Reviewerly/exHarmony huggingface dataset:
Integrating Blockchain in Safety-Critical Systems: An Application to the Nuclear Industry

Safety-Critical Systems (SCSs) often manage sensible data that must be trustworthy, especially in many cases in which different actors participate whose interests may not coincide.Blockchain is a disruptive technology that has emerged to ensure the trustfulness of data.The nuclear industry incorporates many SCSs where blockchain can be applied.This paper focuses on the use of blockchain for the inspection of steam generators of a nuclear power plant.This is a critical process where different actors participate: plant property, external companies in charge of the inspection itself and different administrations.It typically consists of a number of processes that explore the state of different components of the plant in order to find any kind of failure or defect and it generates a great amount of data that must be verifiable and trustworthy.A distributed blockchain-based system is presented where all the nodes share the information and it cannot be altered.As a novelty, automatic inspection algorithms are stored in the blockchain itself by means of smart contracts.The benefits of blockchain are studied for the nuclear industry in general and for the inspection process in particular.In order to explore the possible drawbacks of a blockchain-based system for data management, a simulator has been implemented to recreate the scenario of an inspection.The results obtained show that blockchain architectures are a good alternative to traditional information repositories for nuclear power plant inspections.
Measuring dark web marketplaces via Bitcoin transactions: From birth to independence

This study measures the evolution of the anonymous marketplaces Silk Road, Silk Road 2.0, Agora, Evolution, Nucleus, Abraxas, and AlphaBay, which were the seven leading and most active dark web marketplaces. We identify that all the seven marketplaces use the same software to manage Bitcoin by investigating transactions in these marketplaces. However, the software was no longer used since May 2016 because of its vulnerability to protect anonymity. It indicates that dark web marketplaces advanced to the next stage with anonymity-enhancing tools around in March 2016. Using simple heuristics to identify and trace Bitcoin addresses associated with these marketplaces, purchases on these marketplaces are identified and evaluated. Our method provides evidence on market size, development, and fluctuation over time to fill a gap in previous studies. Dark web marketplaces continue to thrive because users migrate to new marketplaces after the existing ones are shut down. The total sales volume on Silk Road was 192.7 million US dollars between June 2012 and October 2013. The corresponding figures for Silk Road 2.0, Agora, Evolution, Nucleus, and Abraxas were 112.9, 220.7, 69.7, 88.3, and 35.6 million US dollars, respectively. The figures for AlphaBay was 166.0 million US dollars between December 2014 and February 2016.
Device-Free Secure Interaction With Hand Gestures in WiFi-Enabled IoT Environment

Recent research advancement of wireless sensing technology has made device-free interaction in the WiFi-enabled IoT environment possible. Although gesture-based interaction with such a smart environment greatly improves usability, it also introduces many security problems, such as shoulder surfing attacks. By spoofing the gestures of legitimate users, the attacker could easily access private information or services and cause even worse consequences. A secure interaction mechanism for this environment is required to prevent attackers without compromising the usability, while the limited recognition ability and low robustness of WiFi sensing make this target extremely challenging. To this end, we propose a secure interaction mechanism called secure interaction via WiFi Signal (SiWi), which provides the ability to resist shoulder surfing attacks without compromising the usability by using just WiFi signals. SiWi innovates in a concurrent interaction/authentication framework with only three elemental gestures (push, swing, and wave) and four types of identity-related imperceptible/hidden features (time distribution, direction, angle, and distance). HMM and Fresnel model-based algorithms are used to recognize the gestures and extract hidden features robustly and efficiently. Extensive experiments in a real implemented system were conducted to investigate the effectiveness of the proposed secure interaction system. The results show that our system can achieve an average accuracy of 93% to identify legitimate users and 97% to resist the spoofer.
Identifying the Writing Style of Bangla Language Using Natural Language Processing

Bangla is one of the 8th major spoken languages around the world and like other widely spoken languages, it is a very morphologically rich language. It has two styles, one is standard literary style, known as Sadhu Bhasha and the other one is a standard colloquial style which is known as Cholito Bhasha. Mixing both the styles in a written document is considered as a grammatical error in Bangla language known as Guruchondali Dosh. This research aims to develop an algorithm to identify the style of a Bangla paragraph i.e. whether it is in Sadhu Bhasha or Cholito Bhasha from a given Bangla paragraph input. It's a contribution towards finding the Goruchondali Dosh which is a common grammatical mistake in written Bangla language as it was observed that a number of research work for identifying Bangla grammar mistakes is not so notable whereas it is a common trend in other language researchers.
Hierarchical Deep Q-Network from imperfect demonstrations in Minecraft

We present Hierarchical Deep Q-Network (HDQfD) that won first place in the MineRL competition. The HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effective sequence of meta-actions and subgoals from the demonstration data. We present a structured task-dependent replay buffer and an adaptive prioritizing technique that allow the HDQfD agent to gradually erase poor-quality expert data from the buffer. In this paper, we present the details of the HDQfD algorithm and give the experimental results in the Minecraft domain.
Local Differential Privacy for data collection and analysis

Local Differential Privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). In particular, (∊,δ)-LDP has been studied in related statistical tasks like private learning and hypothesis testing, but is mainly achieved by using Gaussian mechanism, leading to the limited data utility. In this paper, we investigate several novel mechanisms that achieve (∊,δ)-LDP with higher data utility in collecting and analyzing users’ data. Specifically, we first design two (∊,δ)-LDP algorithms for mean estimations on multi-dimensional numeric data, which can ensure higher accuracy than the optimal Gaussian mechanism. Then, we investigate different local protocols for frequency estimations on categorical attributes under (∊,δ)-LDP. Based on the proposed mechanisms, we further study on (∊,δ)-LDP-compliant stochastic gradient descent algorithms for machine learning models. Besides, the theoretical analysis of the error bound and the variance of the proposed algorithms are also presented in the paper. We have conducted extensive experiments on both real-world and synthetic datasets and demonstrated the high data utility of our proposed algorithms in the perspectives of simple data statistics tasks and complex machine learning tasks. The experimental results have shown that our proposed algorithms can effectively improve the data utility in different tasks while alleviating the privacy concerns of each individual.
A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network

We present a robot swarm composed of Pi-puck robots that maintain a blockchain network. The blockchain serves as security layer to neutralize Byzantine robots (faulty, malfunctioning, or malicious robots). In the context of this work, we implemented a framework for high-throughput communication using a decentralized mobile ad-hoc network. This work serves as a building block for secure real-world deployments of robot swarms. Our results show that the use of a blockchain is feasible and warranted in embodied robot swarm deployments.
Empathic Chatbot Response for Medical Assistance

Is it helpful for a medical physical health chatbot to show empathy? How can a chatbot show empathy only based on short-term text conversations? We have investigated these questions by building two different medical assistant chatbots with the goal of providing a diagnosis for physical health problem to the user based on a short conversation. One chatbot was advice-only and asked only the necessary questions for the diagnosis without responding to the user's emotions. Another chatbot, capable of showing empathy, responded in a more supportive manner by analyzing the user's emotions and generating appropriate responses with a high empathic accuracy. Using the RoPE scale questionnaire for empathy perception in a human-robot interaction, our empathic chatbot was rated significantly better in showing empathy and was preferred by a majority of the preliminary study participants (N=12).
BanglaSenti: A Dataset of Bangla Words for Sentiment Analysis

Being the fifth most spoken language in the world, use of Bangla or Bengali language has spread its breadth into the world of social media. Huge volume of user-generated Bangla data are produced daily in various social media such as Facebook, Twitter, YouTube; online news portals and various websites. Hence, the importance of understanding the emotion and sentiment of these types of data has gain attraction to the researchers' recently. Bangla Natural Language Processing (BNLP) has emerged as a novel research domain because of these multidisciplinary scopes. In this paper, we have presented “BanglaSenti”, which is a lexicon based corpus or dataset generated solely to identify the sentiment analysis from textual data. This dataset contains 61582 Bangla words with positive, negative and neutral words. These polarities are very significant to understand the overall polarity of the sentences. Not only the corpus, but also a model simulation has been conducted in this paper to understand the usability of this dataset. The dataset is formalized as English SentiWordNet dataset so that researchers' may utilize it with the same format of codes. Though the dataset is developed for sentiment analysis, it could be utilized for emotion detection, opinion/review mining and such applications.
Simulation Environment for Optimal Resource Planning During COVID-19 Crisis

Recent COVID-19 outbreak has affected both human resources and economy around the world dramatically leading towards pandemic and serious global crisis. In this paper, a simulation environment leveraging the synergy of deep learning-based predictions and linear optimization for efficient resource planning is presented. The proposed solution shows satisfiable prediction performance and enables efficient resource exchange by reducing the trading costs.
"Was that successful?" On Integrating Proactive Meta-Dialogue in a DIY-Assistant using Multimodal Cues

Effectively supporting novices during performance of complex tasks, e.g. do-it-yourself (DIY) projects, requires intelligent assistants to be more than mere instructors. In order to be accepted as a competent and trustworthy cooperation partner, they need to be able to actively participate in the project and engage in helpful conversations with users when assistance is necessary. Therefore, a new proactive version of the DIY-assistant Robert is presented in this paper. It extends the previous prototype by including the capability to initiate reflective meta-dialogues using multimodal cues. Two different strategies for reflective dialogue are implemented: A progress-based strategy initiates a reflective dialogue about previous experience with the assistance for encouraging the self-appraisal of the user. An activity-based strategy is applied for providing timely, task-dependent support. Therefore, user activities with a connected drill driver are tracked that trigger dialogues in order to reflect on the current task and to prevent task failure. An experimental study comparing the proactive assistant against the baseline version shows that proactive meta-dialogue is able to build user trust significantly better than a solely reactive system. Besides, the results provide interesting insights for the development of proactive dialogue assistants.
A Novel Median Dendritic Neuron Model for Prediction

Dendritic neuron model (DNM) that utilizes a single dendritic neuron to emulate the information processing in human brains has been successfully applied to classification, approximation, and prediction fields. However, it is still a challenging task to improve the performance for forecasting the time series with outliers. A time series is a collection of data ordered in time. An outlier in time series as an input can affect prediction performance. Thus, in this study, we first propose a novel median dendritic neuron model (MDNM) for prediction to deal with time series with outliers. In MDNM, DNM and median function are combined to achieve better forecasting results. Moreover, states of matter search (SMS) algorithm is used to train MDNM. Twenty real time series and a benchmark time series are adopted to verify the performance of MDNM. The comparison between the MDNM and DNM suggests that MDNM is effective for forecasting time series with outliers.
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection

Hate speech detection on online social networks has become one of the emerging hot topics in recent years. With the broad spread and fast propagation speed across online social networks, hate speech makes significant impacts on society by increasing prejudice and hurting people. Therefore, there are aroused attention and concern from both industry and academia. In this paper, we address the hate speech problem and propose a novel hate speech detection framework called SWE2, which only relies on the content of messages and automatically identifies hate speech. In particular, our framework exploits both word-level semantic information and sub-word knowledge. It is intuitively persuasive and also practically performs well under a situation with/without character-level adversarial attack. Experimental results show that our proposed model achieves 0.975 accuracy and 0.953 macro F1, outperforming 7 state-of-the-art baselines under no adversarial attack. Our model robustly and significantly performed well under extreme adversarial attack (manipulation of 50% messages), achieving 0.967 accuracy and 0.934 macro F1.
Congenial Differential Privacy under Mandated Disclosure

Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is obligated. The enforcement of constraints, when treated as post-processing, adds an extra phase in the production of privatized data. It is well understood in the theory of multi-phase processing that congeniality, a form of procedural compatibility between phases, is a prerequisite for the end users to straightforwardly obtain statistically valid results. Congenial differential privacy is theoretically principled, which facilitates transparency and intelligibility of the mechanism that would otherwise be undermined by ad-hoc post-processing procedures. We advocate for the systematic integration of mandated disclosure into the design of the privacy mechanism via standard probabilistic conditioning on the invariant margins. Conditioning automatically renders congeniality because any extra post-processing phase becomes unnecessary. We provide both initial theoretical guarantees and a Markov chain algorithm for our proposal. We also discuss intriguing theoretical issues that arise in comparing congenital differential privacy and optimization-based post-processing, as well as directions for further research.
A tight scrape: methodological approaches to cybercrime research data collection in adversarial environments

We outline in this article a study of `adversarial scraping' for academic research, which involves the collection of data from websites that implement defences against traditional web scraping tools. Although this is primarily a research methods article, it also constitutes a valuable systematic accounting of the different defensive techniques used by the administrators of illicit online services. Some of these administrators intentionally implement functionality which attempts to prevent web scrapers from gathering data from their site, and some will unintentionally design their sites in ways that make data gathering harder. This is of particular importance for criminological research, where websites such as cryptomarkets and underground forums are publicly available (and hence there is an ethical case for data collection), but the illicit activity involved means that the administrators of these services limit scraping. We classify different anti-crawling techniques taken by websites and outline our developed countermeasures. Based on this, we evaluate which of these methods do and do not succeed at preventing data gathering from a website, as well as those which impact the scraper but do not necessarily prevent the data from being obtained. We find that there are some defences that, if used together, might thwart scraping. There are also a series of defences that are successful at slowing down scrapers, making historical scraping more difficult. On the other hand, we show that many defences are easy to work around and do not impact scraping.
AUASF: An Anonymous Users Authentication Scheme for Fog-IoT Environment

Authentication is a challenging and emerging issue for Fog-IoT security paradigms. The fog nodes toward large-scale end-users offer various interacted IoT services. The authentication process usually involves expressing users' personal information such as username, email, and password to the Authentication Server (AS). However, users are not intended to express their identities or information over the fog or cloud servers. Hence, we have proposed an Anonymous User Authentication Scheme for Fog-IoT (AUASF) to keep the anonymity existence of the IoT users and detect the intruders. To provide anonymity, the user can send encrypted credentials such as username, email, and mobile number through the Cloud Service Provider (CSP) for registration. IoT user receives the response with a default password and a secret Id from the CSP. After that, the IoT user submits the default password for first-time access to Fog Service Provider (FSP). The FSP assigns a One Time Password (OTP) to each user for further access. The developed scheme is equipped with hash functions, symmetric encryptions, and decryptions for security perceptions across fog that serves better than the existing anonymity schemes.
Blockchain: A New Technology to Enhance Data Security and Privacy in Internet of Things

A revolutionary technology well into the world of technology has been in the modern world of technology the Internet of Things. Due to continuing increases as nothing more than a consequence of either the rapid development of computing Internet of things-based applications implementations. Many technologies become increasingly embraced throughout compatible devices such as home automation and also smart cities. These IoT applications become operated on both the Internet, whereby information becomes transported publicly between a network to the next, therefore flowing information requires a great deal of further privacy consideration to guarantee IoT applications become reliable Internet of things technologies have been using various techniques of data protection of security and privacy. However, all such modern security systems have always been sufficient to ensure the Internet of things perceived safety. A Blockchain has become a revolutionary authoritative digital technology that really transforms commercial transactions absolutely. This is also a collaborative service that facilitates it uses a variety of features of cybersecurity. Blockchain technology seems to be the missing piece of the puzzle throughout the IoT to address problems of usability, anonymity, including security. Block chain’s crypto algorithms must enable transparency from customers very confidential. Throughout this research article, we introduce an analysis of IoT security concerns; blockchain gets addressed briefly as well as later addresses the security of IoT applications dependent on blockchain technologies.
Systematic Side-Channel Analysis of Curve25519 with Machine Learning

Abstract Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning techniques to mount side-channel attacks on two implementations of scalar multiplication on the elliptic curve Curve25519. The first implementation follows the baseline implementation with complete formulae as used for EdDSA in WolfSSl, where we exploit power consumption as a side-channel. The second implementation features several countermeasures, and in this case, we analyze electromagnetic emanations to find side-channel leakage. Most techniques considered in this work result in potent attacks, and especially the method of choice appears to be convolutional neural networks (CNNs), which can break the first implementation with only a single measurement in the attack phase. The same convolutional neural network demonstrated excellent performance for attacking AES cipher implementations. Our results show that some common grounds can be established when using deep learning for profiling attacks on very different cryptographic algorithms and their corresponding implementations.
Four Decades of Music Research, Creation, and Education at Padua's Centro di Sonologia Computazionale

Abstract Research in computer music at the University of Padua, Italy, began in the early 1970s and was formalized in 1979 by establishing the Centro di Sonologia Computazionale (CSC). Since its foundation, CSC has established itself as a leading research center in the field of computer music. This article describes the scientific and musical research activities of the center and of the composers and members who worked in association with it in its first four decades. The center's historical background with its musical and scientific precursors is also chronicled, as are important events at CSC. An outline of its scientific research activity is then traced, with aspects of the technical details in its different areas of activities, showing the distinctive research ethos and the changing priorities of the center. Research from the 1970s is also included, as it led to the foundation of the CSC. Moreover, selected musical works, representative of CSC works from historical and scientific points of view, are described. Finally, perspectives for future developments are discussed.