Reviewerly/exHarmony huggingface dataset:
The Effect of the Repetitive Utterances Complexity on User’s Perceived Empathy and Desire to Continue Dialogue by a Chat-oriented Dialogue System

In everyday conversation, it is common for participants to repeat all or part of the other’s utterances, and such repetition is often accompanied by elements such as backchannels. If the repetitive utterances of a dialogue system are monotonous, the user may get bored quickly. In this study, we define complexity as the number of elements and patterns associated with repetitive utterances, and examine the effect of the complexity of repetitive utterances on the user’s perceived empathy and desire to continue dialogue. A dialogue experiment was conducted with five university students using both automatic chat-oriented dialogue system and the Wizard of Oz method. The complexity of the repetitive utterances was divided into three conditions: low, moderate, and high, and templates of repetitive utterances were made according to each condition. The results suggest that moderate complexity may be the most effective in increasing the user’s perceived empathy and desire to continue dialogue.
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning

Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing demand to deploy DRL agents to empower daily-life intelligent devices, while the prohibitive complexity of DRL stands at odds with limited on-device resources. In this work, we propose an Automated Agent Accelerator Co-Search (A3C-S) framework, which to our best knowledge is the first to automatically co-search the optimally matched DRL agents and accelerators that maximize both test scores and hardware efficiency. Extensive experiments consistently validate the superiority of our A3C-S over state-of-the-art techniques.
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling

The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
Position and Orientation Control of Three Wheels Swerve Drive Mobile Robot Platform

Drive technology for mobile robots is currently developing very quickly, especially for the type of wheeled driven platform. The driving models such as Ackermann steering, DDMR, and Omni-wheel robots have been widely implemented as mobile robot platforms. However, to answer the challenges in the robot contest where the competition is getting tougher, research is needed on a mobile platform that is more efficient, faster, and more precise. In this research, the design and fabrication of a mobile robot platform with a swerve drive model will be carried out. Swerve drive has independent driving and steering at each wheel. This model can provide a higher speed and freedom of maneuver for the robot compared to the DDMR, Ackermann steering, and Omni wheel drive models. However, swerve drive requires a higher number of motors as well as a more complex control algorithm in regulating the speed of the wheel drive motor and the steering angle on each independent wheel to be further controlled simultaneously in moving the robot to the target position and orientation. In terms of the control system, a multi-level control will be made where the low-level control will regulate the speed of the wheel drive and its relative direction to the robot’s body. Meanwhile, high-level control will be used to coordinate the movement of each wheel so that the results can make the robot to move according to the given trajectory.
A Fast and Accurate Approximation to the Distributions of Quadratic Forms of Gaussian Variables

In computational and applied statistics, it is of great interest to get fast and accurate calculation for the distributions of the quadratic forms of Gaussian random variables. This paper presents a novel approximation strategy that contains two developments. First, we propose a faster numerical procedure in computing the moments of the quadratic forms. Second, we establish a general moment-matching framework for distribution approximation, which covers existing approximation methods for the distributions of the quadratic forms of Gaussian variables. Under this framework, a novel moment-ratio method (MR) is proposed to match the ratio of skewness and kurtosis based on the gamma distribution. Our extensive simulations show that 1) MR is almost as accurate as the exact distribution calculation and is much more efficient; 2) comparing with existing approximation methods, MR significantly improves the accuracy of approximating far right tail probabilities. The proposed method has wide applications. For example, it is a better choice than existing methods for facilitating hypothesis testing in big data analysis, where efficient and accurate calculation of very small $p$-values is desired. An R package Qapprox that implements related methods is available on CRAN.
Adaptive Regularized Sparse Representation for Weather Radar Echo Super-Resolution Reconstruction

Ability of weather radar to detect extreme weather on medium and small scale is limited by the resolution of its grid data. Low-resolution weather radar data may cause false warnings and forecasts of small and medium scale in extreme weather. The aim of this work is to provide a solution for enhancing the resolution of weather radar data. A super-resolution reconstruction algorithm of weather radar data based on adaptive regularized sparse representation (ARSR) is proposed, which incorporate autoregressive (AR) and non-local (NL) adaptive regularization terms in the sparse representation framework by effectively using local and non-local information from weather radar echo data to improve the reconstruction effect of the edge and detail information of the radar echo. Experimental results show that the ARSR algorithm substantially outperforms common interpolation for &#x00D7;2 and &#x00D7;4 resolution improvement in terms of both objective evaluation metrics and visual quality, especially for the reconstruction of the edge and detail information of the weather radar echo.
Revisiting the Concrete Security of Goldreich’s Pseudorandom Generator

Local pseudorandom generators are a class of fundamental cryptographic primitives having very broad applications in theoretical cryptography. Following Couteau <i>et al.&#x2019;s</i> work at ASIACRYPT 2018, this paper further studies the concrete security of one important class of local pseudorandom generators, i.e., Goldreich&#x2019;s pseudorandom generators. Our first attack is of the guess-and-determine type. Our result significantly improves the state-of-the-art algorithm proposed by Couteau <i>et al.</i>, in terms of both asymptotic and concrete complexity, and breaks all the challenge parameters they proposed. For instance, for a parameter set suggested for 128 bits of security, we could solve the instance faster by a factor of about 2<sup>77</sup>, thereby destroying the claimed security completely. Our second attack further exploits the extremely sparse structure of the predicate <inline-formula> <tex-math notation="LaTeX">$P_{5}$ </tex-math></inline-formula> and combines ideas from iterative decoding. This novel attack, named guess-and-decode, substantially improves the guess-and-determine approaches for cryptographic-relevant parameters. All the challenge parameter sets proposed in Couteau <i>et al.&#x2019;s</i> work in ASIACRYPT 2018 aiming for 80-bit (128-bit) security levels can be solved in about 2<sup>58</sup> (2<sup>78</sup>) operations. We suggest new parameters for achieving 80-bit (128-bit) security with respect to our attacks. We also extend the attacks to other promising predicates and investigate their resistance.
Flight Control of a Multicopter using Reinforcement Learning

Machine Learning, and in particular Reinforcement Learning, is a persistent trend in automation and robotics in recent years. Many researchers worldwide are developing intelligent controllers using Reinforcement Learning techniques. This paper aims to present a proof-of-concept Reinforcement Learning flight controller for a multicopter. The agent has been trained in the Airsim simulation environment to achieve stable flight conditions by controlling its roll, pitch, yaw and throttle. After training, the agent has been tested on the same environment to prove its ability to maintain stable flight conditions while following a determined route.
Multiclass Imbalanced Big Data Classification Utilizing Spark Cluster

Because of the massive increase in data collection and storage that has occurred in recent years, big data applications are increasingly becoming the focus of attention. The difficulty of classification with imbalanced datasets is one of the complexities that make extracting meaningful information difficult, and the key impact arises from its existence in a variety of real-world applications. Because of the variety as well as the veracity of such obtained data, big data is impacted by an imbalance of classes. Furthermore, in real-world data applications, samples from one class, which is the core concern, are frequently vastly dominated by samples from other classes. In this study, we have proposed an approach using block-level undersampling and synthetic data point generation to deal with imbalanced big data. Furthermore, the performance of Random Forest and Decision Tree algorithms in dealing with imbalanced datasets in the big data context has been evaluated. Extensive experiments have been performed utilizing Apache Spark Cluster in the development of the different discussion methods. The proposed technique can handle massive datasets while still offering the assistance required to accurately categories classes with a comparatively less number of instances.
The Return of Eratosthenes: Secure Generation of RSA Moduli using Distributed Sieving

Secure multiparty generation of an RSA biprime is a challenging task, which increasingly receives attention, due to the numerous privacy-preserving applications that require it. In this work, we construct a new protocol for the RSA biprime generation task, secure against a malicious adversary, who can corrupt any subset of protocol participants. Our protocol is designed with generic multiparty computation (MPC), making it both platform-independent and allowing for weaker security models to be assumed (e.g., honest majority), should the application scenario require it. By carefully "postponing" the check of possible inconsistencies in the shares provided by malicious adversaries, we achieve noteworthy efficiency improvements. Concretely, we are able to produce additive sharings of the prime candidates, from multiplicative sharings via a semi-honest multiplication, without degrading the overall (active) security of our protocol. This is the core of our sieving technique, increasing the probability of our protocol sampling a biprime. Similarly, we perform the first biprimality test, requiring several repetitions, without checking input share consistency, and perform the more costly consistency check only in case of success of the Jacobi symbol based biprimality test. Moreover, we propose a protocol to convert an additive sharing over a ring, into an additive sharing over the integers. Besides being a necessary sub-protocol for the RSA biprime generation, this conversion protocol is of independent interest. The cost analysis of our protocol demonstrated that our approach improves the current state-of-the-art (Chen et al.-Crypto 2020), in terms of communication efficiency. Concretely, for the two-party case with malicious security, and primes of 2048bits, our protocol improves communication by a factor of ~37.
Cyber-Bullying Detection Via Text Mining and Machine Learning

Cyber Bullying is one of the most recent evils of social media. With a boom in the usage of social media, the freedom of expression is being exploited. Statistics show that overall 36.5 percent people think they have been cyberbullied in their lifetime. These numbers are more than double of what they were in 2007, and there is an increase from 2018–19, suggesting we are heading in the wrong direction. Solutions to curtail this issue to a certain extent have already been deployed in the market. However, they possess limitations of usage, or simply do not use efficient algorithms. This paper aims at identifying cyberbullying at is origin, meaning when it is being drafted in real-time. Identifying traces of cyberbullying before the content is uploaded on the internet can help reduce circulation of hurtful messages. Using Machine Learning with the support of Natural Language Processing(NLP, results in better performance of cyberbullying detection.
Activity Recognition Based on FR-CNN and Attention-Based LSTM Network

A human activity recognition (HAR) based on the Faster Region-based Convolutional Neural Network (FR-CNN) and attention-based LSTM networks is proposed in this paper. A new structure of posture vector is generated by extracting skeleton joints of human movement using the pre-trained FR-CNN model. The Cornell Activity Dataset (CAD-60) is employed in the training and test phases. An attention-based bidirectional LSTM (Bi-LSTM) network is presented for activity classification. Experimental result shows that the attention-based Bi-LSTM network achieves the precision and recall rate of 97.02% and 96.83%, respectively, in recognizing twelve activities. The result is superior to the other existing systems, indicating the application potential of our work.
Sliced Recursive Transformer

We present a neat yet effective recursive operation on vision transformers that can improve parameter utilization without involving additional parameters. This is achieved by sharing weights across depth of transformer networks. The proposed method can obtain a substantial gain ( $$\sim $$ 2%) simply using naïve recursive operation, requires no special or sophisticated knowledge for designing principles of networks, and introduces minimal computational overhead to the training procedure. To reduce the additional computation caused by recursive operation while maintaining the superior accuracy, we propose an approximating method through multiple sliced group self-attentions across recursive layers which can reduce the cost consumption by 10–30% without sacrificing performance. We call our model Sliced Recursive Transformer (SReT), a novel and parameter-efficient vision transformer design that is compatible with a broad range of other designs for efficient ViT architectures. Our best model establishes significant improvement on ImageNet-1K over state-of-the-art methods while containing fewer parameters. The proposed weight sharing mechanism by sliced recursion structure allows us to build a transformer with more than 100 or even 1000 shared layers with ease while keeping a compact size (13–15 M), to avoid optimization difficulties when the model is too large. The flexible scalability has shown great potential for scaling up models and constructing extremely deep vision transformers. Code is available at https://github.com/szq0214/SReT .
A Novel Generation Method for Diverse Privacy Image Based on Machine Learning

Abstract In recent years, deep neural networks have been extensively applied in various fields, and face recognition is one of the most important applications. Artificial intelligence has reached or even surpassed human capabilities in many fields. However, while artificial intelligence application provides convenience to the human lives, it also leads to the risk of privacy leaking. At present, the privacy protection technology for human faces has received extensive attention. Research goals of face privacy protection technology mainly include providing face anonymization and data availability protection. Existing methods usually have insufficient anonymity and they are not easy to control the degree of image distortion, which makes it difficult to achieve the purpose of privacy protection. Moreover, they do not explicitly perform diversity preservation of attributes such as emotions, expressions and ethnicities, so they cannot perform data analysis tasks on non-identity attributes. This paper proposes a diverse privacy face image generation algorithm based on machine learning, called DIVFGEN. This algorithm comprehensively considers image distortion, identity mapping distance loss and emotion classification loss; transforms the privacy protection target into the problem of generating adversarial examples based on the recognition model; and uses an adaptive optimization algorithm to generate anonymity and diversity of privacy images. The experimental results show that on the Cohn-Kanade+ dataset, our algorithm can reduce the probability of facial recognition by the neural network when it accurately classifies sentiment, from 98.6% to 4.8%.
Visual Analytics of Trajectories with RoseTrajVis

We propose RoseTrajVis, a visual analytics system for studying commuting behaviors using classic rose diagrams, which, given a location in a map, aggregate trajectories by direction, show additional summary information, such as average speed bands, and allow the application of spatial-temporal filters. The rose diagrams also include colored arrows on the border that point to the origin and destination of the trajectories and show the average speed. To support analytical work, the user can also adjust the aggregation radius, move a location marker to a different position on the map with automatic update of the corresponding rose diagram, and create multiple diagrams for the same location with different filters applied. We developed two prototypes of RoseTrajVis, which were evaluated through user studies covering various types of analytical tasks with trajectories. Results suggest that the concept of rose diagrams was well understood, that the system was easy to use, and reveal that most tasks were executed with no errors. RoseTrajVis provides an innovative way of aggregating trajectories and offers features that enable the analysis of commuting movements and other exploratory tasks on a map, to support urban planning and operation.
Designing Historical Tours for Head-Worn AR

Cultural heritage sites, such as historic buildings, ruins, or archaeological digs can provide a glimpse into the past via physical and/or geographic context. Augmented Reality (AR) can be used to enhance real artifacts and places to allow for deeper understanding. However, to date, AR experiences for history education have been primarily handheld experiences on smartphones or tablets. The design and use of head-worn AR for on-site history education needs further investigation. In this work, we aimed to explore best practices for creating such experiences. We describe the design and evaluation of Solitude AR, a head-worn AR experience exploring the history of Solitude, a historic site on the campus of our university. We present findings from a user-experience study of Solitude AR and lessons learned for the design of future head-worn AR experiences for on-site history education.
Optimizing single-photon generation and storage with machine learning

Single photons are at the heart of quantum information processing. The tasks of generating and storing single photons with arbitrary wave-packet shapes are crucial for building quantum networks, but they remain challenging. Here, we present a general machine learning (ML) algorithm with a self-adaptive process to optimize the control of a cavity-atom system for these tasks. This ML algorithm shows high efficiency and fidelity for both generation and storage of single photons. This ML-enhanced single-photon interface may pave the way for building flexible and reliable quantum networks because this ML algorithm can automatically adjust the quantum system according to single-photon wave functions in an ``intelligent'' way.
Policy-Compliant Signatures

We introduce policy-compliant signatures (PCS). A PCS scheme can be used in a setting where a central authority determines a global policy and distributes public and secret keys associated with sets of attributes to the users in the system. If two users, Alice and Bob, have attribute sets that jointly satisfy the global policy, Alice can use her secret key and Bob's public key to sign a message. Unforgeability ensures that a valid signature can only be produced if Alice's secret key is known and if the policy is satisfied. Privacy guarantees that the public keys and produced signatures reveal nothing about the users' attributes beyond whether they satisfy the policy or not. PCS extends the functionality provided by existing primitives such as attribute-based signatures and policy-based signatures, which do not consider a designated receiver and thus cannot include the receiver's attributes in the policies. We describe practical applications of PCS which include controlling transactions in financial systems with strong privacy guarantees (avoiding additional trusted entities that check compliance), as well as being a tool for trust negotiations.
Estimating Gender Based On Bengali Conventional Full Name With Various Machine Learning Techniques

For finding patterns in data, machine learning models are being trained. Gender relations psychology looks for social norms like inter dimensionality, beliefs, social experience and self-perception, and self-respect. Training on gender based text NLP models unknowingly become acquainted with unusual patterns. In this paper, we represent gender recognition by using Bengali conventional full names. We present a review and interpretation of gender classification based on individual names in this correspondence. These days, NLP has demonstrated excellent execution in identifying human gender. In the field of knowledge, gender classification is a demonstrative binary classification phenomenon. We've used a total of seven algorithms in this research. We were added to the dataset with details regarding which features are currently used for prediction along with that it determines how these features are affected by data preprocessing model initialization and architecture selection. Our research compares those classifiers, examines the impact of pretraining moreover, assesses the robustness of the alignment preprocessing through the confusion matrix.. The proposed Neural Network outperforms most approaches and is much more reliable than other models. This model has the best weighted precision of all the models, with such a 73.04 % accuracy score.