Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost

An increasing amount of users' sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users' information. The privacy parameter bounds the information about the data set leaked by the noisy output. Oftentimes, a data set needs to be used for answering multiple queries, so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of privacy budget spending, which should not exceed the given limit of privacy budget. Moreover, if a query has been answered before and is asked again on the same data set, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design an algorithm to reuse previous noisy responses if the same query is asked repeatedly. In particular, considering that different requests of the same query may have different privacy requirements, our algorithm can set the optimal reuse fraction of the old noisy response and add new noise to minimize the accumulated privacy cost. Furthermore, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. As a result, the owner of the data set will have full knowledge about how the data set has been used and be confident that no new privacy cost will be incurred for answering queries once the specified privacy budget is exhausted.
Engineering fast high-fidelity quantum operations with constrained interactions

Abstract Understanding how to tailor quantum dynamics to achieve the desired evolution is a crucial problem in almost all quantum technologies. Oftentimes an otherwise ideal quantum dynamics is corrupted by unavoidable interactions, and finding ways to mitigate the unwanted effects of such interactions on the dynamics is a very active field of research. Here, we present a very general method for designing high-efficiency control sequences that are fully compatible with experimental constraints on available interactions and their tunability. Our approach relies on the Magnus expansion to find order by order the necessary corrections that result in a high-fidelity operation. In the end finding, the control fields are reduced to solve a set of linear equations. We illustrate our method by applying it to a number of physically relevant problems: the strong-driving limit of a two-level system, fast squeezing in a parametrically driven cavity, the leakage problem in transmon qubit gates, and the acceleration of SNAP gates in a qubit-cavity system.
Accelerating Force-Directed Graph Drawing with RT Cores

Graph drawing with spring embedders employs a V ×V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hard-ware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4× to 13× over a CUDA software implementation.
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.A patient's EHR data is organized as a three-level hierarchy from top to bottom: patient journey -all the experiences of diagnoses and treatments over a period of time; individual visit -a set of medical codes in a particular visit; and medical code -a specific record in the form of medical codes.As EHRs begin to amass in millions, the potential benefits, which these data might hold for medical research and medical outcome prediction, are staggering -including, for example, predicting future admissions to hospitals, diagnosing illnesses or determining the efficacy of medical treatments.Each of these analytics tasks requires a domain knowledge extraction method to transform the hierarchical patient journey into a vector representation for further prediction procedure.The representations should embed a sequence of visits and a set of medical codes with a specific timestamp, which are crucial to any downstream prediction tasks.Hence, expressively powerful representations are appealing to boost learning performance.To this end, we propose a novel selfattention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys, based solely on the proposed attention mechanism.We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.The empirical results demonstrate the proposed BiteNet model produces higher-quality representations than state-of-theart baseline methods.
Bengali Spoken Numerals Recognition by MFCC and GMM Technique

Speech is the standard vocalized communication media. Speech is one of the comfortable way for humans to communicate with each other. Similarly, speech recognition system is eagerly necessary to communicate with computer through voice. Speech recognition in English language already helps us to operate English voice command-based applications. But in rural and semi-urban areas, due to lack of knowledge in English in India, it is necessary to implement automatic speech recognition in regional languages. Here, we have built a Gaussian Mixture Model (GMM)-based Bengali (also called Bangla) isolated spoken numerals recognition system where mel frequency cepstral coefficients denoted as MFCC is taken for feature extraction. The proposed system achieved 91.7% correct prediction for the Bangla numeral data set of 1000 audio samples for 10 classes which is satisfactory for previous Bangla spoken digit recognition.
PHD-SLAM 2.0: Efficient SLAM in the Presence of Missdetections and Clutter

This article addresses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneous localization and mapping</i> (SLAM) via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">probability hypothesis density</i> (PHD) filtering. The resulting approach, named PHD-SLAM, has demonstrated its effectiveness, especially when measurements provided by the sensors onboard the vehicle are highly contaminated by missdetections and clutter. However, since the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proposal distribution</i> (PD) of standard PHD-SLAM does not take into account most recently received measurements, a huge amount of particles are typically needed in order to achieve satisfactory performance. In this article, a new PD, which aims to approximate the vehicle pose posterior, is proposed for PHD-SLAM. The resulting algorithm, named PHD-SLAM 2.0, allows for drastically reducing the number of particles, and hence, the computational burden, while preserving the SLAM performance. The computational complexity of PHD-SLAM 2.0 is analyzed, and its performance is assessed via both simulated and real-data experiments.
Detecting Hate Speech in Social Media Articles in Romanized Sinhala

The main aim of this research is to automatically identify the hate content of social media comments and documents written by the Romanized Sinhala Language. Also most of researched done the hate speech recognition study in English or their language but here identify the Sinhala words that's written in English letters that means Romanized Sinhala language. Hate words and other hated texts are growing issue, and to combat this they turn to machine learning and computer science. In this research compare the several features extraction method and four machine learning algorithms and difference N-gram values unigram, bigram and trigram and used the Min-Df value 3. This study will investigate and compare different features for the different classifier when classifying hate speech comments on Facebook. We have achieved a data set of nearly 2500 comments, some containing hate speech, and trained and tested our classifier with different features.
Mapping gold mineral prospectivity based on weights of evidence method in southeast Asmara, Eritrea

Eritrea at the southern Nubian shield in northeast Africa has many gold deposits, and has been an important target for exploration. In view of the arid to semi-arid nature of the terrain, there is continuous quest for new methods of mineral exploration. In this study, we highlight the importance of GIS-based weights of evidence (WofE) method in mapping the gold mineral prospectivity of the Ewanet area, southeast Asmara. For the purpose, data was gathered from geological, metallogenic, geochemical and remote sensing analyses of the study area. Seven evidence maps produced from these spatial data sets were used to calculate posterior probabilities and delineate prospectivity areas for gold deposits. The prediction-area plot provides the evaluation of posterior probabilities and indicates that 83% of the existing mineral occurrences are predicted in 19% of the study area. Two types of gold occurrences, i.e., structure-alteration type and quartz-vein type, were identified during field work. It was found that faults are the main ore-controlling factors, and Au geochemical anomaly is prominently correlated with mineralization. We suggest that the identified prospectivity areas of gold exploration in the Ewanet area are well worth carrying out further mineral exploration.
Examining competencies for the instructional design professional: An exploratory job announcement analysis

Abstract The purpose of this study is to examine core professional competencies for instructional designers through a job announcement analysis involving both qualitative and quantitative produces used on a qualitative data source. Using a conceptual framework as a guide, we extracted and examined a total of N = 1030 unique job announcements obtained from September to October 2019 from three popular job search databases: Glassdoor.com, Indeed.com and Monster.com. Using a systematic content analysis coding procedure, we coded the absence or presence of N = 185 competencies organized into knowledge, skill and ability statements (80 knowledge statements, 43 skill statements and 62 ability statements). These data were analysed using exploratory factor analysis models for dichotomously scored data, resulting in meaningful factors that are representative of these data. Our results highlight critical competencies for professional instructional designers in each domain, including soft skills , the ability to work with diverse stakeholders and more technical competencies like knowledge of video and audio authoring . The study provides a current snapshot of the knowledge, skills and abilities of instructional designers across settings. We provide a comprehensive discussion of our limitations, findings related to similar works, implications and suggestions for future research.
A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images

Background and objective: Nanoparticles present properties that can be applied to a wide range of fields such as biomedicine, electronics or optics. The type of properties depends on several characteristics, being some of them related with the particle structure. A proper characterization of nanoparticles is crucial since it could affect their applications. To characterize a particle shape and size, the nanotechnologists employ Electron Microscopy (EM) to obtain images of nanoparticles and perform measures over them. This task could be tedious, repetitive and slow, we present a Deep Learning method based on Convolutional Neural Networks (CNNs) to detect, segment, infer orientations and reconstruct microscope images of nanoparticles. Since machine learning algorithms depend on annotated data and there is a lack of annotated datasets of nanoparticles, our work makes use of artificial datasets of images resembling real nanoparticles photographs. Methods: Our work is divided into three tasks. Firstly, a method to create annotated datasets of artificial images resembling Scanning Electron Microscope (SEM). Secondly, two models of convolutional neural networks are trained using the artificial datasets previously generated, the first one is in charge of the detection and segmentation of the nanoparticles while the second one will infer the nanoparticle orientation. Finally, the 3D reconstruction module will recreate in a 3D scene the set of detected particles. Results: We have tested our method with five different shapes of basic nanoparticles: spheres, cubes, ellipsoids, hexagonal discs and octahedrons. An analysis of the reconstructions was conducted by manually comparing each of them with the real images. The results obtained have been promising, the particles are segmented and reconstructed accordingly to their shapes and orientations. Conclusions: We have developed a method for nanoparticle detection and segmentation in microscope images. Moreover, we can also infer an approximation of the 3D orientation of the particles and, in conjunction with the detections, create a 3D reconstruction of the photographs. The novelty of our approximation lies in the dataset used. Instead of using annotated images, we have created the datasets simulating the microscope images by using basic geometrical objects that imitate real nanoparticles.
A Bootstrap Framework for Aggregating within and between Feature Selection Methods

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.
Blockchain and Smart Contract based Decentralized Energy Trading Platform

The blockchain technology and smart contracts have opened up several new horizons for the energy sector. One of them is the possibility of local energy trading in which prosumers can trade their excess energy at the desired price without intermediaries. This paper presents a blockchain and smart contract-based trading platform to realize such kind of local energy trading. The blockchain technology is used to record the power deliveries and transactions, while the smart contract is used to check necessary conditions before executing the energy transactions. An interactive web-based user interface is developed to access the decentralized energy trading platform that incorporates the secure financial and energy deviation settlement mechanisms. It aims to provide transparency, energy affordability, and energy self-sufficiency to the prosumers and consumers at the edge of the grid. It also seeks to increase the usage of distributed renewable energy-based generations and promotes long-term energy security.
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks

Trojan attacks on deep neural networks (DNNs) exploit a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backdoor</i> embedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploiting <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STR</u> ong <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ntentional <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> erturbation of inputs that is a multi-domain input-agnostic Trojan detection defence across <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vi</u> sion, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ext and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> udio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.
Twitter sentiment analysis using deep learning models

Twitter is a massive repository and a gold mine of human thoughts that expresses a person's instant feeling. A retrospective review of tweets during the ensuing COVID-19 pandemic may provide valuable insights into the understanding of a person's feelings. With an enormous amount of data, training a model to understand the precise feeling is a daunting job. There are many developments recorded in the field of deep learning to pave the way forward. The purpose of this paper is to compare the tweet sentiment classification using Google BERT, attention based Bidirectional LSTM and Convolutional Neural Networks (CNNs). The final models are trained on the Twitter dataset SemEval-2016, where the embeddings are fine-tuned again. Such models proved to be highly effective and accurate in the study of emotions as opposed to machine learning techniques.
The Capacity of Single-Server Weakly-Private Information Retrieval

A private information retrieval (PIR) protocol guarantees that a user can privately retrieve files stored in a database without revealing any information about the identity of the requested file. Existing information-theoretic PIR protocols ensure perfect privacy, i.e., zero information leakage to the servers storing the database, but at the cost of high download. In this work, we present weakly-private information retrieval (WPIR) schemes that trade off perfect privacy to improve the download cost when the database is stored on a single server. We study the tradeoff between the download cost and information leakage in terms of mutual information (MI) and maximal leakage (MaxL) privacy metrics. By relating the WPIR problem to rate-distortion theory, the download-leakage function, which is defined as the minimum required download cost of all single-server WPIR schemes for a given level of information leakage and a fixed file size, is introduced. By characterizing the download-leakage function for the MI and MaxL metrics, the capacity of single-server WPIR is fully described.
The Talent Training Mode of International Service Design Using a Human–Computer Interaction Intelligent Service Robot From the Perspective of Cognitive Psychology

To effectively improve the efficiency of international service design talent training and make it more in line with society's needs, we analyze the current status of international service design talent training and its professional training focus. Based on the above problems, from the perspective of cognitive psychology, artificial intelligence and human–computer interaction (HCI) technology are used to construct the international service design talent training mode of the HCI intelligent service robot. This mode can be used to solve the existing teaching problems by using novel means to ensure the quality of teaching. Finally, through the actual analysis of teaching cases, the effectiveness of the proposed talent training mode is verified. The HCI system is based on knowledge of cognitive psychology. According to the characteristics and functions of an educational robot, the robot is combined with traditional teaching activities, and the robot-assisted talent training mode is designed. Robot-assisted talent training is a feasible training method that can improve the efficiency of talent training. Students have confidence in their learning skills before the course, and the confidence is further strengthened after the end of the course. After the course, the students have a stronger sense of cooperation. This study can provide theoretical ideas for the research of international service talent training mode.