Reviewerly/exHarmony huggingface dataset:
A Secure Multi-party Computation Protocol Combines Pederson Commitment with Schnorr Signature for Blockchain

Blockchain is being pursued by a growing number of people with its characteristics of openness, transparency, and decentralization. At the same time, how to secure privacy protection in such an open and transparent ledger is an urgent issue to be solved for deep study. Therefore, this paper proposes a protocol based on Secure multi-party computation, which can merge and sign different transaction messages under the anonymous condition by using Pedersen commitment and Schnorr Signature. Through the rationality proof and security analysis, this paper demonstrates the private transaction is safe under the semi-honest model. And its computational cost is less than the equivalent multi-signature model. The research has made some innovative contributions to the privacy computing theory.
Automatic high-precision measurement technology of special-shaped surface parts based on robot arms

Abstract In order to meet the requirements of large-scale, special-shaped surface, high-precision surface measurement of product parts, an automated, high-precision robot arm measurement system based on industrial robots is proposed. According to the D-H method, the relative posture conversion relationship between the robot arm and the elastic probe is discussed. In addition, on the premise of considering the contact deformation between the elastic probe and the measuring surface, the coordinate data of the measuring point is obtained based on the probe deformation model. At the same time, the accuracy calibration is performed by means of visual photogrammetry, and the validity of the coordinate measurement data of the profiled surface is guaranteed.
Uncovering Media Bias via Social Network Learning

It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly analyzing the news content without high-level context. In contrast, social signals (e.g., the network structure of media followers) provide inspiring cues to uncover such bias. In this article, we realize the first attempt of predicting the latent bias of media outlets by analyzing their social network structures. In particular, we address two key challenges: network sparsity and label sparsity . The network sparsity refers to the partial sampling of the entire follower network in practical analysis and computing, whereas the label sparsity refers to the difficulty of annotating sufficient labels to train the prediction model. To cope with the network sparsity, we propose a hybrid sampling strategy to construct a training corpus that contains network information from micro to macro views. Based on this training corpus, a semi-supervised network embedding approach is proposed to learn low-dimensional yet effective network representations. To deal with the label sparsity, we adopt a graph-based label propagation scheme to supplement the missing links and augment label information for model training. The preceding two steps are iteratively optimized to reinforce each other. We further collect a large-scale dataset containing social networks of 10 media outlets together with about 300,000 followers and more than 5 million connections. Over this dataset, we compare our model to a range of state of the art. Superior performance gains demonstrate the merits of the proposed approach. More importantly, the experimental results and analyses confirm the validity of our approach for the computerized prediction of media bias.
For Safer Navigation: Pedestrian-View Intersection Classification

Intersection classification is one of the key components of autonomous navigation. Several related research works have been conducted as a prior task to solve problems such as autonomous driving, aircraft hovering, and navigating in mines. However, to the best of our knowledge, none of these studies support pedestrian-view navigation to guide the small and slow robots for which it is too dangerous to be operated on normal roads along with normal vehicles. To address this problem, we propose: 1) a pedestrian-view-level intersection classification image dataset, 2) ResNet-based architecture fine-tuned on the proposed dataset, and 3) thorough experimentation to explore the capabilities of our proposed architecture. The detailed analysis reported in this paper enabled us to find the network configuration that is neither underfit nor overfit to our data and achieves 80% test accuracy.
Data Mining Classification Approach to Predict The Duration of Contraceptive Use

Family planning program implementation in Indonesia has a plethora of challenges. One of the biggest challenges to implement the family planning program in Indonesia is the huge percentage of contraceptive discontinuation rates for around 29% in 2019. Based on that problem, the data mining classification approach is proposed to produce a model that can predict the duration of contraceptive use by productive couples. Through Cross-Industry Standard Process for Data Mining (CRISP-DM) process, it tested four experiments to seven data mining techniques with 39.594 contraceptives used histories dataset which is sourced from the Demography and Health Survey of Indonesia (DHS) in 2017. The result shows that the Adaboost data mining technique produced the best performance of contraceptive used prediction model, with the accuracy score of the classification model as 85.1%, precision score as 85.1%, recall score as 85.2%, and F1 as 85.1%. The model produced in this study can be used to estimate the length/duration of a particular type of contraceptive method which is used by each productive couple. That information is useful to prevent discontinuation potencies among contraceptive users for a further period.
Eye And Voice Controlled Wheel Chair

The smart wheelchair developed for people with inabilities dependent on the eye-tracking and by utilizing the voice assistant module. The smart wheelchair contains two modules, including a module for processing the image, a wheelchair-controlled voice associate module and constrained by appliances. The module for picture processing comprises a camera mounted on the wheelchair that can capture the picture and handling those images. The captured picture moved into a Raspberry Pie micro controller process using Open CV to get pupil movement in the 2D bearing. The movement of pupil is then moved remotely to the module for managing a wheelchair. The eyeball movement is also used as the controller to regulate the operations. Speech recognition technology is a technology that provides a way of interaction for human with the wheel chair. Hence, the problems faced by the people can be easily solved by using this technology for controlling the wheel chair. This can be implemented by using the smart phone or smart assistant enabled device as an interface between the human and the wheel chair.
Salient Object Detection Based on Multiscale Segmentation and Fuzzy Broad Learning

Abstract Saliency detection has been a hot topic in the field of computer vision. In this paper, we propose a novel approach that is based on multiscale segmentation and fuzzy broad learning. The core idea of our method is to segment the image into different scales, and then the extracted features are fed to the fuzzy broad learning system (FBLS) for training. More specifically, it first segments the image into superpixel blocks at different scales based on the simple linear iterative clustering algorithm. Then, it uses the local binary pattern algorithm to extract texture features and computes the average color information for each superpixel of these segmentation images. These extracted features are then fed to the FBLS to obtain multiscale saliency maps. After that, it fuses these saliency maps into an initial saliency map and uses the label propagation algorithm to further optimize it, obtaining the final saliency map. We have conducted experiments based on several benchmark datasets. The results show that our solution can outperform several existing algorithms. Particularly, our method is significantly faster than most of deep learning-based saliency detection algorithms, in terms of training and inferring time.
A Study on Sports Player Tracking based on Video using Deep Learning

Tracking the players in the game is essential for the correct evaluation of the players. In order to obtain evaluation indicators for players, such as the moving distance and average speed during a game, it is necessary to continuously track the location and trajectory of the player. The players' movement and events' analysis in the games are mainly recorded by professional analysts. To compensate for this, some sports fields use image processing tools for player tracking and event analysis. In this paper, we study the method that shows excellent performance in the detection and tracking objects recently using deep learning, and discuss how to apply it to sports field.
EPID with Malicious Revocation

EPID systems are anonymous authentication protocols where a device can be revoked by including one of its signatures in a revocation list. Such protocols are today included in the ISO/IEC 20008-2 standard and are embedded in billions of chips, which make them a flagship of advanced cryptographic tools. Yet, their security analysis is based on a model that suffers from several important limitations, which either questions the security assurances EPID can provide in the real world or prevents such systems from achieving their full impact. The most prominent example is the one of revocation lists. Although they could be managed locally by verifiers, which would be natural in most use-cases, the security model assumes that they are managed by a trusted entity, a requirement that is not easily met in practice and that is thus tempting to ignore, as illustrated in the corresponding standard.
Communication-efficient Variance-reduced Stochastic Gradient Descent

We consider the problem of communication efficient distributed optimization where multiple nodes exchange important algorithm information in every iteration to solve large problems. In particular, we focus on the stochastic variance-reduced gradient and propose a novel approach to make it communication-efficient. That is, we compress the communicated information to a few bits while preserving the linear convergence rate of the original uncompressed algorithm. Comprehensive theoretical and numerical analyses on real datasets reveal that our algorithm can significantly reduce the communication complexity, by as much as 95%, with almost no noticeable penalty. Moreover, it is much more robust to quantization (in terms of maintaining the true minimizer and the convergence rate) than the state-of-the-art algorithms for solving distributed optimization problems. Our results have important implications for using machine learning over internet-of-things and mobile networks.
A New Hybrid Filter Approach for Image Processing

Today, with the rapidly advancing technology, the importance of image processing techniques is increasing. Image processing is used in many areas from facial recognition to plant disease identification. One of the important image processing stages is the filtering stage used for smoothing images and object detection. Among these filtering techniques, basic filtering techniques such as mean, median and Gaussian are used in image processing. However, these filtering techniques are known to be insufficient to achieve the desired results in some cases. In this study, a new hybrid filtering approach named Mean-Median-Gaussian (MMG) is presented using these three basic filtering techniques. It has been demonstrated that the obtained MMG hybrid algorithm gives more successful results than these three basic filtering techniques in smoothing the images and determining the boundary lines.
Adaptive primal-dual stochastic gradient method for expectation-constrained convex stochastic programs

Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems. A majority of existing works assume no constraints or easy-to-project constraints. In this paper, we consider convex stochastic optimization problems with expectation constraints. For these problems, it is often extremely expensive to perform projection onto the feasible set. Several SGMs in the literature can be applied to solve the expectation-constrained stochastic problems. We propose a novel primal-dual type SGM based on the Lagrangian function. Different from existing methods, our method incorporates an adaptiveness technique to speed up convergence. At each iteration, our method inquires an unbiased stochastic subgradient of the Lagrangian function, and then it renews the primal variables by an adaptive-SGM update and the dual variables by a vanilla-SGM update. We show that the proposed method has a convergence rate of $$O(1/\sqrt{k})$$ in terms of the objective error and the constraint violation. Although the convergence rate is the same as those of existing SGMs, we observe its significantly faster convergence than an existing non-adaptive primal-dual SGM and a primal SGM on solving the Neyman–Pearson classification and quadratically constrained quadratic programs. Furthermore, we modify the proposed method to solve convex–concave stochastic minimax problems, for which we perform adaptive-SGM updates to both primal and dual variables. A convergence rate of $$O(1/\sqrt{k})$$ is also established to the modified method for solving minimax problems in terms of primal-dual gap. Our code has been released at https://github.com/RPI-OPT/APriD .
User Selection Aware Joint Radio-and-Computing Resource Allocation for Federated Edge Learning

Edge intelligence refers to utilize a large number of distributed data and computing resources to learn and inference directly at network edge. Federated edge learning (FEEL) coordinates local model training of edge devices and global model aggregation at a server through wireless connection. To speed up the learning and reduce the resource consumption of the network, we formulate a problem of joint transmission time allocation, computing frequency control and user selection. We propose an efficient solution to solve the non-convex problem. Experiments show that the proposed scheme not only accelerates the learning process but also significantly reduce network cost.
Automated Genre Classification for Gaming Videos

Besides classical videos, videos of gaming matches, entire tournaments or individual sessions are streamed and viewed all over the world. The increased popularity of Twitch or YoutubeGaming shows the importance of additional research on gaming videos. One important pre-condition for live or offline encoding of gaming videos is the knowledge of game-specific properties. Knowing or automatically predicting the genre of a gaming video enables a more advanced and optimized encoding pipeline for streaming providers, especially because gaming videos of different genres vary a lot from classical 2D video, e.g., considering the CGI content, textures or camera motion. We describe several computer-vision based features that are optimized for speed and motivated by characteristics of popular games, to automatically predict the genre of a gaming video. Our prediction system uses random forest and gradient boosting trees as underlying machine-learning techniques, combined with feature selection. For the evaluation of our approach we use a dataset that was built as part of this work and consists of recorded gaming sessions for 6 genres from Twitch. In total 351 different videos are considered. We show that our prediction approach shows a good performance in terms of f1-score. Besides the evaluation of different machine-learning approaches, we additionally investigate the influence of the hyper-parameters for the algorithms.
Research Report: The Parsley Data Format Definition Language

Any program that reads formatted input relies on parsing software to check the input for validity and transform it into a representation suitable for further processing. Many security vulnerabilities can be attributed to poorly defined grammars, incorrect parsing, and sloppy input validation. In contrast to programming languages, grammars for even common data formats such as ELF and PDF are typically context-sensitive and heterogenous. However, as in programming languages, a standard notation or language to express these data format grammars can address poor or ambiguous definitions, and the automated generation of correct-by-construction parsers from such grammar specifications can yield correct and type- and memory-safe data parsing routines. We present our ongoing work on developing such a data format description language. Parsley is a declarative data format definition language that combines grammars and constraints in a modular way. We show how it can be used to capture data formats such as MAVLink, PDF and ELF. We briefly describe the processing pipeline we are designing to generate verified parsers from these specifications.
Transforming requirements for engineering occupations as affected by technological changes

Abstract This article is devoted to studying the influence of technological changes on transforming the necessary set of skills for engineering occupations. The expected changes in qualification requirements for engineers is based on the analysis of industrial revolution. The growing importance of soft skills is acknowledged. The methodological conclusions regarding the need to take into account the level of triple helix awareness, along with the parameters of the labor market when forecasting the directions for qualification requirements transformation for engineers were substantiated. It is noted that being ahead in the formation of new skills may have a positive impact on the growth in innovative activity. It is this aspect of the relationship between technology and qualification development trends that determines the role of educational organizations in the innovation ecosystem. It is shown that the convergence of technologies leads to an expansion of the range of professions referred to as engineering. The use of particular tools for assessing the demand for competencies allows solving only situational research tasks, whereas in the meanwhile scientifically advanced ecosystems require a system for monitoring technological and qualification changes, primarily in relation to engineering qualifications, since they can become a "bottleneck" of technological development. A methodology for systematizing the influence of technological trends on the qualification requirements is proposed. The mentioned methodology is based on bringing into conformity reciprocal trends on the meso-level within the framework of triple helix cooperation on the regional scale. As the labor market acquires flow characteristics, the need for separate monitoring procedures will decrease, with analysis algorithm simplifying.
Variational Bound of Mutual Information for Fairness in Classification

Machine learning applications have emerged in many aspects of our lives, such as for credit lending, insurance rates, and employment applications. Consequently, it is required that such systems be nondiscriminatory and fair in sensitive features user, e.g., race, sexual orientation, and religion. To address this issue, this paper develops a minimax adversarial framework, called features protector (FP) framework, to achieve the information-theoretical trade-off between minimizing distortion of target data and ensuring that sensitive features have similar distributions. We evaluate the performance of the proposed framework on two real-world datasets. Preliminary empirical evaluation shows that our framework provides both accurate and fair decisions.
Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization

For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.
BlockHR – A Blockchain-based Healthcare Records Management Framework: Performance Evaluation and Comparison with Client/Server Architecture

Over the last decade, the management of healthcare records has been revolutionized due to the need for accurate and cost-efficient patient-centric care alongside the technological advances. Currently, Electronic Health Records (EHRs) are managed using a client-server architecture by which healthcare providers retain the data stewardship. However, this approach suffers from security and privacy issues, a single point of failure, data fragmentation and vulnerability. The data replication, immutability, transparency, security and privacy features of blockchain have a promising future in the healthcare domain addressing the existing issues. In this paper, we propose BlockHR, a healthcare records management framework for healthcare providers and patients enabling better prognosis/diagnosis and follow-up. We analyze the effectiveness of BlockHR in providing security and privacy compared to the client-server approach. We also evaluate the performance of BlockHR versus the client-server approach. Our experimental results demonstrate that client-server approach takes 2.6 times less execution time for data write operation compared to BlockHR. The data retrieval for BlockHR is 20 times faster compared to the client-server approach. We analyze the impact of increasing number of medical records in our evaluation.
Comparing Methods for Mapping Facial Expressions to Enhance Immersive Collaboration with Signs of Emotion

We present a user study comparing a pre-evaluated mapping approach with a state-of-the-art direct mapping method of facial expressions for emotion judgment in an immersive setting. At its heart, the pre-evaluated approach leverages semiotics, a theory used in linguistic. In doing so, we want to compare pre-evaluation with an approach that seeks to directly map real facial expressions onto their virtual counterparts. To evaluate both approaches, we conduct a controlled lab study with 22 participants. The results show that users are significantly more accurate in judging virtual facial expressions with pre-evaluated mapping. Additionally, participants were slightly more confident when deciding on a presented emotion. We could not find any differences regarding potential Uncanny Valley effects. However, the pre-evaluated mapping shows potential to be more convenient in a conversational scenario.