Reviewerly/exHarmony huggingface dataset:
Application of Computer Multimedia Technology in College English Teaching

Abstract The 21st century is the era of multimedia, which has been applied to all walks of life, especially the education industry. With the rapid development of economy, the process of global economic integration is faster and faster, which requires higher quality of English Teaching (hereinafter referred to as ET). Therefore, multimedia technology (hereinafter referred to as MT) has become an important tool in College ET, which can not only promote the cultivation of language expression ability and thinking logic ability, but also enhance students’ interest in learning. Through MT, colleges can change the traditional teaching methods and means, which will improve students’ interest and enthusiasm. Through the introduction of computer MT, colleges effectively improve the students’ learning quality. First, this paper analyzes the important role of computer MT in ET. Then, this paper constructs the important teaching platform function of MT in Colleges. Finally, some suggestions are put forward.
Front-end Control Mechanism of Electronic Records

In the digital era, how to ensure the authenticity and integrity of electronic records has become an open challenging issue. Front-end control is an important concept as well as a basic principle in electronic record manageme... | Find, read and cite all the research you need on Tech Science Press
CCF-Net: Composite Context Fusion Network with Inter-Slice Correlative Fusion for Multi-Disease Lesion Detection

Detecting lesions from computed tomography (CT) scans relies on two aspects of the input: intra-slice texture information from the key slice and inter-slice structural context information from the adjacent slices. However, most existing methods ignore the correlation and complementarity between texture and structural information resulting in unexpected loss of performance. In this paper, a novel Composite Context Fusion Network (CCF-Net) is proposed to jointly model intra-slice and inter-slice features so as to prove the effectiveness of the two-steam framework. To extract both texture and structural information, two streams of 2D and 3D convolutional modules are employed in each stage. Moreover, a Composite Fusion architecture equipped with Inter-slice Correlative Fusion (ICF) modules is proposed to achieve stage-by-stage feature fusion in order to excavate and exchange information between texture-aware and context-aware features. Extensive experiments show that the proposed CCF-Net is able to achieve state-of-the-art detection performance on the multi-disease CT lesion detection task and significantly surpass the baseline methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
CT Segmentation of Liver and Tumors Fused Multi-Scale Features

Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images ... | Find, read and cite all the research you need on Tech Science Press
Collaborative Filtering Based on Clustering and Simulated Annealing

Collaborative filtering (CF) is a typical and widely used recommendation method that can be categorized into user-based CF and item-based CF. For user-based CF, how the approach chosen more suitable similar users is important. To solve this problem, we first define the user-genre vector as the objects used for clustering. The user-genre vector uses not only genre information to enrich the source data for recommendation but also users' rating biases, which can be differentiated, to certain extent. Then, we propose a k-means clustering method enhanced by simulated annealing to ensure stable clustering results. Finally, we define comprehensive similarity, which combines original and transformed ratings, to select more appropriate similar users for recommendation. Our experimental results showed that the proposed method can make recommendations accurately and reliably.
Representation Learning of Knowledge Graph with Semantic Vectors

Knowledge graph (KG) is a structured semantic knowledge base, which is widely used in the fields of semantic search, such as intelligent Q&A and intelligent recommendation. Representation learning, as a key issue of KG, aims to vectorize entities and relations in KG to reduce data sparseness and improve computational efficiency. Translation-based representation learning model shows great knowledge representation ability, but there also are limitations in complex relations modeling and representation accuracy. To address these problems, this paper proposes a novel representation learning model with semantic vectors, called TransV, which makes full use of external text corpus and KG’s context to accurately represent entities and complex relations. Entity semantic vectors and relation semantic vectors are constructed, which can not only deeply extend semantic structure of KG, but also transform complex relations into precise simple relations from a semantic perspective. Link prediction and triple classification tasks are performed on TransV with public datasets. Experimental results show that TransV can outperform other translation-based models. Mean Rank is reduced by 66 and Hits@10 is increased by 20% on average for link prediction task on FB15K.
A behavior model for improving realism in a VR team sport

Personality, moods and emotions play a vital role in real-life interactions, especially for team activities like sports. Diverse player personalities lead to distinct emotional reactions impacting game play. One of the major challenges while developing a realistic virtual environment is simulating human-like behaviors and imitations. We aim to improve the believability of virtual players by incorporating personality, moods and emotions for them. We propose a novel framework for a multi-player VR Volleyball game by integrating various behavioral changes during game play. This is achieved by simulating player reactions and rendering changes in the player&#x2019;s body postures, which are dependent on game environment parameters and scenarios. We showcase visual analysis of individual players&#x2019; mood changes during game play to further corroborate the presented VR framework. Incorporating the proposed model, we exhibit the progression and variations in moods for players with different personalities when presented with varied game situations. We conducted a user study with 10 participants, which consisted of seven males and three females. We used two versions of the VR Volleyball game. One version of the game is without any behavioral changes, and the other version included the proposed behavioral model. Our results shows that behavioral changes setting had significantly higher ratings compared to the version with non-behavioral changes setting.
The Text Captcha Solver: A Convolutional Recurrent Neural Network-Based Approach

Although several different attacks or modern security mechanisms have been proposed, the captchas created by the numbers and the letters are still used by some websites or applications to protect their information security. The reason is that the labels of the captcha data are difficult to collect for the attacker, and protector can easily control the various parameters of the captchas: like the noise, the font type, the font size, and the background color, then make this security mechanism update with the increased attack methods. It can against attacks in different situations very effectively. This paper presents a method to recognize the different text-based captchas based on a system constituted by the denoising autoencoder and the Convolutional Recurrent Neural Network (CRNN) model with the Connectionist Temporal Classification (CTC) structure. We show that our approach has a better performance for recognizing, and it solves the identification problem of indefinite character length captchas efficiently.
Game theoretic modeling of economic denial of sustainability (EDoS) attack in cloud computing

Abstract In this paper, we develop a novel game theoretic model of the interactions between an EDoS attacker and the defender based on a signaling game that is a dynamic game of incomplete information. We then derive the best defense strategies for the network defender to respond to the EDoS attacks. That is, we compute the perfect Bayesian Nash Equilibrium (PBE) of the proposed game model such as the pooling PBE, separating PBE and mixed strategy PBE. In the pooling equilibrium, each type of the attacker takes the same action and the attacker's type is not revealed to the defender, whereas in the separating equilibrium, each type of the attacker uses different actions and hence the attacker's type is completely revealed to the defender. On the other hand, in the mixed strategy PBE, both the attacker and the defender randomize their strategies to optimize their payoffs. Numerical illustration is also presented to show the efficacy of the proposed model.
Unsupervised Feature Selection Based on Spectral Clustering with Maximum Relevancy and Minimum Redundancy Approach

In this paper, two novel approaches for unsupervised feature selection are proposed based on the spectral clustering. In the first proposed method, spectral clustering is employed over the features and the center of clusters is selected as well as their nearest-neighbors. These features have a minimum similarity (redundancy) between themselves since they belong to different clusters. Next, samples of data sets are clustered employing spectral clustering so that to the samples of each cluster a specific pseudo-label is assigned. After that according to the obtained pseudo-labels, the information gain of the features is computed that secures the maximum relevancy. Finally, the intersection of the selected features in the two previous steps is determined that simultaneously guarantees both the maximum relevancy and minimum redundancy. Our second proposed approach is very similar to the first one whose only but significant difference with the first method is that it selects one feature from each cluster and sorts all the features in terms of their relevancy. Then, by appending the selected features to a sorted list and ignoring them for the next step, the algorithm continues with the remaining features until all the features to be appended into the sorted list. Both of our proposed methods are compared with state-of-the-art methods and the obtained results confirm the performance of our proposed approaches especially the second one.
An IoT Based Intelligent Control System for Physically Disabled People

Smart wheelchairs have been created for quite a while to help incapacitated individuals with a few inability levels. Much of the time, the eye muscles of incapacitated individuals are one of only a handful couple of controllable muscles that still capacity well. In this way, utilizing the eye-stare as an interface for deadened or physically crippled individuals has been of intrigue. The proposed framework exhibits the IoT based control System for physically Disabled People. It means to give a doable answer for physically impaired individuals who don’t be able to move the wheelchair and to control the different home apparatuses without anyone else. These incorporate individuals with genuine incapacitated condition. In this eye following based innovation, three Proximity Infrared sensor are mounted on an eye casing to follow the development of the iris. Since, IR sensors distinguish just white items, an interesting arrangement of computerized bits is produced relating to each eye development. These IoT based sign are then prepared by means of arduino controller IC to control the engines of the wheelchair. To control the Home apparatuses transfers are utilized with the Arduino. The potential and effectiveness of recently created restoration frameworks that utilization head motion control, jaw control, taste n-puff control, voice acknowledgment, and EEG flag variedly have likewise been investigated in detail. They were observed to be awkward as they served either constrained ease of use or non-moderateness. After different relapse examinations, the proposed plan was created as a savvy, adaptable and stream-lined option for individuals who experience difficulty embracing ordinary assistive advancements. The proposed IoT based control systems is used to control the overall operation of physically disabled persons.
URL filtering using big data analytics in 5G networks

The future generations networking technologies such as 5G and 6G will provide tremendous performance, network capacity, quality of service and connectivity. Therefore, the convergence of these with technologies with big data analytics in today's smart ecosystem will provide tremendous opportunities. The existing URL filtering techniques do not do real-time filtering, and lack fault-tolerance and scalability. We have addressed these issues and have developed a real-time, fault-tolerant and scalable machine learning based binary classification model, which handles streams of URL traffic and classifies it into obscene or clean material, in real-time. We have only used the URL based features for classification, and have still achieved a good accuracy of 93% on logistic regression classifier and 88%. Our model can filter 2 million URLs in 55 seconds. The proposed model achieved precision, recall and f1-score values of 0.92, 0.95 and 0.93 respectively.
Introduction to “Advances in Longitudinal HCI Research”

Aimed as an educational resource for graduate students and researchers in HCI, this book brings together a collection of chapters, addressing theoretical and methodological considerations, and presenting case studies of longitudinal HCI research. In this short introduction to the book, we reflect on the need for longitudinal studies in human–computer interaction research, we define what is and what is not longitudinal research and outline the selected contributions.
Geomorphological and Statistical Assessment of Tilt-Block Tectonics in the Garhwal Synform: Implications for the Active Tectonics, Garhwal Lesser Himalaya, India

Active tectonics is manifested in geomorphological features such as drainage basins and drainage patterns. Geomorphic parameters asymmetry factor (AF) and transverse topography symmetry factor (T) is calculated for 94 third order basins of the Garhwal synform to decipher the tilt-block tectonics based on remote sensing and geographical information system (GIS) techniques. The quantitative analysis of the AF suggests that all the 94 basins are asymmetric and gentle to steeply tilted, indicating active tectonics and early and late stage of development, respectively. The mean vector magnitude (θv) of T suggests the migration of the basin stream towards the south in most basins (60%), suggesting a unidirectional tilting of the tectonic block. The χ2 test for statistical significance indicates that the θv is significant for southern and northern limb basins. The χ2 test affirms that the third order basin position on either side of the main channel of the river basin influences the tilt direction. The regional tectonics suggests migration of the Lansdowne klippe towards the south, as the majority of third order basins show southward tilt. The study provides a quick appraisal of tilting in the tectonic blocks of active margins, such as in the Himalayas.
Video Quality Measurement For Buffering Time Based On EEG Frequency Feature

Currently, user-based quality of experience (QoE) measurement methods (e.g., mean opinion score, MOS) are often employed. However, their results might be affected by human subjective experience and thoughts. Physiological measurement methods can overcome these disadvantages. In the field of video quality models, the video buffering problem caused by poor network conditions is an important factor that affects QoE. In this paper, a reasonable psychophysiological measurement method, electroencephalography (EEG), is proposed to quantitatively analyze QoE changes when users face different levels of video buffering time. By extracting the band power of EEG signals as the feature and analyzing the correlation and variance, a more objective buffering time EEG (BT-EEG) score model of video quality under the single-factor video buffering time is established, which solves the problem of uneven subjective data quality.
Blockchain for bounded-error-pruned content protection

IoT content usually comes from power-constrained sensors and requires applying different levels of protection. We design a bounded-error-pruned IoT content market (BIoTCM) framework with preserving privacy protection (BEPDPP) scheme using blockchain smart contract. According to payment level applied in smart contract of BIoTCM, our BEPDPP will prune original data within a given level of bounded-error to provide different resolutions of content for consumers. As the real data is intrinsically hidden, content protection is provided with privacy-preserving. Experimental results generated by five different types of IoT content demonstrate that our content protection solution is cost-effective in sensor lifetime.
FlexACC: A Programmable Accelerator with Application-Specific ISA for Flexible Deep Neural Network Inference

Deep neural networks (DNN) have become ubiquitous and dominant in various application domains due to its state-of-the-art learning capabilities. To run compute and memory intensive DNN models, designing specialized hardware accelerators becomes the common choice. However, the performance improvement in accelerators comes with limitations on programmability, which has become crucial given the rapid evolution of DNN models. In this work, we first conduct workload analysis on a diverse set of DNN models, including CNN, LSTM, Transformer, and GCN to demonstrate the challenges of generalizing DNN acceleration. Next, we present a high-programmable accelerator, referred as FlexACC, with a novel application-specific ISA for flexible DNN inference. To increase the programmability, the general-purpose RISC-V instructions are tightly coupled with DNN instructions in FlexACC ISA. Compared with standalone fixed-datapath CNN and LSTM engines, FlexACC only has small latency and area overhead, while it provides much higher programmability and flexibility.
Human vs. Deep Neural Network Performance at a Leader Identification Task

Control of robotic swarms through control over a leader(s) has become the dominant approach to supervisory control over these largely autonomous systems. Resilience in the face of attrition is one of the primary advantages attributed to swarms yet the presence of leader(s) makes them vulnerable to decapitation. Algorithms which allow a swarm to hide its leader are a promising solution. We present a novel approach in which neural networks, NNs, trained in a graph neural network, GNN, replace conventional controllers making them more amenable to training. Swarms and an adversary intent of finding the leader were trained and tested in 4 phases: 1-swarm to follow leader, 2-adversary to recognize leader, 3-swarm to hide leader from adversary, and 4-swarm and adversary compete to hide and recognize the leader. While the NN adversary was more successful in identifying leaders without deception, humans did better in conditions in which the swarm was trained to hide its leader from the NN adversary. The study illustrates difficulties likely to emerge in arms races between machine learners and the potential role humans may play in moderating them.
Structured sparsity learning for large-scale fuzzy cognitive maps

Fuzzy cognitive map (FCM) as a kind of intelligent soft computing method, by combining the advantages of neural network and fuzzy logic, can be used to mine the causal relationships between concepts and make reasoning. However, how to effectively learn the large-scale FCMs is still an open problem. In this article, by means of structured sparsity learning, a robust learning method for large-scale FCMs based on iterative smoothing algorithm is proposed. Firstly, in terms of sparse signal reconstruction, the objective function of learning method is constructed by using elastic and total variation (TV) penalties, which can be conducive to capture the sparse structure information of FCM and improve the robustness of network reconstruction. Due to the non-smoothness of the TV penalty, Nesterov's smoothing technique is used to solve the non-smooth problem, thus transforming the problem into a convex optimization problem. Subsequently, in order to quickly solve the convex optimization, the algorithm based on proximal gradient descent is applied. In the experiment part, synthetic FCM models with different densities, sizes and noises are used to evaluate the proposed method, and the experimental results demonstrate the proposed method can make full use of the observations to learn the structural information of FCM. Moreover, the real-world data from the gene regulatory networks (GRNs) are further used to evaluate the effect of network reconstruction, and a higher reconstruction accuracy can be verified.
Back–Projection Pipeline

We propose a simple extension of residual networks that works simultaneously in multiple resolutions for the problem of image super-resolution. Our network design is inspired by the iterative back-projection algorithm and seeks the more difficult task of learning how to enhance images. Compared to similar approaches, we propose a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow. Features are updated at multiple scales in each layer of the network. The update dynamic through these layers includes interactions between different resolutions in a way that is causal in scale, and it is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets.
Personalised Employee Training

Every individual is unique in their own way and they learn and understand things at their own pace and in their own style more efficiently. Currently learning systems have a major shortcoming which is that they do not consider an individual learner's style and goals. Companies spent thousands of dollars on training programs without desired results. And thus there is a dire need of a good personalized learning system. Existing solutions are a tad bit inefficient as ignore a lot of parameters since they do not take advantage of the entire design. Thus to overcome the above issues we propose a learner-centric e-learning system using adaptive learning in this paper. This system is designed as such that it will be most suitable to the learner and flexible according to their style as they learn and grow. The important part of the design is the neural network model created for every individual based on their interaction and performance and accordingly recommends most suitable learning content. Also the system adjusts to user's pace and uses gamification in activities to improve experience. The aim of this paper is the introduction of a learning system which increases user engagement and maximizes their learning abilities.