Reviewerly/exHarmony huggingface dataset:
A Fast Robustness Quantification Method for Evaluating Typical Deep Learning Models by Generally Image Processing

Deep learning especially image recognition techniques have been extensively used in various applications such as unmanned driving. The robustness of deep learning models is of critical importance since fault image recognition results may result in serious incidents. In this paper, we propose a fast quantifying method by general image processing to evaluate the robustness of five typical deep learning models under the Keras framework (i.e., VGG16, InceptionV3, ResNet50, DenseNet, and MobileNet). We analyze six metrics in terms of accuracy, precision, recall, F1, recognition time, and impact factor. The evaluation data is publicly accessible image data sets from Kaggle. In our evaluation, the adversary samples are generated by generally image processing methods such as gray-scaling, color-reversing, and image-flipping, which is ordinary operations and easily launched. The different models over various image processing methods are evaluated and compared comprehensively. The evaluation results show that DenseNet performs best over three conditions such as baseline, grayscaling and horizontal flipping. MobileNet costs the shortest delay in decision over all image processing methods. F1 score varies with different attack intensity. InceptionV3 presents overall robustness in most conditions.
Compressive Autoencoders in Wireless Communications

Autoencoder, one of the most promising and successful architectures in deep learning (DL), has been widely used in wireless communications. However, fast-increasing size of neural networks (NNs) in autoencoder leads to high storage requirement and heavy computational overhead, which poses a challenge to practical deployment of autoencoders in real communications systems. In this paper, we investigate two representative NNs compression methods and propose two compressive autoencoder schemes for wireless communications by combining the compression techniques with autoencoder architecture. Our proposed schemes are capable to reduce memory consumption and execution time. Numerical experiments demonstrate that our methods can effectively compress the autoencoder’s size without degrading the model performance or distorting the constellations of transmitted signal.
Cyber Crimes and Cyber Laws : A Perspective of Women Victimization

Women from all the parts of society are facing cybercrime against them. Being a victim of cyber crime, women experiences several psychological impact which deeply effects their life. Sometimes they even commit suicide. The National Crime Records Bureau (NCRB) has reported increased rate of cyber crime against women in the last few years. Cyber crime against women takes place in the form of cyber defamation, sexual harassment and abuse, email-misrepresentation and so forth . Cyber crime itself is traumatic for women to deal with and on the other side the challenges that are encountered by a women to proceed for a legal action against the culprit is also matter of concern for them. This research paper is an attempt to discuss upon the various types of cybercrimes that are inflicted upon women in India. It is also brief analysis of the legal rights of women to protect against cyber crime, its application and challenges that are experienced by women to get those rights.
Object Detection: State of the Art and Beyond

As one of the fundamental problems of scene understanding and modeling, object detection has attracted extensive attention in the research communities of computer vision and artificial intelligence. Recently, inspired by the success of deep learning, various deep neural network-based models have been proposed and become the de facto solution for object detection. Therefore, in this chapter, we propose to present an overview of object detection techniques in the era of deep learning. We will first formulate the problem of object detection in the framework of deep learning, and then present two mainstream architectures, i.e., the one-stage model and the two-stage model, with the widely used detectors such as Fast R-CNN, YOLO, and their variants. Lastly, we will also discuss the potential and possible improvements on current methods and outline trends for further study.
Intellectual Property Exhaustion and Parallel Imports of Pharmaceuticals: A Comparative and Critical Review

This Chapter addresses the topic of intellectual property (IP) exhaustion in the context of the parallel trade of pharmaceuticals. These imports, which are controversial in general, are more complex with respect to pharmaceuticals, which require additional marketing and import authorizations. Nevertheless, individual countries remain free to accept these imports under the flexibility of Article 6 of the Agreement on Trade Related Aspect to Intellectual Property Rights (TRIPS Agreement). This Chapter reviews several national approaches—in developed, developing, and least developed countries (LDCs)—from the perspective of the exhaustion of patent rights as well as other IP rights. Through this review, it highlights that several countries today accept parallel trade. A large of number of these countries are, however, developed countries, whereas several developing countries and LDCs instead prohibits parallel imports. This finding is perplexing, and the reasons for this restrictive approach are unclear as developing countries and LDCs need flexible policies and can largely benefit from parallel trade. In addition, despite the claim by the pharmaceutical industry that parallel trade would increase the price of medicines in these countries—as originator would increase prices due to the fear of parallel imports—medicines are sold at lower prices mostly because of governments’ pricing or after the expiration of patent protection. Based on this review, this Chapter concludes that national legislations, which are not taking advantage of the flexibility in Article 6 of the TRIPS Agreement, may consider reviewing their policies and allow parallel imports.
Sentiment Analysis for E-Learning Counting on Neuro-Fuzzy and Fuzzy Ontology Classification

Nowadays, the utmost effective rising approach in learning procedure is E-Learning. To be specific, the combined learning is meant to be a worthwhile approach for assisting and comprehending schoolchildren and their acquisition matters. Because of E-Learning platforms and their collaborative tools, schoolchildren might communicate with other schoolchildren and share doubts on definite course or with teaching staff. When we try to enhance E-Learning, it is exceptionally worthwhile to have enlightenment about the users’ sentiments available. On one side, this information might be utilized by adaptational E-Learning systems for the drive of assisting personalized learning, by considering the user’s emotional state when recommending him/her the utmost suitable activities to be undertaken at each time. Contrarily, the schoolchildren’ sentiments concerning a course might serve as feedback for teaching staff, specifically in reference to online learning, where face-to-face interrelationship has been considered to be a lesser great deal of frequency. The worth of this study in the perspective of E-Learning, both for teaching staff and for adaptive systems, is looked at too. Adoption of an innovative means for sentiment analysis through Facebook is inspected, beginning with messages written by schoolchildren to mine enlightenment about the schoolchildren’s sentiment polarity (positive, neutral or negative), as conveyed in the messages written by them. We have used this methodology in SentBuk, a Facebook application also put forward in this study. The classification methodology implemented follows a hybrid approach: It combines neuro-fuzzy algorithm and fuzzy ontology. Results acquired through this model show that it is feasible to carry out sentiment analysis in Facebook within height accuracy.
A Federated Search System for Online Property Listings Based on SIFT Algorithm

Facilitating access to accurate real estates information can contribute positively in the social and economic development of developing countries. Recently, in Mauritius, it has been observed that despite the increasing number of real estate agents marketing their products on the Internet, potential buyers are still facing difficulties in obtaining relevant information due to several duplications with varying information observed across multiple real estate platforms. Such a situation can leave buyers confused and reluctant to take a decision, thereby preventing progress. In an attempt to help buyers obtain adequate information about a property advertised online, a federated search system, based on the SIFT algorithm, that can aggregate and display similar real estate property listings from multiple websites into one location is proposed. Buyers are able to then view all the details for the same property in a single location to decide which online platform to visit and pursue with their quest for a property acquisition. In this paper, the architecture, implementation and evaluation results of the proposed system are presented.
A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage

As an essential application in cloud computing, cloud storage offers client versatile, adaptable and astounding data stockpiling and calculation administrations. A developing number of data proprietors outsource data records to the cloud. Since cloud storage servers are not completely reliable, data proprietors require tried and true intends to check the possession ship for their documents outsourced to remote cloud servers. To address this vital issue, some remote data possession checking (RDPC) conventions have been displayed. In any case, numerous current plans have vulnerabilities in effectiveness or data progression. In this paper, we give another proficient RDPC convention in light of homomorphic hash function. The new plan is provably secure against fraud assault, supplant assault and replay assault in light of a normal security demonstrate. To help data flow, an operation record table (ORT) is acquainted with track tasks on document squares. We additionally give another upgraded usage for the ORT which makes the cost of getting to ORT almost consistent. Also, we make the extensive execution investigation which demonstrates that our plan has focal points in calculation and correspondence costs and also provides data regain ability if it is found modified. Model execution and examinations show that the plan is practical for genuine applications.
Implementation of SM4 Based on Random State to Resist DPA

Since the Chinese block cipher standard SM4 was released in 2012, it has caused research on its security in the cryptographic industry. DPA (Differential Power Analysis), as a powerful side-channel attack method, poses a huge threat to SM4. In this paper, we have implemented a new SM4 circuit that is resistant to power consumption attacks. This work achieves random power consumption by randomizing the intermediate data generated in the encryption process. Experiments show that even with 50,000 power consumption curves, the key cannot be cracked, which proves the effectiveness of the design against DPA. Our Random State circuit implementation scheme is 31% less in area than the General Mask, and its security is higher than the Normal SM4.
Using an analytic hierarchy process-based index and geographic information system in geochemical exploration of gold

In this study, a gold exploration index (GEI) that reduces financial expenditure and time losses during exploration studies has been developed using the analytical hierarchy process in a region where a high-sulfidation epithermal gold (Au) deposit exists. The GEI can be used to predict the location of the target element by evaluating the maps obtained from related element distributions together with a GEI-based prediction map. The hierarchical structure of the index has been established based on geochemistry of the rock samples. The elements used in the design of the hierarchical structure are arsenic (As), silver (Ag), antimony (Sb), copper (Cu), manganese (Mn), lead (Pb) and zinc (Zn), which are determined by the correlation analysis and experts’ opinions. The efficiency scores of the alternatives are converted into prediction maps called GEI-based anomaly distribution maps. These are compared with the maps derived from both geographic information system-based overlay analysis of the rock samples and spatial gold distribution. The efficiency scores of the alternatives in these maps are categorized into three groups as ‘high’, ‘medium’ and ‘weak’ in terms of gold potential. Comparison of the results with those derived using principal component analysis, weighted sum and weighted product models shows that the produced index yields reliable information that can be used to determine where gold enrichment occurs, especially in high-sulfidation epithermal environments. Supplementary material: Geochemical analysis results are available at https://doi.org/10.6084/m9.figshare.c.5443218
Mental Integrations as Functional Wholes

Abstract It is argued that a mental integration is formed only if the result is a functional whole. This idea is then used to clarify the definition of a concept and discuss problems in which an instance may belong to different conceptual classes depending on the context. The same idea is also applied to the rules for dealing with an entity when it is formed out of sub-entities. Specific examples of how such rules are frequently violated in literature as well as colloquially are discussed.
Modelling Heterogenous Robot Squads in Unstable Situations

The report considers the issues of information interaction at destabilizing the state of the mixed robotic grouping. The equilibrium state is achieved by applying the principles of maintaining the quasi-stability state of the system and exchanging of pertinent data flows between objects. Within the framework of this, the spheres of their application, schemes of possible information interaction are examined using the example of unmanned aerial vehicles. In addition, an approach is described for planning the actions of a group of unmanned aerial vehicles under the conditions of “balancing on the brink” and the basic principle of pertinence. Pertinent information flows correspond to the specific information needs of each object in the mixed robotic group, what does not require additional resources for processing the received information. Moreover, the results of modeling the behavior of mixed robotic groups in the conditions of “balancing on the brink” with the exchange of the information, streams by the example of a group of unmanned aerial vehicles are presented. The simulation was performed using the developed special software and described using the IDEF0 functional modeling methodology. The developed functional model shows all the requirements, limitations, information and other resources involved in the process of achieving the goal by a group of unmanned aerial vehicles.
A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction

Kernel least mean square (KLMS) algorithm is a popular method for online time-series prediction. It has the advantages of high robustness, low computational complexity, model simplicity, and online learning ability. Unfortunately, as input data grows, its dictionary size increases and the computational complexity rises significantly. In addition, how to improve the adaptability in uncertain environments with noise is also one of the main challenges. In this paper, we propose a Quantized Kernel Least-mean-square Based on Echo State Network (ES-QKLMS) algorithm in response to the above problems. In the new algorithm, the reservoirs of the echo state network (ESN) are integrated with the KLMS algorithm for online prediction. The ESN reservoir can store the associated information of historical data, which improves the algorithm's ability to track temporal characteristics. Then, the vector quantization method is adopted to suppress the size of the KLMS dictionary. Moreover, the adaptive weight adjustment is used to improve the noise immunity in an uncertain environment. At last, The Lorenz time series and the El Nino-Southern Oscillation (ENSO) time series are discussed to demonstrate the efficacy of the ES-QKLMS algorithm.
Implementation of Various Classification Systems in Library

Organizing library materials cannot be separated from the application of the classification system which is used as a class-based knowledge determination with the aim of facilitating librarians and librarians in retrieval of collections in the library. This study uses the library research method by comparing the classification systems in 5 libraries from 5 different journals. The results of the study indicate that there are different implementation of classification systems depending on the type of library. The classification system used is Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), Mandala Classification, Classification System Libraries, Department of Orthotics, Prosthetics, Health Polytechnic, Ministry of Health, Jakarta and Archaeological Library Classification System. The conclusion can be used any classification system as long as it still has the same goal, which is to make it easier for librarians and users to find the desired source of information.
The Construction of Smart Library Based on Data Mining Technology

Abstract Data mining technology is a technology that mines and analyzes data deeply. Applying data mining technology to the construction of a smart library can quickly extract information about books and documents. This provides support for the management of document resources in smart libraries. At the same time, it not only optimizes the collection structure, but also expands the service model. It improves the quality of information services.
An Integrative and Privacy Preserving-Based Medical Cloud Platform

With the rapid development of cloud computing which has been extensively applied in the health research, the concept of medical cloud has become widespread. In this paper, we proposed an integrated medical cloud architecture with multiple applications based on privacy protection. The scheme in this paper adopted attribute encryption to ensure the PHR files encrypted all the time in order to protect the health privacy of the PHR owners not leaked. In addition, the medical cloud architecture proposed in this paper is suitable for multiple application scenarios. Different from the traditional domain division which has public domain (PUD) and private domain (PSD), the PUD domain is further divided into PUD1and PUD2 with finer granularity based on different permissions of the PHR users. In the PUD1, the PHR users have read or write access to the PHR files, while the PHR users in the PUD2 only have read permissions. In the PSD, we use key aggregation encryption (KAE) to realize the access control. For PHR users of PUD1 and PUD2, the outsourcable ABE technology is adopted to greatly reduce the computing burden of users. The results of function and performance test show that the scheme is safe and effective.
Uncertainty evaluation of a 300-mm-aperture vertical Fizeau interferometer

In the development of a high-precision vertical Fizeau interferometer with 300-mm aperture, uncertainty evaluation is a significant method to assess the accuracy of interferometer. In this paper, we reported an uncertainty evaluation method. Then, the uncertainty evaluation of a 300-mm-aperture vertical Fizeau interferometer was carried out based on the method. The uncertainties of interference cavity measurement and absolute testing (e.g., three-flat method) were used as indicators of uncertainty evaluation. In addition, the gravity-induced deformation of three-flat method can be compensated accurately by finite element method modeling. Finally, we obtained the uncertainties of interference cavity measurement and three-flat method. With 300-mm aperture, the expanded uncertainty of interference cavity measurement is 3.7×10-3 λ, and the expanded uncertainty of three-flat method is 1.47×10-2 λ.
Asset Management Mode of Higher Vocational Colleges Based on Big Data Technology

The integration research and development of asset information in higher vocational colleges (HVC) is the core component of the education informatization project, and an important form and method of promoting modern education with educational informatization in china. It fully reflects the development direction and measures of the current social education system. The purpose of this study is to study the financial management (FM) mode and strengthen the current FM work in colleges and universities (CAU), so that CAU can get considerable development in FM, and build an efficient FM mode to ensure the financial stability of HVC, and to ensure adequate education funds. Based on the reform of china's higher education system, through the use of big data technology for reference, this paper analyzes the shortcomings of the asset management mode of a higher vocational college in Jiangxi province, and finds corresponding solutions. The results show that the expenditure of teachers and teaching assistants accounts for 26.3% of the total education cost. However, books and grants accounted for 22.4% and 4.8% respectively. Housing construction accounts for the largest proportion of fixed assets in HVC, with an increase rate of 33.8% from 53.4 million yuan in 2016 to 71.45 million yuan in 2019.
ASM Specification and Refinement of a Quantum Algorithm

In this paper we use the Abstract State Machine (ASM) method for high-level system design and analysis created by Egon Börger to formally specify Grover's quantum database search algorithm, stepwise refining it from its highest abstraction level down to its implementation as a quantum circuit. Our aim is to raise the question of whether the ASM method in general and quantum ASMs in particular can improve the current practices of quantum system engineering; providing accurate high-level modelling and linking the descriptions at the successive stages of quantum systems development through a chain of rigorous and coherent system models at stepwise refined levels of abstraction.
Improving Inertial Navigation Accuracy with Bias Modeling

Navigation-grade gyroscopes and accelerometers are well-known to have bias in their output. This bias contributes significantly to the error in the time-evolving position solution of the inertial navigation systems (INS). The innovation in this paper is the approach of modeling the bias at the solution level as linear function and estimating the bias using unbiased velocity measurement which is available in the sensor suite for underwater vehicles. Moreover, a model switching scheme is used to more accurately correct the position estimates when the vehicle maneuvers. Motion models for nearly straight line motion and turning models are used.
Research on Computer Basic Teaching Mode Under the Cultivation of Applied Talents

Applied talents are specialized talents to meet the needs of society, and also the goal of talent cultivation in Colleges and Universities. The basic computer teaching for non-computer majors is more important. In order to further adapt to the cultivation of applied talents, this paper puts forward the corresponding reform strategies.
Blockchain and IoT: A Systematic Literature Review for Access Control Issues

IoT has been one of the emerging technologies for the past decade because of its facilities and it’s also becoming widely used for monitoring purposes. Therefore, many questions are rising about the security of devices that can operate either in an everyday life or industry. It was found that IoT networks are using low-safety communication protocols that might be questionable and this paper reveals fair alternatives to replace these technologies using blockchain and smart contracts that will enforce the protection of these devices from security violation by setting up access control policies and roles to every peer in the network.