Reviewerly/exHarmony huggingface dataset:
Understanding the Manipulation on Recommender Systems through Web Injection

Recommender systems have been increasingly used in a variety of web services, providing a list of recommended items in which a user may have an interest. While important, recommender systems are vulnerable to various malicious attacks. In this paper, we study a new security vulnerability in recommender systems caused by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">web injection</i> , through which malicious actors stealthily tamper any unprotected in-transit HTTP webpage content and force victims to visit specific items in some web services (even running HTTPS), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> ., YouTube. By doing so, malicious actors can promote their targeted items in those web services. To obtain a deeper understanding on the recommender systems of our interest (including YouTube, Yelp, Taobao, and 360 App market), we first conduct a measurement-based analysis on several real-world recommender systems by leveraging machine learning algorithms. Then, web injection is implemented in three different types of devices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> ., computer, router, and proxy server) to investigate the scenarios where web injection could occur. Based on the implementation of web injection, we demonstrate that it is feasible and sometimes effective to manipulate the real-world recommender systems through web injection. We also present several countermeasures against such manipulations.
Demonstration of a Quantum Key Distribution Trusted Node on an Electric Utility Fiber Network

We present a trusted node quantum key distribution system deployed at an electric utility. We show the QKD distance limitation can be extended using trusted nodes with realistic utility fiber infrastructure, demonstrating the possibility of quantum technologies in providing information security to electric grid devices.
An improved reversible watermarking scheme using weighted prediction and watermarking simulation

For the reversible watermarking scheme using the prediction error expansion and histogram shifting (PEE–HS), improving the prediction accuracy facilitates performance enhancement, which still remains a challenging problem in this field. To this end, the paper improves the state-of-the-art local predictor (LP) by designing the following approaches: 1) enlarging the prediction context; 2) partitioning the prediction block surrounding the target pixel into the watermarked and original regions, and imposing different weights on prediction values from these two regions to generate the final prediction for the target pixel; and 3) conducting watermarking simulation on the original region via random noises to further enhance the prediction performance. These three approaches are then integrated to result in an improved LP using weighted prediction and watermarking simulation (LP-WPWS). By exploiting the LP-WPWS for prediction error generation, we thus construct a new PEE–HS-based reversible watermarking scheme. Extensive simulation shows that the proposed scheme outperforms the state-of-the-art LP and is comparable to the excellent methods exploiting the sorting, multiple histograms modification, and hybrid dimensional histogram generation with adaptive mapping selection.
Efficient workflow scheduling in cloud computing for security maintenance of sensitive data

Summary The seamless conveying of information's considering internet as a virtual space, connecting all users from the world is cloud computing. In simpler terms it is a means of storing and accessing information over internet irrespective of place and time. The cloud proceeds with the process of storing, retrieving and allowing access, on demand as a paid service. As the benefits of cloud is attractive, the number of users adopting to the cloud also increases, so the workflow management becomes tedious and more challenging in the cloud. The decision to provide with an enhanced work flow management requires proper scheduling recollecting value of the information. So the paper proposes with an efficient method of managing the work flow considering the value of the information by categorizing between the much value and non‐value information's and framing the algorithm that functions as scheduler using the parallel implementation in natural process of genetic algorithm (GA) with secured frame work for the information's of high value. The proposed work shows an overwhelmed performance than the conventional techniques in the time taken for the execution and its total cost. The performance of the scheduler is validated in the WORKFLOWSIM on the grounds of time of execution and total cost.
Breaking bad systems with Human Factors and Ergonomics: Using Work Domain Analysis to identify strategies to disrupt trading in dark net marketplaces

The trading of illicit goods in dark net marketplaces is a significant and growing global problem. This paper describes the findings from a study which involved the use of Work Domain Analysis in conjunction with a node breaking process to identify interventions designed to disrupt trading in dark net marketplaces. The analysis identified a series of critical disruption points and opportunities for disrupting dark net trading, including strategies to disrupt critical functions such as marketplace administration, reputation management, and product management. The analysis also showcases the potential use of Human Factors and Ergonomics for disrupting, as well as optimising, complex sociotechnical systems. The benefits of using Human Factors and Ergonomics to disrupt complex systems, as well as optimise them, are discussed along with potential application areas.
Application of Approximate Matrix Multiplication to Neural Networks and Distributed SLAM

Computational efficiency is a critical constraint for a variety of cutting-edge real-time applications. In this work, we identify an opportunity to speed up the end-to-end runtime of two such compute bound applications by incorporating approximate linear algebra techniques. Particularly, we apply approximate matrix multiplication to artificial Neural Networks (NNs) for image classification and to the robotics problem of Distributed Simultaneous Localization and Mapping (DSLAM). Expanding upon recent sampling-based Monte Carlo approximation strategies for matrix multiplication, we develop updated theoretical bounds, and an adaptive error prediction strategy. We then apply these techniques in the context of NNs and DSLAM increasing the speed of both applications by 15-20% while maintaining a 97% classification accuracy for NNs running on the MNIST dataset and keeping the average robot position error under 1 meter (vs 0.32 meters for the exact solution). However, both applications experience variance in their results. This suggests that Monte Carlo matrix multiplication may be an effective technique to reduce the memory and computational burden of certain algorithms when used carefully, but more research is needed before these techniques can be widely used in practice.
Multi-Path Planning Method for UAVs Swarm Purposes

Advances in the field of robotics have culminated in the cooperative and autonomous use of different types of vehicles, to efficiently tackle a wide range of tasks. One of the main elements, to allow the use of a collaborative and autonomous system of multiple vehicles, is the planning of safe routes, which the movement of different agents is guaranteed through the work area, without colliding with obstacles in the environment. Consequently, in this document, multiple path planning is proposed for a Multi-Robot System consisting of several Unmanned Aerial Vehicles; based on the creation of Probabilistic Roadmaps (PRM). The objective of this paper is the implementation of a multi-path planning method, for a Multi-Robot system, which must achieve different objectives in a set of proposed situations and the demonstration that the development of Probabilistic Roadmaps constitute an optimal and effective solution for multi-path problem solving. In addition, this solution is integrated in a Robot Operating System (ROS) architecture that provides simple implementation of the algorithms presented in a real fleet of vehicles. Finally, the results obtained show that methods based on PRM create optimal solutions in terms of computing time in swarms of scalable agents and for different situations analyzed, along with the possible integration in a versatile framework such as ROS.
Deep Dive into Anonymity: Large Scale Analysis of Quora Questions

Anonymity forms an integral and important part of our digital life. It enables us to express our true selves without the fear of judgment. In this paper, we investigate the different aspects of anonymity in the social Q&A site Quora. Quora allows users to explicitly post anonymous questions and such activity in this forum has become normative rather than a taboo. Through an analysis of millions of questions, we observe that at a global scale almost no difference manifests between the linguistic structure of the anonymous and the non-anonymous questions posted on Quora. We find that topical mixing at the global scale to be the primary reason for the absence. However, the differences start to feature once we “deep dive” and (topically) cluster the questions and compare them. In particular, we observe that the choice to post the question as anonymous is dependent on the user’s perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity. Subsequently, to gain further insights, we build an anonymity grid to identify the differences in the perception on anonymity of the user posting the question and the community of users answering it. We also look into the first response time of the questions and observe that it is lowest for topics which talk about personal and sensitive issues, which hints toward a higher degree of community support.
Linear Algebra-Based Triangle Counting via Fine-Grained Tasking on Heterogeneous Environments : (Update on Static Graph Challenge)

Triangle counting is a representative graph problem that shows the challenges of improving graph algorithm performance using algorithmic techniques and adopting graph algorithms to new architectures. In this paper, we describe an update to the linear-algebraic formulation of the triangle counting problem. Our new approach relies on fine-grained tasking based on a tile layout. We adopt this task based algorithm to heterogeneous architectures (CPUs and GPUs) for up to 10.8× speed up over past year's graph challenge submission. This implementation also results in the fastest kernel time known at time of publication for real-world graphs like twitter (3.7 second) and friendster (1.8 seconds) on GPU accelerators when the graph is GPU resident. This is a 1.7 and 1.2 time improvement over previous state-of-the-art triangle counting on GPUs. We also improved end-to-end execution time by overlapping computation and communication of the graph to the GPUs. In terms of end-to-end execution time, our implementation also achieves the fastest end-to-end times due to very low overhead costs.
A Multivariate Blind Ring Signature Scheme

Abstract Blind signatures are an important and useful tool in designing digital cash schemes and electronic voting protocols. Ring signatures on the other hand provide the anonymity of the signer within the ring of users. In order to fit to some real-life applications, it is useful to combine both protocols to create a blind ring signature scheme, which utilizes all of their features. In this paper, we propose, for the first time, a post-quantum blind ring signature scheme. Our scheme is constructed based on multivariate public key cryptography, which is one of the main candidates for post-quantum cryptography.
ConvAI2 Dataset of Non-goal-Oriented Human-to-Bot Dialogues

Conversational Intelligence Challenge (ConvAI) is a competition of non-goal-oriented dialogue systems (chatbots). It aims at (1) improving state-of-the-art chatbots and (2) creating an evaluation setup that allows performing unbiased evaluation and comparison of chatbots manually and automatically. The task of the second ConvAI competition is smalltalk about common topics such as hobbies, work, family, pets. This report contains the description of human-to-bot dialogues collected during ConvAI2. We analyse this data and compare it with dialogues from the first ConvAI (discussion of Wikipedia articles). We found that the task of ConvAI2 is both more engaging for user and less challenging for chatbots than the task of the first ConvAI. Our comparison of performance of paid workers and volunteers demonstrated that paid workers generate dialogues of better quality and score chatbots higher. However, in order to make the competition closer to real-world cases of chatbot usage the task should be more engaging for volunteers.
On Post-processing the Results of Quantum Optimizers

The use of quantum computing for applications involving optimization has been regarded as one of the areas it may prove to be advantageous (against classical computation). To further improve the solutions, post-processing techniques are often used on the results of quantum optimization. One such recent approach is the Multi Qubit Correction (MQC) algorithm by Dorband. In this paper, we will discuss and analyze the strengths and weaknesses of this technique. Based on our discussion, we perform an experiment on (i) how pairing heuristics on the input of MQC can affect the results of a quantum optimizer and (ii) a comparison between MQC and the built-in optimization method that D-wave Systems offers. Among our results, we are able to show that the built-in post-processing rarely beats MQC in our tests. We hope that by using the ideas and insights presented in this paper, researchers and developers will be able to make a more informed decision on what kind of post-processing methods to use for their quantum optimization needs.
GDPR compliance in Video Surveillance and Video Processing Application

In this paper we will present what is the General Data Protection Regulation (GDPR) and its impact on businesses that use and market video surveillance systems. We will detail the regulations and guidelines of both GDPR and the European Data Protection Supervisor (EDPS) regarding the recording and processing of personal and sensitive data and why it is classified as high-risk. We will consider the impact of these laws and regulations on the the field that we are operating in, namely machine learning applied for video analytics. To do that, we will briefly present two of our applications that are affected by those changes: Facial Recognition and Identification and Person Detection and how to adapt them to be GDPR-compliant. Finally we will also present a lightweight piece of software that can be easily applied to existing software with minimal computational overhead.
Learning No-Reference Quality Assessment of Multiply and Singly Distorted Images With Big Data

Previous research on no-reference (NR) quality assessment of multiply-distorted images focused mainly on three distortion types (white noise, Gaussian blur, and JPEG compression), while in practice images can be contaminated by many other common distortions due to the various stages of processing. Although MUSIQUE (MUltiply-and Singly-distorted Image QUality Estimator) Zhang et al., TIP 2018 is a successful NR algorithm, this approach is still limited to the three distortion types. In this paper, we extend MUSIQUE to MUSIQUE-II to blindly assess the quality of images corrupted by five distortion types (white noise, Gaussian blur, JPEG compression, JPEG2000 compression, and contrast change) and their combinations. The proposed MUSIQUE-II algorithm builds upon the classification and parameter-estimation framework of its predecessor by using more advanced models and a more comprehensive set of distortion-sensitive features. Specifically, MUSIQUE-II relies on a three-layer classification model to identify 19 distortion types. To predict the five distortion parameter values, MUSIQUE-II extracts an additional 14 contrast features and employs a multi-layer probability-weighting rule. Finally, MUSIQUE-II employs a new most-apparent-distortion strategy to adaptively combine five quality scores based on outputs of three classification models. Experimental results tested on three multiply-distorted and six singly-distorted image quality databases show that MUSIQUE-II yields not only a substantial improvement in quality predictive performance as compared with its predecessor, but also highly competitive performance relative to other state-of-the-art FR/NR IQA algorithms.
Computing Grounded Extensions Of Abstract Argumentation Frameworks

Abstract An abstract argumentation framework is a directed graph $(V,E)$ such that the vertices of $V$ denote abstract arguments and $E \subseteq V \times V$ represents the attack relation between them. We present a new ad hoc algorithm for computing the grounded extension of an abstract argumentation framework. We show that the new algorithm runs in $\mathcal{O}(|V|+|E|)$ time. In contrast, the existing state-of-the-art algorithm runs in $\mathcal{O}(|V|+|S||E|)$ time where $S$ is the grounded extension of the input graph.
Present state and future prospect of autonomous control technology for industrial drones

Abstract This article includes of a technical and general discussion about a future prospective of flying robot. Drones or unmanned aerial vehicles (UAVs) should be evolving to real and bio‐inspired flying robots. The concept of guidance, navigation and control is first explained and defined. Next, the autonomy (safety) class of drones, future roadmaps, and a correlation diagram between the flight level of drone and autonomy (safety) will be described, and a fault‐tolerant control system is introduced. Finally, the ideal state of autonomous control in the near future and realization of autonomy, as well as top‐level autonomy as Class A by a supervisor (guidance system), will be defined. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
A sequential pattern mining approach to identifying potential areas for business diversification

Although many quantitative models have been presented to identify potential areas for business diversification, most have focused on the assessment of technological capabilities and/or similarities using patent information. New data sources and scientific methods have thus seldom been addressed. We propose a sequential pattern mining approach to identifying potential areas for business diversification using the historical business segment data. Our approach includes (1) sequential pattern mining to identify potential areas for business diversification by extracting the significant changing patterns of firms’ business segments; and (2) index analysis to assess the market and financial characteristics of the areas identified. Taken together, three diversification strategy maps are developed to provide comprehensive views of analysis results. An empirical analysis of 25,126 unique firms with 1320 business segments confirms that the proposed approach enables a wide-ranging search for potential areas for business diversification and the quick assessment of their characteristics.
Enhanced Security of IoT Data Sharing Management by Smart Contracts and Blockchain

In recent years, the incredible growth and diversity of IoT systems and applications have generated an enormous amount of sensing data which play an essential role in IoT-based smart systems. So far, it has consumed much time and cost to collect these sensing data enough for these intelligent systems, which is the main reason leading to the needs of sharing available data to shorten time-consuming and cost savings for the data collection process. However, there are many challenges in ensuring integrity, security, and fairness in the data sharing process. In this paper, the authors propose a model leveraging the emerging Blockchain technology as an alternative solution to enhance the security of IoT data sharing management in terms of three principal criteria including confidentiality, integrity, and availability. Our prototype on Ethereum Blockchain demonstrates the feasibility of the proposed model.
Comparison Performance of Naive Bayes Classifier and Support Vector Machine Algorithm for Twitter’s Classification of Tokopedia Services

Abstract Tokopedia is one of the online shopping centers in Indonesian that carries the business model marketplace. Positive and negative opinions in Twitter from Tokopedia users about company services are source of information for the management. Naive Bayes Classification (NBC) and Support Vectore Machine (SVM) are techniques in data mining used to classify data or users opinion. The algorithm of NBC is very simple since it only use text frequency to compute the posterior probability for each classes. While SVM algorithm is more complex than NBC. SVM develop hyperplane equation which separate data into classes perfectly. The researcher wants to compare the performance of the NBC and SVM algorithms and use them to classify user opinions on Tokopedia’s services, because these two algorithms have different approaches and difficulty levels. Classification included positive and negative class only. Accuracy, precision and recall value are used to compare the performance of both algorithms. Research evaluation shows that SVM linear kernel technique outperform NBC technique with the accuracy 83.34%.
Card-Based Protocol Against Actively Revealing Card Attack

In 1989, den Boer presented the first card-based protocol, called the “five-card trick” that securely computes the AND function using a deck of physical cards via a series of actions such as shuffling and turning over cards. This protocol enables a couple to confirm their mutual love without revealing their individual feelings. During such a secure computation protocol, it is important to keep any information about the inputs secret. Almost all existing card-based protocols are secure under the assumption that all players participating in a protocol are semi-honest or covert, i.e., they do not deviate from the protocol if there is a chance that they will be caught when cheating. In this paper, we consider a more malicious attack in which a player as an active adversary can reveal cards illegally without any hesitation. Against such an actively revealing card attack, we define the t-secureness, meaning that no information about the inputs leaks even if at most t cards are revealed illegally. Subsequently, we design a 1-secure AND protocol. Thus, our contribution is the construction of the first formal framework to handle actively revealing card attacks and their countermeasures.