Reviewerly/exHarmony huggingface dataset:
Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.
Two-sample Tests for Functional Data Using Characteristic Functions

In this paper, we consider the two-sample problem for univariate and multivariate functional data. To solve this problem, we use tool of characteristic function and a basis function representation of functional data. We construct test statistics for conformity of distributions based on a weighted distance between characteristic functions of random vectors obtained in basis representation. Different weight functions result in different test statistics, whose distributions are approximated by permutation method. Testing procedures are implemented in the R program and the code is available. Simulation study shows good finite sample properties of proposed methods, while real data example illustrates the application of them.
ALEM at CASE 2021 Task 1: Multilingual Text Classification on News Articles

We participated CASE shared task in ACL-IJCNLP 2021. This paper is a summary of our experiments and ideas about this shared task. For each subtask we shared our approach, successful and failed methods and our thoughts about them. We submit our results once for every subtask, except for subtask3, in task submission system and present scores based on our validation set formed from given training samples in this paper. Techniques and models we mentioned includes BERT, Multilingual BERT, oversampling, undersampling, data augmentation and their implications with each other. Most of the experiments we came up with were not completed, as time did not permit, but we share them here as we plan to do them as suggested in the future work part of document.
LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model

Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text.In this paper, we describe our solutions to tackle toxic spans detection.The first solution, which follows a supervised approach, is based on SpanBERT model.This latter is intended to better embed and predict spans of text.The second solution, which adopts an unsupervised approach, combines linear support vector machine with the Local Interpretable Model-Agnostic Explanations (LIME).This last is used to interpret predictions of learningbased models.Our supervised model outperformed the unsupervised model and achieved the f-score of 67,84% (ranked 22/85) in Task 5 at SemEval-2021: Toxic Spans Detection.
Industrial Internet of Things (IIoT) of Forensic and Vulnerabilities

The Industrial Internet-of-Things (IIoT) have changed the present world and future technology-based Industry 4.0, however the understanding of Industrial Internet of things (IIoT) has turned out to be big challenge as far as security concern. The main purpose of adopting and going with new technologies will bring new challenges with cybersecurity and will have more expose uncertain vulnerabilities in terms of AI and BI applications and usage with forensic investigation and accuracy of information sharing between smart devices. This paper composed on the utilization of Artificial Intelligence in securing required evidence for forensic investigation process. The legal methods are different as per region and industry, but the back-frame work and case-based thinking are similar. This framework is made from Intelligence systems such as AI and BI too dependent on the digital information from cloud server. The information from Business Intelligence (BI) and Artificial Intelligence (AI) intersects with data on cloud-based server requires more secure network process and firewall to prevent cyber intruders. This paper has discovered a few gaps on security issues and vulnerabilities where as it will cater proper IIoT based procedure for the Digital Forensic Investigation process.
AStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-Training

This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection.Our solution is built upon RoBERTa language model and Conditional Random Fields (CRF).We pre-trained RoBERTa on Civil Comments dataset, enabling it to create better contextual representation for this task.We also employed the semi-supervised learning technique of selftraining, which allowed us to extend our training dataset.In addition to these, we also identified some pre-processing steps that significantly improved our F1 score.Our proposed system achieved a rank of 41 with an F1 score of 66.16%.
Robust Image Hashing Based on Cool and Warm Hue and Space Angle

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.
SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model

This paper presents one of the top winning solution systems for task 7 at SemEval2021, "Ha-Hackathon: Detecting and Rating Humor and Offense".The shared task 7 consists of two parts, task-1 with three sub-tasks 1a,1b, and 1c, and task-2.The goal of task-1 is to predict if the text would be considered humorous or not, then if it is yes, predict how humorous it is and whether the humor rating would be perceived as controversial.The goal of task-2 is to predict how the text is considered offensive for users in general.The proposed solution, Sar-casmDet, has been developed using RoBERTa pre-trained model with ensemble techniques.The paper describes the submitted system's architecture with the experiments and the hyperparameter fine-tuning that led to this robust system.Our model ranked third and fourth places out of 50 teams in tasks 1c and 1a with F1-Score of 0.6270 and 0.9675, respectively.At the same time, the model ranked one of the top 10 models in task 1b and task 2 with RMSE scores of 0.5446 and 0.4469, respectively.
CapsTM: capsule network for Chinese medical text matching

Abstract Background Text Matching (TM) is a fundamental task of natural language processing widely used in many application systems such as information retrieval, automatic question answering, machine translation, dialogue system, reading comprehension, etc. In recent years, a large number of deep learning neural networks have been applied to TM, and have refreshed benchmarks of TM repeatedly. Among the deep learning neural networks, convolutional neural network (CNN) is one of the most popular networks, which suffers from difficulties in dealing with small samples and keeping relative structures of features. In this paper, we propose a novel deep learning architecture based on capsule network for TM, called CapsTM, where capsule network is a new type of neural network architecture proposed to address some of the short comings of CNN and shows great potential in many tasks. Methods CapsTM is a five-layer neural network, including an input layer, a representation layer, an aggregation layer, a capsule layer and a prediction layer. In CapsTM, two pieces of text are first individually converted into sequences of embeddings and are further transformed by a highway network in the input layer. Then, Bidirectional Long Short-Term Memory (BiLSTM) is used to represent each piece of text and attention-based interaction matrix is used to represent interactive information of the two pieces of text in the representation layer. Subsequently, the two kinds of representations are fused together by BiLSTM in the aggregation layer, and are further represented with capsules (vectors) in the capsule layer. Finally, the prediction layer is a connected network used for classification. CapsTM is an extension of ESIM by adding a capsule layer before the prediction layer. Results We construct a corpus of Chinese medical question matching, which contains 36,360 question pairs. This corpus is randomly split into three parts: a training set of 32,360 question pairs, a development set of 2000 question pairs and a test set of 2000 question pairs. On this corpus, we conduct a series of experiments to evaluate the proposed CapsTM and compare it with other state-of-the-art methods. CapsTM achieves the highest F-score of 0.8666. Conclusion The experimental results demonstrate that CapsTM is effective for Chinese medical question matching and outperforms other state-of-the-art methods for comparison.
Is Face Recognition with Masks Possible?

With the recent outbreak of the COVID-19 pandemic, wearing face masks has become extremely important to protect us, and to reduce the spread of the virus.This measure has made many existing face recognition systems ineffective as they were trained to work with unmasked faces.In this paper, several methods have been proposed for masked face recognition.Two pre-trained deep learning architectures (VGG16, and MobileNetV2) and the Histogram of Gradients (HOG) technique were used to extract the relevant features from face images of celebrities.A SoftMax layer and Support Vector Machines (SVM) were used for classification.Five scenarios were devised to assess the different models and approaches.With an accuracy of 96.8%, the best model was obtained with MobileNetV2 with a SoftMax layer on the dataset consisting of a mixture of masked and unmasked images.Three different types of masks were also used in this study.The mean accuracy was 91.35% when the same type of mask is used for training and testing.However, the accuracy dropped by an average of 5.6% when a different type of mask is used for training and testing.A contactless attendance system using the best masked face recognition model has also been implemented.
Deep Reinforcement Learning Boosted Partial Domain Adaptation

Domain adaptation is critical for learning transferable features that effectively reduce the distribution difference among domains. In the era of big data, the availability of large-scale labeled datasets motivates partial domain adaptation (PDA) which deals with adaptation from large source domains to small target domains with less number of classes. In the PDA setting, it is crucial to transfer relevant source samples and eliminate irrelevant ones to mitigate negative transfer. In this paper, we propose a deep reinforcement learning based source data selector for PDA, which is capable of eliminating less relevant source samples automatically to boost existing adaptation methods. It determines to either keep or discard the source instances based on their feature representations so that more effective knowledge transfer across domains can be achieved via filtering out irrelevant samples. As a general module, the proposed DRL-based data selector can be integrated into any existing domain adaptation or partial domain adaptation models. Extensive experiments on several benchmark datasets demonstrate the superiority of the proposed DRL-based data selector which leads to state-of-the-art performance for various PDA tasks.
The Effect of Using Social Media on Academic Performance of Faculty Members during Covid-19 Pandemic

The use of social media has increased in schools and universities due to the new norms of teaching and learning during the Covid-19 pandemic. However, this educational paradigm shift requires further surveys to explore the potentials and restrictions, particularly in the context of Afghanistan. Therefore, a quantitative method and descriptive survey design are adopted to examine the effect of using social media (Facebook, WhatsApp, and Telegram) on the academic performance (Mean Scores of GPA) of Afghanistan faculty members who are pursuing their postgraduate studies at Universiti Teknologi Malaysia (UTM). The participants are (N=103 male and female) who are randomly selected. The data is collected through a structured survey google form. Frequency and percentage statistical analysis, Independent-Samples T-test, and Independent-Measures ANOVA are performed as data analysis techniques. The results show a large number of WhatsApp users in terms of frequency and percentage among faculty members of Afghanistan who are using social media. The findings also examine that there is no significant difference between the mean scores of GPA in the academic performance of faculty members using different types of social media. In conclusion, the study contributes to establishing an in-depth understanding of the effect of using social media on the academic performance among faculty members based on the new policies of e-learning implementation by the Ministry of Higher Education during the Covid-19 pandemic.
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI Interaction

Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under differing assumptions, thus precluding the possibility of designing a single framework that captures these measures under the same assumptions. In this paper, we present a unifying Bayesian framework that models a human observer's evolving beliefs about an agent and thereby define the problem of Generalized Human-Aware Planning. We will show that the definitions of interpretability measures like explicability, legibility and predictability from the prior literature fall out as special cases of our general framework. Through this framework, we also bring a previously ignored fact to light that the human-robot interactions are in effect open-world problems, particularly as a result of modeling the human's beliefs over the agent. Since the human may not only hold beliefs unknown to the agent but may also form new hypotheses about the agent when presented with novel or unexpected behaviors.
Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study

Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is also found statistically significant.
An Efficient Velocity Estimation Approach for Face Liveness Detection using Sparse Optical Flow Technique

Objectives: To propose a new liveness detection algorithm using optical flow to ensure the presence of actual live face into a photograph or 2D masks in face recognition biometric security systems.Methods: This work proposes an antispoofing model namely Sparse Optical Flow Technique with Velocity Estimation Approach (SOFT_VEA).Optical flow is an effective method for tracking objects in motion.It is adapted in this work to capture facial movements and decide the liveness state.The proposed algorithm considers real faces and two kinds of photo imposters.Findings: From the input video, the motion information of specific facial landmark points is captured by an optical flow algorithm.Then, the velocity of those landmark points is estimated via Euclidean distance.Based on this calculated velocity, the fake face is discriminated from the real face using a threshold value.The Empirical study shows that the proposed face liveness detection model is effective with an accuracy of 88% and Half Total Error Rate (HTER) of 2.45.Novelty: The proposed work is based on real face and photo imposters.The liveness detection algorithm is developed with a novel velocity estimation approach.It is very helpful for biometric security systems.
Learning from Complementary Labels via Partial-Output Consistency Regularization

In complementary-label learning (CLL), a multi-class classifier is learned from training instances each associated with complementary labels, which specify the classes that the instance does not belong to. Previous studies focus on unbiased risk estimator or surrogate loss while neglect the importance of regularization in training phase. In this paper, we give the first attempt to leverage regularization techniques for CLL. By decoupling a label vector into complementary labels and partial unknown labels, we simultaneously inhibit the outputs of complementary labels with a complementary loss and penalize the sensitivity of the classifier on the partial outputs of these unknown classes by consistency regularization. Then we unify the complementary loss and consistency loss together by a specially designed dynamic weighting factor. We conduct a series of experiments showing that the proposed method achieves highly competitive performance in CLL.
Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling

Electronic healthcare records (EHRs) are comprehensive longitudinal collections of patient data that play a critical role in modeling the disease progression to facilitate clinical decision-making. Based on EHRs, in this work, we focus on sepsis -- a broad syndrome that can develop from nearly all types of infections (e.g., influenza, pneumonia). The symptoms of sepsis, such as elevated heart rate, fever, and shortness of breath, are vague and common to other illnesses, making the modeling of its progression extremely challenging. Motivated by the recent success of a novel subsequence clustering approach: Toeplitz Inverse Covariance-based Clustering (TICC), we model the sepsis progression as a subsequence partitioning problem and propose a Multi-series Time-aware TICC (MT-TICC), which incorporates multi-series nature and irregular time intervals of EHRs. The effectiveness of MT-TICC is first validated via a case study using a real-world hand gesture dataset with ground-truth labels. Then we further apply it for sepsis progression modeling using EHRs. The results suggest that MT-TICC can significantly outperform competitive baseline models, including the TICC. More importantly, it unveils interpretable patterns, which sheds some light on better understanding the sepsis progression.
Research on Visual Tracking Algorithm Based on Peak Sidelobe Ratio

Aiming at the problems of illumination changes, target deformation and background clutter in the target tracking field, a visual tracking algorithm based on peak sidelobe ratio is proposed.The object-interference model is used to represent the target appearance model, and context information is added to the relevant filtering framework.Training the filter internally to enhance the ability of filter discrimination.In the model update process, it is easier to introduce samples that cannot characterize the target, and use peak sidelobe comparison to update the tracking parameters, which can enhance the generalization ability of the model.Tested with some classic and recently algorithms in the OTB50, OTB100, UAV123, TC128 experimental video data set, the experiment' results show that the visual tracking algorithm that is proposed in the article can track the target more accurately.It has important research on the development of intelligent video surveillance value. INDEX TERMS Target tracking,
Uncertainty-aware Binary Neural Networks

Binary Neural Networks (BNN) are promising machine learning solutions for deployment on resource-limited devices. Recent approaches to training BNNs have produced impressive results, but minimizing the drop in accuracy from full precision networks is still challenging. One reason is that conventional BNNs ignore the uncertainty caused by weights that are near zero, resulting in the instability or frequent flip while learning. In this work, we investigate the intrinsic uncertainty of vanishing near-zero weights, making the training vulnerable to instability. We introduce an uncertainty-aware BNN (UaBNN) by leveraging a new mapping function called certainty-sign (c-sign) to reduce these weights' uncertainties. Our c-sign function is the first to train BNNs with a decreasing uncertainty for binarization. The approach leads to a controlled learning process for BNNs. We also introduce a simple but effective method to measure the uncertainty-based on a Gaussian function. Extensive experiments demonstrate that our method improves multiple BNN methods by maintaining stability of training, and achieves a higher performance over prior arts.
Referring expression generation from images via deep learning object extraction and fuzzy graphs

We propose an approach to obtain referring expressions for objects in images. Segmentation and categorization is performed via deep learning, providing the basis for a graph-based representation of the context that is later enriched with fuzzy properties. Referable objects and their corresponding expressions are obtained from the graph using extraction algorithms following a classical REG approach. The proposal is more flexible and scalable than end-to-end deep learning techniques.
Combining Reinforcement Learning and Causal Models for Robotics Applications

The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to improve their respective learning processes, especially in the context of our application area (service robotics). The preliminary results obtained so far are a good starting point for thinking about the success of our research project.