. We focus on two of the most important fields: stochastic optimal control, with its roots in deterministic optimal control, and reinforcement learning, with its roots in Markov decision processes. Building on prior work, we describe a unified framework that covers all 15 different communities and note the strong parallels with the modeling framework of stochastic optimal control. By contrast, we make the case that the modeling framework of reinforcement learning, inherited from discrete Markov decision processes, is quite limited. Our framework (and that of stochastic control) is based on the core problem of optimizing over policies. We describe four classes of policies that we claim are universal and show that each of these two fields has, in their own way, evolved to include examples of each of these four classes.
Leveraging feature selection to detect potential tax fraudsters

Tax evasion is any act that knowingly or unknowingly, legally or unlawfully, leads to non-payment or underpayment of tax due. Enforcing the correct payment of taxes by taxpayers is fundamental in maintaining investments that are necessary and benefits a society as a whole. Indeed, without taxes it is not possible to guarantee basic services such as health-care, education, sanitation, transportation, infrastructure, among other services essential to the population. This issue is especially relevant in developing countries such as Brazil. In this work we consider a real-world case study involving the Treasury Office of the State of Ceará (SEFAZ-CE, Brazil), the agency in charge of supervising more than 300,000 active taxpayers companies. SEFAZ-CE maintains a very large database containing vast amounts of information concerning such companies. Its enforcement team struggles to perform thorough inspections on taxpayers accounts as the underlying traditional human-based inspection processes involve the evaluation of countless fraud indicators (i.e., binary features), thus requiring burdensome amounts of time and being potentially prone to human errors. On the other hand, the vast amount of taxpayer information collected by fiscal agencies opens up the possibility of devising novel techniques able to tackle fiscal evasion much more effectively than traditional approaches. In this work we address the problem of using feature selection to select the most relevant binary features to improve the classification of potential tax fraudsters. Finding out possible fraudsters from taxpayer data with binary features presents several challenges. First, taxpayer data typically have features with low linear correlation between themselves. Also, tax frauds may originate from intricate illicit tactics, which in turn requires to uncover non-linear relationships between multiple features. Finally, few features may be correlated with the targeted class. In this work we propose Alicia, a new feature selection method based on association rules and propositional logic with a carefully crafted graph centrality measure that attempts to tackle the above challenges while, at the same time, being agnostic to specific classification techniques. Alicia is structured in three phases: first, it generates a set of relevant association rules from a set of fraud indicators (features). Subsequently, from such association rules Alicia builds a graph, which structure is then used to determine the most relevant features. To achieve this Alicia applies a novel centrality measure we call the Feature Topological Importance. We perform an extensive experimental evaluation to assess the validity of our proposal on four different real-world datasets, where we compare our solution with eight other feature selection methods. The results show that Alicia achieves F-measure scores up to 76.88%, and consistently outperforms its competitors.
An inaudible voice attack to context-based device authentication in smart IoT systems

As a typical application of Cyber-physical systems (CPS), a smart Internet of Things (IoT) system integrates computation, networking, and physical processes from embedded computing devices. To protect the confidentiality and authenticity of IoT systems, device authentication/pairing is a critical operation for securing networking and communication. Context-based pairing aims to provide more practical methods of pairing, using the common events in the same trust domain as the basis for device authentication. However, the inaccuracy of event recognition and context noise sensitivity are inherent flaws that greatly affect the robustness and efficiency of these approaches. In this paper, we analyze the weakness of context-based pairing methods and present a jamming-based attack named Pairjam, which interferes with the device authentication of these context-based device pairing methods. Due to the unknown noise threshold in signal detection, we design two attack model and validate that both attacks lead to misclassified of attack signal and decrease the similarity of fingerprint. The experiment results demonstrate that Pairjam can successfully disturb the device pairing by interfere with the fingerprint similarity of the microphone-equipped devices with other non-acoustic IoT devices.
MoDAr-WA: Tool Support to Automate an MDA Approach for MVC Web Application

Model-driven engineering (MDE) uses models during the application development process. Thus, the MDE is particularly based on model-driven architecture (MDA), which is one of the important variants of the Object Management Group (OMG). MDA aims to generate source code from abstract models through several model transformations between, and inside the different MDA levels: computation independent model (CIM), platform independent model (PIM), and platform specific model (PSM) before code. In this context, several methods and tools were proposed in the literature and in the industry that aim to automatically generate the source code from the MDA levels. However, researchers still meet many constraints—model specifications, transformation automation, and level traceability. In this paper, we present a tool support, the model-driven architecture for web application (MoDAr-WA), that implements our proposed approach, aiming to automate transformations from the highest MDA level (CIM) to the lowest one (code) to ensure traceability. This paper is a continuity of our previous works, where we automate transformation from the CIM level to the PIM level. For this aim, we present a set of meta-models, QVT and Acceleo transformations, as well as the tools used to develop our Eclipse plug-in, MoDAr-WA. In particular, we used QVT rules for transformations between models and Acceleo for generating code from models. Finally, we use MoDAr-WA to apply the proposed approach to the MusicStore system case study and compare the generated code from CIM to the original application code.
Initial Coin Offerings: Tokens as Innovative Financial Assets

In this chapter we describe the phenomenon of Initial Coin Offerings (ICOs), i.e. unregulated offerings of digital tokens, built on the innovative blockchain technology, as to provide a means to collect finance for a project on the Internet, disintermediating any external platform, payment agent or professional investor. ICO tokens allow the access to platform services, may serve as cryptocurrencies, or grant profit rights; they are traded on electronic exchanges and represent a new financial asset. We highlight the issues emerged with respect to information asymmetries and moral hazard and we review the nascent empirical literature exploring the ICO token market.
Complex Neural Networks for Radio Frequency Fingerprinting

We use deep learning to design a radio frequency (RF) fingerprint algorithm that takes complex-valued wireless signals as input, and outputs the identity of the device that transmitted the signal. We study how performance accuracy varies due to changes in input representation, choices of labels, and treatment of complex values. We report sensitivity to number of devices, training set size, signal-to-noise ratio, and environmental channel. Training data are real-time transmissions from thousands of devices.
Authorized Keyword Searches on Public Key Encrypted Data With Time Controlled Keyword Privacy

Recently, more and more data have been stored in the cloud with keyword indices so that the data users can make search over the databases. In some of these database applications, the query frequency analysis of keywords is quite important to the optimization of the databases and it is easy to be implemented when the data are not encrypted. However, with the growing demands of data privacy, it is desired that the data should be encrypted before uploaded to the cloud. Searchable encryption has been proposed which enables users to make keyword search over the encrypted data with keyword privacy. In a secure searchable encryption scheme, it is required that the keyword in each query should not be revealed. So it becomes challenging to analyze the query frequency of keywords in an encrypted database which has the pressing need of optimization. In this paper, we first consider this problem and present an efficient solution to it which enables the query frequency analysis of keywords without destroying the privacy of the encrypted data and the identity privacy of data users. We also simulate our solution and show that it is practical to the real applications.
A new image denoising model utilizing the conformable fractional calculus for multiplicative noise

Reducing noise from images is an essential structure of the image processing study. Noises can arise with images through achievement on diffusion. The existence of noise can delay the right operation of these images for many applications such as satellite and medical images. Reducing denois in images multiplicatively (DIM) has been developed and modified by many researchers during the past few years. DIM can destroy almost all data of the original image, especially the texture of images. Our aim is to present a new technique to solve this problem. The technique is based on a new fractional calculus called the conformable fractional calculus (CFC). This type of calculus has advantages because of its formula involves a controller, which can be applied to complex problems such as DIM. The proposed structures of CFC windows are given by four masks suggested for x and y directions. On four directional angles, a convolution operational product of the input image pixels with a CFC mask window has been completed. The visual observation and peak signal-to-noise ratio with Root Mean Square Error are employed for measurements. The experiments showed that the skillful filtering outcomes are indicated high score than some well known filers such as Gaussian filter, Sobel edge filter, Canny edge filter and gray-level co-occurrence matrix. Compering is illustrated as well with newly researches.
A decentralized and secure blockchain platform for open fair data trading

Summary As the value of data has received considerable attention, data trading shows broad market prospects. The existing data trading methods, including private trades and centralized trades, have high risks regarding transaction security and data protection. To solve this problem, we propose a decentralized trading solution for open fair data trading by deploying the smart contract on the blockchain network. The data for sale are encrypted and stored on the distributed storage platform but not directly on the blockchain network. Because the trading content is the decryption key of the data, the proposed new method can alleviate the storage pressure of the blockchain by reducing the transaction cost. We conduct a security analysis which shows that our scheme achieves secure, practical, open, and fair trading. We implement our trading contract with solidity and test it on the Ethereum's test network, and extensive experiments demonstrate desirable feasibility of our proposal.
TDR: Two-stage deep recommendation model based on mSDA and DNN

Recently, deep learning techniques have been widely used in recommendation tasks and have attained record performance. However, the input quality of the deep learning model has a great influence on the recommendation performance. In this work, an efficient and effective input optimization method is proposed. Specifically, we propose an integrated recommendation framework based on two-stage deep learning. In the first stage, with user and item features as the original input, a low-cost marginalized stacked denoising auto-encoder (mSDA) model is used to learn the latent factors of users and items. In the second stage, the resulting latent factors are combined and used as input vector to the DNN model for fast and accurate prediction. Using the latent factor vector as the input to the deep learning-based recommendation model not only captures the high-order feature interaction, but also reduces the burden of the hidden layer, and also avoids the model training falling into local optimum. Extensive experiments with real-world datasets show that the proposed model shows much better performance than the state-of-the-art recommendation methods in terms of prediction accuracy, parameter space and training speed.
Know-How in Programming Tasks: From Textual Tutorials to Task-Oriented Knowledge Graph

Accomplishing a program task usually involves performing multiple activities in a logical order. Task-solving activities may have different relationships, such as subactivityof, precede-follow, and different attributes, such as location, condition, API, code. We refer to task-solving activities and their relationships and attributes as know-how knowledge. Programming task know-how knowledge is commonly documented in semi-structured textual tutorials. A formative study of the 20 top-viewed Android-tagged how-to questions on Stack Overflow suggests that developers are faced with three information barriers (incoherent modeling of task intent, tutorial information overload and unstructured task activity description) for effectively discovering and understanding task-solving knowledge in textual tutorials. Knowledge graph has been shown to be effective in representing relational knowledge and supporting knowledge search in a structured way. Unfortunately, existing knowledge graphs extract only know-what information (e.g., APIs, API caveats and API dependencies) from software documentation. In this paper, we devise open information extraction (OpenIE) techniques to extract candidates for task activities, activity attributes and activity relationships from programming task tutorials. The resulting knowledge graph, TaskKG, includes a hierarchical taxonomy of activities, three types of activities relationships and five types of activity attributes, and enables activity-centric knowledge search. As a proof-of-concept, we apply our approach to Android Developer Guide. A comprehensive evaluation of TaskKG shows high accuracy of our OpenIE techniques. A user study shows that TaskKG is promising in helping developers finding correct answers to programming how-to questions.
Deep Learning Assisted Smart Glasses as Educational Aid for Visually Challenged Students

Computer Vision Technology has played a significant role in assisting visually challenged people to carry out their day to day activities without much dependency on other people. Smart glasses in one such solution which enables blind or visually challenged people to “read” images. This paper is an attempt in this direction to build a novel smart glass which has the ability to extract and recognize text captured from an image and convert it to speech. It consists of a Raspberry Pi 3 B+ microcontroller which processes the image captured from a webcam super-imposed on the glasses of the blind person. Text detection is achieved using the OpenCV software and open source Optical Character Recognition (OCR) tools Tesseract and Efficient and Accurate Scene Text Detector (EAST) based on Deep Learning techniques. The recognized text is further processed by Google's Text to Speech (gTTS) API to convert to an audible signal for the user. A second feature of this solution is to provide location-based services to the blind people by identifying locations in an academic building using the RFID technology. This solution has been extensively tested in a university environment for aiding visually challenged students. The novelty of the implemented solution lies in providing the desired computer vision functionalities of image/text recognition which is economical, small-sized, accurate and uses open source software tools. This solution can be potentially used for both educational and commercial applications.
Blockchain-enabled deep semantic video-to-video summarization for IoT devices

The rapid development of multimedia technologies gave birth to large video data that demands effective and fast video summarization methods along with ability to assure the authenticity of digital data. Video summarization provides consolidated view of original video in short and compact form of a video. The summarized videos should be transmitted to remote IoT devices through secure channels. To prevent data manipulation and to verify the transmitted data, we exploited the blockchain technology. This paper focuses on different steps of keyframe extraction from a video followed by secure and trusted transmission to the consumers. The proposed system enables the users to summarize a video based on human and objects as parameters. Cryptographic hashes are used with blockchain, hashes are generated from summarized video blocks, signed and transmitted through blockchain. Cumulus blockchain technique is utilized to ensure the video integrity. System allows remote users to get tamper-proofed summarized video of their business sites or any sensitive premises on their smartphones.
Incremental and Multi-Task Learning Strategies for Coarse-To-Fine Semantic Segmentation

The semantic understanding of a scene is a key problem in the computer vision field. In this work, we address the multi-level semantic segmentation task where a deep neural network is first trained to recognize an initial, coarse, set of a few classes. Then, in an incremental-like approach, it is adapted to segment and label new objects’ categories hierarchically derived from subdividing the classes of the initial set. We propose a set of strategies where the output of coarse classifiers is fed to the architectures performing the finer classification. Furthermore, we investigate the possibility to predict the different levels of semantic understanding together, which also helps achieve higher accuracy. Experimental results on the New York University Depth v2 (NYUDv2) dataset show promising insights on the multi-level scene understanding.