Reviewerly/exHarmony huggingface dataset:
A Reinforcement Learning Approach to Redirected Walking with Passive Haptic Feedback

Various redirected walking (RDW) techniques have been proposed, which unwittingly manipulate the mapping from the user’s physical locomotion to motions of the virtual camera. Thereby, RDW techniques guide users on physical paths with the goal to keep them inside a limited tracking area, whereas users perceive the illusion of being able to walk infinitely in the virtual environment. However, the inconsistency between the user’s virtual and physical location hinders passive haptic feedback when the user interacts with virtual objects, which are represented by physical props in the real environment.In this paper, we present a novel reinforcement learning approach towards RDW with passive haptics. With a novel dense reward function, our method learns to jointly consider physical boundary avoidance and consistency of user-object positioning between virtual and physical spaces. The weights of reward and penalty terms in the reward function are dynamically adjusted to adaptively balance term impacts during the walking process. Experimental results demonstrate the advantages of our technique in comparison to previous approaches. Finally, the code of our technique is provided as an open-source solution.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.While there are many legitimate applications of such models, there is also a rising need to distinguish machine-generated texts from human-written ones (e.g., fake news detection).However, to our best knowledge, there is currently no benchmark environment with datasets and tasks to systematically study the so-called "Turing Test" problem for neural text generation methods.In this work, we present the TURINGBENCH benchmark environment, which is comprised of (1) a dataset with 200K human-or machine-generated samples across 20 labels {Human, GPT-1, GPT-2_small, GPT-2_medium, GPT-2_large, GPT-2_xl, GPT-2_PyTorch, GPT-3, GROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large, FAIR_wmt19, FAIR_wmt20, TRANS-FORMER_XL, PPLM_distil, PPLM_gpt2}, (2) two benchmark tasks-i.e., Turing Test (TT) and Authorship Attribution (AA), and (3) a website with leaderboards.Our preliminary experimental results using TURINGBENCH show that FAIR_wmt20 and GPT-3 are the current winners, among all language models tested, in generating the most human-like indistinguishable texts with the lowest F1 score by five state-of-the-art TT detection models.
Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems

Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the proposed algorithm are described and then the mathematical modeling of the SLOA is presented in order to implement it on different optimization problems. A standard set of objective functions, including twenty-three functions, is used to evaluate the ability of the proposed algorithm to optimize and provide appropriate solutions for optimization problems. Also, the optimization results obtained from the proposed SLOA are compared with eight other well-known optimization algorithms. The optimization results show that the proposed SLOA has a high ability to solve various optimization problems. Also, the analysis and comparison of the optimization results obtained from the SLOA with the other eight algorithms shows that the SLOA is able to provide more appropriate quasi-optimal solutions and closer to the global optimal, and with better performance, it is much more competitive than similar algorithms.
Software Supply Chain Attacks, a Threat to Global Cybersecurity: SolarWinds’ Case Study

Exploitation of a vulnerability that compromised the source code of the Solar Winds’ Orion system, a software that is used widely by different government and industry actors in the world for the administration and monitoring of networks; brought to the fore a type of stealth attack that has been gaining momentum: supply chain attacks. The main problem in the violation of the software supply chain is that, from 85% to 97% of the code currently used in the software development industry comes from the reuse of open source code frameworks, repositories of third-party software and APIs, creating potential vulnerabilities in the development cycle of a software product. This research analyzes the SolarWinds case study from an exploratory review of academic literature, government information, but also from the articles and reports that are published by different cybersecurity consulting firms and software providers. Then, a set of good practices is proposed such as: Zero trust, Multi-Factor authentication mechanisms (MFA), strategies such as SBOM and the recommendations of the CISA guide to defend against this type of attack. Finally, the research discusses about how to improve response times and prevention against this type of attacks, also future research related to the subject is suggested, such as the application of Machine Learning and Blockchain technologies. Additionally for risk reduction, in addition to the management and articulation of IT teams that participate in all the actors that are part of the software life cycle under a DevSecOps approach.
Multimodal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing

In this article, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed cross-modal info-max hashing (CMIMH). First, to learn informative representations that can preserve both intramodal and intermodal similarities, we leverage the recent advances in estimating variational lower bound of MI to maximizing the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modeled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intramodal and intermodal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.
A Comparative Study of Post-Quantum Cryptosystems for Internet-of-Things Applications

The existence of quantum computers and Shor’s algorithm poses an imminent threat to classical public-key cryptosystems. These cryptosystems are currently used for the exchange of keys between servers and clients over the Internet. The Internet of Things (IoT) is the next step in the evolution of the Internet, and it involves the connection of millions of low-powered and resource-constrained devices to the network. Because quantum computers are becoming more capable, the creation of a new cryptographic standard that cannot be compromised by them is indispensable. There are several current proposals of quantum-resistant or post-quantum algorithms that are being considered for future standards. Given that the IoT is increasing in popularity, and given its resource-constrained nature, it is worth adapting those new standards to IoT devices. In this work, we study some post-quantum cryptosystems that could be suitable for IoT devices, adapting them to work with current cryptography and communication software, and conduct a performance measurement on them, obtaining guidelines for selecting the best for different applications in resource-constrained hardware. Our results show that many of these algorithms can be efficiently executed in current IoT hardware, providing adequate protection from the attacks that quantum computers will eventually be capable of.
Cryptographic approach to quantum metrology

We consider a cryptographically motivated framework for quantum metrology in the presence of a malicious adversary. We begin by devising an estimation strategy for a (potentially) altered resource (due to a malicious adversary) and quantify the amount of bias and the loss in precision as a function of the introduced uncertainty in the resource. By incorporating an appropriate cryptographic protocol, the uncertainty in the resource can be bounded with respect to the soundness of the cryptographic protocol. Thus the effectiveness of the quantum metrology problem can be directly related to the effectiveness of the cryptography protocol. As an example, we consider a quantum metrology problem in which resources are exchanged through an unsecured quantum channel. We then construct two protocols for this task which offer a trade-off between difficulty of implementation and efficiency.
Gamification as a Promoting Tool of Motivation for Creating Sustainable Higher Education Institutions

Higher Educational Institutions (HEIs) are responsible for creating healthy and sustainable environments for students and teachers through diverse educational paradigms such as gamification. In this sense, the Healthy People 2030 and the Sustainable Development Goals indicated the imperative to provide inclusive and equitable quality education to promote a healthy environment and life. The principal objective was to analyse the impact of gamification on health development in HEIs, highlighting their positive and negative effects. To achieve such an objective, a bibliometric analysis was carried out. The 257 documents showed no significant increasing trend in the last decade (p > 0.05) related to the pandemic. Most of the publications were conferences (45%), and the few published articles were the documents with more citations (p < 0.001). According to their index in Journal Citation Reports, there were significant differences between the citations of articles published in journals (p < 0.001). The analysis of journal co-citations showed that the leading journals (such as Computers in Human Behavior) had a significant part in the clusters formed (p < 0.001), conditioning also the keywords, especially the term “motivation”. These findings were discussed, concluding that the experimental studies focused on the teachers’ adverse effects are yet to come.
Improved Constructions of Anonymous Credentials from Structure-Preserving Signatures on Equivalence Classes

Anonymous attribute-based credentials (ABCs) are a powerful tool allowing users to authenticate while maintaining privacy. When instantiated from structure-preserving signatures on equivalence classes (SPS-EQ) we obtain a controlled form of malleability, and hence increased functionality and privacy for the user. Existing constructions consider equivalence classes on the message space, allowing the joint randomization of credentials and the corresponding signatures on them. In this work, we additionally consider equivalence classes on the signing-key space. In this regard, we obtain a signer-hiding notion, where the issuing organization is not revealed when a user shows a credential. To achieve this, we instantiate the ABC framework of Fuchsbauer, Hanser, and Slamanig (FHS, Journal of Cryptology ’19) with a recent SPS-EQ scheme (ASIACRYPT ’19) modified to support a fully adaptive NIZK from the framework of Couteau and Hartmann (CRYPTO ’20). We also show how to obtain Mercurial Signatures (CT-RSA, 2019), extending the application of our construction to anonymous delegatable credentials. To further increase functionality and efficiency, we augment the set-commitment scheme of FHS19 to support openings on attribute sets disjoint from those possessed by the user, while integrating a proof of exponentiation to allow for a more efficient verifier. Instantiating in the CRS model, we obtain an efficient credential system, anonymous under malicious organization keys, with increased expressiveness and privacy, proven secure in the standard model.
Children’s trust in and learning from voice assistants.

As children increasingly interact with digital voice assistants, it is important to know whether they treat these devices as reliable information sources. Two studies investigated children's trust in and recall of statements made by a novel voice assistant and a human informant. In Study 1, children ages 4-5 (Mage = 5.05; 20 boys, 20 girls) and 7-8 (Mage = 7.98; 18 boys, 22 girls) from predominately White, upper middle-class families heard each informant respond to questions from multiple categories. With increasing age, children showed greater trust in the voice assistant for factual information and greater trust in the human for personal information about the experimenter identified as her friend. Endorsement of each informant's statements also predicted later recall. In Study 2, children ages 4-5 (Mage = 5.00; 20 boys, 20 girls) and 7-8 (Mage = 8.03; 19 boys, 21 girls) from predominately White, upper middle-class families chose whether to seek out information from a voice assistant or human informant. With increasing age, children showed an increasing preference to seek factual information from the voice assistant and an increasing preference to seek personal information from the human. Additionally, children's preferences were not related to attributions of epistemic capacities to each informant nor the presence of a voice assistant in children's homes. These results suggest that children's trust in voice assistants varies with age and depends on the type of information involved. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A social hybrid recommendation system using <scp>LSTM</scp> and <scp>CNN</scp>

Abstract With the ever‐increasing use of Internet and social networks that generate a vast amount of information, there is a serious need for recommendation systems. In this article, we propose a recommender system utilizing deep neural networks that simultaneously considers both the users' ratings to the movies and the visual features of the movie poster and trailer. For this purpose, a hybrid movie recommender system, RSLCNet, has been developed using CNN and LSTM architectures. The proposed system considers the dynamics of users' interests in the collaborative filtering engine using the LSTM network that receives user‐rating sequences. In the content‐based filtering engine, utilizing CNN, the visual features of movie posters and trailers are extracted, and along with the actors and the directors, similar movies are recommended to the user. Moreover, each user's social influence is calculated employing the social information available on the user's Twitter account and used in the average movie rating to improve the effectiveness of the content‐based filtering part. The required datasets have been collected from MovieTweetings, Mise‐en‐scène, and OMDB. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved in terms of MAE and RMSE compared to the best available methods.
Mining association rules from COVID-19 related twitter data to discover word patterns, topics and inferences

This work utilizes data from Twitter to mine association rules and extract knowledge about public attitudes regarding worldwide crises. It exploits the COVID-19 pandemic as a use case, and analyzes tweets gathered between February and August 2020. The proposed methodology comprises topic extraction and visualization techniques, such as WordClouds, to form clusters or themes of opinions. It then uses Association Rule Mining (ARM) to discover frequent wordsets and generate rules that infer to user attitudes. The goal is to utilize ARM as a postprocessing technique to enhance the output of any topic extraction method. Therefore, only strong wordsets are stored after discarding trivia ones. We also employ frequent wordset identification to reduce the number of extracted topics. Our findings showcase that 50 initially retrieved topics are narrowed down to just 4, when combining Latent Dirichlet Allocation with ARM. Our methodology facilitates producing more accurate and generalizable results, whilst exposing implications regarding social media user attitudes.
Visibility and Distortion Measurement for No-Reference Dehazed Image Quality Assessment via Complex Contourlet Transform

Recently, most dehazed image quality assessment (DQA) methods have focused on estimating remaining haze and omitting distortion impact from the side effect of dehazing algorithms, which leads to their limited performance. Addressing this problem, we propose a method for learning both visibility and distortion-aware features no-reference (NR) dehazed image quality assessment (VDA-DQA). Visibility-aware features are exploited to characterize clarity optimization after dehazing, including the brightness-, contrast-, and sharpness-aware features extracted by the complex contourlet transform (CCT). Then, distortion-aware features are employed to measure the distortion artifacts of images, including the normalized histogram of the local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT subbands corresponding to the chroma and saturation map. Finally, all the above features are mapped into quality scores by support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of the proposed VDA-DQA method in terms of consistency with subjective visual perception and outperform state-of-the-art methods.
Quality Metric Guided Portrait Line Drawing Generation From Unpaired Training Data

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods.
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

Abstract In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, that is, the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches based on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best model achieves F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of our dataset. Our dataset and code are available at https://mcgill-nlp.github.io/topiocqa.
Non-human Personas. Including Nature in the Participatory Design of Smart Cities

For the past decades humans have been placed at the centre of designing information and communication technologies (ICT), leading to the rise in prominence of human-centred design. The field of smart cities has equally adopted notions of citizen participation as a way to ensure that technological solutions improve people’s livelihoods. However, these kinds of processes treat the urban environment as separate from nature, promoting human comfort and convenience over planetary health and wellbeing. Motivated by these growing concerns that highlight the urgency to reconsider how we define and practice participation in smart cities and in human-centred ICT solutions more broadly, this article assesses how the personas method can be adapted to include more-than-human perspectives in the design process. Based on a case study, which involved designing smart urban furniture for human and non-human use, we introduce a framework for developing and employing non-human personas. As a key element of the framework, we describe a middle-out approach for forming a coalition that can speak on behalf of the non-human species that are impacted by design decisions. We demonstrate how the framework can be used through its retrospective application on two research-led smart city projects. The article concludes with a discussion of key principles for creating and using non-human personas in design projects.
Friend or a Foe: Understanding Generation Z Employees' Intentions to Work with Service Robots in the Hotel Industry

Through multiple studies, this research examines Gen Zers' perceptions and acceptance of working with service robots in the hotel industry. Study 1 used interviews with 30 respondents and extensive literature to generate a list of items to measure determinants of Gen Zers' acceptance of working with service robots. Study 2 purified and refined the item pool, generated in Study 1 using the data collected from 200 Gen Zers via an online survey to conduct an exploratory factor analysis. Study 3 used a second dataset (N = 552) to conduct confirmatory factor analysis on the 19-item scale by employing partial least squares-based structural equation modelling. Results indicate that hedonic motivations, utilitarian motivations, and social skills towards service robots have a positive whereas insecurity, and technical and interactional barriers have a negative impact on Gen Zers' intentions to work with service robots. Theoretical implications for technology adoption in the hotel industry and relevant practical implications are also discussed.