Reviewerly/exHarmony huggingface dataset:
On the Case of Privacy in the IoT Ecosystem: A Survey

IoT has enabled the creation of a multitude of personal applications and services for a better understanding of urban environments and our personal lives. These services are driven by the continuous collection and analysis of user data in order to provide personalized experiences. However, there is a strong need to address user privacy concerns as most of the collected data is of sensitive nature. This paper provides an overview of privacy preservation techniques and solutions proposed so far in literature along with the IoT levels at which privacy is addressed by each solution as well as their robustness to privacy breaching attacks. An analysis of functional and non-functional limitations of each solution is done, followed by a short survey of machine learning applications designed with these solutions. We identify open issues in the privacy preserving solutions when used in IoT environments. Moreover, we note that most of the privacy preservation solutions need to be adapted in the light of GDPR to accommodate the right to privacy of the users.
Distribution‐free simultaneous tests for location–scale and Lehmann alternative in two‐sample problem

Abstract The paper deals with the classical two‐sample testing problem for the equality of two populations, one of the most fundamental problems in biomedical experiments and case–control studies. The most familiar alternatives are the difference in location parameters or the difference in scale parameters or in both the parameters of the population density. All the tests designed for classical location or scale or location–scale alternatives assume that there is no change in the shape of the distribution. Some authors also consider the Lehmann‐type alternative that addresses the change in shape. Two‐sample tests under Lehmann alternative assume that the location and scale parameters are invariant. In real life, when a shift in the distribution occurs, one or more of the location, scale, and shape parameters may change simultaneously. We refer to change of one or more of the three parameters as a versatile alternative. Noting the dearth of literature for the equality two populations against such versatile alternative, we introduce two distribution‐free tests based on the Euclidean and Mahalanobis distance. We obtain the asymptotic distributions of the two test statistics and study asymptotic power. We also discuss approximating p ‐values of the proposed tests in real applications with small samples. We compare the power performance of the two tests with several popular existing distribution‐free tests against various fixed alternatives using Monte Carlo. We provide two illustrations based on biomedical experiments. Unlike existing tests which are suitable only in certain situations, proposed tests offer very good power in almost all types of shifts.
Skipping RNN State Updates without Retraining the Original Model

Recurrent Neural Networks (RNNs) break a time-series input (or a sentence) into multiple time-steps (or words) and process it one time-step (word) at a time. However, not all of these time-steps (words) need to be processed to determine the final output accurately. Prior work has exploited this intuition by incorporating an additional predictor in front of the RNN model to prune time-steps that are not relevant. However, they jointly train the predictor and the RNN model, allowing one to learn from the mistakes of the other. In this work we present a method to skip RNN time-steps without retraining or fine tuning the original RNN model. Using an ideal predictor, we show that even without retraining the original model, we can train a predictor to skip 45% of steps for the SST dataset and 80% of steps for the IMDB dataset without impacting the model accuracy. We show that the decision to skip is not easy by comparing against 5 different baselines based on solutions derived from domain knowledge. Finally, we present a case study about the cost and accuracy benefits of realizing such a predictor. This realistic predictor on the SST dataset is able to reduce the computation by more than 25% with at most 0.3% loss in accuracy while being 40× smaller than the original RNN model.
Constant-Round Interactive Proofs for Delegating Computation

The celebrated ${\sf IP}={\sf PSPACE}$ theorem [Lund, Fortnow, Karloff, and Nisan, J. ACM, 39 (1992), pp. 859--868; Shamir, J. ACM, 39 (1992), pp. 869--877] allows an all-powerful but untrusted prover to convince a polynomial-time verifier of the validity of extremely complicated statements (as long as they can be evaluated using polynomial space). The interactive proof system designed for this purpose requires a polynomial number of communication rounds and an exponential-time (polynomial-space complete) prover. In this paper, we study the power of more efficient interactive proof systems. Our main result is that for every statement that can be evaluated in polynomial time and bounded-polynomial space there exists an interactive proof that satisfies the following strict efficiency requirements: (1) the honest prover runs in polynomial time, (2) the verifier is almost linear time (and under some conditions even sublinear), and (3) the interaction consists of only a constant number of communication rounds. Prior to this work, very little was known about the power of efficient, constant-round interactive proofs (rather than arguments). This result represents significant progress on the round complexity of interactive proofs (even if we ignore the running time of the honest prover) and on the expressive power of interactive proofs with polynomial-time honest prover (even if we ignore the round complexity). This result has several applications, and in particular it can be used for verifiable delegation of computation. Our construction leverages several new notions of interactive proofs, which may be of independent interest. One of these notions is that of unambiguous interactive proofs where the prover has a unique successful strategy. Another notion is that of probabilistically checkable interactive proofs ($\mathsf{PCIP}$s), where the verifier only reads a few bits of the transcript in checking the proof (this could be viewed as an interactive extension of $\mathsf{PCIP}$s). An equivalent notion to $\mathsf{PCIP}$s, called interactive oracle proofs, was recently introduced in an independent work of Ben-Sasson, Chiesa, and Sponcer [Proceedings of TCC, 2016, pp. 31--60].
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models

Deep video action recognition models have been highly successful in recent years but require large quantities of manually-annotated data, which are expensive and laborious to obtain. In this work, we investigate the generation of synthetic training data for video action recognition, as synthetic data have been successfully used to supervise models for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation, physics models and other components of modern game engines. With this model we generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. PHAV contains a total of 39,982 videos, with more than 1000 examples for each of 35 action categories. Our video generation approach is not limited to existing motion capture sequences: 14 of these 35 categories are procedurally-defined synthetic actions. In addition, each video is represented with 6 different data modalities, including RGB, optical flow and pixel-level semantic labels. These modalities are generated almost simultaneously using the Multiple Render Targets feature of modern GPUs. In order to leverage PHAV, we introduce a deep multi-task (i.e. that considers action classes from multiple datasets) representation learning architecture that is able to simultaneously learn from synthetic and real video datasets, even when their action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance. Our approach also significantly outperforms video representations produced by fine-tuning state-of-the-art unsupervised generative models of videos.
Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content

We present a demo application of a web-based recommender systems that is powered by the “Movie Genome”, i.e., a rich semantic description of a movie's content, including state-of-the-art audio and visual descriptors and metadata (genre and tags). The current version of the Movie Genome web application implements content-based filtering approaches. Due to its modular implementation and the free availability of its source code, it can be easily extended to various context-aware and user-aware scenarios. A personality questionnaire is already integrated into the web application, which allows it to also serve as testbed for personality-aware recommendation algorithms.
Deep Learning Based Sentiment Analysis and Text Summarization in Social Networks

Sentiment analysis aims to reveal semantic knowledge of written texts where users share their feelings and thoughts on sharing platforms such as personal blogs and social networks. The data shared by users on social networks consist of short texts. A text classification problem has arisen for data reaching large dimensions shared on social networks. Although language libraries have been developed in other languages to solve the problem of sentiment analysis, studies for the Turkish language are limited. In this study, two categories of emotion analysis were studied with data obtained from many social networks. Also, there is a feeling class on a topic on Twitter, and text is summarized in the class. Sentiment analysis considered a classification problem. To increase the success rate, the study was carried out by focusing on the words with semantic context word embedding methods. LSA is used for text summarization. In this study, where both emotion analysis and text summation are carried out, the main goal is to analyze the sentiments and thoughts about a subject and present brief information to the user. The primary model was created with data collected from many social networks. Analysis and summary of the text were made with data from a hashtag on Twitter. The methods used in the analysis of emotions were compared to the methods of word embedding and a success rate of 93% was obtained.
Relation Understanding in Videos

In this paper, we present our solutions to the grand challenge task "Relation Understanding in Videos" in ACM Multimedia 2019. The challenge task aims to detect instances of target visual relations in a video, where a visual relation instance is represented by a relation triplet <subject, predicate, object> with the trajectories of the subject and object. It seems that it is similar to the image relation detection task with the input changed from images to videos. However, video relation detection requires a much more complex pipeline which not only needs to detect objects in each frame, but also track them in the temporal direction. In this challenge, we follow the basic pipeline structure which consists of 3 main separate components: an object detector, an object tracker and a relation predictor. Based on our analysis, there exist data unbalance and label missing problems in the VidOR dataset. We exploit two simple but effective methods to alleviate these problems. We also use trajectory Non-Maximum Suppression and a sliding window method to address the redundancy of trajectory proposals. Experimental results on the challenge task demonstrate that by applying these proposed approaches over the state-of-the-art relation prediction model, the video relation detection performance can be improved with the [email protected] and [email protected] reaching 0.3305 and 0.3507 respectively.
American Sign Language Translation via Smart Wearable Glove Technology

In the world of sound, for those without it, sign language is a powerful tool to make their voices heard. The ASL, short for American Sign Language is the most widely used sign language across the globe with certain variations according to the country. In this work, we developed a wearable wireless gesture decoder module that can translate the fundamental set of ASL gestures into corresponding alphabets and words. Our project utilizes a glove that houses a series of flex sensors on the metacarpal and inter-phalangeal joints of the fingers to detect the bending of fingers, through piezoresistive effect. The glove is outfitted with an accelerometer as well, that helps to detect the hand movements. Simple classification algorithms from machine learning are then applied to translate the gestures into alphabets or words. With the assistance of a bluetooth module, the data is sent wirelessly. The result is then displayed and heard on the android smartphone using an android application named “Dastaana”, which is an Urdu word for glove. Portability, compactness, cost-effectiveness and a user-friendly android application gives this system an edge.
A Distributed Video Analytics Architecture Based on Edge-Computing and Federated Learning

The prevalence of high definition video cameras in our environments provides countless types of essential services. The current model of sending video streams to the cloud for processing is facing many challenges such as latency and privacy. Delegating workloads of the cloud to the nearer Edge computing nodes is becoming the trend, which offers very low latency video analytics services. However, it opens up new challenges such as the issue of coordination between edge nodes and the fragmentation of their object detection models. In this paper, we introduce a distributed video analytics architecture based on edge-computing and the newly emerging federated learning. Its design allows for real-time and distributed object detection, in addition to a privacy-preserving scheme for detection model updating. A use-case scenario of a road surveillance system is given to support the proposed architecture. Finally, Challenges and future work are pointed out.
Analysis of Inverse Kinematics Solutions for Full-Body Reconstruction in Virtual Reality

Many Virtual Reality (VR) applications usually visualize only the VR controllers or floating hands. However, to create an immersive experience, a full-body avatar is essential. We reconstruct a full-body avatar by tracking the position and orientation of the head, hands, feet, and hip. To track the arm movements, the user has to hold two HTC Vive controllers. Additionally, the user has to bind Vive trackers to both ankles and the hip. We apply some of the most popular Inverse Kinematics (IK) methods to estimate the full-body pose. We perform parameter optimization to analyze the damping constant, the maximum number of iterations, and error value for the position as well as rotation. We made several tests between the IK methods in terms of the accuracy and the time to solve the IK problem. The results show that Damped Least Squares (DLS) outperforms the other methods. We furthermore conducted a user study to evaluate the subjective quality of the DLS method. Evaluation results show that the motion reconstruction for lower-body is very accurate; however, for the upper-body, some inaccuracies can occur. Such a motion reconstruction approach can be used in VR exergames, e.g., users can learn different poses while observing the movements in a virtual mirror and by looking down towards their own body.
Formation of Smart Contracts under Contract Law

This chapter examines in the first place the formation of (blockchain-based) smart contracts. The term “smart contract” is used to refer to software programs that are often (but not necessarily) built on blockchain technology as a set of promises, specified in digital form, including protocols within which the parties perform on these promises. It is regularly said that smart contracts are neither legal contracts in the traditional sense nor are they smart and that the term therefore is a misnomer. The crucial question this chapter is trying to answer is whether the traditional (Common Law) concept of contract formation is seriously challenged by the rise of smart contracts. So are smart contracts the end of contract formation as we know it, or is it at the end just much ado about nothing? And if contract law is in principle fit for the formation of smart contracts, do they have features which do challenge the traditional contract law, and if yes what are those features? These are some of the questions this contribution is trying to answer.
Sentiment Analysis for Urdu News Tweets Using Decision Tree

In the last few years, with rapid growth in use of networking sites such as twitter and Facebook has been increased greatly. This also attracted the researcher to use social networks data for sentiment analysis. Sentiment analysis is also known as opinion mining is the process of finding out the emotion such as positive, negative and neutral from the series of words. In present, on internet huge amount of data has been generated and to extract useful information from data is also become interest for the researchers. Sentiment analysis has been done mostly in English and Chinese languages. In this paper, sentiment classification is done on Urdu news tweets. The proposed methodology consists upon two steps. In first step data preprocessing is done such as removal of hash tag and removal of stop words is done. In second step feature vector is designed. The feature vector is formulated by through the identification of number of positive words, negative words, and presence of negation and use of POS tags. After formulation of feature vector the decision tree is used as classification algorithm. The decision tree classifies the tweet as positive, negative and neutral. The experimental result of the proposed methodology shows significant success in terms of accuracy and sentiment analysis.
Optimal Strategies for Live Video Streaming in the Low-latency Regime

Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds.
Spatiotemporally scalable matrix recovery for background modeling and moving object detection

Separating moving objects and backgrounds from a video is an important yet challenging task for video analysis due to complex moving behaviors, camera jitters/movements, and huge data amount in real-world applications. To deal with these issues, this paper proposes a unified framework called spatiotemporally scalable matrix recovery (SSMR), which has a moderate computational and space complexity scalable to temporal and spatial resolution of videos. In the proposed model, the inherent batch-mode nuclear norm for low-rank approximation is replaced with an explicitly low-rank matrix factorization in order to achieve online implementation. Motion information extracted by an optical flow method is incorporated into the data term to facilitate the separation of moving objects from the background. Affine transformation is embedded into the model and simultaneously optimized with other variables to handle camera motions. In addition, we proposed a pyramidal scheme to achieve spatial scalability for high definition videos. Experimental results demonstrate that our method outperforms many other state-of-the-art methods and can handle videos of various complex scenarios.
AG: Adaptive Switching Granularity for Load Balancing with Asymmetric Topology in Data Center Network

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipments widely exist in production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make full use of available paths, they are prone to experience packet reordering problem under asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called AG, which adaptively adjusts switching granularity according to the asymmetric degree of multiple paths. AG increases switching granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing switching granularity to obtain high link utilization under small degrees of topology asymmetry. AG is deployed on the switches with negligible overhead, while making no modification on end-hosts. We evaluate AG through both Mininet testbed and large-scale NS2 simulations. The experimental results show that AG reduces the average and 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> flow completion time by up to 51% and 56% over the state-of-the-art load balancing schemes, respectively.
Underwater image restoration through a combination of improved dark channel prior and gray world algorithms

Due to the absorption and scattering of light caused by particles suspended in water and the variable attenuation of light with different wavelengths, underwater images are typically characterized by low contrast, blurred features, and severe color bias. To improve the quality of underwater images, an underwater image restoration method is proposed. First, based on similarities between the underwater optical imaging model and the atmosphere model, an improved dark channel prior algorithm is used to enhance the original underwater image and an improved gray world algorithm is used to calibrate the image. Then, an improved wavelet fusion algorithm is adopted to combine the results produced by the two algorithms. The experimental results show that the proposed method can effectively improve feature clarity and color accuracy in underwater images with low quality and illumination, resulting in a better visual effect.
Motion Capture from Pan-Tilt Cameras with Unknown Orientation

In sports, such as alpine skiing, coaches would like to know the speed and various biomechanical variables of their athletes and competitors. Existing methods use either body-worn sensors, which are cumbersome to setup, or manual image annotation, which is time consuming. We propose a method for estimating an athlete's global 3D position and articulated pose using multiple cameras. By contrast to classical markerless motion capture solutions, we allow cameras to rotate freely so that large capture volumes can be covered. In a first step, tight crops around the skier are predicted and fed to a 2D pose estimator network. The 3D pose is then reconstructed using a bundle adjustment method. Key to our solution is the rotation estimation of Pan-Tilt cameras in a joint optimization with the athlete pose and conditioning on relative background motion computed with feature tracking. Furthermore, we created a new alpine skiing dataset and annotated it with 2D pose labels, to overcome shortcomings of existing ones. Our method estimates accurate global 3D poses from images only and provides coaches with an automatic and fast tool for measuring and improving an athlete's performance.