Reviewerly/exHarmony huggingface dataset:
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network

Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook the rich interactions among neighbor nodes, which introduces an early summarization issue. In this article, we propose GraphHINGE ( H eterogeneous IN teract and aggre G at E ), which captures and aggregates the interactive patterns between each pair of nodes through their structured neighborhoods. Specifically, we first introduce Neighborhood-based Interaction (NI) module to model the interactive patterns under the same metapaths, and then extend it to Cross Neighborhood-based Interaction (CNI) module to deal with different metapaths. Next, in order to address the complexity issue on large-scale networks, we formulate the interaction modules via a convolutional framework and learn the parameters efficiently with fast Fourier transform. Furthermore, we design a novel neighborhood-based selection (NS) mechanism, a sampling strategy, to filter high-order neighborhood information based on their low-order performance. The extensive experiments on six different types of heterogeneous graphs demonstrate the performance gains by comparing with state-of-the-arts in both click-through rate prediction and top-N recommendation tasks.
A Real-Time Traffic Surveillance and Security System using Transfer Learning and Edge Computing

Two Wheeler's are one of the most commonly used vehicles worldwide, and the helmet is the most critical gear/safety equipment. There is no proper system that validates whether all the users are equipped with a helmet while they are riding. The existing traffic surveillance and security systems are designed only to identify the vehicles that over speed and jump over the red lights. These systems use interceptors at highways to capture the violators and consume a lot of manpower and vehicles for patrolling. An efficient traffic surveillance system to monitor whether the motorcyclist and the pillion rider is wearing a helmet is missing. This paper proposes an efficient and reliable traffic surveillance system using edge computing and transfer learning that continuously monitors the two-Wheeler's and sends alerts and information on the non-helmet riders to the nearest police vehicles. The edge computing is being applied at the input/entry-level (CCTV camera) and helps in reducing the delay of the entire system. The classification models are created by applying Transfer Learning on ResNet50, VGG16 architectures, and applied to real-time video footage from the CCTV camera to detect non-helmet riders. The proposed system gives an accuracy of 97.50%, reduces the manpower, and enables the police interceptors to work efficiently.
Investigation and Research on Student Satisfaction Evaluation of Campus Node Square—Taking Southwest University of Science and Technology as an Example

With the development and maturity of higher education in China, the construction of university campus is also facing many problems, among which the research and design of campus node square cannot be ignored.Aiming at investigating students' satisfaction, this paper establishes a satisfaction evaluation index system.Relevant data of each evaluation index were obtained by questionnaire survey.By means of the fuzzy comprehensive evaluation method, this paper studies the relationship between the perceptual characteristics of four typical campus node squares in Qingyi Campus of Southwest University of Science and Technology and students' satisfaction, which provides a reference for the humanized design of campus node space.The research shows that the four indexes of humanistic atmosphere, interest, ground pavement and atmosphere are the key points in the design and construction of campus node square.The four squares have different shapes.Except for the Light of Science and Technology Square, the satisfaction rate is 67%, and the other three squares are not satisfied by the majority of students.Finally, aiming at the problems of four squares, we put forward to optimize the layout, and improve the infrastructure and functional configuration, etc.
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing

Knowledge tracing refers to a family of methods that estimate each student’s knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an overall knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error.
Unified framework of dense convolution neural network for image super resolution

In the last few years, Image Enhancement is a very necessary task in computer vision and to enhance the image. Super Resolution (SR) technique has been used to enhance the resolution of image. Super Resolution is a technique which is used to convert High Resolution (HR) from Low Resolution Image (LR). In this paper, to understand SR problems and to design an efficient and robust model, Convolution neural network (CNN) is used. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on other datasets and acheived advance results as compared to existing works.
Robust Visual Tracking Based On Convolutional Sparse Coding

This paper proposes a target tracking algorithm based on convolutional sparse coding (CSC). This algorithm first uses the CSC to divide the interest region into a smooth image and four detail images with different fitting degrees. Then, the smooth image is tracked to produce the initial result based on the kernel correlation filtering (KCF). According to the initial value of the target area and the linear combination of four detail images, the appearance model of the details is constructed. And the matching between samples and the appearance model is performed according to the overlap rate and Euclidean distance to determine the tracking results of the detail images. In the end, the two tracking results including the one based on the smooth image and the other one based on the detail images are linearly combined to determine the final position of the target in the new frame. Many experiments on the video sequences from Tracking Benchmark 2015 show that: our method produces much better result than most of present visual tracking methods.
Wavefront phase representation by Zernike and spline models: a comparison

A comparative analysis of spline and Zernike models is presented for wavefront phase construction. The techniques are analyzed on the basis of representation accuracy, computational costs, and the number of samples used for representation. The strengths and weaknesses of each model over a set of various wavefront phases with different domain shapes are analyzed. The findings show that both models efficiently represent a simple wavefront phase at irregular domain shapes. On the other hand, when complex wavefront phases at irregular domain shapes are represented, the spline model performs much better than the Zernike model. Further, results show that the spline model evaluation speed is significantly faster than the Zernike model.
Incorporating Pre-trained Model into Neural Machine Translation

Since Transformer is proposed, it has boosted the neural machine translation baseline. In the past few years, although many researchers have tried to update the original transformer for each module, these approaches gain a little improvement. Pre-trained models have proved their power in natural language understanding tasks, how to incorporate the pre-trained model into natural language generation tasks such as machine translation is still an opening problem. This paper proposes a novel framework call Transformer-Pt,which combines the BERT and GPT-2 with Transformer. Experiment result on the WMT-14 dataset demonstrates our proposed method achieve improvement compared to other models.
A Homomorphic Encryption Method for Power Data Based on Improved Paillier Algorithm

In the context of wide application of power big data, data security is not only related to user privacy and enterprise development, but also related to national financial security. In recent years, many scholars have proposed various encryption algorithms for the application of power data, trying to find a reliable encryption algorithm to meet the increasingly high security requirements of power data. However, excessively high data security requirements make it difficult for enterprises to share data, which also leads to the frequent occurrence of "data island" phenomenon, which becomes an obstacle for further in-depth analysis of power data. Given this phenomenon, this paper proposes an improved Paillier homomorphic encryption algorithm (I-Paillier) to make the power data available but not visible between different enterprises or institute, which provides technical support for the cooperation of data between different enterprises to get more economic returns. The I-Paillier algorithm proposed in this paper improves the power mode operation of the original Paillier algorithm without changing its security, reduces the number of operations, and improves the efficiency of power data encryption and decryption. A large number of simulation experiments show that the efficiency of I-Paillier algorithm in power data encryption and decryption is higher than that of the original Paillier algorithm.
Dentacoin

Blockchain is a decentralized digital ledger of transactions shared amongst all participating web nodes, over which every data is recorded. Since the first blockchain was conceptualized in 2008, much research has been done to expand its applications to non-financial purposes. Dentacoin is the first cryptocurrency ever created worldwide that strives to create a dental industry community by rewarding people with a given token—Dentacoin cryptocurrency—for specific actions that have a desirable effect on the Dentacoin ecosystem. This concept aims to improve the global dental market by applying blockchain technology advantages and promoting intelligent prevention in dental healthcare. The purpose of this chapter is to review the concept of blockchain-based Dentacoin ecosystem, as it is expected that, in the future, this method will significantly improve dental health and oral hygiene habits, thus improving the quality of life for individuals resulting in overall health enhancement and increased longevity.
A Cockpit for the Development and Evaluation of Autonomous Database Systems

Databases are highly optimized complex systems with a multitude of configuration options. Especially in cloud scenarios with thousands of database deployments, determining optimized database configurations in an automated fashion is of increasing importance for database providers. At the same time, due to increased system complexity, it becomes more challenging to identify well-performing configurations. Therefore, research interest in autonomous or self-driving database systems has increased enormously in recent years. Such systems promise both performance improvements and cost reductions. In the literature, various fully or partially autonomous optimization mechanisms exist that optimize single aspects, e.g., index selection. However, database administrators and developers often distrust autonomous approaches, and there is a lack of practical experimentation opportunities that could create a better understanding. Moreover, the interplay of different autonomous mechanisms under complex workloads remains an open question. The presented cockpit enables an interactive assessment of the impact of autonomous components for database systems by comparing (autonomous) systems with different configurations side by side. Thereby, the cockpit enables users to build trust in autonomous solutions by experimenting with such technologies and observing their effects in practice.
Bootstrapping Information Extraction via Conceptualization

Bootstrapping enables us to use existing knowledge to find patterns and extract new knowledge from free texts, from which more patterns can be found. Due to its minimally supervised, domain-independent, and language-independent nature, it has been widely adopted in real-world applications. However, as iterations go on, semantic drift may happen. The extraction may shift from the target class to other classes and result in errors, which propagate in the succeeding iterations and hurt the performance significantly. Existing solutions simply throw away bad patterns, sacrificing recall to ensure high precision. However, we argue that most of these patterns and instances can be kept as long as being applied selectively, guided by prior knowledge. In this paper, we propose a pattern-based extraction framework with three distinguished features: (1) it uses conceptual taxonomies to guide the extraction to reduce semantic drift; (2) it uses the knowledge of existing triples to improve the precision; (3) it integrates all patterns to form a generalized pattern set with quantified confidence measurement. The proposed solution is applied on enriching two real-world knowledge bases and achieves higher precision and recall compared to existing solutions.
Stasis type particle stability in a stochastic model of particle swarm optimization

Particle Swarm Optimization (PSO) is a heuristic optimization technique where particles explore optima of the fitness landscape defined in the search space. It is a practical approach but vulnerable to incorrect particle movement parameter tuning. Therefore, stochastic models of a particle and a swarm are subject to analysis. In this paper, we study the stability properties of particles controlled by a universal update equation proposed by Cleghorn and Engelbrecht. We propose a new concept of particle state, namely the stasis state when the difference between two consecutive particle location variance values is not changing much after a sufficient time. We also propose a convergence time measure, representing a minimal number of steps a particle location variance needs to reach stasis. Finally, we visualize the convergence time characteristics obtained in simulations.
KCF-Match Target Tracking Algorithm for Tracking Swing Angle of Coupler Based on Video

The coupler is an essential component on the train that has the function of connecting and buffering. The actual dynamic performance of the coupler directly influences the safety and comfort of the vehicle. When the heavy haul train passes through the curve, the extreme swing angles of the couplers will seriously threaten the safety of the train. Therefore, the kernelized correlation filter-template matching (KCF-Match) target tracking algorithm is proposed to track the position and calculate the swing angles of the couplers. After the tracked area is selected, the corresponding data of the area are input into the KCF target tracking model for tracking. During the tracking process, if the tracking effects are not satisfied with the given evaluation indexes, the template matching algorithm will be used to track again. Experiments show that KCF-Match target tracking algorithm can achieve 99.8% accuracy rate and 99.9% success rate on the premise of ensuring real-time performance.
Preserving Privacy of Data with Efficient Attribute-based Encryption Schemes 

Attribute-based encryption (ABE) schemes and their variations are often applied to preserve the privacy of data. In particular, ABE schemes proposals are resilient to multiple attacks, including attacks in interception, interruption, modification, fabrication, unauthorized authentication, and access of data. Existing proposals have several limitations, such as the generation, verification, and distribution of digital certificates incur extra computation and communication overhead which are not suitable for resource-constrained computing. Furthermore, in most of the ABE schemes, a certification authority (CA) generates the public/secret keys according to a set of attributes. However, the compromise of CA can endanger the secret keys, therefore, the secrecy of encrypted messages. Some of the existing ABE schemes are based on bilinear pairing that requires large security parameters, which make ABE schemes unsuitable for resource-constrained computing devices.
The Use of Agile Project Management and Related Tools for Book Moves

Abstract In this article Beth Flerlage and Renae Satterley reflect on the library staffs’ experience of using Agile project management techniques and cloud-based tools to restructure resources at Middle Temple Library.
Blockchain, Decentralisation and the Public Interest: The need for a Decentralisation Conceptual Framework for dApps

Blockchain and Distributed Ledger Technology (DLT) enable for disintermediation of services and decentralisation of the software processes enabling them. By doing so, processes can become transparent, verifiable, tamper-proof, and immutable. They have the capacity to provide an unchangeable history, or log, of all actions that have taken place which could be traced back to specific stakeholders. It can be said that such features allow for the implementation of use-cases that strive towards the common good such as financial inclusion, ethical supply chains, and community empowerment – however, decentralisation may not always work towards the common good (Ellul & Pace, 2018). The question of whether and how exactly blockchain can help towards the common good is, indeed, too wide-ranging. What exactly the term common good is has changed, and has been debated since Aristotelian times (Dupre, 1993). In this paper a narrower concept, public interest, and its relation to decentralisation will be explored, after which we aim to provide a conceptual framework that can aid in visualising the decentralisation complexities of DLT. The proposed conceptual framework can thereafter aid in determining a system’s relevance to de/centralisation goals in aid of the public interest.
Orbit Verification of Small Sat Constellations

We present a scheme for verifiable location reporting of small satellites, or small sats. The decreasing size of small sats makes them harder to track optically. We propose to verify satellite locations by having the satellites verify the correctness of each other's positions by occasionally completing cryptographically-secure, telemetry-based challenges that are secured on a permissioned blockchain.In the scheme, we update an a posteriori position estimate using self-reporting. The self-reported data is verified by requiring that satellites periodically complete challenges through which it becomes possible to determine the correctness of position using bilateration. To avoid equivocation, the challenges are cryptographically secured by wrapping a challenge in layers that can be removed only by the satellite that is targeted by the challenger. The blockchain is used to secure a list of locations so that verification can be done asynchronously. This is necessary because satellite communication can be significantly delayed. The blockchain is permissioned and operated by constellations, not individual satellites, and does not require that any satellite host a node.
IOT-Based Occlusion Invariant Face Recognition System

Nowadays, one of the most important challenges of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The method that proposed is a robust approach which consists of detecting the face region that is not occluded and uses this region to obtain the face recognition. Face images are captured from the USB web camera on the Raspberry Pi. To segment the occluded and non-occluded parts, fuzzy C-means clustering is used, and for recognition, block-based mean weighted local binary patterns (MWLBP) are used. The input image is divided into number of sub-blocks, and each block is checked if occlusion present or not, and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results show that our method is giving promising results when compared to the other conventional techniques.
Efficient Video Captioning on Heterogeneous System Architectures

Video captioning is the core technology to drive the development of many important multidisciplinary applications, such as AI-assisted medical diagnosis, storytelling through videos, video question answering, lip-reading, just to name a few. Video captioning employs a hybrid CNN+RNN neural network model to translate video scenes into natural language descriptions. For deep learning inference, a typical approach is running both the CNN and the RNN on a GPU. Such a GPU-only approach often suffers long inference time due to underutilization of the computing power offered by the CPU+GPU heterogeneous system architecture, which is a common architecture in modern computers.This work is an early effort to tackle the performance issue of performing deep learning inference using a hybrid CNN+RNN model on a heterogeneous system with a CPU and a GPU. This is a challenging task because of (1) CNN and RNN exhibit very different computing behaviors. This raises the question of how to split the two models into computing tasks and properly assign the tasks to the CPU and the GPU to minimize the inference time for a video frame, and (2) Data dependency exists between the CNN and the RNN within a video frame, as well as between the adjacent RNNs across two video frames. These data dependencies prohibit full parallelization of the hybrid model. To solve these two problems, we propose two optimizations: a fine-grained scheduling scheme for mapping computation and devices within a video frame, and a pipeline scheduling scheme to exploit maximum parallelism between the execution of the video frames. To facilitate our optimizations, we also develop an accurate regression-based cost model to predict the computation time of CNN/RNN operations and the communication time for moving data between CPU and GPU. Experimental results show that our optimization improves the performance of video captioning by up to 3.24× on the CPU+GPU system, compared with the GPU-only execution.