Reviewerly/exHarmony huggingface dataset:
Comprehensive Defense System against Vulnerabilities Based on Return-Oriented Programming

It is difficult or impossible to develop software without included errors. Errors can lead to an abnormal order of machine code execution during data transmission to a program. Program splitting into routines causes possible attacks by using return instructions from these routines. Most of existing security tools need to apply program source codes to protect against such attacks. The proposed defensive method is intended to a comprehensive solution to the problem. Firstly, it makes it difficult for an attacker to gain control over program execution, and secondly, the number of program routines, which can be used during the attack, decreases. Specific security code insertion is used at the beginning and end of the routines to make it complicated to gain control over the program execution. The return address is kept secure during a call of the protected routine, and the protected routine is restored after its execution if it was damaged by the attacker. To reduce the number of suitable routines for attacks, it was suggested to use synonymous substitutions of instructions that contain dangerous values. It should be mentioned that proposed defensive measures do not affect the original application`s algorithm. To confirm the effectiveness of the described defensive method, software implementation and its testing were accomplished. Acknowledging controls were conducted using synthetic tests, performance tests and real programs. Results of testing have demonstrated the reliability of the proposed measures. It ensures the elimination of program routines suitable for attacks and ensures the impossibility of using standard return instructions for conducting attacks. Performance tests have shown a 14 % drop in the operating speed, which approximately matches the level of the nearest analogues. The application of the proposed solution declines the number of possible attack scenarios, and its applicability level is higher in comparison with analogues.
Device for Position Tracking System with GPS for Elderly Person’s Health Aspect to Make Them Equally Accessible in the Developmental and Monitoring Process in the Society

India is the one of the world-largest population in the world. Out of 15–25% people at the age of 65 years loss their memory due to old age. People detected as Alzheimer’s patient sometimes are not able to reach their destination. Device for the position tracking with GPS system may help the parent/caretaker of the patient to identify their location. The system includes a GPS modem that continuously tracks the patient’s location in the form of latitude and longitude along with the heartbeat and body temperature as physical parameter. With the help of the sensors, the data will be sent to a microcontroller interfaced to a GSM modem. In this paper, an attempt has been made to develop a device with tracking system including GPS as well as to collect the health status of the old person. This device may help the country adopting a policy for the elderly person’s proper health care and management.
Research on Trustworthiness Evaluation Based on Web Service Composition

The loose coupling and distributed nature of service-oriented architecture (SOA) can easily lead to trustworthiness problem of service composition. The current Web service composition trustworthiness evaluation method is biased towards the service provider itself, while ignoring the trustworthiness of the service discoverer and user. This paper mainly studies a multi-angle Web service composition trustworthiness evaluation method, comprehensively considers the three aspects of Web service composition, and uses the service finder to expand the service center. The experiment proves that this kind of trustworthiness evaluation method of Web service composition can improve the accuracy and comprehensiveness of trustworthiness evaluation.
Text Matching Technique Based Intelligent Web Crawler in Hybrid Mode

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
An Efficient Class-incremental Learning Strategy with Frozen Weights and Pseudo Exemplars

In this paper, we propose a novel and efficient class-incremental learning approach that does not necessitate the storage of old data after training each task. The proposed approach uses the autoencoder's decoder to generate pseudo data to consolidate the model and sets a subset of relevant weights in the encoder layers to learn new knowledge while freezing most weights. It uses no extra storage space to save old data. The experimental results also show the performance of the proposed approach.
Against Music? Heuristics and Sense-Making in Listening to Contemporary Popular Music

Is the ubiquity of contemporary popular music akin to a deliberate aggression to the hearing, making the listening experience devoid of any sense? If so, is there any strategy to morph this supposed confusion into meaningful stimuli? Relying on epistemology, we attempt at promoting the act of listening as a proper way of world-making and refer to Mark Reybrouck, Bruno Nettl, Steven Brown and Joseph Jordania—among others—to gather appropriate heuristic tools. In the last part of the essay, we advance the concept of timbral quotation as an additional means to grasp meaningful cues in the timbrical richness of contemporary popular compositions. We shall sustain the particular fitness of this tool especially with regard to nowadays’ Western popular music, more and more timbre-centered rather than harmony-centered.
Better Mechanistic Understanding of Big Data Through Order Search Using Causal Bayesian Networks

Every year, biomedical data is getting larger at an increasing rate and being collected from many different sources such as hospitals (clinical big data), laboratories (genomic and proteomic big data) and the Internet (online big data). In this article we introduce and evaluate a practical causal discovery algorithm that uses modern statistical, machine learning, and informatics approaches that have been used in learning causal relationships from biomedical big data that integrates clinical, omics (genomic and proteomic) and environmental aspects.
LERA—an interactive platform for synoptical representations of multiple text witnesses

Abstract In the humanities, text comparison is essential for scholarly editing the traditions of a text with its different witnesses under study. For the computer-aided creation of a critical apparatus, there have been established approaches and tools for many years. However, they mainly focus on subsentences or sentences. An efficient and easy-to-use automatic comparison of entire chapters or books still represents a research desideratum. The Locate, Explore, Retrace and Apprehend complex text variants (LERA) working environment presented here solves this issue. It is based on a two-stage collation approach: an efficient, fully automatic alignment of text segments, which can be paragraphs, subparagraphs or sentences, with interactive post-processing options, followed by the detailed comparison at segment level. Because aligning text segments, such as paragraphs, for more than two text witnesses is algorithmically challenging, we discuss the heuristics we developed in more detail. LERA combines the entire process of document management, tokenization/segmentation, normalization, alignment, and visualization with interactive control options and exploratory tools. It has already been and is being successfully applied in several Digital Humanities projects of different languages, e.g. for Arabic, French, Hebrew as well as German and English texts.
Highly Scalable Beaver Triple Generator from Additively Homomorphic Encryption

In a convolution neural network, a composition of linear scalar product, non-linear activation function and maximum pooling computations are intensively invoked. As such, to design and implement privacy-preserving, high efficiency machine learning mechanisms, one highly demands a practical crypto tool for secure arithmetic computations. SPDZ, an interesting framework of secure multi-party computations is a promising technique deployed for industry-scale machine learning development if one is able to generate Beaver (multiplication) triple offline efficiently. This paper studies secure yet efficient Beaver triple generators leveraging privacy-preserving scalar product protocols which in turn can be constructed from additively homomorphic encryptions (AHEs). Different from the state-of-the-art solutions, where a party first splits her private input into a shared vector and then invokes an AHE to compute scalar product of the shared vectors managed MPC servers, we formalize Beaver triple generators in the context of 2-party shared scalar product protocol and then dispense the generated shares to MPC servers. As such, the protocol presented in this paper can be viewed as a dual construction of the state-of-the-art AHE based solutions. Furthermore, instead of applying the Paillier encryption as a basis of our previous constructions or inheriting from somewhat fully homomorphic encryptions, we propose an alternative construction of AHE from polynomial ring learning with error (RLWE) which results in an efficient implementation of Beaver triple generators.
Matching Social Issues to Technologies for Civic Tech by Association Rule Mining using Weighted Casual Confidence

More than 80 civic tech communities in Japan are developing information technology (IT) systems to solve their regional issues. Collaboration among such communities across different regions assists in solving their problems because some groups have limited IT knowledge and experience for this purpose. Our objective is to realize a civic tech matchmaking system to assist such communities in finding better partners with IT experience in their issues. In this study, as the first step toward collaboration, we acquire relevant social issues and information technologies by association rule mining. To meet our challenge, we supply a questionnaire to members of civic tech communities and obtain answers on their faced issues and their available technologies. Subsequently, we match the relevant issues and technologies from the answers. However, most of the issues and technologies in this questionnaire data are infrequent, and there is a significant bias in their occurrence. Here, it is difficult to extract truly relevant issues--technologies combinations with existing interestingness measures. Therefore, we introduce a new measure called weighted casual confidence, and show that our measure is effective for mining relevant issues--technologies pairs.
Application of Artificial Intelligence Towards Digital Government Establishment

The paper will introduce the implementation of Artificial intelligence in the context of digital government. The application, based on this technology, is intended to expand the capabilities of governmental agencies in public administration and make operations more efficient. However, the rapid evolutionary nature of neural networks may lead to unforeseen externalities, reducing all gains from the technology. Thus, the authors explore the concept of trusted AI, which is prophesied to guarantee privacy and safety owing to the legislative framework and emerging technologies.
Interactive Sentiment Analysis with Ranking Graph Convolutional Networks

In the past decade, various methods have been proposed to address document-level sentiment classification. However, user-product relations have long been overlooked in those works. We propose to learn discriminative user/product representations from the interactive data for classification task. A ranking graph convolution network is proposed for the representation learning, where the semantics of the edges are seamlessly encoded in the propagation process of each layer, so as to discriminate connections of different semantics. To preserve local semantics among the neighbors and learn discriminative representations, a latent matching module with pair-wise ranking objective is proposed, and the learning objective is enforced in each layer of the graph to guarantee the discriminative representation learning. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed model over the state-of-the-art baselines.
Research on Method of Undesirable Text Recognition based on Deep Learning and Knowledge Graph

Now the Internet is flooded with a large amount of undesirable information, which harms the network ecological environment and it should be cleared. At present, it mainly relies on manual methods, and there are problems such as slow and incomplete identification in undesirable text recognition. In order to solve the problems, this paper proposes a method of undesirable text recognition based on deep learning and knowledge graph. It combines the undesirable information knowledge graph and the context relationship, together with the user's search intention, extracts the vector and input by the deep learning neural network to obtain the target result. Experiment shows that compared with other traditional methods, this method can achieve better classification result and improve the accuracy of web undesirable text recognition for different topics.
Automated Intention Mining with Comparatively Fine-tuning BERT

In the field of software engineering, intention mining is an interesting but challenging task, where the goal is to have a good understanding of user generated texts so as to capture their requirements that are useful for software maintenance and evolution. Recently, BERT and its variants have achieved state-of-the-art performance among various natural language processing tasks such as machine translation, machine reading comprehension and natural language inference. However, few studies try to investigate the efficacy of pre-trained language models in the task. In this paper, we present a new baseline with fine-tuned BERT model. Our method achieves state-of-the-art results on three benchmark data sets, outscoring baselines by a substantial margin. We also further investigate the efficacy of the pre-trained BERT model with shallower network depths through a simple strategy for layer selection.
Human Resources and their Tendency to Information Security Crimes Based on Holland Theory

Security systems are often the target of cyber-criminals and professional hackers, but often they fail in hiding all traces of the attack, thereby leaving critical evidence that could lead to identifying and arresting the criminal. However, hacking skills vary from one hacker to another depending on the hacker's personal traits, behavior, and intellectual tendencies. The aim of this study is to develop a proposed descriptive model of the behavioral patterns and motives of hackers based on programmable psychological theories, modeled using object-oriented programming models. The study proposes a descriptive model of an inverse algorithm that simulates Holland's Theory of Behavioral Patterns. Findings show that this descriptive model is applicable to be produced as a code map for the human resources of an investigative nature.
Random Polygon Cover for Oracle Bone Character Recognition

Deep Convolutional neural networks are widely used in computer vision research because of their good feature extraction ability, which can often show good performance in related tasks. Performance of deep convolution network models is not only related to its own architecture design, but also closely related to training data. When training model with large dataset and the images are clear and noise in images is less, it can get good result. But in the case of small dataset and low image quality, it is easy to appear that the model can fit the data in the training process and perform badly in testing, that is, overfitting problem. Our work proposes random polygon cover algorithm to simulate the possible damage object and partial content loss in training dataset, which is also a data augmentation technique. We'll use experiments to prove the effectiveness of this approach, while trying to reveal how data augmentation works and how our method differs from dropout.
Polynomial state estimation in infectious diseases

State estimation of nonlinear stochastic systems has been a difficult and challenging task due to their complexities and nonlinearities. However, many efforts have been made and different methods have been proposed through the years. In this chapter a continuous-time filter for a class of stochastic nonlinear polynomial systems driven by additive white Gaussian noises is presented. A closed-form solution of this filter is obtained for a nonlinear polynomial function of order n by expressing the conditional expectations of polynomial terms as functions of the estimate and the error covariance matrix. As a particular case, a finite-dimensional equation for a second-degree polynomial function is obtained. To show effectiveness of the presented approach two different models widely used for modeling viral infectious diseases are considered and compared with the extended Kalman filter algorithm. Numerical results show the applicability of the polynomial approach.
Technospheric Curation and the Swedish Allah Ring: Refiguring Digitisations and Curatorial Agency as Ecological Compositions, and Eco-curating as Planetary Inhabitations

Abstract In this chapter I unravel human-centred understandings of digitisations working with the digitisation of a silver-alloy finger ring inscribed in Arabic Kufic writing with the words “il-la-lah” (“For/to Allah”) and re-theorise it through a novel ontological, new-materialisms, posthuman, ecological mode of thinking (Cameron, Fiona R. 2018. Posthuman Museum Practices. In The Posthuman Glossary , ed. Rosi Braidotti and Maria Hlavajova, 349–352. London / New York: Bloomsbury Academic; Cameron, Fiona R. 2019. Theorizing Digitisations in Global Computational Infrastructures. In The International Handbook of New Digital Practices in Galleries, Libraries, Archives, Museums and Heritage Sites , ed. Hannah Lewi, Wally Smith, Steve Cooke, and Dirk vom Lehn, 55–67. London: Routledge). The ring is significant as evidence of interactions between Viking and Islamic worlds and as an artefact directed to promoting intercultural respect during the Syrian refugee crisis and the rise of far-right anti-Muslim sentiments. Significantly, the ring digitisation also becomes planetary in reach and distribution as an unruly, more-than-digital ecological composition in which curatorial agency is refigured as radical, eco-systemic processes involving the action and vitality of many different coordinates in their unfolding (Cameron, Fiona R. 2021. The Future of Digital Data, Heritage and Curation in a More-Than-Human World . Abington: Routledge., 129).
An Assisted Instruction System Designed for Online Teaching and Learning

In 2020 education had to stop its engine and let the e-education for teaching and learning, without a certified standard educational system. This is an argument to promote the idea that e-education includes education. The keyword was “online”, and the environment was the technological infrastructure available at the moment of the decision. So, e-education it is used like an extension of the education. This image reflects an aggregate approach, but a functional solution should integrate education in the e-education. Assisted design is the keyword in order to clarify the context. A standard E-Classroom should be the new classroom in education and then online teaching and learning should be an option. This paper describes an assisted instruction approach developed as a teaching and learning system in a standard E-Classroom, improved for a T-Class (teaching and learning in the same time, but in different places). The T-Class is the previous version of the E-Class, but the diversity of the user’s cognitive infrastructure tends to react as a priority, relating to the conceptual infrastructure or the technological one.
When AI Meets Digital Pathology

Pathology diagnosis relies mainly on the pathologists performing the examination on specimen glasses through microscopic. The process is time-consuming and highly experience-dependent. Recent advance in scanning speed, scanning resolution, and image quality shunts the lights on digitizing pathology images. Many promising and successful stories of artificial intelligence (AI) in diagnosing assistance further boost the acceptance of digital pathology. It is anticipated that with pathology slides digitized, tremendous benefits from AI assistive analysis can be brought in. While the lights have been shone, several issues also arise with the processing of digitized pathology images. The digitized pathology image has an image size of gigabytes. The targets for analysis could be very small and distributed all over the whole slide of images or mixed with various nontarget tissues. Some targets could appear of very large or very small sizes. These pose challenges on the labelling and processing of the whole slide pathology images. This chapter presents the issues and methods which have been developed to address the issues, when AI has been brought in as the assisted tools for digital pathology. Several examples on liver pathology image analysis are also presented.
Animal Attribute Recognition via Multi-task Learning Based on YOLOX

The task of attribute recognition is to obtain high-level semantic information of the target, which plays an important role in video or image analysis. The current popular attribute recognition datasets consist of cropped low-resolution single target pictures, which are not suitable for end-to-end multi-target attribute prediction, thus are not convenient for application. This research firstly proposed a new animal dataset Animal-Att without cropping, with each picture containing at least one target. On the basis of the existing anchor-free target detection network YOLOX, our multi-task learning network is realized by designing a new decoupling head. As object detection task and attribute recognition tasks share the same backbone network, animal attribute recognition head only increase little calculation and inference time. In multi-task network, it can also realize the attribute recognition of multiple targets at one time, which avoids redundant post-processing of the previous single target attribute detection. Finally, we verified the effectiveness and feasibility of this method in our dataset.
Hybrid Machine Learning Model for Face Recognition Using SVM

Face recognition systems have enhanced human-computer interactions in the last ten years.However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques.Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples.Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN.PCA-SVM, however, is ineffective and inefficient in detecting human faces in cases in which there is poor lighting, long hair, or items covering the subject's face.This study proposes a novel PCA-SVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection.The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.