Reviewerly/exHarmony huggingface dataset:
StyleDGPT: Stylized Response Generation with Pre-trained Language Models

Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training.In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks.To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level.Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform stateof-the-art methods in terms of both style consistency and contextual coherence.
Towards Learning Convolutions from Scratch

Convolution is one of the most essential components of architectures used in computer vision. As machine learning moves towards reducing the expert bias and learning it from data, a natural next step seems to be learning convolution-like structures from scratch. This, however, has proven elusive. For example, current state-of-the-art architecture search algorithms use convolution as one of the existing modules rather than learning it from data. In an attempt to understand the inductive bias that gives rise to convolutions, we investigate minimum description length as a guiding principle and show that in some settings, it can indeed be indicative of the performance of architectures. To find architectures with small description length, we propose $\beta$-LASSO, a simple variant of LASSO algorithm that, when applied on fully-connected networks for image classification tasks, learns architectures with local connections and achieves state-of-the-art accuracies for training fully-connected nets on CIFAR-10 (85.19%), CIFAR-100 (59.56%) and SVHN (94.07%) bridging the gap between fully-connected and convolutional nets.
Fully Private Coded Matrix Multiplication From Colluding Workers

Recently, coded computation has been used to reduce the completion time in distributed computing by mitigating straggler effects with erasure codes. As a variation of coded computation, fully private coded matrix multiplication (FPCMM) has been proposed to preserve a master's privacy in a scenario where the master wants to obtain a matrix multiplication result from the libraries which are shared at the workers, while concealing both of the two indices of the desired matrices from each worker. In this letter, we propose a new FPCMM scheme to keep a master's privacy from up to T colluding workers. Furthermore, we compare the performance of our scheme with those of the existing private coded matrix multiplication schemes for non-colluding workers and for concealing the index of a single desired matrix.
Drone versus Bird Flights: Classification by Trajectories Characterization

This paper presents an alternative approach to identify and classify the small flying drones from the birds in near field, by examining the pattern of their flight paths and trajectories. The trajectories of the drones and birds were extracted from multiple clips of videos including various natural and synthetic database. Small drones that are piloted both automatically and manually usually fly in a stable manner. Their flight paths are usually straight and smooth with sharp angle turns. Whereas birds being natural flyers have flight paths that are intrinsically periodic due to their flapping motion with occasional straight glides and soaring sections. Five (5) trajectories characteristics and observed from the object's flight paths: turning angle, periodicity (frequency), curvature, object pace (velocity and acceleration). Subsequently, principal component analyses were applied to reduce the number of these trajectory features from 5 to 2 parameters. Classification by support vector machine with Non-linear transformation kernel was used. Sample test results show that the prediction was ≥80% accurate even at confident level of 90%. These predictions can be improved by gather more training data to cover more cases.
A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss

At present, occlusion and appearance similarity pose severe challenges to person re-identification tasks.Although many robust deep convolutional neural networks alleviate these problems, convolutional layers with limited receptive fields cannot model global semantic information well.In addition, in the person re-identification model, many metric losses ignore or destroy the intra-class structure of the sample, which makes the model difficult to be optimized.Therefore, we design a discriminative Re-identification model with global-local attention and adaptive weighted rank list loss (GLWR).Specifically, our global-local attention (GL-Attention) learns the semantic context in the channel and spatial dimensions.By learning the dependencies between features, GL-Attention integrates global semantic information into local features to extract discriminative features.Unlike rank list loss, our adaptive weighted rank list loss (WRLL) adaptively assigns weights according to the metric distance between the negative sample and the input image, which further improves the performance of the model.Experimental studies on three public datasets (Market-1501, DukeMTMC-ReID and CUHK03) indicate that the performance of our GLWR is significantly superior to many of the latest algorithms.
Physical-model-based reconstruction of axisymmetric three-dimensional sound field from optical interferometric measurement

Optical interferometric measurement methods for sound fields have garnered considerable attention owing to their contactless nature. The capabilities of non-invasive measurement and reconstruction of three-dimensional sound fields are significant for characterizing acoustic transducers. However, three-dimensional reconstructions are typically time consuming because of the two-dimensional scanning and rotation of the measurement system. This paper presents a scan and rotation-free reconstruction of an axisymmetric sound field in the human hearing range. A physical-model-based algorithm is proposed to reconstruct an axisymmetric sound field from optical interferograms recorded using parallel phase-shifting interferometry and a high-speed polarization camera. We demonstrate that audible sound fields can be reconstructed from data measured in 10 ms. The proposed method is effective for the rapid evaluation of axially symmetric acoustic transducers.
Bilinear constraint based ADMM for mixed Poisson-Gaussian noise removal

In this paper, we propose new operator-splitting algorithms for the total variation regularized infimal convolution (TV-IC) model [6] in order to remove mixed Poisson-Gaussian (MPG) noise. In the existing splitting algorithm for TV-IC, an inner loop by Newton method had to be adopted for one nonlinear optimization subproblem, which increased the computation cost per outer loop. By introducing a new bilinear constraint and applying the alternating direction method of multipliers (ADMM), all subproblems of the proposed algorithms named as BCA (short for Bilinear Constraint based ADMM algorithm) and BCA$ _{f} $ (short for a variant of BCA with $ {\bf f} $ully splitting form) can be very efficiently solved. Especially for the proposed BCA$ _{f} $, they can be calculated without any inner iterations. The convergence of the proposed algorithms are investigated, where particularly, a Huber type TV regularizer is adopted to guarantee the convergence of BCA$ _f $. Numerically, compared to existing primal-dual algorithms for the TV-IC model, the proposed algorithms, with fewer tunable parameters, converge much faster and produce comparable results meanwhile.
Corrigendum to “Object Detection from the Video Taken by Drone via Convolutional Neural Networks”

1], there was a spell error in author Jinhiu She's name in the author list, where "Jinhiu She" should have read as "Jinhui She." is is corrected as shown above.
Shades of hatred online: 4chan duplicate circulation surge during hybrid media events

The 4chan /pol/ platform is a controversial online space on which a surge in hate speech has been observed. While recent research indicates that events may lead to more hate speech, empirical evidence on the phenomenon remains limited. This study analyzes 4chan /pol/ user activity during the mass shootings in Christchurch and Pittsburgh and compares the frequency and nature of user activity prior to these events. We find not only a surge in the use of hate speech and anti-Semitism but also increased circulation of duplicate messages, links, and images and an overall increase in messages from users who self-identify as “white supremacist” or “fascist” primarily voiced from English-speaking IP-based locations: the U.S., Canada, Australia, and Great Britain. Finally, we show how these hybrid media events share the arena with other prominent events involving different agendas, such as the U.S. midterm elections. The significant increase in duplicates during the hybrid media events in this study is interpreted beyond their memetic logic. This increase can be interpreted through what we refer to as activism of hate. Our findings indicate that there is either a group of dedicated users who are compelled to support the causes for which shooting took place and/or that users use automated means to achieve duplication.
Utilization of DIVAYANA Formula in Evaluating of Suitable Platforms for Online Learning in the Social Distancing

The selection of a suitable platform to facilitate online learning in &lt;em&gt;social distancing&lt;/em&gt; becomes a very important thing to maintain the quality and smoothness of the learning process was carried out at home. Some educational evaluation models were able to be used to provide recommendations in choosing an online learning platform that was suitable to use in &lt;em&gt;social distancing&lt;/em&gt;, such as &lt;em&gt;CIPP&lt;/em&gt;, &lt;em&gt;Countenance&lt;/em&gt;, and &lt;em&gt;CSE-UCLA&lt;/em&gt;. However, those models are only able to provide recommendations based on narrative evaluation components so raised different understandings and high subjectivity in its implementation in the field. Those models haven’t formulas that specifically and accurately provide quantitative results in determining the most priority platforms is used in online learning. Therefore, it is essential to know there is a new formula in the field of educational evaluation to determine the suitable platform for online learning that is done at home. One of the new formulas that can be used and contributed to solving problems in the field of educational evaluation is the &lt;em&gt;DIVAYANA&lt;/em&gt; formula. This formula can show an accurate calculation mechanism in determining one of the platforms that the most priority from the various choices of online learning platforms. The purpose of this research was to show the mechanism for calculating the &lt;em&gt;DIVAYANA&lt;/em&gt; formula to determine the priority platform suitable for online learning. This research used an evaluative approach that focused on the nominate stage in the &lt;em&gt;DIVAYANA&lt;/em&gt; model because the &lt;em&gt;DIVAYANA&lt;/em&gt; formula is located in that nominate stage. Eighty respondents were involved in the initial data collection by evaluating the platform selection criteria for online learning. The subjects who were involved in testing the effectiveness of the &lt;em&gt;DIVAYANA&lt;/em&gt; formula were eight experts. Questionnaires were used as the initial data collection tools and testing tools for the effectiveness of the &lt;em&gt;DIVAYANA&lt;/em&gt; formula. The method was used to analyze the data of effectiveness test results on the &lt;em&gt;DIVAYANA&lt;/em&gt; formula was by comparing that test results with the effectiveness standard that refers to five scales. The results of the effectiveness test showed the percentage of effectiveness level was 89.79%. It means that the &lt;em&gt;DIVAYANA&lt;/em&gt; formula is effective to use in determining priority platforms suitable for online learning at home.
Cryptanalysis of a Chaotic Block Cryptographic System Against Template Attacks

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. [Formula: see text]32% less attack traces than correlation power analysis, when the templates are properly built.
Action Recognition Based on the Modified Twostream CNN

Human action recognition is an important research direction in computer vision areas.Its main content is to simulate human brain to analyze and recognize human action in video.It usually includes individual actions, interactions between people and the external environment.Space-time dual-channel neural network can represent the features of video from both spatial and temporal perspectives.Compared with other neural network models, it has more advantages in human action recognition.In this paper, a action recognition method based on improved space-time two-channel convolutional neural network is proposed.First, the video is divided into several equal length non-overlapping segments, and a frame image representing the static feature of the video and a stacked optical flow image representing the motion feature are sampled at random part from each segment.Then these two kinds of images are input into the spatial domain and the temporal domain convolutional neural network respectively for feature extraction, and then the segmented features of each video are fused in the two channels respectively to obtain the category prediction features of the spatial domain and the temporal domain.Finally, the video action recognition results are obtained by integrating the predictive features of the two channels.Through experiments, various data enhancement methods and transfer learning schemes are discussed to solve the over-fitting problem caused by insufficient training samples, and the effects of different segmental number, pre-training network, segmental feature fusion scheme and dual-channel integration strategy on action recognition performance are analyzed.The experiment results show that the proposed model can better learn the human action features in a complex video and better recognize the action.
Modified Genetic Based Algorithm for Load Balancing in Cloud Computing

Cloud-based Computation is one of the important computing technologies where demand-based services and resources from the user's application to cloud data center with the help of internet connection can be achieved as pay on demand. The software, resources, applications are shared among different computing devices in a cloud-based approach. Balancing of Load in cloud computation technology is meant for the distribution of workload to balance loads among different cloud servers connected with different virtual machines. The main idea of load balancing is to optimize resource usage, cost of data center and virtual machines, maximization of throughput, reduction of response time and avoidance of overloading in different virtual machines as well as cloud servers. In cloud load balancing fitness checking of different cloud servers as well as virtual machines are playing an important role in cloud applications. In this proposed research paper genetic-based algorithmic approach has been used to handle the load balancing in the cloud environment. Genetic Algorithm can be used for getting solution of an optimization problem. Here it is used to find the fittest virtual machines connected with different Data Centers. Our proposed work is more appropriate compared to different algorithms discussed in [1][2], as in proposed algorithm cloudlets are taking a smaller amount time for execution and performs the load balancing in cloud environment with more efficient way by using strong fittest function for allocating cloudlets into appropriate virtual machines of a data center in cloud environment.
Applying Video for Writing Descriptive Text in Senior High School in the Covid-19 Pandemic Transition

The emergence of the Covid-19 as the global pandemic with its social and physical distancing consequence has affected educational practices. This paper reports the expereince of carrying out a study on applying youtube video for writing of descriptive text in EFL Class. The purpose of this research was to find out whether the videos can develop studentsâ€™ ability in writing descriptive text or not.Â The study took place in the time before and during the covid-19 emergence. As quantitative research, this study applied pre-experimental design. The participant of this research was the 1st year students of one of the prominence senior high schools in Manado, Indonesia. They were 26 students of language class. The results indicated that the mean score of the post-test is significantly higher that of the pre-test. This points out that the use of YouTube video in teaching the writing of descriptive text is effective. However, the emergence of Covid-19 pandemic influenced the implementation of intervention that later affected studentsâ€™ achievement. The result of this study is not fully in line with the outcome of the previous studies which hypothetically resulted in two to four gains of scores. In addition, it implies that the use of YouTube video in this pandemic transition period seems to be helpful mostly to low level students as they displayed higher gains. Besides, this study denotes the challenges for teachers and students in the Covid-19 pandemic era namely the competence in online learning, and the internet connectivity access and learning mode system.
Sentence Matching with Syntax- and Semantics-Aware BERT

Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks.However, previous studies mainly focused on exploiting either syntactic or semantic information for sentence matching, and no studies consider integrating both of them.In this study, we propose integrating syntax and semantics into BERT with sentence matching.In particular, we use an implicit syntax and semantics integration method that is less sensitive to the output structure information.Thus the implicit integration can alleviate the error propagation problem.The experimental results show that our approach has achieved state-of-the-art or competitive performance on several sentence matching datasets, demonstrating the benefits of implicitly integrating syntactic and semantic features in sentence matching.
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software

This paper describes the first, three-year phase of a project at the National Research Council of Canada that creates software to assist Indigenous communities in preserving their languages and extending their use.The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central.Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen'kéha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for speech recordings, software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.
HandKey: An Efficient Hand Typing Recognition using CNN for Virtual Keyboard

This paper proposes an efficient framework that recognizes hand typing motions and gestures for making a virtual keyboard by using a single RGB camera. There are several works related to virtual keyboard in the Human-computer interaction (HCI) area. Most of them use hand pose estimation, hand shape and external equipment (depth sensor, leap motion, control glove, touch screen etc.). Whereas, our framework does not require additional equipment or prior experience from users, it works like a regular typing action in the air which is similar to typing on a real QWERTY keyboard. It uses convolutional neural networks (CNN) to classify 2 hand typing gestures (touch and non-touch). Also, we train 11 gestures which are non-touch and touching for each 10 fingers of two hands gestures. Proposed CNN model achieves a 99.2% classification accuracy for the 2 gestures case and a 91% classification accuracy for the 11 gestures case.
Private Hypothesis Selection

We provide a differentially private algorithm for hypothesis selection. Given samples from an unknown probability distribution P and a set of m probability distributions H, the goal is to output, in a ε-differentially private manner, a distribution from H whose total variation distance to P is comparable to that of the best such distribution (which we denote by α). The sample complexity of our basic algorithm is O(log m/α <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + log m/αε), representing a minimal cost for privacy when compared to the non-private algorithm. We also can handle infinite hypothesis classes H by relaxing to (ε, δ)-differential privacy. We apply our hypothesis selection algorithm to give learning algorithms for a number of natural distribution classes, including Gaussians, product distributions, sums of independent random variables, piecewise polynomials, and mixture classes. Our hypothesis selection procedure allows us to generically convert a cover for a class to a learning algorithm, complementing known learning lower bounds which are in terms of the size of the packing number of the class. As the covering and packing numbers are often closely related, for constant α, our algorithms achieve the optimal sample complexity for many classes of interest. Finally, we describe an application to private distribution-free PAC learning.