Reviewerly/exHarmony huggingface dataset:
DS-Net: Dynamic spatiotemporal network for video salient object detection

As moving objects always draw more attention of human eyes, the temporal motion information is always exploited complementarily with spatial information to detect salient objects in videos. Although efficient tools such as optical flow have been proposed to extract temporal motion information, it often encounters difficulties when used for saliency detection due to the movement of camera or the partial movement of salient objects. In this paper, we investigate the complementary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information. We construct a symmetric two-bypass network to explicitly extract spatial and temporal features. A dynamic weight generator (DWG) is designed to automatically learn the reliability of corresponding saliency branch. And a top-down cross attentive aggregation (CAA) procedure is designed to facilitate dynamic complementary aggregation of spatiotemporal features. Finally, the features are modified by spatial attention with the guidance of coarse saliency map and then go through decoder part for final saliency map. Experimental results on five benchmarks VOS, DAVIS, FBMS, SegV2, and ViSal demonstrate that the proposed method achieves superior performance than state-of-the-art algorithms. The source code is available at https://github.com/TJUMMG/DS-Net.
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning

While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy Deep Neural Network (DNN) models in security-critical areas. Recently, many research studies have been conducted to develop defense techniques enabling more robust models. In this paper, we target detecting adversarial samples by differentiating them from their clean equivalents. We investigate various metrics for detecting adversarial samples. We first leverage moment-based predictive uncertainty estimates of DNN classifiers derived through Monte-Carlo (MC) Dropout Sampling. We also introduce a new method that operates in the subspace of deep features obtained by the model. We verified the effectiveness of our approach on different datasets. Our experiments show that these approaches complement each other, and combined usage of all metrics yields 99 % ROC-AUC adversarial detection score for well-known attack algorithms.
Design and Implementation of Wheel Chair Control System Using Particle Swarm Algorithm

About 10–20% of every country’s population is disable. There are at least 650 million people with a kind of disability worldwide. Assistance and support are perquisites for many handicap people for participating in society. Electric powered wheelchairs provide efficient mobility to motor impaired persons. In this paper a smart controller of a wheel chair mobile robot using Particle Swarm Optimization Proportional controller (PSO-P) was proposed where (PSO) algorithm was utilized to tune the proportional controller’s gains for each axis. Aiming to improve wheelchair tracking trajectory, a kinematic model of a robot with linear and angular velocities parameters was developed. The controller performance was examined using the system parameters with respect to trajectory references. A simulation of the proposed controller showed good correlation of the trajectory track under different loading conditions.
Adaptive Attention-based High-level Semantic Introduction for Image Caption

There have been several attempts to integrate a spatial visual attention mechanism into an image caption model and introduce semantic concepts as the guidance of image caption generation. High-level semantic information consists of the abstractedness and generality indication of an image, which is beneficial to improve the model performance. However, the high-level information is always static representation without considering the salient elements. Therefore, a semantic attention mechanism is used for the high-level information instead of conventional of static representation in this article. The salient high-level semantic information can be considered as redundant semantic information for image caption generation. Additionally, the generation of visual words and non-visual words can be separated, and an adaptive attention mechanism is employed to realize the guidance information of image caption generation switching between new fusion information (fusion of image feature and high-level semantics) and a language model. Therefore, visual words can be generated according to the image features and high-level semantic information, and non-visual words can be predicted by the language model. The semantics attention, adaptive attention, and previous generated words are fused to construct a special attention module for the input and output of long short-term memory. An image caption can be generated as a concise sentence on the basis of accurately grasping the rich content of the image. The experimental results show that the performance of the proposed model is promising for the evaluation metrics, and the captions can achieve logical and rich descriptions.
A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.
Healthify: A Blockchain-Based Distributed Application for Health care

Blockchain technology has received significant popularity, with a growing interest in various domains, including data processing, financial services, information security, and IoT to the healthcare and medical research industries. There has also been a tremendous trend in using blockchain technologies to provide efficient data protection in health care. However, through secure and efficient data storage, blockchain turns traditional healthcare approaches into a more robust means of effective treatment and cure. In this chapter, we examine both current and latest innovations in the healthcare sector through the application of blockchain as a platform. We propose a secure distributed application called Healthify, a wide-range healthcare data protection approach focused on distributed ledger technology where medical data is encoded to provide a safer environment. The objective of this approach is to provide a practical application that offers a permanent database and offers simple accessibility to the gadgets. The application’s basis is specified by the smart contract, which provides rules and regulations for the users. Also, the architecture of the distributed application promotes the delivery of secure healthcare services within the medical system.
Vectorizer Comparison for Sentiment Analysis on Social Media Youtube: A Case Study

Youtube is a popular social media used by several companies to market their products, both in the form of advertisements and videos. Nokia is one company that uses Youtube as social media to advertise and market its products until now. Nokia was a cellphone company that had fallen in 2013 due to the company's unwillingness to follow the operating system trend at the time. Nokia continues to rise and launch new products that are increasingly sophisticated. In seeing and summarizing public opinion towards the revival of the Nokia company, this research will classify the sentiment given by the public towards latest Nokia products through comments on the videos of Nokia products on Youtube. This research using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) algorithm to classify, with comparing performance of three vectorizers, namely CountVectorizer, TFIDFVectorizer and HashingVectorizer. Compared to other algorithms and vectorizers, SVM with TFIDFVectorizer has the highest accuracy with score of 97.5%. The best vectorizer in this research is TFIDFVectorizer because there are almost no errors in predicting negative values, and also has many positive predictive values compared to other vectorizers. So, the best way to do classification is using SVM algorithm with TFIDFVectorizer.
Cybersecurity Threat Intelligence Augmentation and Embedding Improvement - A Healthcare Usecase

The implementation of Internet of Things (IoT) devices in medical environments, has introduced a growing list of security vulnerabilities and threats. The lack of an extensible big data resource that captures medical device vulnerabilities limits the use of Artificial Intelligence (AI) based cyber defense systems in capturing, detecting, and preventing known and future attacks. We describe a system that generates a repository of Cyber Threat Intelligence (CTI) about various medical devices and their known vulnerabilities from sources such as manufacturer and ICS-CERT vulnerability alerts. We augment the intelligence repository with data sources such as Wikidata and public medical databases. The combined resources are integrated with threat intelligence in our Cybersecurity Knowledge Graph (CKG) from previous research. The augmented graph embeddings are useful in querying relevant information and can help in various AI assisted cybersecurity tasks. Given the integration of multiple resources, we found the augmented CKG produced higher quality graph representations. The augmented CKG produced a 31% increase in the Mean Average Precision (MAP) value, computed over an information retrieval task.
The Latest in Immersive Telepresence to Support Shared Engineering Education

Work in Progress: In this paper we outline our initial findings on the potential of state-of-the-art immersive telepresence to support the practical and collaborative group work element in our remotely taught international degree programmes, specifically a Software Engineering Masters degree. Whilst we adopt widely used distance learning approaches generally, the challenge remains in supporting live synchronous practical sessions that depend heavily, on one hand, on the expertise of the educator and their presence in the shared learning environment, and on the other hand, the students' active engagement within teams partially distributed physically. Any technical progress in improving the "sense of presence" can radically renew our conceptions of (distance) teaching and learning, maybe also in terms of the 21st century skills or even those that we are not yet aware of. The question to address is that if sufficiently high-fidelity, and unintrusive, immersive capture technology is used then how could this be game changing in this area? Previous work using similar immersive technology has failed to achieve the level of quality or scope necessary, however, the past 4 years have seen significant progress in the hardware and algorithms required. To support our degree programmes we have designed and developed a custom live 3D capture system for a higher fidelity immersive experience targeting small groups of 2-6 people collaborating both locally and remotely. Here we will present the system and scope out some of the initial plausible affordances for education through the Conceive, Design, Implement, Operate (CDIO)-model. We focus on the affordances offered by the technology to support the learning of the much needed competences of communication, collaboration, critical thinking and creativity which are very challenging to enhance by conventional, content delivery oriented distance learning approaches.
Two-boson quantum interference in time

The celebrated Hong-Ou-Mandel effect is the paradigm of two-particle quantum interference. It has its roots in the symmetry of identical quantum particles, as dictated by the Pauli principle. Two identical bosons impinging on a beam splitter (of transmittance 1/2) cannot be detected in coincidence at both output ports, as confirmed in numerous experiments with light or even matter. Here, we establish that partial time reversal transforms the beamsplitter linear coupling into amplification. We infer from this duality the existence of an unsuspected two-boson interferometric effect in a quantum amplifier (of gain 2) and identify the underlying mechanism as timelike indistinguishability. This fundamental mechanism is generic to any bosonic Bogoliubov transformation, so we anticipate wide implications in quantum physics.
Smart Home Care System with Fall Detection Based on the Android Platform

In this paper, the authors propose a smart home-care system built on an Android smartphone. The database and application programming interface (API) are set up on the server side. The database collects information from various sensors and stores it, and the API acts as a bridge between the mobile phone and the database. The API prevents the leakage of private data. In the associated Android smartphone app, two functions are provided: instant monitoring based on in-home sensor data and fall detection using the three-axis accelerometer, gyroscope, and orientation sensor inbuilt into the smartphone. When the sensor data are abnormal, the remote controller is notified immediately. Moreover, the remote controller can view real-time images by using an IP camera to guarantee home safety. As for fall detection, given that falls cause severe injuries in elder people and children, the proposed app can detect a fall event, send a help message, and indicate the user's location by using the global positioning system and Google Maps API. According to the simulation results obtained in this study, the proposed system exhibited a fall-detection sensitivity of 92.5% and specificity of 97.6%, thus proving that the system can be effectively used for home care.
Visible Thermal Person Reidentification via Mutual Learning Convolutional Neural Network in 6G-Enabled Visual Internet of Things

Visible thermal person reidentification (VT Re-ID) in the 6G-enabled Visual Internet of Things (VIoT) is essential for implementing 24-h surveillance in wide-area space. The existing methods mainly focus on reducing the visual difference between visible and thermal visual streams (V-streams). However, they cannot fully exploit the information of visible and thermal V-streams, which leads to suboptimal representations. In this article, we propose a novel deep network termed the mutual learning convolutional neural network (MLCNN) to transfer useful information between visible and thermal V-streams for VT Re-ID in 6G-enabled VIoT. The proposed MLCNN consists of the feature generation module and the mutual learning module. The feature generation module aims to map the input samples into a more discriminative feature space in order to decrease the intramodality difference. The mutual learning module employs the identification loss to supervise the predictive identity accuracy and utilizes the Kullback–Leibler divergence to constitute the mutual learning loss for useful information transfer. Extensive experiments on two popular visible thermal data sets (SYSU-MM01 and RegDB) prove the effectiveness of the proposed MLCNN.
Dominant values of professional development education spaces of cross-border regions in the aspect of postmodernism

Based on an extensive review of the current literature and research in terms of axiological approach, this article considers the strategies of post-modernism pluralistic concepts of socio-culture spaces, and discusses the ideas of andragogy supporting the value sphere of the individual of pedagogues, FLT teachers, who are responsible for the creative education development spaces in the classrooms. As a result of the study the authors present a comprehensive model of course design elements as a theory for guiding the use of new technologies in distance education through culture-oriented pedagogical strategies and activities. In conclusion there discussed some of the fruitful examples of the Tutorials in a polyculture &amp; polylingua situation and event spaces of cross-border regions of Russia, in terms of mentoring and fostering a culure-determined learning and a self-determining individual.
Intelligent assistants in language learning: an analysis of features and limitations

Learning a second language is a challenging endeavour which requires various degrees of support. The proliferation of smart technologies includes chatbots and conversational agents which have the potential to ‘assist’ language learners (Kukulska-Hulme, 2019). However, whilst a growing number of researchers and developers are working on such intelligent assistants across different disciplines, little is known about their application to language learning. The aim of this project was to review relevant research literature over a ten-year period (2010-2020) in order to uncover the capabilities and limitations of Intelligent Assistants (IAs) in relation to language learning. Results suggest that IAs can assist learners in a variety of ways, including provision for conversation and pronunciation practice. These tools can also fail to comprehend meaning or accented pronunciation. The analysis highlighted gaps in research around skills development, task design, pedagogy, and the use of chatbots in virtual worlds.
How Well Just-In-Time Defect Prediction Techniques Enhance Software Reliability?

Many Just-In-Time defect prediction (JIT) techniques, which anticipate defect-prone software changes, have been proposed in recent years. Researchers have evaluated these techniques from different perspectives and have drawn inconsistent conclusions about which JIT defect prediction techniques are the most effective and efficient. This paper evaluates JIT techniques from a reliability perspective. For short-term early evaluation, we measure JIT predictive performance on early exposed defects. While for long-term evaluation, we quantify the overall reliability improvement resulted from JIT. A case study applying 11 state-of-the-art JIT methods on 18 large open-source projects has shown: 1) Different JIT methods have their own individual strengths for different purposes, 2) in general, RandomForest is the most effective method in short-term software reliability improvement, and CBS+ performs best in long-term reliability improvement; 3) JIT prediction accuracy is highly correlated to overall reliability improvement.
A salient object segmentation framework using diffusion-based affinity learning

In this paper, a salient object segmentation framework by using diffusion-based affinity learning and based on absorbing Markov chain (AMC) is proposed. Traditional approaches for structural modeling of images via local information and pairwise similarity graph by using, e.g. Gaussian heat kernel function, are insufficient for capturing the faithful relationships among the regions. According to the AMC principles, the more strong relationships result in lowering the time that a transient node becomes an absorbed one and consequently increases the transition probability between those nodes. To this end, a dense transition probability matrix is constructed based on an affinity matrix which learned using a diffusion process. Computing tensor product of the initial similarity graph with itself provides credible information about inter-relationships of nodes. Since conducting similarity propagation over such a tensor product graph imposes high computational costs, an iterative diffusion process is leveraged that introduces the same complexity as applying traditional diffusion processes on the original graph. As a fundamental benefit, such a process will enhance the accuracy and preciseness of saliency detection. Finally, as a complementary step, the saliency map will be refined by revisiting the saliency value of every single pixel. The experimental results on three major benchmark datasets demonstrate the efficiency of the proposed framework. More specifically, as expected, taking advantage of full learned affinity matrix can significantly improve the precision of the process.
Tracking control of two qubit entanglement using piecewise time-independent method

Abstract The tracking control of quantum correlations between two coupled qubits is analyzed, where only one of the qubits is coupled to a Markovian environment. Such a system is a generalized model that can be used, for example, to study the problem of a nuclear spin coupled to the electron spin exposed to the environment. Using an external control field, we increase the time for which the system remains coherent and entangled. The control field is an external potential applied to the system, which contains two adjustable parameters, the intensity and the phase of the field. Furthermore, we present quantum control protocols with different targets. First, for population control of both qubits, second for the coherence control of both two-level systems, and finally for the control of entanglement. Due to analytical for X states the target function can directly be identified with the entanglement. Furthermore, we have shown that when smaller dissipation rates are considered, the control method generates larger stable entanglement between the qubits.
Personalized quantification of facial normality: a machine learning approach

Abstract What is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically along their own continuum of normality, and to measure any surgical changes against such a personalized benchmark. This has not previously been possible. We have solved this problem by designing a computerized model that produces realistic, normalized versions of any given facial image, and objectively measures the perceptual distance between the raw and normalized facial image pair. The model is able to faithfully predict human scoring of facial normality. We believe this work represents a paradigm shift in the assessment of the human face, holding great promise for development as an objective tool for surgical planning, patient education, and as a means for clinical outcome measurement.
Recognition of Handwritten Digit Using Convolutional Neural Network

In recent decades, Convolutional Neural Network (CNN) has achieved remarkable results in both the research field and the application field due to the significant achievement acquired in computer technology. However, handwritten digit recognition still has great development space due to its complexity. At present, the recognition of handwriting has received intensive attention from many researchers. In this paper, we introduce the Convolutional Neural Network (CNN) based on TensorFlow framework is introduced, and use the MINIST data set which is widely used in handwritten digit recognition to analyze the structure and parameters of the CNN model. Furthermore, we utilize different functions and structures and analyze the problems in experiments, so as to provide some reference for the research and development aiming at handwritten digit recognition.