Reviewerly/exHarmony huggingface dataset:
Quadratic Privacy-Signaling Games and Payoff Dominant Equilibria

We consider a privacy-signaling game problem in which a transmitter with privacy concerns and a receiver, which does not pay attention to these privacy concerns, communicate. In this communication scenario, the transmitter observes a pair of correlated random variables which are modeled as jointly Gaussian. The transmitter constructs its message based on these random variables with the aim to hide one of them and convey the other one. In contrast, the objective of the receiver is to accurately estimate both of the random variables so as to gather as much information as possible. These conflicting objectives are analyzed in a game theoretic framework where depending on the commitment conditions (of the sender), we consider Nash or Stackelberg equilibria. We show that a payoff dominant (i.e., most desirable for both players) Nash equilibrium is attained by affine policies and we explicitly characterize these policies. In addition, the strategies at the characterized Nash equilibrium is shown to form also a Stackelberg equilibrium. Furthermore, we show that there always exists an informative Stackelberg equilibrium for the multidimensional parameter setup. We also revisit the information bottleneck problem within our Stackelberg framework under the mean squared error distortion criterion where the information bottleneck setup has a further restriction that only one of the parameters is observed at the sender. We fully characterize the Stackelberg equilibria under certain conditions and when these conditions are not met we establish the existence of informative equilibria.
Effects of Brightness on Distance Judgments in Head Mounted Displays

Many HMD applications are improved when people perceive and act similarly in the virtual world as they would in an equivalent real environment. However, multiple studies show people underestimate egocentric distances in HMDs while being reasonably accurate in the real world. Some studies also show that brightness in the periphery may be one factor influencing these distance judgments. We built upon this previous work and examine if the brightness of the entire screen affects distance judgments. Using direct blind-walking, we compared a bright environment to an artificially darkened one with a between-subject experiment. The results of the experiment indicate that overall brightness has no significant effect on egocentric distance judgment. We discuss the implications of our findings in the context of previous work. Our findings may help designers of HMD software featuring dark environments (e.g., some entertainment applications).
New Lattice-Based Digital Multi-signature Scheme

The traditional digital multi-signature schemes are mostly based on large integer factorization and the discrete logarithm, which cannot be secured in quantum environment. The paper presents a lattice-based multi-signature scheme that can resist the quantum attack using the hardness of average-case short integer solution problem (SIS). Multi-signature includes the simultaneous signature and sequential signature. The paper describes respectively the key generation, multi-signature generation and multi-signature verification of the two types of schemes. Moreover, experimental results prove that the digital multi-signature scheme based on lattice is especially efficient and secure to multi-signature generation.
Spatial Sentiment and Perception Analysis of BBC News Articles Using Twitter Posts Mining

Over the past few decades, an exponential growth is seen in social media, online resources and microblogging websites such as Twitter. There has been a gush of user generated content and production of huge amount of data through news and event sharing on these sites is no exception. Data generated by these resources is a rich source of information for data mining. Sentiment Analysis is a current and important research area that attempts to determine the polarity of text. Determining the sentiments on happening events around the globe has become extremely important. In this paper, a subjective lexicon-based approach is proposed to mine the unstructured data into meaningful information from a popular microblogging website, Twitter, in order to determine the semantic orientation of real-time reactions and opinions. The main focus is to extract the audience’s sentiment related to BBC news articles being shared on Twitter. Firstly, our approach will extract all comments on shared articles and determine their polarity. Secondly, it categorizes the extracted users based on their location and shows the collective opinion of users in different regions. Thirdly, a visualization tool has been developed for viewing the obtained results.
Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.
A Method for Fast Computing the Algebraic Degree of Boolean Functions

The algebraic degree of Boolean functions (or vectorial Boolean functions) is an important cryptographic parameter that should be computed by fast algorithms. They work in two main ways: (1) by computing the algebraic normal form and then searching the monomial of the highest degree in it, or (2) by examination the algebraic properties of the true table vector of a given function. We have already done four basic steps in the study of the first way, and the second one has been studied by other authors. Here we represent a method for fast computing (the fastest way we know) the algebraic degree of Boolean functions. It is a combination of the most efficient components of these two ways and the corresponding algorithms. The theoretical time complexities of the method are derived in each of the cases when the Boolean function is represented in a byte-wise or in a bitwise manner. They are of the same type Θ(n.2n) for a Boolean function of n variables, but they have big differences between the constants in Θ-notation. The theoretical and experimental results shown here demonstrate the advantages of the bitwise approach in computing the algebraic degree---they are dozens of times faster than the byte-wise approaches.
No-Reference Quality Prediction for DIBR-Synthesized Images Using Statistics of Fused Color-Depth Images

With the increasing prevalence of multi-view and free-viewpoint videos, the view synthesis has gained extensive attention. In view synthesis, the new viewpoint is generated from color and depth images by a depth-image-based rendering (DIBR) algorithm. The quality of color and depth images is crucial for generating a new high-quality viewpoint. However, DIBR is computationally expensive, thus how to infer the quality of DIBR-synthesized image by only using the input color and depth images is highly desired. With this motivation, this paper presents a no-reference metric to predict the quality of DIBR-synthesized images using statistics of fused color-depth images without actual DIBR process. Specifically, a wavelet-based fusion strategy is first proposed to simulate the interactions between color and depth distortions during the DIBR process. Then the multi-scale statistical features are extracted from the fused color-depth image. Finally, the back propagation (BP) regression network is employed for building the quality prediction model for DIBR-synthesized images. The experimental results demonstrate the superiority of the proposed metric over the state-of-the-arts.
Learning with Small Data

In the era of big data, data-driven methods have become increasingly popular in various applications, such as image recognition, traffic signal control, fake news detection. The superior performance of these data-driven approaches relies on large-scale labeled training data, which are probably inaccessible in real-world applications, i.e., "small (labeled) data" challenge. Examples include predicting emergent events in a city, detecting emerging fake news, and forecasting the progression of conditions for rare diseases. In most scenarios, people care about these small data cases most and thus improving the learning effectiveness of machine learning algorithms with small labeled data has been a popular research topic.
Use of trainable wavelet transform with adaptive threshold filtering for noise reduction of speech signals

Abstract A method of noise reduction based on an adaptive threshold filtering over trainable wavelet transform was proposed. The restrictions on the trainable transform filter parameters were provided by a set of quadratic regularization terms. As an analog of the “hard” and “soft” threshold functions we used their smooth infinitely differentiable versions. The parameters of trainable wavelet transform, and threshold values were estimated by backpropagation and gradient-based optimization algorithm with an adaptive momentum estimation. Results of the proposed method were compared with an approach based on fixed discrete wavelet transform and non-adaptive global and level-depended threshold algorithms on the model problem. We used signal-to-noise ratio between suppressed and clean signals to numerically estimate the efficiency of noise reduction. We showed that the best results were obtained when the proposed trainable method with Daubechies 4 wavelet filters fine-tuning and adaptive level-dependent thresholding were applied.
Hierarchical embedding for DAG reachability queries

Current hierarchical embeddings are inaccurate in both reconstructing the original taxonomy and answering reachability queries over Direct Acyclic Graph. In this paper, we propose a new hierarchical embedding, the Euclidean Embedding (EE), that is correct by design due to its mathematical formulation and associated lemmas. Such embedding can be constructed during the visit of a taxonomy, thus making it faster to generate if compared to other learning-based embeddings. After proposing a novel set of metrics for determining the embedding accuracy with respect to the reachability queries, we compare our proposed embedding with state-of-the-art approaches using full trees from 3 to 1555 nodes and over a real-world Direct Acyclic Graph of 1170 nodes. The benchmark shows that EE outperforms our competitors in both accuracy and efficiency.
FPGA-based accelerator for convolution operations

Convolutional neural networks have been widely used in many deep learning applications. Convolutional neural networks have a large number of convolution operations, which poses a huge challenge to real-time performance. In recent years, FPGA implementations of convolutional accelerators have received much attention due to their high performance and energy efficiency. In this paper, we implement an accelerator for convolution operations through the systolic array architecture on Xilinx ZedBoard device. The experimental results show that ours designed accelerators achieving performance density of up to 0.032 Gop/s/DSP.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks

Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains. In this work, we introduce a new retrospective loss to improve the training of deep neural network models by utilizing the prior experience available in past model states during training. Minimizing the retrospective loss, along with the task-specific loss, pushes the parameter state at the current training step towards the optimal parameter state while pulling it away from the parameter state at a previous training step. Although a simple idea, we analyze the method as well as to conduct comprehensive sets of experiments across domains - images, speech, text, and graphs - to show that the proposed loss results in improved performance across input domains, tasks, and architectures.
On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent

We study stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. Building upon the recent advances in algorithmic high-dimensional robust statistics, in each SGD iteration, master employs a non-trivial decoding to estimate the true gradient from the unbiased stochastic gradients received from workers, some of which may be corrupt. We provide convergence analyses for both strongly-convex and non-convex smooth objectives under standard SGD assumptions. We can control the approximation error of our solution in both these settings by the mini-batch size of stochastic gradients; and we can make the approximation error as small as we want, provided that workers use a sufficiently large mini-batch size. Our algorithm can tolerate less than 1/3 fraction of Byzantine workers. It can approximately find the optimal parameters in the strongly-convex setting exponentially fast, and reaches to an approximate stationary point in the non-convex setting with linear speed, i.e., with a rate of 1/T, thus, matching the convergence rates of vanilla SGD in the Byzantine-free setting.
On Validating Attack Trees with Attack Effects

Threats or attacks can be decomposed into more primitive attacks/events by attack trees. These trees can show possible scenarios of threats. In addition, the quantitative properties of attacks, called attributes, can be integrated along with the tree structures. This paper introduces a formal system for attack trees focusing on refinement scenarios, and enriches attack trees with effects of attacks, which allows the evaluation of the validity of attack decomposition systematically. The property that sub-attacks refine an attack is described by the relationship among their effects, that is defined as consistency of a branch. Consistent attack trees support a systematic approach for the entire attack tree process. Furthermore the effects of attacks in consistent attack trees are well-behaved as an attribute. These ideas are applied to the case study of a vehicular network system. As an application, possible degrees of mitigation for attacks in attack trees are discussed.
Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity

Pattern mining is one of the core topics of data mining. We consider the problem of mining a succinct set of patterns that together explain the data in terms of mutual exclusivity and co-occurence. That is, we extend the traditional pattern languages beyond conjunctions, enabling us to capture more complex relationships, such as replacable sub-components or antagonists in biological pathways.
Computational Code-Based Single-Server Private Information Retrieval

A new computational private information retrieval (PIR) scheme based on random linear codes is presented. A matrix of messages from a McEliece scheme is used to query the server with carefully chosen errors. The server responds with the sum of the scalar multiple of the rows of the query matrix and the files. The user recovers the desired file by erasure decoding the response. Contrary to code-based cryptographic systems, the scheme presented here enables to use truly random codes, not only codes disguised as such. Further, we show the relation to the so-called error subspace search problem and quotient error search problem, which we assume to be difficult, and show that the scheme is secure against attacks based on solving these problems.
Design of Reinforced Hybrid Fuzzy Rule-Based Neural Networks Driven to Inhomogeneous Neurons and Tournament Selection

In this article, we introduce novel reinforced hybrid fuzzy rule-based neural networks (RHFNNs). This article is concerned with the development of the design methodologies of hybrid fuzzy rule-based model for constructing the network structure and enhancing its predictive abilities through the combination of inhomogeneous neurons [i.e., clustering-based polynomial neurons (CPNs) and polynomial neurons (PNs)] and tournament selection. The key points of the proposed RHFNN are enumerated as follows: The first layer of the proposed network consists of CPNs. CPN can effectively reflect the complex nonlinear structure encountered in the data space, and refine (granulate) it with the help of the clustering algorithm. Two types of CPNs including hard C-means (HCM) clustering-based polynomial neuron (HCPN) and fuzzy C-means (FCM) clustering-based polynomial neuron (FCPN) are designed. According to the type of CPN used in the first layer, RHFNN can be categorized into two types, namely, RHFNN based on HCPN (HRHFNN) and RHFNN based on FCPN (FRHFNN). We use PNs to construct the second and consecutive layers. PN can identify and approximate the nonlinear relationship among system's inputs and outputs. A tournament-based performance selection (TPS) algorithm stemming from evolutionary computation is used for selection of neuron. TPS not only ensures that the candidate nodes have sufficient fitting ability but also enhances the individual diversity in the node set and provides the abilities to generate better prediction nodes. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization is considered to reduce the deviation between coefficients and ameliorate overfitting as well as boost generalization ability. The performance of RHFNN is discussed through a variety of publicly available machine learning datasets. From the experimental results, we conclude that RHFNN achieves the best prediction accuracy on 13 of 15 datasets; the statistical analysis also confirms the superiority of RHFNN.
A Detection Method for DeepFake Hard Compressed Videos based on Super-resolution Reconstruction Using CNN

The DeepFake video detection method based on convolutional neural networks has a poor performance in the dataset of hard compressed DeepFake video. And a large number of false tests will occur to the real data. To solve this problem, a networks model detection method for super-resolution reconstruction of DeepFake video is proposed. First of all, the face area of real data is processed by Gaussian blur, which is converted into negative data, and the real data and processing data are input into neural network for training. Then the residual network is used for super-resolution reconstruction of test data. Finally, the trained model is used to test the video after super-resolution reconstruction. Experiments show that the proposed method can reduce the false detection rate and improve the accuracy in detection of single frames.
An Evaluation of the Efficiency of Popular Personal Space Pointing versus Controller based Spatial Selection in VR

We detail the results of two experiments that empirically evaluate the efficiency of spatial selection performance of popular pointing and controller based direct 3D interaction within the participants' maximum arms reach or personal space in an immersive virtual environment. In the first experiment, targets were presented in a circular configuration of 13 targets in accordance with the ISO 9241-9 Fitts' Law standard, with a target distance of 5, 9 or 18 cm and target width of 0.65, 0.85, or 1.05cm, and with each circular configuration presented in a random distance of 60%, 75% or 90% of maximum arms length of the participant. The targets were centered at eye level, and in a within-subjects protocol participants selected the targets using a pointer at the tip of the controller or at the tip of their index finger on the dominant hand in the pointing based condition. The second experiment presented each target in a similar width and distance configuration as the previous experiment, but each target also varied in depth from one trial to the next between 60%, 75% or 90% of maximum arms reach. The selections were performed in an HTC Vive head mounted display VR world, using the popular Leap Motion gesture tracking device or the Vive controller. The results of both our experiment taken together revealed that with commodity 3D input devices, participants were more efficient in spatial selection performance in the popular controller based condition, as compared to the pointing based condition. Implications for VR developers and consumers are discussed in the latter sections.
Naive Learning Through Probability Matching

We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions random decision rules are sufficient to achieve agreement in finite time in any strongly connected network. Our main result establishes that naive learning can be achieved in any large strongly connected network. That is, if beliefs satisfy a high level of inertia, then there exist corresponding decision rules coinciding with probability over-matching such that the eventual agreement action matches the true state, with a probability converging to one as the network size goes to infinity.
A Secure and Privacy-Preserving Data Collection (SPDC) Framework for IoT Applications

Mobile patient monitoring systems monitor and treat chronic diseases by collecting health data from wearable sensors through mobile devices carried out by patients. In the future, these systems may be hosted by a third-party service provider. This would open a number of security and ID privacy issues. One of these issues is the inference attack. This attack allows a single service provider from inferring the patient's identity by collecting a number of contextual information about the patient such as the pattern of interaction with the service provider. Thus a security and ID privacy mechanisms must be deployed. In this paper, we propose a framework called Secure and Privacy-Preserving Data Collection (SPDC) that allows the patient to encrypt the data and then upload the encrypted data on different service providers rather than one while allowing an anonymous linkage for the patient's data which are scattered across different service providers. In this framework, each patient is allowed to select the service providers involved in the data collection, assigns one as the home while the others consider foreign. The patient uses the foreign to upload data while the home is responsible for anonymously collecting the patient's data from multiple foreign service providers and deliver them to the healthcare provider. This framework also shows a novel mechanism to conduct anonymous authentication across different distributed service provides. The framework has been analyzed against the specified design requirements and security threats.