Reviewerly/exHarmony huggingface dataset:
Using Phoneme Representations to Build Predictive Models Robust to ASR Errors

Even though Automatic Speech Recognition (ASR) systems significantly improved over the last decade, they still introduce a lot of errors when they transcribe voice to text. One of the most common reasons for these errors is phonetic confusion between similar-sounding expressions. As a result, ASR transcriptions often contain "quasi-oronyms", i.e., words or phrases that sound similar to the source ones, but that have completely different semantics (e.g., "win" instead of "when" or "accessible on defecting" instead of "accessible and affecting"). These errors significantly affect the performance of downstream Natural Language Understanding (NLU) models (e.g., intent classification, slot filling, etc.) and impair user experience. To make NLU models more robust to such errors, we propose novel phonetic-aware text representations. Specifically, we represent ASR transcriptions at the phoneme level, aiming to capture pronunciation similarities, which are typically neglected in word-level representations (e.g., word embeddings). To train and evaluate our phoneme representations, we generate noisy ASR transcriptions of four existing datasets - Stanford Sentiment Treebank, SQuAD, TREC Question Classification and Subjectivity Analysis - and show that common neural network architectures exploiting the proposed phoneme representations can effectively handle noisy transcriptions and significantly outperform state-of-the-art baselines. Finally, we confirm these results by testing our models on real utterances spoken to the Alexa virtual assistant.
Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest Recommendation

Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
Visually Imbalanced Stereo Matching

Understanding of human vision system (HVS) has inspired many computer vision algorithms. Stereo matching, which borrows the idea from human stereopsis, has been extensively studied in the existing literature. However, scant attention has been drawn on a typical scenario where binocular inputs are qualitatively different (e.g., high-res master camera and low-res slave camera in a dual-lens module). Recent advances in human optometry reveal the capability of the human visual system to maintain coarse stereopsis under such visually imbalanced conditions. Bionically aroused, it is natural to question that: do stereo machines share the same capability? In this paper, we carry out a systematic comparison to investigate the effect of various imbalanced conditions on current popular stereo matching algorithms. We show that resembling the human visual system, those algorithms can handle limited degrees of monocular downgrading but also prone to collapses beyond a certain threshold. To avoid such collapse, we propose a solution to recover the stereopsis by a joint guided-view-restoration and stereo-reconstruction framework. We show the superiority of our framework on KITTI dataset and its extension on real-world applications.
Towards a pedagogical and sociotechnical framework for the strategic integration of mobile learning in higher education in low and middle income countries

Using mobile devices to facilitate teaching and learning (m-learning) in higher education has continued to gain momentum. To ensure meaningful m-learning, several models and frameworks, including those focused on pedagogies and the learning environment, have been developed. However, the majority of these are derived from the context of high-income countries with advanced educational technologies and may not be suitable in low- and middle-income countries where educational technology systems are not as functional. This paper reports on the four phases of a project, which set out to develop a suitable m-learning framework for low- and middle-income countries. The proposed framework was originally derived from the extant literature, then revised following the outcome of two empirical studies conducted in a Nigerian university using the mixed methods research approach. The revised framework provides a roadmap for the strategic integration of m-learning in higher education, taking into consideration the pedagogical and socioeconomic context of low- and middle-income countries.
Pathfinding Algorithms in Game Development

Abstract This review paper provides an overview of a pathfinding algorithm for game development which focuses on the algorithms and their contribution to game development. The algorithms were categorised based on their search performance. The aim of this paper is to investigate and provide insights into pathfinding algorithms for game development in the last 10 years. We summarise all pathfinding algorithms and describe their result in terms of performance (time and memory). The result of this paper is metaheuristic techniques have better performance in terms of time and memory compared to heuristic techniques as a pathfinding algorithm.
Using Digital Data to Protect and Promote the Most Vulnerable in the Fight Against COVID-19

OPINION article Front. Public Health, 12 June 2020Sec. Digital Public Health Volume 8 - 2020 | https://doi.org/10.3389/fpubh.2020.00296
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach

Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions. A lot of research has been done for either multi-label text categorization or image annotation. However, there is a lack of published work that targets at item tagging specifically for IR. Directly applying a traditional multi-label classification model for item tagging is sub-optimal, due to the ignorance of unique characteristics in IR. In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes. To enrich the representation of items, we leverage the query logs available in IR tasks, and construct a query-item-tag tripartite graph. This formulation results in a TagGNN model that utilizes heterogeneous graph neural networks with multiple types of nodes and edges. Different from previous research, we also optimize both full tag prediction and partial tag completion cases in a unified framework via a primary-dual loss mechanism. Experimental results on both open and industrial datasets show that our TagGNN approach outperforms the state-of-the-art multi-label classification approaches.
Accelerating Large Scale GCN Inference on FPGA

We propose an algorithm-architecture co-optimization framework to accelerate large-scale GCN inference on FPGA. We first perform data partitioning to fit each partition in the limited on-chip memory of FPGA. Then, we use the two-phase pre-processing algorithm consisting of sparsification and node reordering. The first phase (sparsification) eliminates edge connections of high-degree nodes by merging common neighbor nodes. The second phase (re-ordering) effectively groups densely connected neighborhoods to improve on-chip data reuse. Incorporating the above algorithmic optimizations, we propose an FPGA architecture to efficiently execute the two key computational kernels of GCN - feature aggregation and weight transformation. We evaluate our design on a state-of-the-art FPGA device. Compared with multi-core and GPU baselines, our design reduces the inference latency by up to $30 \times $ and $2 \times $ respectively.
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence).Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent.We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish.Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences.We conclude by relating these results to broader concerns about the relationship between comprehension (i.e.typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.
On attaining user-friendly hand gesture interfaces to control existing GUIs

Hand gesture interfaces are dedicated programs that principally perform hand tracking and hand gesture prediction to provide alternative controls and interaction methods. They take advantage of one of the most natural ways of interaction and communication, proposing novel input and showing great potential in the field of the human-computer interaction. Developing a flexible and rich hand gesture interface is known to be a time-consuming and arduous task. Previously published studies have demonstrated the significance of the finite-state-machine (FSM) approach when mapping detected gestures to GUI actions. In our hand gesture interface, we broadened the FSM approach by utilizing gesture-specific attributes, such as distance between hands, distance from the camera, and time of occurrences, to enable users to perform unique GUI actions. These attributes are obtained from hand gestures detected by the RealSense SDK employed in our hand gesture interface. By means of these gesture-specific attributes, users can activate static gestures and perform them as dynamic gestures. We also provided supplementary features to enhance the efficiency, convenience, and user-friendliness of our hand gesture interface. Moreover, we developed a complementary application for recording hand gestures by capturing hand keypoints in depth and color images to facilitate the generation of hand gesture datasets. We conducted a small-scale user survey with fifteen subjects to test and evaluate our hand gesture interface. Anonymous feedback obtained from the users indicates that our hand gesture interface is adequately facile and self-explanatory to use. In addition, we received constructive feedback about minor flaws regarding the responsiveness of the interface. We proposed a hand gesture interface along with key concepts to attain user-friendliness and effectiveness in the control of existing GUIs.
User Verification Based on Mouse Dynamics: a Comparison of Public Data Sets

In this paper, we compare the performance of user verification systems based on three publicly available mouse dynamics data sets. One of these datasets is our new DFL data set which contains the mouse dynamics of 21 different users. Two aspects of mouse dynamics based user verification systems are investigated: the effect of the quantity of training data on the performance, and the effect on the performance of the number of consecutive mouse actions used for user identity predictions. Measurements show that it is advisable to use approximately 1000 mouse actions for training (60 minutes of active mouse movements on average), and at least 10 mouse actions for user identity prediction.
Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

The performance and learning speed of the Cascade Correlation neural network (CasCor) may not be optimal because of redundant hidden units’ in the cascade architecture and the tuning of connection weights. This study explores the limitations of CasCor and its variants and proposes a novel constructive neural network (CNN). The basic idea is to compute the input connection weights by generating linearly independent hidden units from the orthogonal linear transformation, and the output connection weights by connecting hidden units in a linear relationship to the output units. The work is unique in that few attempts have been made to analytically determine the connection weights on both sides of the network. Experimental work on real energy application problems such as predicting powerplant electrical energy, predicting seismic hazards to prevent fatal accidents and reducing energy consumption by predicting building occupancy detection shows that analytically calculating the connection weights and generating non-redundant hidden units improves the convergence of the network. The proposed CNN is compared with that of the state-of-the-art machine learning algorithms. The work demonstrates that proposed CNN predicts a wide range of applications better than other methods.
Using Exploration to Alleviate Closed Loop Effects in Recommender Systems

Recommendation systems are often trained and evaluated based on users' interactions obtained through the use of an existing, already deployed, recommendation system. Hence the deployed recommendation systems will recommend some items and not others, and items will have varying levels of exposure to users. As a result, the collected feedback dataset (including most public datasets) can be skewed towards the particular items favored by the deployed model. In this manner, training new recommender systems from interaction data obtained from a previous model creates a feedback loop, i.e. a closed loop feedback. In this paper, we first introduce the closed loop feedback and then investigate the effect of closed loop feedback in both the training and offline evaluation of recommendation models, in contrast to a further exploration of the users' preferences (obtained from the randomly presented items). To achieve this, we make use of open loop datasets, where randomly selected items are presented to users for feedback. Our experiments using an open loop Yahoo! dataset reveal that there is a strong correlation between the deployed model and a new model that is trained based on the closed loop feedback. Moreover, with the aid of exploration we can decrease the effect of closed loop feedback and obtain new and better generalizable models.
Efficient Unsupervised Dimension Reduction for Streaming Multiview Data

Multiview learning has received substantial attention over the past decade due to its powerful capacity in integrating various types of information. Conventional unsupervised multiview dimension reduction (UMDR) methods are usually conducted in an offline manner and may fail in many real-world applications, where data arrive sequentially and the data distribution changes periodically. Moreover, satisfying the requirements of high memory consumption and expensive retraining of the time cost in large-scale scenarios are difficult. To remedy these drawbacks, we propose an online UMDR (OUMDR) framework. OUMDR aims to seek a low-dimensional and informative consensus representation for streaming multiview data. View-specific weights are also learned in this article to reflect the contributions of different views to the final consensus presentation. A specific model called OUMDR-E is developed by introducing the exclusive group LASSO (EG-LASSO) to explore the intraview and interview correlations. Then, we develop an efficient iterative algorithm with limited memory and time cost requirements for optimization, where the convergence of each update is theoretically guaranteed. We evaluate the proposed approach in video-based expression recognition applications. The experimental results demonstrate the superiority of our approach in terms of both effectiveness and efficiency.
A meta-analysis of the deterrence theory in security-compliant and security-risk behaviors

Deterrence theory has been widely adopted in the study of information security management; however, evidence frequently presents sometimes contradictory results. Prior meta-analytic studies have focused primarily on the use of formal deterrence constructs to predict security-compliant behavior, while informal deterrence constructs and security-risk behavior are often neglected as a result. This study aims to meta-analyze the relationships formed between both formal/informal deterrence constructs and security-compliant/risk behaviors in a comprehensive manner beyond what has taken place in prior IS security meta-analysis based on deterrence theory. By searching multiple electronic databases, we have located 40 studies, along with 108 effect sizes, pertinent to our study's purpose. Inverse variance method weighted with sample sizes was used to determine mean effect sizes. The random-effects model was used to report meta-analysis results since Q, I2, and H index showed some degree of heterogeneity existent in the collected data. Publication bias was assessed by means of fail-safe N. All proposed relationships occurring between formal/informal deterrence constructs and security-compliant/-risk behaviors were supported. Formal deterrence constructs exerted weak to moderate effects on security behavior, while informal deterrence constructs exerted moderate to strong effects on security behavior. Further, informal deterrence constructs showed greater mean effect sizes than formal deterrence constructs. Additionally, prediction intervals of deterrence constructs, along with detection certainty, included zero, which indicated that moderators may be present. Based on these findings, the mean effect sizes of deterrence constructs may be more clearly identified when dividing security behavior into both compliant- and risk- behaviors. Further moderators might be employed to improve the inconsistent findings evidenced in deterrence theory.
Deep Neural Network Approach to GNSS Signal Acquisition

This paper investigates the use of data-driven models, popular in the machine learning literature, as an alternative to well-engineered signal processing blocks used in state-of-the-art GNSS receivers. Acknowledging that the latter are optimally designed and extensively tested, it is also agreed that when the nominal models do not hold the performance of the receiver might degrade. Particularly, we investigate the use of data-driven models in the signal acquisition stage of the receiver by addressing a classification problem from Cross Ambiguity Function (CAF) delay/Doppler maps. A discussion on the training of such models and future perspectives is provided. The detection results in nominal situations are then compared to the theoretical bound in the receiver operating characteristic (ROC) plots.
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification

Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method. In this paper, we propose a multi-guided learning approach which utilizing the information of attributes and meanwhile introducing two novel random augments to improve the robustness during training. What's more, we propose an attribute constraint method and group re-ranking strategy to refine matching results. Our method achieves mAP of 66.83% and rank-1 accuracy 76.05% in the CVPR 2020 AI City Challenge.
Learning Neighborhood-Reasoning Label Distribution (NRLD) for Facial Age Estimation

In this paper, we propose to learn a neighborhood-reasoning label distribution (NRLD) for facial age estimation. Unlike conventional label distribution methods with fixed-structural aging patterns, in this work, our NRLD aims to reason about more resilient and adaptive label distribution by disentangling the graph of face neighbors. In particular, our model holds the assumption on that the sample-specific age label distribution is principally influenced by a mixture of interpretable and meaningful factors, which typically cause plausible edges connected to the anchors. Under the scenario of each factor, we specifically collect the subset of graph edges and then convolute them with face samples to regress a mean-variance label distribution. During the training process, the mixture hyperparameters of our label distribution are iteratively optimized by following the Expectation-Maximization schema. Extensive experimental results on three challenging widely-evaluated datasets indicate the superiority in comparisons with most state of the arts.
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation

Many interactive online systems, such as social media platforms or news sites, provide personalized experiences through recommendations or news feed customization based on people's feedback and engagement on individual items (e.g., liking items). In this paper, we investigate how we can support a greater degree of user control in such systems by changing the way the system allows people to gauge the consequences of their feedback actions. To this end, we consider two important aspects of how the system responds to feedback actions: (i) immediacy, i.e., how quickly the system responds with an update, and (ii) visibility, i.e., whether or not changes will get highlighted. We used both an in-lab qualitative study and a large-scale crowd-sourced study to examine the impact of these factors on people's reported preferences and observed behavioral metrics. We demonstrate that UX design which enables people to preview the impact of their actions and highlights changes results in a higher reported transparency, an overall preference for this design, and a greater selectivity in which items are liked.
Learning With Differential Privacy

The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.