Reviewerly/exHarmony huggingface dataset:
Automated Motion Detection Security System Notifier using Raspberry Pi with Telegram

Technology is a double-edged sword when it changes the way we perform everyday activities for the better. Unfortunately, misfits will always find new ways to misuse technology to facilitate their misdeeds. CCTV are great security measures that can be used to catch culprits in action, but other than banks, hospitals, shopping malls, academic institutes etc., a CCTV solution is beyond most household budgets and require continuous monitoring. Because mobile phones are the primary hand-held device, it is only logical to integrate these with a CCTV solution. Motion Detection Security System Notifier using Raspberry Pi with Telegram is as the name suggests. Using Raspberry Pi, a motion detection system is developed through the method of background subtraction, which works by subtracting the static from the background frame from the constantly changing foreground. The change of value in noise and pixel density between the background frame and the foreground indicates motion. Each time a motion is detected, the system will automatically send the frame to the user via Telegram to notify that there has been movement in the area as captured in the frames. A prototype is first developed in a virtual environment using VirtualBox software before running it on the Raspberry Pi. This paper will show the relevance of the project.
Real time anomaly detection and categorisation

Abstract The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method.
Contouring reflective plates through image resizing and Iterative Intensity Integration Technique (IIIT)

Abstract Iterative Intensity Integration Technique (IIIT) is an intensity based correlation technique used to obtain the slope and curvature of loaded reflecting plates. Energy conservation principle forms the basis for this technique. The total quantity of light reflected by specimen before and after loading is assumed to be identical. It is best suited for contouring reflective plates that produce constant image size in load and unload state. But the corner and simply supported plates having the free edges to rotate while loading make reflected image enlarge/shrinks. Hence, preprocessing one of the images is mandatory to make it equal to size of other. In this present study, reflecting plates of corner supported rhombus and rectangular plate with holes are chosen and the experiments are conducted for central loading. One of images is resized and those images are subjected to IIIT. A suitable correction procedure is applied to results obtained from IIIT to get the final result. The same plates are modelled using ANSYS for known displacement and the partial slope and curvature values are determined. The obtained ANSYS results are exported to MATLAB for comparison of results with experiments and the least square errors are presented.
Sharing Economy: Implementing Decentralized Privacy-Preserving Parking System

Recently, smart-parking systems were proposed employing blockchain technology to ensure security, transparency, and availability of services. Building on a principle of complete system decentralization, we present the concept and implementation of a privacy-preserving solution for renting parking spaces. The anonymity of users is ensured by a combination of zero knowledge proofs, commitment schemes, differential privacy and fungible tokens. We compare implementations on three different public blockchain networks: Ethereum, EOS, and Tron. We analyze security and privacy of the proposed system and evaluate how ensuring anonymity affects user experience. We conduct user-oriented test to explore the performance (transaction time and cost) of the system in realistic situations. Results indicate that the proposed concept is feasible while employing current blockchain technology. EOS and Tron implementations currently provide performance that matches existing centralized systems. The performance of Ethereum implementation would probably not meet user expectations.
Learning recommendation based on hybrid collaborative filtering algorithm

Abstract To solve the sparse matrix problem of learning resource recommendation algorithm based on collaborative filtering, a hybrid recommendation algorithm combining user learning behaviour and rating is proposed. First of all, learning behaviour characteristics are defined and different characteristics are given weight values; then, according to the user’s learning behaviour log, the method of weight accumulation to calculate the user’s learning behaviour value of resources is used; finally, the learning behaviour matrix and rating matrix is used to build a hybrid collaborative filtering recommendation algorithm. Experimental results show that the hybrid recommendation algorithm has lower root mean square error than the traditional collaborative filtering algorithm.
A New Fully Homomorphic Signatures from Standard Lattices

Recently, Gorbunov, Vaikuntanathan and Wichs [6] propose a new powerful (fully) homomorphic trapdoor function (HTDF) based on small integer solution (SIS) problem in standard lattices, and construct the first fully homomorphic signature (FHS) schemes. Later Wang et al. [10] extend the notion of HTDF to identity-based setting with strongly security and construct the first identity based fully homomorphic signature (IBFHS) schemes. In this paper, we provide a new IBHTDF which satisfies claw-free and collision-resistant. Moreover, we find a homomorphic algorithm for our new IBHTDF where the noise level of multiplication gate is the same as that of addition gate. So, the noise level of IBHTDF for evaluating a circuit of depth d is reduced from $$O(4^d m\beta )$$ to $$O(2^d\beta )$$ . Finally, we construct a new leveled strongly-unforgeable identity-based fully homomorphic signature (IBFHS) schemes based on our IBHTDF.
Context-based Multi-stage Offline Handwritten Mathematical Symbol Recognition using Deep Learning

We propose a context-based multi-stage machine learning (ML) architecture for offline handwritten mathematical symbol recognition. In the absence of context information, the first stage of the architecture acts as a generalized method of training a Multi-Column Deep Neural Network (MCDNN) model for isolated symbol recognition. The second stage trains a deep convolutional neural network that further classifies ambiguous symbols based on each symbols context information. To further improve the classification accuracy, we develop a set of rules in the third stage to classify ambiguity symbols that would avoid violating some mathematical syntax rules. The proposed method is evaluated using the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) dataset. Experiments show that the proposed architecture outperforms all other previous approaches, and results the state-of-the-art accuracy on both the CROHME 2013 and 2016 datasets in offline handwritten mathematical symbol recognition. We believe the proposed multi-stage context-based ML architecture would have wide applications on handwritten symbol recognition.
Controlling biases and diversity in diverse image-to-image translation

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
Convention of Generations in the Modern Educational Environment

This article deals with the problem of intergenerational interactions between teachers and students in the modern educational environment. The authors analyzed the “convention of generations” to determine the nature of interactions between teachers and pupils. This approach was developed based on the generation theories and the age structure of the pedagogical community of the Russian Federation. The fundamental principle of interaction between the student and teacher within the framework of the new methodological approach is the principle of the convention of generations. This approach is presented in comparison to the existing approaches as a new conceptual framework that defines the goals, principles, methods, and results of organizing the interaction of all subjects of the modern educational environment. The subjects are viewed as representatives of different but equal generations in the environment of lifelong learning. The result of this lifelong learning is the achievement of a cooperative educational effect.
Effective Use of Deep Learning and Image Processing for Cancer Diagnosis

The area of medical image processing obtains its significance with the requirement of precise and effective disease diagnosis over a short period. With manual processing becoming more complicated, stagnant and unfeasible with higher data size, there necessitates automatic processing that can transform contemporary medicine. Deep learning mechanisms can arrive at a higher rate of accuracy in processing and classifying images in comparison with human-level performance. Deep learning not only assist in selecting and extracting features but also possesses the potentiality of measuring predictive target audience and bestows prediction in a more action format to help doctors significantly. Unsupervised Deep Learning for cancer diagnosis is advantageous whenever the involvement of unlabeled data is huge. By bestowing unsupervised deep learning techniques to such unlabeled data, features of pixels that are superior compared to manually obtained features of pixels are said to be learned. Supervised Discriminating Deep Learning directly provides discriminating potentiality for cancer diagnosis purposes. Finally, hybrid deep learning for labeled and unlabeled data is specifically used for cancer diagnosis with a resource or poor pixel representations and hence early detection and diagnosis performed via bank features. Deep Neural Network, as the name implies includes several layers, emphasizing the complex non-linear relationships between the features present in the images, therefore contributing to higher accuracy. Deep Belief Network used in both supervised and unsupervised deep learning adopting greedy mechanism, maximizing the likelihood nature of detection and diagnosis at an early stage. Sequential event analysis is said to be performed by Recurrent Neural Network with the weights being shared across all neurons, contributing diagnosis accuracy. Certain fine-tuned learning parameters of consideration for better and precise learning are Interaction and Non-linear Rectified Activation function, Circumventing over-fitting via Dropout and Optimal Epoch Batch Normalization. In the last section, challenges about the application of deep learning for cancer diagnosis are discussed.
Tabula in action

In this paper, we demonstrate Tabula, a middleware that sits between the data system and the geospatial visualization dashboard to increase user interactivity. The proposed system adopts a sampling cube approach that stores prematerialized spatial samples and allows data scientists to define their own accuracy loss function such that the produced samples can be used for various user-defined visualization tasks. The system ensures that the difference between the sample fed into the dashboard and the raw query answer never exceeds the user-specified loss threshold. For demonstration purposes, we connect Apache Zeppelin, a visualization dashboard, to the system and show how Tabula accelerates interactive visualizations on NYC Taxi Trip data, Yelp review data and San Diego Smart Streetlights data.
Multi-Container Application Migration with Load Balanced and Adaptive Parallel TCP

Application migration in Wide Area Network (WAN) is needed in many scenarios: disaster recovery and service hand-off in edge cloud. Modern distributed applications are virtualized with multiple virtual machines or containers. This paper focuses on parallel multi-container migration in WAN environments by utilizing multiple TCP connections over a single direct path (a.k.a parallel TCP). Our application migration middleware framework utilizes a feedback controller to determine a proper number of parallel container migration (i.e., parallel window) based on changing network bandwidth. Then a middleware's scheduler selects migration requests for the parallel window to load balance multiple pairs of hosts. The goal of our migration is to achieve the best possible balance between optimizing the total migration time and average individual migration time. This differs from most existing live migration works that attempt to optimize mainly the down time. Our proposed framework is generalized and not restricted to any particular virtualization technology implementation. For performance evaluation, we conducted a WAN-emulated experiment in static and dynamic network settings. The performance evaluation results show that the total migration time using our feedback controller is less than that of the sequential migration by 32.7% in the static network, and 43.9% in the dynamic network. Moreover, while achieving total migration time comparable to that of the best fixed parallel migration window, our approach can reduce the average individual migration time by 62.7% in the dynamic network setting.
Improving Multi-Speaker Tacotron with Speaker Gating Mechanisms

In this paper, we present two speaker gating mechanisms for multi-speaker Tacotron, a popular end-to-end text-to- speech (TTS) neural system, to improve the performance of generating multiple voices. With our presented mechanisms, the model can work better in both generalization and accuracy. As a starting point, we introduce the original multi-speaker Tacotron as a baseline model because of its excellent performance and straightforward structure. Employing gated linear units (GLUs), two different speaker gating mechanisms are then proposed for this model. Extensive experiments on VCTK dataset are conducted to demonstrate the validity of our methods. Conclusively, we find that it is promising to incorporate the speaker identity information by using the proposed speaker gating mechanisms.
An Experimental Study of Operator Choices in the $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm

We study the influence of the particular choice of operators on the running time of the recently proposed \((1+(\lambda ,\lambda ))\) genetic algorithm. In particular, we consider three choices for the mutation operator, six choices of the crossover operator, three strategies for sampling population sizes based on non-integer parameter values, and four choices of what to do when the best mutant is better than the parent.We test all these 216 configurations on four optimization problems and in three adjustment flavours: the fixed \(\lambda \), the unlimited self-adjustment of \(\lambda \) and the logarithmically capped one. For each of these configurations, we consider both the default values of the hyperparameters and the ones produced by the irace parameter tuning tool.The result of our experimental analysis showed that there exists a configuration that is robust on linear functions and is roughly two times faster compared to the initially proposed one and 12% faster on the OneMax problem compared to one of the similar previous studies. An even more robust configuration exists, which has a slightly worse performance on OneMax but is better on satisfiability problems. Such configurations can be the default choices for the practical evaluation of the \((1+(\lambda ,\lambda ))\) GA.
Optimal Path Planning Algorithm of AUV State Space Sampling Based on Improved Cost Function

In order to solve the problem of autonomous underwater vehicle (AUV) trajectory planning for local obstacle avoidance, this paper proposes an optimal trajectory planning algorithm based on improved cost function of AUV state space sampling. This algorithm samples the end states of the s-direction and q-direction in the Frenet coordinate system with the initial position, speed, direction, acceleration, and reference path of the AUV, and uses a fifth-order polynomial to generate a series of alternative trajectories. And combined with the potential field strength to optimize the selection of the traditional cost function, by calculating the end state of the minimum trajectory of the cost function in each sampling period as the starting state of the next period, iteratively obtain the trajectories in two directions and couple with the time parameter t get the optimal trajectory. Simulation results show that the algorithm can be used to plan a set of trajectories that can efficiently avoid underwater emerging obstacles and meet the actual braking constraints of AUV.
Exposing Rural Indian Students to Mobile Assisted Language Learning: A Case Study

Mobile learning has huge potential for developing countries like India where mobile devices have greater penetration even in rural areas recently. There is a remarkable difference in the quality of education between urban and rural India. Rural Indian students being the first generation learners of English, they have poor facilities for learning English both inside and outside the class. Mobile Assisted Language Learning (MALL) plays a vital role in India Proficiency in English is considered essential for employment and higher education opportunities for millions of rural youth. The present paper aims at studying how rural Indian students respond to MALL individually and as a group to different learning activities and benefit from the same. The study is based on the data collected through a questionnaire and focus group discussions and the assessment tests at the beginning and end of the study. Statistical analysis of the data indicates positive results for group learning activities like contests and debates using social media whereas individual activities like recording one minute speeches have shown mixed results. Some of the best collaborative learning activities based on flipped classroom methods have implications for the practitioners, course designers and future research.
Resource distillation in convex Gaussian resource theories

It is known that distillation in continuous variable resource theories is impossible when restricted to Gaussian states and operations. To overcome this limitation, we enlarge the theories to include convex mixtures of Gaussian states and operations. This extension is operationally well-motivated since classical randomness is easily accessible. We find that resource distillation becomes possible for convex Gaussian resource theories-albeit in a limited fashion. We derive this limitation by studying the convex roof extension of a Gaussian resource measure and then go on to show that our bound is tight by means of example protocols for the distillation of squeezing and entanglement.
Codesigners of Classroom 52: A Case Study for Codesign in Active Citizenship

This paper analyses the potential of working with the principle of codesign in active citizenship as an objective and a process in the vocational education at the secondary level. The authors of this paper developed and implemented a case study of graphic design education through codesign practice in Porto, Portugal, with youngsters of age between 15 to 17 years old. The key findings of the paper stress that the: i) stronger connection between the designer’s professional role and civic responsibility could be encouraged by staging the codesign in active citizenship; ii) design students have the capability to share and own responsibility of codesign when working together on the real-case scenarios; iii) additional research on the impact of individual design methods is needed, especially when working with non-designers.
Video Conferencing and Webinars: Integration of Online Tools in Traditional Forms of Educational and Scientific Activities

The introduction of information technology at the institutional level in modern society requires a change in the methods and organizational forms of education.Unlike traditional educational technology, the use of electronic systems for organizing educational and scientific activities creates conditions to provide the educational process with the quality of continuity through the technological integration of online tools in traditional forms of educational and scientific activities of students.The article presents the views of scientists on the theoretical and practical aspects of organizing video conferences and webinars.The opportunities offered by video conferencing and webinars for educational and scientific activities of students, as well as the main areas of preliminary work on the video conferencing and webinars preparation, are determined in the article basing on the results of an expert survey.
Frequency domain based data hiding for encrypted medical images

This chapter presents a novel, separable, and blind joint encryption/watermarking algorithm that will provide security for transmitted medical images. The frequency domain algorithm combines cryptography as a preprotection mechanism and digital watermarking as a postprotection mechanism to provide the required security requirements. The algorithm is based on a special type of watermarking called reversible data hiding that guarantees the exact recovery of the original image after the embedded data have been extracted. The main advantages of using the frequency domain in the proposed algorithm are the high embedding capacity it provides. Moreover, modifying the coefficient values instead of the pixels distributes the distortion that results from the irregular embedding process over the entire image, which makes the watermark hard to detect. Extensive experiments using four different medical modalities and four different sizes of the same modality have been conducted to evaluate the performance of the proposed algorithm.