Reviewerly/exHarmony huggingface dataset:
Preliminary findings of the impact of COVID-19 on drugs crypto markets

The COVID-19 pandemic has had unprecedented consequences on the world economy. The impact of the pandemic on illicit drug market remains scant. We expose the potential consequences the COVID-19 pandemic could have on the ability of people who use drugs to source their illicit drugs via cryptomarkets.We analyzed 262 self-reported submissions of illicit drug transactions on the darkweb. The self-reports include the date of the transaction, the types of illicit drugs bought/sold, and whether the shipment of the illicit drugs succeeded, had issues (ex. unusually long delivery, an error in the type of drug shipped, quantity or concentration of the drug), or failed.Between January 1st, 2020 and March 21, 2020, successful deliveries represented 60% to 100% of transactions. Starting on March 21 however, the share of shipments that had issues or failed increased rapidly and represented a majority of all shipments. At the peak of the market disruption, the successful deliveries represented only 21% of all transactions.Illicit drug transactions on the darkweb were disrupted at the same time as lockdowns were put in place in the United States and in the United Kingdom. While no causation link can be established, the correlation suggests that lockdowns could have disrupted drug cryptomarkets activities. We discuss the market disruption in light of the literature.
STOPGAP: A Software Package for Subtomogram Averaging and Refinement

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Reasoning About Generalization via Conditional Mutual Information

We provide an information-theoretic framework for studying the generalization properties of machine learning algorithms. Our framework ties together existing approaches, including uniform convergence bounds and recent methods for adaptive data analysis. \nSpecifically, we use Conditional Mutual Information (CMI) to quantify how well the input (i.e., the training data) can be recognized given the output (i.e., the trained model) of the algorithm. We show that bounds on CMI can be obtained from VC dimension, compression schemes, differential privacy, and other methods. We then show that bounded CMI implies various forms of generalization.
A Gesture Air-Writing Tracking Method that Uses 24 GHz SIMO Radar SoC

Gesture air-writing is an advanced nontouching interaction method that replaces traditional typewriter keyboards, touch screens and other input devices. Due to its low power consumption, noncontact detection and independence from light conditions, millimeter wave radar has become a valuable gesture air-writing solution. In this paper, we proposed a prototype of a gesture air-writing tracking system that is based on the 24-GHz frequency-modulated continuous-wave (FMCW) radar system-on-chip (SoC). The transmitted chirp signal of this radar chip covers up to a 4-GHz bandwidth, which provides sufficient range resolution to track hand gestures. With the development of single input, multiple output (SIMO) antennas, the air-writing symbols can be reconstructed in an observation plane. A system design and signal processing algorithm for gesture air-writing applications is proposed for the prototype system in this paper. To test the performance of the proposed method, experiments with cases of five simple gestures, air-writing numbers and letter tracking are carried out. The experimental results verify the efficiency and accuracy of the proposed method.
Towards Guidelines for Assessing Qualities of Machine Learning Systems

Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the system and its components (such as ISO/IEC 25010). Due to the different nature of ML, we have to adjust quality aspects or add additional ones (such as trustworthiness) and be very precise about which aspect is really relevant for which object of interest (such as completeness of training data), and how to objectively assess adherence to quality requirements. In this article, we present the construction of a quality model (i.e., evaluation objects, quality aspects, and metrics) for an ML system based on an industrial use case. This quality model enables practitioners to specify and assess quality requirements for such kinds of ML systems objectively. In the future, we want to learn how the term quality differs between different types of ML systems and come up with general guidelines for specifying and assessing qualities of ML systems.
Dark Design Patterns: An End-User Perspective

The number of websites and mobile applications available is growing continually, as are the persuasive approaches to influence human behavior and decision making. Although designing for persuasion offers several potential benefits, recent developments expose various deceptive designs, that is, dark patterns, that utilize psychological factors to nudge people toward, from someone else’s perspective, desired directions. This paper contributes to an increased awareness of the phenomenon of dark patterns through our exploring how users perceive and experience these patterns. Hence, we chose a qualitative research approach, with focus groups and interviews, for our exploration. Our analysis shows that participants were moderately aware of these deceptive techniques, several of which were perceived as sneaky and dishonest. Respondents further expressed a resigned attitude toward such techniques and primarily blamed businesses for their occurrence. Users considered their dependency on services employing these practices, thus making it difficult to avoid fully dark patterns.
Trustworthy Deep Learning in 6G-Enabled Mass Autonomy: From Concept to Quality-of-Trust Key Performance Indicators

Mass autonomy promises to revolutionize a wide range of engineering, service, and mobility industries. Coordinating complex communication among hyperdense autonomous agents requires new artificial intelligence (AI)-enabled orchestration of wireless communication services beyond 5G and 6G mobile networks. In particular, safety and mission-critical tasks will legally require both transparent AI decision processes and quantifiable quality-of-trust (QoT) metrics for a range of human end users (consumer, engineer, and legal). We outline the concept of trustworthy autonomy for 6G, including essential elements such as how explainable AI (XAI) can generate the qualitative and quantitative modalities of trust. We also provide XAI test protocols for integration with radio resource management and associated key performance indicators (KPIs) for trust. The research directions proposed will enable researchers to start testing existing AI optimization algorithms and develop new ones with the view that trust and transparency should be built in from the design through the testing phase.
Learning Generalized Transformation Equivariant Representations Via AutoEncoding Transformations

Transformation equivariant representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of translation equivariance underlying the success of convolutional neural networks (CNNs). For this purpose, we present both deterministic AutoEncoding Transformations (AET) and probabilistic AutoEncoding Variational Transformations (AVT) models to learn visual representations from generic groups of transformations. While the AET is trained by directly decoding the transformations from the learned representations, the AVT is trained by maximizing the joint mutual information between the learned representation and transformations. This results in generalized TERs (GTERs) equivariant against transformations in a more general fashion by capturing complex patterns of visual structures beyond the conventional linear equivariance under a transformation group. The presented approach can be extended to (semi-)supervised models by jointly maximizing the mutual information of the learned representation with both labels and transformations. Experiments demonstrate the proposed models outperform the state-of-the-art models in both unsupervised and (semi-)supervised tasks. Moreover, we show that the unsupervised representation can even surpass the fully supervised representation pretrained on ImageNet when they are fine-tuned for the object detection task.
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors

In this paper, a novel benchmark is introduced for evaluating local image descriptors. We demonstrate limitations of the commonly used datasets and evaluation protocols, that lead to ambiguities and contradictory results in the literature. Furthermore, these benchmarks are nearly saturated due to the recent improvements in local descriptors obtained by learning from large annotated datasets. To address these issues, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and verification. This allows for more realistic, thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors is able to boost their performance to the level of deep learning based descriptors once realistic benchmarks are considered. Additionally we specify a protocol for learning and evaluating using cross validation. We show that when training state-of-the-art descriptors on this dataset, the traditional verification task is almost entirely saturated.
Space-Time Video Super-Resolution Using Temporal Profiles

In this paper, we propose a novel space-time video super-resolution method, which aims to recover a high-frame-rate and high-resolution video from its low-frame-rate and low-resolution observation. Existing solutions seldom consider the spatial-temporal correlation and the long-term temporal context simultaneously and thus are limited in the restoration performance. Inspired by the epipolar-plane image used in multi-view computer vision tasks, we first propose the concept of temporal-profile super-resolution to directly exploit the spatial-temporal correlation in the long-term temporal context. Then, we specifically design a feature shuffling module for spatial retargeting and spatial-temporal information fusion, which is followed by a refining module for artifacts alleviation and detail enhancement. Different from existing solutions, our method does not require any explicit or implicit motion estimation, making it lightweight and flexible to handle any number of input frames. Comprehensive experimental results demonstrate that our method not only generates superior space-time video super-resolution results but also retains competitive implementation efficiency.
Simulating the Effects of Social Presence on Trust, Privacy Concerns &amp; Usage Intentions in Automated Bots for Finance

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.
Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 Pandemic: A Blockchain Perspective

Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including 1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas? To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named Bychain. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.
Conversational Semantic Parsing for Dialog State Tracking

Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction

Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image. In this work, we approach this problem by addressing two issues that have been under-researched in the open literature: sampling strategy (data term) and graph construction (prior term). First, instead of the popular random sampling strategy, we suggest that Poisson disk sampling is a much more effective solution to create sparse depth map from a dense version. We experimentally compare a class of quasi-random sampling strategies and demonstrate that an optimized sampling strategy can significantly improve the performance of depth completion for the same number of sparse samples. Second, instead of the traditional square kernel, we suggest that dynamic construction of local neighborhood is a better choice for interpolating the missing values. More specifically, we proposed an end-to-end network with a graph convolution module. Since the neighborhood relationship of 3D points is more effectively exploited by our novel graph convolution module, our approach has achieved not only state-of-the-art results for depth completion of indoor scenes but also better generalization ability than other competing methods.
A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

Emotion and sentiment classification in dialogues is a challenging task that has gained popularity in recent times.Humans tend to have multiple emotions with varying intensities while expressing their thoughts and feelings.Emotions in an utterance of dialogue can either be independent or dependent on the previous utterances, making the task complex and interesting.Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting.On the other hand, sentiment analysis in dialogue or conversation helps in understanding the perspective of the user with respect to the ongoing conversation.Besides text, additional information in the form of audio and video assists in identifying the correct emotions with the appropriate intensity and sentiments in an utterance of a dialogue.Lately, quite a few datasets have been made available for emotion and sentiment classification in dialogues.Still, these datasets are imbalanced in representing different emotions and consist of only a single emotion.Hence, we present at first a large-scale balanced Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD) collected from different TV series that has textual, audio, and visual features, and then establish a baseline setup for further research.
Collaborative Blockchain for Space-Air-Ground Integrated Networks

A space-air-ground integrated network (SAGIN) provides comprehensive interconnection and quality of service. Such an integrated network poses security concerns by bringing together a variety of devices from different untrusted authorities. Blockchain, as a decentralized ledger, although showing great potential to solve the security issues in SAGIN, may bring up intolerable latency by sequentially validating requests and take up numerous storage resources by keeping a complete replica of the entire chain in every node in a large-scale network. It becomes a feasible solution to use multiple parallel blockchains to efficiently manage different segments of SAGIN and blockchain collaboration technology to enable necessary inter-segment cooperation. Toward this end, we propose a collaborative blockchain architecture for SAGIN to achieve secure and efficient management of diverse resources. Each segment of SAGIN (i.e., space, air, and ground network) maintains a specially designed block-chain while blockchain collaboration is enabled. We then design an incentive-based collaborative blockchain for space-air-ground mobility management to encourage the participation of different network segments and ensure the security of collaboration using relay chain technology. Numerical results show the effectiveness of the proposed framework.