Reviewerly/exHarmony huggingface dataset:
Intrinsic Temporal Regularization for High-resolution Human Video Synthesis

Fashion video synthesis has attracted increasing attention due to its huge potential in immersive media, virtual reality and online retail applications, yet traditional 3D graphic pipelines often require extensive manual labor on data capture and model rigging. In this paper, we investigate an image-based approach to this problem that generates a fashion video clip from a still source image of the desired outfit, which is then rigged in a framewise fashion under the guidance of a driving video. A key challenge for this task lies in the modeling of feature transformation across source and driving frames, where fine-grained transform helps promote visual details at garment regions, but often at the expense of intensified temporal flickering. To resolve this dilemma, we propose a novel framework with 1) a multi-scale transform estimation and feature fusion module to preserve fine-grained garment details, and 2) an intrinsic regularization loss to enforce temporal consistency of learned transform between adjacent frames. Our solution is capable of generating 512\times512 fashion videos with rich garment details and smooth fabric movements beyond existing results. Extensive experiments over the FashionVideo benchmark dataset have demonstrated the superiority of the proposed framework over several competitive baselines.
The Effect of My Simple Show in Writing Recount Text at the Indonesian Junior High School

The objectives of this research are to find out whether there is any significant of students in teaching writing recount text between before and after by using My Simple Show and how the student’s perception towards the use of this application in teaching writing recount text. The method used is quantitative by using a pre-experimental design and questionnaire, To get data, the researcher gave a Pre-test, and Post-test to students. Based on the result of the research, there are some research that can be represented. First, there is significance of My Simple Show to write recount text. Second, the students perception of My Simple Show to increase recount text. Based on the result of this research, the researcher hope can help the students to increase writing ability in recount text by using this application, and give more motivation to be creative.
Multi-Objective Neural Network for Despeckling with a General Statistical Model

Among the different deep learning-based methods proposed for SAR image despeckling, the main issue seems to construct reliable training data sets. In the statistical-based solution MONet, which assumes square root Gamma distributed speckle in the simulation, the authors showed that despeckling results on actual SAR images are stringently related to the considered training dataset and its statistical distributions. This paper develops realistic simulated data sets for feeding the MONet architecture, including backscattering mechanisms arising in different existing SAR scenarios. We consider a generalized Gaussian coherent scatterer model for SAR correlated clutter simulation for this aim. The use of such simulation has a twofold effect within the considered framework: from one side, it allows generating several noisy patches, used as input data; on the other, it allows including different speckle distributions for different actual SAR scenarios. Results on SAR images show the effectiveness of such simulation.
Blockchain-Based Pharmaceutical Supply Chain: A Literature Review

Recently, blockchain technology was introduced to the public in order to provide a secure environment that is immutable, consensus-based and transparent in the finance technology world. However, there have been many efforts to apply blockchain to other fields where trust and transparency are a requirement. The ability to reliably share pharmaceutical information between various stakeholders is essential. The use of blockchain technology adds traceability and visibility to supply chains such as pharmaceuticals to provide all the information from end to end. Currently, the data is stored and managed by large manufacturers and pharmacy retailers using their own centralized systems. Several existing approaches and methods that allow pharmaceutical information to be stored and shared between the healthcare provider and other stakeholders in a centralized manner have been discussed in the literature. Due to the lack of comprehensive literature review studies that focus on pharmaceutical supply chain using blockchain technology, thus this paper highlights and addresses this gap. This paper reviews several studies which have applied blockchain technology in pharmaceutical supply chains. This paper overviews the knowledge on blockchain technology, discusses and explores the most recent and relevant studies that adopt blockchain technology in the field of pharmaceutical supply chains, describes the challenges associated with blockchain technology, and presents some ideas for future work.
Distributed Recommendation Algorithm Based on Fuzzy Clustering

In recent years, the Internet is sneaking into every corner of users' lives at a speed visible to the naked eye. Information has shown a geometric growth with the development of the Internet. This article mainly studies the application of distributed recommendation algorithm based on fuzzy clustering. In this paper, the data set used is divided into training set, test set and validation set in a random manner. The ratio of the three number sets is 7:1:2. This paper randomly selects 70% of consumers' historical data as the training data set, and the last 10% as the test set. The USCensus 1990 raw data set is divided into three groups, numbered 1, 2, and 3, and the number of samples is 400000, 600000 and 800000 respectively. Under the Hadoop platform, the MrDPCND-FCM algorithm is used to cluster the three sets of data to be tested, the number of nodes is increased from 2 to 5, and the time taken to cluster different data sets to be tested under different numbers of nodes is recorded. Therefore, offline testing uses items that the user has already evaluated. If the user has not scored the item, it cannot be evaluated. When the number of clusters is 60, the data sparsity drops from 92% to 51%. When the number of clusters reaches 5, the sparsity drops to around 3%. The algorithm in this paper solves the problem of large-scale data to a certain extent, and improves the efficiency and accuracy.
Exploring the potential for use of AI to help researchers improve their research funding relevance and performance

Researchers and scientists face globally, and parallel to their core research activities, increased pressure to successfully lead or participate in fundraising activities. The field has been experiencing fierce competition with success rates of proposals falling dramatically down, while the complexity of the funding instruments and the need for acquiring a wide understanding of issues related to impacts, research priorities in connection to wider national and transnational (e.g. EU-wide) policy aspects, increases discomfort levels for the individual researchers and scientists. In the paper we suggest the use of transdisciplinary AI tools to support (semi-)automation of several steps of the application and proposal preparation processes. (Abstract)
Hardware-software implementation of a DNN for 3D object detection using FINN - a demo

In this demo, we present a hardware-software system for 3D object detection in LiDAR point clouds based on a deep neural network. The PointPillars architecture was used in this research, as it is a reasonable compromise between detection accuracy and computational complexity. The Brevitas tool was used for network quantisation and the FINN tool for hardware-software implementation in the reprogrammable Zynq UltraScale+ MPSoC device.
Design of power quality abnormal data identification and verification system

Aiming at the problem that abnormal data in the data set greatly reduces the quality of data, this paper studies several existing mainstream methods of abnormal data identification and verification, and selects three kinds of abnormal data detection methods according to the characteristics of power quality basic data, including setting threshold discrimination method, data horizontal comparison method and improved K-MEANS algorithm. This paper also designs the power quality abnormal data identification and verification system, using the above three methods to identify and verify the power quality abnormal data.
Convexity Properties for a Function of Two Integer Variables

Sufficient conditions are given for any local minimum of a function of two integer variables to be a global minimum.An example is given to show that a function of two integer variables need not be discrete convex for this condition to hold.
Minimizing Outputs’ Differences of Classifiers with Different Responsibilities for Unsupervised Adversarial Domain Adaptation

The adversarial domain adaptation has made many achievements in the field of Unsupervised Domain Adaptation (UDA). However, initial adversarial domain adaptation methods do not consider the category-level domain adaptation, causing confusion between different categories in the different domains. This paper propose a UDA approach called Minimizing Outputs' Differences of Classifiers with Different Responsibilities (MODCDR) to improve this situation. Its framework consists of one generator and two different task-specific classifiers, one working on the source domain, while the other working on the target domain. By training the feature generator to minimize the outputs' discrepancy of these two classifiers, this method not only generates discriminative features, but also achieves the category-level feature distribution alignment simultaneously. In addition, these two classifiers can also produce and filter the reliable pseudo-labels on the target domain, being used for assisting our training. This paper tested this method on three publicly available transfer learning object recognition datasets, with comparison to recent unsupervised adversarial domain adaptation approaches. The results show that the proposed method has superior performance.
Iterative mapping for high-precision calibration and displacement measurements

The precision of calibration severely affects the precision of subsequent measurements. In the calibration, the corresponding image and global target coordinates are two distinct input datasets. The image coordinates may be estimated precisely with the use of an optimal feature detection algorithm. However, the global coordinates are usually chosen as the given 3D manufacturing centers, and are different from the reconstructed three-dimensional (3D) optical centers. In this study, an iterative coordinate mapping (ICM) method is proposed to optimize the 3D optical centers for high-precision calibration and displacement measurements. Precise optical 3D coordinates for a stereo-vision system calibration scheme are generated based on the results of the left and right camera calibrations. Herein, the principles of single-camera calibration, ICM method, precise coordinate generation, and stereo-vision system calibration schemes are presented. Computer simulations were performed to verify the effectiveness of the correction of 3D coordinates. Experiments were conducted to analyze the stability of the calibrated parameters and the precision of the reconstructed 3D coordinates. The results show that the ICM is with higher precision.
Heuristic-word-selection Genetic Algorithm for Generating Natural Language Adversarial Examples

Using adversarial examples as test cases is an effective way to detect the security and reliability problems of Deep Neural Networks (DNNs). Genetic Algorithm (GA) performs well in generating Natural Language Processing (NLP) adversarial examples due to its excellent ability to search for optimal solutions. However, the existing GAs need substantial time to generate NLP adversarial examples. Moreover, the semantics of examples generated by GAs is quite different from real texts, which makes it impossible to generate efficient and reliable adversarial examples. To address these two problems, this paper proposes a Heuristic-word-selection Genetic Algorithm (HGA) to generate NLP adversarial examples. Initial experimental results indicate that HGA not only improves the efficiency of GA to generate adversarial examples, but also enhances the preservation of text semantics, while maintaining the existing GAs' advantages.
“User”-Centered Design

Luxury drug and alcohol rehabs exhibit the hallmarks of wealth and comfort as selling points, in direct contrast to the 12-step programming they most often use for treatment. 12-step programs are designed by and for recovering addicts, and center service, unity, and recovery as their priorities, explicitly rejecting materialism and marketing of any kind. Analysis of the different communication materials used by luxury rehab facilities and 12-step programs reveals two differing rhetorics of recovery. Technical communicators have an opportunity to construct new modes of communicating with and informing addicts seeking recovery using the rhetoric they have constructed as creator-members of 12-step programs. With a thorough understanding of 12-step programs’ rhetorics of recovery, technical communicators can move beyond the goal of selling recovery and towards building community and participatory design which centers addicts’ unique needs and modes of understanding their addictions.
Calibration method for monocular 3D imaging systems based on reference planes

We propose a calibration method for a monocular 3D imaging system based on the reference plane. Our idea is to use the calibration plate to approximate replace the reference plane.
Strong admissibility for abstract dialectical frameworks

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling argumentation allowing general logical satisfaction conditions and the relevant argument evaluation. Different criteria used to settle the acceptance of arguments are called semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. However, the notion of strongly admissible semantics studied for abstract argumentation frameworks has not yet been introduced for ADFs. In the current work we present the concept of strong admissibility of interpretations for ADFs. Further, we show that strongly admissible interpretations of ADFs form a lattice with the grounded interpretation as the maximal element. We also present algorithms to answer the following decision problems: (1) whether a given interpretation is a strongly admissible interpretation of a given ADF, and (2) whether a given argument is strongly acceptable/deniable in a given interpretation of a given ADF. In addition, we show that the strongly admissible semantics of ADFs forms a proper generalization of the strongly admissible semantics of AFs.
Prior Information Guided Fuzzy Clustering with Spatial Neighborhood Information for MRI Image Segmentation

Various fuzzy clustering algorithms have been proposed for image segmentation. The PIGFCM algorithm uses prior information to initialize the cluster centers in the segmentation process, thus overcoming the problem of random cluster initialization. But, it does not perform satisfactorily on noisy images and produces incorrect segmentation results. There is a high probability that pixels in the same neighborhood are highly correlated. By using this fact, we have introduced a spatial neighborhood information term in the PIGFCM algorithm. Brain Web dataset which consists of simulated MR images is used for the purpose of evaluating the performance of the proposed method. The results are compared with other algorithms such as FCM_S, FLICM, KFCM, ARKFCM, IIFCM, KIFCM, and IFCMSNI, which demonstrates the superiority of the proposed PIGFCMSNI method.
ClickIndia: Indian Language Learning Mobile Application

Loss of native languages among immigrant children is a well-known problem yet very little effort has been made to address this issue. This project aims to build an iOS mobile application to facilitate the vocabulary learning of such languages for English speaking children. Currently, two Indian languages, namely Punjabi and Hindi, are used as the testbed and therefore the mobile application is called ClickIndia but its scalable architecture allows to incorporate other languages including endangered languages in the future. The goal is to take advantage of smartphone technologies and constructivist principles of learning to allow users to easily master commonly used Indian vocabulary words. ClickIndia provides multi-sensory learning experience via multimedia presentation of vocabulary words using audio, text and pictures. Category-based vocabulary word list is formed to build a connection between word and group for faster learning. Also, social context is incorporated in learning environment by using location detection feature to teach location-specific vocabulary. Further, it uses the novel concept of machine learning object recognition technology to develop an engaging vocabulary learning game. The game-oriented learning keeps users motivated to learn more by performing tasks to construct their own knowledge. Moreover, ClickIndia generates crowdsourced picture tagging, i.e., pictures clicked by users are stored on the cloud to build a tagged picture dataset to be incorporated in future release. Overall, ClickIndia promotes self-management and construction of knowledge by engaging users in a multimedia learning environment.
Assessing the future prospects for cryptocurrencies

Cryptocurrencies are attracting considerable attention around the world because of the various advantages that they offer. On the other hand, they also carry some inherent risks. Although monetary authorities broadly agree that cryptocurrencies do not engender an immediate threat to national and global financial systems, the future is full of unknowns. In this regard, drawing a framework based on the current drivers of demand for cryptocurrencies would help visualize the prospects for these assets and create a roadmap to avoid or manage any disruptive risks. This discussion paper aims to contribute to the literature by examining the key factors that will determine the future performance of cryptocurrencies. The main conclusion derived from the discussion is that national regulations will potentially affect the direction of cryptocurrencies, as well as the need for any special efforts in the domain of monetary policy
Fake Gradient: A Security and Privacy Protection Framework for DNN-based Image Classification

Deep neural networks (DNNs) have demonstrated phenomenal success in image classification applications and are widely adopted in multimedia internet of things (IoT) use cases, such as smart home systems. To compensate for the limited resources on the IoT devices, the computation-intensive image classification tasks are often offloaded to remote cloud services. However, the offloading-based image classification could pose significant security and privacy concerns to the user data and the DNN model, leading to effective adversarial attacks that compromise the classification accuracy. The existing defense methods either impact the original functionality or result in high computation or model re-training overhead. In this paper, we develop a novel defense approach, namely Fake Gradient, to protect the privacy of the data and defend against adversarial attacks based on encryption of the output. Fake Gradient can hide the real output information by generating fake classes and further mislead the adversarial perturbation generation based on fake gradient knowledge, which helps maintain a high classification accuracy on the perturbed data. Our evaluations using ImageNet and 7 popular DNN models indicate that Fake Gradient is effective in protecting the privacy and defending against adversarial attacks targeting image classification applications.
Research of LMD-STFT Algorithm Based on Wavelet Packet in rectal dynamic function diagnosis

Fecal incontinence (FI) is that the anal sphincter losses the ability to control the expulsion of intestinal contents. With the development of medical technology, the cure rate of patients with anal incontinence is increasing, but the detection of rectal motility after treatment is relatively lacking. A model of rectal information collection and functional diagnosis system is proposed in this study. It can collect many kinds of signals from the patient's rectum at a time to analyze their rectal motility function. This paper proposes a local mean decomposition and short-time Fourier transform (LMD-STFT) algorithm based on wavelet packet to process the collected rectal pressure signal. This program uses wavelet packets to denoise the rectal pressure signal, and decomposes the denoised signal through LMD, and analyzes the decomposed signal with STFT to get the dynamic characteristics of the rectum. Experiments show that this method can improve the efficiency of local signal decomposition, eliminate the influence of noise on the diagnosis results, preprocess the rectal signal, obtain the dynamic characteristics of the rectum, and diagnose the rectal function. This study laid a foundation for rectal information collection and function diagnosis system to achieve rectal function diagnosis.
Intent-based Product Collections for E-commerce using Pretrained Language Models

Building a shopping product collection has been primarily a human job. With the manual efforts of craftsmanship, experts collect related but diverse products with common shopping intent that are effective when displayed together, e.g., backpacks, laptop bags, and messenger bags for freshman bag gifts. Automatically constructing a collection requires an ML system to learn a complex relationship between the customer’s intent and the product’s attributes. However, there have been challenging points, such as 1) long and complicated intent sentences, 2) rich and diverse product attributes, and 3) a huge semantic gap between them, making the problem difficult. In this paper, we use a pretrained language model (PLM) that leverages textual attributes of web-scale products to make intent-based product collections. Specifically, we train a BERT with triplet loss by setting an intent sentence to an anchor and corresponding products to positive examples. Also, we improve the performance of the model by search-based negative sampling and category-wise positive pair augmentation. Our model significantly outperforms the search-based baseline model for intent-based product matching in offline evaluations. Furthermore, online experimental results on our e-commerce platform show that the PLM-based method can construct collections of products with increased CTR, CVR, and order-diversity compared to expert-crafted collections.
Combining Visual and Social Dialogue for Human-Robot Interaction

We will demonstrate a prototype multimodal conversational AI system that will act as a receptionist in a hospital waiting room, combining visually-grounded dialogue with social conversation. The system supports visual object conversation in the waiting room (e.g. looking for available seats or personal belongings), task-based dialogues regarding navigation and check-in procedures in the hospital, as well as access to the latest news, and a quiz game about coronavirus. The prototype system therefore demonstrates how to weave together a wide range of natural, daily conversations with end users that vary in complexity; from complex visual dialogue to chitchat and quiz games, to task-oriented domain-specific conversations. We are currently able to demonstrate the system via a web-based interface. It will soon be deployed on the ARI robot in a hospital waiting room.