Reviewerly/exHarmony huggingface dataset:
Adjacency Pairs-Aware Hierarchical Attention Networks for Dialogue Intent Classification

Dialogue intent classification is a fundamental and essential task in dialogue systems. Although sentence-level and document-level text classification have made dramatic progress in recent years with the help of deep learning technology, dialogue-level classification remains challenging. Dialogue has unique characteristics that distinguish it from other types of text. Dialogue is interactive, with feedback between speakers, and turn-taking. These unique features suggest that model architecture should take dialogue structure into account to learn a better representation. In this paper we propose an Adjacency Pairs-Aware Hierarchical Attention Network (AP-HAN) for dialogue intent classification. A dialogue reconstruction strategy is designed to match the question and answer utterances properly and then make the dialogue to be presented as a sequence of adjacent pairs. Then, the adjacency pairs features are incorporated into the hierarchical attention network. Experimental results on public CCL2018-Task1 corpus show the better performance of the proposed model.
Optimized Reward Function Based Deep Reinforcement Learning Approach for Object Detection Applications

Reinforcement learning is considered a powerful artificial intelligence method that can be used to teach machines through interaction with the environment and learning from their mistakes. More and more applications are coming to the fore where Reinforcement learning has been newly and successfully implemented. It is frequently used especially in the game industry and robotics. In this article, a deep reinforcement learning approach, which uses our own developed neural network, is presented for object detection on the PASCAL Voc2012 dataset. Our approach is by moving a bounding box step-by-step towards the goal in order to fully frame the object in the picture. The created neural network consists of a 5-layer structure. In addition, it is aimed to maximize the mAP value by optimizing the reward function. The right choice in the reward policy will certainly affect the outcome and will play an important role in the training of the agent. Thanks to the optimized reward function, ground truth and the bounding box intersect at the highest rate, contributing positively to the result. As a result of the training that lasted for approximately 36 hours, the test results of 6 randomly selected classes were compared with the results of previous similar studies. Within the scope of this article, some artificial neural networks and basic studies in the literature using the Reinforcement learning approach for object detection are examined.
A dual-pathways fusion network for seeing background objects in light field

Background objects obscured in some sub-apertures of light-field cameras can be seen by other sub-apertures. Consequently, occluded surfaces are possible to be reconstructed from LF images. So far, Current foreground occlusion elimination approaches based on LF usually extract only the complementary information about background objects among different sub-aperture images to get an occlusion-free center view, which cannot get ideal performances in reconstructing visually realistic and semantically plausible pixels for occluded areas. In this paper, we suggest a easy but efficient LF foreground occlusions elimination way using a dual-pathways fusion network, which is a encoder-decoder network using convolution operations. In our method, we first construct all sub-aperture images(SAIs) as an input tensor and then render it to the encoder to incorporate information between SAIs. In particular, except for a pathway to synthesize center view, we also set another pathway to predict the foreground occlusion. By fusing these two pathways’ outputs, we not only reserve more information belonging to occluded surfaces but also fill the occluded regions with better visual effects. Experimental results indicate that our method is superior to the state-of-the-art approaches and the occlusion-free view looks more realistic. Our source codes will be available.
Towards A Common Speech Analysis Engine

Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-supervised representation learning-based systems are not yet considered state-of-the-art.We propose leveraging recent advances in self-supervised-based speech processing to create a common speech analysis engine. Such an engine should be able to handle multiple speech processing tasks, using a single architecture, to obtain state-of-the-art accuracy. The engine must also enable support for new tasks with small training datasets. Beyond that, a common engine should be capable of supporting distributed training with client in-house private data.We present the architecture for a common speech analysis engine based on the HuBERT self-supervised speech representation. Based on experiments, we report our results for language identification and emotion recognition on the standard evaluations NIST-LRE 07 and IEMOCAP. Our results surpass the state-of-the-art performance reported so far on these tasks.We also analyzed our engine on the emotion recognition task using reduced amounts of training data and show how to achieve improved results.
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds

Centroid based clustering methods such as k-means, k-medoids and k-centers are heavily applied as a go-to tool in exploratory data analysis. In many cases, those methods are used to obtain representative centroids of the data manifold for visualization or summarization of a dataset. Real world datasets often contain inherent abnormalities, e.g., repeated samples and sampling bias, that manifest imbalanced clustering. We propose to remedy such a scenario by introducing a maximal radius constraint r on the clusters formed by the centroids, i.e., samples from the same cluster should not be more than 2r apart in terms of ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distance. We achieve this constraint by solving a semi-definite program, followed by a linear assignment problem with quadratic constraints. Through qualitative results, we show that our proposed method is robust towards dataset imbalances and sampling artifacts. To the best of our knowledge, ours is the first constrained k-means clustering method with hard radius constraints. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Image calibration technology for radiation hardened CMOS image processor

Affected by the total ionizing dose, the CMOS image sensor degrades and the fixed pattern noise increases. In order to improve the removal efficiency and effect of the current fixed pattern noise, an image correction method based on statistical filtering and wavelet analysis was proposed. Firstly, the image is segmented by selective down sampling. Next, a particular shaped Gaussian filter kernel is used to blur the image. The fuzzy image and the original image are then statistical extracted and compared. Finally, after median processing, the correction result is obtained. Experimental results show that SFAWA has a better processing performance.
Improving Smart Cities Safety using Sanity-Check Deep Neural Network Algorithm

Nowadays, in a smart city, unmanned aerial vehicles (UAVs) have widespread usage to manage citizens&#x0027; requirements. Due to the widespread usage of UAVs, the safety of citizens has become a great concern as UAVs are also used for several malicious activities to spread vulnerabilities or carry explosives. Detection of UAVs is the most important step in keeping citizens safe. The hardware-level fault has a critical impact on deep neural network (DNN) model accuracy. The purpose of our study is to detect hardware-level faults in the DNN model and improve model accuracy. In our work, we have used the sanity-check DNN. Our implemented function improves the DNN accuracy for UAV detection. Our study improves the safety of citizens.
Using a Single Input to Forecast Human Action Keystates in Everyday Pick and Place Actions

We define action keystates as the start or end of an action that contains information such as the human pose and time. Existing methods that forecast the human pose use recurrent networks that input and output a sequence of poses. In this paper, we present a method tailored for everyday pick and place actions where the object of interest is known. In contrast to existing methods, ours uses an input from a single timestep to directly forecast (i) the key pose the instant the pick or place action is performed and (ii) the time it takes to get to the predicted key pose. Experimental results show that our method outperforms the state-of-the-art for key pose forecasting and is comparable for time forecasting while running at least an order of magnitude faster. Further ablative studies reveal the significance of the object of interest in enabling the total number of parameters across all existing methods to be reduced by at least 90% without any degradation in performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup>
In Pursuit of Preserving the Fidelity of Adversarial Images

Adversarial examples have emerged as a severe concern for the security of neural networks. However, the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> -distances, typically used as a similarity constraint, often fail to capture human perceived similarity. Under challenging scenarios, such as attacking a defended model, this discrepancy leads to the severe degradation of image fidelity. In this paper, we find adversarial examples that better match the natural distribution of the input domain by integrating signal processing techniques into the attack framework, dynamically altering the allowed perturbation with a Rule Adjustable Distance (RAD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</inf> ). The framework allows us to easily in-corporate structural similarity, Otsu’s method, or variance filtering to increase the fidelity of adversarial images while still adhering to an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> -bound.
Training Privacy-Preserving Video Analytics Pipelines by Suppressing Features That Reveal Information About Private Attributes

Deep neural networks are increasingly deployed for scene analytics, including to evaluate the attention and reaction of people exposed to out-of-home advertisements. However, the features extracted by a deep neural network that was trained to predict a specific, consensual attribute (e.g. emotion) may also encode and thus reveal information about private, protected attributes (e.g. age or gender). In this work, we focus on such leakage of private information at inference time. We consider an adversary with access to the features extracted by the layers of a deployed neural network and use these features to predict private attributes. To prevent the success of such an attack, we modify the training of the network using a confusion loss that encourages the extraction of features that make it difficult for the adversary to accurately predict private attributes. We validate this training approach on image-based tasks using a publicly available dataset. Results show that, compared to the original network, the proposed PrivateNet can reduce the leakage of private information of a state-of-the-art emotion recognition classifier by 2.88% for gender and by 13.06% for age group, with a minimal effect on task accuracy.
Approaches Toward Physical and General Video Anomaly Detection

In recent years, many works have addressed the problem of finding never-seen-before anomalies in videos. Yet, most work has been focused on detecting anomalous frames in surveillance videos taken from security cameras. Meanwhile, the task of anomaly detection (AD) in videos exhibiting anomalous mechanical behavior, has been mostly overlooked. Anomaly detection in such videos is both of academic and practical interest, as they may enable automatic detection of malfunctions in many manufacturing, maintenance, and real-life settings. To assess the potential of the different approaches to detect such anomalies, we evaluate two simple baseline approaches: (i) Temporal-pooled image AD techniques. (ii) Density estimation of videos represented with features pretrained for video-classification.Development of such methods calls for new benchmarks to al-low evaluation of different possible approaches. We introduce the Physical Anomalous Trajectory or Motion (PHANTOM) dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which contains six different video classes. Each class consists of normal and anomalous videos. The classes differ in the presented phenomena, the normal class variability, and the kind of anomalies in the videos. We also suggest an even harder benchmark where anomalous activities should be spotted on highly variable scenes.
Embedded low illumination enhanced network for object detection

Object detection under low lighting is still a serious challenge because feature details of a target in an image with low illumination are so indistinct that it is very difficult to extract sufficient features for target detection. Therefore, many lowilluminance enhancement networks are proposed for feature extraction. However, most of the current low-illuminance enhancement methods employ a single network to enhance the input images with different illuminances, resulting in the problem of excessive enhancement for some input images. And it is inevitable to produce excessive noise during the image enhancement. To address such two issues, we propose an embedded low illumination enhanced network based on the Scale-YOLOv4 object detection network, iteratively generating enhancement images with different illumination levels, and among the enhanced images, automatically selecting the one which is best suitable for target detection, and adding a noise removal module to eliminate the generated noise. The proposed embedded low illumination enhanced network is tested on our (ExDark + MS COCO unlabeled) data set, and the effect of 55.7% mAP (70.4% AP50) is achieved, which demonstrates the effectiveness of the proposed network.
Improved Point 5P Formula for Twisted Edwards Curve in Projective Coordinate Over Prime Field

Computer security is essential in today&#x2019;s world of technological advancement. Providing high security for constrained devices with limited capacity, memory, and power consumption has become one of the prominent challenges in computer security. Elliptic curve cryptography (ECC) is a powerful cryptography approach that generates security keys using the mathematics of elliptic curve (EC) to achieve an equivalent level of security with smaller key sizes. The efficiency of ECC depends on an EC operation known as scalar multiplication (SM). SM in Affine coordinate that has an inversion can be accelerated by employing projective coordinates. At the point arithmetic level, this paper improved the precomputed point by proposing a new point 5P formula in projective coordinates using EN for the Twisted Edwards curve over prime field. By comparison to the Twisted Edwards curve with temporary variables method, the algorithm using the proposed 5P formula saved 3M with 16.3% cost reduction. The proposed method also saved 3.2M, 7.2M, and 4.2M with 17.2%, 31.9% and 21.4% cost reduction, when compared to 5P using the Weierstrass curve with point addition, Edwards curve with points addition and doubling, and Edwards curve with computable and point addition methods respectively. The proposed point 5P can be applied to improve the ECC of SM algorithm.
Improving Class Activation Map for Weakly Supervised Object Localization

We propose a Weakly Supervised Object Localization (WSOL) method that can locate an object within a given image using a pre-trained network learned with only class labels without location annotations. Most existing WSOL methods rely on thresholding a Class Activation Map (CAM) generated by the pre-trained network to highlight and localize the object. However such approaches often produce incomplete object bounding boxes, as only the discriminative parts of the object are selected during thresholding. We revisit current CAM-based WSOL approaches and propose a pipeline to: (1) refine the CAM map using Weighted Global Average Pooling (WGAP), (2) recombine weights to make use of the negative features, (3) adaptively select a suitable threshold to achieve better object localization. Our method does not require additional learning or hyperparameter tuning. We show that our simple approach can achieve competitive results when evaluated on the CUB-200-2011 and ILSVRC 2016 datasets against other state-of-the-art methods.
Divergence-Guided Feature Alignment for Cross-Domain Object Detection

Domain shift causes performance drop in cross-domain object detection. To alleviate the domain shift, a prevailing approach is global feature alignment with adversarial learning. However, such simple feature alignment has defects of unawareness of fore-ground/background regions and well-aligned/poorly-aligned regions. To remedy the defects, in this paper, we propose a novel divergence-guided feature alignment method for cross-domain object detection. Specifically, we generate source-like images of the target domain and seek cues of foreground regions and poorly-aligned regions from prediction divergence of the source-like and original images. The feature alignment is guided by the divergence maps and consequently results in adaptation performance superior to alignment unaware of the cues. Different from most previous studies focusing on two-stage object detection, this paper is devoted to adapting one-stage object detectors which have simpler and faster inference. We validated the effectiveness of our method by conducting experiments in cross-weather, cross-camera, and synthetic-to-real adaptation scenarios.
Making The Unknown More Certain: A Stacked Ensemble Classifier for Open Gesture Recognition with a Social Robot

We introduce a novel stacked ensemble classifier for the unconstrained recognition of known and unknown gestural input data in nonverbal communication with a social robot. The architecture utilizes three separate CNNs of different expected data input size and combines their output predictions to a unified estimate. Analysis shows that in comparison to a single CNN architecture, the combined estimate reduces prediction confidence values for unknown gestural movement segments, making the system able to identify unknown data input with higher certainty under both laboratory and real environment conditions. In a human-robot interaction experiment, we are able to improve unknown class detection accuracy by up to 40% under maintained or equal known class recognition performance, and hence considerably enhance the overall robustness of the recognition system.
A study of Cyber-defense strategies adopted by major Public and Private sector Banks of India

The banking and financial services sector and banking institutions across the globe faces three hundred percent greater cyber-attacks as compared to any other industry. The increasing reliance of the modern banking sector on the internet and computer technologies to operate their businesses is the major factor behind the increasing threats and security breaches in recent years. The cyber-attacks against banks increased by 238% from February 2020 to April 2020. More and more banks are adopting Cyber Security frameworks to protect them against cyber-crimes. The goal of the study is to examine the cyber defense strategies adopted by major Public and Private sector banks of India. This paper analyzes the cyber-security and risk assessment measures taken by the banks of India. The study is a comparative analysis of private and public sector banks to know what banks are doing to protect themselves against cyber-attacks. Secondary data has been taken from the reports published by the Reserve Bank of India and the Annual reports of the Top Public and Private sector banks of India. The study provides implications to Banks for assessing their Cyber-security frameworks.
IoT in Fault Detection and Prediction

The objective of this research is to study industries using IoT with other technologies in fault detection and come up with a convenient and universal solution for fault detection. This paper will benefit practitioners, implementers, and managers in implementing it in their environment. It is a concept paper and we propose a solution to increase the uptime of the machines/systems. We will be collecting and analyzing qualitative data on the current trends of IoT in fault detection and performing our research on secondary data. We will be analysing different approaches made tackle faults in many different industries.By analyzing the industries, we would try to come up with a universal solution to help different industries in fault detection and predicting any faults beforehand. With the help of various other technologies for example cloud computing, machine learning, big data, and analytics the usability and universality of IoT have increased. It helps us in getting real-time stats for processing and making better business decisions.
Identification of Parasitized Single Cell from Normal Using Deep Learning Approach

Patients' cells need to be examined, thus healthcare facilities require changes as well as advancements in terms of instruments and technology, notably software that aids in the diagnosis of certain symptoms and diseases by looking at them. This aids in the identification and diagnosis of intracellular parasites in a person's cell, making it easier to identify a person's health condition. These parasites are responsible for a variety of acute and chronic illnesses. The paper aims to provide an enhanced model for cell classification. This will help to increase the accuracy of detection for intercellular parasites within the patient cell and easily diagnose a person&#x2019;s health condition. In response to that, the system implements a deep learning technique in cell categorization using the YOLOv3 algorithm. Having a model with 90.6% mean Average precision, made a cell classification with 99.06% precision determining whether the subjected single cell is parasitized or normal.
Chinese-Vietnamese cross-language topic discovery method based on generative adversarial networks

The cross-language news topic discovery task aims to cluster news texts in different languages that describe the same topic and classify the topic in the form of keywords. At present, most cross-language topic discovery methods are based on machine translation or external resources like bilingual dictionaries and parallel sentences to solve cross-language problems. However, Vietnamese is a low resource language and it is difficult and expensive to manually annotate ChineseVietnamese bilingual aligned corpora. To solve this problem, this paper proposes a Chinese-Vietnamese cross-language topic discovery method based on generative adversarial networks (GAN). Firstly, News texts are represented as vectors by BERT, and then the bilingual vectors are mapped to the same semantic space by GAN. Finally, k-means clustering algorithm is used to cluster the representation vectors and extract the topics. Experiments on the Chinese-Vietnamese bilingual news topic discovery corpus show that the proposed method is superior to the baseline.