Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving and Fault-Tolerant Aggregation of Time-Series Data With a Semi-Trusted Authority

Time-series data aggregation in Internet of Things applications is a useful operation, where the time-series data is sensed by a group of users, and gathered by the aggregator for real-time analysis. However, some security and privacy challenges still affect the collection and aggregation process. Although existing privacy-preserving solutions achieve strong privacy guarantees, they introduce a fully trusted TA that is difficult to realize in the real world. Besides, they cannot be directly applied in time-series data aggregation scenarios due to unacceptable efficiency. In this article, we propose a privacy-preserving time-series data aggregation scheme with a semi-trusted authority. Moreover, our scheme also supports arbitrary aggregate functions and fault tolerance to enhance the reliability and scalability of data aggregation. Security analysis demonstrates that our proposed scheme achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n-k)$ </tex-math></inline-formula> -source anonymity even if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k(k\leq (n-2))$ </tex-math></inline-formula> data providers collude with the cloud server. We also conduct thorough experiments based on a simulated data aggregation scenario to show the high computation and communication efficiency of our scheme.
A Methodology of Trusted Data Sharing across Telecom and Finance Sector under China’s Data Security Policy

Data security policies have significant impacts on big data and artificial intelligence applications, and yield data silo due to the data inaccessibility among the commercial companies for privacy-preserving. On September 1st, 2021, China officially implemented the Data Security Law. This paper timely proposes, validates, and productizes a trusted data sharing solution based on Vertical Federated Learning (VFL) technology across telecom and finance companies. A VFL model with Hetero Secure Boost Tree (HSBT) algorithm is proposed to overcome the data silo problems and to preserve user data privacy. Experimental results demonstrate that the model with both financial and telecom data improves the success rate of marketing for a tier-1 commercial bank in China. The solution has also been successfully implemented to greatly improve the marketing efficiency, resulting an approximately 50% cost reduction.
HarX: Real-time harassment detection tool using machine learning

Cybersecurity has a great deal of importance over the digital market for organizations in this modern era. Nowadays all kinds of communications and connections are established by using the internet. Chatting is a main source of communication. The major problem faced by users is harassment. User starts to get harassed frequently as most of users does not know what to do and how to take action or how to stop this. In this work, we employ machine learning and natural language processing to tackle online harassment. This study proposed a real time machine learning based algorithm which detects harassment actively and alert user to take action against it. For detection mechanism, Naïve Bayes classification is used. The proposed approach attain approximately 77% accuracy. The result shows that the algorithm actively detects harassing keywords in chat messages.
Deep Multiscale Multi-Instance Networks With Regional Scoring for Mammogram Classification

Mammography is one of the most commonly used methods for breast cancer screening. Most mammogram classification or segmentation models are trained with additional manual segmentation annotation data, which is difficult to obtain. Meanwhile, these models rarely take into account the fact that lesion features may vary at different scales. Moreover, most models have difficulty in quantifying the effect of single lesion regions on prediction results. To solve the above problems, we propose deep multiscale multi-instance networks with regional scoring. This model classifies mammograms automatically by image labels without using any other annotation information during the training procedure. The proposed multiscale structures include large scale, medium scale, and small scale, and they can extract features of mammograms from global to local. Because there may be more than one breast lesion region in a mammogram, the proposed multi-instance structure divides the image into multiple regions and selects few regions of interest to represent the whole image together. The regional scoring mechanism quantifies the contribution of each region to the prediction results, and thus, makes the results interpretable. In addition, the proposed method can roughly localize the lesion area by the score of each region. The experimental results on a publicly available dataset and a private dataset have shown that the proposed method achieved the highest AUC on both datasets compared with the state-of-the-art models.
THE IMPLEMENTATION OF SELF-DIRECTED LEARNING STRATEGY IN TEACHING READING NARRATIVE TEXT IN DISTANCE LEARNING DURING COVID-19

This study aims to know how to implement a self-directed learning strategy in teaching reading narrative text in distance learning during covid 19 periods in ninth-grade students of SMP Yadika 12 Depok in the 2020/2021 school year. The data sources used are primary and secondary. Primary sources are data sources that directly provide data collection data where these sources are obtained from English language teachers. Meanwhile, secondary sources are data sources that do not directly provide data collection data where these sources are obtained from 49 students. This research method used a qualitative method with a descriptive analysis approach, so the authors conducted research directly to obtain in-depth information. The data collection techniques in this study used interviews and documentation. The research results in implementing a self-directed learning strategy in teaching narrative text in distance learning during Covid-19 determine the text to be read to practice reading skills. Then, through a guide made by the teacher, students practice independently.
On the Efficiency of the SST Planner to Find Time Optimal Trajectories Among Obstacles With a DDR Under Second Order Dynamics

In this work, we study a sampling based motion planner able to deal with a kinodynamic problem. We want to move a robot from an initial state to a final one, along the minimum cost trajectory in an environment with obstacles. In particular, we study the effect of using extremal controls as the inputs for two sampling-based algorithms, namely, the Stable Sparse Rapidly Exploring Random Tree (SST), and the asymptotically optimal planner SST*, in terms of the speed at which such methods converge and the resulting cost of a given stable trajectory. To exemplify our analysis and demonstrate the usefulness of the present study, we elaborate on the case of finding time optimal trajectories among obstacles for a differential drive robot (DDR) considering second-order dynamics. To further show the generality of the approach, we also present an experimental study comparing the use of extremal controls against the use of the entire range of controls, for other four systems. We found that utilizing extremal controls improves the convergence of the addressed algorithms.
A Global Cost-aware Container Scheduling Strategy in Cloud Data Centers

Large-scale Internet applications running on data centers are typically instantiated as a set of containers. Assigning a container to its affinity machine can reduce communication and transport costs while assigning it to the anti-affinity machine may affect the proper operation of the container. Existing container scheduling methods cannot accommodate these two types of requirements. In order to reduce the operation and maintenance cost of data centers, this paper focuses on the container instance allocation problem in heterogeneous server cluster, and proposes a global cost-aware scheduling algorithm (GCCS) to solve it. The purpose is to minimize the total power consumption of the cluster from a global perspective, while trying to meet the affinity/anti-affinity requirements of applications. We study the number of containers per server selected by the application, model it as an integer linear program (ILP), and then propose a heuristic search algorithm to repair the relaxation solution of the ILP into a suboptimal feasible solution. In particular, we use Bayesian optimizer to perform a number of automated development and exploration processes for the selection of the cost coefficient. The experiments are carried out with the best cost coefficient recommended by Bayesian optimizer. Finally, the results demonstrate that GCCS can significantly reduce the total power consumption of the cluster, while maintaining a high affinity satisfaction ratio.
High‐speed parallel reconfigurable <i>F</i><sub><i>p</i></sub> multipliers for elliptic curve cryptography applications

Abstract Elliptic curve cryptography (ECC) protocols due to higher security strength per bit have been widely accepted and deployed. Finite field multiplication is the most computational intensive operation in data security protocols developed using ECC. This paper presents two high‐speed parallel re‐configurable finite field multipliers: PIMD‐2 and PIMD‐3 over prime field ( F p ) for ECC applications. The proposed designs are based on the new novel optimized interleaved multiplication algorithms. This work first identifies room of parallelism by investigating independent operations in the standard interleaved multiplication method and subsequently proposes high‐speed hardware architectures that allow the parallel execution of these operations. Due to the introduced modifications, the critical path delays and clock cycle consumption in the PIMD‐2 and PIMD‐3 designs are reduced simultaneously. The proposed F p multipliers are synthesized using Xilinx ISE Design Suite and implemented on Virtex‐5 and Virtex‐6 field programmable gate array (FPGA) platforms for common ECC key sizes 160–521 bits. The implementation results reveal that the proposed designs are highly efficient, provided up to 3× improvement in latency with lower area‐delay product and higher throughput per FPGA slice as compared to the state‐of‐the‐art.
Study on the Impacts of Feature Indexes on Intelligent Identification of Communication Modulation Mode

The traditional recognition method of signal modulation mode gradually presents the phenomena of low accuracy and poor interference resistance. This paper proposes an intelligent recognition method of signal modulation mode based upon time-frequency domain, multi-entropy feature extraction and Support Vector Machine (SVM). The multi-entropy feature extraction includes permutation entropy, sample entropy, fuzzy entropy, wavelet entropy, and information entropy. In this paper, we use the methods of multiple feature extraction to extract the features of the modulating signals. Then, we use the SVM model to realize the classification and recognition of signals. The simulation results indicate that with multi-entropy, the comprehensive recognition accuracy of the model is over 87.00%, which is significantly higher than that of the model with only time-frequency domain features. Moreover, this paper further studies the impacts of different feature indexes on intelligent recognition of signal modulation mode in the communication system.
Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data

Although data quality is of paramount importance in algorithmic decision–making, most existing methods for supervised classification use training data without ever questioning their fidelity. At the same time, counterfactual explanation approaches widely used for post–hoc explanation of algorithmic decisions may result in unrealistic recommendations when left unconstrained. This work highlights a significant research problem, and introduces a novel framework to improve supervised classification in the presence of untrustworthy data, while offering actionable suggestions when an undesirable decision has been made (e.g., loan application rejection). Evaluation results spanning datasets from different domains demonstrate the superiority of the proposed approach, and its comparative advantage as the percentage of mislabeled instances increases.
Social Interaction in Virtual Shopping

With the increasing popularity of e-commerce and the coronavirus situation, an increasing number of shoppers are opting for online shopping because of store closures and their fear of contracting the coronavirus in public. While conventional retail provides consumers with a full spectrum of interaction, online shopping has been deficient in these types of experiences. Therefore, the virtual reality technology is used to bridge the gap between the two shopping techniques and create a more natural and intuitive shopping environment. The aim of this research is to investigate how, in a virtual store environment, consumers’ interaction affect their shopping experience. A virtual supermarket with the interaction facility was designed. An experiment was conducted to track the effect of the social interaction on the consumers using different metrics. The results showed that consumers’ social interaction in the form of avatar- mediated communication has a beneficial impact on their social presence. It also demonstrates that consumers felt more immersed and socially engaged to the shopping environment via the social interaction among avatars.
Impacts of Homophone Normalization on Semantic Models for Amharic

Amharic is the second-most spoken Semitic language after Arabic and serves as the official working language of the government of Ethiopia. In Amharic writing, there are different characters with the same sound, which are called homophones. The current trend in Amharic NLP research is to normalize homophones into a single representation. This means, instead of character <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We have used the IPA notation for Amharic character transliteration, , and , the character will be used; instead of , and , the character will be replaced; and so on. This was done by the assumption that they are repetitive alphabets as they have the same sound. However, the impact of homophone normalization for Amharic NLP applications is not well studied. When one homophone character is substituted by another, there will be a meaning change and it is against the Amharic writing regulation. For example, the word is “poverty” while means “salvage”. These two words are homophones, but they have different meanings. To study the impacts of homophone normalization, we develop different general-purpose pre-trained embedding models for Amharic using regular and normalized homophone characters. We fine-tune the pre-trained models and build some Amharic NLP applications. For PoS tagging, a model that employs a regular FLAIR embedding model performs better, achieving an F1-score of 77%. For sentiment analysis, the model from regular RoBERTa embedding outperforms the other models with an F1-score of 60%. For IR systems, we achieve an F1-score of 90% using the normalized document. The results show that normalization is highly dependent on the NLP applications. For sentiment analysis and PoS tagging, normalization has negative impacts while it is essential for IR. Our research indicates that normalization should be applied with caution and more effort towards standardization should be given.
Model Predictive Control based Cooperative Robot Manipulation and Collision Avoidance in Shared Workspaces

We present a model predictive control algorithm for cooperative trajectory planning of robot manipulators subject to collision avoidance in a shared confined environment. To this end, we propose the approximation of the geometry of the robots by smooth Bézier curves and spherical objects. Addition-ally, we introduce velocity constraints for collision avoidance, enforcing such robot geometry approximations to slide along tangential planes defined by two collision spheres placed at the corresponding nearest points of the two robots. These velocity constraints are embedded into a centralized model predictive control algorithm for the computation of minimum-time trajectories for two cooperative robot manipulators. The performance of the proposed control scheme is demonstrated by simulations of highly critical coordinated synchronous pick-and-place robot task executions.
Automated Code Refactoring upon Database-Schema Changes in Web Applications

Modern web applications manipulate a large amount of user data and undergo frequent data-schema changes. These changes bring up a unique refactoring task: updating application code to be consistent with data schema. Previous study and our own investigation show that this type of refactoring is error-prone and time-consuming for developers. This paper presents EvolutionSaver, a static code analysis and transformation tool that automates schema-related code refactoring and consistency checking. EvolutionSaver is implemented as an IDE plugin that works for both Rails and Django applications. The source code of EvolutionSaver is available on Github [1] and the plugin can be downloaded from Visual Studio Marketplace [2], with its tutorial available at https://www.youtube.com/watch?v=qBiMkLFIjbE and DOI 10.5281/zenodo.5276127.
On-Orbit Real-Time Variational Image Destriping: FPGA Architecture and Implementation

On-orbit real-time image processing is of increasing demands due to requirements for quick-response missions. Image destriping is usually an important pre-processing step to improve image quality in practice. The unidirectional variational models have shown impressive destriping performance. However, they are not easy for real-time implementation for high-computation complexity and there are few attempts for hardware implementation. This article is the first hardware implementation of variational image destriping algorithm, which achieves high throughput for large-swath remote-sensing images. In this article, a fully pipelined hardware architecture is proposed. First, the involved iteration loop is unrolled and a coarse-grained parallelism is obtained. Second, for each deployed iteration computation blocks (ICBs), a dedicated timing arrangement is designed to alleviate the bottleneck caused by the data dependency within each ICB, obtaining a fine-grained parallelism. Moreover, to further optimize the critical path, an approximate simplification scheme is proposed, saving the resource usage and reducing computing delay. The proposed architecture is implemented and verified on a XILINX 6vcx240t field programmable gate array (FPGA); it achieves a maximum frame rate up to 41.9 frames/s with delay of only tens of row cycles for 8-bit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${{2048}} \times {{2048}}$ </tex-math></inline-formula> images. It performs all the processing on the pixels in raster scan order on- the-fly as they are being transmitted from camera payload, which significantly facilitates on- orbit real-time processing of large-swath remote-sensing images with high data rate.
From linear to non-linear: investigating the effects of right-rail results on complex SERPs

Modern search engine result pages (SERPs) become increasingly complex with heterogeneous information aggregated from various sources. In many cases, these SERPs also display results in the right rail besides the traditional left-rail result lists, which change the linear result list to a non-linear panel and might influence user search behavior patterns. While user behavior on the traditional ranked result list has been well studied in existing works, it still lacks a thorough investigation of the effects caused by the right-rail results, especially on complex SERPs. To shed light on this research question, we conducted a user study, which collected participants' eye movements, detailed interaction behavioral logs, and feedback information. Based on the collected data, we analyze the influence of right-rail results on users' examination patterns, search behavior, perceived workload, and satisfaction. We further construct a user model to predict users' examination behavior on non-linear SERPs. Our work contributes to understanding the effects of the right-rail results on users' interaction patterns, benefiting other related research, such as the evaluation and UI optimization of search systems.
Graph Signal Denoising Methods Using Sparseness and Bandlimitedness Priors in GFT Domain

In this paper, graph signal denoising methods using sparseness and bandlimitedness priors in graph Fourier transform (GFT) domain are presented. First, the conventional smoothness prior method in vertex domain is described. Then, the sparseness prior method in GFT domain is developed to denoise the graph signal by using convex optimization. Next, the bandlimitedness prior method in GFT domain is presented to solve the denoising problem whose solution is obtained by using least squares (LS) method. Finally, the irregular temperature data collected from the sensor network is used to demonstrate the effectiveness of the proposed signal denoising methods and comparisons are made.
Subcarrier wave quantum key distribution with leaky and flawed devices

In this paper, we consider the influence of device imperfections on the performance of the subcarrier wave quantum key distribution system. For instance, we consider the utilization of a non-phase-randomized coherent light source, the possibility of detector blinding, and its consequences. The common opinion is that the presence of a coherent light source in the system provides the possibility for a photon number splitting attack to an eavesdropper. We show that the attack is inapplicable or inefficient in the case of the considered states (without assumption of phase randomization). We also provide the conditions for estimation of a detector blinding attack. Moreover, we demonstrate that not only hardware countermeasures are applicable to revealing the attack, but also utilization of special protocol construction can be implied. Thus, we conclude that the imperfections of the considered devices can easily be leveled due to the features of implemented states and protocol construction.