Reviewerly/exHarmony huggingface dataset:
K-Zones: a Machine Learning-Based System to Estimate Social Distancing Violations During Pandemic Eras

The outbreak of pandemics adversely influences various aspects of people's lives, including economies, education, careers, and social relations. Therefore, many authorities worldwide resort to imposing social distancing regulations to flatten the curve of new confirmed cases. This paper proposes a Machine Learning-based social distancing violation detection system. Unlike many contributions in the literature that use pairwise distance computation running in quadratic execution time, this paper introduces a novel technique that runs in linear time. The solution is considered a Video Surveillance System, and the experimental results show how the system effectively detects not only social distancing violations but also the severity of those violations.
Enhanced Task Scheduling Mechanism in Logistics Based on Improvised Round Robin Technique

Task mechanization gives the fundamental capacity to plan the executives tasks by utilizing task scheduler in cloud computing. An important element of any cloud computing system is efficient task scheduling. Cloud computing system and has a direct impact on resource utilization. Task Manager is a product which show every one of the running projects, cycles and administration on the framework. At the point when more than one clients are associated with the framework the undertaking supervisor can be utilized to see what all identity is associated with the framework. In which tasks are being performed by them and it can likewise send messages to the different hubs associated with the framework. Overseeing and characterizing numerous assignments over various physical and advanced work areas are finished by utilizing different strategies. The cloud environment would be able to offer high Quality of Service (QOS) if the scheduling algorithm performs effectively. In this paper focused on the resource management based tasks where the executives devices can uphold the task on a work area. The vital idea in any of the working framework which permits the specific framework to help performing various tasks framework and so forth is Task Scheduling. The proposed Enhanced Round Robin (ERR) scheduling algorithm will be introduced and evaluated in this paper. By taking in consideration of delayed product details like bandwidth, storage, processing capacity and task deadline. The proposed method helps in reducing the task migration and task completion time. By analysing the proposed algorithm’s performance with the current approaches, it’s performance is studied.
A Novel Approach for Detecting the Malignant Features of Breast Cancer using Algorithms of ML

Machine learning (ML) is trending knowledge tool for finding disease. It facilitates systems that can learn data automatically. Breast cancer (BC) is second largest disease among the women. In all over world 50 % women are dying due to the BC. ML is used in finding the results in BC as malignant or benign because predication of BC at early stage is going to be very challenging. This proposed work is to identify the BC disease before time stage with the help of machine learning algorithms viz. K-Nearest Neighbor, Random Forest, Naive Bayes, Support Vector Machine (SVM) & Decision Tree. Herein projected work, the dataset used has been collect from the UCI repository. In Breast cancer dataset Out of total 450 samples, 150 samples are found either benign or malignant. The results obtained to achieve higher performance measures show that RF gives best outcome by smallest amount inaccuracy rate.
Robust Monitor for Industrial IoT Condition Prediction

The robustness of machine learning (ML) models has gained much attention along with their wide application on various safety-required Industrial Internet of Things (IIoT) paradigms. Researchers found that some specific attacks added on sensor measurements can maliciously disturb IIoT monitors that are designed using ML architectures. The Traditional detection methods could judge whether the measurements are attacked to prevent the failure of monitors. Unfortunately, recent works argue that the commonly used detection methods could be circumvented through adaptive attacks that could acquire the mechanism of detectors; they could not truly enhance the robustness of ML models. Instead, general robust mechanisms should be performed to authentically enhance the robustness of models against any potential attacks with specific restrictions. On the basis of the above argument, we design a robust condition monitor for predicting the fault condition of IIoT systems using the adversarial training technique called robust temporal convolutional network (RTCN). The model is designed to be formally robust to attacks with restricted magnitude. The temporal convolutional network (TCN) is employed to design the base structure of the monitor. TCN can capture temporal information from sensors to enhance the feature extraction performance of models. We also present a novel false data injection (FDI) attack-generating method that utilizes the conception of adversarial perturbations to disturb well-trained monitors. The experimental results verify the efficiency of feature extraction performance of our model from IIoT systems. Furthermore, adversarial training mechanism through a min–max manner could effectively improve the reliability of ML-based IIoT monitors against strong FDI attacks.
A Few-Shot Relation Extraction Method for Enhancing Entity Attention

The aim of the few-shot relation extraction (FSRE) method is to study the relation classification problem with fewer samples. An effective few-shot relation extraction model EnAttConceptFERE is proposed to effectively classify relationships through externally introduced entity concept information and the greater use of internal information. First, we introduce an entity-level vector representation, which uses selects appropriate entity concepts by comparing the similarity between the semantics of entity pairs in a sentence and the semantics of the concepts corresponding to the entities. In addition, the access to external resources is often limited and the introduction of noise cannot be avoided. Therefore, this paper is based on fully mining the effective information of the sample itself, and by introducing the entity self-attention module, the model can pay greater attention to the information of entity pairs that have an impact on relationship extraction. In order to verify the performance of EnAttConceptFERE, experiments are conducted on the FSRE benchmark dataset FewRel. Under the few-shot task setting of 5 way1shot (N=5,K=1) and 10way1shot (N=10,K=1), the accuracy rate is improved by 2.53% and 1.06%, and under the task setting of 5way5shot(N=5,K=5), the accuracy was improved by 1.31% compared with the TD-Proto model, demonstrating the effectiveness and superiority of the EnAttConceptFERE model.
On Interval Perturbation of the ɑ-Symmetric Implicational Algorithm

Perturbation is an important property of fuzzy reasoning algorithms. It is an important task to estimate the upper and lower bounds of the output after a perturbation (such as an interval perturbation) is given to the input. In this study, the interval perturbation problem of a fuzzy reasoning algorithm, i.e., the ɑ-symmetric implicational algorithm, is researched systematically. For FMP (Fuzzy Modus Ponens) and FMT (Fuzzy Modus Tollens) problems of fuzzy reasoning, aiming at the case of a single rule, the estimation for the upper and lower bounds are obtained for the input interval disturbance, in which R-implications and (S, N)-implications are mainly employed. Then the stability is verified on account of the upper and lower bounds. Following that, for the multi-rule case, the upper and lower bounds of the output are also given, and the corresponding stability is validated. In conclusion, it is found that the ɑ-symmetric implicational algorithm has good stability.
Deep Learning

Abstract The core of this book are deep learning methods and neural networks. This chapter considers deep feed-forward neural (FN) networks. We introduce the generic architecture of deep FN networks, and we discuss universality theorems of FN networks. We present network fitting, back-propagation, embedding layers for categorical variables and insurance-specific issues such as the balance property in network fitting, as well as network ensembling to reduce model uncertainty. This chapter is complemented by many examples on non-life insurance pricing, but also on mortality modeling, as well as tools that help to explain deep FN network regression results.
Regression Approach to Lossles Compression Algorithm for Hyperspectral Images

At present, there is a significant increase in interest in solving applied problems using hyperspectral aerospace images obtained from satellites of spacecraft for remote sensing of the Earth. Hyperspectral images show significant spectral correlation, the use of which is critical for compression. In this paper, we propose an efficient approach to hyperspectral image compression using a lossless regression algorithm. The main idea of the proposed transformation is an algorithm with finding pairs of correlated channels and then creating lossless transformed blocks using regression analysis, which makes it possible to reduce the size of the aerospace image channels and transform them before compression.
Incremental Two-Stage Logo Recognition with Knowledge Distillation

The recognition of logos can be useful in developing autonomous checkout systems, or monitoring brand presence and advertisement in shopping malls. The continuous generation and update of new brand logos imposes the definition of a flexible solution to the problem. We therefore define a two-stage logo recognition system composed of an agnostic logo detector, to locate image regions that possess generic logo-like characteristics, and an incremental logo classifier, to progressively update the set of known logo classes. We investigate our solution's sensitivity to regularization and availability of training samples, and we develop two alternative techniques for model compression. Results are presented and compared with state of the art solutions, showing promising results. Our code is made available for public download.
Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts

Automatic summarization captures the most relevant information and condenses it into an understandable text in natural language. Such a task can be classified as either extractive or abstractive summarization. Research on Brazilian Portuguese-based abstractive summarization is still scarce. This work explores abstractive summarization in Portuguese-based texts using a deep learning-based approach. The results are relatively satisfactory considering the ROUGE measurements and the quality of the generated summaries. Still, there are some problems regarding coherence, readability, and grammar. We strongly believe they are linked to the inherent complexity of generating an abstract and the degradation of text quality by the translation steps. These results should be seen as preliminary, serving as a basis for future research.
Container Lead Seal Detection Based on Nano-CenterNet

In the process of container loading and unloading, manual inspection is still used in the process of container lead seal inspection, which has the problems of low efficiency, high labor cost, and high safety risk. Using visual object detection technology to replace manual lead seal automatic detection technology is an effective way to improve the efficiency of container operation. To address the problem of the tiny area of the seal in the image, the significant variation in scale, and the random location of its appearance, this paper proposes a Nano-CenterNet model. Based on the CenterNet, the lightweight feature extraction network is introduced, and the lightweight feature fusion network is added; the enhancement module was used to enhance the small object feature. The loss function of the algorithm is optimized to improve the imbalance between positive and negative samples. The Nano-CenterNet model was applied to the detection of container lead seals. The 3200 samples collected at the port entrance were used as the training set, and 400 samples were used as the test set. The measured precision rate was 96.5%, the recall rate was 95.4%, and the detection speed reached 18FPS, which met industrial application requirements.
An Improved Ciphertext Deduplication Scheme Based on Ciphertext Feature Transformation

Due to the diversity of information dissemination, the data forms produced are ever-changing, and the amount of accumulated data is increasing day by day, making the data in the network transmission exponential growth. On the premise of protecting data security and privacy, users choose to encrypt data and upload it to the cloud server. This paper proposes an improved ciphertext deduplication scheme based on ciphertext feature transformation. To solve the problem that different users encrypt the same data and generate different ciphertext, the linear equations are used to convert the key into a consistent form, so that users can download the same file uploaded by other users. The safety analysis shows that the scheme has high practicability and safety.
Minimal Hand Pose Estimation for Touchable Projector-Depth Systems

Projector-depth systems (PDS) have the potential to transform any horizontal area into a touchable surface. However, robust touch detection in such systems is difficult to achieve due to measurement noise. We propose a machine learning based approach for a minimal hand pose estimation that relies solely on depth information. Our model enables the localization of fingertips on the surface, which can then be used for touch detection. This improves robustness by considerably minimizing the margin for noise-based errors. Compared to previous works, our method does not depend on any spatial pre-processing, but can be trained end-to-end. To generate the dataset, an efficient and autonomous data recording procedure has been implemented. Eventually, a dataset of 10'450 samples originating from 29 different test users was collected. The proposed model achieves a mean average precision (mAP) of 97.3 ± 2.8e-2 and a mean absolute error (MAE) of 15.2 ± 3.0 pixels using 5-fold cross-validation. Our approach enables accurate detection and localization of multiple hands in real time, making a valuable step towards identifying whole gestures in PDS.
TAFIM: Targeted Adversarial Attacks Against Facial Image Manipulations

Face manipulation methods can be misused to affect an individual's privacy or to spread disinformation. To this end, we introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by causing the manipulation model to produce a predefined manipulation target (uniformly colored output image in our case) instead of the actual manipulation. In addition, we propose to leverage differentiable compression approximation, hence making generated perturbations robust to common image compression. In order to prevent against multiple manipulation methods simultaneously, we further propose a novel attention-based fusion of manipulation-specific perturbations. Compared to traditional adversarial attacks that optimize noise patterns for each image individually, our generalized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones (Project Page: https://shivangi-aneja.github.io/projects/tafim ).
Multi-Sequence Dilated Network for Object Detection

Scale variation is one of the key challenges in the object detection. Most previous object detectors remedy this by using dilated convolution to enlarge the receptive fields of the vanilla convolutional layers. However, these methods focus on either the spatial information of small objects or the semantics of middle and large objects, which still fail to effectively adapt the scale variance of different objects, resulting in a sub-optimal performance for the object detection. In this paper, we propose a novel Multi-Sequence Dilated Network (MSDN) that stacks different dilated convolutions with different orders in parallel for improving the performance of the object detection. Concretely, MSDN contains a sequential dilated module and a dilated attention module. The former aims to generate scale-specific feature maps with fine-spatial and semantic information of objects at different scales, while the latter further selects more powerful information to adaptively enlarge the receptive fields of object features at different scales. Facilitated with these modules, MSDN well obtains the fine-spatial and semantic information of objects at different scales, thus solving the problem of the scale variation. Comprehensive experimental results over two public object detection benchmarks clearly demonstrate the effectiveness of our proposed MSDN. Particularly, on the COCO dataset, the mAP value of MSDN is 48.7%, outperforming existing state-of-the-art methods in a single model manner.
Image Encryption Using Hyper Elliptic Curve and Phase Truncate Transformation

These days images are widely used in communication over different networks. It is therefore very important to make image transfer as secure as possible. This paper exhibits an algorithm based on image encryption using hyper elliptic curve mapping and phase truncate transformation. Features of phase truncate transformation and high security of hyper elliptic curve mapping are combined and enhance security for color scale image cryptosystem For starters image is masked and then a first primary key using phase truncate transformation is generated. Hyper elliptic curve is used for scrambling. Phase truncate transformation is then used to create the second primary key. This dual encryption increases the randomness of the encrypted image. Algorithm is implemented on MATLAB and to validate the proposed system and check the authenticity of the proposed system Result obtained after implementing the algorithm on a few benchmarks image gave very good measures of entropy, PSNR, NPCR, UACI, and MSE against multiple attacks.
Controllable Scene Generation from Natural Language

We propose a novel framework to generate recognizable scenes conditioned on natural language (NL) descriptions. The proposed modular approach decomposes the scene synthesis process into several manageable steps, in which it first infers a spatial layout of the desired scene from input descriptions by a spatial layout generator and generates the scene with a scene generator. Specifically, the proposed approach allows interactive tuning of the synthesized scene via NL, which helps to generate more complex and meaningful scenes, and to correct training errors or bias. We demonstrate the capability of the proposed approach on the challenging MS-COCO dataset and show that our approach can improve the quality of generated scenes, interpretability of the drawn scenes and semantic alignment to the input language descriptions.
Utilizing Convolutional Neural Networks to Create a Wearable Sign Language Detection System with the Internet of Things

Because having hearing or speech impairments might make it more difficult for an individual to communicate, the objective of the strategy that has been offered is to narrow the gap in communication that exists between a person with a disability and a typical person. Our goal is to create a hand glove that is capable of not only translating sign language into voice but also displaying text on an app. Additionally, the application will be able to do the task in reverse, translating spoken words into sign language. To develop a full sign language translator app that is capable of translating speech into sign language and sign language into speech vice versa. The technique that has been suggested is intended to help persons with hearing and speech impairments overcome the challenges they experience and improve their ability to communicate with others. Arduino, Convolutional Neural Network (CNN), and Natural Language Processing are the technologies that are used to accomplish it (NLP). In order to get data on motion, Arduino will be paired with a number of other sensors, such as flex sensors. The convolutional neural network (CNN) will be implemented in Python by making use of the Keras library. All of them will be combined into a single application called Streamlit, which will be developed using the dart programming language.
Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved.
Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach

Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks’ arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).