Reviewerly/exHarmony huggingface dataset:
Association Rules Generation of Outsourced Transaction Data with Privacy-Preserving using Paillier Encryption

Most of the distributed storage network uses the ideal knowledge mining model, which emerges as a prominent alternative in all the aspects to save money for the protection, supervision and maintenance expenses in an IT institution. A mining affiliate / store will outsource their mining to a cloud computing centre. For each case both the partnership rules and outsourced database object set are considered as private property of the organization. To maintain client protection, the data owner encrypts the information sent to server. User sends mining queries to the server and then the server undertakes data collection and returns the user with mixed cases. Customers search out mixed cases to be honest with goodness. This paper addresses the topic of link outsourcing in an enterprise security program. Data mining is therefore an analysis field that remains stressed as it is regarded with the security selected from eventually identifiable information in data mining process. The Rob Frugal encryption technique is familiar with the beating outsourced information security flaws, which are also based on modified replacement statistics for the issues like bogus database events. In any case, it includes multiple fake diagrams that raise the point of disturbance overhead. In the process, the weighted support for products is increased to substantially reduce the amount of fabricated illustrations and the degree of security for less multifaceted outsourced content that is revised to address this issue. The overhead is transferred to the grid scheme by using the false table data. The simple modest Rob count can also be used to determine the attack and the man-in-the-middle ambush Pallier encryption is used to resolve these strikes with the clear and legitimate goal of providing outsourced mining security in the background of Rob Frugal encryption. The proposed work has increased protection because the framework does not make it feasible and also minimizes the time consumed.
Methodology for formation of professional communicative competence of future IT specialists

Abstract At present, due to significant attention of the employers in the IT sphere to key competencies of University graduates the system of professional communicative competence formation is required. This paper presents an overall design and implementation scheme of the course “Translation in the Field of Professional Communication” as an integral part of the cycle of disciplines in the area of “Infocommunication Technologies and Communication Systems”. The foreign language intersubject status is strengthened by the developed course. The paper focuses on basic components of this course providing integrity of educational process. Based on theoretical and practical approaches: study of psychological and pedagogical literature; analysis and synthesis of theoretical generalization of research results, comparison and interpretation of new facts and specific manifestations of the research subject, abstracting, questioning, testing, observation, pedagogic experiment and statistical data processing methods the efficiency of a proposed experimental training system was confirmed. Its positive influence on the level of acquisition of knowledge in the English language discipline and, correspondingly, on formation of the professional communicative competence of a software engineer and on development of positive motivation for professional activity was analysed. Besides, the development of positive motivation for the self-development of the personality and for the growth of interest in communication was described.
Learning Crisp Edge Detector Using Logical Refinement Network

Edge detection is a fundamental problem in different computer vision tasks. Recently, edge detection algorithms achieve satisfying improvement built upon deep learning. Although most of them report favorable evaluation scores, they often fail to accurately localize edges and give thick and blurry boundaries. In addition, most of them focus on 2D images and the challenging 3D edge detection is still under-explored. In this work, we propose a novel logical refinement network for crisp edge detection, which is motivated by the logical relationship between segmentation and edge maps and can be applied to both 2D and 3D images. The network consists of a joint object and edge detection network and a crisp edge refinement network, which predicts more accurate, clearer and thinner high quality binary edge maps without any post-processing. Extensive experiments are conducted on the 2D nuclei images from Kaggle 2018 Data Science Bowl and a private 3D microscopy images of a monkey brain, which show outstanding performance compared with state-of-the-art methods.
An effective image transmission with DWT over fading channel

As the communication technologies are evolving significantly in wireless medium, this work aims to transmit and receive images with reduced bit error rate and increased peak signal to noise ratio. In this work, binary phase shift keying scheme is used to work in two phases to reduce the bit error rate. Along with binary phase shift keying, wavelet type transform, Symlet wavelet transform is also used to afford and analyze the image in both domains (time and frequency). The most advantageous part is the image with less noise can be improvised as a series of images or to video transmission hence can really take the transmission of information to next level. The advantage of the proposed work is shown in line with the existing works. The proposed work utilizes MATLAB wavelet tool box for achieving an efficient computation.
The Potential Adoption of Second Life as a Platform for Learning

Second Life (SL), an advanced virtual world technology used in education, creates an environment that facilitates engagement, collaboration, and conceptualization of knowledge to improve teaching and learning. The chapter investigates the potential of SL as a learning platform and students' perceptions on the ease of use of SL and usefulness as a learning platform, and challenges with SL implementation. The chapter adopts the qualitative research method, the Technology Acceptance Model, and a purposive sample drawn from fourth year Information Systems and Technology (IS&amp;T) students at the University of KwaZulu-Natal in South Africa. The chapter reports that SL is perceived to be a useful learning platform by students and improves students' instinctive and creative learning ability. However, inadequate training and insufficient information technology (IT) infrastructure are the challenges that hamper SL implementation efforts. Thus, the chapter suggests adequate training, provision of infrastructure and creation of a support pathway for SL as a learning platform as the possible solutions.
Document-Level Event Subject Pair Recognition

In recent years, financial events in the stock market have increased dramatically. Extracting valuable information automatically from massive financial documents can provide effective support for the analysis of financial events. This paper just proposes an end-to-end document-level subject pair recognition method. It aims to recognize the subject pair, i.e. the subject and the object of an event. Given one document and the predefined event type set, this method will output all the corresponding subject pairs related to each event type. Subject pair recognition is certainly a document-level extraction task since it needs to scan the entire document to output desired subject pairs. This paper constructs a global document-level vector based on sentence-level vectors which are encoded from BERT. The global document-level vector aims to cover the information carried by the entire document. It is utilized to guide the extraction process conducted sentence by sentence. After considering global information, our method obtains superior experimental results.
CUSUM Based Concept Drift Detector for Data Stream Clustering

The last few decades mark an unprecedented growth in the number of applications producing high-speed data streams. Learning from such fast data streams has many inherent challenges. The dynamic change in the concept of the stream is a significant challenge to be handled by the learning systems. This problem termed concept drift is given due focus in data stream classification scenarios. But, data stream clustering algorithms usually treat concept drift implicitly as part of the learning process. The need for explicit drift detection and adaptation is often neglected. This paper discusses a statistical method of change detection for data stream clustering problems. The change detection is done based on CUSUM test. On identifying a change, the model is re-built using the recent samples from the stream. The change detection process has been validated using real and synthetic datasets.
Ranking method for uncertain problems using TpFCMs model

Occurrence of any situation in the world has its own cause and outcome. The whole process of the situation depends on the decision making of any one person who is authorized for it. The process of decision making is an extremely a tedious job, for any type of person who is dealing with. From the lower category to the higher level, decision making process varies accordingly to the environment. It is possible for anyone to discuss any type of situation theoretically. But, when these situation is modelled mathematically, one could easily find out a solution for the problem accurately. This would enhance the process of decision making in a more appropriate manner. One such type of uncertain problem is analysed in this paper using the fuzzy model Trapezoidal Fuzzy Cognitive Maps. This fuzzy model is able to rank the factors affecting the problem.
Feature Store for Enhanced Explainability in Support Ticket Classification

In order to maximize trust between human and ML agents in an ML application scenario, humans need to be able to easily understand the reasoning behind predictions made by the black box models commonly used today. The field of explainable AI aims to maximize this trust. To achieve this, model interpretations need to be informative yet understandable. But often, explanations provided by a model are not easy to understand due to complex feature transformations. Our work proposes the use of a feature store to address this issue. We extend the general idea of a feature store. In addition to using a feature store for reading pre-processed features, we also use it to interpret model explanations in a more user-friendly and business-relevant format. This enables both the end user as well as the data scientist personae to glean more information from the interpretations in a shorter time. We demonstrate our idea using a service ticket classification scenario. However, the general concept can be extended to other data types and applications as well to gain more insightful explanations.
Implementation of Recognition System of People and Computers in a Classroom Through Artificial Vision

This work encompasses the implementation of an autonomous system that recognizes people and object in a classroom to later develop a smart classroom. This system uses artificial intelligent, artificial vision and deep learning technics to process images, sent by a camera that was previously installed in the class. The information is processed through a YOLO-type convolutional neural network, which is open source and is already trained and validated for real-time object detection applications, this information will contribute to the creation of an intelligent classroom. The aim of the system is getting the number of people and laptops that are inside a defined work area. The smart classroom goal is to influence the student’s behavior to: improve their academy performance, cognitive skills, and most importantly create a dynamic and interactive learning environment where the relationship between classmates and teacher improves.
Single Image Super-Resolution Based on Sparse Coding and Joint Mapping Learning Framework

Many image super-resolution algorithms can be formulated in a mapping framework based on the natural image prior. Generally, the mapping function with free parameters is learned by minimizing the reconstruction mapping error. In this paper, we obtain a considerable image super-resolution algorithm which gains in accuracy and speed by combining joint mapping learning with fast approximations of sparse coding. A novel “dictionary” training method for single image super-resolution based on feed-forward neural network is proposed. The training algorithm alternates between solving sparse coding problem and learning joint mapping relation problem. The learning process enforces that the sparse coding of a low-resolution image patch can be regarded as the shared latent coordinates for reconstructing its underlying high-resolution image patch with the image high-resolution dictionary. Experiments validate that our learning method shows excellent results both quantitatively and qualitatively.
Application of waveform and data flow analysis in engine sensor fault determination - a case study of Volkswagen B7 engine

Taking Volkswagen B7 engine as the main research object, this paper analyzed the mechanism of typical sensor faults and summarized the causes of common faults. Then, the use of engine fault diagnosis instrument, B7 comprehensive analyzer, KT - 600 engine equipment structures, experimental platform, the use of waveform analysis and data flow analysis fault test is carried out for the part of the sensor test, it is concluded that using waveform and data flow of the general regularity of auto sensor fault diagnosis and has high practical significance and value.
Approaches to Chernoff fusion with applications to distributed estimation

In large scale information fusion networks the statistical dependencies between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection and its generalization are perhaps the most well-known. Owing to its relationship with the statistical divergence known as Chernoff information the latter approach is referred to here as Chernoff fusion. Using Chernoff fusion requires tuneup for the fusion parameters, where in some applications, the Chernoff fusion may cause overhead. This work is concerned with the practical application of Chernoff fusion for distributed estimation. We provide a technique for fast particle filters fusion based on a linear variant of Chernoff fusion as well as a recursive tuning technique of the underlying mixing weights. The viability of the proposed schemes are demonstrated in such applications as distributed object tracking, cooperative robots localization, and image classification with ensembles of convolutional neural networks.
Alternative Psychological Treatment for Patients with a Diagnosis of Entomophobia with Virtual Reality Environments

Virtual reality is a term that emphasizes the establishment of virtual spaces that simulate the context. Thus, the use of this technology is an alternative for the treatment of phobias caused by irrational and unexplained fears, through the practice of some traumatic experience. Virtual reality helps to create places that simulate three-dimensional existence, in which objects or situations can be useful for the psychological treatment of patients with serious mental illnesses, such as phobias, which are considered an irrational fear of a stimulus that can cause dangerous consequences. Sometimes, fear is so aggressive that patients refuse to be treated, even knowing that they can free themselves from their fear. Nowadays, Entomophobia or insectophobia is the most common phobias that often do not justify the size or harmlessness of insects, the application of alternative treatments makes patients able to live with this type of fear or end the association of stimulus-response.
A Food Safety Text Filtering Method Based on Text Classification Techniques

In order to filter out food safety texts from unstructured textual data of various types, we proposed a Chinese food safety text filtering method. Firstly, the data is collected and preprocessed by the web crawler; secondly, the BERT pre-training model is fine-tuned by small-scale data; then the document vector calculation is carried out using a feature extraction method combining TF-IDF values and word vectors of keywords proposed in this paper; finally, the SVM classifier is trained by document vectors to screen out food safety text. The experiments show that the SVM classifier is able to filter out food safety texts from various types of text data with high performance, which basically achieves the expected results.
Damage Sensitive and Original Restoration Driven Thanka Mural Inpainting

Thangka murals are an important part of the cultural heritage of Tibet, but many precious murals were damaged during the Tibetan history. Three reasons cause existing methods to fail to provide a feasible solution for Thanka mural restoration: 1) damaged Thanka murals contain multiple large irregular broken areas; 2) damaged Thanka murals should be repaired with the original content instead of imaginary content; and 3) there is no large Thanka dataset for training. We propose a damage sensitive and original restoration driven (DSORD) Thanka inpainting method to resolve this problem. The proposed method consists of two parts. In the first part, instead of using existing arbitrary mask sets, we propose a novel mask generation method to simulate real damage of the Thanka murals, both masked Thanka and the mask generated by our method are inputted into a partial convolution neural network for training, which makes our model familiar with a variety of irregular simulated damages; and in the second part, we propose a 2-phase original-restoration-driven learning method to guide the model to restore the original content of the Thanka mural. Experiments on both simulated and real damage demonstrated that our DSORD approach performed well on a small dataset (N = 3000), generated more realistic content, and restored better the damaged Thanka murals.
A Programmable SoC Implementation of the DGK Cryptosystem for Privacy-Enhancing Technologies

Additively homomorphic encryption has many applications in privacy-enhancing technologies because it allows a cloud service provider to perform simple computations with users' data without learning the contents. The performance overhead of additively homomorphic encryption is a major obstacle for practical adaptation. Hardware accelerators could reduce this overhead substantially. In this paper, we present an implementation of the DGK cryptosystem for programmable systems-on-chip and evaluate it in real hardware. We demonstrate its efficiency for accelerating privacy-enhancing technologies by using it for computing squared Euclidean distances between a user's input and a server's database. We also provide comparisons with a recent implementation of Paillier cryptosystem and show that DGK offers major speedups. This work represents the first implementation of the DGK cryptosystem that uses hardware acceleration and demonstrates that the DGK benefits greatly from the hardware/software codesign approach.
When algorithm selection meets Bi-linear Learning to Rank: accuracy and inference time trade off with candidates expansion

Abstract Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either needs to train the models specifically for every algorithm or relies on the sparse one-hot encoding as the algorithms’ representation. To escape these intermediate steps and form the mapping function directly, we borrow the learning to rank framework from Recommender System (RS) and embed the bi-linear factorization to model the algorithms’ performances in AS. This Bi-linear Learning to Rank (BLR) has proven to work with competence in some AS scenarios and thus is also proposed as a benchmark approach. Thinking from the evaluation perspective in the modern AS challenges, precisely predicting the performance is usually the measuring goal. Though approaches’ inference time also needs to be counted for the running time cost calculation, it’s always overlooked in the evaluation process. The multi-objective evaluation metric Adjusted Ratio of Root Ratios (A3R) is therefore advocated in this paper to balance the trade-off between the accuracy and inference time in AS. Concerning A3R, BLR outperforms other benchmarks when expanding the candidates range to TOP 3. The better effect of this candidates expansion results from the cumulative optimum performance during the AS process. We take the further step in the experimentation to represent the advantage of such TOPK expansion, and illustrate that such expansion can be considered as the supplement for the convention of TOP 1 selection during the evaluation process.
Network anomaly detection with net-GAN, a generative adversarial network for analysis of multivariate time-series

We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial networks (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate time-series, exploiting temporal dependencies through RNNs. Net-GAN discovers the underlying distribution of the baseline, multivariate data, without making any assumptions on its nature, offering a powerful approach to detect anomalies in complex, difficult to model network monitoring data. We present preliminary detection results in different monitoring scenarios, including anomaly detection in sensor data, and intrusion detection in network measurements.
Depth Estimation Using Convolutional Neural Network with Transfer Learning

Predicting depth is crucial to understand the 3D geometry of a scene. While, “for stereo images, local correspondence suffices for estimation, finding depth from a single image is less straightforward, requiring integration of both global and local information”. In this chapter, we deal with depth estimation using Convolutional Neural Network (CNN) using transfer learning as a baseline. We propose a not so deep CNN, which uses transfer learning to extract low-level features. We employ a fully convolutional architecture, which first extracts low-level image features by pre-trained ResNet-50 and VGG19 network. We do transfer learning by using the initial layers of ResNet-50 and VGG19, which are connected in parallel without downsampling anywhere in the proposed architecture to recover the size of the depth map. This CNN network is able to do inference in real-time on images because it is not such a deep architecture. In this study, we have compared our approach with the pure CNN network, which illustrates the effectiveness of transfer learning. We demonstrate that our method of deep CNN with transfer learning yields a better result than one pure CNN and is faster to converge. Moreover, we have shown the influence of loss functions on training. We have compared the qualitative visualization and quantitative metrics.