Reviewerly/exHarmony huggingface dataset:
Accelerating Robust Watermarking Through Parallelization

The rapid online usage of digital images has pushed us to accelerate the watermarking method to protect it. Lately, in order to accelerate the applied science and engineering methods multicore architectures are used more and more. Therefore, at this research we have tried to increase the speed of watermarking method on GPU by CUDA. This acceleration by CUDA allows rapid embedding and extraction of the watermarks. For the sake of the fact that better methods have more computational cost, parallelization leads up to the use of a better watermarking method which has a better performance in terms of imperceptibility, robustness, and security. It assists to the rapid secure use of digital images, as well. The results show that the speeds of CUDA implementations are superior to multi cores which have been implemented by OpenMP, although, when the images are big enough OpenMP implementations show a slightly better performance than sequential implementations. The proposed method is evaluated on an NVIDIA 940MX GPU which can reduce the execution time by 50% in comparison with sequential implementations.
Online Hate Crimes Against Women (CYBER VAWG)

In this article, a review of Cyber VAWG is provided with its meaning, potential impacts, the state of legislation to address Cyber VAWG, and recommendations to improve processes and systems to tackle Cyber VAWG. A literature review of academic and nonacademic literature has been undertaken. It is found that legislation and enforcement are still in infancy and there are currently complexities and barriers that are discouraging the reporting of Cyber VAWG. The article serves as a starting point for further research which should address the actual implementation of legislative and system changes to avoid and punish (in cases where it does occur) Cyber VAWG.
A Whole Slide Ki-67 Proliferation Analysis System for Breast Carcinoma

The expression of Ki-67 with IHC stain has been utilized to assess the prognosis of breast cancer, and the degree of cellular differentiation and proliferation rate. Recently, some researchers utilize the index to predict metastasis of breast carcinoma. In traditional pathological screening, manual assessment of Ki-67 proliferative index may be limited by manual evaluation from different pathologists. Especially, inconsistent biopsy staining would affect the quantitation of Ki-67 proliferation so that developing an automatic system to assess Ki-67 proliferation index poses a big challenge. The goal of this paper is to propose an automatic analysis system to evaluate the degrees of Ki-67 proliferation on IHC stained cells of breast tissue using image processing and machine intelligence techniques. The proposed system not only can assist physicians diagnose, but also provides important information of treatment and prognosis. In order to validate the evaluation performance, we compared with visual assessments by a pathologist and the ImmnuoRatio (i.e., a web-based evaluation system in Ki-67 expression) developed by Vilppu J Tuominen et al.[1] via a number of Ki-67 stained samples for patients with breast carcinoma. Experimental results also demonstrate that the proposed system can automatically, accurately and reliably assess the Ki-67 proliferation index on the breast tissue images with a precision of around 87.37%. However, the accuracy evaluating with ImmunoRatio only can reach 75.82% with the same samples. Moreover, our proposed system also provides various interaction functions including browsing, navigation, and quantitative analyses for pathologists who evaluate the expression of the Ki-67 proliferation.
A Secure Distributed e-Health System for the Management of Personal Health Metrics Data

The definition of smart city as a broad concept values the versatile acquisition, storage, and processing of relevant data for the city’s community. In this context, health data occupies a privileged place. The reliable gathering of personal health information has become recently possible through wearable medical devices. These devices usually do not store the data locally and offer, in the most favourable case, limited basic data processing features, and virtually no advanced processing capabilities for the collected personal health data. This paper describes an integrated distributed e-Health system, which collects health data from the enrolled city residents, and allows secure storage and processing of medical data in cloud by using a comprehensive encryption model to preserve the data privacy, which is based on the NTRU public-key cryptosystem. The system collects the user data through a client application module that is installed on the user’s smartphone or smartwatch, and securely transports it to the cloud backend. The homomorphic processing of the encrypted data is performed using the Apache Spark service. The event-based handlers are triggered by the IBM OpenWhisk programming service. The prototype has been tested using a real-world use case, which involves five hundred residents of Brasov City, Romania.
Learning Global-to-local Constraint Network for Face Alignment

In this paper, we propose a global-to-local constraint network (GLCN) to improve the robustness of face alignment. Especially in the challenging cases when faces undergo large variations including severe poses and occlusions. Unlike conventional cascade regression which based on average faces to conduct regressing, which does not take full advantage of face information. Our GLCN learns a custom shape for every input images through global information. Then local adjustments will be applied for these initializations for robust feature representation. To achieve this goal, we design a local constraint network, which models the global-to-local constraint mechanism. We employ an hourglass network to learn global feature representation, and typically incorporates with five subnets to optimize facial landmarks. To further reinforce the alignment performance, we integrate our designed model in the paradigm of RNN network. Experimental results demonstrate the robustness of our GLCN versus many challenging cases especially.
Coordinate Difference Matrices

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called vector geometry problems (VGPs). A closely related field studies distance geometry problems (DGPs), where only the Euclidean distance between pairs of points is available. This has been extensively studied in the literature and is often associated with Euclidean distance matrices (EDMs). Although similar to DGPs, VGPs have received little attention in the literature; our goal is to fill in this gap and introduce a framework to solve VGPs. Inspired by EDM-related approaches, we arrange the differences in what we call a coordinate difference matrix (CDM) and introduce a methodology to reconstruct a set of points from CDM entries. We first propose a reconstruction scheme in 1D and then generalize it to higher dimensions. We show that our algorithm is optimal in the least-squares sense, even when we have only access to partial measurements. In addition, we provide necessary and sufficient conditions on the number and structure of measurements needed for a successful reconstruction, as well as a comparison with EDMs. In particular we show that compared to EDMs, CDMs are simpler objects, both from an algorithmic and a theoretical point of view. Therefore, CDMs should be favored over EDMs whenever vector differences are available. In the presence of noise, we provide a statistical analysis of the reconstruction error. Finally, we apply the established knowledge to five practical problems to demonstrate the versatility of this theory and showcase the wide range of applications covered by the CDM framework.
Deep-Learning-Based Detection of Obstacles in Transit on Trams

Due to the pervasive employment of trams, the measurements to keep the transit of trams safe are necessary and emergent. In this sense, we put forward a Neural Network based on Convolutional Neural Network, in which we made some modifications to make it more flexible. Such as passthrough layer to ensure some small objects detectable, anchor boxes to ensure a high-speed detection, and batch-normalization layers to make the network be malleable for objects with different distributions. With this network, we can efficiently detect possible obstacles, such as pedestrians, cars and some other objections that may endanger the trams. We test the network among several databases with 5000 samples, and the average accuracy rate is 94.12%, the average detecting speed is 30 FPS, the smallest detectable object’s size is 20 × 20 pixels, these all show remarkable result.
Shape Signature for Recognizing Human Emotions

In the previous chapter, we discussed how distance signature could be used efficiently as a descriptor for facial expression recognition. After finding the distances from the grid, we see that many triangles are formed within the considered grid. This is another idea of facial expression recognition.
Power Combination Network for Image Classification on Small Samples with Data Loss

Neural networks with deeper convolutional layers are difficult to train. Improving information flows helps to simplify the training process and achieve the better accuracy. In this study, we propose a power combination network, which called PowNet. The PowNet uses fewer convolution layers than other networks before. We consider the flow of information in and between layers, rather than just maximizing the flow of information between layers. In each layer, we divide the feature maps into partitions, and use intra-layer power combination to learn the relationship between partitions to get the feature of each layer. We employ inter-layer power combination to learn the relationship between the features in different level and get the output. This comprehensive combination of structures allows the networks to learn more global information and optimize the propagation of gradients. Therefore, the networks using fewer convolutional layers can have better performance in unfavorable training or test environments. Furthermore, the networks can decrease the generalization errors. In the CIFAR-10 dataset, we show that networks using intra-layer and inter-layer power combinations can use fewer convolutional layers to achieve the similar classification accuracy. We separately evaluated the performance of PowNets and other networks in the original dataset, small samples training, and data loss, including training accuracy, test accuracy and error between them. It has shown that networks using power combination had more advanced performance. To verify the power combination's ability in reducing generalization errors, we also performed experiments in STL-10. The similar phenomenon was obtained.
Learning Tibetan-Chinese Cross-Lingual Word Embeddings

The idea of Word Embedding is based on the semantic distribution hypothesis of the linguist Harris (1954), who believes that words of the same semantics are distributed in similar contexts. Learning of vector-space word embeddings is a technique of central importance in natural language processing. In recent years, cross-lingual word vectors have received more and more attention. Cross-lingual word vectors enable knowledge transfer between different languages, the most important It is this transfer that can take place between resource-rich and low-resource languages. This paper uses Tibetan and Chinese Wikipedia corpus to train monolingual word vectors, mainly using the fastText word vector training method, and the two monolingual word vectors are analyzed by CCA correlation, thus obtaining Tibetan-Chinese cross-lingual word vectors. In the experiment, we evaluated the resulting word representations on standard lexical semantic evaluation tasks and the results show that this method has a certain improvement on the semantic representation of the word vector.
A Two-Stage Architecture for Differentially Private Filtering

This chapter presents an architecture generalizing the input and output mechanisms to process dynamic data streams while enforcing differential privacy. A privacy-sensitive signal that we want to process in order to publish real-time statistics is first shaped by certain pre-filter, then perturbed to obtain a differentially private signal, and finally post-filtered to mitigate the effect of the noise and the pre-filter. A general methodology is provided for the design of such two-stage architectures, and an example demonstrates the significant performance improvements achievable over the input and output mechanisms.
PENGARUH PENERAPAN SKS, MOTIVASI EKSTRINSIK, DAN DISIPLIN BELAJAR TERHADAP PRESTASI BELAJAR EKONOMI SISWA KELAS XI IPS SMAN 3 PURWOKERTO

Abstract&#x0D; The purpose of the study was to analyze the effect of applying credits, extrinsic motivation, and learning discipline on economic learning achievement in class XI IPS SMAN 3 Purwokerto. This research uses a quantitative approach with a survey method. The population of this research was all students of class XI of SMAN 3 Purwokerto. To analyze hypotheses using multiple linear regression analysis. The Conclusions are: 1. The application of the Semester Credit System (SKS) does not influence the economic Significantly learning achievement of students of class XI IPS SMAN 3 Purwokerto; 2. extrinsic motivation has a positive effect on students' achievement of economic learning in class XI IPS SMAN 3 Purwokerto; 3. Learning discipline has a positive economic effect on the learning achievement of students of class XI IPS SMAN 3 Purwokerto; 4.&#x0D; Keywords: SKS application, extrinsic motivation, learning discipline, Learning Achievement.
Error feedback sampling for CNN training in upper gastrointestinal diseases classification

With the continuous development of deep learning technology, the application scope of convolutional neural network (CNN) in image recognition is expanding. The classification of medical images has become a new application field of CNN. How to quickly train the CNN with high accuracy has become an important problem in the process of training. The training method based on error feedback sampling effectively improves the training efficiency by adding the error samples in each epoch to the next epoch while training. This method obtains a higher accuracy rate than the general sampling method in the test of upper digestive tract diseases classification.
Forecasting of Product Quality Through Anomaly Detection

Forecasting of product quality by means of anomaly detection is crucial in real-world applications such as manufacturing systems. In manufacturing systems, the quality is assured through tests performed on sample units randomly chosen from a batch of manufactured units. One of the major issues is to detect defective units among the sample test units as early as possible in terms of test time and of course as accurate as possible. Traditional way of detecting defective units is to make use of human experts during test. However, human intervention is prone to errors and it is time consuming. On the other hand, automated systems are efficient alternatives and of assistance to human experts. There are on-line and off-line approaches for automated systems. Our ultimate aim is to design a system that automates the detection of defective units among the sampled freezer units manufactured in high volumes in a factory of one of the leading home appliances manufacturers. We start by analyzing the data of the test units sampled from the batches of freezer units. For analysis, we first embedded data in two-dimensional space to observe if there are any structures exist in the data. Clustering was then applied to see if the data can be grouped into two classes without their labels. As off-line approaches, state-of-the-art classifier methods including one-class-classifier are employed. Finally, a deep learning method for time-series analysis combined with a classifier is applied as an on-line method.
Two Efficient Image Bag Generators for Multi-instance Multi-label Learning

Image annotation plays a vital role in dealing with effective organization and retrieval of a large number of digital images. Multi-instance multi-label (MIML) learning can deal with complicated objects by solving the ambiguity in both input and output space. Image bag generator is a key component of MIML algorithms. A bag generator takes an image as its input and generates a set of instances for that image. These instances are the various subparts of the original image and collectively describe the image in totality. This paper proposes two new bag generators which can generate an instance for every possible object present in the image. The proposed bag generators effectively utilize the correlations among pixels to generate instances. We demonstrate that the proposed bag generators outperform the state-of-the-art bag generator methods.
Human Emotion Recognition from Distance-Shape-Texture Signature Trio

The previous Chaps. 2 , 3 , and 4 consist of feature descriptors individually distance signature, shape signature, and texture signature and Chaps. 5 , 6 , and 7 are considered as combined descriptors such as distance and shape (D-S), distance and texture (D-T), and shape and texture (S-T). In course of Distance-Shape-Texture (D-S-T) signature trio respective stability indices and statistical measures supplement the signature features with a view to enhance the performance task of facial expression classification. Incorporation of these supplementary features is duly justified through extensive study and analysis of results obtained thereon.
Strategy Reasoning in the Multi-agent Systems

Recently, the popular logical frameworks on strategy specification and verification in the multi-agent concurrent systems, such as alternating-time temporal logic and Strategy Logic (SL), mainly focus on the strategic abilities of one coalition. In them, the notion of strategy is just viewed as a pure syntax and/or semantics object, we cannot represent or reason about the internal structures of strategies, let alone strategic abilities under these ones. In this paper, based on linear dynamic logic, by extending SL, we propose a new strategic logic, named Dynamic Strategy Logic (DSL), which includes the existential/ universal quantification operators about regular expressions with test expressions. By introducing action atoms, we can specify both the evolution of time and structured strategies by means of regular expressions. Hence, in this logic, we can represent and reason about much more expressive strategic abilities under structured strategies of coalitions, and prove that DSL is more expressive than SL.
Eye Movements Recognition Using Electrooculography Signals

Nowadays, the number of people who suffer from severe motor disabilities is increasing remarkably. This disability gradually prevents them from moving all their limbs and they can communicate with their external environment only through the movement of their eyes. Hence, Human Computer Interfaces (HCI) have come out to provide those people with new communication way based on detecting eye movements. Eye movements are recorded by Electrooculogram (EOG). These signals are captured by placing electrodes horizontally and vertically around the eyes. In this work, the five eye movements left, right, up, down and blinking are classified by investigating both horizontal and vertical EOG signals. Statistical and geometrical features are extracted from EOG signals after applying Discrete Wavelet Transform (DWT). Six classifiers are examined in this study using both horizontal and vertical EOG features. The experimental results show the superiority of Naïve Bayes classifier.
Risk Management of E-Commerce Security in Cloud Computing Environment

The emergence of cloud computing technology has changed the traditional business economic model and provided convenience for the application of e-commerce in the financial field. This paper uses cloud computing as the entry point to analyze the security of e-commerce in a cloud computing environment in detail. It also proposes relevant solutions to how e-commerce can ensure the security of its own data in the context of cloud computing. At the same time, it also pays attention to reducing the user's perception of information security risks from the user's perspective, and improving the user's trust, satisfaction and loyalty to the information system.
Differentially Private Filtering for Stationary Stochastic Collective Signals

This chapter builds on the two-stage architecture for differentially private filtering, and presents mechanisms with better performance than the zero-forcing equalization mechanism, for the situation where we have some knowledge about the statistics of the privacy-sensitive input signals, which moreover are assumed to be stationary. The mechanisms described use as second stage in the architecture a Wiener filter, and the performance of the overall mechanism is then optimized, following the general methodology outlined in Chap. 3 .
Healthcare Outlier Detection with Hierarchical Self-Organizing Map

The amount of data in healthcare repositories along with their high dimensionality nature, requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends and relationships embedded in that data. Historically, healthcare entities in both public and private sectors have relied upon business rules and outlier models to mine claims data for fraud, waste and abuse patterns. We propose the use of Hierarchical Self-Organizing Map (HSOM) algorithm to perform clustering analysis, dimensionality reduction and outlier detection in healthcare data. HSOM provides an appropriate framework to perform the clustering task based on individual types of data and is more powerful and sensitive than standard Self-Organizing Map (SOM) for outlier detection.
Study on Correctness Judgement of Handwritten Chinese Characters Based on Feature Matrix for Similarity Matching

With the unprecedented development of Computer Aided Instruction, integrating information technology into the teaching of Chinese character writing has become a trend. As an important part of Chinese character writing teaching supported by mobile platform, the correctness of judgment automatically plays an important role. On this basis, this paper designs and develops a method for judging the correctness of handwritten Chinese characters based on feature matrix. It firstly extracts the stroke features which includes stroke orientation, stroke length, absolute position and combination relationship of the stroke, then similarity matching is achieved by the feature matrix. This method can realize the one-to-one correspondence between the user's handwritten Chinese strokes and the standard ones, making the whole character correctness judgement and the specific error strokes and error types locating possible, which can be applied to Chinese character writing training and teaching.
Modal Logics of Some Hereditarily Irresolvable Spaces

A topological space is hereditarily k-irresolvable if none of its subspaces can be partitioned into k dense subsets. We use this notion to provide a topological semantics for a sequence of modal logics whose n-th member K4\(\mathbb {C}_n\) is characterised by validity in transitive Kripke frames of circumference at most n. We show that under the interpretation of the modality \(\Diamond \) as the derived set (of limit points) operation, K4\(\mathbb {C}_n\) is characterised by validity in all spaces that are hereditarily \(n+1\)-irresolvable and have the T\(_D\) separation property. We also identify the extensions of K4\(\mathbb {C}_n\) that result when the class of spaces involved is restricted to those that are crowded, or densely discrete, or openly irresolvable, the latter meaning that every non-empty open subspace is 2-irresolvable. Finally, we give a topological semantics for K4M, where M is the McKinsey axiom.