Reviewerly/exHarmony huggingface dataset:
Granular Analysis of Pretrained Object Detectors

Object detectors have become the fundamental building blocks of many real-world machine learning applications. Even though different problem domains require their own unique object detector specifications, it is common practice to take a pretrained object detector off the shelf and either use it as-is or fine-tune it with limited amounts of labeled training data. However, the image distribution that such object detectors are trained on is more often times than not different from the targeted problem domain of interest. In this work, we scrutinize whether existing state-of-the-art object detectors have the ability to generalize across different domains. Specifically, we evaluate whether widely used pretrained state-of-the-art objectors such as Faster-RCNN and YOLOv3 generalize to images sampled from an autonomous vehicle application. For this purpose, we evaluate the performance of detectors on localizing humans and vehicles on images from the KITTI dataset and report results of detailed subgroup analysis on multiple factors. Our analysis shows that the detectors exhibit different levels of performance on varying levels of object-object occlusion and object size. Moreover, we report the performance drop of the object detectors with different image-altering hazardous factors.
A Zero-Watermarking Against Large-Scale Cropping Attack

Considering the digital copyright protection of the color image, and aiming at the shortcomings of the existing color image zero-watermarking algorithms against various attacks including image information loss, e.g., cropping, rows and columns removal, and smearing, etc. A color image zero-watermarking algorithm based on quaternion fractional-order generalized Laguerre moments (QFr-GLMs) is proposed. The novelties of the proposed scheme are as follows: (1) QFr-GLMs are applied the first time in the digital copyright protection based on color image zero-watermarking scheme; (2) The proposed zero-watermarking scheme can efficiently resist traditional noisy and smoothing filter attacks, moreover, when the image is attacked by large-scale cropping or smearing, the proposed zero-watermarking scheme can still detect the watermark correctly; Simulation experimental results prove that the performance of the proposed scheme is superior to that of the graying-based and the single channel-based zero-watermarking schemes.
A Siamese Network for real-time object tracking on CPU

Visual object tracking methods depend upon deep networks that can hardly meet real-time processing requirements on mobile platforms with limited computing resources. In this work, we propose a real-time object tracking framework by enhancing a lightweight feature pyramid network with Transformer architecture to construct a robust target-specific appearance model efficiently. We further introduce the pooling attention module to avoid the computation and memory intensity while fusing pyramid features with the Transformer. The optimized tracker operates over 45 Hz on a single CPU, allowing researchers to deploy it on any mobile device with limited power resources.
A Study on the Impact of Using Industry Standard Tools and Practices on Software Engineering Courses Projects

His overarching research interest is to investigate release engineering practices in software systems.His research works are driven by the desire to determine the empirical factors that lead to a successful software development and release.His research interests also include understanding the significance of software architecture for long lasting software systems and providing tool support to the community to nurture software feature architecture.Dr. Rahman is the first author who extracted feature-architecture while understanding the best practices of feature management and its impact on software architecture, which is another major research interest of his.Understanding and visualizing feature architecture is necessary to advance software development and engineering by maintaining a controlled architectural growth of software systems.Dr. Rahman is currently focusing on the following research areas: software feature-architecture, release management in trunk based development, software quality in trunk-based rapid-release cycles, developing knowledge-base to predict potential architectural drift using artificial intelligence.Tajmilur Rahman was a doctoral student at Concordia University.As a doctoral student he worked on empirical understanding on software projects with various lengths of release cycle, he extracted feature-architecture of Google Chrome web browser, he updated the 12 years old browser reference architecture and also he was working on software quality due to post-release bugs in a trunk based development environment.
Multi-Loss Function for Distance-to-collision Estimation

Estimation of the drone&#x2019;s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.
Unsupervised Person Re-identification via Multi-branch Network

Currently, the mainstream unsupervised method uses a clustering algorithm to cluster unlabeled data and generate labels for training samples based on the clustering results. The critical step is to obtain the features. Most unsupervised methods are based on single-branch networks. Still, multi-branch networks that fusion local and global features have been proved to improve supervised effectively. This paper studies the problem of unsupervised person re-identification using the multi-branch network. The goal is to extract more reliable feature representation through the multi-branch network and obtain a model with a more vital distinguishing ability. Specifically, an OSNet-based multi-branch network simultaneously extracts global, local, and channel features. It applies a bottom-up clustering algorithm based on the hierarchy to create labels for unlabeled training samples and uses Softmax-Triplet joint loss to optimize the model. We verify the proposed method on two benchmark re-ID datasets, such as the Market-1501 and DukeMTMC-reID datasets. Compared with the baseline method, the accuracy of Rank-1 and mAP are improved by 5.5 and 12.6% on Market-1501, and 5.5 and 7.6% on DukeMTMC-reID.
Performance Improvement Method of the Video Visual Relation Detection with Multi-modal Feature Fusion

Video visual relation detection is a novel research problem that aims to detect instances of visual relations of interest in a video. In this paper, we propose a performance improvement method of the video visual relation detection with multi-modal feature fusion. First, we introduce a spatial feature extraction method that is designed to include the relative positions of objects itself and between objects in the image. Next, we suggest a relationship classifier that is designed to accommodate the complexity of the input features. Our proposed method achieves 6.65 mAP, and ranked the 2nd place in the visual relation detection task of Video Relation Understanding Challenge (VRU), the ACM Multimedia 2020.
Cartoon-Texture Decomposition with Patch-Wise Decorrelation

Cartoon-Texture decomposition (CTD) is a fundamental task and has wide applications in image processing and computer vision. To enhance separation of the cartoon and texture, existing models explicitly introduce correlation terms to decorrelate the two components. However, existing correlations usually ignore the local geometric structure information. In this work, we propose the patchwise cosine similarity to decorrelate the cartoon and texture. The proposed decorrelation term takes the local geometric information into account and is more effective in separating cartoon and texture. Combining our decorrelation term with the RTV and div( L p ), we propose a new CTD model. Extended experiments show that the proposed model outperforms existing methods in CTD, especially in preserving edges of the cartoon.
A New Security Scheme for Cloud Storage Systems and Its Performance Evaluation using Avalanche Effect

We introduce a new protocol that achieves Shannon perfect secrecy performance using significantly lower key size than the original approach of one-time pad. We achieve this goal by taking advantage of high computational complexity of servers that is available in data centers. We demonstrate that there is a tradeoff between computational complexity and the size of the key. Our approach is based on redefining the perfect secrecy problem by producing keys for multiple packets simultaneously. Then, we use two intermediate keys called temporary keys and common keys. A unique combination of these two keys produces the final keys that are used for encryption. However, when we combine the common and temporary keys, we take advantage of the location of the final keys as a source of uncertainty. Our claim is that we can obtain the required entropy to achieve perfect secrecy from a combination of random bits and other sources of randomness in order to construct uniformly distributed and unique final keys. By doing this, we create the required entropy needed to achieve perfect secrecy. An iterative algorithm is introduced that allows us to reduce the ratio of final keys to the ratio of the information bits. In order to evaluate the performance of this approach, we use the avalanche effect and investigate the effectiveness of our approach in comparison with AES. Our results show that this technique succeeds in maintaining the avalanche effect requirement.
Instrumented Activity Dice for Assessing Throwing Performance: A Pilot Study

Assessment tests are crucial but often viewed as tedious work by the participants and the experimentalists. Consequently, research has been done to integrate assessments into serious games and to automate observations using computer vision or wearable sensors. However, the use of camera generates privacy concerns. Moreover, the behavior of a person can change when the person feels being recorded or when the person has to wear sensors. Hence, we investigate a different methodology to assess the physical status of a person - by integrating sensors into a tangible toy commonly used by the participants - an activity dice that can be embedded in serious games. We demonstrate that with this instrumented activity dice, limitations of throwing performance of a person can be assessed purely based on the recording taken from the activity dice when the person throws the activity dice. We further demonstrate that variables extracted from the dice are similar to the variables extracted from a video.
On Some Fuzzy Clustering Algorithms with Cluster-Wise Covariance

In many fuzzy clustering algorithms, the KL-divergence-regularized method based on the Gaussian mixture model, fuzzy classification maximum likelihood, and a fuzzy mixture of Student’s-t distributions have been proposed for cluster-wise anisotropic data, whereas more other types of fuzzification technique have been applied to fuzzy clustering for cluster-wise isotropic data. In this study, some fuzzy clustering algorithms are proposed based on the combinations between four types of fuzzification—namely, the Bezdek-type fuzzification, KL-divergence regularization, fuzzy classification maximum likelihood, and q-divergence-basis—and two types of mixture model—namely, the Gaussian mixture model and t-mixture model. Numerical experiments are conducted to demonstrate the features of the proposed methods.
Effect of the Period of the Fourier Series Approximation for Binarized Neural Network

The construction of low complexity models for the neural networks is an important issue in practical, real-world scenarios. One of the most famous construction methods for a simple neural network model is to represent weights and activations by the 1-bit quantization, called binarized neural networks (BNNs). However, it is still under research on how to represent the gradient in the backpropagation of BNNs because the activation function is the sign function whose gradients are zero almost everywhere. One way to address this problem is to approximate the gradient of the sign function by the Fourier series representation. In this paper, we analyze the effect of the period and the number of terms of the Fourier series representation on the network accuracy. Since the period has a direct relationship with the degree of the approximation for the sign function and the oscillation behavior of the gradient function, the choice of the period significantly affects the accuracy of the BNN model. The experiments on the CIFAR-10 dataset demonstrate that a proper choice of the period can outperform the conventional BNNs with straight through estimator.
Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for Online Video Services

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.
Utilizing Blockchain to Enhance the Privacy and Block Validity in Healthcare Systems

Healthcare systems have severe privacy and security concerns. Several solutions had been proposed to address the privacy and security issues; however, these solutions have several limitations. Block-chain technology has limited applicability in healthcare systems security because of many limitations in confidentiality and integrity of patient data. This research aims to improve privacy for efficient data collection with keyless signature infrastructure to create a reliable and secure environment. The proposed system consists of an enhanced privacy and block validity that generates a hash signature, block header, and blocks of data along with its public and private key pairs that ensure data authenticity. Differential privacy is used to increase the specific time for creating the file because it provides the contribution proof of each file. Our simulation studies results show minimum intrusion and found the probability of falsification attack in the range of 0-1. It provides an increase in differential privacy by 34.5 -35.6% and block validity by 13- 13.5%. The proposed system focuses on data transmission techniques that permit data subjects to monitor, agree, and notify the processing of their sensitive data r. Finally, this study enhances the security ad privacy issues of data transmission with blockchain technology during data transmission in healthcare systems.
Moment Detection and Monitoring System

This paper labels an actual interaction permitted eye stare tracing software that feet precision with an extraordinary, strict appraisal of a user in the centre. This camera trails skull actions upholding the user-centred. Sometimes while tracing errors may occur, a face from a webcam through a widespread area vision is needed to localize the eye and immediately recuperate the tracing procedure. A distinct form was broken for permitting the optimization of processing the image procedures established for this product. Subsequently, a standardization formula, a link of stare, is committed using the user flash vector. The vertical and horizontal points of the eyes have been authenticated by using iris summary, which eliminates the glint falsification owed to the differences in warp on the ocular sphere. These suggested processes define user centre having fill in-pixel firmness, reducing a quantity mistake in user flash vector. There three main phases which have a significant role in this product. They are mouth recognition, eye blink detection, and face movement detection, which play an essential role in moving the mouse cursor.
Clustering Methods from Proximity Variance Analysis

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Credit Card Fraud Detection Using K-Means Combined with Supervised Learning

The last decade has witnessed incredible growth in the credit card ecosystem, changing the dynamics of payment mechanisms. Credit cards provide ease of use and security to the customers, but there are various challenges in the domain. Recently, the surge in fraudulent cases involving credit cards has led to an increased risk of online transactions. Various financial institutions and researchers are trying to mitigate this problem by detecting fraudulent transactions. Therefore, we propose a novel approach by combining clustering and classification algorithms to identify credit card fraudulent transactions with high performance. The proposed system uses K-Means algorithms for clustering followed by different classification algorithms to construct the state-of-the-art credit-card fraud detection system. The proposed system based on random forest achieved the highest precision of 99.94%, recall 99.94%, F1-score 99.93%, and accuracy 99.9%.
A stereoscopic approach for cloud base height assessment over an optical ground station

Telecommunication and remote sensing data exchanged between Earth and space has increased dramatically in recent years. Radio frequencies, the medium used for several decades has shown to reach its capacity. Free space optical communications, achieved with laser transmitter devices, is an alternative with better transmission rate and increased security. However, a major drawback of this technology is that laser signal cannot cross through thick clouds, which imply to set up a network of optical ground stations with handover management strategies based on Cloud Optical Depth (COD) observation and Cloud Motion forecasts. Ground-based long-wave infrared observations from a sky imager can be used to estimate COD. However, as a low thin cloud may have similar thermal signature than a high thick cloud, additional Cloud Base Height (CBH) measurement is needed to retrieve accurate COD in any weather situation [1]. The CCSDS currently recommends to use a ceilometer for this purpose [2]. Here, we explore an alternative approach, retrieving CBH directly from a pair of thermal sky imagers and stereoscopic methods. Our CBH retrievals have been compared with the measurement of a reference ceilometer. The results showed a good agreement for a continuous period of acquisition over several days, with a Root Mean Squared Error of 15%. In addition, stereoscopy offers a wider field of view and thus, may increase the forecasting horizon and accuracy, compared to a sky imager+ceilometer configuration [3] [4] [5]. We believe this approach is promising for the cloud monitoring of Optical Ground Stations and should be further investigated.
The Impact of Purchasing Cyber Insurance on the Enhancement of Operational Cyber Risk Mitigation of U.S. Banks - A Case Study

Cyberspace is complex, fragile and risky resulting in potential financial loss, disruption, damage or destruction to enterprises from information technology systems failure. As a result, banks, as early adopters of information technologies, have been forced to deal with increasing threats, vulnerabilities and consequences of this evolving cyber risk. To counter non-systemic cyber risk, banks have turned to a myriad of cyber risk mitigation options, two of which are theorized to work in conjunction with each other, purchasing cyber insurance and enhancing operational cyber risk mitigation programs and activities, making the two collectively more effective at reducing the gap between cyber risk and cybersecurity. However, the literature, to date, supporting this theory is primarily theoretical due to the lack of empirical data. This case study addresses this deficiency by collecting and analyzing empirical data on the purchase of cyber risk insurance and the enhancement of operational cyber risk mitigation programs and activities. Using a mixed-method approach, the results found that while most of the cyber insurance variables in this study had either a random or negative impact on operational cyber risk mitigation programs and activities, three of the independent cyber insurance variables of how many years has your bank been purchasing cyber privacy insurance, is your bank's cyber risk, cyber network security or cyber privacy insurance coverage first-party (purchased by the bank) or is it third-party (purchased by your contractor(s)) and does your bank use a third-party contractor(s) to supply your IT and cybersecurity needs have both positive qualitative and quantitative results that enhance the dependent operational cyber risk mitigation functions variables in large banks in the State of New Jersey. These results are useful to both academic scholars and banking practitioners.
Automatic Dialect Detection for Low Resource Santali Language

Among different natural language processing (NLP) tasks, language detection is considered a difficult task especially for similar languages, varieties, and dialects. To build any tools or technologies in NLP, it is required reliable and robust language detection tool. Although such language detection tools and technologies are available for many high-resource languages, it is difficult to find them for low-resource languages due to the lack of training datasets. In this work, we compiled a language detection dataset for the low resource language Santali. We considered the Sanatali written in Odia script and standard Odia script. We used a deep supervised autoencoder for language detection. The deep supervised autoencoder can detect both Odia and Santali languages although written using the same Odia script. We obtained an accuracy of 100% on the test set. Our proposed dataset will be enriched Santali language and help researchers to build tools and technologies for this low resource language.
LGA-GAN: landmarks guided attentive generative adversarial network for facial expression manipulation

Recent advances in Generative Adversarial Networks (GANs) have shown impressive improvements for facial expression manipulation. However, previous methods still generate undesired artifacts and blurs in large-gap and large-angle situations. To address these problems, we propose a novel Landmark Guided Attentive GAN (LGA-GAN). A novel Expression Extraction Network (EENet) is proposed to extract expression-related features. At the heart of our method is a new landmark guided attentive (LGA) matrix that calculates where the expression of a pixel in the reference image should be applied in the synthesized result. With the help of LGA matrix and source image, the Expression Injection Network (EINet) decodes the transferred feature and outputs the synthesized image. Extensive experiments on both quantitative and qualitative evaluation demonstrate the improvements of our proposed approach.