Reviewerly/exHarmony huggingface dataset:
Meaningful Experience in Service Design for the Elderly: SAPAD Framework and its Case Study

Abstract A growing trend of aging population of China has brought tremendous pressure on the domestic care system, and community education is one of the important content for elderly services. Based on the framework of SAPAD, the community English class in Guangzhou City is taken for case study. Depth research on three stakeholeders-the elderly, social workers and volunteers are carried out by interview, user observation and field research. 6 levels (physical level, syntactic level, empirical level, semantic level, pragmatic level and social level) are extracted based on SAPAD framework, and the behavior- object-significance mapping is completed. Significant clusters of multiple users at different levels are analyzed, and 16 core significant clusters are jointly built. By linking with clustering results of the syntactic level, 6 new function modules are obtained. Finally, the community elderly education service system is built through personas, service blueprint, touch points and storyboard. The new service system will improve learning efficiency, satisfactions and emotional appeals for the elderly, and work efficiency of social workers and volunteers.
Configurable Texture Unit for Convolutional Neural Networks on Graphics Processing Units

To accelerate Convolutional Neural Networks (CNN) operations on resource-limited mobile graphics processing units (GPUs), taking advantage of the common characteristics between texture filtering and convolutional layer, we propose a configurable texture unit called tensor and texture unit (TTU) to offload the computation from shader cores. With adding a new datapath for loading weight parameters in the texture unit, reusing the original texture cache, increasing the flexibility of the filtering unit, and packing the input data and weight parameters to fixed-point format, we make the texture unit be able to support convolutional and pooling layers with only small modifications. The proposed architecture is verified by integrating TTU into a GPU system in RTL level. Experimental results show that 18.54× speedup can be achieved with the overhead of only 8.5% compared with a GPU system with a traditional texture unit.
Analysis of the Algorithm for Detecting a Point Object on a Sequence of Video Images Using Decisive Statistics of Marks

Detection of point objects on a sequence of video images is an important step in optical monitoring systems for UAVs, since they allow them to be detected on the maximum range. This task is quite complicated because the intensity of the object in the image is low due to the low signal / noise ratio (SNR). Decisive statistics of marks are used to increase the effectiveness of object tracking. The paper presents a method for detecting point objects on a video images sequence according to Wald test using distributions of decisive statistics in likelihood ratio. Results of statistical modeling show that the synthesized algorithm reduces the detection time and reduce computational complexity compared with known algorithms detect a moving object IMM-PDAF-AI, which uses the decisive statistics of marks and IMM-PDAF, which does not use decisive statistics of marks.
Digitalization of Education as a Basis for the Competence Approach

The authors analyze the electronic information and educational environment of the university as an innovative component of the competence approach under the digital transformation of the economy and defines its relationship with the formation of information and communication competence of students. The innovation of the proposed approaches is the transformation of the traditional system of education and its integration with electronic educational resources. The study presents the features of the electronic information and educational environment of the university, analyzes the theoretical foundations of the educational process in economic and mathematical courses using innovative technologies, and develops guidelines for the introduction of innovative technologies to train students in the modern information and educational environment.
A Threat Analysis for Virtual Reality Media

This research paper proposes an initial threat analysis of virtual reality (VR) for the media industry. It focuses on the use of VR for media consumption or media-related applications. Pure gaming is not within its scope. Nevertheless, this study tackles casual gaming applications related to movies or TV shows. After introducing a taxonomy of VR (360 VR, Theatrical VR, and Interactive VR) in the media industry, we determine all of the assets that may need protection. Indeed, many more assets than the media content may require adequate protection. We describe some specific threats using the Attack-Defense Tree (ADT) methodology. This threat analysis discloses a new attack coined Denial of Experience to induce cybersickness. How should the industry prepare? We propose some priorities in the definition of a proper defense. As an example, the first recommendation is to build a risk aversion table. Some defenses may benefit from a coordinated, standardized approach.
Personalizing the Prediction: Interactive and Interpretable machine learning

While many applications with machine learning provide enough utilities for users, they mostly target average of users. Although it might be acceptable in certain domains, there are domains such as health and medical-care where it is crucial to provide personalized service. In such cases, personalization of machine learning model usually does not depend on end users to make change to the system. As machine learning models are black-box, the only information that the users can acquire is input and output of certain decision made by the model. Thus, with no reason behind specific prediction provided by the system, users cannot understand how the system works and make amendments to the system. This shortcoming is directly related to users' credibility in the system. In this paper, we present an interface where the system provides users the reason behind the decision made by the machine learning model and users provide feedback to the model. Moreover, we present the principle behind the suggested interface and prototype that instantiates the suggested interface. Our interface's effectiveness is evaluated through users' surveys regarding two main attributes: (1) how well users understand the system and more importantly, (2) how it influences users to trust in the system.
Accurate And Fast Fine-Grained Image Classification via Discriminative Learning

Currently, most top-performing Weakly supervised Fine-grained Image Classification (WFGIC) schemes tend to pick out discriminative patches. However, those patches usually contain much noise information, which influences the accuracy of the classification. Besides, they rely on a large amount of candidate patches to discover the discriminative ones, thus leading to high computational cost. To address these problems, we propose a novel end-to-end Self-regressive Localization with Discriminative Prior Network (SDN) model, which learns to explore more accurate size of discriminative patches and enables to classify images in real time. Specifically, we design a multi-task discriminative learning network, a self-regressive localization sub-network and a discriminative prior sub-network with the guided loss as well as the consistent loss to simultaneously learn self-regressive coefficients and discriminative prior maps. The self-regressive coefficients can decrease noise information in discriminative patches and the discriminative prior maps through learning discriminative probability values filter thousands of candidate patches to single figure. Extensive experiments demonstrate that the proposed SDN model achieves state-of-the-art both in accuracy and efficiency.
Visual Analytics of Taxi Trajectory Data via Topical Sub-trajectories

GPS-based taxi trajectories contain valuable knowledge about movement behaviors for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectories. However, previous methods generally ignore the direction of trajectories. In this paper, we employ the bigram topic model instead of traditional topic models to analyze textualized trajectories to take into account the direction information of trajectories. We further propose a modified Apriori algorithm to extract frequent sub-trajectories and use them to represent each topic as topical sub-trajectories. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore topics, sub-trajectories, and trips. We demonstrate the effectiveness of our system via case studies with Chengdu taxi trajectory data.
Elderly Action Prediction and Anomalous Activity Detection in Smart Homes through Profiling Residents’ Behavior

Background: Elderly healthcare is one of the important issues in an aging society. Smart homes in healthcare domain help the elderly to be continuously monitored, instead of being under supervision at expensive health centers, and hence enable them to live independently. This service requires detecting and monitoring of residentsâ normal activities of daily living in smart homes. Objectives: By profiling residentsâ behavior and identifying changes in normal activities of the elderly over time, one can detect anomalous behavior and determine whether their health status declines. Hence, the possibility of preventive care for some elderly people or providing assistance to the elderly will be partly provided in case of occurrence of the anomalies. Methods: In this paper, first a method was proposed for detection and prediction of elderly activities by extracting several features from available information. In the second step, statistical measures were applied on the features to profile the elderlyâs behavior. The AdaBoost learning algorithm was used for detecting the anomalies and modeling normal/abnormal behavior. Results: For detection and prediction of the activity, the proposed method was tested using a dataset collected in the âeHealth Monitoring Open Data Projectâ. The accuracy of 98.48% was obtained by considering features of start time, end time, duration, location, previous action, water, and electric device use. Anomalous behavior was detected in the same dataset with the average f-score of 90%. Conclusions: Results of the present study revealed that, the proposed methods are effective for detecting abnormal actions of the residents in smart homes to a fairly good level. This enables the elderly to live independently while being under continuous monitoring and significantly reduces the elderly health care costs.
DANN: Digital Audio Neural Network

Neural networks have been widely used in computer audio to deal with many synthesis and processing parameters. Experiments have also led to direct synthesis of audio using dynamic neural networks that exhibit oscillating behaviour, as they are clocked at audio rates. There is clearly great potential in convolutional networks as they directly implement complex resonances enabling acoustic interaction. It thus transpires that many different neural network types were used for direct audio synthesis and processing. The proposed innovation extends a fully-connected network of computational cells to facilitate long feedback delays (needed for waveguides) intertwined with zero delays (needed for polynomials). Another improvement is the network computing algorithm that allows for randomisation of delays by adjusting the order of calculations. The DANN model is a specification for network of cells and links that allows direct translation from digital audio synthesis and processing schemes. Taking basic examples such as exponential decay, logistic chaos and biquad filter it transpires that there are exact DANN equivalents to these basic building blocks. It is therefore suggested that complex DANN schemes can reproduce myriad synthesis and processing techniques in a transparent fashion. As a matter of proving the concept a simple snare drum synthesizer is translated to a DANN scheme in order to explore the parameter space in this domain. Thanks to the large amount of training schemes there is great potential be found in training networks for velocity sensitive synthesis to include complex physical interaction sensors. Audio signal processing applications may include modelling of analogue equipment and symbolic acoustic instrument processing. The simplicity of the network grants easy implementation on fast architectures to facilitate near real-time processing and higher sample rates. Beyond the practical uses proposed it is further suggested that network simplification procedures may lead to yet unknown compact schemes for complex signal processing problems in the audio domain, potentially reverberation and pitch-shifting.
Research on Topological Structure Optimization of Underwater Constant-Current Remote Supply System Based on MMAS Optimization Algorithm

Based on the reliability and construction cost of the submarine cable section, primary and secondary nodes, a topological structure model of a typical chain double-ended constant-current remote supply system is proposed. In the topology model, the underwater unit is equivalent to a series-parallel circuit for simulation analysis. Furthermore, the optimization analysis is carried out from the perspective of system power supply and underwater unit power consumption based on the MMAS optimization algorithm. The results of calculation and simulation shows that the designed model of system has high reliability and has reference significance for the construction of the actual cabled underwater information networks.
Similarity-Aware Deep Adversarial Learning for Facial Age Estimation

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making access to limited training samples which likely leads to sub-optima, our SADAL seeks sets of unobserved and plausible hard-examples based on existing training samples, which typically reinforces the discriminativeness of the learned feature descriptor for ages. Motivated by the fact that age labels are usually correlated in the real-world applications, we carefully develop a similarity-aware function in our approach, which dynamically measures each face pair with different weights based on different age value gaps. During the learning process, we jointly optimize both procedures of generating hard-examples and learning age estimator via a sequence of adversarial-game iterations. As a result, the smoothing aging pattern is exploited in the reconstructed hard-example space for robust age estimation. Experimental results on two standard benchmarking datasets show that our approach achieves superior performance compared with most state-of-the-art age estimation methods.
Gradient Outlier Removal for Gradient‐Domain Path Tracing

Abstract We present a new outlier removal technique for a gradient‐domain path tracing (G‐PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G‐PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.
Stage Actor Tracking Method Based on Kalman Filter

Aiming at the problem that the traditional tracking algorithm could not accurately track the characters in the stage lighting environment, an improved method using Kalman filter is proposed. In the traditional background subtraction method based on hybrid Gaussian model, Kalman filter is introduced to establish the data structure of the tracking target, and the filtering prediction position and the detection position are matched to solve the problem of error detection area under the light interference. The experimental results show that the method has a good effect on the tracking of characters in the environment of stage lighting.
A Causal Power Semantics for Generic Sentences

Abstract Many generic sentences express stable inductive generalizations. Stable inductive generalizations are typically true for a causal reason. In this paper we investigate to what extent this is also the case for the generalizations expressed by generic sentences. More in particular, we discuss the possibility that many generic sentences of the form ‘ k s have feature e ’ are true because (members of) kind k have the causal power to ‘produce’ feature e . We will argue that such an analysis is quite close to a probabilistic based analysis of generic sentences according to which ‘relatively many’ k s have feature e , and that, in fact, this latter type of analysis can be ‘grounded’ in terms of causal powers. We will argue, moreover, that the causal power analysis is sometimes preferred to a correlation-based analysis, because it takes the causal structure that gives rise to the probabilistic data into account.
Vehicle Detection and Classification in Traffic Images Using ConvNets With Constrained Resources

This paper proposes a convolutional neural network model for detection and classification of vehicles present in digital images into six categories, namely Bus, Microbus, Minivan, Sedan, SUV, and Truck. Experimental results with the BIT-Vehicle Dataset report a mean average precision (mAP) of 92.40% and the average intersection over a union (IoU) of 81.26% with the average inference latency under 70 milliseconds in an environment equipped with a graphics processing unit. We conclude that the model is discriminative and capable of generalizing the patterns of the vehicle type classification task while not requiring expensive computational resources. These features suggest that the model can be useful in the development of embedded intelligent traffic systems improving accuracy and decision latency.
Digitization: The Bar’s Aspect

The article is directed at researching the application of new laws on digital rights, digital money, electronic transactions, digitalization of legal proceedings, advocacy, including the part of the rules on managing of law files in lawyer practice. The use of digital technologies in the implementation of advocacy should be correlated with the protection of attorney-client confidentiality, personal data and privacy. The ratio of private law and public interest is particularly pronounced in the sphere of realization of non-property rights, where there is no intermediary in the form of rights to property, which has the equivalent of the right enshrined in its value terms. the conclusion is made the general direction when considering issues of digitalization of legal assistance - non-entrepreneurial activities is the establishment of a model of legal assistance of an attorney who observes the rights of the principal - both in terms of providing qualified assistance and in terms of the opportunities contained in electronic resources. As a result of the analysis of advocacy concluded the topic of applying digital technologies in advocacy is divided into two clusters - on the one hand, advocates should know what legal norms should be applied tomorrow to relations related to digital products as objects of civil rights and participate in creating adequate practice on this subject, on the other hand, to have skills in handling digital technology objects, to be able to use legal systems, and official Internet resources of the legislative, executive authorities, courts, specialized computer programs and databases.
A Deep Learning Model for Multi-label Classification Using Capsule Networks

During the past few years, single-label classification has been extensively studied. However, in public datasets, the number of multiple-labeled images is much larger than the number of single-labeled images, which means that the study of multi-label image classification is more important. Most of the published network for multi-label image classification uses a CNN with a sigmoid layer, which is different from the single-label classification network using a CNN with a softmax layer. The binary cross entropy is often used as loss function for multi-label image classification. But due to the complex underlying object layout and feature confusion caused by multiple tags, the effect of CNN with a sigmoid layer on multi-label image classification is not satisfactory. Recently, in order to break some restrictions of CNN, the concept of capsule networks has been proposed. In this paper, a capsule network layer has been used to replace the traditional fully-connected layer and the sigmoid layer in the CNN network to improve the effect of multi-label image classification. In order to solve the deep network’s convergence problem due to insufficient training data, fine-tuning DCNNs techniques have been applied to the capsule network architecture. In the experiments, three datasets, PASCAL VOC 2007, PASCAL VOC 2012 and NUS-WIDE, have been used. The proposed CNN+Capsule architecture has been compared with the traditional CNN+FullyConnected architecture. It has been shown that with different parameter settings the proposed CNN+Capsule architecture can consistently achieve better performance than the CNN+FullyConnected architecture.
English Translation Model Design Based on Neural Network

In recent years, machine translation research has flourished and machine translation effects have improved greatly. However, machine translation research has also encountered difficulties such as insufficient bilingual data and lack of effective feature representation, and the effect is still unsatisfactory. At the same time, as a new machine learning method, deep learning is able to learn how to abstract feature and establish a complex mapping relationship between input and output signals, which provides a new idea for statistical machine translation research. This paper uses deep neural networks to learn the key problems in statistical machine translation to better describe the representation of translation phenomena and let the performance of statistical machine translation become well. A new neural network is proposed to deal with the translation decoding process. A three-step semi-supervised training method was used to train this model. In addition, we also explored the representation of translated phrase pairs and proposed a phrase pair representation based on translation confidence. Chinese-to-English translation result show that this method can significantly improve translation.
Development and Research on the Informationization of Physical Education in Colleges and Universities

The deep integration of information technology and education and teaching will be the key development direction of the future education reform. Therefore, it is necessary to explore the application of information technology in college physical education. Based on the author’s learning and practical experience, this paper first analyzes the development and application of information technology in college physical education, then summarizes the problems in college physical education informatization, and finally puts forward the reform approach of college physical education informatization in the new era.