Reviewerly/exHarmony huggingface dataset:
Application of Multimedia Technology in Business English Interactive Teaching

Abstract The application of multimedia technology is a comprehensive and creative application of traditional teaching forms. In particular, multimedia courseware has the advantages of integration of display, selection of hypertext links, operation of human-computer interaction, richness of large-capacity storage, and convenience of high-speed transmission and so on. It can realize the unified combination of classroom teaching integrity, science, art and technology. Higher education has always emphasized the student-centered, teacher-led teaching model, and flexible use of multimedia in business English teaching has achieved remarkable teaching results. This paper expounds the current situation and problems of the application of multimedia technology in business English teaching, and puts forward some innovative application methods of multimedia in business English teaching.
Gradient Boosting with Neural Networks

Gradient boosting machines form a family of powerful machine learning techniques that have been applied with success in a wide range of practical applications. Ensemble techniques rely on simple averaging of models in the ensemble. The family of boosting methods adopts a different strategy to construct ensembles. In boosting algorithms, new models are sequentially added to the ensemble. At each iteration, a new weak base-learner is trained with respect to the error of the whole ensemble built so far.
Detecting Callback Related Deep Vulnerabilities in Linux Device Drivers

Extensibility is an important design goal for software frameworks that are expected to evolve in a variety of dimensions. Callback mechanism is utilized extensively in large frameworks to achieve extensibility. However, callback mechanism introduces implicit control-flow dependencies that make program comprehension and analysis difficult. This paper presents an automated approach for detecting deep bugs/vulnerabilities that involve callbacks. Our approach consists of several stages to balance scalability and precision. Specifically, it uses a light-weight static analysis for extracting callback related interactions between the application modules and the framework modules. This information is used to extend the basic call graph of the application modules to incorporate implicit call chains due to callbacks. The second stage, summary mode, summarizes bug relevant data-flow facts for paths that start at callbacks. The third stage, summary-aware mode, uses the extended call graph to incorporate data-flow facts due to implicit paths that lead to the callbacks and detects deep bugs. We have implemented the presented model extraction and bug detection approach in a framework called MOXCAFE and applied it to Linux device drivers. Using our approach, we could detect several deep vulnerabilities.
Research on Electronic Evidence Management System Based on Knowledge Graph

With the development of information technology, electronic data cues have been playing an important role in the investigation of criminal cases. However, the difficulty of analyzing massive data have also brought great challenges to criminal investigations. At present, Big data, artificial intelligence and other technologies are used to store and analyze electronic evidence such as text, pictures, videos, forms, etc. However, to deeply mine and make full use of the semantic information of evidence and to break the barrier between various heterogeneous data are still hard to be settled. To solve these problems, the innovation of storage form of data is desperately needed to construct a unified expression of data for heterogeneous information. This paper proposes to build a case-oriented knowledge graph, and provides solutions to the association and the relation of the knowledge graph with external information and non-electronic data evidence respectively. This knowledge graph can be used to realize the reasoning and judgment of cross-structure information, and assist the public security organs to detect cases.
Adversarial Domain Alignment Feature Similarity Enhancement Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) attempts to transfer knowledge learned from labeled source domain to unlabeled target domain. Its main challenge is distribution gap between two domains. Most of works focus on reducing domain shift by domain alignment methods. Although these methods can reduce the domain shift, the samples far from the class center of target domain are still easily misclassified. To solve the problem, we propose a new approach named Adversarial Domain Alignment Feature Similarity Enhancement Learning (AASE). It learns domain invariant features by adversarial game and correlation alignment to reduce the domain gap, and makes these features having better discrimination via joint central discrimination and feature similarity enhancement. AASE makes the learned features have better intra-class compactness and inter-class separability. AASE is evaluated on two datasets, and the results show that AASE has critical improvement in the performance of UDA.
Digitizing the Txalaparta: Computer-Based Study of a Traditional Practice

Abstract This article describes a software implementation dealing with the ancient Basque musical tradition of the txalaparta. The research is different from earlier studies of the txalaparta in that, by digitizing the instrument and its performance rules, we have had to formalize and make explicit conventions that hitherto have been tacit knowledge of improvisational practice. Analysis through software development is an unusual case of musicological analysis as it demands clarity and precision, and often requires multidisciplinary approaches to understand the studied subject. We have developed software in order to analyze and understand a practice that has received little musicological analysis. By expounding musical patterns and performers' behaviors that have hitherto been difficult to analyze, we reveal the social and cultural aspects of performance practice. The txalaparta is a two-performer instrument and the software produces txalaparta rhythms and plays along with a human player, while learning and adapting to the player's style. The system helps novices to explore the rules of the txalaparta and more-experienced performers to approach the instrument from a new perspective. In this research we have applied a user-centered approach, where feedback from players using the digital txalaparta was collected. This feedback allowed us to approach the reflective vision of txalaparta players and their thoughts on the results of our research.
An evaluation of augmented reality music notation

We conducted a focus group study of a prototype application to test the opportunities and limitations of augmented reality music notation for musical performance and rehearsal.
Driverless Government: Speculation, Citizenship and Collective Civic Intelligence

This article examines data, algorithms and machine learning as the new materials that make cities smart. Analyzing the threat of relying on digital tools made by corporations to govern and regulate will also provide insight into possible areas in which citizens can insert their own agency. Using a speculative case study, Driverless Government, the project imagines the full potential of using digital tools in a city council meeting by mixing the political, digital and physical to create a hybridized and multimodal proposal. The speculative design provocation challenges the current American bureaucratic system by presenting tools that allow citizens to dispute an optimization-led and corporate agenda by envisioning representation in new forms. Specifically, the project imagines how citizens can create artificial intelligence representatives that advocate for themselves and other agents. By imagining, possibilities and responsibilities for the American citizenry are outlined in order to create cities that are for people.
OntoDS: An Ontology-Aware Distributed Storage Scheme for RDF Graphs

With the development of the Semantic Web, the amount of RDF data has been increasing rapidly. It is no longer feasible to store entire data sets on a single machine, and still be able to access the data at reasonable performance. Consequently, the requirement for clustered RDF database systems is becoming more and more important. At the same time, the native storage scheme of RDF data is less mature in many aspects compared with relational storage scheme. SQL-on-Hadoop is a distributed relational database engine for big data with many factors, which uses Hadoop to improve the fault tolerance of the system and is fully transactional. However, currently, there is no SQL-on-Hadoop relational database that realizes a subsystem for RDF data storage. In this paper, we propose an Ontology-aware Distributed Storege scheme for RDF, called OntoDS, which modifies the relational RDF data storage scheme DB2RDF to build a novel scheme for RDF data and optimizes the partitioning of RDF graphs by distributing RDF triples based on ontologies to meet the need for RDF graph data storage and query load. The experimental results on the benchmark datasets show that our distributed RDF storage scheme is about 1–1.5 times faster than the state-of-the-art native storage schemes.
GPU-Accelerated Decoding of Integer Lists

An inverted index is the basic data structure used in most current large-scale information retrieval systems. It can be modeled as a collection of sorted sequences of integers. Many compression techniques for inverted indexes have been studied in the past, with some of them reaching tremendous decompression speeds through the use of SIMD instructions available on modern CPUs. While there has been some work on query processing algorithms for Graphics Processing Units (GPUs), little of it has focused on how to efficiently access compressed index structures, and we see some potential for significant improvements in decompression speed.
Awareness Learning for Balancing Performance and Diversity in Neural Network Ensembles

An ensemble learning system requires a set of cooperative modules that solve a task together. It is believed that awareness modules being aware of other modules in an ensemble could build strong cooperations each other. In this paper, two levels of awareness are introduced into negative correlation learning in order to control the differences among the individual modules in an ensemble. At the ensemble level, whether individual modules would learn to be more different to the rest of modules on a given data point would depend on how well the data point had been learned by the ensemble. At the individual level, each individual module would try to learn to be more different to the rest of modules on a given data point only if it had not been far away from the other individuals based on the distances measured by their outputs on the data point. Negative correlation learning with such awareness learning was tested on training different structures of neural network ensembles where the relations between differences and cooperation were analyzed in the learning process.
Statistical Analysis of Cloud Based Scheduling Heuristics

Scheduling of cloudlets (tasks) on virtual machines in cloud has always been of prime concern. Various heuristics have already been proposed in this area of research and are well documented. In this work, authors have proposed a unique method of statistically evaluating the results of simulation of these heuristics for cloud-based model. The results are evaluated for a standard set of performance metrics. The statistical method applied proves the reliability of simulation results obtained and can be applied to evaluation of all heuristics. In addition to this a recent and more advanced CloudSim Plus simulation tool is used as there is paucity of work that demonstrates using this tool for this research problem. The simulations use a standard model of task and machine heterogeneity that is pertinent to cloud computing. To make the simulation environment more realistic, Poisson distribution is used for the arrival of cloudlets, and exponential distribution for length (size) of cloudlets (tasks).
Clustering Steam User Behavior Data using K-Prototypes Algorithm

Abstract The use of user telemetry to gather player behavioral data on video games can be very beneficial to game developers with a certain business model. With the help of user telemetry in game development, it can provide access to data on user behavior from installed game clients platform such as Steam. These behavioral data can be used to find out the Steam user behavioral patterns on playtime distributions that can be studied by developers in order to have a deeper understanding of the behaviors of their players. In this study, the data are clustered using the k-prototypes algorithm, a combination of k-means and k-modes algorithm that can be used to cluster mixed attributes. The result shows that the clusters represent the types and preferences of the players.
DeepIdentifier: A Deep Learning-Based Lightweight Approach for User Identity Recognition

Identifying a user precisely through mobile-device-based sensing information is a challenging and practical issue as it is usually affected by context and human-action interference. We propose a novel deep learning-based lightweight approach called DeepIdentifier. More specifically, we design a powerful and efficient block, namely funnel block, as the core components of our approach, and further adopt depthwise separable convolutions to reduce the model computational overhead. Moreover, a multi-task learning approach is utilized on DeepIdentifier, which learns to recognize the identity and reconstruct the signal of the input sensor data simultaneously during the training phase. The experimental results on two real-world datasets demonstrate that our proposed approach significantly outperforms other existing approaches in terms of efficiency and effectiveness, showing up to 17 times and 40 times improvement over state-of-the-art approaches in terms of model size reduction and computational cost respectively, while offering even higher accuracy. To the best of our knowledge, DeepIdentifier is the first lightweight deep learning approach for solving the identity recognition problem. The dataset we gathered, together with the implemented source code, is public to facilitate the research community.
An efficient stereo matching based on superpixel segmentation

The traditional semi-global matching methods provide a good trade-off between accuracy and complexity compared with the local matching methods and global matching methods, however, they still need to traverse the full disparity search range to find the best matching point. Therefore, it still needs high computational cost especially for stereo images with large disparity search range. We proposes an efficient semi-global matching method that disparity search range is reduced based on 3D plane fitting. Firstly, the simple linear iterative clustering (SLIC) algorithm is adopted to segment the stereo images. Secondly, the dense SIFT keypoints are extracted and matched from the left and right images. Thirdly, similar adjacent superpixels are merged based on the gray mean and variance, and for each merged region, 3-D plane is fitted based on matched keypoints. Finally, the pixel-wise disparity search range is limited into several pixels for more-global matching method which can reduce the computational complexity and obtain an accurate disparity map. Experimental results demonstrate that the computational speed of the new semi-global matching method is several times faster than that of the original method, as well as offering a more accurate disparity map.
Challenges and Lessons Deploying a Physical System for Resource Exchange in Local Communities

This interactive poster will discuss challenges and lessons learned designing and deploying ShareBox, a hardware-based system that enables people to share physical resources within local communities. Our goal in sharing the insights and struggles we encountered creating ShareBox is to help other researchers working on similar platforms to avoid the pitfalls that impacted our research.
Multi-scale Iterative Network for Underwater Image Restoration

Degraded underwater images have seriously affected the exploration of the marine environment, causing inconvenience to the marine military, marine engineering, marine research, and other fields. In this paper, we address the underwater image restoration problem by proposing a multiscale iterative network framework that restores the content and details of the image from coarse to fine. First, a endocer-decoder network based on residual blocks and jump connection is designed, which includes several symmetric convolution layers and deconvolution layers to learn an end-to-end mapping of a degraded underwater image to clear image. Then, a multi-scale iterative training strategy is employed to gradually restore the underwater image. Last, a multi-scale loss function is designed to train the network. The experiment results show that the recovery results of the proposed method have better visual perception and quantitative results.
A Low Complexity Image Compression Algorithm for IoT Multimedia Applications

This paper focuses on a novel lossless image compression algorithm which can be efficiently used for Internet of Things (IoT) multimedia applications. The proposed algorithm has low memory requirements and relies on a simple and efficient encoding scheme. Thus it can be easily implemented even in low-cost microcontrollers as those commonly used in several IoT platforms. Despite its simplicity, comparison results on different image datasets show that the proposed algorithm achieves compression ratios comparable with other more complex state-of-the-art solutions.
Hybrid Solution for Privacy-Preserving Data Mining on the Cloud Computing

The continuous evolution of the cloud has attracted many enterprises to outsource their data to the cloud to accomplish data mining tasks and other services. So that data owners can enjoy the benefits of the cloud without fear from violations of data privacy, PPDM approaches came to protect the privacy of data while preserving the usefulness of data. This article reviews the most popular models of PPDM over the cloud along with their strengths and the weaknesses to conclude the research gap. Some of the current PPDM models still vulnerable to various types of attacks like the K-anonymity model whereby surface from background knowledge attack, homogeneity attack, similarity attack and probabilistic inference attack, others of them consume great computational complexity, for example, the methods which depended on cryptography. The hybrid solution was proposed to protect the data privacy and overcome the current problems of PPDM include identify and attributes disclosures whereby preservation the privacy of data before to outsourced on the cloud is the main focus. K-Anonymization is combined with homomorphic encryption to rid from their limitations and take their advantage together to enhance data privacy-preserving and maintain the data utility of outsourced data.
Isopod

Kubernetes is an open-source cluster orchestration system for containerized workloads to reduce idiosyncrasy across cloud vendors [2]. Using Kubernetes, Cruise has built a multi-tenant platform with thousands of cores and tens of terabytes of memory. Such a scale is possible in part thanks to the declarative abstraction of Kubernetes, where desired states are described in YAML manifests [5].
Innovative Management Technologies of Decision Making in Modern Business Systems

Purpose The purpose of the chapter is to study the possibility of applying modern intellectual methods and technologies of decision-making for managing complex poorly structured systems.
Analysis of the security level of modified CBC algorithm cryptography using avalanche effect

Abstract There have been many modifications of cryptographic algrotima aimed at increasing the security of the algorithm. One cryptographic algorithm that has been modified is CBC (Cipher Block Chaining) cryptography. CBC is a block-based modern cryptographic algorithm. The disadvantages that exist in CBC is the encryption process in each block plainteks using the same key, resulting in if the cryptanalyst finds the key that is used it will be easy to decrypt the ciphertext. Modifications made to CBC aimed at increasing security is by combining the Vigenere algorithm in determining the key and block transposition on the final reading of the encryption process. Parameters for assessing the algorithm are good or not by looking at the value of avalanche effect. A cryptographic algorithm will be categorized as good if the value of avalanche effect ranges between 45%-60% and bernilia is very good if the value of avalanche effect is 50%. The results of this study indicate that the modified CBC cryptographic algorithm resulting from the encryption process of several plaintexts has Avalanche effect values in the range above 50% and below 60%, which means that the modified CBC algorithm is of good value.
Insurance Block: An Insurance Data Security Transaction Authentication Scheme Suitable for Blockchain Environment

In order to solve the problems existing in the sensitive data transaction communication in the insurance industry, such as poor security, high management cost of transaction center, regulatory difficulties and easy data tampering, etc. This paper puts forward a kind of insurance data security transaction authentication scheme based on block chain. This paper proposed a insurance data security transaction authentication scheme based on block chain. With the characteristics of insurance distributed authorization management, the data exchange center is reliable, safe and efficient operation. The scheme can share books, design and implement consensus mechanism of insurance data, intelligent contract and special chain storage structure, as well as multi-party co-storage of data and decentralization of business. Experimental results show that, the block chain insurance data security authentication scheme in this paper has high security and practicability.
Heightening Satellite Image Display via Mobile Augmented Reality – A Cutting-Edge Planning Model

This paper summarizes on object detection, classification, analysis, and display for optical satellite image. Initially, all the existing object detection and object viewing system based on AI techniques are introduced. Various optical imaginary methods and the possibility of immersing optical and 3D data with other data sources are also explained. The surveyed literatures show that in most of the case, the detected objects are taken as resource for planning. We also observed that the image viewing and displaying model was ignored by many authors which is one of the key concepts for next phase. Satellite AR plays a vital role in displaying the images. Overall, it can be seen that optical image view along with AR display can be used for better planning, which is one of the popular research topics and has an excessive operational potential which is the need of the hour dealing with analyzing, predicting, and viewing large amount of data.