Reviewerly/exHarmony huggingface dataset:
Dual Image Reversible Data Hiding Using Rhombus Prediction

The importance of securing digital information against illegal access or making copies is increasing day-by-day because of the advance of the Internet. Reversible Data Hiding (RDH) can be used to hide secret data in a cover image which can later be used to verify or authenticate the user. This work proposes a dual image RDH algorithm using Prediction Error Expansion (PEE). The main drawback of the conventional single image PEE techniques is that they exhibit low Embedding Capacities (EC). This work uses the dual-image RDH approach with Rhombus Prediction to improve the EC. First, the prediction errors are generated using the Rhombus Predictor. The prediction error histogram is then shifted to embed secret data and generate two stego images. It can be seen from the results that the average EC increases from 0.33bpp to 0.67bpp while maintaining a Peak Signal to Noise Ratio (PSNR) of 42.45dB.
Computer Aid-System to Identify the First Stage of Prostate Cancer Through Deep-Learning Techniques

Nowadays, there are high rates of discordance between pathologists when they analyse the biopsy samples to diagnose prostate cancer according to the Gleason scale. Thus, we designed a computer-aid system capable of accurately differentiating between normal tissues and pathological ones at the first stage. Specifically, we made use of an original segmentation algorithm to identify regions of interest and distinguish from them between artefacts (false glands), benign glands and Gleason grade 3 glands. Regarding the building of predictive models, we applied, for the first time, deep-learning algorithms on the previously segmented gland candidates. We compared the results reported by two different convolutional neural networks (CNNs) addressed with distinct classification strategies. The best model reached a multi-class classification accuracy of 0.812±0.033, after performing an in-depth data partitioning per medical history.
Are These Pairing Elements Correct?

Using a set of pairing product equations (PPEs) to verify the correctness of an untrusted set of pairing elements with respect to another set of trusted elements has numerous cryptographic applications. These include the design of basic and structure-preserving signature schemes, building oblivious transfer schemes from "blind" IBE, finding new verifiable random functions and keeping the IBE/ABE authority "accountable" to the user.
Immersing Future Middle-Level Science Educators in a Blended Learning Environment

Current pre-service teachers are considered digital natives in that they have grown up with the use of technology. However, these future teachers often need explicit and modeled experiences to become a digital educator as they will be the future teachers in what has become an increasingly technological world that utilizes digital tools and applications. This chapter delves into the literature related to blended learning and preparing these preservice teachers to utilize technological pedagogical content knowledge (TPACK) within a blended learning environment (BLE). Through immersion in a middle school science methods course that utilized a BLE, preservice teachers were provided with relative experiences for their future teaching career. Lessons learned and reflections from the preservice teachers provide foundational information for future planning.
An Unsupervised Monocular Image Depth Prediction Algorithm Based on Multiple Loss Deep Learning

In order to improve the predication accuracy with low execution time in the process of image depth map generation, we mainly investigate the unsupervised monocular image depth prediction. In this paper, an unsupervised monocular image depth prediction method based on multiple loss deep learning is designed from following two aspects. First, a monocular image depth estimation algorithm based on multi-scale feature extraction is proposed, which includes two parts: a feature extraction network and a deconvolution prediction network. The feature extraction network extracts image features at different levels of the network and introduces the acquired multi-scale features into the deconvolution layer, without changing the image resolution. Through training, the left and right disparity map can be eventually predicted. Second, we provide a new multiple loss function with the asymmetric parameters of the training model and constraint theorem of polar geometry. The Multi-Scale-Structural Similarity Index (MS-SSIM) algorithm and L1 algorithm are combined as the loss function of image reconstruction, the left-right disparity consistency and the flipped left-right disparity consistency are incorporated in the loss function of the network model training. The simulation results show that this method can effectively improve the prediction results accuracy, particularly for complex images with mirrors, transparent, and shadows. KITTI dataset is further utilized to evaluate our method, which can achieve end-to-end results that even exceed those of a supervised method.
A SECO Meta-model

Software development companies are venturing towards collaborative approach and software ecosystems (SECO) participation. Over the years, many papers have been written and different modelling languages were proposed to capture the interactions between the SECO participants. What is missing, however, is a comprehensive meta-model describing possible entities and relationships that constitute a SECO. The goal of this paper is to create a common language for academic researchers for software ecosystems by creating such a meta-model. We constructed the meta-model by extracting and grouping entities and relationships from research papers. The meta-model consists of 5 themes: actors and roles, products and platforms, boundaries, ecosystem health and strategy. We advocate that our meta-model allows for easy sharing and comparing of case studies and the generalization of results across studies. We present the results from initial expert evaluation of the meta-model.
Single Image Dehazing Algorithm Based on Sky Region Segmentation

In this paper a hybrid image defogging approach based on region segmentation is proposed to address the dark channel priori algorithm's shortcomings in de-fogging the sky regions. The preliminary stage of the proposed approach focuses on segmentation of sky and non-sky regions in a foggy image taking the advantageous of Meanshift and edge detection with embedded confidence. In the second stage, an improved dark channel priori algorithm is employed to defog the non-sky region. Ultimately, the sky area is processed by DehazeNet algorithm, which relies on deep learning Convolutional Neural Networks. The simulation results show that the proposed hybrid approach in this research addresses the problem of color distortion associated with sky regions in foggy images. The approach greatly improves the image quality indices including entropy information, visibility ratio of the edges, average gradient, and the saturation percentage with a very fast computation time, which is a good indication of the excellent performance of this model.
Different Fixed-Phases for Quantum Search Operators

Amplitude amplification techniques are the basic building blocks in many quantum algorithms. Amplifying the probability of success to the highest possible probability to find solutions in the search space is an essential aim. To simplify the construction of the hardware, the search operators with fixed phases must be used to handle the whole search space. This paper presents more reliable fixed-phase quantum search algorithm, where the phase shifts that used in the oracle and the diffusion operator are not matched, in contrary to the wide belief in literature that the matching condition between the phase shifts is the best choice. This paper shows that there are many phase shifts which can be used for the search operators to give better results when the number of solutions is known in advance. The minimum probability of success of the proposed algorithm is 99.61% in quadratic speed-up which is better than any other known fixed operator quantum search algorithms.
Fault Tolerant Arithmetic Logic Unit

Low power design is most challenging in this era of IC technology and shaken the Moore's law. Various techniques have been invented for low power design and researchers are taking lot of pain to investigate new steps and methods to achieve low power goal. Reversible logic is seeking attention from last one decade and can be employed to balance between power and performance. Reversible logic is based on principle of no bit loss and claims almost no power dissipation. Although paradigm shifts from conventional logic to reversible logic is tedious but there is no other way looking around to reduce power dissipation. Many Reversible logic based arithmetic and logic units are available in literature but incorporating fault tolerance is demand of various applications. This paper aims in designing fault tolerant arithmetic logic unit based on high functionality conservative and parity preserving logic based gates. The quantum cost of used gates in proposed design is verified using RCViewer+ tool and performance of proposed design is evaluated with respect to existing designs in literature.
A Novel Approach to Implement Decentralized Voting System Using Blockchain

The primary target of the democracy is “vote” by which the general population can choose the possibility for shaping a proficient government to fulfill their requirements and demands with the end goal that their standard living can be improved. The significance of casting a ballot is lost among the rushing about of city life ([TechCrunch in Liquid democracy uses blockchain to fix politics, and now you can vote for it. [1]). The election commission follows manual voting mechanism which is done by electronic voting machine. This machine is set in the poll booths and is observed by higher authorities. Because of some unlawful exercises, the booths are abused and individuals’ vote to right has been denied in many parts of the country. Our principle point of the proposed framework is to build up a perfect system with high-security Aadhaar-based login system together with the immutable machine (blockchain) which has proven its mettle in various other applications till date. The proposed framework is fundamentally intended for our nation.
Virtual machine migration for IoT applications

To integrate heterogeneous devices in IoT (Internet of Things), we design and implement an IoT system consisting of smart meters, smartphones, and cloud servers. In our system, a smartphone can rent its idle resources to the third party, which can install an application to conduct various tasks, such as collecting and processing data from nearby smart meters. Built on the top of Xen hypervisor and MiniOS, our system cannot only ensure smartphone's security by providing isolated resources to the application, but also guarantee data integrity and privacy of the application. We are the first to build a lightweight virtual machine migration system on an ARM-architecture smartphone.
Public driven and public perceptible innovation of environmental sector

This chapter compares two complementary approaches to understanding opinions at a deep level regarding the controversial topic of power sources. The first method is the emerging and increasingly popular technology of text mining, which uses text as inputs from people, analyzing the structure of the texts. Freely emitted text allows for methods such as sentiment analysis to understand the underlying minds of the people generating the text. The second method, mind genomics, is composed of experiments using systematically varied text and experiments with people, designed to understand the algebra of the mind. Our empirical study involved a mind genomics assessment of solar energy and nuclear energy, respectively. Data from mind genomics suggest that people focus primarily on who should use solar and nuclear energy. We compare the two methods to show the similarities, differences, and potential connections between the two approaches to understanding people's opinions.
PRME-GTS: A New Successive POI Recommendation Model with Temporal and Social Influences

Successive point-of-interest (POI) recommendation is an important research task which can recommend new POIs the user has not visited before. However, the existing researches for new successive POI recommendation ignore the integration of time information and social relations information which can improve the prediction of the system. In order to solve this problem, we propose a new recommendation model called PRME-GTS that incorporates social relations and temporal information in this paper. It can models the relations between users, temporal information, points of interest, and social information, which is based on the framework of pair-wise ranking metric embedding. Experimental results on the two datasets demonstrate that employing temporal information and social relations information can effectively improve the performance of the successive point-of-interest (POI) recommendation.
Mistakes in games

We define a new concept of "mistake" strategies and actions for strategic-form and extensive-form games, analyze the relationship to prior main game-theoretic solution concepts, study algorithms for computation, and explore practicality. This concept has potential applications to cybersecurity, for example detecting whether a human player is illegally using real-time software in online poker.
Downright: A Framework and Toolchain for Privilege Handling

We propose Downright, a novel framework based on Seccomp, Berkeley Packet Filter, and PTrace, that makes it possible to equip new and existing C applications with a request broker architecture. An extensive configuration language allows AppArmor-like configuration that supports programmers in building rules for system call parameter validation and result sanitization. Access to these privileged function calls can be restricted both within Linux kernel and user spaces. Downright's main strength compared to related approaches is that it implements a complete mediation request broker architecture, in which all system calls are vetted before execution, either by the kernel or by a request broker, which runs as another process. This isolates the main program from many failures due to programming bugs and attacks, which would have to pass not only the attacked program, but the request broker also. We argue that this makes acquiring and releasing elevated privileges easier and safer. Downright eliminates the need to write Seccomp programs, instead allowing policies to be expressed declaratively through a rich policy language. We demonstrate the viability of this approach by instrumenting nginx, an industrial-strength web server and reverse proxy. While this instrumentation takes only a single line of code, we argue that even this effort can be avoided by suitable C runtime code. We show that Downright's overhead is substantial, halving nginx's perfomance, but propose measures for optimisation.
Smartphones for rapid kits

This chapter is focused on application of smartphones to testing based on rapid kits. Currently, there are many tests that are designed for rapid screening either at point of care or at home. They are also very useful in resource limited setting, when full diagnostic capabilities are not within the reach, and quick screening is necessary to detect problems that might later require travel to remotely located hospitals and testing laboratories. In addition to that, for home use they provide such important benefits, as relatively low cost, confidentiality, and availability on-demand. The main reason to use smartphones in combination with rapid kits is to increase accuracy of the test. Although, in simple cases, such as pregnancy testing, the answer can be just “yes” or “no,” many other tests require much more detailed quantification that cannot be precisely addressed with simple visual assessment. Under those circumstances, smartphones can provide sensitive digital cameras and real-time image processing that can greatly increase accuracy. Many of the discussed technologies are only available as lab prototypes demonstrating the proof of concept, while others are already FDA approved and can be purchased and used.
Vehicle Service Management and Live Monitoring With Predictive Maintenance System

In this paper an efficient vehicle service management system is presented which can automatically manage complete servicing process and at the same time monitor the changes and operations done on the vehicle. Regular vehicle service is required to keep a check on various parts of the vehicle to ensure proper functioning and efficient working of the vehicle. Vehicle service management and monitoring system constantly checks the state of the vehicle after frequent intervals. It warns through android application about any improper values received and suggests that servicing is now required. When the vehicle is at any of the service centers, previous details of vehicle service can be shared with garage on authentication by the customer using the same android application. IoT technology is used to update various sensors data in the application in real time. Sensor interfaced with raspberry pi constantly monitors different subsystems of the vehicle. It also provides customers with live streaming of garage when their vehicle is in service centers. This paper also talks about predictive vehicle maintenance. It predicts the time after which vehicle's, components may need servicing.
Fault Intensity Map Analysis with Neural Network Key Distinguisher

Physical cryptographic implementations are vulnerable to side-channel attacks, including fault attacks, which can be used to recover a secret key. Using a deep neural network (NN) with fault intensity map analysis (FIMA), we present a new highly efficient statistical fault analysis technique called FIMA-NN. This technique employs a convolutional neural network (CNN) to rank the key candidates based on multiple features in data distribution under fault with varying intensities, and generalizes most existing statistical techniques including fault sensitivity analysis (FSA), differential fault intensity analysis (DFIA), statistical ineffective fault analysis (SIFA), and FIMA. As FIMA-NN does not rely on a single feature of data distribution, it is successful even in the presence of a wide variety of countermeasures against fault analysis. Using a simulated fault mechanism on an FPGA implementation of AES, we demonstrate that, in terms of required amount of collected ciphertexts, FIMA-NN is 7.3 and 4.5 times more efficient than statistical techniques using bias alone, at low and medium fault intensities, respectively. Further, in the presence of error-detection and infective countermeasures, FIMA-NN is 10.7 and 7.9 times more efficient than biased-based techniques, respectively.
Learnings from an Ongoing Deployment of an IVR-based Platform for Voter Awareness

Spreading awareness among voters is a critical first step towards ensuring democratic participation. We report results from an ongoing deployment in India where we use an Interactive Voice Response (IVR) system to raise awareness about voting. People can call in to the number and answer a few questions on voting. They receive a mobile airtime top-up if they answer correctly, and an explanation of the correct answer if they do not. The system also serves as a survey instrument and we use it to collect data on the percentage of people who have their voter ID cards that enable them to vote. Extending past work on IVR-based mass awareness, we employ a different way of presenting content---a quiz instead of a tutorial---and of incentivizing usage---no monetary incentive for referrals except for specified referrers. In 24 days of deployment, over 1900 people have called the system, out of which 1245 answered all questions correctly in their first attempt, and 234 answered correctly after learning from their initial incorrect answers.
Efficient Face Alignment with Fast Normalization and Contour Fitting Loss

Face alignment is a key component of numerous face analysis tasks. In recent years, most existing methods have focused on designing high-performance face alignment systems and paid less attention to efficiency. However more face alignment systems are now applied on low-cost devices, such as mobile phones. In this article, we design a common efficient framework that can team with any face alignment regression network and improve the overall performance with nearly no extra computational cost. First, we discover that the maximum regression error exists in the face contour, where landmarks do not have distinct semantic positions, and thus are randomly labeled along the face contours in training data. To address this problem, we propose a novel contour fitting loss that dynamically adjusts the regression target during training so the network can learn more accurate semantic meanings of the contour landmarks and achieve better localization performance. Second, we decouple the complex sample variations in face alignment task and propose a Fast Normalization Module (FNM) to efficiently normalize considerable variations that can be described by geometric transformation. Finally, a new lightweight network architecture named Lightweight Alignment Module (LAM) is also proposed to achieve fast and precise face alignment on mobile devices. Our method achieves competitive performance with state-of-the-arts on 300W and AFLW2000-3D benchmarks. Meanwhile, the speed of our framework is significantly faster than other CNN-based approaches.
Cybersecurity Within the Curricula of Informatics: The Estonian Perspective

While the needs for generic IT staff have been generally addressed in many places, the lack of specialized workforce is a growing problem, especially in cybersecurity - falling behind in addressing cybercrime can lead to serious problems. Not only should the common cybersecurity awareness be raised, but more people should also be guided to specialize in cybersecurity. The cost of cybersecurity education and awareness campaigns is far less than the price of dealing with enormous consequences of cybercrime – in 2018, 600 Billion was reported in CSIS [17] by Internet Society, and it doubles annually. In some countries, the problem has been recognized more than in others. The Estonian CyberSecurity Strategy (2019) points out the need to educate all citizens, improve education in schools and find new talents for the field [11]. The new curriculum of Informatics of which $$\frac{1}{3}$$ covers the cyber skills was developed in 2017, new materials will be available in 2019. In this paper we provide an overview of Estonian digital safety and cybersecurity curricula as well as discuss how to educate the masses and what to do with talents (how to detect talent and build up the talent pool). We will look at three years’ work and studies (including the Cyber Olympics program) to offer ideas and perhaps inspiration to other countries. We also list a number of initiatives and tools that could help in updating the cybersecurity education and channeling resources in a more effective way.