Reviewerly/exHarmony huggingface dataset:
Problem Student Prediction Model Based on Convolution Neural Network and Least Squares Support Vector Machine

Problem students are an unavoidable special group in the education system. How to educate and guide problem students is a key link that affects the harmonious and stable operation of students themselves, families, schools and even society. Early judgment and early intervention can effectively reduce the occurrence of accidents, but the traditional way of student management does not pay enough attention to the psychological status of problem students. Commonly, the students are educated after the problems appear, and the psychological intervention of problem students is not timely. Based on the data of learning behavior, life behavior and psychological behavior in the campus big data environment, this paper designs an early warning model of problem students based on convolution neural network and least squares support vector machine, which makes up for the lack of current student work and has strong practicability and operability.
Implementation-Free Forensic Watermarking for Adaptive Streaming with A/B Watermarking

Forensic watermarking enables the identification of digital pirates after they illegally re-distribute copyright-protected videos. For adaptive streaming, these methods are best combined with A/B watermarking, in which two watermarked versions are created for each video segment, and subsequently mixed in order to create a large number of uniquely watermarked videos. Although good video quality and low bitrate are key characteristics of a good watermarking system, existing methods objectively lower the compression efficiency. Additionally, they often require complex implementations. Therefore, this paper proposes an implementation-free, rate-distortion-preserving watermarking technique to be used with the scalable A/B watermarking concept. Even though the embedding is performed during compression, it does not change the existing video encoder implementation. Instead, it only changes the target bitrate parameter in order to create different compression artifacts. These artifacts represent the watermark but are not noticeable due to high-quality coding. As such, the rate-distortion performance is nearly equal to that of ordinary, unwatermarked compression (i.e., a BD-rate of 0.02% and \(-0.10\)% when applied with H.264/AVC and H.265/HEVC encoders, respectively). Furthermore, the robustness is equal or better than state-of-the-art methods with comparable embedding complexities. More specifically, in case of recompression attacks, nonzero false negative rates are only reported when a watermarked video is initially compressed with a high quality and degraded to a very low quality. Consequently, the proposed scheme can be used in practice by adaptive-streaming platforms without a quality decrease, bitrate increase, or implementation overhead.
Attack Time Analysis in Dynamic Attack Trees via Integer Linear Programming

Attack trees (ATs) are an important tool in security analysis, and an important part of AT analysis is computing metrics. However, metric computation is NP-complete in general. In this paper, we showcase the use of mixed integer linear programming (MILP) as a tool for quantitative analysis. Specifically, we use MILP to solve the open problem of calculating the min time metric of dynamic ATs, i.e., the minimal time to attack a system. We also present two other tools to further improve our MILP method: First, we show how the computation can be sped up by identifying the modules of an AT, i.e. subtrees connected to the rest of the AT via only one node. Second, we define a general semantics for dynamic ATs that significantly relaxes the restrictions on attack trees compared to earlier work, allowing us to apply our methods to a wide variety of ATs. Experiments on a synthetic testing set of large ATs verify that both the integer linear programming approach and modular analysis considerably decrease the computation time of attack time analysis.
A Human Action Recognition Algorithm in Dynamic Scene of Emergency Rescue

Action recognition is a hot topic in computer vision. As an emerging interdisciplinary discipline, rescue medicine has been paid more and more attention by more countries and industries. However, there is currently fewer research on action recognition in emergency rescue scenes, and this problem is susceptible to dynamically changing noisy background and other factors. Aiming at the problem, this paper makes the dataset and proposes a GC-SlowFast network combining the attention mechanism. The proposed algorithm combines the SlowFast network with the attention module global context block (GC Block), which can effectively integrate the dependent information between remote key features on the basis of the original network, weaken the interference of the background on target action recognition, and improve the accuracy of action recognition. With R(2+1)D as the backbone of GC-SlowFast, the number of parameters can be significantly reduced by optimizing the network structure. In addition, in view of the problem for small data easily leading to overfitting, this paper enhances data on the self-built dataset. GC-SlowFast is pre-trained on the HACS Clips dataset and then fine-tuned on the self-built dataset.
Software Fault Prediction Using Data Mining Techniques on Software Metrics

Software industries have enormous demand for fault prediction of the faulty module and fault removal techniques. Many researchers have developed different fault prediction models to predict the fault at an early stage of the software development life cycle (SDLC). But the state-of-the-art model still suffers from the performance and generalize validation of the models. However, some researchers refer to data mining techniques, machine learning, and artificial intelligence play crucial roles in developing fault prediction models. A recent study stated that metric selection techniques also help to enhance the performance of models. Hence, to resolve the issue of improving the fault prediction model's performance and validation, we have used data mining, instance selection, metric selection, and ensemble methods to beat the state-of-the-art results. For the validation, we have collected the 22 software projects from the four different software repositories. We have implemented three machine learning algorithms and three ensemble methods with two metric selection methods on 22 datasets. The statistical evaluation of the implemented model performed using Wilcoxon signed-rank test and the Friedman test followed by the Nemenyi test to find the significant model. As a result, the Random forest algorithm produces the best result with an average median of 95.43% (accuracy) and 0.96 (f-measure) on 22 software projects. Based on the Nemenyi test, Random forest (RF) is performing better with 4.54 (accuracy mean score) and 4.41 (f-measure mean score) shown in the critical diagram. Experimental study shows that data mining techniques with PCA provide better accuracy and f-measure.
Text Line Segmentation in Handwritten Documents with Generative Adversarial Networks

In document analysis and recognition applications, segmenting the text lines accurately is a crucial step. Text line segmentation in handwritten documents is still a challenging task because of numerous factors that can decrease the segmentation accuracy. In this work, generative adversarial networks are proposed to segment the text lines in handwritten documents and text line segmentation problem is considered as an image-to-image translation problem and generative models are used to extract text lines from document images. Generative model is trained with a diverse and challenging Arabic dataset and segmentation performance of the method is evaluated with visual and numerical results. Proposed generative model can segment the text lines having 0.81 precision, recall and F-measure results. Also, visual results show that generative model is highly capable of segmenting the text lines having various behaviors.
Tiny Person Pose Estimation via Image and Feature Super Resolution

Although great progress has been achieved on human pose estimation in recent years, we notice the performance drops dramatically when the scale of target person becomes small. In this paper, we start with analysis on tiny person pose estimation and find that the failure is mainly caused by blurriness and ambiguous edges in up-sampled images, which are harmful for pose estimation. Based on the above analysis, we propose to apply an additional super resolution network on top of an existing pose estimation method to better handle tiny persons. Specifically, we propose three super resolution (SR) networks which apply on image level, feature level and both levels, respectively. Furthermore, a novel task-driven loss function tailored to pose estimation is proposed for SR networks. Experimental results on the MPII and MSCOCO datasets show that our proposed pose super resolution methods bring significant improvements over the baseline for tiny persons.
Tool for Measuring Productivity in Software Development Teams

Despite efforts to define productivity, there is no consensus in the software industry regarding what the term productivity means and, instead of having only one metric or factor that describes productivity, it is defined by a set of aspects. Our objective is to develop a tool that supports the productivity measurement of software development teams according to the factors found in the literature. We divided these factors into four groups: People, Product, Organization, and Open Source Software Projects. We developed a web system containing the factors that influence productivity identified in this work, called Productive, to support software development teams in measuring their productivity. After developed the tool, we monitored its use over eight weeks with two small software development teams. From the results, we found that software development companies can use the system to support monitoring team productivity. The results also point to an improvement in productivity while using the system, and a survey applied to users demonstrates the users’ positive perception regarding the results obtained. In future work, we will monitor the use of the tool and investigate the users’ perceptions in other project contexts.
Poster: Privacy Preserving Divisible Double Auction with A Hybridized TEE-Blockchain System

Divisible double auction enables distributed agents to trade divisible resources (e.g., electricity, mobile data, and cloud resources) by dynamically submitting the bid profiles of both buyers and sellers. In such systems, smart contract can be utilized to ensure transaction consensus among mutually distrustful agents, and strong integrity/availability of its execution on the blockchain. However, severe privacy risks emerge if disclosing the sensitive bid profiles in the transactions. Preserving privacy and consensus by designing cryptographic schemes will result in huge burden to the blockchain. To address such concerns, we design a hybridized TEE-Blockchain system (including both system and auction mechanism) to privately execute the divisible double auction while ensuring privacy, truthfulness and high efficiency.
Which Minimizer Does My Neural Network Converge To?

The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error. We explain how common variants of the standard NN training procedure change the minimizer obtained. First, we make explicit how the size of the initialization of a strongly overparameterized NN affects the minimizer and can deteriorate its final test performance. We propose a strategy to limit this effect. Then, we demonstrate that for adaptive optimization such as AdaGrad, the obtained minimizer generally differs from the gradient descent (GD) minimizer. This adaptive minimizer is changed further by stochastic mini-batch training, even though in the non-adaptive case, GD and stochastic GD result in essentially the same minimizer. Lastly, we explain that these effects remain relevant for less overparameterized NNs. While overparameterization has its benefits, our work highlights that it induces sources of error absent from underparameterized models.
Tomography of a Feedback Measurement with Photon Detection

Measurement and discrimination of quantum states are essential tasks in quantum information processing. A measurement strategy consisting of photon detection preceded by a displacement operation, that is actively feedback-controlled depending on the photon detection outcomes, is known to be a powerful measurement technique for quantum state discrimination [1] . In our work, we experimentally develop such a feedback measurement with photon detection and characterize its positive operator valued measure (POVM) via quantum detector tomography using coherent states. Quantum detector tomography has been demonstrated for various types of static measurements [3] , but not before for a dynamically updated measurement. As a potential application of the feedback measurement for quantum state discrimination, we analyze the performance of our measurement for the discrimination of the two orthogonal superpositions of vacuum and single photon states $| \pm \rangle = (|0\rangle \pm |1\rangle )/\sqrt 2 $ , which are the conjugate basis states of the optical single-rail qubit.
Development of a Physiological Responsive CANDU (CANada Deuterium Uranium) Fuel Channel Assembly VR Tool Prototype

Virtual reality (VR) has been gaining momentum as an engaging platform for delivering immersive and engaging learning experiences. The navigation of virtual worlds allows users to explore environments and objects otherwise unavailable in a classroom setting. For example, understanding the assembly and inspection of the CANDU (CANada Deurerium Uranium) 6 reactor's fuel channels require students to rely on pictures, diagrams, videos, and computer-assisted engineering tools that lack immersion and realism. Understanding the fuel channel components in proper scale is relevant to identify sag deformations that can cause pressure tube failure to force a reactor shutdown. This is critical as fuel channel inspections need to be conducted under timed conditions to avoid harmful radiation exposure. To better understand how stress scenarios may affect user performance, physiological measurements have been used in virtual training and serious games. We hypothesize that physiological measures can be proactively used to provide real-time feedback while performing a task in VR to increase awareness about challenges otherwise unknown to the user. This paper presents the development of a VR prototype for CANDU assembly, where skin response and muscle activity measures provide information about challenges experienced during a preliminary study where participants assembled a fuel channel and answered some questions about the process.
Crowd Counting with Dilated Inception Convolution

Convolutional neural network (CNN) has been successfully applied to image-based crowd density estimation. However, large computational resources are required in previous CNN-based methods. Therefore, to overcome these drawbacks, this paper proposes a lightweight crowd density map estimation architecture with Dilated Inception Convolution Neural Network (DICNN). The proposed method not only extracts scale-aware informative features, but also effectively reduces the number of parameters of the CNN architecture. In addition, the proposed method is trained along with DICNN in an end-to-end fashion via both pixel-wise Euclidean distance and density-level relevant (DLR) loss for global optimization. Extensive experiments on several publicly available datasets have shown that the proposed method outperforms state-of-the-art approaches in almost all datasets with far fewer parameters.
Effect of Digital Learning With an Interactive eBook on Electrocardiogram Interpretation Among Clinical Nurses

In digital learning, the use of an interactive eBook is perceived as helpful for students. However, the effect of interactive eBooks on learning among clinical nurses has not been explored yet. This study used an interactive electrocardiogram eBook to explore the effect of digital learning on the promotion of electrocardiogram interpretation competence, confidence, knowledge retention, and learning satisfaction among clinical nurses. A single-group quasi-experimental study with three repeated measures was conducted. A total of 80 nurses from the emergency room, critical units, and medical-surgical units completed the measures. The results showed that digital learning is an effective method that significantly improved nurses' electrocardiogram competence, learning retention, confidence, and learning satisfaction. Most nurses were satisfied with the convenience and content design of this eBook. Few nurses reported drawbacks regarding loading speed and individual learning habits. It is recommended that more preset learning exercise questions should be created for trial and error so that nurses can have repeated practice for self-assessment. Specific feedback mechanisms should be established to promote motivation for digital self-learning.
Path Planning for Lunar Rover Based on Bi-RRT Algorithm

A new method based on the Bi-RRT (Bidirectional Rapidly exploring Random Tree) algorithm, APF (Artificial Potential Field) algorithm, and DOM (Digital Orthophoto Map) is presented in this paper to solve the lunar rover path planning problem. First, we use DOM to build an occupancy grid map through an image processing algorithm. Then we build a repulsive potential field based on the obstacle and an attractive potential field based on the target. We then designed an improved Bi-RRT algorithm based on a novel potential field guidance growth method and a goal bias sampling method. Finally, a simulation was carried out on the lunar DOM to verify the efficiency and safety of the proposed Bi-RRT-APF algorithm. The results show that the searching efficiency of the Bi-RRT-APF algorithm is increased by twice compared with the Bi-RRT algorithm and is increased by four times compared with the RRT-APF algorithm. The safe coefficient of the Bi-RRT-APF algorithm is increased by 42.7% compared with the Bi-RRT algorithm.
Chinese Character Style Transfer Based on the Fusion of Multi-scale Content and Style Features

Style transfer has received much attention, and Chinese character style transfer is an important application. In the previous Chinese character style transfer methods, most of the methods separately establish a mapping from source font to target Chinese character for each specific target font. It is impossible to extend a single model to other styles. The model training is based on the amount of Chinese character data from a single font, and the demand of the character is greater. In fact, different font style transfer tasks have certain commonalities. Therefore, we use multiple fonts in the data set and combine these fonts into multiple sets of Chinese character style transfer tasks. These tasks will be trained at the same time, instead of that only one font style transfer task training is achieved at a time like the previous work. In order to achieve the purpose of extracting and fusing the content features and style feature, we propose a style transfer network composed of content encoder, style encoder, feature fusion module, and decoder, based on the fusion of multi-scale content and style features. Through the experimental results, it can be found that, using our method, training the style transfer of multiple fonts at the same time can help any one of them to generate better results with only a few samples.
Integration of Artificial Intelligence and the Internet of Things with Blockchain Technology

Artificial intelligence (AI), blockchain, and the Internet of Things (IoT) give a unique opportunity to the public sector and enterprises. All institutions that leverage these innovations develop advanced goods and services for new customer generation by associating streamline and improving modern processes and building entirely new business models. The internet, mobile devices, and personal computing put a supercomputer in our hands. We have been exposed to distracting technology earlier and recognized that it changes the world as we know it. However, several of these examples from past times appeared in isolation steadily and objectively. Before the origination of the internet, we used personal computing, and the game changed again. We were internet-savvy lay before smartphones put the internet in our pocket. Several new developments are evolving at once: 5G broadband, 3D printing, virtual and augmented reality, plus more. As these technologies provide the business with exciting opportunities and major challenges, we consider three to be truly transformative: IoT, AI, and blockchain. All three alone would have the ability to alter industry, leisure, and culture as a whole. However, together they will have an unparalleled transformational effect. First time in civilized history, the very generation has created three revolutionary innovations; furthermore, it is a sight of what is coming. The digital revolution means technological transformation has shifted from a phenomenon once in a lifetime to a continuous phase. Now any company that falls behind will soon be able to close an insurmountable distance. In this paper, we will look into how you can develop your company for AI, IoT, and blockchain and enable your business to use them both forthwith and in the future. We will explore how the cloud functions as both an enabler and an accelerant—paving the way to adopt any new technology.
An Adaptive Template Update Network for Siamese Trackers

Visual object tracking has been a fundamental topic in recent years and many Siamese structure based trackers have achieved state-of-the-art performance on multiple benchmarks. However, most Siamese trackers default the first frame of the video sequence as the template frame, and the template is not updated or updated linearly in the subsequent tracking process. When the target is rapidly moving, deformed, partially blocked, the tracker is prone to tracking drift. To tackle this issue, in this paper, we propose a novel adaptive template update network (ATUNet), which takes the template of the object at different moments including the initial template, the accumulated template and the predicted template as the input of a frame residual difference module to update the required template of the current frame. Furthermore, we introduce a n-step iterative training to avoid cumbersome and inefficient training process. The ATUNet is compact and can easily be integrated into existing Siamese trackers. We demonstrate the generality of the proposed approach by applying it to three Siamese trackers, SiamFC and SiamRPN and SiamDW. Extensive experiments on VOT2016, VOT2017 datasets demonstrate that our ATUNet effectively predicts the new target template, outperforming the standard linear update.