Reviewerly/exHarmony huggingface dataset:
Finnish parliament ASR corpus

Public sources like parliament meeting recordings and transcripts provide ever-growing material for the training and evaluation of automatic speech recognition (ASR) systems. In this paper, we publish and analyse the Finnish Parliament ASR Corpus, the most extensive publicly available collection of manually transcribed speech data for Finnish with over 3000 h of speech and 449 speakers for which it provides rich demographic metadata. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. Similarly, there are two official, corrected test sets covering different times, setting an ASR task with longitudinal distribution-shift characteristics. An official development set is also provided. We developed a complete Kaldi-based data preparation pipeline and ASR recipes for hidden Markov models (HMM), hybrid deep neural networks (HMM-DNN), and attention-based encoder-decoders (AED). For HMM-DNN systems, we provide results with time-delay neural networks (TDNN) as well as state-of-the-art wav2vec 2.0 pretrained acoustic models. We set benchmarks on the official test sets and multiple other recently used test sets. Both temporal corpus subsets are already large, and we observe that beyond their scale, HMM-TDNN ASR performance on the official test sets has reached a plateau. In contrast, other domains and larger wav2vec 2.0 models benefit from added data. The HMM-DNN and AED approaches are compared in a carefully matched equal data setting, with the HMM-DNN system consistently performing better. Finally, the variation of the ASR accuracy is compared between the speaker categories available in the parliament metadata to detect potential biases based on factors such as gender, age, and education.
Comparing functions of and recollection with recently taken and recently deleted smartphone camera photos

Abstract The current study examined why people take and delete photos with smartphone cameras, and participants' recollective experiences with saved and deleted photos. Two mixed‐methods surveys asked undergraduates (Study 1) and an international online sample (Study 2) to review both recently taken and recently deleted photos from their smartphones' photo album or trash bin. Participants recollected the photographed events for both types of photos and rated the quality of their recollections. Events recollected with saved photos were remembered more vividly, positively, and emotionally intensely compared to recollection with deleted photos. Participants also gave short responses about why they originally took both types of photos, as well as why they had deleted the recently deleted photos. Consistent with prior work, different photo‐taking goals were associated with different recollective experiences, especially for photos taken as mementos compared to photos taken to offload information.
MDS codes with Euclidean and Hermitian hulls of flexible dimensions and their applications to EAQECCs

The hull of a linear code is the intersection of itself with its dual code with respect to certain inner product. Both Euclidean and Hermitian hulls are of theorical and practical significance. In this paper, we construct several new classes of maximum distance separable (MDS) codes via (extended) generalized Reed-Solomon (GRS) codes and determine their Euclidean or Hermitian hulls. As a consequence, four new classes of MDS codes with Hermitian hulls of flexible dimensions and six new classes of MDS codes with Euclidean hulls of flexible dimensions are constructed. As applications, for the former, we further construct four new families of entanglement-assisted quantum error-correcting codes (EAQECCs) and four new families of MDS EAQECCs of length $$n>q+1$$ . Meanwhile, many of the distance parameters of our MDS EAQECCs are greater than $$\lceil \frac{q}{2} \rceil $$ or q; for the latter, we show some examples on Euclidean self-orthogonal and one-dimensional Euclidean hull MDS codes. In addition, two new general methods for constructing extended GRS codes with $$(k-1)$$ -dimensional Hermitian hull and Hermitian self-orthogonal extended GRS codes are also provided.
Meta Hierarchical Reinforced Learning to Rank for Recommendation: A Comprehensive Study in MOOCs

The rapid development of Massive Open Online Courses (MOOCs) surges the needs of advanced models for personalized online education. Existing solutions successfully recommend MOOCs courses via deep learning models, they however generate weak “course embeddings” with original profiles, which contain noisy and few enrolled courses. On the other hand, existing algorithms provide the recommendation list according to the score of each course while ignoring the personalized demands of learners. To tackle the above challenges, we propose a Meta hierarchical Reinforced Learning to rank approach MRLtr, which consists of a Meta Hierarchical Reinforcement Learning pre-trained mechanism and a gradient boosting ranking method to provide accurate and personalized MOOCs courses recommendation. Specifically, the end-to-end pre-training mechanism combines a user profile reviser and a meta embedding generator to provide course embedding representation enhancement for the recommendation task. Furthermore, the downstream ranking method adopts a LightGBM-based ranking regressor to promote the order quality with gradient boosting. We deploy MRLtr on a real-world MOOCs education platform and evaluate it with a large number of baseline models. The results show that MRLtr could achieve $$\varDelta NDCG_{4}$$ = 7.74%–16.36%, compared to baselines. Also, we conduct a 7-day A/B test using the realistic traffic of Shanghai Jiao Tong University MOOCs, where we can still observe significant improvement in real-world applications. MRLtr performs consistently both in online and offline experiments.
A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots

Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
Gaze Speedup: Eye Gaze Assisted Gesture Typing in Virtual Reality

Mid-air text input in augmented or virtual reality (AR/VR) is an open problem. One proposed solution is gesture typing where the user performs a gesture trace over the keyboard. However, this requires the user to move their hands precisely and continuously, potentially causing arm fatigue. With eye tracking available on AR/VR devices, multiple works have proposed gaze-driven gesture typing techniques. However, such techniques require the explicit use of gaze which are prone to Midas touch problems, conflicting with other gaze activities in the same moment. In this work, the user is not made aware that their gaze is being used to improve the interaction, making the use of gaze completely implicit. We observed that a user's implicit gaze fixation location during gesture typing is usually the gesture cursor's target location if the gesture cursor is moving toward it. Based on this observation, we propose the Speedup method in which we speed up the gesture cursor toward the user's gaze fixation location, the speedup rate depends on how well the gesture cursor's moving direction aligns with the gaze fixation. To reduce the overshooting near the target in the Speedup method, we further proposed the Gaussian Speedup method in which the speedup rate is dynamically reduced with a Gaussian function when the gesture cursor gets nearer to the gaze fixation. Using a wrist IMU as input, a 12-person study demonstrated that the Speedup method and Gaussian Speedup method reduced users' hand movement by and respectively without any loss of typing speed or accuracy.
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce suboptimal output or fail to work altogether. The field of explainable AI (XAI) has sought to develop techniques that improve the interpretability of black-box systems. While most XAI work has focused on single-classification tasks, the subfield of explainable AI planning (XAIP) has sought to develop techniques that make sequential decision making AI systems explainable to domain experts. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always optimal. In this work, we examine novice user interactions with a non-robust IDS system – one that occasionally recommends suboptimal actions, and one that may become unavailable after users have become accustomed to its guidance. We introduce a new explanation type, subgoal-based explanations, for plan-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance in the presence of IDS recommendations, improve user ability to distinguish optimal and suboptimal IDS recommendations, and are preferred by users. Additionally, we demonstrate that subgoal-based explanations enable more robust user performance in the case of IDS failure, showing the significant benefit of training users for an underlying task with subgoal-based explanations.
Categorical and Continuous Features in Counterfactual Explanations of AI Systems

Recently, eXplainable AI (XAI) research has focused on the use of counterfactual explanations to address interpretability, algorithmic recourse, and bias in AI system decision-making. The proponents of these algorithms claim they meet users' requirements for counterfactual explanations. For instance, many claim that the output of their algorithms work as explanations because they prioritise "plausible", "actionable" or "causally important" features in their generated counterfactuals. However, very few of these claims have been tested in controlled psychological studies, and we know very little about which aspects of counterfactual explanations help users to understand AI system decisions. Furthermore, we do not know whether counterfactual explanations are an advance on more traditional causal explanations that have a much longer history in AI (in explaining expert systems and decision trees). Accordingly, we carried out two user studies to (i) test a fundamental distinction in feature-types, between categorical and continuous features, and (ii) compare the relative effectiveness of counterfactual and causal explanations. The studies used a simulated, automated decision-making app that determined safe driving limits after drinking alcohol, based on predicted blood alcohol content, and user responses were measured objectively (users' predictive accuracy) and subjectively (users' satisfaction and trust judgments). Study 1 (N=127) showed that users understand explanations referring to categorical features more readily than those referring to continuous features. It also discovered a dissociation between objective and subjective measures: counterfactual explanations elicited higher accuracy of predictions than no-explanation control descriptions but no higher accuracy than causal explanations, yet counterfactual explanations elicited greater satisfaction and trust judgments than causal explanations. Study 2 (N=211) found that users were more accurate for categorically-transformed features compared to continuous ones, and also replicated the results of Study 1. The findings delineate important boundary conditions for current and future counterfactual explanation methods in XAI.
Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI – rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users' reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI's prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user's task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user's decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI.
Bottlenecks CLUB: Unifying Information-Theoretic Trade-Offs Among Complexity, Leakage, and Utility

Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair machine learning algorithms, design of privacy-assuring mechanisms, and appear as information-theoretic performance bounds in various multi-user communication problems. In this work, we propose a general family of optimization problems, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complexity-leakage-utility bottleneck (CLUB)</i> model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights for the generative compression models, and (iv) can be used to obtain fair generative models. We first formulate the CLUB model as a complexity-constrained privacy-utility optimization problem. We then connect it with the closely related bottleneck problems, namely information bottleneck (IB), privacy funnel (PF), deterministic IB (DIB), conditional entropy bottleneck (CEB), and conditional PF (CPF). We show that the CLUB model generalizes all these problems as well as most other information-theoretic privacy models. Then, we construct the deep variational CLUB (DVCLUB) models by employing neural networks to parameterize variational approximations of the associated information quantities. Building upon these information quantities, we present unified objectives of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">supervised</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> DVCLUB models. Leveraging the DVCLUB model in an unsupervised setup, we then connect it with state-of-the-art generative models, such as variational auto-encoders (VAEs), generative adversarial networks (GANs), as well as the Wasserstein GAN (WGAN), Wasserstein auto-encoder (WAE), and adversarial auto-encoder (AAE) models through the optimal transport (OT) problem. We then show that the DVCLUB model can also be used in fair representation learning problems, where the goal is to mitigate the undesired bias during the training phase of a machine learning model. We conduct extensive quantitative experiments on colored-MNIST and CelebA datasets.
Long Short-Term Memory-Based Neural Networks for Missile Maneuvers Trajectories Prediction<sup>⋆</sup>

Due to its extensive applications in different contexts, moving target tracking has become a hot topic in the last years, above all in the military field. Specifically, missile tracking research received a great effort, mainly for its importance in terms of security and safety. Herein, traditional solutions, e.g. Interacting Multiple Model (IMM) based on the Kalman estimation theory, achieve good performance under the main restrictive assumption of the a priori knowledge of the target model, so neglecting the unavoidable presence of model uncertainties and limiting the achievable tracking accuracy only by the presence of the measurement noise. With the specific aim of overcoming this narrowness, this work investigates the capability of deep neural networks in predicting the missile maneuvering trajectories in a model-free fashion. The idea is to leverage the Long-Short Term Memory (LSTM) net due to its excellent capability in learning long-term dependencies of temporal information. Two different LSTM-based architectures have been hence designed to predict both position and velocity of a missile using raw and noisy measurements provided by a realistic radar system, exploiting a large database abundant of realistic off-line data. Training results and theoretical derivations are verified through non-trivial scenarios in order to assess the capability of predicting unknown and realistic 3D missile maneuvers. Finally, the proposed approach has been also compared with a performing model-based IMM algorithm, suitably tuned to deal with realistic missile maneuvers, confirming the excellent generalization abilities of the developed data-driven architectures for different datasets.
Construction and Optimization of Dynamic S-Boxes Based on Gaussian Distribution

Block ciphers are widely used for securing data and are known for their resistance to various types of attacks. The strength of a block cipher against these attacks often depends on the S-boxes used in the cipher. There are many chaotic map-based techniques in the literature for constructing the dynamic S-Boxes. While chaos-based approaches have certain attractive properties for this purpose, they also have some inherent weaknesses, including finite precision effect, dynamical degradation of chaotic systems, non-uniform distribution, discontinuity in chaotic sequences. These weaknesses can limit the effectiveness of chaotic map-based substitution boxes. In this paper, we propose an innovative approach for constructing dynamic S-boxes using Gaussian distribution-based pseudo-random sequences. The proposed technique overcomes the weaknesses of existing chaos-based S-box techniques by leveraging the strength of pseudo-randomness sequences. However, one of the main drawbacks of using Gaussian distribution-based pseudo-random sequences is the low nonlinearity of the resulting S-boxes. To address this limitation, we introduce the use of genetic algorithms (GA) to optimize the nonlinearity of Gaussian distribution-based S-boxes while preserving a high level of randomness. The proposed technique is evaluated using standard S-box performance criteria, including nonlinearity, bit independence criterion (BIC), linear approximation probability (LP), strict avalanche criterion (SAC), and differential approximation probability (DP). Results demonstrate that the proposed technique achieves a maximum nonlinearity of 112, which is comparable to the ASE algorithm.
Analysing the Accuracy of Detecting Phishing Websites using Ensemble Methods in Machine Learning

Phishing attacks are now one of the prevalent dangers that firms, service providers and internet users must deal with. Rather than targeting software vulnerabilities, it targets human vulnerabilities. It is the act of enticing users to attain their personal data using fake emails and websites. Like how e-commerce sectors are growing, phishing attacks are also developing. Preventing phishing attempts is a critical aspect of protecting online transactions. Since hacktivists, spy agencies and cybercriminals now have a rich field in which they can operate sophisticated phishing attacks, prompt detection of phishing attempts is more critical than ever. To properly respond to various phishing attacks, it is required to gain a thorough understanding of these attacks, and suitable response techniques must be used. The challenges faced in this research is finding the appropriate datasets and Feature extraction prompted the study of several modules, in addition to understanding every module and attaining the desired outcome from it. Machine learning techniques are used to accurately identify phishing attacks before cause harm to a user. Being able to handle the changing nature of phishing attempts and offering an accurate method of classification, it is one of the most practical ways to approach the situation.
Individual Performance Model for E-Learning in University

During a pandemic in the world, the use of learning methods through online learning continues to increase. Individual performance of students is a concern if the learning method is implemented with e-learning. The focus in the reported study is to examine the connect between individual student performance on the concept of online learning through support from continuance intention and student behavior (satisfaction). The study was administered by studying students' experience of e-learning around 491 students. Data was collected through a quantitative questionnaire with the tool of analysis used SmartPLS. The study was conducted by testing the model through two stages, namely PLS Algorithm and Bootstrapping process. The outcomes of the research show a positive correlation between e-learning satisfaction on continuance intention and individual performance directly. On the other hand, it was stated that continuance intention was able to mediate between e-learning satisfaction and increasing student individual performance on the online learning concept. The study knowledge of the model related to individual student performance on the e-learning method. Individual performance research models can be used as recommendations for improving the learning performance of students at universities.