Reviewerly/exHarmony huggingface dataset:
POSTER: Blockchain-Based Differential Privacy Cost Management System

Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation of privacy cost. To tackle this limitation, this paper explores the integration of a secured decentralised ledger, blockchain. Blockchain will be able to keep track of all noisy responses generated with differential privacy algorithm and allow for certain queries to reuse old responses. In this paper, a demo of a proposed blockchain-based privacy management system is designed as an interactive decentralised web application (DApp). The demo created illustrates that leveraging on blockchain will allow the total privacy cost accumulated to decrease significantly.
3D Human Body Shape and Pose Estimation from Depth Image

This work addresses the problem of 3D human body shape and pose estimation from a single depth image. Most 3D human pose estimation methods based on deep learning utilize RGB images instead of depth images. Traditional optimization-based methods using depth images aim to establish point correspondences between the depth images and the template model. In this paper, we propose a novel method to estimate the 3D pose and shape of a human body from depth images. Specifically, based on the joints features and original depth features, we propose a spatial attention feature extractor to capture spatial local features of depth images and 3D joints by learning dynamic weights of the features. In addition, we generalize our method to real depth data through a weakly-supervised method. We conduct extensive experiments on SURREAL, Human3.6M, DFAUST, and real depth images of human bodies. The experimental results demonstrate that our 3D human pose estimation method can yield good performance.
Safe Motion Planning for an Uncertain Non-Holonomic System with Temporal Logic Specification

We propose a sampling-based motion planning algorithm for systems with complex dynamics and temporal logic specifications allowing to tackle sophisticated missions. By complex dynamics we refer to non-holonomy and disturbance that prevent implementation of an exact steer function. We instead construct a set of feedback motion primitives guaranteeing bounded state uncertainty (and thus safety) allowing the system to follow an arbitrarily long trajectory without replanning. The motion primitives allow to use A*-based algorithm to provably accomplish the temporal logic mission. We propose a heuristics for the A*-based algorithm via construction of backward trees. We illustrate the approach on several case studies, including simulations of a rover and fixed wing drone. We further show that construction of backward trees allows for faster re-planning compared to the state-of-the-art.
Buffer Sizes Reduction for Memory-efficient CNN Inference on Mobile and Embedded Devices

Nowadays, convolutional neural networks (CNNs) are the core of many intelligent systems, including those that run on mobile and embedded devices. However, the execution of computationally demanding and memory-hungry CNNs on resource-limited mobile and embedded devices is quite challenging. One of the main problems, when running CNNs on such devices, is the limited amount of memory available. Thus, reduction of the CNN memory footprint is crucial for the CNN inference on mobile and embedded devices. The CNN memory footprint is determined by the amount of memory required to store CNN parameters (weights and biases) and intermediate data, exchanged between CNN operators. The most common approaches, utilized to reduce the CNN memory footprint, such as pruning and quantization, reduce the memory required to store the CNN parameters. However, these approaches decrease the CNN accuracy. Moreover, with the increasing depth of the state-of-the-art CNNs, the intermediate data exchanged between CNN operators takes even more space than the CNN parameters. Therefore, in this paper, we propose a novel approach, which allows to reduce the memory, required to store intermediate data, exchanged between CNN operators. Unlike pruning and quantization approaches, our proposed approach preserves the CNN accuracy and reduces the CNN memory footprint at the cost of decreasing the CNN throughput. Rus, our approach is orthogonal to the pruning and quantization approaches, and can be combined with these approaches for further CNN memory footprint reduction.
A Cross-Modal Classification Dataset on Social Network

Classifying tweets into general categories, such as food, music and games, is an essential work for social network platforms, which is the basis for information recommendation, user portraits and content construction. As far as we know, nearly all existing general tweet classification datasets only have textual content. However, textual content in tweets may be short, meaningless, and even none, which would harm the classification performance. In fact, images and videos are widespread in tweets, and they can intuitively provide extra useful information. To fill this gap, we construct a novel Cross-Modal Classification Dataset constructed from Weibo called CMCD. Specifically, we collect tweets with three modalities of text, image and video from 18 general categories, and then filter tweets that can easily be classified by only textual contents. Finally, the whole dataset consists of 85,860 tweets, and all of them have been manually labelled. Among them, 64.4% of tweets contain images, and 16.2% of tweets contain videos. We implement classical baselines for tweets classification and report human performance. Empirical results show that the classification over CMCD is challenging enough and requires further efforts.
Key-Elements Graph Constructed with Evidence Sentence Extraction for Gaokao Chinese

Multiple choice questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between sentences in the article and strong ability to handle long text. Face the above challenges, we propose a key-elements graph to enhance context semantic representation and a comprehensive evidence extraction method inspired by existing methods. Our model first extracts evidence sentences from a passage according to the corresponding question and options to reduce the impact of noise. Then combines syntactic analysis techniques with graph neural network to construct the key-elements graph bases on the extracted sentences. Finally, fusing the learned graph nodes representation into context representation to enhancing syntactic information. Experiments on Gaokao Chinese multiple-choice dataset demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of accuracy.
Uncertainty in Continuous Scatterplots, Continuous Parallel Coordinates, and Fibers

In this paper, we introduce uncertainty to continuous scatterplots and continuous parallel coordinates. We derive respective models, validate them with sampling-based brute-force schemes, and present acceleration strategies for their computation. At the same time, we show that our approach lends itself as well for introducing uncertainty into the definition of fibers in bivariate data. Finally, we demonstrate the properties and the utility of our approach using specifically designed synthetic cases and simulated data.
Non-Differentiable Learning of Quantum Circuit Born Machine with Genetic Algorithm

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
An Image Cryptosystem based on Tent Map

With the increasing growth of multimedia applications, security has become an important aspect of the transmission of images. Encryption is one form to guarantee safety. Digital images need security in storing and communication and are made use of in numerous domains namely medical systems, audiovisual applications, military etc. Here, an image encryption scheme which is chaotic that provides security is described. Also, the proposed system is enhanced with row and column shuffling. Further, four variants in shuffling are provided based on the coordinates.
Peterson–Gorenstein–Zierler algorithm for differential convolutional codes

Differential Convolutional Codes with designed Hamming distance are defined, and an algebraic decoding algorithm, inspired by Peterson–Gorenstein–Zierler’s algorithm, is designed for them.
HAMLET: A Hierarchical Agent-based Machine Learning Platform

Hierarchical Multi-Agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this paper, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research and democratization of geographically and/or locally distributed machine learning entities. The proposed system models a machine learning solutions as a hypergraph and autonomously sets up a multi-level structure of heterogeneous agents based on their innate capabilities and learned skills. HAMLET aids the design and management of machine learning systems and provides analytical capabilities for research communities to assess the existing and/or new algorithms/datasets through flexible and customizable queries. The proposed hybrid machine learning platform does not assume restrictions on the type of learning algorithms/datasets and is theoretically proven to be sound and complete with polynomial computational requirements. Additionally, it is examined empirically on 120 training and four generalized batch testing tasks performed on 24 machine learning algorithms and 9 standard datasets. The provided experimental results not only establish confidence in the platform's consistency and correctness but also demonstrate its testing and analytical capacity.
A Comparative Analysis of Deep Learning based Super-Resolution Techniques for Thermal Videos

Video streams acquired from thermal cameras are proven to be beneficial in diverse number of fields including military, healthcare, law enforcement, and security. Despite the hype, thermal imaging is increasingly affected by poor resolution, where it has expensive optical sensors and inability to attain optical precision. In recent years, deep learning based super-resolution algorithms are developed to enhance the video frame resolution at high accuracy. This paper presents a comparative analysis of super resolution (SR) techniques based on deep neural networks (DNN) that are applied on thermal video dataset. SRCNN, EDSR, Auto-encoder, and SRGAN are also discussed and investigated. Further the results on benchmark thermal datasets including FLIR, OSU thermal pedestrian database and OSU color thermal database are evaluated and analyzed. Based on the experimental results, it is concluded that, SRGAN has delivered a superior performance on thermal frames when compared to other techniques and improvements, which has the ability to provide state-of-the art performance in real time operations.
A robust personalized location recommendation based on ensemble learning

Recommender systems (RSs) have attracted considerable attention with the aim of optimizing location service efficiency since a large volume of information is generated by location-based social networks. Prediction accuracy is generally considered the main performance evaluation criterion, while the stability of RSs, which might be affected by uncertainty, has rarely been documented. To guarantee the robustness of RSs on the basis of two essential elements, accuracy and stability, this paper proposes an ensemble-based personalized location recommendation (EPLR) algorithm, in which several different categories of individual models are pre-trained to provide a knowledge base, and the accuracy metric in terms of F1 and the information gain (IG) are individually calculated for each user and are used as personalized weights to integrate the individual models. Notably, IG is used as an evaluation index of system stability. To demonstrate and evaluate EPLR in detail, four representative recommendation algorithms, i.e., user-based collaborative filtering (UBCF), singular value decomposition (SVD), friend-based collaborative filtering (FCF) and kernel density estimation (KDE), are selected as individual models for demonstration. Extensive experiments are conducted on two popular datasets: Brightkite and Gowalla. Additionally, three published ensemble recommendation algorithms and four individual models are implemented for comparison. The experimental results show that EPLR outperforms the other considered algorithms in terms of prediction accuracy and exhibits a promising advantage in system stability.
Scam Augmentation and Customization: Identifying Vulnerable Users and Arming Defenders

Why do "classical" attacks such as phishing, IRS scams, etc., still succeed? How do attackers increase their chances of success? How do people reason about scams and frauds they face daily? More research is needed on these questions, which is the focus of this paper. We take a well-known attack, viz. company representative fraud, and study several parameters that bear on its effectiveness with a between-subjects study. We also study the effectiveness of a coherent language generation technique in producing phishing emails. We give ample room for the participants to demonstrate their reasoning and strategies.
Detecting Physical Activities Using Body-Worn Accelerometers

This paper addresses the task of using data from accelerometers attached to a person's body to determine the kind of physical activity being performed by that person. The activities of interest are routine ones such as sitting, walking up a flight of stairs, walking, and jogging. The paper describes methods for segmenting the time-series data from accelerometers and for extracting features that are effective for determining activities when used in conjunction with well established classification algorithms. These methods are implemented in a prototype that is used to evaluate their effectiveness on a publicly available dataset of tagged accelerometer traces. The prototype also provides intuitive visualizations of the accelerometer traces, allowing a human expert to gain a better understanding of both the dataset and the predictions from the classifiers. Although the methods in this paper use fewer and simpler features extracted from the raw accelerometer data, they provide higher accuracies when compared to those reported in prior work on the experimental dataset.
Bayesian Distance Weighted Discrimination

Distance weighted discrimination (DWD) is a linear discrimination method that is particularly well-suited for classification tasks with high-dimensional data. The DWD coefficients minimize an intuitive objective function, which can solved efficiently using state-of-the-art optimization techniques. However, DWD has not yet been cast into a model-based framework for statistical inference. In this article we show that DWD identifies the mode of a proper Bayesian posterior distribution, that results from a particular link function for the class probabilities and a shrinkage-inducing proper prior distribution on the coefficients. We describe a relatively efficient Markov chain Monte Carlo (MCMC) algorithm to simulate from the true posterior under this Bayesian framework. We show that the posterior is asymptotically normal and derive the mean and covariance matrix of its limiting distribution. Through several simulation studies and an application to breast cancer genomics we demonstrate how the Bayesian approach to DWD can be used to (1) compute well-calibrated posterior class probabilities, (2) assess uncertainty in the DWD coefficients and resulting sample scores, (3) improve power via semi-supervised analysis when not all class labels are available, and (4) automatically determine a penalty tuning parameter within the model-based framework. R code to perform Bayesian DWD is available at https://github.com/lockEF/BayesianDWD.
A range characterization of the single-quadrant ADRT

This work characterizes the range of the single-quadrant approximate discrete Radon transform (ADRT) of square images. The characterization follows from a set of linear constraints on the codomain. We show that for data satisfying these constraints, the exact and fast inversion formula by Rim [Appl. Math. Lett. 102 (2020), 106159] yields a square image in a stable manner. The range characterization is obtained by first showing that the ADRT is a bijection between images supported on infinite half-strips, then identifying the linear subspaces that stay finitely supported under the inversion formula.
An experimental study on the perceived quality of natively graded versus inverse tone mapped high dynamic range video content on television

High Dynamic Range (HDR) television promises to display higher brightness and deeper black levels and thus more vivid and realistic images. However, home video distribution and video broadcasting were historically designed for what we now call standard dynamic range screens (SDR). In order to display SDR content on an HDR screen, it is explicitly or implicitly converted, in a process called inverse tone mapping (iTMO). This paper’s goal is to assess the perceived quality of converted SDR content in comparison to natively graded HDR content. In doing so, this paper aims to enable content creators/distributors to make informed choices between creating/broadcasting HDR content or relying on conversion. To this end, a psychophysical experiment was performed to tests how viewers evaluate the difference between natively graded HDR and a set of SDR to HDR conversion options in a television setup. Results indicate that viewers prefer natively graded HDR content, followed by inverse tone mapping algorithms starting from videos with a compressed dynamic range. When comparing conversion options, users clearly prefer conversion from ‘compressed dynamic range’ SDR over ‘clipped dynamic range’ SDR. Users disliked videos that were naively stretched from standard SDR. In addition, a significant effect of type of sequence was found, with a preference for light scenes with low contrast.
Indonesian Phonemicization Model Using N- Gram-Based Bidirectional Long Short-Term Memory

Building a phonemic model of words is a description of a function in mapping the spelling of words to a series of phonetic symbols that represent the pronunciation of the word. The conversion of spelled symbols (grapheme) into pronunciation symbols (phoneme) is very dependent on the characteristics of the language being reviewed. Basically, the word phonemicization is the same as a grapheme-to-phoneme (G2P) conversion. A language in general has a grapheme-to-phoneme conversion system that is different from other languages. The speech synthesis system from text needs to be done in the process of converting graphemes into phonemes, so a G2P is needed here. A widely used implementation of the grapheme-to-phoneme conversion system is a rule-based conversion system. Rule-based can be established through a variety of techniques and methods. The conversion of graphemes into phonemes will represent the mapping of each grapheme or spelling symbol in any word to the phonemic representation or pronunciation symbol. One of the method that can be used is deep learning algorithm. In this paper, a word phonemicization model is developed using a bidirectional long short-term memory (BLSTM). A rule-based re-optimization procedure is proposed to enhance the model. An evaluation of a dataset of 50 k Indonesian formal words shows that the proposed model gives a word error rate (WER) of 0.73%, which is much lower than all previous models. The errors are mostly caused by converting grapheme $\lt \mathrm{e}\gt $.
Human Motion Recognition for Industrial Human-Robot Collaboration based on a Novel Skeleton Descriptor

In the industrial human-robot collaboration environment, workers and robots often need to collaborate to complete complex tasks in a shared space. Accurate and robust motion recognition is the key to improving the productivity and safety of human-robot collaboration. Accurately tracking and recognizing workers' motions can provide clues about tasks to be performed, thus ensuring the safety of collaborative workspaces. Aiming at the differences in postures and characteristics of workers' motions in the working environment, the human body's self-occlusion and partial occlusion, this paper uses a skeleton recognition algorithm to estimate the 3D skeleton information in monocular video. A novel skeleton descriptor that describes static and dynamic features of the 3D skeletons in the short time around the current frame is proposed. Besides, we train a deep neural network to recognize the human motion. Experimental results show that the proposed skeleton descriptor can improve the accuracy of motion recognition by about 4%, and the neural network we use outperforms the other three classical classification methods.
Energy Optimization of an Elastic Computer Cluster with a SDN Controller

Number of people using online applications has increased in a fast manner in the last decade, and companies providing applications have shifted their data centers to cloud based on-demand resource provisioning. Focus of the previous era of research was only on improving the computational power or performance of applications or resources. But due to the larger needs now the challenge has come up on optimizing the energy of the application or resource. The research carried out in this paper highlights a novel model for energy optimization of elastic computing clusters. The cluster used in the proposed system is set on top of an Open Nebula private cloud platform and an SDN controller using flood light: The energy optimization model is based on optimizing the energv required for intra cluster and inter cluster communication using SDN. The results show that there is a considerable decrease in the energy of the cluster when SDN is used.