Reviewerly/exHarmony huggingface dataset:
Image Security Enhancement using DCT &amp; DWT Watermarking Technique

The success of internet technology transform the world of technology and fashioned our life such a lot easier. The matter of duplication and unauthorized use of information become a great threat within the field of technology. To beat these problems, techniques like digital watermarking, steganography and cryptography were introduced. The approach of embedding a secret data associated with the digital signal inside the signal itself is digital watermarking. For embedding and detecting the watermark different techniques are used; spatial domain techniques like Least Significant Bit (LSB) and Patch Work Algorithm, then the frequency domain techniques such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) are some of them. Combination of DCT & DWT technique for digital watermarking is proposed here. The proposed methodology is implemented in MATLAB 2017a simulator and result is analysed using evaluation parameters like Peak Signal to Noise Ratio (PSNR) & Mean Square Error (MSE). PSNR value obtained for the case of watermarking using DWT is 27.46 and MSE value is 17.45. The value of PSNR for watermarking using DCT is 41.4 and for MSE the value is 0.67. The PSNR and MSE values obtained for watermarking using LSB technique is 50.55 and 0.58. The proposed method improves the watermarked image quality and the MSE & PSNR value obtained is 0.52 and 51.017 respectively.
Event-Driven Framework for Detecting Unusual Patterns in AAL Environments

An aging population has motivated research into Ambient Assisted Living (AAL) with the aim of supporting people to continue to live in their homes as they age or with chronic health problems. As part of this work, some researchers have focused on identifying and reporting daily activities of individuals at home in order to try to reduce the workload on caregivers and health professionals. Such an environment is usually monitored by non-wearable sensors that collect a vast amount of data. The use of this data requires computational methods that can process it in a reasonable time. This paper proposes an event-driven framework to detect unusual patterns in AAL environments. A Fog-Cloud paradigm and Lambda architecture are adopted as the framework to support computational solutions to deal with the volume of data, and machine learning techniques are used for recognition of daily activities. The framework was evaluated through a case study based on data collected in a real environment. Results point to the feasibility of the proposal.
Vividness of news push notifications and users’ response

With the media environment becoming increasingly digital, the shift to online news has weakened the direct relationship between publishers and readers (Newman et al., 2019). The current challenge for news media, therefore, is to build traffic towards both their apps and web sites. The most popular means in meeting such a challenge is through the effective use of "push notifications." This paper analyzes how vivid push notifications can influence the click-through rate. It proposes that vivid (as opposed to "not vivid") wording of headlines will have a positive influence on click-through rates. The use of vivid images will also have a positive influence on click-through rates. Additionally, this paper proposes that vivid images will moderate the relationship between vivid headline text and click-through rate only when they are congruent with the central theme of the news item. In order to test this moderation model, we conducted quasi-experimental field research, using a random sample of 770 news push notifications from mainstream media, on the subject of general affairs. Data were analyzed using model 3 of the PROCESS macro. The results support the hypothesis and contribute to the scant literature on push notifications in various areas (notably in news media), by providing a better understanding of the underlying mechanism explaining user response: the vividness evoked by these messages contributes to users willing to deepen the content of the news. It also discusses practical implications, adding to the growing literature on mobile news effects.
A Predictive Analytics Framework to Anomaly Detection

Online fraudulent activities cost the global economy billions of dollars each year. In the world of big data from large number of transactions, detecting and preventing anomaly cases like fraudulent activities is an essential ingredient to success not only to big banks, insurance companies, small businesses, but also to consumers. Forms and levels of sophistication of online fraud are constantly changing, hence there is a need for a flexible framework that can predict and adjust to new fraudulent cases. One major problem in building a predictive model for anomaly detection, is the scarcity of fraudulent data records in comparison to non-fraudulent, which makes the training data imbalanced. In this paper, we present a comprehensive predictive analytics framework that aims at detecting anomaly cases and most importantly mitigating the problem of imbalanced datasets in training anomaly detection models. The framework is developed through experimentation of an ensemble of sampling algorithms, feature engineering methods, and an array of machine learning algorithms. The framework presented in this study provides a data-driven approach applied to financial credit card data and can be adapted to other domains where deviations from what is normal occur in large datasets.
Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification

In multi-label classification problems, multiple class labels are assigned to each instance. Two approaches have been studied in the literature. One is a data transformation approach, which transforms a multi-label dataset into a number of singlelabel datasets. However, this approach often loses the correlation information among classes in the multi-class assignment. The other is a method adaptation approach where a conventional classification method is extended to multi-label classification. Recently, some explainable classification models for multi-label classification have been proposed. Their high interpretability has also been discussed with respect to the transparency of the classification process. Although the explainability is a well-known advantage of fuzzy systems, their applications to multi-label classification have not been well studied. Since multi-label classification problems often have vague class boundaries, fuzzy systems seem to be a promising approach to multi-label classification. In this paper, we propose a new multiobjective evolutionary fuzzy system, which can be categorized as a method adaptation approach. The proposed algorithm produces nondominated classifiers with different tradeoffs between accuracy and complexity. We examine the behavior of the proposed algorithm using synthetic multi-label datasets. We also compare the proposed algorithm with five representative algorithms. Our experimental results on real-world datasets show that the obtained fuzzy classifiers with a small number of fuzzy rules have high transparency and comparable generalization ability to the other examined multi-label classification algorithms.
Security impacts of sub-optimal DevSecOps implementations in a highly regulated environment

This work presents lessons learned from a multi-year support effort of a large and well-funded software development project. The focus is on the security impacts to the DevSecOps culture, process, and pipeline. These impacts stem from faulty implementations of requirements in order to achieve a full DevSecOps environment. The faulty implementations resulted in a lax security posture facilitating potential compromise in many areas of the software development environment. We discuss each of the faulty implementations in detail and provide recommendations to avoid in future engagements. The main lesson learned was the organization's inability to strictly adhere to DevSecOps principles resulted in a dysfunctional software development environment and a reduced security posture.
Self-Driving Car Safety Quantification via Component-Level Analysis

In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how separate perception system and operational design domain statistical analyses can be used to prove or disprove safety at the vehicle level.
Dagger: Towards Efficient RPCs in Cloud Microservices With Near-Memory Reconfigurable NICs

Cloud applications are increasingly relying on hundreds of loosely-coupled microservices to complete user requests that meet an application's end-to-end QoS requirements. Communication time between services accounts for a large fraction of the end-to-end latency and can introduce performance unpredictability and QoS violations. This letter presents our early work on Dagger, a hardware acceleration platform for networking, designed specifically with the unique qualities of microservices in mind. The Dagger architecture relies on an FPGA-based NIC, closely coupled with the processor over a configurable memory interconnect, designed to offload and accelerate RPC stacks. Unlike the traditional cloud systems that use PCIe links as the NIC I/O interface, we leverage memory-interconnected FPGAs as networking devices to provide the efficiency, transparency, and programmability needed for fine-grained microservices. We show that this considerably improves CPU utilization and performance for cloud RPCs.
Interactive sound propagation for dynamic scenes using 2D wave simulation

Abstract We present a technique to model wave‐based sound propagation to complement visual animation in fully dynamic scenes. We employ 2D wave simulation that captures geometry‐based diffraction effects such as obstruction, reverberation, and directivity of perceptually‐salient initial sound at the source and listener. We show real‐time performance on a single CPU core on modestly‐sized scenes that are nevertheless topologically complex. Our key ideas are to exploit reciprocity and use a perceptual encoding and rendering framework. These allow the use of low‐frequency finite‐difference simulations on static scene snapshots. Our results show plausible audio variation that remains robust to motion and geometry changes. We suggest that wave solvers can be a practical approach to real‐time dynamic acoustics. We share the complete C++ code of our “Planeverb” system.
A Novel SDN-Based Application-Awareness Mechanism by Using Deep Learning

With the rapid development of the Internet of Things (IoT) and smart cities, more and more types of applications have been emerging. In fact, different applications have different features and different requirements on services. In order to satisfy users' Quality of Service (QoS) requirements, the application-awareness technique should be leveraged to distinguish different applications for providing the differentiated services. However, the traditional Internet only can obtain the local network view, which belongs to the offline awareness mode and cannot adapt to the dynamical network environment. At the right time, Software-Defined Networking (SDN) has been accepted as a new networking paradigm thanks to its network awareness on the global status information, which can greatly facilitate the online application-awareness. At present, three ways, i.e., port number, depth packet inspection and deep learning can be used for the application-awareness. To the best of our knowledge, the deep learning based application-awareness method is the most cutting-edge technique. In spite of this, the previous related schemes fail to effectively guarantee the correctness and stability. To this end, this paper proposes a Convolutional Neural Network (CNN) based deep learning mechanism to do the application-awareness, including three phases, i.e., traffic collection, data pre-processing and application-awareness. The SDN environment is implemented based on the MiniNet and the simulation experiments are made based on the TensorFlow. The experimental results show that the proposed application-awareness mechanism outperforms three benchmarks on recall ratio, precision ratio, F value and stability.
Performance Comparison Between Deep Learning-Based and Conventional Cryptographic Distinguishers

While many similarities between Machine Learning and cryptanalysis tasks exists, so far no major result in cryptanalysis has been reached with the aid of Machine Learning techniques. One exception is the recent work of Gohr, presented at Crypto 2019, where for the first time, conventional cryptanalysis was combined with the use of neural networks to build a more efficient distinguisher and, consequently, a key recovery attack on Speck32/64. On the same line, in this work we propose two Deep Learning (DL) based distinguishers against the Tiny Encryption Algorithm (TEA) and its evolution RAIDEN. Both ciphers have twice block and key size compared to Speck32/64. We show how these two distinguishers outperform a conventional statistical distinguisher, with no prior information on the cipher, and a trail distinguisher based on the differential trails presented by Biryukov and Velichkov at FSE 2014. We also present some variations of the DL-based distinguishers, discuss some of their extra features, and propose some directions for future research.
A Neural NLP toolkit for Greek

We present a neural NLP toolkit for Greek, currently integrating modules for POS tagging, lemmatization, dependency parsing and text classification. The toolkit is based on language resources including web crawled corpora, word embeddings, large lexica, and corpora manually annotated at different levels of linguistic analysis. We show how we integrate these diverse resources in the development of the toolkit, achieving high accuracies on held-out evaluation data for each module.
A Soft Sensor Approach Based on an Echo State Network Optimized by Improved Genetic Algorithm

In the process of fault diagnosis and the health and safety operation evaluation of modern industrial processes, it is crucial to measure important state variables, which cannot be directly detected due to limitations of economy, technology, environment and space. Therefore, this paper proposes a data-driven soft sensor approach based on an echo state network (ESN) optimized by an improved genetic algorithm (IGA). Firstly, with an ESN, a data-driven model (DDM) between secondary variables and dominant variables is established. Secondly, in order to improve the prediction performance, the IGA is utilized to optimize the parameters of the ESN. Then, the immigration strategy is introduced and the crossover and mutation operators are changed adaptively to improve the convergence speed of the algorithm and address the problem that the algorithm falls into the local optimum. Finally, a soft sensor model of an ESN optimized by an IGA is established (IGA-ESN), and the advantages and performance of the proposed method are verified by estimating the alumina concentration in an aluminum reduction cell. The experimental results illustrated that the proposed method is efficient, and the error was significantly reduced compared with the traditional algorithm.
Interval-Valued Fuzzy c-Means Algorithm and Interval-Valued Density-Based Fuzzy c-Means Algorithm

Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the membership value of the fuzzy set as an interval. IVFS assumes that membership interval can better represent the uncertainty in the data. Accordingly, IVFS can be used to obtain good clustering results since it can represent the uncertainty more appropriately. Thus, this paper proposes the interval-valued fuzzy c-means algorithm (IVFCM) which uses IVFSs to represent the data. The concept of the proposed IVFCM is then extended to introduce the interval-valued density based fuzzy c-means (IVDFCM) algorithm based on the distance measure of IVFSs. Both IVFCM and IVDFCM are simulated over various UCI benchmark datasets to show their suitability and supremacy over their existing counterparts.
A theoretical justification of warping generation for dewarping using CNN

Dewarping is a necessary preprocessing step to recognize text from a distorted camera captured document image. According to recent literature, deep learning-based approaches perform with higher accuracy in similar domains. The deep learning-based neural networks are not yet fully explored in the domain of dewarping. To fill this gap, we propose a dewarping approach based on the convolutional neural network. A large number of images are required to train such networks. However, it is a tedious job to capture such a large number of images. Hence, it is required to generate synthetic warped images for the training phase of the deep learning-based neural network. The existing synthetic warped image generation methods are heuristic-based. In this paper, we propose a novel mathematical model for the generation of warped images. The proposed model takes some parameters such as depth of the surface, camera angle, and camera position and generates the corresponding warped image. These parameters are the ground truth for that particular warped image. We use a Convolutional Neural Network (CNN) based model to estimate the warping parameters from a 2D warped image for dewarping. In the training phase of CNN based model, the synthetic images and their corresponding ground truth are used. Next, the trained model is used to dewarp the unknown warped images. The performance of the proposed warping model is analyzed. Finally, the proposed dewarping method is compared with existing approaches. In both cases, the results are encouraging.
A New Supervision Strategy based on Blockchain for Electronic Health Records

According to Medical Statistics conducted in the USA, the number of patients who die every year from Medical errors are estimated to be around 200 to 400 thousand. The most common types of medical mistakes are (Billing errors, incorrect medication/incorrect dosage). Moving towards Electronic Health Records (EHR) instead of the paper-based system can prevent Medical errors. The (EHR) faces challenges regarding the issues of security and privacy. Utilizing blockchain can provide the proper solution to the above-stated challenges. This paper aims to introduce a new supervising strategy regarding accessing, sharing, and storing the (EHR). The proposed strategy enhances the security in a distributed network environment, through applying blockchain technology and hash table. Furthermore, it is applying a newly developed collision-resistant hash function in generating a unique ID for each patient in the EHR System. The Proposed scenario will improve the overall performance of the EHR system making it more efficient and reliable than the traditional system.
Eye fixations and electrodermal activity during low‐budget virtual reality embodiment

Summary As motion‐sensing devices become more accessible to consumers, it is important to understand how users experience embodiment when using these devices. In our 3 × 2 between‐groups study, we explored eye fixations and electrodermal activity (EDA) in order to more objectively understand potential interaction effects between the self‐avatar body, and the presence of a mirror within the context of low‐budget embodiment. We developed six experimental conditions concerning Body (human, mannequin, and zombie self‐avatars) and Mirror (mirror and no mirror) factors, and presented participants with a virtual environment in which they could control their self‐avatars by using HTC Vive controllers and trackers. In addition to eye fixations and EDA, we assessed self‐reported data concerning body ownership, agency, self‐location, as well as enjoyment of the experience. Our results suggest that the Body may have been more influential in eliciting body ownership than the Mirror , and that an interaction effect between Gender and Body may influence eye gaze behavior. Additionally, female participants reported significantly higher agency than males. We consider logical next steps for similar research which might elaborate upon our findings.
Deep Learning Technique Based Surveillance Video Analysis for the Store

AI technology has developed so fast, and it has been applied to the commercial area. In order to predict the customer preference and adjust the placement of product or advertisement, etc., the intelligent surveillance video analysis technique has been proposed to gather the sufficient customer information and realize crowd counting and density map drawing. In this paper, a series of deep learning techniques are adopted to realize surveillance video analysis. This work covers different subproblems such as object detection, tracking and human identification. A skeleton recognition algorithm is adopted instead of object detection algorithm to overcome the severe occlusion problem. A multiple human tracking algorithm combing the human re-identification technology is adopted to realize the human tracking and counting. Finally, the density map and statistics information are obtained which can be used to evaluate and adjust the current business plan. A real store surveillance video is analyzed by the algorithm, and the results show the advantage of the algorithm.
An Approach to BI-RADS Uncertainty Levels Classification Via Deep Learning with Transfer Learning Technique

This work combines the transfer learning technique with Convolutional Neural Networks (CNN) to classify the pathology within BI-RADS levels 3 and 4 for malignancy of breast masses. These BI-RADS levels represent the zone of the uncertainty of the degree of malignancy of the found mass, making it difficult for the human experts in classifying as malignant or benign. Eleven CNN architectures were used as feature extractors and combined with four traditional classification models: Bayes, Multilayer Perceptron (MLP), Support Vector Machines, and Random Forest. The combination DenseNet201-MLP achieved an accuracy higher than 63%, surpassing the performance of a human expert by 9.0%.
Arduino-controlled Reflectance Transformation Imaging to the study of cultural heritage objects

This article examines the development of a low-cost and portable set-up controlled by an Arduino board to perform Reflectance Transformation Imaging technique, from the information derived from 45 digital photographs of an object acquired using a stationary camera. The set-up consists of 45 high-intensity light emitting diodes (LEDs) distributed over a hemispherical dome of 70 cm in diameter and a digital camera on the top of the dome. The LEDs are controlled by an Arduino board, and the user can individually control the LEDs state (ON or OFF) and duration of illumination. An old manuscript written with iron-gall ink and a set of 1 Euro coins mint in 2002 were photographed with the set-up. The interactive re-lighting and the mathematical enhancement of the object's surface revealed corrosion, loss of material, scratches and other details, which were not perceived in standard images. These unique features, which can be extracted using edge detection processing, have immediate application in different fields such as cultural heritage or forensic studies, where they can be used as fingerprints to identify unique objects, allowing also recognizing the use of tools to alter the surface of coins to increase the price in the market.