Reviewerly/exHarmony huggingface dataset:
PalliCare: A disruptive and a novel solution for Palliative Care

There are a huge number of patients that require Palliative Care. These patients usually suffer from diseases like various types of cancer, AIDS, and many more. One of the most important problems that occur when solving Palliative Care is there are no tools available in the technical domain that can aid the patients. It faces another issue that there are no concrete methodologies to treat the patients effectively. Hence, there is no widespread availability to treat patients that need Palliative Care. The paper proposes a system that provides the faced in Palliative Care with the help of technology. The system, called PalliCare, aims to resolve a multitude of problems faced by the patients. This can be helpful to improve the lives of the patients and their families. The features include deep learning-based Facial Sentiment Analysis, a Medicine Delivery System, and a Therapy System that allows the patient to conduct therapy sessions online. The proposed system aims to integrate a variety of individuals - therapists, pharmaceutical companies, and NGOs to provide the above-mentioned features. The paper reviews in detail the existing technologies that can be used to build the system and expounds on their benefits. The goal is to serve an ever-growing need of patients requiring Palliative Care. Using modern technologies, PalliCare has the potential of revolutionizing the treatment of Palliative Care.
Quantum sensor could detect dementia

A new quantum sensor could help clinicians identify diseases such as dementia, Alzheimer’s and Parkinson’s by tracking patients’ brain waves.
Autopilot Flight Testing of Unmanned Aerial Vehicle

This paper presents the Autopilot Flight Testing of Unmanned Aerial Vehicle (UAV) that we designed the control system to control the flight of an Unmanned Aerial Vehicle that has to fly for an automated mission. After the design and development of the autopilot flight control system, it is imperative to test the flight to verify that the designed control system can operate autonomous flight control for its mission purpose. Furthermore, this research presents a test of autonomous flight with a prototype autopilot flight control system that we use the Fuzzy PD + I controller [1],[2]. The results of this test flight will use a commercially available off-the-shelf remote control aircraft which an autopilot flight control system installed into the aircraft, and then perform a flight test with an expert pilot man. The flight test results after adjusting the control gain, and showed that the aircraft was able to maintain an altitude and can flyable to the desired waypoints very well.
Research on Cloud End-User Behavior Trust Evaluation Model Based on Sliding Window

As a new service-oriented computing paradigm, cloud computing facilitates users to share and use resources. However, due to the dynamic and openness of its operating environment, only relying on traditional identity authentication technology can no longer fully meet the security requirements of cloud computing. The trust evaluation of user behavior has become the key to improve the security of cloud computing. Therefore, in view of some problems existing in our current research on user behavior trust, this paper optimizes and improves the construction of the evaluation index system and the calculation of trust value, and proposes a cloud end-user behavior trust evaluation model based on sliding window. Finally, the model is proved to be scientific and effective by simulation experiments, which has certain significance for the security protection of cloud resources.
Randomness study of the concatenation of generalized sequences

Abstract Keystream sequences should look as random as possible, i.e. should present no logical pattern to be exploited in cryptographic attacks. The generalized self-shrinking generator, a sequence generator based on irregular decimation, produces a family of sequences with good cryptographic properties. In this work, we display a detailed analysis on the randomness of the sequences resulting from the concatenation of elements of this family. We apply the most important batteries of statistical and graphical tests providing powerful results and a new method to construct sequences with good cryptographic properties.
Real-time Face Mask Detection System on Edge using Deep Learning and Hardware Accelerators

Real-time face mask detection with the use of Artificial Intelligence is one of the most advanced ways of detecting face masks and their wearing condition in public or private areas. In this work, a system based on Object Detection models is proposed which can detect and classify the type of mask wearing conditions in real-time. The system is implemented with two latest deep convolutional neural networks; YOLOv5s and YOLOv5l. The proposed system can efficiently detect and classify face masks based on their wearing condition as well as count them and store the count into a CSV file format with a timestamp. To perform real-time inference, the deep learning models were deployed on Nvidia Jetson Nano and Jetson Xavier NX which are embedded solutions inspired by Edge AI. The detection algorithms achieved mAP of 86.43 and 92.49 for YOLOv5s and YOLOv5l respectively. Comparing the mAP of both detection models, YOLOv5l achieved higher mAP than YOLOv5s while comparing fps on both hardware, Nvidia Jetson Xavier NX provides more fps than Nvidia Jestion Nano for realtime inference.
Framework for Eliciting Security Requirements of Web Application from Business Users

Security requirements play a vital role in the software development process to gain secured software. The security levels of each software system can be varied depending on multiple factors, such as information processing or storing in the software system, business criticality of the software system, or features and functions of the system. However, the understanding of these factors has not been done early and has little or no involvement from the business users (end-users or system owners), who have the best understanding of how the system should be used in their business and the business impacts. As a result, the system design is done without awareness of these factors. Most of the time designing the system to address security concerns is done late in the development lifecycle, which causes extra money, effort, and time from the development team. In this paper, we propose a framework to help elicit the security requirements from business users based on the survey questions. The framework recommends a set of security requirements and implementation methods that are suitable for the business operations of the system. Two web applications with 12 business users for each application were selected to experiment with the effectiveness of the framework. The framework's suggested results were tested for conformity against a set of security recommendations from four security experts. We found that the security requirements and recommendations from the framework have an average 89% conformity for the WFMS system and a 93% conformity for the ASMS system with the expert recommendations.
DASH rate control using game theory to consider user video preference

Dynamic adaptive streaming over HTTP (DASH) is the most widespread internet video delivery service system. In DASH, a bitrate is selected dynamically to appropriately share the bandwidth of the server among multiple users. While most of the existing rate-selection methods have been based on improving quality of service (QoS), quality of experience (QoE) methods that express user satisfaction with the quality of video content have been attracting the attention of service providers and network providers. In addition, some studies show that user preference affects QoE. Therefore, we both propose and demonstrate the effectiveness of a rate-selection method based on game theory that considers user preference for video streaming.
Target Denoising Method Based on Fusion of Adaptive Filter and Sparse Representation

Abstract Acoustic signals are often used to detect and track targets in target detection field. Because of the complicated underwater, air and solid environment, the echo signals are often affected by environment that affects the analysis and processing of the detection signal. Aiming at this problem, this paper analyzes and studies the use of adaptive filter, and analyzes the model making use of the reference. Signal source is used to denoise the target signal. The method is applied for processing the data. The simulation results show that the proposed method greatly improves the target identification, enhances the SNR and highlights the features of echo signal. This method improves the recognition accuracy significantly.
Exploring Layerwise Decision Making in DNNs

While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still poorly understood. Conversely, common decision trees are easily interpretable and theoretically well understood. We show that by encoding the discrete sample activation values of nodes as a binary representation, we are able to extract a decision tree explaining the classification procedure of each layer in a ReLU-activated multilayer perceptron (MLP). We then combine these decision trees with existing feature attribution techniques in order to produce an interpretation of each layer of a model. Finally, we provide an analysis of the generated interpretations, the behaviour of the binary encodings and how these relate to sample groupings created during the training process of the neural network.
Self-Supervised Learning via Multi-view Facial Rendezvous for 3D/4D Affect Recognition

In this paper, we present Multi-view Facial Rendezvous (MiFaR): a novel multi-view self-supervised learning model for 3D/4D facial affect recognition. Our self-supervised learning architecture has the capability to learn collaboratively via multi-views. For each view, our model learns to compute the embeddings via different encoders and robustly aims to correlate two distorted versions of the input batch. We additionally present a novel loss function that not only leverages the correlation associated with the underlying facial patterns among multi-views but it is also robust and consistent towards different batch sizes. Finally, our model is equipped with distributed training to ensure better learning along with computational convenience. We conduct extensive experiments and report ablations to validate the competence of our model on widely-used datasets for 3D/4D FER.
Irregular Ellipse Detection Method by Confined Space Sweep

A method for the detection of elliptical figures with irregular contours in images is proposed. This method overcomes the problem of heteroclite contours that causes the dispersion of the parameters during the accumulation of votes in the Hough parametric domain, effect that is usually reduced by increasing the discretization step of the parameters, although with the consequence of reducing the precision of the results. With the use of an inter ellipses region of interest (IEROI), an ellipse pixel search area is defined, and in turn, the geometric characteristics of the irregular object are delimited. This IEROI can be adapted to detect, both, irregular and uniform contours. Similar to the Hough transform, the parameters of the detected ellipses are stored in a 5-dimensional parametric space, but the traditional voting method is replaced by the use of a value that corresponds to the Confinement Grade of the object contour within the IEROI. Finally, the functionality of the proposed method is validated for different cases, using real and synthetic images with uniform and irregular elliptical contours.
Improving the Performance of Image Captioning Models Trained on Small Datasets

Recent work in image captioning seems to be driven by increasingly large amounts of training data, and requires considerable computing power for training. We propose and investigate a number of adjustments to state-of-the-art approaches, with an aim to train a performant image captioning model in under two hours on a single consumer-level GPU using only a few thousand images. Firstly, we address the issue of sparse object and scene representation in a small dataset by combining visual attention regions at various levels of granularity. Secondly, we suppress semantically unlikely caption candidates through the introduction of language model rescoring during inference. Thirdly, in order to increase vocabulary and expressiveness, we propose an augmentation of the set of training captions through the use of a paraphrase generator. State-of-the-art performance on the Flickr8k test set is achieved, across a number of evaluation metrics. The proposed model also attains competitive test scores compared to existing models trained on a much larger dataset. The findings of this paper can inspire solutions to other vision-and-language tasks where labelled data is scarce.
Image Stitching Algorithm Based on Region Division for Underwater Dam Crack Image

The surface crack of underwater dam is one of the important indexes to evaluate the normal operation of the dam. Complete crack image is an important means to improve the accuracy of evaluation. In view of the limitations of traditional Algorithms in underwater crack image stitching, we propose an underwater dam surface crack image stitching algorithm based on region division(ISA-RD). First of all, an image enhancement algorithm aiming at increasing the number of feature points is used. Secondly, we simplify the process of feature point selection and matching by relying on the features of multiple regions in the local crack image, and improve the matching accuracy by mining the close relationship between the matching of feature points and different regions. Finally, the high matching feature point pairs are used for image fusion. We take the crack image of the real scene as the research object. Compared with the classical image stitching algorithm, the feature point matching algorithm proposed in this paper improves the accuracy of feature point matching. Obviously, the image quality after stitching is improved.
Research on content and mode recommendation of humanistic quality education for science and engineering students based on collaborative filtering algorithm

The implementation of quality education needs the guarantee of education quality. Training high-quality talents needs to take curriculum as the basic way. Curriculum is the design and planning of education. The training objectives of the school must be achieved through specific courses. The educational policy can be implemented only through specific courses. Teaching activities can be realized only by relying on the curriculum plan. The teaching content can be transformed into students’ quality and ability through courses. It can be seen that curriculum is the basic way to implement quality education. As a new educational concept, quality education can be implemented in the whole educational process only when it is internalized into the curriculum and the thoughts of educators. However, with the rapid development of modern science and technology and the establishment of the idea of quality education, the setting of the original curriculum system in China’s universities of science and technology has exposed many disadvantages that do not adapt to the development of modern society. Based on this, on the basis of deepening the theoretical research on curriculum, this paper deeply discusses the achievements and existing problems of humanistic quality curriculum in China’s universities of science and technology, and then puts forward a set of countermeasures and suggestions for humanistic quality curriculum in Universities of science and technology suitable for China’s national conditions.
Building a Python Extension Module

In this chapter and the next, we look at building Python extension modules. These are components that connect C/C++ to Python. Python has been around for quite some time, and over the years, a number of different approaches to accomplishing this have been developed. Table 7-1 lists some of the approaches.
Goal and Feature Model Optimization for the Design and Self-Adaptation of Socio-Cyber-Physical Systems

Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.
Semi-supervised Anomaly Detection and Location Based on Generative Adversarial Network

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames.
Benchmarking Shallow and Deep Neural Networks for Contextual Representation of Social Data

Representing the underlying context in text data is a much-explored research domain, where language model construction for the sizeable unstructured corpus is the central premise. To date, several deep language embedding representation techniques have been put forth for context-aware modelling of text data, focusing on word, sentence and document-level representations for specific tasks. In this paper, we experiment with shallow and deep embedding representation techniques for social media text data to predict Atherosclerotic Heart Disease (AHD) mortality rate. We employed Word2Vec, Doc2Vec, and LSTM based embedding techniques for this experimentation and analyzed the performance on standard datasets. Experimental evaluation evidence suggests that Doc2Vec, a shallow network, outperforms deep neural networks by attaining a Pearson correlation value of 0.8199 for tuned hyper-parameters, exceeding Word2Vec and Bi-LSTM models by a margin of 60 per cent.
Asymmetric Cryptosystem for Color Images Based on Unequal Modulus Decomposition in Chirp-Z Domain

In this paper, we have presented an asymmetric encryption algorithm for color images in Chirp-Z domain. In the proposed cryptosystem, unequal modulus decomposition provides the private keys that are different from the encryption keys. Chaotic logistic map is utilized for pixel scrambling and provides additional layer of security to the cryptosystem. To evaluate the security of proposed cryptosystem, various statistical metrics such as correlation coefficient, mean squared error, and peak signal noise ratio are used. The encryption algorithm is also tested against statistical cryptographic attacks such as histogram, mesh plots, and correlation distribution analysis of adjacent pixels. The results show that the proposed cryptosystem resists the cryptographic attacks and exhibits a high level of security.
Analysis of big data and cloud computing technology based on the library lending system

In today's continuous update and progress of Internet information, repeated online resources have become a major obstacle for users' access to important information. At the same time, although the traditional library is relatively convenient for the physical environment, the preservation of books is still a major problem, and the external environment will also become a major obstacle to preserving books. After the simple exploration of the Internet in the early stage, today's cloud technology era has gradually entered the human life, and the future of the digital library is also closely related to it. The combination of the cloud computer technology and the digital library has become the most basic step in the library activities. Based on the research of library borrowing system, and based on explaining the importance of cloud computer to library borrowing system, this paper discusses the design help of intelligent library, and proposes relevant optimization suggestions to meet the needs of different service objects.
Research on Video Expression Recognition Based on Migrated Convolutional Neural Network

Face expression recognition is an important part of artificial intelligence research and development, and it is also one of the important technologies to ensure the human-computer friendly interaction to truly achieve the intelligent goal. After understanding the dynamic time warping and its improved algorithm, this paper analyzes how to design video facial expression recognition system based on 3D-CNN according to the construction and application of convolutional neural network.
Professional Self-identification of Student’s Majoring in Engineering

The concept of consciousness, as a scientific problem, has been the object of many scientists’ research. Basically, humanities scientists dealt with this problem. Due to the fact that consciousness is an intangible phenomenon, it is quite difficult to study it without applying the inter- or transdisciplinary approach including data from History, Social sciences, Psychology and other cognitive studies. It is hard to imagine the modern research of the consciousness, without the use of technology. The paper studies the socio-psycholinguistic aspects of self-identification of students majoring in Engineering. The authors identify the special features of the perception of the engineering profession throughout the time, focusing on the contemporary peculiarities of the matter.