Reviewerly/exHarmony huggingface dataset:
Video Deraining Using the Visual Properties of Rain Streaks

In computer vision applications, the visibility of the video content is crucial to perform analysis for better accuracy. The visibility can be affected by several atmospheric interferences in challenging weather &#x2013; one such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the TAWL (<i>Temporal Appearance, Width, and Location</i>) method. The proposed TAWL method adaptively uses features from different resolutions and frame rates. Moreover, it progressively processes features from the upcoming frames so that it can remove rain in real-time. Experiments have been conducted using video sequences with both real rain and synthetic rain to compare the performance of the proposed method against the relevant state-of-the-art methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by removing more rain streaks while keeping other moving regions.
O-Minimal Invariants for Discrete-Time Dynamical Systems

Termination analysis of linear loops plays a key rôle in several areas of computer science, including program verification and abstract interpretation. Already for the simplest variants of linear loops the question of termination relates to deep open problems in number theory, such as the decidability of the Skolem and Positivity Problems for linear recurrence sequences, or equivalently reachability questions for discrete-time linear dynamical systems. In this article, we introduce the class of o-minimal invariants , which is broader than any previously considered, and study the decidability of the existence and algorithmic synthesis of such invariants as certificates of non-termination for linear loops equipped with a large class of halting conditions. We establish two main decidability results, one of them conditional on Schanuel’s conjecture is transcendental number theory.
Adaptive Playback Control: A Framework for Cinematic VR Creators to Embrace Viewer Interaction

Content creators have been trying to produce engaging and enjoyable Cinematic Virtual Reality (CVR) experiences using immersive media such as 360-degree videos. However, a complete and flexible framework, like the filmmaking grammar toolbox for film directors, is missing for creators working on CVR, especially those working on CVR storytelling with viewer interactions. Researchers and creators widely acknowledge that a viewer-centered story design and a viewer’s intention to interact are two intrinsic characteristics of CVR storytelling. In this paper, we stand on that common ground and propose Adaptive Playback Control (APC) as a set of guidelines to assist content creators in making design decisions about the story structure and viewer interaction implementation during production. Instead of looking at everything CVR covers, we set constraints to focus only at cultural heritage oriented content using a guided-tour style. We further choose two vital elements for interactive CVR: the narrative progression (director vs. viewer control) and visibility of viewer interaction (implicit vs. explicit) as the main topics at this stage. We conducted a user study to evaluate four variants by combining these two elements, and measured the levels of engagement, enjoyment, usability, and memory performance. One of our findings is that there were no differences in the objective results. Combining objective data with observations of the participants’ behavior we provide guidelines as a starting point for the application of the APC framework. Creators need to choose if the viewer will have control over narrative progression and the visibility of interaction based on whether the purpose of a piece is to invoke emotional resonance or promote efficient transfer of knowledge. Also, creators need to consider the viewer’s natural tendency to explore and provide extra incentives to invoke exploratory behaviors in viewers when adding interactive elements. We recommend more viewer control for projects aiming at viewer’s participation and agency, but more director control for projects focusing on education and training. Explicit (vs. implicit) control will also yield higher levels of engagement and enjoyment if the viewer’s uncertainty of interaction consequences can be relieved.
Personalised Federated Learning with BERT Fine Tuning. Case Study on Twitter Sentiment Analysis

Ruiz-Millán, Jose AntonioMartínez-Cámara, EugenioVictoria Luzón, M.Herrera, FranciscoFederated learning is a machine learning approach where many clients jointly train a model under the orchestration of a central server, while keeping the data sequestered on the clients. It allows to preserve data privacy and to reach a higher learning generalisation capacity. Also, the global model of federated learning has to adapt to the particularities of the data of the clients. We propose the use of fine tuning as a personalisation method for adapting a global federated learning model to the data of new clients. We use sentiment Twitter data as case of study, and we show that the use of fine tuning improve the performance of the global model and of the local model of the clients.
Active Learning in Digital Heritage: Introducing Geo-localisation, VR and AR at Holocaust Historical Sites

Currently, Holocaust history education faces two challenges: the authentication and availability of its information and the need for more effective pedagogical methods for Historical and Cultural Learning. This study presents the impact of the Future Memory tablet application which combines the active, embodied exploration of historical information with geo-localised presentation using virtual and augmented reality technology. We report three recent studies where the application was tested with a total of 147 participants. The results confirm that active, embodied exploration of historical information facilitates learning more as compared to standard exhibitions. In addition, our results show that memory consolidation and learning is facilitated by prior knowledge while negative emotions can have a detrimental effect on learning outcomes. Lastly, our results show that combining historical information with a spatialised VR/AR environment leads to significantly better learning outcomes as compared to using a spatial representation alone. These results provide strong support for the model of Historical and Cultural Learning pursued in the Future Memory project, which grounds the learning experience in the situated, active and individualised engagement with historical sources.
IPv6 Landmark Mining Based on Domain Name Screening and IPv4-IPv6 Connection

Existing IP landmark mining technologies mainly focus on IPv4, and less attention is paid to the association between IPv4 and IPv6. The current IPv6 network has fewer reliable landmarks with limited accuracy, while the IPv4 network already has a large number of reliable landmarks. If IPv4 landmarks can be used in IPv6 landmark mining, the accuracy and efficiency of IPv6 landmark acquisition will be improved. To this end, this paper proposes an IPv6 landmark mining method based on domain name screening and IPv4-IPv6 connection. This method first uses the domain names of government agencies, top universities, and top websites as seed domain names, and continuously obtains a large number of domain names through web crawlers; then performs DNS resolution on the obtained domain names to obtain their corresponding IPv4 address and IPv6 address; and finally evaluate and analyze whether the two addresses are located in the same location, and determine whether to use it as an IPv6 landmark. This method successfully associates the IPv4 geolocation with the IPv6 geolocation, and uses the IPv4 landmarks to obtain and evaluate the IPv6 landmarks. Experimental results show that this method can obtain city-level IPv6 network landmarks more efficiently, and it also has a certain ability in street-level landmark mining.
Application of Isolation Forest for Detection of Energy Anomalies in ADS-B Trajectory Data

In recent years, the aviation industry has seen a large increase in the volume of operations. The maintenance and improvement of safety at acceptable levels is one of the most important concerns in civil aviation operations. Reactive methods to aviation safety improvement are being augmented with proactive and predictive approaches that leverage large amounts of routinely collected aviation data. Due to the increased availability of airborne sensor data and improvements in computing power, application of machine learning methods to various aviation safety problems for identifying, isolating, and reducing risk has gained momentum. Work in this domain focuses on identifying anomalies or abnormal operations as a first step towards identification of potentially risky situations using aircraft sensor data, such as ADS-B trajectory data. Specifically, detecting anomalies in energy metrics derived from ADS-B trajectory data has been an active area of research. While various machine learning techniques have been leveraged to detect anomalies in the energy dimension, many, such as those that are clustering-based, rely on a distance function to be defined. However, definition of a distance function is not straightforward and may be considered to be a limitation. Recently, use of the Isolation Forest (iForest) algorithm has become prevalent in other domains. The iForest algorithm does not rely on the definition of a distance function. Rather, the iForest algorithm recursively generates partitions of the data set to isolate anomalies. In this paper, a novel application of the iForest algorithm is proposed to identify anomalies in arriving aircraft operations. The application is demonstrated on 120 days of data collected for San Francisco International Airport as a case study. Energy states between nominal and anomalous flights are compared and an analysis between anomalous flights and unstable approach is conducted. The developed method has the potential to aid air traffic controllers in identifying abnormal trajectories such that these trajectories may be further investigated to determine a level of risk.
Neural Network based Biometric Attendance System

In the modern world, education system has reached a new destination due to the introduction of concept called “smart classroom”. However, when we are speaking about any classroom the attendance system still remains primitive. The traditional attendance system where the teacher/lecturer calls the name of students to mark their attendance in an attendance register is a manual method which is found to be not suitable for a smart class due to a list of disadvantages. The automatic attendance management will replace the manual method. This dynamic attendance management system will consider the physiological features of the human beings for uniquely identifying them. Hence we are using a biometric based attendance system. There are many biometric processes, among which face recognition is the best method. In the proposed project, we are going to describe the attendance without human interference. In this method a camera, fixed within the classroom will capture the image, the faces are detected and then they are compared with the faces in the database and finally the attendance is marked. It also proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Freely available machine learning and deep learning tools like dlib, Keras are used for making the face recognition faster and accurate one. This makes the system suitable in a real life scenario.
Neural Machine Translation Using Attention Mechanism

Neural Machine Translation (NMT) is the latest Machine Translation procedure that promotes exceptional upgrades compared to Rule-Based and Statistical Machine Translation procedures by conquering many shortcomings in conventional methods. NMT accepts preferences in a straightforward design and can catch lengthy relations in a sentence, demonstrating an immense possibility of turning into a different standard. Particularly for Indian Languages, NMT techniques show greater accuracy. The Attention Mechanism drastically improves the accuracy. The way to utilize the Attention Mechanism that guarantees the speed and accuracy of the translation together has likewise become a significant issue for scientists to explain. In this paper, we implement Attention Mechanism in the Machine Translation for Indian Languages and improve accuracy. Our primary objective is to test the model with the Hindi-English pair. Our NMT for this language pair showed a BLEU score of 0.8274. For future enhancement, we plan to work on other Indian Language pairs.
[Spatial Distribution and Source Analysis of Soil Heavy Metals in a Small Watershed in the Mountainous Area of Southern Ningxia Based on PMF Model].

In order to ascertain the distribution characteristics of soil heavy metal pollution and main pollution sources in a small watershed in the southern mountainous area of Ningxia and to ensure the quality of the soil environment, the contents of heavy metals Pb, Ni, Zn, Mn, Cu, Cr, and Cd in 260 surface soil samples (0-20 cm) were collected and determined. Based on the soil background value in Ningxia, the soil heavy metal pollution status and potential ecological risk were evaluated through the single factor index, Nemera composite index, and potential ecological risk index, and the method of combining positive definite matrix factor analysis (PMF) and Kriging interpolation was used to analyze the soil heavy metal spatial distribution and source. The results showed that the average contents of ω(Pb), ω(Ni), ω(Zn), ω(Mn), ω(Cu), ω(Cr), and ω(Cd) were 31.42, 36.22, 62.89, 546.18, 22.26, 61.90, and 0.18 mg·kg-1, respectively. Except for Ni, the other elements were higher than the background value of Ningxia but lower than the background value of agricultural soil pollution risk selection criteria and green food environmental quality standards. The Nemera composite index showed that the proportions of mild, moderate, and severe heavy metal pollution were 71.92%, 19.23%, and 1.54%, respectively. The potential ecological risks were mainly minor risks, accounting for 98.85%. In addition, there were a very small number of samples with medium potential ecological risk levels, accounting for 1.15% of the total number of samples. Source analysis showed that the main sources of soil heavy metals in the small watershed in the mountainous area of southern Ningxia were mixed sources of fertilization and industrial emissions (12.6%), agricultural activity sources (23.5%), natural parent material sources (27.6%), mixed sources of pesticide use and mining development emissions (17.7%), and atmospheric deposition sources (18.6%).
A Study on the Application and the Advancement of Deep Neural Network Algorithm

Abstract Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.
EdgeDIPN

With the rapid growth of e-commerce in recent years, e-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. To improve online shopping experience for consumers and increase sales for sellers, it is important to understand user intent accurately and be notified of its change timely. In this way, the right information could be offered to the right person at the right time. To achieve this goal, we propose a unified deep intent prediction network, named EdgeDIPN, which is deployed at the edge, i.e., mobile device, and able to monitor multiple user intent with different granularity simultaneously in real-time. We propose to train EdgeDIPN with multi-task learning, by which EdgeDIPN can share representations between different tasks for better performance and saving edge resources in the meantime. In particular, we propose a novel task-specific attention mechanism which enables different tasks to pick out the most relevant features from different data sources. To extract the shared representations more effectively, we utilize two kinds of attention mechanisms, where the multi-level attention mechanism tries to identify the important actions within each data source and the inter-view attention mechanism learns the interactions between different data sources. In the experiments conducted on a large-scale industrial dataset, EdgeDIPN significantly outperforms the baseline solutions. Moreover, EdgeDIPN has been deployed in the operational system of Alibaba. Online A/B testing results in several business scenarios reveal the potential of monitoring user intent in real-time. To the best of our knowledge, EdgeDIPN is the first full-fledged real-time user intent understanding center deployed at the edge and serving hundreds of millions of users in a large-scale e-commerce platform.
Dynamic manifold Boltzmann optimization based on self‐supervised learning for human motion estimation

It is a challenge work to estimate the 3D human motion from image sequence. There are some problems, such as unsatisfactory estimation error, ambiguous matching and transient occlusion. Although the prior information of learning large-scale samples exists, these problems are still difficult to be solved. How to extract the feature of the high-dimensional (HD) sample of 3D human motion and find the desired one will become the key to solve these problems above. Some dimension reduction methods can extract the sample features and build the low-dimensional (LD) space to view their LD features, but how to search the relevant valid and desired LD samples remains the bottleneck problem, which can be used to reconstruct the 3D human motions denoted by the corresponding high-dimensional samples. Thus, a new method called dynamic manifold Boltzmann optimization (DMBO) is proposed to estimate the 3D human motion from multi-view images. DMBO can find the best matching 3D human motion model by the help of the self-supervised learning from Gaussian incremental dimension reduction model (GIDRM). DMBO can avoid the local optimum during searching and solve the problems above, so that the generation of the accurate 3D human motion corresponding to multi-view images can be achieved.
Two-Party Signing For ISO/IEC Digital Signature Standards

Abstract Two-party signing can be used to provide a high level of key protection especially in the blockchain systems where the safety of money relies on the safety of the signing key. With a two-party signing protocol, the signing key is distributed among two devices, thus the funds are safe as long as one device remains uncorrupted. In this paper, we study the two-party signing protocols for all ISO/IEC signature standards. The mechanisms based on elliptic curve discrete logarithm in ISO/IEC can be divided into three types: Schnorr-type, Elliptic Curve Digital Signature Algorithm (ECDSA)-type and SM2-type. There have already been efficient two-party protocols based on Schnorr signature scheme which can be easily extended into all Schnorr-type standards. However, it is particularly hard to construct efficient distributed SM2-type and ECDSA-type protocols due to their nonlinear signing equations. In this paper, we present the first secure and efficient two-party protocol over SM2-type signature standard. We prove its security in the generic group model. We then construct a more efficient two-party ECDSA protocol that is secure in the generic group model and outperforms all previous works.
A Chatbot Tutor Can Lessen the Gender Confidence Gap in Information Systems Learning

Women are underrepresented in the information systems discipline as well as other STEM fields.A common explanation for the significant gender gap is the difference in women's and men's self-efficacy concerning information systems (IS) tasks and roles during their secondary education.As a potential solution, this study explores how chatbot tutors impact confidence differently between 136 women and men in an introductory programming course.Our findings confirmed prior research showing that while men have greater confidence in information systems tasks, there is no difference in performance in those tasks between women and men.Next, we found that a chatbot used during learning can improve confidence of all students.Finally, and most importantly, we found that the effect of a chatbot tutor is stronger for women than for men.Therefore, chatbot may be a valid tool to lessen the gender gap in the information systems discipline.
Multi-agent Pick and Delivery with Capacities: Action Planning Vs Path Finding

Motivated by autonomous warehouse applications in the real world, we study a variant of Multi-Agent Path Finding (MAPF) problem where robots also need to pick and deliver some items on their way to their destination. We call this variant the Multi-Agent Pick and Delivery with Capacities (MAPDC) problem. In addition to the challenges of MAPF (i.e., finding collision-free plans for each robot from an initial location to a destination while minimizing the maximum makespan), MAPDC asks also for the allocation of the pick and deliver tasks among robots while taking into account their capacities (i.e., the maximum number of items one robot can carry at a time). We study this problem with two different approaches, action planning vs path finding, using Answer Set Programming with two different computation modes, single-shot vs multi-shot.
“Trust Pass” - Blockchain-Based Trusted Digital Identity Platform Towards Digital Transformation

According to the United States Census Bureau, by June 2019 world population on earth was 7.5 billion, which exceeds the world population of 7.2 billion as of 2015. Each of these citizens needs to prove their identity to fulfil their day-to-day routine. In this current digital revolution whole world is transforming to digitalization. Therefore, proving someone's identity in the digital space is a must. Being able to track a person digitally can eliminate identity theft and most incidents related to online harassment. With the focus on data privacy and security of citizens, we have proposed “Trust Pass”: Cyber Security Intelligence-based trusted digital identity platform capable of registering and verifying service providers based on document validation neural network model (95.4% accuracy) and allowing citizens to authenticate themselves to service providers with three-factor biometrics authentication with liveness detection neural network model (99.8% accuracy). The requests of the whole system are secured with Cyber Security Threat Intelligence System, and unusual activities of users are monitored through Informative Data Analytics Engine. All the sensitive user data is saved using a blockchain to ensure user privacy while reducing the system's vulnerability.