Reviewerly/exHarmony huggingface dataset:
A Self-Supervised Model for Language Identification Integrating Phonological Knowledge

In this paper, a self-supervised learning pre-trained model is proposed and successfully applied in language identification task (LID). A Transformer encoder is employed and multi-task strategy is used to train the self-supervised model: the first task is to reconstruct the masking spans of input frames and the second task is a supervision task where the phoneme and phonological labels are used with Connectionist Temporal Classification (CTC) loss. By using this multi-task learning loss, the model is expected to capture high-level speech representation in phonological space. Meanwhile, an adaptive loss is also applied for multi-task learning to balance the weight between different tasks. After the pretraining stage, the self-supervised model is used for xvector systems. Our LID experiments are carried out on the oriental language recognition (OLR) challenge data corpus and 1 s, 3 s, Full-length test sets are selected. Experimental results show that on 1 s test set, feature extraction model approach can get best performance and in 3 s, Full-length test, the fine-tuning approach can reach the best performance. Furthermore, our results prove that the multi-task training strategy is effective and the proposed model can get the best performance.
Service Robots, Agency, and Embarrassing Service Encounters

Purpose – Extant research focused mostly on potentially negative customer responses to service robots. In contrast, our study is the first to explore a service context where service robots are the preferred service delivery mechanism over human frontline employees. Specifically, we examine how customers respond to service robots in the context of potentially embarrassing service encounters.Design/methodology/approach – This study employs a mixed-method approach whereby a qualitative in-depth study (Study 1) is followed by two lab experiments (Studies 2 and 3). Findings – Results show that interactions with service robots attenuated customers’ anticipated embarrassment. Study 1 identifies a number of factors that can reduce embarrassment. These include the perception that service robots have reduced agency (e.g., are not able to make moral or social judgements) and emotions (e.g., are not able to have feelings), a text-based interaction style, and enhanced interaction privacy. Study 2 tests our base model and shows that people feel less embarrassed during a potentially embarrassing service encounter when interacting with service robots compared to frontline employees. Finally, Study 3 confirms that perceived agency, but not emotion, fully mediates frontline counterparty (employee vs. robot) effects on anticipated embarrassment. Practical implications – Service robots can add value by reducing potential customer embarrassment because they are perceived to have less agency than service employees. This makes service robots the preferred service delivery mechanism for at least some customers in potentially embarrassing service encounters (e.g., in certain medical contexts). Originality/value – This study is the first to examine a context where service robots are the preferred service delivery mechanism over human employees.
Minimal universality criterion sets on the representations of binary quadratic forms

For a set S of (positive definite and integral) quadratic forms with bounded rank, a quadratic form f is called S-universal if it represents all quadratic forms in S. A subset S0 of S is called an S-universality criterion set if any S0-universal quadratic form is S-universal. We say S0 is minimal if there does not exist a proper subset of S0 that is an S-universality criterion set. In this article, we study various properties of minimal universality criterion sets. In particular, we show that for 'most' binary quadratic forms f, minimal S-universality criterion sets are unique in the case when S is the set of all subforms of the binary form f.
Unsupervised Discovery of Transitional Skills for Deep Reinforcement Learning

By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn skills without extrinsic reward. However, when considering using multiple consecutive skills to complete a specific task, the transition from one to another cannot guarantee the success of the process due to the evident gap between skills. In this paper, we propose a novel unsupervised reinforcement learning approach to learn transitional skills in addition to pursuing diverse primitive skills. By introducing an extra latent variable for exploring the dependence between skills, our method discovers both primitive and transitional skills by optimizing a novel information theoretic objective. Considering various robotic tasks, our results demonstrate the effectiveness on learning both diverse primitive skills and transitional skills, and further exhibit the superiority of our method in smooth transition of skills over the baselines. Videos of transitional skills can be found on the project website: https://sites.google.com/view/udts-skill.
Temporally coherent person matting trained on fake-motion dataset

We propose a novel neural-network-based method to perform matting of videos depicting people that does not require additional user input such as trimaps. Our architecture achieves temporal stability of the resulting alpha mattes by using motion-estimation-based smoothing of image-segmentation algorithm outputs, combined with convolutional-LSTM modules on U-Net skip connections. We also propose a fake-motion algorithm that generates training clips for the video-matting network given photos with ground-truth alpha mattes and background videos. We apply random motion to photos and their mattes to simulate movement one would find in real videos and composite the result with the background clips. It lets us train a deep neural network operating on videos in an absence of a large annotated video dataset and provides ground-truth training-clip foreground optical flow for use in loss functions.
Hierarchical Knowledge-Based Graph Embedding Model for Image–Text Matching in IoTs

The development of Internet of Things systems (IoTs) and 5G technology has allowed image and text information to be collected and spread at an unprecedentedly high speed. To improve the data processing capabilities of IoTs, the semantic relations between images and text should be extracted efficiently and accurately. Therefore, to reduce the enormous semantic differences between images and text, existing methods introduce consensus knowledge graphs into image–text matching tasks. However, these methods result in noisy edges during the graph construction stage and overlook detailed knowledge extraction, leading to reduced performance in semantic matching. In this article, a two-layer heterogeneous knowledge graph network is proposed to solve the above problems. The proposed model incorporates category knowledge and local knowledge for improved data representation. Specifically, a category-based hierarchical knowledge graph is constructed to learn representations of knowledge concepts through a hierarchical correlation graph embedding (HCGE) module. Then, a globally guided local attention (GLA) module is used to extract fine-grained local knowledge. Finally, the similarity between the input image and text is calculated based on knowledge-fused features to complete the matching process. Extensive experiments show that the proposed model can learn more effective knowledge features to improve the efficacy of image–text matching in.
Framework of the Employee Attendance System with QR Code in the Pandemic Covid-19

Attendance system is the process of identifying an employee's attendance as one of the foundation of the company in providing salary and other rewards. The problem of this research is working "work from office" priority in maintaining the health protocol of the new normal era covid-19 in the industrial world avoiding crowds, using non-touch attendance technology, and eliminate cheating in employee attendance data. The purpose of this paper is to develop a framework for employee attendance system in the pandemic period by considering the safety and accuracy of the data. Less contact economy minimizes direct interaction between employees and touch with the attendance device The research methodology uses the approach of developing an attendance system framework using an android smartphone, qr-code scanner, RaspBerry Pi application server. The result of writing is a framework for employee attendance systems using a personal smartphone through detection from a QR- Code system scanner.
Online Evaluation Methods for the Causal Effect of Recommendations

Evaluating the causal effect of recommendations is an important objective because the causal effect on user interactions can directly leads to an increase in sales and user engagement. To select an optimal recommendation model, it is common to conduct A/B testing to compare model performance. However, A/B testing of causal effects requires a large number of users, making such experiments costly and risky. We therefore propose the first interleaving methods that can efficiently compare recommendation models in terms of causal effects. In contrast to conventional interleaving methods, we measure the outcomes of both items on an interleaved list and items not on the interleaved list, since the causal effect is the difference between outcomes with and without recommendations. To ensure that the evaluations are unbiased, we either select items with equal probability or weight the outcomes using inverse propensity scores. We then verify the unbiasedness and efficiency of online evaluation methods through simulated online experiments. The results indicate that our proposed methods are unbiased and that they have superior efficiency to A/B testing.
Tracing of Spare Parts Replacement to Improve the Usage of Genuine Parts with Blockchain Technology

The problem of counterfeit vehicle parts is something that is still happening today. Various techniques facilitate to prevent counterfeit spare parts in the market. One of many vehicles distributed is a vehicle repair shop—the previous study, stating that blockchain technology can trace and track spare parts. However, in previous studies, the model or method of implementation had not been found. Therefore, this research tries to simulate replacing replacement parts in the Blockchain to make it easier to track. This qualitative research involved a workshop for field observations and discussions with experts in related fields. In this study, a simulation is carried out directly of vehicle parts recorded in blockchain-based (multichain) applications. The results of this study indicate that Blockchain technology possible use for recording vehicle parts.
Supervised classification by thresholds: Application to automated text categorization and opinion mining

Abstract Over recent years, the world has experienced explosive growth in the volume of textual data, which makes a manual analysis impossible. Machine learning techniques provided an effective solution to this problem. Due to its capacity to organize the huge and varied amounts of data, it offered valuable insights and it has become an emerging investigative field for the research community. Classification techniques are used to classify data into different classes according to desired criteria. By their simplicity, they give rise to a variety of applications: automated text categorization, opinion mining, and so forth. These processes go through three stages: text representation, features extraction, and the classification process; they still face many difficulties due both to the complex nature of text databases and to the high dimensionality of texts representations. This article presents a new classification approach that learns to classify texts from the most reliable features more accurately. The added advantage of the proposed approach is that it automatically classifies a text without necessarily processing all its features. The experimental results showed that this new classification by thresholds outperforms the state‐of‐the‐art methods. As a result, the obtained f‐measure on automatic text categorization was 95.06% while it is lower on opinion mining.
DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery

Detecting abusive and fraudulent claims is one of the key challenges in online food delivery. This is further aggravated by the fact that it is not practical to do reverse-logistics on food unlike in e-commerce. This makes the already-hard problem of harvesting labels for fraud even harder because we cannot confirm if the claim was legitimate by inspecting the item(s). Using manual effort to analyze transactions to generate labels is often expensive and time-consuming. On the other hand, typically, there is a wealth of ‘noisy’ information about what constitutes fraud, in the form of customer service interactions, weak and hard rules derived from data analytics, business intuition and domain understanding.
Online disagreement in WhatsApp groups: A comparative study of Spanish family members and work colleagues

The purpose of this paper is to compare disagreement in two different WhatsApp groups: one for members of the same family, and another for work colleagues. After the analysis, 427 instances of disagreement were identified in the family group, and 161 in the interactions between work colleagues. The most common strategy in both corpora is ‘Giving opposite opinions’. Nevertheless, the rest of the results present very significant dissimilarities, most notably the higher presence of disagreement in the family WhatsApp group. This higher tolerance for disagreement is corroborated by the choice of linguistic strategies made. Whereas the family members tended to give emotional or personal reasons for disagreement, as well as negative comments on the topic, the colleagues preferred to express their disagreement with mitigating expressions and token agreement. Thus, in the corpora studied, the expression of disagreement seems to be less face threatening for family members than for work colleagues.
Obstacle Avoidance for Autonomous Mobile Robots Based on Mapping Method

In recent years, the mobile robot has been considerable attention to researchers for its application in various environments. For a mobile robot navigating its way from a starting point to a goal point while traversing through deterrents, needs to recognize the obstacles and generate new trajectories to reach the destination. This paper presents an obstacle avoidance method for mobile robots using an open-source robot operation system (ROS) combined with the dynamic window approach (DWA) algorithm. The experiments are carried out using a mobile robot in which the navigation method is based on data collected from a laser scanner. The experimental results show that the robot can work well in environments containing static and dynamic obstacles.
Universal Adversarial Attack on Deep Learning Based Prognostics

Deep learning-based time series models are being extensively utilized in engineering and manufacturing industries for process control and optimization, asset monitoring, diagnostic and predictive maintenance. These models have shown great improvement in the prediction of the remaining useful life (RUL) of industrial equipment but suffer from inherent vulnerability to adversarial attacks. These attacks can be easily exploited and can lead to catastrophic failure of critical industrial equipment. In general, different adversarial perturbations are computed for each instance of the input data. This is, however, difficult for the attacker to achieve in real time due to higher computational requirement and lack of uninterrupted access to the input data. Hence, we present the concept of universal adversarial perturbation, a special imperceptible noise to fool regression based RUL prediction models. Attackers can easily utilize universal adversarial perturbations for real-time attack since continuous access to input data and repetitive computation of adversarial perturbations are not a prerequisite for the same. We evaluate the effect of universal adversarial attacks using NASA turbofan engine dataset. We show that addition of universal adversarial perturbation to any instance of the input data increases error in the output predicted by the model. To the best of our knowledge, we are the first to study the effect of the universal adversarial perturbation on time series regression models. We further demonstrate the effect of varying the strength of perturbations on RUL prediction models and found that model accuracy decreases with the increase in perturbation strength of the universal adversarial attack. We also showcase that universal adversarial perturbation can be transferred across different models.
GUIS2Code: A Computer Vision Tool to Generate Code Automatically from Graphical User Interface Sketches

It is a typical task for front-end developers to repetitively transform the graphical user interface model provided by the designer into code. Automatically converting the design draft provided by the designer into code can simplify the task of the front-end engineer and avoid a lot of simple and repetitive work. In this paper, we propose GUIS2Code using deep neural network, which is trained on the datasets of the design drafts to detect the UI elements of the input sketches and generate corresponding codes through the UI parser. Our method can generate code for three different platforms (i.e., iOS, Android, and Web). Our experimental results illustrates that GUIS2Code achieves an average GUI-component classification accuracy of 95.04% and generates code that can restore the target sketches more accurately while exhibiting reasonable code structure.
Framework for Digitally Managing Academic Records Using Blockchain Technology

Research studies report that there are a growing number of falsified educational certificates being produced by dishonest job seekers and higher education applicants across the world. Technological development in the image-processing domain makes editing the document so simple that any individual can perform this kind of forgery without having high-level skills in image editing. Most national governments have put in place stringent policies and procedures to verify and authenticate academic documents. However, due to the amount of human intervention in the process, the efficiency of such measures is debatable. Such systems leave open the possibility that unethical insiders may engage in forgery. In addition, the process of document verification and authentication consumes substantial amounts of time and money. Existing systems of document attestation do not provide a simple and instant way of verifying the authenticity of certificates from transcript level onward. In response to the above issues, this project proposes a prototype model for digitally managing and attesting the academic records using permissioned blockchain technology. By this method, the block-chaining of a student record begins from the time of admission to the Higher Education Institute (HEI) and continues to record the academic progress until graduation, having the graduation details stored as the last block in the chain. The whole blockchain of the student record will remain in the system with the participants enabling any indirect stakeholder to verify the details instantly based on the hash code or QR code. Additional privileges will be provided for direct stakeholders such as (here in Oman) the Ministry of Manpower and the Ministry of Higher Education to access further details of the certificate-holder. The system includes the student as a stakeholder and also as a participant to ensure transparency for her/his academic records.
A novel algorithm of dorsal hand vein image segmentation by integrating matched filter and local binary fitting level set model

The performance of dorsal hand vein image segmentation is limited due to low contrast and intensity inhomogeneity. In this paper, a novel method is proposed by integrating matched filter and local binary fitting level set model with the aim of overcoming the fault or incomplete segmentation in dorsal hand vein image. Following is the main work and contributions of this paper. First, 12-direction matched filters are adopted to enhance the vein patterns. Then, the local binary fitting level set model is introduced to segment the image enhanced by the first step. Third, a spurious vascular removal solution is presented to reduce the interference of metacarpal bones. 380 dorsal hand vein images collected from 69 subjects are used to evaluate the performance of the proposed algorithm. Compared with 5 existing vein segmentation methods, the proposed method achieves superior accuracy and shows great potential in image segmentation.
An Analysis of Supervised Machine Learning Algorithms for Spam Email Detection

Email users are increasing at a high rate and a huge number of people’s privacy is getting risked by spam email and it also kills valuable time of people most often. Spam email can be malicious as well as it can be of commercial use as in for marketing which are not desirable to us. Hence, detecting and filtering spam emails from several emails is a must work to do. There are enormous machine learning (ML) algorithms and some of them can be used to detect and analyze spam and unwanted emails. In this paper, we use the supervised ML technique on an existing email classification dataset where we explore Naïve Bayes, Support Vector Machine, Random Forest Classifier. Along with observing the accuracy from these algorithms, we showed other performance metric like precision, recall and F1 score etc. We got a high rate of accuracy in each algorithm such as we got 98.8%, 97.6%, 91.5%, 97.8%, 98.5% accuracy in Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Random forest classifier, Support vector machine (SVM) respectively.
Graph-Aware Collaborative Filtering for Top-N Recommendation

Recommender systems based on collaborative filtering has always suffered from sparsity and cold start problems. Therefore, researchers attempt to address the issues with various side information such as user profiles and item attributes. In this paper, we proposed Graph-aware Collaborative Filtering (GCF), an end-to-end framework, in which user-item bipartite graph and the knowledge graph of items (side information) are integrated to improve recommendation performance. In GCF, we aggregate the neighbors in the candidate item knowledge graph to refine the item representation. Similarly, we aggregate the user interaction neighbors in the user-item bipartite graph to refine the user representation. The collaborative signals in the knowledge graph and user-item bipartite graph are successfully captured through the neighborhood aggregation operation. Experimental results on three real datasets indicate that the proposed GCF is superior to the existing models in terms of accuracy and can also effectively solve the data sparsity problem of the recommender system.