Reviewerly/exHarmony huggingface dataset:
BadDet: Backdoor Attacks on Object Detection

Backdoor attack is a severe security threat which injects a backdoor trigger into a small portion of training data such that the trained model gives incorrect predictions when the specific trigger appears. While most research in backdoor attacks focuses on image classification, backdoor attacks on object detection have not been explored but are equally important. Object detection has been adopted as an essential module in various security-sensitive applications such as autonomous driving. Therefore, backdoor attacks on object detection could pose severe threats to human lives and properties. We propose four backdoor attacks and a backdoor defense method for object detection tasks. These four kinds of attacks can achieve different goals attacking: 1) Object Generation Attack: a trigger can falsely generate an object of the target class; 2) Regional Misclassification Attack: a trigger can change the prediction of a surrounding object to the target class; 3) Global Misclassification Attack: a single trigger can change the predictions of all objects in an image to the target class; and 4) Object Disappearance Attack: a trigger can make the detector fail to detect the object of the target class. We develop appropriate metrics to evaluate the four backdoor attacks on object detection. We perform experiments using two typical object detection models - Faster-RCNN and YOLOv3 on different datasets. Empirical results demonstrate the vulnerability of object detection models against backdoor attacks. We show that even fine-tuning on another benign dataset cannot remove the backdoor hidden in the object detection model. To defend against these backdoor attacks, we propose Detector Cleanse, an entropy-based run-time detection framework to identify poisoned testing samples for any deployed object detector.
VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver

This paper proposes the implementation of BCH Encoder in the ZYNQ-7000 AP SOC to guarantee the sensitive data that is acquired from capsule endoscopy is transmitted without any errors though a wireless medium. The BCH Encoder and Decoder is designed, Synthesized and Simulated using Xilinx Vivado. On successful Simulation, the code is dumped to the ZYNQ-7000 which features a single-core ARM Cortex™-A9 processor mated with 28 nm Artix-7-based programmable logic. ZYNQ-7000 is proposed because it is the most flexible & scalable platform for maximum reuse and best TTM, also Industry-leading design tools, C/C++, and Open CL design abstractions with Largest portfolio of SW & HW design tools, SoMs, design kits, and reference designs. To ensure the safety and security of sensitive encoded data, Encryption is done using the Triple DES algorithm. Triple DES is the sophisticated version of DES. It is a block cipher technique based on Feistel Structure and it uses symmetric-key block cipher which applies the DES algorithm thrice to each data block. TDES comprises a key bundle of three keys. By doing this sensitive data’s are secured, and provides no chances for stealing or modifying the data. The abstract should summarize the contents of the paper in short terms, i.e., 150–250 words.
Design of a Novel Side Chaining Model for Improving the Performance of Security Aware E-Voting Applications

Electronic voting (E-Voting) has been described as one of the most efficient methods of collecting consensus-based decisions about a particular entity. These systems are useful for a wide variety of application scales, which range from selecting candidates at small corporations to nationwide elections. But voting systems face inherent security and quality of service (QoS) issues, which limits their public-domain deployments. Due to many sources of vulnerability, such as mutability, poor traceability, reduced trust levels, and centralized computing design, these systems are susceptible to attack by hackers and other adversaries. The usage of blockchain-based computing models, in which each set of votes is translated into a transaction, and these transactions are kept inside smart contracts, can be used to tackle these problems. These smart contracts are turned into blocks and kept in a decentralized blockchain database for storage. This database uses an improved unidirectional linked list, where each block is connected to the next block via a unique hash value. Due to the uniqueness of connecting hashes, this model exhibits immutability, which is one of the main reasons for its use in e-Voting systems. Secondly, hashes are generated using a decentralized mining mechanism, due to which the blockchain database is stored on multiple nodes, and is resilient against denial of service (DoS), Sybil, masquerading, and other server-based attacks. Similarly, the blockchain model also possesses transparency, and traceability, which makes it an ideal candidate for e-Voting systems. But the delay of voting increases exponentially w.r.t. the number of transactions, which is due to the fact that the addition of each block requires mining nodes to generate a new unique hash, which requires scanning of the entire blockchain. This limits the scalability of the blockchain model, which makes it unusable for larger-scale networks. In order to remove this drawback, a novel sidechaining mechanism is proposed in this text, wherein sidechains are created and managed using a firefly optimization model, which uses a number of parties and cast votes per party parameters. Due to this dynamic model for sidechain creation and management, the proposed method is capable of reducing transaction delay by 28% when compared with a single blockchain, and 16% when compared with static sidechain methods. Additionally, the model was tested on medium to large-scale e-Voting applications, and it was discovered that, when compared to other cutting edge models, it is capable of improving throughput by 8% and reducing storage cost by 18%. The proposed sidechain paradigm can be used for a wide range of e-Voting application deployments as a result of these benefits.
Checking the Box on the Efficiency of CME Bitcoin Futures Options

The adoption of new financial instruments is naturally met with skepticism and apprehension. Capital markets as we know them today possess the amazing capability to package any exposure into a digestible instrument for market participants to utilize. However, prudence requires us to verify that these instruments do not suffer from inefficiencies that may prove hazardous to investors. In this article, the authors verify the efficiency of CME Bitcoin Futures Options by testing boundary arbitrage, put-call parity arbitrage, and box spread arbitrage conditions. The results strongly suggest that no reasonable arbitrage opportunities exist and that CME Bitcoin Futures Options are well-suited for institutional scale investing. Hence, we believe their use by institutions to hedge, speculate, and/or facilitate transactions between decentralized markets (“DeFi”) and traditional markets (“TradFi”) will grow significantly in the next few years.
Big Data Analytics Using Artificial Intelligence

Data analytics using artificial intelligence is the process of leveraging advanced AI techniques to extract insights and knowledge from large and complex datasets [...]
A Vocabulary-Based Framework for Sentiment Analysis

The variety of knowledge analysis that is earned from the news reports, user reviews and social media updates or a small blogging website is named opinion mining. Its associated approaches are employed to analyse the sentiments provided as input file. The reviews of people towards events, brands, product, or company are often legendary part of sentiment analysis. The responses of general public are collected and measured temporarily by researchers to perform evaluations. The recognition of sentiment analysis is growing these days since the numbers of views being shared by folks on the small blogging sites are increasing. All the emotions are often categorized into three totally different classes referred to as positive, negative and neutral. We have used lexicon-based approach to determine the scores of sentiment sentences. Python language is employed during this analysis to implement the classification algorithmic programme on the collected knowledge.
Virtual Reality Gaming in Rehabilitation after Musculoskeletal Injury—User Experience Pilot Study

The purposes of this study were twofold: (1) to describe a new technological solution for the rehabilitation of musculoskeletal injuries based on virtual reality (VR) gaming, and (2) to analyze the variation in rated perceived exertion (RPE) and heart rate (HR) of the participants when undergoing a rehabilitation session based on different VR games. Thirty-seven participants aged 23.7 ± 7.0 years tested during five customized VR games to provide a complete rehabilitation session after a musculoskeletal injury. The results of the Friedman test indicated a statistically significant difference in RPE scale and HR across the five VR games throughout a complete rehabilitation session (χ2 (4, n = 35) = 75.59; p &lt; 0.001 and χ2 (4, n = 35) = 27.75, p &lt; 0.001, respectively). RPE and HR increased significantly from Game 1 to Game 2 (z = −5.16, p &lt; 0.001), from Game 1 to Game 3 (z = −5.05, p &lt; 0.001), from Game 1 to Game 4 (z = −4.87, p &lt; 0.001), and from Game 1 to Game 5 (z = −3.61, p &lt; 0.001). Moreover, the results showed a high perceived usability of the system, greater intrinsic motivation to perform the rehabilitation exercises, a high level of immersion, and a good experience in the VR gaming environment. Our study stimulates extended intervention programs following-up on this immersive virtual reality rehabilitation system to support soccer players recovering from musculoskeletal injuries.
Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection

Reliable spatial uncertainty evaluation of object detection models is of special interest and has been subject of recent work. In this work, we review the existing definitions for uncertainty calibration of probabilistic regression tasks. We inspect the calibration properties of common detection networks and extend state-of-the-art recalibration methods. Our methods use a Gaussian process (GP) recalibration scheme that yields parametric distributions as output (e.g. Gaussian or Cauchy). The usage of GP recalibration allows for a local (conditional) uncertainty calibration by capturing dependencies between neighboring samples. The use of parametric distributions such as Gaussian allows for a simplified adaption of calibration in subsequent processes, e.g., for Kalman filtering in the scope of object tracking. In addition, we use the GP recalibration scheme to perform covariance estimation which allows for post-hoc introduction of local correlations between the output quantities, e.g., position, width, or height in object detection. To measure the joint calibration of multivariate and possibly correlated data, we introduce the quantile calibration error which is based on the Mahalanobis distance between the predicted distribution and the ground truth to determine whether the ground truth is within a predicted quantile. Our experiments show that common detection models overestimate the spatial uncertainty in comparison to the observed error. We show that the simple Isotonic Regression recalibration method is sufficient to achieve a good uncertainty quantification in terms of calibrated quantiles. In contrast, if normal distributions are required for subsequent processes, our GP-Normal recalibration method yields the best results. Finally, we show that our covariance estimation method is able to achieve best calibration results for joint multivariate calibration. All code is open source and available at https://github.com/EFS-OpenSource/calibration-framework .
Survey on artificial intelligence-based human action recognition in video sequences

In the artificial intelligence domain, human action recognition (HAR) has evolved as one of the major active research topics as a reason of diverse applications, namely video surveillance. The extensive variation types amidst routine human activities make the recognition procedure much more intricate. As a reason for an increment in the cameras’ usage, automated systems are essential to categorize activities, such as these utilizing computationally intelligent methodologies, namely machine learning and deep learning (DL). This paper’s organization covers all the characteristics of the HAR’s general framework. This work talks about the public dataset’s characteristics utilized that are aimed at HAR. After that, it summarizes and classifies the recently published research enhancement under the common framework. The merits and demerits of dimensionality reduction, action representation, and as well as action analysis methodologies are offered. After that, the outcomes exhibit the DL methodologies’ performance via graphs. This article gives a clear vision of what we can explore in the activity recognition area. The work talks about HAR’s challenges and its future directions.
Coverage Motion Planning Based on 3D Model’s Curved Shape for Home Cleaning Robot

Research on the automated control of robots for wiping curved surfaces includes studies on cleaning contaminated areas and controlling the end-effector posture to apply a constant force along curved surfaces. However, such robots should also clean difficult-to-find contaminants such as dust and dirt. One of previous studies has considered motion generation as a generalized traveling salesman problem. In particular, a point cloud is acquired from an RGBD camera, and a large graph is created to represent the surface based on the point cloud. The system then finds an appropriate end-effector posture for each node and sets up multiple coordinate systems. Consequently, an efficient cleaning motion can be generated; however, path generation using the previous study is a highly time-consuming process. Therefore, in this study, a 3D model is used to generate cleaning actions more efficiently. A point cloud is then generated from the mesh data of the 3D model, based on which the surface is represented as a simple graph that can be solved as a traveling salesman problem, thereby reducing the computational time. The optimal end-effector posture is determined based on the shape of the surface and is set as the coordinate system for each node. Finally, experiments are conducted to compare the cleaning results with the previous study results, thereby verifying that results can be obtained in less than one-tenth the computational time required for the previous study results.
Assessment of Student Satisfaction with Technology and Organization of the Learning Process at the University

A university graduate must possess a set of necessary competencies that meet modern requirements. The authors identify three groups of competencies, in addition to professional knowledge. The first group includes language competences. They allow you to know the skills of business communication in a foreign language, know the international professional terminology, and understand technical documentation that has not been translated. The second group includes social competences. The language of socio-cultural communications allows university graduates to fully interact in a multicultural and multi-ethnic environment. The third group includes IT competences of the future - knowledge of different programming languages, digital services, application in practice of modern technologies, information management methods, and ways to create information systems. Today, students are focused on the needs of employers. The labor market values both professional and additional competences of university graduates. This, in turn, increases students’ requirements to the content, process, learning technologies and forms of learning activities. The article deals with the results of a survey to identify the level of student satisfaction with various aspects of the educational process. Regular assessment of student satisfaction with technologies and organization of the learning process allows: 1) identify problems and improve the quality of educational services provided; 2) shift the focus from purely professional competencies to extended multilingual: language, social, IT competencies. The results obtained can be used in the organization of the educational process in higher education institutions.
MMCAN: Multi-Modal Cross-Attention Network for Free-Space Detection with Uncalibrated Hyperspectral Sensors

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. However, there exist cases where sensors cannot be well-calibrated. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. The method can be directly used on the pictures taken in the field without complex preprocessing. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations.
GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images

Predicting patients’ survival from gigapixel Whole-Slide Images (WSIs) has always been a challenging task. To learn effective WSI representations for survival prediction, existing deep learning methods have explored utilizing graphs to describe the complex structure inner WSIs, where graph node is respective to WSI patch. However, these graphs are often densely-connected or static, leading to some redundant or missing patch correlations. Moreover, these methods cannot be directly scaled to the very-large WSI with more than 10,000 patches. To address these, this paper proposes a scalable graph convolution network, GraphLSurv, which can efficiently learn adaptive and sparse structures to better characterize WSIs for survival prediction. GraphLSurv has three highlights in methodology: (1) it generates adaptive and sparse structures for patches so that latent patch correlations could be captured and adjusted dynamically according to prediction tasks; (2) based on the generated structure and a given graph, GraphLSurv further aggregates local microenvironmental cues into a non-local embedding using the proposed hybrid message passing network; (3) to make this network suitable for very large-scale graphs, it adopts an anchor-based technique to reduce theorical computation complexity. The experiments on 2268 WSIs show that GraphLSurv achieves a concordance-index of 0.66132 and 0.68348, with an improvement of 3.79% and 3.41% compared to existing methods, on NLST and TCGA-BRCA, respectively. GraphLSurv could often perform better than previous methods, which suggests that GraphLSurv could provide an important and effective means for WSI survival prediction. Moreover, this work empirically shows that adaptive and sparse structures could be more suitable than static or dense ones for modeling WSIs.
Histopathological Classification of Colorectal Polyps using Deep Learning

Early diagnosis and classification of colorectal polyps are critical in reducing the morbidity and mortality rate of colorectal cancer (CRC). This paper proposes an automated method for histopathologically classifying colorectal polyps from 7000 µm H&E-stained images. First, a number of state-of-the-art deep learning models are developed and fine-tuned using transfer learning and ImageNet pre-trained weights. Subsequently, a baseline architecture is selected by comparing the trained models, and its performance is then optimized using data augmentation methods such as rotation, rescaling, mixup and cutout. Moreover, an extended variant of the adaptive moment estimation (Adam) optimizer called rectified Adam (Radam) and label smoothing are also used to boost the model performance. Based on the experimentation results using an open dataset, the proposed method achieved an accuracy of 90%, a precision of 90%, a recall of 89% and an F1-score of 0.91%.
Dynamic Feature Aggregation for Efficient Video Object Detection

Video object detection is a fundamental yet challenging task in computer vision. One practical solution is to take advantage of temporal information from the video and apply feature aggregation to enhance the object features in each frame. Though effective, those existing methods always suffer from low inference speeds because they use a fixed number of frames for feature aggregation regardless of the input frame. Therefore, this paper aims to improve the inference speed of the current feature aggregation-based video object detectors while maintaining their performance. To achieve this goal, we propose a vanilla dynamic aggregation module that adaptively selects the frames for feature enhancement. Then, we extend the vanilla dynamic aggregation module to a more effective and reconfigurable deformable version. Finally, we introduce inplace distillation loss to improve the representations of objects aggregated with fewer frames. Extensive experimental results validate the effectiveness and efficiency of our proposed methods: On the ImageNet VID benchmark, integrated with our proposed methods, FGFA and SELSA can improve the inference speed by $$31\%$$ and $$76\%$$ respectively while getting comparable performance on accuracy. Codes are available at https://github.com/YimingCuiCuiCui/DFA .
Neural Architecture Search Using Differential Evolution in MAML Framework for Few-Shot Classification Problems

Model-Agnostic Meta-Learning (MAML) algorithm is an optimization based meta-learning algorithm which aims to find a good initial state of the neural network that can then be adapted to any novel task using a few optimization steps. In this study, we take MAML with a simple four-block convolution architecture as our baseline, and try to improve its few-shot classification performance by using an architecture generated automatically through the neural architecture search process. We use differential evolution algorithm as the search strategy for searching over cells within a predefined search space. We have performed our experiments using two well-known few-shot classification datasets, miniImageNet and FC100 dataset. For each of those datasets, the performance of the original MAML is compared to the performance of our MAML-NAS model under both 1-shot 5-way and 5-shot 5-way settings. The results reveal that MAML-NAS results in better or at least comparable accuracy values for both of the datasets in all settings. More importantly, this performance is achieved by much simpler architectures, that is architectures requiring less floating-point operations.