Reviewerly/exHarmony huggingface dataset:
Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

Mammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task. Basically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images. Reported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate. Reported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.
Design of an Automatic High Precision Solar Tracking System with an Integrated Solar Sensor

A sustainable powered standalone automatic Solar Tracking System is designed and successfully simulated to provide the best alignment of solar panel with position of the sun automatically, to extract an increased efficiency by 40 percent. A very low cost (approx. 5 USD), high precision Solar Tacking Sensor has been designed to rotate the solar panel coupled to the stepper motor rotates 25.70 degree at single step and to tracks the whole 180-degree trace in 8 (eight) steps per day with greater accuracy. Microcontroller (ATmega16) is used as main control unit where its ADC ports are used to interface the Sensor unit and ULN2000A motor driver is used to interface the stepper motor to rotate the solar panel at maximum solar energy angle. The results found from the simulation and analysis shows that, the system required 12.04 mA and 37.163 mA current, and 144.48 mW and 445.956 mW power, during standby and panel rotating mode respectively. The maximum current drawn by the Solar Tracking Sensor is less than 0.5 mA. ‘Proteus ISIS 7.7 professional’ is used for design and simulation, and ‘Code Vision AVR v2.5’ is used to write the program code and burning into the ATmega16 for simulation and analysis.
A Framework for Distributed Deep Neural Network Training with Heterogeneous Computing Platforms

Deep neural network (DNN) training is generally performed by cloud computing platforms. However, cloud-based training has several problems such as network bottleneck, server management cost, and privacy. To overcome these problems, one of the most promising solutions is distributed DNN model training which trains the model with not only high-performance servers but also low-end power-efficient mobile edge or user devices. However, due to the lack of a framework which can provide an optimal cluster configuration (i.e., determining which computing devices participate in DNN training tasks), it is difficult to perform efficient DNN model training considering DNN service providers' preferences such as training time or energy efficiency. In this paper, we introduce a novel framework for distributed DNN training that determines the best training cluster configuration with available heterogeneous computing resources. Our proposed framework utilizes pre-training with a small number of training steps and estimates training time, power, energy, and energy-delay product (EDP) for each possible training cluster configuration. Based on the estimated metrics, our framework performs DNN training for the remaining steps with the chosen best cluster configurations depending on DNN service providers' preferences. Our framework is implemented in TensorFlow and evaluated with three heterogeneous computing platforms and five widely used DNN models. According to our experimental results, in 76.67% of the cases, our framework chooses the best cluster configuration depending on DNN service providers' preferences with only a small training time overhead.
Automatic Decomposition of IoT Aware Business Processes – a Pattern Approach

The automatic decomposition of Internet of Things (IoT) aware business processes aims at transferring business logic to IoT devices, in order to reduce both the exchanged messages and the battery usage of such devices. Existing approaches are concerned with block structured processes or ignore data flow dependencies, which cannot be easily applied to processes modelled using the Business Process Model and Notation (BPMN). In this paper, we extend our previous proposal by generalizing the patterns we use to identify the parts of the BPMN processes that IoT devices can execute, and to define the decomposition transformation, assuring that decomposed models maintain the flow dependencies of their original counterparts. This generalization allows for the application of the patterns in more situations than our previous work, further reducing the number of exchanged messages and increasing battery lifespan.
Pixel Value Difference Based Image Steganography with One Time Pad Encryption

Pixel value differencing (PVD) and Least Significant Bit (LSB) embedding are well known spatial domain steganographic techniques. PVD utilizes the sharp changes of intensities among adjacent pixels where a large number of secret bits could be embedded without any perceptible change. one-time pad (OTP) symmetric cryptography is known for its security. In the proposed scheme one or more LSB bits of the selected pixels are used depending on the pixel intensity difference with neighboring pixels in 2 × 2 image blocks of the cover image. Secret bits are encrypted using OTP with randomly generated pre-shared key. Such encrypted bits are completely random and resemble noise hence make the scheme robust against different statistical attacks. Comparative simulations with some well-known PVD-based techniques show good results in terms of visual imperceptibility and different quality metrics such as MSE, PSNR, SSIM etc.
Adaptive Speech Information Hiding Method Based on K-Means

In secure communication, finding bits that used for steganography is essential for effective and high-quality speech information hiding. This paper presents an adaptive speech information hiding method based on K-means, which can quickly find hidden bits. This method uses K-means algorithm to detect effective hidden bits through clustering and embeds secret information with different matrices to achieve good efficiency utilization of hidden bits. This proposed method embeds secret information according to the clustering result and the matrix coding at the transmitting end and extracts the secret information according to the corresponding embedding matrix ate the receiving end. Thereby implementing secure communication over VoIP. The experimental results indicate that the proposed method has better speech quality (PESQ is reduced by about 1.5%) while achieving a higher embedding capacity (up to 976 bit/s), and it shows strong concealment.
Generative Correlation Discovery Network for Multi-label Learning

The goal of Multi-label learning is to predict multiple labels of each single instance. This is a challenging problem since the training data is limited, long-tail label distribution, and complicated label correlations. Generally, more training samples and label correlation knowledge would benefit the learning performance. However, it is difficult to obtain large-scale well-labeled datasets, and building such a label correlation map requires sophisticated semantic knowledge. To this end, we propose an end-to-end Generative Correlation Discovery Network (GCDN) method for multi-label learning in this paper. GCDN captures the existing data distribution, and synthesizes diverse data to enlarge the diversity of the training features; meanwhile, it also learns the label correlations based on a specifically-designed, simple but effective correlation discovery network to automatically discover the label correlations and considerately improve the label prediction accuracy. Extensive experiments on several benchmarks are provided to demonstrate the effectiveness, efficiency, and high accuracy of our approach.
Trust Modeling for Blockchain-Based Wearable Data Market

Wearable devices continuously produce physiological data that can provide individuals critical information about their daily routine or fitness level in combination with their smartphones without requiring manual calculations or maintaining log-books. Real-time participant-generated data can enable large scale observational studies of health conditions, provide better insights into medical conditions of individuals and streamline clinical trial processes in medical research. However, privacy is a major concern for health data and there can be a lack of trust among different parties in the health data collection process. In addition, individuals often do not have sufficient control over the sharing of their data from the wearable devices. The lack of control, trust and privacy are key barriers to research participants being prepared to share their personal data from wearable devices. In this work, we propose a trust model to overcome the trust deficit among different parties. Then, we present a reference system architecture, rooted on the developed trust model, that provides incentive for individuals to securely share their health data through a data marketplace. By encouraging individuals to share their real-time health data, researchers will have access to large data sets at low cost.
Software Bug Categorization Technique Based on Fuzzy Similarity

Categorization of software bugs is an important task in software repository mining. Most of the information about the software bugs are in textual form, and it is difficult to categorize these bugs into a particular category as the some of the terms present in the software bugs can be common to multiple categories. Fuzzy similarity technique can be utilized to identify the belongingness of these bugs into different categories. In this paper, a binary software bug categorization technique using fuzzy similarity measure is proposed to classify the bugs as bugs or non-bugs. The fuzzy similarity of a software bug is computed and based on a user-defined threshold value the bug can either be assigned to bug or non-bug category. Experiments are performed on available software bug data sets and performance of proposed fuzzy similarity based classifier is evaluated using the parameters accuracy, F-measure, precision, and recall. The proposed algorithm is also compared with the existing standard machine learning algorithms.
Research On Aircraft Virtual Assembly Technology Based On Gesture Recognition

With the continuous development of computer, virtual simulation and human-computer interaction technology, virtual reality technology has been continuously improved and matured. Especially in recent years, with the intelligentization of mobile phones and the birth of VR glasses and helmets, the research of virtual reality technology has formed in various industries around the world. VR technology has much lower cost and difficulty in creating virtual scenes than building real world.. This article selects HTC Vive pro kit and Leap Motion as the hardware platform, and uses Unity3D and related plug-ins and Leap Motion plug-in as the software platform to develop a set of aircraft virtual equipment maintenance system based on gesture recognition. In the process of gesture recognition, the theory of machine learning was introduced to model, train and identify the gesture information collected by Leap Motion sensor. It can be equipped with the correct virtual tool in the virtual scene to complete the assembly of the corresponding components, and has a good sense of immersion.
Predicting Personal Susceptibility to Phishing

Phishing is a confidence trick with damaging impacts on both individuals and society as a whole. In this paper, we examine the possible role of thinking styles, as assessed by the Cognitive Reflection Test (CRT), and other factors to predict personal susceptibility to phishes. We report the results of two large-scale national studies conducted on cross-sectional populations in Norway. Using a binary logistic regression method, we analyzed the relationship between CRT scores, willingness to share data and demographical variables, to susceptibility to comply with phishes. Our main finding was that both an intuitive thinking style, as operationalized by the CRT scores, and willingness to share personal, significantly predict the probability of falling for phishing. As these results are based on two large-scale studies of national populations, they can be expected to have greater validity than earlier studies. The finding that CRT scores and other personal characteristics can predict the likelihood of falling for phishing suggests methods of pre-emptive testing of individuals as part of private and organizational strategies for encouraging improved resistance to phishing and other forms of personal data theft.
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Edge machine learning involves the deployment of machine learning algorithms at the network edge so as to leverage massive mobile data and distributed computation resources. Many edge learning frameworks (e.g., federated learning) have been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for aggregation for updating a global AI model. Since each gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead may become a bottleneck for edge learning. In this work, we propose a novel gradient compression scheme to reduce the aforementioned overhead. Specifically, in the proposed scheme, the norm of the stochastic gradient is quantized using a uniform quantizer while the normalized stochastic gradient is decomposed into block gradients. A Grassmannian codebook is applied to quantizing each normalized block gradients. Their quantized versions are assembled using a so-called hinge vector, which is quantized using another Grassmannian codebook. Furthermore, a practical bit-allocation strategy is developed. By simulations, we show that similar learning performance can be achieved with substantially lower communication overhead as compared to the one-bit scalar quantization schemes used in the state-of-the-art design, namely signed SGD.
Superposition of causal orders for quantum discrimination of quantum processes

We address the superposition of causal orders in the quantum switch as a convenient framework for quantum process discrimination in the presence of noise in qubit systems, using Bayes strategy. We show that, for different kinds of qubit noises, the indefinite causal order between the unitary to be discriminated and noise gives enhancement compared to the definite causal order case without reaching the ultimate bound of discrimination in general. Whereas, for entanglement breaking channels, the enhancement is significant, where the quantum switch allows for the attainability of the ultimate bound for discrimination posed by quantum mechanics. Memory effects escorting the superposition of causal orders are discussed, where we point out that processes describing an indefinite causal order, violate the notion of Markov locality. Accordingly, a suggestion for the simulation of indefinite causal orders in more generic scenarios beyond the quantum switch is given.
Identity-based encryption with security against the KGC: A formal model and its instantiations

The key escrow problem is one of the main barriers to the widespread real-world use of identity-based encryption (IBE). Specifically, a key generation center (KGC), which generates secret keys for a given identity, has the power to decrypt all ciphertexts. At PKC 2009, Chow defined a notion of security against the KGC, that relies on assuming that it cannot discover the underlying identities behind ciphertexts. However, this is not a realistic assumption since, in practice, the KGC manages an identity list, and hence it can easily guess the identities corresponding to given ciphertexts. Chow later amended this issue by introducing a new entity called an identity-certifying authority (ICA) and proposed an anonymous key-issuing protocol. Essentially, this allows the users, KGC, and ICA to interactively generate secret keys without users ever having to reveal their identities to the KGC. Unfortunately, since Chow separately defined the security of IBE and that of the anonymous key-issuing protocol, his IBE definition did not provide any formal treatment when the ICA is used to authenticate the users. Effectively, all of the subsequent works following Chow lack the formal proofs needed to determine whether or not it delivers a secure solution to the key escrow problem. In this paper, based on Chow's work, we formally define an IBE scheme that resolves the key escrow problem and provide formal definitions of security against corrupted users, KGC, and ICA. Along the way, we observe that if we are allowed to assume a fully trusted ICA, as in Chow's work, then we can construct a trivial (and meaningless) IBE scheme that is secure against the KGC. Finally, we present two instantiations in our new security model: a lattice-based construction based on the Gentry–Peikert–Vaikuntanathan IBE scheme (STOC 2008) and Rückert's lattice-based blind signature scheme (ASIACRYPT 2010), and a pairing-based construction based on the Boneh–Franklin IBE scheme (CRYPTO 2001) and Boldyreva's blind signature scheme (PKC 2003).
Content Extraction from Lecture Video via Speaker Action Classification Based on Pose Information

Online lecture videos are increasingly important e-learning materials for students. Automated content extraction from lecture videos facilitates information retrieval applications that improve access to the lecture material. A significant number of lecture videos include the speaker in the image. Speakers perform various semantically meaningful actions during the process of teaching. Among all the movements of the speaker, key actions such as writing or erasing potentially indicate important features directly related to the lecture content. In this paper, we present a methodology for lecture video content extraction using the speaker actions. Each lecture video is divided into small temporal units called action segments. Using a pose estimator, body and hands skeleton data are extracted and used to compute motion-based features describing each action segment. Then, the dominant speaker action of each of these segments is classified using Random forests and the motion-based features. With the temporal and spatial range of these actions, we implement an alternative way to draw key-frames of handwritten content from the video. In addition, for our fixed camera videos, we also use the skeleton data to compute a mask of the speaker writing locations for the subtraction of the background noise from the binarized key-frames. Our method has been tested on a publicly available lecture video dataset, and it shows reasonable recall and precision results, with a very good compression ratio which is better than previous methods based on content analysis.
Preventing Waiting List Manipulation And Black Marketing of Donated Organs Through Hyperledger Fabric

Thousands of lives can be saved every year through organ transplantation. Shortage of donors and less secure systems are limiting factors to the increase in transplantation rates. Influential Individuals try to exercise their influence over the donated organs through money resulting in jumping of waiting list and black marketing of organs. Increase in the number of donors, by a more secure and accountable platform, is necessary to facilitate more transplants before patients on the organwaiting list die. To address this serious health care problem a secure, accountable and transparent platform is developed using Hyperledger Fabric which improves security by enforcing transparency and immutability through its ledger and consensus protocol. For each activity the accountable action can be easily tracked leaving no scope for manipulation and other malpractices.
Evolving Levels for General Games Using Deep Convolutional Generative Adversarial Networks

Deep Convolutional Generative Adversarial Networks (DCGANs) are a machine learning approach that can learn to mimic any distribution of data. DCGANs consist of a generator and discriminator where generator generates new content and discriminator finds whether the generated content is real or fake. Procedural Content Generation (PCG) for level generation could potentially benefit from such models, particularly for a game where there is an existing level to emulate. In this paper, DCGANs are trained on existing levels generated through a random generator. Three different games (Freeway, Zelda, and Colourescape) are selected from the General Video Game Artificial Intelligence (GVG-AI) framework for level generation. The proposed approach successfully generates various levels which mimic the training levels. Generated levels are also evaluated using agent-based testing to ensure their play-ability.
Infection prediction using physiological and social data in social environments

The ability of detecting infections at an early stage in clinical environments is an important clinical problem. When an infection is not diagnosed on time, it may not only affect the health of the infected patient, but also spread and infect other people. In this paper, we propose the development of a clinical decision support system (CDSS) for diagnosing infections using clinical signals from patients. This system is designed to be able to cope with small amounts of data (a single record per day and patient), making it convenient for environments under strict constraints (such as low resources or bad connectivity). Additionally, we have incorporated data from external sources, in order to enrich the quality of the models. In particular, we have considered social data arising from web searches, retrieved from Google Trends, as well as weather data. Clinical data was recorded between April 2018 and July 2019 in two nursing homes in Spain and one in Dominican Republic, where nurses had also tested patients for infections. Feature extraction was carried out by aggregating measurements from days before to the infection (lead) and after the infection was detected (lag), and these features were used to train supervised learning models. The best model attained using only clinical data attains an AUROC of 0.734. When data is enriched with external sources, this performance increases up to an AUROC of 0.798. In the case of prognosis (i.e., only measurements before the manual annotation of the infection are used) an AUROC of 0.719 is obtained using only clinical data, and up to 0.757 when combining additional sources of data. In conclusion, the CDSS provides a good recognition performance given the small amounts of data available. This performance can be increased by including social data, which are readily available, and can therefore be useful in scenarios where clinical data acquisition is expensive or unfeasible.