Reviewerly/exHarmony huggingface dataset:
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions

Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.
Exploring the Use of Invalid Action Masking in Reinforcement Learning: A Comparative Study of On-Policy and Off-Policy Algorithms in Real-Time Strategy Games

Invalid action masking is a practical technique in deep reinforcement learning to prevent agents from taking invalid actions. Existing approaches rely on action masking during policy training and utilization. This study focuses on developing reinforcement learning algorithms that incorporate action masking during training but can be used without action masking during policy execution. The study begins by conducting a theoretical analysis to elucidate the distinction between naive policy gradient and invalid action policy gradient. Based on this analysis, we demonstrate that the naive policy gradient is a valid gradient and is equivalent to the proposed composite objective algorithm, which optimizes both the masked policy and the original policy in parallel. Moreover, we propose an off-policy algorithm for invalid action masking that employs the masked policy for sampling while optimizing the original policy. To compare the effectiveness of these algorithms, experiments are conducted using a simplified real-time strategy (RTS) game simulator called Gym-μRTS. Based on empirical findings, we recommend utilizing the off-policy algorithm for addressing most tasks while employing the composite objective algorithm for handling more complex tasks.
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification

Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extracting bottleneck (BN) features, the key considerations include training targets, activation functions, and loss functions. In this paper, we systematically study the impact of these choices on the performance of TD-SV. For training targets, we consider speaker identity, time-contrastive learning (TCL), and auto-regressive prediction coding, with the first being supervised and the last two being self-supervised. Furthermore, we study a range of loss functions when speaker identity is used as the training target. With regard to activation functions, we study the widely used sigmoid function, rectified linear unit (ReLU), and Gaussian error linear unit (GELU). We experimentally show that GELU is able to reduce the error rates of TD-SV significantly compared to sigmoid, irrespective of the training target. Among the three training targets, TCL performs the best. Among the various loss functions, cross-entropy, joint-softmax, and focal loss functions outperform the others. Finally, the score-level fusion of different systems is also able to reduce the error rates. To evaluate the representation learning methods, experiments are conducted on the RedDots 2016 challenge database consisting of short utterances for TD-SV systems based on classic Gaussian mixture model-universal background model (GMM-UBM) and i-vector methods.
Evaluation and improvement of HMD-based and RGB-based hand tracking solutions in VR

Hand tracking has become a state-of-the-art technology in the modern generation of consumer VR devices. However, off-the-shelf solutions do not support hand detection for more than two hands at the same time at distances beyond arm’s length. The possibility to track multiple hands at larger distances would be beneficial for colocated multi-user VR scenarios, allowing user-worn devices to track the hands of other users and therefore reducing motion artifacts caused by hand tracking loss. With the global focus of enabling natural hand interactions in colocated multi-user VR, we propose an RGB image input-based hand tracking method, built upon the MediaPipe framework, that can track multiple hands at once at distances of up to 3 m. We compared our method’s accuracy to that of Oculus Quest and Leap Motion, at different distances from the tracking device and in static and dynamic settings. The results of our evaluation show that our method provides only slightly less accurate results than Oculus Quest or Leap motion in the near range (with median errors below 1.75 cm at distances below 75 cm); at larger distances, its accuracy remains stable (with a median error of 4.7 cm at the distance of 2.75 m) while Leap Motion and Oculus Quest either loose tracking or produce very inaccurate results. Taking into account the broad choice of suitable hardware (any RGB camera) and the ease of setup, our method can be directly applied to colocated multi-user VR scenarios.
Solving blockchain trilemma using off‐chain storage protocol

Abstract Trilemma in blockchain refers to the infamous problem of simultaneously not delivering the three critical aspects of a ledger: security, scalability, and decentralisation. While security and scalability hinder decentralisation, security is jeopardised if the scalability is escalated. This deficiency of not maintaining a balance among these three crucial factors restricts the broader adoption of blockchain technology and cryptocurrencies in the industries. This paper proposes a solution to the blockchain trilemma by implementing a public ledger using The InterPlanetary File System (IPFS) and a newly introduced strategy called the double‐chain technique. The scalability and decentralisation features are guaranteed by the distributed file system of IPFS and the public nature of the blockchain suggested in this study. Although any consensus can be plugged into our system, the proof‐of‐work consensus is utilised to ensure that the security is not compromised while stabilising scalability and decentralisation.
Vote at Any Polling Place: A Case Study of St. Louis County, Missouri

In 2020, St. Louis County, Missouri, instituted a new procedure allowing Election Day voters to cast their ballot at any of the county's polling places (similar to vote centers in other states). This change was accelerated by the COVID-19 pandemic, during which private venues withdrew from polling place commitments. The system adopted by St. Louis County is new among states that do not allow early or mail voting. We rely on interviews with key election staff and data on election administration and voting behavior to document the implementation of the new voting procedures, as well as administrative and voting outcomes. We use a systems framework to examine this case of local election administration. The new voting process required new voting technology, but also changes in polling place siting and staffing, poll book procedures, as well as other technological and personnel adjustments. All of these changes had to be managed seamlessly for the new system to succeed. We observe several positive outcomes in St. Louis County, and few problems. For example, St. Louis County experienced a massive decline in rejected absentee and provisional ballots in the 2020 election, developments not matched by any other Missouri jurisdiction. This case illustrates the interdependent nature of many features of election administration and one of the many successes of election administration in 2020.
New kid on the block? a conceptual systematic review of digital agency

Abstract The digitalisation of education has been emphasised over the last two decades, affecting and developing both the theories and practices of teaching and learning. Considering these developments, the need for addressing teachers’ and students’ digital competence has gained ground. In the last few years, the concept of digital agency has been noted in empirical and theoretical research – broadly focusing on the integration of information and communication technologies (ICT) in education. Nonetheless, how digital agency is understood and how it relates to the more established concept of digital competence remains unclear. To address this gap, we conducted a conceptual systematic review and examined the current state of the knowledge on digital agency. Specifically, we review the conceptualisations of digital agency, its underlying theoretical frameworks, and how it relates to digital competence and similar concepts. Database searches resulted in 32 publications, with the majority published in education and design journals and stemming from Scandinavian countries. Our findings show that out of 32 studies, only one aimed at defining digital agency explicitly. Nevertheless, for the last three years, digital agency has been more frequently used in the body of literature, emphasising the design and transformation of teaching and learning with technology towards a ‘new normal’ considering the post-pandemic era and lessons learned. Reviewing the extant body of knowledge on digital agency, we review the strengths and weaknesses of the concept and compare it to the more established notion of digital competence. Finally, we discuss implications for policy, research, and practice in education.
Defending against social engineering attacks: A security pattern‐based analysis framework

Abstract Social engineering attacks are a growing threat to modern complex systems. Increasingly, attackers are exploiting people's "vulnerabilities" to carry out social engineering attacks for malicious purposes. Although such a severe threat has attracted the attention of academia and industry, it is challenging to propose a comprehensive and practical set of countermeasures to protect systems from social engineering attacks due to its interdisciplinary nature. Moreover, the existing social engineering defence research is highly dependent on manual analysis, which is time‐consuming and labour‐intensive and cannot solve practical problems efficiently and pragmatically. This paper proposes a systematic approach to generate countermeasures based on a typical social engineering attack process. Specifically, we systematically ‘attack’ each step of social engineering attacks to prevent, mitigate, or eliminate them, resulting in 62 countermeasures. We have designed a set of social engineering security patterns that encapsulate relevant security knowledge to provide practical assistance in the defence analysis of social engineering attacks. Finally, we present an automatic analysis framework for applying social engineering security patterns. We applied the case study method and performed semi‐structured interviews with nine participants to evaluate our proposal, showing that our approach effectively defended against social engineering attacks.
Effect of AI Chatbot’s Interactivity on Consumers’ Negative Word-of-Mouth Intention: Mediating Role of Perceived Empathy and Anger

Although much research has examined chatbot interactivity as one of the most human-like qualities, replicating a conversation norm in human-to-human dialogue, far less has examined the "native state" of two-way communication between humans and chatbots. This study fills the gap by substantiating a synchronous two-way conversation between users and chatbots and testing how chatbot interactivity shapes the users’ perceived empathy. Guided by the computers-are-social-actors (CASA) paradigm, like skilled customer service representatives interacting with customers during product failures, this study designs a between-subject experiment (N = 300) to embody the warm and competent types of human conversation and suggests two versions of chatbot interactivities: social interactivity (high/low) and message interactivity (high/low). We further investigate whether a chatbot’s warm or competent persona manifested in its interaction with consumers elicits empathic responses from consumers, such as calming down their anger in line with social response theory. As a result, both social and message interactivity in chatbots effectively elicit users’ perception of empathy, alleviating customer anger and reducing adverse word-of-mouth (WOM) intentions. The findings provide empirical evidence that supports the explanatory framework of social response theory. This research demonstrates how technological features, particularly interactivity, contribute to creating experiential value for customers. Specifically, it elucidates how customers perceive empathy displayed by chatbots, which subsequently influences their level of anger and inclination to engage in negative word-of-mouth communication. Notably, these results offer novel insights into the roles of chatbot interactivity and empathy as psychological mechanisms that enable consumers to express their dissatisfaction by interacting with chatbot service agents, particularly in situations involving product failures. The implications of this study extend to the practical realm, providing valuable guidance on the design and implementation of empathetic chatbots in frontline consumer service contexts.
Public sector transparency and sustainable development: A focus on <scp>Sub‐Saharan</scp> Africa

This study empirically addresses the relevance of public transparency in promoting sustainable development in Sub‐Saharan Africa. Using the generalized method of moments techniques in order to account for concerns of endogeneity and simultaneity, the study utilizes a panel of forty‐eight (48) countries in Sub‐Saharan African countries for the period 2000‐2019. We then establish the subsequent findings. Public transparency maintains overall positive net effects on both the human development index (i.e., social sustainability); adjusted net national income growth (i.e., economic sustainability), and renewal energy consumption (i.e., environmental sustainability). Overall, we establish that public transparency enhances sustainable development. This study recommends that policymakers should ensure that public transparency processes are enhanced in order to achieve the post‐2015 agenda.
Research Thesis for Undergraduate Engineering Programs in the Digitalization Era: Learning Strategies and Responsible Research Conduct Road to a University Education 4.0 Paradigm

Many university students have doubts about how or where to start writing their undergraduate thesis work; it is common not to be clear how to identify a research problem or even towards which topic their research is oriented, and there are doubts about how to process and systematize so much information available in the era of digitalization. This article presents learning strategies to formulate a research thesis for engineering undergraduate programs, with an emphasis on the use of information and communication technologies (ICTs) toward a University Education 4.0 paradigm. The main themes and issues discussed in this article, carried out through learning strategies for students based on the scientific method, are the following: (i) Recommendations for choosing a research topic, (ii) Guidelines for problem identification and question research, (iii) Suggestions for choosing a thesis advisor, (iv) Instructions for finding suitable sources of information, (v) Structure of the table of contents for writing the thesis manuscript, and (vi) Indications for preparing an oral defense of thesis research. This article highlights recommendations and precautions directed towards professors and students with the use of the chatbot-type artificial intelligence (AI) tool called ChatGPT for the formulation of the thesis under a responsible conduct approach in research. This article concludes that the application of tutoring/guidance strategies between professors and students requires an adequate ethical use of information and communication technologies (ICTs) during the development of a research thesis to generate a comprehensive educational environment that encourages research and develops a sustainable learning process in the context of the University Education 4.0 paradigm.
Using Networked Ontologies to Support the Development of Software Systems with Adaptive User Interface

The new ways of interacting with computers, smartphones, and other devices have brought new challenges, such as the need to ensure that different types of users can easily use the same system. Adaptive User Interface (AUI) systems have been recognized as a solution to this matter. They change the user interface to better meet the needs of different users. However, developing such systems is not trivial. It is necessary to capture the users’ characteristics and preferences and constantly adapt the system accordingly. In this paper, we discuss the use of ontologies to support the development of AUI systems. We argue that by providing structured knowledge about such systems, ontologies help understand how they work and offer a basis to structure them, identify the necessary adaptations and implement mechanisms to make them happen in run-time. We have explored the use of ontologies from an ontology network (i.e., networked ontologies) to develop a social network about academic subjects that automatically adapts its interface according to the low vision and colorblind user’s needs and usage characteristics. This exploratory study showed that using networked ontologies to develop an AUI system is useful and feasible. The ontology was useful at the conceptual level by serving as a basis to define the system’s structural model and at the operational level by providing the semantics used in a reasoning engine to adapt the UI at run-time. The first version of an ontology-based process to guide the development of AUI systems emerged from this experience and it is also presented in this paper.
WAD-X: Improving Zero-shot Cross-lingual Transfer via Adapter-based Word Alignment

Multilingual pre-trained language models (mPLMs) have achieved remarkable performance on zero-shot cross-lingual transfer learning. However, most mPLMs implicitly encourage cross-lingual alignment in pre-training stage, making it hard to capture accurate word alignment across languages. In this paper, we propose Word-align ADapters for Cross-lingual transfer (WAD-X) to explicitly align word representations of mPLMs via language-specific subspace. Taking a mPLM as the backbone model, WAD-X constructs subspace for each source-target language pair via adapters. The adapters use statistical alignment as the prior knowledge to guide word-level aligning in the corresponding bilingual semantic subspace. We evaluate our model across a set of target languages on three zero-shot cross-lingual transfer tasks: part-of-speech tagging (POS), dependency parsing (DP), and sentiment analysis (SA). Experimental results demonstrate that our proposed model improves zero-shot cross-lingual transfer on three benchmarks, with improvements of 2.19, 2.50, and 1.61 points in POS, DP, and SA tasks over strong baselines.
FREDA: Few-Shot Relation Extraction Based on Data Augmentation

The primary task of few-shot relation extraction is to quickly learn the features of relation classes from a few labelled instances and predict the semantic relations between entity pairs in new instances. Most existing few-shot relation extraction methods do not fully utilize the relation information features in sentences, resulting in difficulties in improving the performance of relation classification. Some researchers have attempted to incorporate external information, but the results have been unsatisfactory when applied to different domains. In this paper, we propose a method that utilizes triple information for data augmentation, which can alleviate the issue of insufficient instances and possesses strong domain adaptation capabilities. Firstly, we extract relation and entity pairs from the instances in the support set, forming relation triple information. Next, the sentence information and relation triple information are encoded using the same sentence encoder. Then, we construct an interactive attention module to enable the query set instances to interact separately with the support set instances and relation triple instances. The module pays greater attention to highly interactive parts between instances and assigns them higher weights. Finally, we merge the interacted support set representation and relation triple representation. To our knowledge, we are the first to propose a method that utilizes triple information for data augmentation in relation extraction. In our experiments on the standard datasets FewRel1.0 and FewRel2.0 (domain adaptation), we observed substantial improvements without including external information.
Trapped ions quantum logic gate with optical tweezers and the Magnus effect

We consider the implementation of quantum logic gates in trapped ions using tightly focused optical tweezers. Strong polarization gradients near the tweezer focus lead to qubit-state-dependent forces on the ion. We show that these may be used to implement quantum logic gates on pairs of ion qubits in a crystal. The qubit-state-dependent forces generated by this effect are located on the plane perpendicular to the direction of propagation of the laser beams opening alternate ways of coupling to motional modes of an ion crystal. The proposed gate does not require ground-state cooling of the ions although the waist of the tightly focused beam needs to be comparable to its wavelength in order to achieve the needed field curvature. Furthermore, the gate can be implemented on both ground-state and magnetic-field-insensitive clock-state qubits without the need for counterpropagating laser fields. This simplifies the setup and eliminates errors due to phase instabilities between the gate laser beams. Finally, we show that imperfections in the gate execution, in particular, a 30-nm tweezer alignment error, lead to an infidelity of $\ensuremath{\sim}{10}^{\ensuremath{-}3}$. In the absence of experimental imperfections and within the limits of the gate model explored in this paper the fidelity is predicted to be $\ensuremath{\sim}0.999\phantom{\rule{0.16em}{0ex}}88$ when using a Laguerre-Gaussian beam to suppress photon scattering errors.