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Chills or goosebumps, also called frisson, is a phenomenon that is often associated with an aesthetic experience e.g., music or some other ecstatic experience. The temporal and spatial cause of frisson in the brain has been one of the biggest mysteries of human nature. Accumulating evidence suggests that aesthetic, namely subjective, affective, and evaluative processes are at play while listening to music, hence, it is an important subjective stimulus for systematic investigation. Advances in neuroimaging and cognitive neuroscience, have given impetus to neuro-aesthetics, a novel approach to music providing a phenomenological brain-based framework for the aesthetic experience of music with the potential to open the scope for future research. In this paper, we present an affordable, wearable, easy-to-carry device to measure phenomenological goosebumps intensity on our skin with respect to real-time data using IoT devices (Raspberry pi 3, model B). To test the device subjects were asked to provide a list of songs that elicit goosebumps. Wireless earphones were provided, allowing participants to walk around and dance while listening to their music. (Some subjects moved during sessions). Results indicate that goosebumps were reliably detected by the device after visual inspection of the videos/music. The effective measurement when interfaced with neurophysiological devices such as electroencephalography (EEG) can help interpret biomarkers of ecstatic emotions. The second part of the study focuses on identifying primary brain regions involved in goosebump experience during musical stimulation.

Various industries such as finance, meteorology, and energy generate vast amounts of heterogeneous data every day. There is a natural demand for humans to manage, process, and display data efficiently. However, it necessitates labor-intensive efforts and a high level of expertise for these data-related tasks. Considering that large language models (LLMs) have showcased promising capabilities in semantic understanding and reasoning, we advocate that the deployment of LLMs could autonomously manage and process massive amounts of data while displaying and interacting in a human-friendly manner. Based on this belief, we propose Data-Copilot, an LLM-based system that connects numerous data sources on one end and caters to diverse human demands on the other end. Acting like an experienced expert, Data-Copilot autonomously transforms raw data into visualization results that best match the user's intent. Specifically, Data-Copilot autonomously designs versatile interfaces (tools) for data management, processing, prediction, and visualization. In real-time response, it automatically deploys a concise workflow by invoking corresponding interfaces step by step for the user's request. The interface design and deployment processes are fully controlled by Data-Copilot itself, without human assistance. Besides, we create a Data-Copilot demo that links abundant data from different domains (stock, fund, company, economics, and live news) and accurately respond to diverse requests, serving as a reliable AI assistant.

Crypto wallets are a key touch-point for cryptocurrency use. People use crypto wallets to make transactions, manage crypto assets, and interact with decentralized apps (dApps). However, as is often the case with emergent technologies, little attention has been paid to understanding and improving accessibility barriers in crypto wallet software. We present a series of user studies that explored how both blind and sighted individuals use MetaMask, one of the most popular non-custodial crypto wallets. We uncovered inter-related accessibility, learnability, and security issues with MetaMask. We also report on an iterative redesign of MetaMask to make it more accessible for blind users. This process involved multiple evaluations with 44 novice crypto wallet users, including 20 sighted users, 23 blind users, and one user with low vision. Our study results show notable improvements for accessibility after two rounds of design iterations. Based on the results, we discuss design implications for creating more accessible and secure crypto wallets for blind users.

The digitalization of the reproductive body has engaged myriads of cutting-edge technologies in supporting people to know and tackle their intimate health. Generally understood as female technologies (aka female-oriented technologies or 'FemTech'), these products and systems collect a wide range of intimate data which are processed, transferred, saved and shared with other parties. In this paper, we explore how the "data-hungry" nature of this industry and the lack of proper safeguarding mechanisms, standards, and regulations for vulnerable data can lead to complex harms or faint agentic potential. We adopted mixed methods in exploring users' understanding of the security and privacy (SP) of these technologies. Our findings show that while users can speculate the range of harms and risks associated with these technologies, they are not equipped and provided with the technological skills to protect themselves against such risks. We discuss a number of approaches, including participatory threat modelling and SP by design, in the context of this work and conclude that such approaches are critical to protect users in these sensitive systems.

The COVID-19 pandemic has significantly transformed the healthcare sector, with telehealth services being among the most prominent changes. The adoption of telehealth services, however, has raised new challenges, particularly in the areas of security and privacy. To better comprehend the telehealth needs and concerns of medical professionals, particularly those in private practice, we conducted a study comprised of 20 semi-structured interviews with telehealth practitioners in audiology and speech therapy. Our findings indicate that private telehealth practitioners encounter difficult choices when it comes to balancing security, privacy, usability, and accessibility, particularly while caring for vulnerable populations. Additionally, the study revealed that practitioners face challenges in ensuring HIPAA compliance due to inadequate resources and a lack of technological comprehension. Policymakers and healthcare providers should take proactive measures to address these challenges, including offering resources and training to ensure HIPAA compliance and enhancing technology infrastructure to support secure and accessible telehealth.

Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.

With the explosive advancement of AI technologies in recent years, the scene of the disinformation research is also expected to rapidly change. In this viewpoint article, in particular, we first present the notion of "disinformation 2.0" in the age of AI where disinformation would become more targeted and personalized, its content becomes very difficult to distinguish from real news, and its creation and dissemination become more accelerated by AI. Then, we discuss how disinformation 2.0 and cybersecurity fit and a possible layered countermeasure to address the threat in disinformation 2.0 in a holistic manner.

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.

The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias. However, this comes at the price of an underestimation of action values, in addition to increased memory requirements and a slower convergence. In this paper, we introduce a new way to address the maximization bias in the form of a "self-correcting algorithm" for approximating the maximum of an expected value. Our method balances the overestimation of the single estimator used in conventional Q-learning and the underestimation of the double estimator used in Double Q-learning. Applying this strategy to Q-learning results in Self-correcting Q-learning. We show theoretically that this new algorithm enjoys the same convergence guarantees as Q-learning while being more accurate. Empirically, it performs better than Double Q-learning in domains with rewards of high variance, and it even attains faster convergence than Q-learning in domains with rewards of zero or low variance. These advantages transfer to a Deep Q Network implementation that we call Self-correcting DQN and which outperforms regular DQN and Double DQN on several tasks in the Atari 2600 domain.