Reviewerly/exHarmony huggingface dataset:
Adversarial Neural Pruning with Modified Latent Vulnerability utilizing Feature Robustness

Today, vision recognition with Convolutional Neural Network (CNN) shows performance good enough to be employed in safety-critical autonomous driving. However, CNN models are vulnerable to adversarial attacks. To overcome this vulnerability, many methods have been studied Pruning is regarded as one of the effective methods to make the network more robust against adversarial attacks. In this paper, we introduce a new vulnerability loss to suppress the vulnerability better when the pruning is used to counter adversarial attacks. With this vulnerability suppression, we achieve up to 1.12% better accuracy against the adversarial examples compared to a previous study called ANP-VS.
Data Rearrange Unit for Efficient Data Computation in Embedded Systems

Recently demands for computation intensive applications such as convolutional neural networks (CNNs) have been increasing. In these applications, valid data for computation are allocated in non-continuous addresses. Therefore, common burst memory access pattern results in a low spatial locality of valid data per access. As a result, computation of data parallel execution units degrades in throughput, as computation resource is wasted by computing invalid data. This is especially a problem in embedded systems in which constraints in power consumption provoke a requirement for high computation efficiency. In this paper, we introduce a Data Rearrange Unit (DRU), a hardware unit rearranging computation data to increase spatial locality of valid data. The DRU drastically reduces the main memory access rate and increases computation efficiency by decreasing memory access to reduce power consumption. We demonstrate the effectiveness of our DRU by implementation on the RMTP SoC [1] [2] improving convolution throughput on a data parallel execution unit by a maximum of 94times, while only increasing the total cell area by about 13%.
Research of Scale Recurrent Generative Network on Image Inpainting

Existing learning-based inpainting methods have recently reached notable success in filling irregular holes. However, the quantity of network parameters in these methods also grows rapidly, thus making them difficult for training and deployment on resource-limited platforms. In this paper, we propose a Scale Recurrent Generative Network (SRGN), in which a new scale recurrent structure is raised and deployed on top of the general learning-based inpainting methods. The scale recurrent procedure stores the context information in different scales to achieve better memorability while keeping the network parameters in the same order of magnitude. To add the iterations on scale dimension, we combine max pooling and average pooling in the downsampling procedure and introduce scale factor in the loss function. The qualitative and quantitative comparisons on the Places2 dataset show that the texture and detail of our generated image are significantly improved in comparison with peer works.
Smart Real-Time Recommendation of Mobile Services

In this paper, a new vision is presented for highly personalized, customized, and contextualized real-time recommendation of services to mobile users (consumers) by considering the current consumer-, network-, and service context. A smart service recommendation system is elaborated, which builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices, thereby achieving the best quality of experience (QoE) as perceived by those consumers when utilizing different mobile services. The algorithm-driven recommended mobile services, accessible anytime-anywhere-anyhow through any kind of mobile devices via heterogeneous wireless access networks, range from typical telecommunication services (e.g., outgoing voice calls) to Internet services (e.g., multimedia streaming). These algorithms also may be further enriched by their being adapted and expanded to cover more sophisticated services such as helping the consumer’s health and security needs, an example being the finding (with subsequent dynamic changing, if required) of the most 'healthy' or 'secure' driving/biking/jogging/walking route to follow so as to avoid areas posing particular, consumer-specific, health or safety risk.
Interactive Evolutionary Design Method of Tile Shape by GAN Reflecting Adjectives

A method of designing tile shapes using interactive evolutionary computing (IEC) with generative adversarial network (GAN) is proposed. Designing complex tile shape is difficult for ordinary people due to mathematical constraints and artistry requirements. The proposed method automatically generates shapes which reflect adjectives the user wants to express using GAN, and the shapes are refined by human interaction with IEC, so that the obtained shape becomes fully satisfactory to the user. Here, datasets of shapes are prepared for several adjectives first, and GAN is trained using random latent vectors to generate shapes for each adjective. The values in the latent vector are controlled by IEC to consider the user&#x2019;s preference. The proposed method is shown to be effective in computer simulation and its subjective test.
Setting Up and Exploration of Security in a Hybrid Cloud

Public Cloud services offer various compute and storage resources. However, organisations often require the large number of resources offered by public cloud providers, as well as the security associated with on-premise servers. This scenario is optimal to make use of a hybrid model combining public and private cloud infrastructures. In this paper, we propose a hybrid cloud infrastructure for document storage of a university. AWS S3 is used for the public cloud storage and Ceph for the private cloud storage. The hybrid cloud setup will store documents that the university authorizes to be made public, like research publications, study material etc on the public cloud and confidential documents/files on the private cloud. A sequence of tests are designed to explore security vulnerabilities in the hybrid cloud infrastructure, like data in motion, data at rest, web application vulnerabilities, and ways to mitigate any such potential vulnerabilities are explored.
Innovative Activity: State Regulatory Legal Acts and Possibilities of Their Application in Library Practice

Innovative ways of the development of libraries is a topic that has a sufficient degree of elaboration. However, the documents that provide regulatory and strategic support for the creation and implementation of innovations, as well as the boundaries of their applicability in the library sphere, are not sufficiently examined. The article presents the regulatory legal acts and documents of the strategic planning system that influence the development of libraries and can be used to create and implement sectoral library development programs. Most of these documents have status of regulatory legal acts. They can be used as a conceptual and methodological basis for creating programs and strategies for the development of libraries, given that the availability of such documents has already become mandatory for many branches and organizations, not only in the industrial or commercial spheres of the economy.
Method of Masking Information in the Contours of Video Images

This article contains information on the analysis of existing steganographic methods of hiding information in order to create a new one. Media files, including video ones, are considered as a container for embedding information in this research. The aim of the research is to develop new steganographic method of data embedding. Due to the fact of using outlines as a container, the developed method allows embedding a large amount of information without significantly changing the appearance of the container. The presence of several levels of abstraction from specific values increases the security of built-in data from external interference.
Self-Organization of Students of Non-Linguistic Specialties as a Factor in New-Formation Graduate Development

The aim of the research is to substantiate the need for the formation of a flexible skill “Time management” as the most important component of the universal competence “Self-organization and self-development” at the stage of obtaining professional education through the inclusion of a training module in the programs of general scientific disciplines. The article contains a comprehensive analysis of using active educational technologies in teaching students of non-linguistic specialties within the framework of the chosen competence development. On the basis of the developed diagnostic tools for determining the level of possession of the above mentioned competence, a survey and interpretation of the results were carried out, on the basis of which a draft module was proposed for developing students’ time-management skills as a factor in training a specialist of a new formation, adapted to the modern labor market. The scientific originality of the research lies in the substantiation of the need for the introduction and implementation of an educational module on the development of time-management skills in the form of a block inclusion in the program of general scientific disciplines at the university. As a result, time management is considered as a means of professional adaptation of a young specialist, the methods of forming the universal competence “Self-organization and self-development” are revealed, a map of the component of this competence - “Time management” - is proposed.
Protection and detection of physical hazards for the intelligent system under industrial Internet of Things by neural network

Abstract Aiming at detecting and avoiding many security risks brought by various intelligent equipment and sensors in industrial Internet of Things (IoT) intelligent system, the Kalman back propagation neural network (Kalman‐BPNN) algorithm is designed, based on the neural network and with the help of the unique advantages of Kalman filtering algorithm and back propagation neural network (BPNN) algorithm. Introductions are made on the architecture of IoT and the theory of system security, and descriptions are made on the principle and process of related algorithms in detail. The advantages of traditional algorithms are integrated to design a Kalman‐BPNN algorithm. The simulation experiment analysis is carried out, and the designed detection method is compared with the traditional detection method. The voltage test of the Kalman‐BPNN algorithm based on the neural network is no more than 4.1%, as the experiment shows. The true positive rate of Kalman‐BPNN algorithm is 91.121%, which is very close to the value of 1, indicating that the algorithm has a great probability to predict the normal voltage. The false positive rate is 9.341%, which is very close to 0, indicating that the algorithm has a small probability of false judgment.
Distributed Algorithm over Tree Networks for Optimization with Total Constraints

This paper addresses distributed optimization with total constraints over tree networks. To express the total constraints, data aggregation over the tree networks is utilized. The result is remarkable in that the alternating direction method of multipliers (ADMM) without the aggregator can dispersively solve the optimization problem with a proposed formulation.
{GRD} 2.1: An update to {GRD} for {SPSS} 27 and above

GRD is a popular tool to genenrate random data on the fly.It is most useful in statistic classes where the students can generate with a single short syntax, or using a graphical interface, random data that differs on every run but yet can implement effect sizes, outliers, etc.With the new versions of SPSS (version 27 and above) which is now using a new version of Python, it was necessary to upgrade the extension.Here, GRD 2.1 is presented which works with SPSS 27 and above.
You Can See, But You Cannot Photograph: An Intelligent Content Protection Scheme for Videos on Screens

Nowadays, with the development of digital image, video, and other multimedia visualization techniques, electronic screens are ubiquitous in various scenarios, and these screens provide vivid communication and presentation methods. Unfortunately, at the same time, the security and safety defenders have noticed that it is more and more difficult to prevent people from photographing the contents on the screens, especially when the photographic equipment is undergoing miniaturization and intelligentization, and that promotes the information and privacy leakage inevitably. There are plenty of technical solutions that have been proposed previously, but few of the exiting schemes are feasible for daily and widespread applications because most of them require some additional and customized hardware to be mounted on the screens or audiences. Therefore, in this paper, we propose an intelligent content protection scheme for videos on screens, which is designed to prevent audiences from photographing the entire picture of the screen while having no observable influence for direct observation with naked eyes. Furthermore, our experiments demonstrate that, for the 1080P and 60 fps videos on ordinary LCD screens, our scheme outperforms the existing schemes in terms of occlusion rate, effective distance and so on.
Analysing the action techniques of basketball players’ shooting training using calculus method

Abstract This article uses the calculus method and three-dimensional analysis system to photograph and analyse the action techniques of China's famous basketball players’ shooting training. Combining with the action technical parameters of outstanding male basketball players at home and abroad, this article analyses several main basketball players’ shooting training actions. Quantitative analysis of the calculus method was carried out in the technical link, and the problems existing in the basketball player's movement technology were found. Further, from the comparison of the physical fitness of excellent athletes, the reasons for the gap between the level of basketball in China and the world were discovered, and the physical quality of excellent male basketball players was established. Level evaluation models and standards provide a reliable guarantee for accurately grasping the development of athletes’ physical fitness, clarifying the status of each physical fitness in training, optimally controlling the basketball training process, and achieving scientific basketball training.
Image recognition based on Boltzmann machine algorithm

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.
A Service Driver Based Application Execution and Development Method in Multi-cloud Context

To reduce the cost of cloud tenants and avoid the phenomenon of vendor lock-in, multi-cloud technique is proposed and competent for this goal. However, it leads to another difficultly on supporting application development and runtime update process in the multi-cloud context. The key points for this problem are: (i) implementing application runtime switch among cloud services for varied service requirements and status; (ii) supporting runtime agile and low-cost update, and redevelopment for emerging new cloud services. To overcome this problem and advance multi-cloud applications execution, we present a support technique based on service driver concept. Service driver can help applications shield the heterogeneity and switch among cloud services. To support the implementation of service driver, we have finished the following three areas: (i) proposing a multi-cloud application service access model which is implemented through multi-agent system technique; (ii) presenting a multi-cloud application runtime service update and invocation process; (iii) introducing a multi-cloud application development framework. Furthermore, to help and direct developers to design and implement multi-cloud application, we describe a multi-cloud application (re)development method. This method includes multi-cloud application development process and runtime multi-cloud application agile redevelopment process. Finally, we interpret the multi-cloud application development support techniques and methods via case study.
Tsunami early warning chain assessment utilizing social network analysis

Abstract The application of rapid and accurate disaster early warning information has served as the key to successful mitigation for natural disaster. For example, in a tsunami disaster condition, precise information from authorized stakeholders must be immediately and accurately conveyed to the people potentially affected by the disaster. However, in practice in the field, the disseminated information has been however uncertain, failing to reach the lowest level of society. For this reason, a study is required to identify the information network that existed in the community when the tsunami disaster occurred. One method in determining this information includes Social Network Analysis (SNA), referring to a study method of structural relationships among the interacting network members, involving: individuals, organizations, or institutions. In this study, a survey was conducted to 90 respondents at the disaster location. From the results, it was found that 14 actors were involved in disseminating information on the tsunami disaster in the lowest community. The result of the Social Network Analysis indicated that the value of the degree of centrality, actor of mosque information obtained the highest value of 0.231, implementing that most of the community at the lowest level received tsunami information from announcements disseminated through information from mosques. Meanwhile, the lowest value of network closeness centrality was hamlet (RT/RW), having a value of 0.876, indicating that information from the mosque was beneficial as disaster early warning information. Upon calculating the degree of centrality and closeness at the lowest level of society, information from mosques and hamlet (RT/RW) serves as the most influential actor in disseminating tsunami information in Pandeglang Regency, Banten, Indonesia.
Few-Shot Classification With Intra-Class Unrelated Multi-Prototype Representation and Episode Adaptation Strategy

The episode training strategy, which trains models by many episodes to recognize unseen object categories using one or a few samples, is used by many existing approaches to solve the few-shot classification. An episode can be regarded as a classification task for the few-shot classification. However, this training strategy will be tricky when the task feature differences between the training episodes are big. To improve on this shortcomimg, we propose an Episode Adaptation Loss (EAL) to reduce these gaps for optimizing the models efficiently. Furthermore, to enhance the ability of each class feature representation and preserve diversities of the intra-class features, we propose Multi-Prototypical Representations (MPR) and a Multi-Prototype Unrelated Loss (MPUL). In addition, we introduce the multi-prototypes inductive inference as a classification strategy for our method. We evaluate our method on miniImageNet and Fewshot-CIFAR100 benchmarks. Experimental results demonstrate that our method outperforms the baseline approaches. The ablation study validates that the components of the proposed method all provide positive effects on few-shot learning.
Potential distribution of water in Malang city based on Multitemporal Sentinel 2A image data

Abstract Malang City is one of the cities in East Java with the status of a second-level city, with Surabaya City as the first-level city. Malang City also has various advantages that can attract many people outside the city to live in Malang City. As time goes by, Malang City experiences an increase in built-up land area, which is considered to be the cause of increased flooding in Malang City. This study attempted to identify the areas vulnerable to potential inundation (flooding) using several influencing variables, consisting of data on land cover, slope, land altitude, intensity of rainfall, and NDWI. This study employed the SMCE (Spatial Multi-Criteria Evaluation) method to obtain data on the distribution of potential areas for rainwater puddles (floods) in Malang City. Based on the analysis results, most of Malang City in 2019 is dominated by areas categorized as vulnerable with a percentage of 71% or an area of 7,827.21 hectares. At the same time, the remaining 29%, or an area of 3,130.10 hectares, is an area included in the safe category from potential puddles. The percentage of this vulnerable area, when compared to 2014, increased by 6%.
Modern techniques for identifying mineralization in virgin area

Mineral exploration is a complex process involving rigorous field work to collect data and conceptualization to fit the data into a plausible theory. Mineral exploration proceeds step by step taking into account aerial photographs, satellite imagery, geological maps, geophysical maps, and geochemical maps to delineate areas of interest. Data generated by these techniques are stored in a thematic and textual database using suitable software. Various techniques of combining and compressing these data have developed over the years, where computers play an important part. These data can be utilized for 2D or 3D modeling or for data mining. The data can be selectively combined to obtain information for likely locating of ore deposits. Different techniques of data modeling and data interpretation that have recently developed are discussed and how and where they have been used has been described briefly.
Physical Application of Mechanical Parts and Components Simulate in Receivable Auxiliary Multifunction Mechanical Hand and Claw

Abstract Based on the analysis of current office work status and office robots, this paper proposes a design scheme of intelligent mechanical gripper which can transfer data and move objects independently to assist daily office work. It is proposed to design mechanical structure, including mechanism motion design and part structure design, and to use Autodesk Inventor to design and simulate mechanical parts and components. Dimensional and strength checks are carried out on key components, and stress analysis is simulated by using stress analysis module included in Autodesk Inventor. Finally, the mechanical structure design scheme and control system design scheme for the mechanical gripper are proposed. The overall design mainly centers on the functions expected to be completed, and innovates different organizations in combination to achieve the intended objective.