Reviewerly/exHarmony huggingface dataset:
A Model Training Mechanism based on Onchain and Offchain Collaboration for Edge Computing

Blockchain as a new decentralized chain structure can be used in edge computing to solve the security issue caused by edge nodes in model training. However, large amounts of data exchanges in the process of model training of edge computing reduce the performance of blockchain, and meanwhile, the block needed to be saved in the edge node challenges storage capacity of the edge node. Therefore, in the paper we propose a safe and efficient model training mechanism based on onchain and offchain collaboration. In the mechanism, edge nodes train models locally, store the model parameters in offchain and only return identifiers for model aggregation. By the method, the storage pressure of the edge node is reduced and the efficiency of executing consensus algorithms are increased. Moreover, in the mechanism we design a reputation evaluation model based on confidence factors to avoid the uploading of random and wrong data of edge nodes. Evaluation results show that our schemes can reduce the average delay and resources consumption, increase transaction throughput and maintain security compared with a state-of-the-art scheme.
Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation, in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore, forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern (LTrP) features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform (DWT) for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally, Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results, three datasets are used, namely MICC-F220, MICC-F2000, and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.
TDMatcher: A topic-based approach to task-developer matching with predictive intelligence for recommendation

Artificial Intelligence is currently gripping the business world, which is the next step on the journey from Big Data to full automation. As crowdsourcing has been widely adopted by more enterprises and developers, the software crowdsourcing platform is able to collect enough data. Therefore, we introduce predictive intelligence to solve complex problems. This provides a bridge between software developers and enterprises: developers look for suitable tasks, whose aim is to gain revenues with respect to their interests and abilities; enterprises look for developers that are able to complete crowdsourcing tasks and/or solve hard problems. One main problem is the prediction challenge, i.e., how to perfectly predict the developers for the software crowdsourcing tasks and make appropriate recommendations. To solve the problem, this paper introduces predictive intelligence and proposes TDMatcher, which can effectively perform task-developer pairs prediction and recommendations for software crowdsourcing. First, we builds a unified model for tasks and developers such that they can be matched in the same domain space. Second, we quantitatively measures the matching degree between tasks and developers. Third, we randomly generates potential matchings between developers and crowdsourcing tasks and then employs an MCMC sampling approach to optimize the whole process. Highly matched task-developer pairs can be achieved in the sampling process. In order to solve the cold-start problem, we constructs a social network for each new developer, which indicates that the developer’s interests/abilities to be modeled We implemented TDMatcher and evaluated it against the state-of-the-art approaches on the real-world dataset. The experimental results clearly demonstrate the superiority of TDMatcher. We measured our proposed TDMatcher through the accuracy, diversity and Harmonic Mean of TDMatcher, and found that: (1) TDMatcher outperforms the state-of-the-arts by 15+% in the prediction accuracy and 30% in diversity; and (2) TDMatcher achieves a balance between accuracy and diversity. We believe that TDMatcher provides crowdsourcing platforms with much more capabilities in finding appropriate developers to complete crowdsourcing tasks or vice versa.
Design of Leakage Resilient and DPA Attack Immune Architecture of S BOX

Electronic gadgets such as mobile phones, laptops, IPad, home appliances and other gaming gadgets have become a part and parcel of our life and they have made the human life most comfortable. With the shirking of technology the gadgets have become handy and security of the devices is taken care by the field of cryptography. One of the best Cryptographic algorithm secured the data is Advanced Encryption Standard (AES) which is a combination of various steps. One of the major action involved in making the encryption is by Substitution of data with the help of Substitution box called as S Box. The threat on to the chips fabricated is Side Channel Attacks (SCA) and the popular SCA attacks are Differential Power Analysis (DPA). In cryptographic systems such are Smart cards, RFID tags, military applications and E-commerce applications cryptography plays a vital role. In this paper a Low power adiabatic logic with attack immunity is used to construct a Sub Byte transformation called as S Box of AES. It is implemented and found that the energy dissipated is reduced by 26% and 44% against the NDDPL and PFSAL Techniques. The efficiency of the proposed Dual Rail Single Clock Positive Feedback Adiabatic Logic (Dual Rail SCPFAL) S Box is verified by comparing it with the PFSAL based Rijndael S-Box. The DPA attack immunity is verified with the help of an efficiency factor called Coefficient of Deviation (CoD) or ϵ and is improved by 32% and 48% against NDDPL and PFSAL logic. The proposal is implemented and results are verified in Cadence Virtuoso 45nm technology and the power and current traces are compared at different temperature.
Using Throughput-Centric Byzantine Broadcast to Tolerate Malicious Majority in Blockchains

Fault tolerance of a blockchain is often characterized by the fraction f of “adversarial power” that it can tolerate in the system. Despite the fast progress in blockchain designs in recent years, existing blockchain systems can still only tolerate f below 0.5. Can practically usable blockchains tolerate a malicious majority, i.e., f above 0.5? This work presents a positive answer to this question. We first note that the well-known impossibility of byzantine consensus for f above 0.5 does not carry over to blockchains. To tolerate f above 0.5, we use byzantine broadcast, instead of byzantine consensus, as the core of the blockchain. A major obstacle in doing so, however, is that the resulting blockchain may have extremely low throughput. To overcome this central technical challenge, we propose a novel byzantine broadcast protocol OverlayBB, that can tolerate f above 0.5 while achieving good throughput. Using OverlayBB as the core, we present the design, implementation, and evaluation of a novel Proof-of-Stake blockchain called BCube. BCube can tolerate a malicious majority, while achieving practically usable transaction throughput and confirmation latency in our experiments with 10000 nodes and under f=0.7. To our knowledge, BCube is the first blockchain that can achieve such properties.
Map Reduce Overview and Functionality

In today's modern world the data has become one of the essential entities to be taken care of properly. New social media platforms that have emerged during last decade, has plethora of data volume and the complexity of that data is also a big concern which could not be solved by the traditional programming models. Hence Map Reduce was introduced. This is one of the best models to perform various tasks on a huge amount of data which was not possible by traditional way. Many real world tasks are expressed in MapReduce model, which is a programming model that processes and generated large datasets. MapReduce makes use of user specified functions map() and reduce() to perform its functionality. The former one process a key-value pair and generates set of key-value pairs which are used by later one for merging. The run-time system takes responsibility of partitioning input, scheduling and handling failures as well. MapReduce is widely used nowadays.
Trade-Offs on Number and Phase Shift Resilience in Bosonic Quantum Codes

Quantum codes typically rely on large numbers of degrees of freedom to achieve low error rates. However each additional degree of freedom introduces a new set of error mechanisms. Hence minimizing the degrees of freedom that a quantum code utilizes is helpful. One quantum error correction solution is to encode quantum information into one or more bosonic modes. We revisit rotation-invariant bosonic codes, which are supported on Fock states that are gapped by an integer $g$ apart, and the gap $g$ imparts number shift resilience to these codes. Intuitively, since phase operators and number shift operators do not commute, one expects a trade-off between resilience to number-shift and rotation errors. Here, we obtain results pertaining to the non-existence of approximate quantum error correcting $g$-gapped single-mode bosonic codes with respect to Gaussian dephasing errors. We show that by using arbitrarily many modes, $g$-gapped multi-mode codes can yield good approximate quantum error correction codes for any finite magnitude of Gaussian dephasing and amplitude damping errors.
ON THE ACTIVATION OF STUDENTS THROUGH AUGMENTED REALITY EXPERIENCES

Abstract Over the last ten years, several stakeholders from the higher education sector have been involved in the area of student activation. This is due to the fact that, among other things, lecturers can get better feedback from the learners through active learning and thus can make the lecture more goal-oriented. Due to technological progress and currently available learning platforms, interactive methods are often used, which can be carried out with the help of online tools. Among the most commonly used methods are Peer Instruction (PI) and Just in Time Teaching (JiTT). All kinds of Mixed Reality are increasingly used in industrial and social context, such as product development processes. Through Augmented Reality (AR) technology and the constant availability of powerful mobile devices, 3D-based content can be virtually linked to the real environment. This enables teaching complex content efficiently, which is otherwise difficult to achieve using conventional methods. This paper shows an example on the creation of an AR-Experience to activate students even in a situation, that prevents attandence learning. The procedure is explained along the design and calculation of a non-switchable clutches within the lecture "machine elements".
Transfer learning using Pre-trained AlexNet for Marathi Handwritten Compound Character Image Classification

Transfer learning uses to train the data faster and avoid over-fitting when the size of the dataset is small. The purpose of this work is to investigate Handwritten Marathi Compound Characters and Handwritten Marathi 0-9 digits using Pre-trained Convolutional Neural Network. In this article, AlexNet mainly used to train the handwritten compound characters of Marathi Script for image classification. The tests conducted on Marathi Handwritten characters with 3,283 sample images of three compound characters, 400 images of two compound character and 1,000 Marathi Handwritten numbers with some preprocessing of resizing images into 227x 227 pixels. For three combining characters AlexNet gives the maximum accuracy of 96.77%, 97.3% and for Marathi handwritten digits gives 100% accuracy.
A Teacher Training Proposal for Classroom Conflict Management through Virtual Reality

This paper addresses the use of Virtual Reality (VR) in the training of Secondary Education teachers in Spain as an integral part of the new ecology of learning and proposes some premises from which to design a training program to improve teachers' communicative competence and their ability to manage conflict affecting the classroom climate. First, the paper explains the experiential and experimental potential in which VR immerses us, its ability to create challenging and personalized virtual worlds, as well as the possibility to generate instant feedback and feedforward. Finally, an example of a prototype scenario designed on this conceptual basis is provided and some ideas are discussed to introduce VR into initial teacher training.
Fast-Convergent Federated Learning with Adaptive Weighting

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose Fed erated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. Through mathematical and empirical analysis, we observe the implicit connection between the gradient of local training and data distribution on local node. We then propose to assign different weight for updating global model based on node contribution adaptively through each training round, which is measured by the angle between local gradient vector and global gradient vector, and is quantified by a designed non-linear mapping function. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, that results in communication round reduction drastically. With extensive experiments performed in Pytorch and PySyft, we show that FL training with FedAdp can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to the commonly adopted Federated Averaging (FedAvg) algorithm.
Opportunistic Coded Distributed Computing: An Evolutionary Game Approach

Task offloading has been proposed and studied to overcome the problem of energy and computation constrained terminals. Computationally intensive tasks are often parallelable, and therefore the execution time can be further improved via a coded distributed computing (CDC) approach, as CDC offers robustness against stragglers by introducing redundant computational tasks. In this paper, we study a user-centric task offloading problem, in which the edge performs the of-floaded computation with CDC. Furthermore, the extent of the straggler's effect on servers is also unknown to the user. This requires users to explore server and code settings of the CDC, and “opportunistically” select the best combo to maximize the utility. For simplicity, we refer to this scenario as opportunistic coded distributed computing. We formulate the problem as an evolutionary game in which each user is self-interested. The payoff is calculated based on the monetary cost of CDC-as-a-Service and total delay, weighted by user-defined parameter values. For the game solution, an evolutionary stable equilibrium (ESS) is used, i.e., probabilistic joint selection of server and code configuration. To obtain the ESS, we present an iterative algorithm based on the revision protocol. A theoretical analysis of equilibrium in terms of existence, uniqueness, stationarity, and stability is provided. Numerical simulations are conducted to support the theoretical findings and the adaption of equilibrium states to the hyper-parameters.
Repetitive patterns in the locations of touch errors for two-thumb text entry on a smartphone

This study investigated repetitive patterns in the locations of touch errors as a function of the shapes and positions of soft buttons on a smartphone for two-thumb text entry. Forty-three right-handed college students with smartphone-use experience were recruited for testing. An experimental application was developed, and the locations and frequencies of touch errors were measured for the button combinations of seven shapes and eight positions. More than 70.0 % of touch errors occurred within 2 mm from the boundaries of the buttons. In terms of direction, touch errors were primarily observed below the buttons, across all the button shapes and positions. Simultaneously, touch errors often appeared on the lateral sides of the buttons: (1) close to the proximal phalange of the thumbs when the buttons were placed near the initial positions of the thumbs and (2) close to the initial positions of the thumbs when the buttons were placed near the top and bottom ends of the keyboard.
The development of motion comic storyboard based on digital literacy and elementary school mathematics ability in the new normal era during covid-19 pandemic

Abstract In this Covid-19 pandemic situation, educators are demanded to quickly adapt to a new approach one of which in terms of preparing online learning media that is in line with learning objectives and is digital-based. Motion comics are digital comics that are modified so that the character appears to move on each page; thus, they become more interesting than conventional comics. For this reason, this RnD research was conducted in order to create a representative motion comic. One of the stages in the development of a motion comic is the stage of creating a storyboard. This article will discuss in more depth the stages of making a motion comic storyboard with the learning topic about fractions by paying attention to the principles of excellent educational learning media as well as the characteristics of elementary school students. There are several stages of making a storyboard including conducting research, creating the story, writing scripts, writing the screenplay, drawing characters, creating a design environment, and making a storyboard. This research involved teachers, elementary school mathematicians, IT experts, and elementary school students. This storyboard is a reference in developing a motion comic prototype about fractions to improve the math skills of elementary school students.
Saliency based human fall detection in smart home environments using posture recognition

The implementation of people monitoring system is an evolving research theme. This paper introduces an elderly monitoring system that recognizes human posture from overlapping cameras for people fall detection in a smart home environment. In these environments, the zone of movement is limited. Our approach used this characteristic to recognize human posture fastly by proposing a region-wise modelling approach. It classifies persons pose in four groups: standing, crouching, sitting and lying on the floor. These postures are obtained by calculating an estimation of the human bounding volume. This volume is estimated by obtaining the height of the person and its surface that is in contact with the ground according to the foreground information of each camera. Using them, we distinguish each postures and differentiate lying on floor posture, which can be considered as the falling posture from other postures. The global multiview information of the scene is obtaining by using homographic projection. We test our proposed algorithm on multiple cameras based fall detection public dataset and the results prove the efficiency of our method.
Separated smooth sampling for fine-grained image classification

Discovering diverse significant regions (e.g., beaks and wings for some bird species) and extracting discriminative features from them is vitally important in fine-grained image recognition. Currently, the attention-based approaches present promising performance, which generally extract the fine-grained features by cropping or sampling significant parts. However, the cropping methods usually suffer from a fixed number of parts and difficulty to highlight irregular regions, and existing sampling methods may produce extremely distorted images. To effectively capture the fine-grained features, we propose an end-to-end separated smooth sampling network (SSSNet) in this paper. Specifically, we propose a separated smooth sampling module to highlight diverse significant regions of an image. Different from previous methods, we adopt smooth sampling on two separated coordinates to process images, which can effectively highlight discriminative contents and meanwhile avoid extreme distortion. We further propose an iterative masking method to embed into SSSNet, which can produce multiple attention maps without overlap to represent different significant regions. We conduct extensive experiments on CUB-200–2011, Stanford-Cars, and FGVC-Aircraft datasets. The results show the effectiveness of separated smooth sampling, and our SSSNet achieves better performance against previous state-of-the-art approaches under the same settings.
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study

Advances in sensing and learning algorithms have led to increasingly mature solutions for human detection by robots, particularly in selected use-cases such as pedestrian detection for self-driving cars or close-range person detection in consumer settings. Despite this progress, the simple question which sensor-algorithm combination is best suited for a person detection task at handƒ remains hard to answer. In this paper, we tackle this issue by conducting a systematic cross-modal analysis of sensor-algorithm combinations typically used in robotics. We compare the performance of state-of-the-art person detectors for 2D range data, 3D lidar, and RGB-D data as well as selected combinations thereof in a challenging industrial use-case.We further address the related problems of data scarcity in the industrial target domain, and that recent research on human detection in 3D point clouds has mostly focused on autonomous driving scenarios. To leverage these methodological advances for robotics applications, we utilize a simple, yet effective multi-sensor transfer learning strategy by extending a strong image-based RGB-D detector to provide cross-modal supervision for lidar detectors in the form of weak 3D bounding box labels.Our results show a large variance among the different approaches in terms of detection performance, generalization, frame rates and computational requirements. As our use-case contains difficulties representative for a wide range of service robot applications, we believe that these results point to relevant open challenges for further research and provide valuable support to practitioners for the design of their robot system.