Reviewerly/exHarmony huggingface dataset:
Developing a supervised learning-based social media business sentiment index

The fast-growing digital data generation leads to the emergence of the era of big data, which become particularly more valuable because approximately 70% of the collected data in the world comes from social media. Thus, the investigation of online social network services is of paramount importance. In this paper, we use the sentiment analysis, which detects attitudes and emotions toward issues of society posted in social media, to understand the actual economic situation. To this end, two steps are suggested. In the first step, after training the sentiment classifiers with several big data sources of social media datasets, we consider three types of feature sets: feature vector, sequence vector and a combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers is assessed: MaxEnt-L1, C4.5 decision tree, SVM-kernel, Ada-boost, Naïve Bayes and MaxEnt. In the second step, we collect datasets that are relevant to several economic words that the public use to explicitly express their opinions. Finally, we use a vector auto-regression analysis to confirm our hypothesis. The results show the statistically significant relationship between public sentiment and economic performance. That is, "depression" and "unemployment" lead to KOSPI. Also, it shows that the extracted keywords from the sentiment analysis, such as "price," "year-end-tax" and "budget deficit," cause the exchange rates.
Tests for Einstein-Podolsky-Rosen steering in two-mode systems of identical massive bosons

In a previous paper tests for entanglement for two mode systems involving identical massive bosons were obtained. In the present paper we consider sufficiency tests for EPR steering in such systems. We find that spin squeezing in any spin component, a Bloch vector test, the Hillery-Zubairy planar spin variance test and squeezing in two mode quadratures all show that the quantum state is EPR steerable. We also find a generalisation of the Hillery-Zubairy planar spin variance test for EPR steering. The relation to previous correlation tests is discussed. This paper is based on a detailed classification of quantum states for bipartite systems. States for bipartite composite systems are categorised in quantum theory as either separable or entangled, but the states can also be divided differently into Bell local or Bell non-local states in terms of local hidden variable theory (LHVT). For the Bell local states there are three cases depending on whether both, one of or neither of the LHVT probabilities for each sub-system are also given by a quantum probability involving sub-system density operators. Cases where one or both are given by a quantum probability are known as local hidden states (LHS) and such states are non-steerable. The steerable states are the Bell local states where there is no LHS, or the Bell non-local states. The relationship between the quantum and hidden variable theory classification of states is discussed.
Content-Centric Group User Authentication for Secure Social Networks

Content-centric computing systems, which can obtain users' attributes by analyzing information from content, have been widely used in many fields. In social networks, the computing resources of users are restricted. Group authentication schemes are required to ensure the validity of the user and the accuracy of the shared data. In this paper, we propose a content-centric group user authentication scheme to guarantee the security and accuracy of shared data. The proposed group user authentication scheme makes use of the user's content to generate the user's feature vector. Furthermore, the group authentication scheme based on the identity of each user can guarantee the network security before data sharing. In addition, regular operations in the network are not affected or damaged by incidents that occur during the authentication process. The security and performance analyses show that our scheme is secure against various attacks and has lower computational cost than the existing schemes.
Data Mining: Classification and Prediction

Data mining is the set of computational techniques and methodologies aimed to extract knowledge from a large amount of data, by using sophisticated data analysis tools to highlight information structure underlying large data sets. Machine learning methods represent one of these tools, allowing, not only data management but also analysis and prediction operations. Supervised learning, a kind of machine learning methodology, uses input data and products outputs of two type: qualitative and quantitative, respectively describing data classes and predicting data trends. Classification task provides qualitative responses whereas prediction or regression task offers quantitative outputs
Probabilistic Semantic Retrieval for Surveillance Videos With Activity Graphs

We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos. The wide diversity of queries coupled with unavailability of annotated activity data limits our ability to train activity models. To bridge the semantic gap we propose to let users describe an activity as a semantic graph with object attributes and inter-object relationships associated with nodes and edges, respectively. We learn node/edge-level visual predictors during training and, at test-time, propose to retrieve activity by identifying likely locations that match the semantic graph. We formulate a novel CRF based probabilistic activity localization objective that accounts for mis-detections, mis-classifications and track-losses, and outputs a likelihood score for a candidate grounded location of the query in the video. We seek groundings that maximize overall precision and recall. To handle the combinatorial search over all high-probability groundings, we propose a highest precision subgraph matching algorithm. Our method outperforms existing retrieval methods on benchmarked datasets.
Nine-frequency-path quantum interferometry over 60 km of optical fiber

The archetypal quantum interferometry experiment yields an interference pattern that results from the indistinguishability of two spatiotemporal paths available to a photon or to a pair of entangled photons. A fundamental challenge in quantum interferometry is to perform such experiments with a higher number of paths and over large distances. We demonstrate that using indistinguishable frequency paths instead of spatiotemporal ones allows for robust, high-dimensional quantum interferometry in optical fibers. In our system, twin photons from an Einstein-Podolsky-Rosen pair are offered up to nine frequency paths after propagation in long-haul optical fibers and we show that the multipath quantum interference patterns can be faithfully restored after the photons travel a total distance of up to $60\phantom{\rule{0.28em}{0ex}}\mathrm{km}$.
Selection Problems in the Presence of Implicit Bias

chapter Share on Selection Problems in the Presence of Implicit Bias Author: Manish Raghavan Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer ScienceView Profile Authors Info & Claims The Societal Impacts of Algorithmic Decision-MakingSeptember 2023https://doi.org/10.1145/3603195.3603209Published:08 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Markov equivalence of marginalized local independence graphs

Symmetric independence relations are often studied using graphical representations.Ancestral graphs or acyclic directed mixed graphs with mseparation provide classes of symmetric graphical independence models that are closed under marginalization.Asymmetric independence relations appear naturally for multivariate stochastic processes, for instance, in terms of local independence.However, no class of graphs representing such asymmetric independence relations, which is also closed under marginalization, has been developed.We develop the theory of directed mixed graphs with μ-separation and show that this provides a graphical independence model class which is closed under marginalization and which generalizes previously considered graphical representations of local independence.Several graphs may encode the same set of independence relations and this means that in many cases only an equivalence class of graphs can be identified from observational data.For statistical applications, it is therefore pivotal to characterize graphs that induce the same independence relations.Our main result is that for directed mixed graphs with μ-separation each equivalence class contains a maximal element which can be constructed from the independence relations alone.Moreover, we introduce the directed mixed equivalence graph as the maximal graph with dashed and solid edges.This graph encodes all information about the edges that is identifiable from the independence relations, and furthermore it can be computed efficiently from the maximal graph.
Low Area-Overhead Low-Entropy Masking Scheme (LEMS) Against Correlation Power Analysis Attack

The low-entropy masking scheme (LEMS) is a costsecurity tradeoff solution that ensures a certain level of security with much lower overheads than a full-entropy masking scheme (FEMS). However, most existing LEMSs are based on a look-up-table (LUT) and limited to the first-order, which is vulnerable to classical higher-order correlation power analysis (CPA) attack and other special types of attack (e.g., collision attack). This paper proposes a new type of LEMS for a block cipher in which the S-box consists of power functions and an affine function. First, a low masking-complexity algorithm for evaluating S-boxes is developed by fully utilizing the property of a hybrid addition-chain (AC) named LUT-AC. Next, an LEMS for block ciphers is proposed. This LEMS provides two different masking modes to realize various cost-security tradeoff schemes. Due to the “masked invariant property” of the LUTAC, the masking complexity of the proposed LEMS is equal to O(d), whereas under FEMS it is equal to O(d2). Compared with existing LEMSs, the proposed LEMS has following advantages: higher security in terms of the masking entropy; resistance against collision attacks; and scalability to higher-order schemes. Per the proposed algorithm, an architecture without any nonlinear multiplication for evaluating AES is developed by replacing the LUT with seven scalar multiplications. The different LEMSs based on this architecture are developed. Their area overheads are evaluated by implementing different schemes in 65 nm CMOS process. The security of the first-order LEMS with rotation mode is verified by performing CPA on the SAKURA-G FPGA board. From the experimental success rates, it shows that the proposed first-order LEMS can resist CPA without revealing the correct subkey for up to 100 000 power traces, whereas the unprotected scheme is broken at 1100 traces.
A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

In the age of Big Genomics Data, institutions such as the National Human Genome Research Institute (NHGRI) are challenged in their efforts to share volumes of data between researchers, a process that has been plagued by unreliable transfers and slow speeds. These occur due to throughput bottlenecks of traditional transfer technologies. Two factors that affect the efficiency of data transmission are the channel bandwidth and the amount of data. Increasing the bandwidth is one way to transmit data efficiently, but might not always be possible due to resource limitations. Another way to maximize channel utilization is by decreasing the bits needed for transmission of a dataset. Traditionally, transmission of big genomic data between two geographical locations is done using general-purpose protocols, such as hypertext transfer protocol (HTTP) and file transfer protocol (FTP) secure. In this paper, we present a novel deep learning-based data minimization algorithm that 1) minimizes the datasets during transfer over the carrier channels; 2) protects the data from the man-in-the-middle (MITM) and other attacks by changing the binary representation (content-encoding) several times for the same dataset: we assign different codewords to the same character in different parts of the dataset. Our data minimization strategy exploits the alphabet limitation of DNA sequences and modifies the binary representation (codeword) of dataset characters using deep learning-based convolutional neural network (CNN) to ensure a minimum of code word uses to the high frequency characters at different time slots during the transfer time. This algorithm ensures transmission of big genomic DNA datasets with minimal bits and latency and yields an efficient and expedient process. Our tested heuristic model, simulation, and real implementation results indicate that the proposed data minimization algorithm is up to 99 times faster and more secure than the currently used content-encoding scheme used in HTTP of the HTTP content-encoding scheme and 96 times faster than FTP on tested datasets. The developed protocol in C# will be available to the wider genomics community and domain scientists.
DriverAuth: Behavioral biometric-based driver authentication mechanism for on-demand ride and ridesharing infrastructure

On-demand ride services and the rideshare infrastructure primarily focus on the minimization of travel time and cost. However, the safety of riders is overlooked by service providers. For driver authentication, existing identity management methods typically check the driving license, which can be easily stolen, forged, or misused. Further, background checks are not performed at all; instead, social profiles and peer reviews are used to foster trust, thereby compromising the safety and security of riders. Moreover, the present mechanism seems ineffective in discontinuing a malicious driver from offering the services. In this paper, we present DriverAuth—a fully transparent and easy-to-use authentication scheme for drivers that is based on common behavioral biometric modalities, such as hand movements, swipes, and touch-strokes while the drivers interact with the dedicated smartphone-based application for accepting the booking. A preliminary study of behavioral biometric-based approaches offers a usable verification mechanism on smartphones that could be a potential solution to improve the safety of riders in the emerging on-demand ride and the rideshare infrastructure.
Restoring Aspect Ratio Distortion of Natural Images With Convolutional Neural Network

We propose a method to restore aspect ratio distortion of images using convolutional neural network (CNN). The “aspect ratio,” which is focused on this research, means degree of horizontal stretching of images. Indeed an image can be distorted by vertical or horizontal stretching, which does not maintain the aspect ratio. In the proposed method, we construct an aspect ratio estimator whose input is a (possibly distorted) image and output is a scalar value of aspect ratio. Since estimation of aspect ratio from image can be regarded as regression problem, we modeled the estimator by CNN. Once we have a reliable estimate of aspect ratio of an image, the restoration can be done straightforwardly by inverse stretching. In the experiments, we evaluated performance of the model trained on Pascal VOC natural image dataset. Our method can precisely restore the distortion within 1.4% of stretch from original images on average, which outperforms average human performance (i.e., about 13%). In terms of accuracy, 99.86% of distorted images are successfully restored. We also propose training methods to enhance the robustness of the CNN against particular types of disturbance.
Graphical structures for design and verification of quantum error correction

Abstract We introduce a high-level graphical framework for designing and analysing quantum error correcting codes, centred on what we term the coherent parity check (CPC). The graphical formulation is based on the diagrammatic tools of the ZX -calculus of quantum observables. The resulting framework leads to a construction for stabilizer codes that allows us to design and verify a broad range of quantum codes based on classical ones, and that gives a means of discovering large classes of codes using both analytical and numerical methods. We focus in particular on the smaller codes that will be the first used by near-term devices. We show how CSS codes form a subset of CPC codes and, more generally, how to compute stabilizers for a CPC code. As an explicit example of this framework, we give a method for turning almost any pair of classical <?CDATA $[n,k,3]$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> <mml:mo stretchy="false">]</mml:mo> </mml:math> codes into a <?CDATA $[[2n - k + 2, k, 3]]$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">[</mml:mo> <mml:mo stretchy="false">[</mml:mo> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> <mml:mo>−</mml:mo> <mml:mi>k</mml:mi> <mml:mo>+</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> <mml:mo stretchy="false">]</mml:mo> <mml:mo stretchy="false">]</mml:mo> </mml:math> CPC code. Further, we give a simple technique for machine search which yields thousands of potential codes, and demonstrate its operation for distance 3 and 5 codes. Finally, we use the graphical tools to demonstrate how Clifford computation can be performed within CPC codes. As our framework gives a new tool for constructing small- to medium-sized codes with relatively high code rates, it provides a new source for codes that could be suitable for emerging devices, while its ZX -calculus foundations enable natural integration of error correction with graphical compiler toolchains. It also provides a powerful framework for reasoning about all stabilizer quantum error correction codes of any size.
Improved structure tensor for fine-grained texture inpainting

Partial differential equations (PDEs) have emerged as a useful tool for recovering structures when solving image inpainting problems. In the research presented in this paper, we extend structure tensor (ST)-based PDE to fine-grained texture inpainting by improving the ST with three modifications to enhance its suitability to more accurately process fine-grained textures of which the small-scale patterns repeat non-locally. These modifications are then inserted into an anisotropic PDE to recover damaged textures. The result is a subtly-conducted anisotropic diffusion inpainting algorithm free of detail-blurring artifacts produced by the classic tensor diffusion model and dislocation deficiencies affecting patch-based completion approaches. This is made possible by the following strategies: (1) construct a fractional ST (FST 1 ) composed of the inner product of the fractional derivative vector and its transposition to more effectively address complex fractal-like texture details because of the characteristics of the fractional differential; (2) because FST is composed of the inner product of two vectors, it is necessary to represent the tensor at a higher resolution than for the original image to avoid spectrum aliasing, which is automatically implemented by the calculation of the twofold oversampling fractional derivative (i.e., double-sampling FST, DFST 2 ); (3) apply a non-local regularizing method to all four channels of the resulting DFST based on the repeatability of textures, which integrates non-local geometric characteristics to effectively infer how damaged textures extend. Finally, we insert the modified non-locally regularized DFST (NDFST 3 ) into an anisotropic PDE to effectively guide the diffusion process and design its numerical implementation scheme. The experimental results show that, for fine-grained texture images, the proposed NDFST is equipped to accurately detect subtle and complex texture features, and the resulting NDFST-based PDE is particularly effective not only for recovering non-homogeneous structures, but also for the restitution of fine-grained textures.
Analysis of Error-Correcting Codes for Lattice-Based Key Exchange

Lattice problems allow the construction of very efficient key exchange and public-key encryption schemes. When using the Learning with Errors (LWE) or Ring-LWE (RLWE) problem such schemes exhibit an interesting trade-off between decryption error rate and security. The reason is that secret and error distributions with a larger standard deviation lead to better security but also increase the chance of decryption failures. As a consequence, various message/key encoding or reconciliation techniques have been proposed that usually encode one payload bit into several coefficients. In this work, we analyze how error-correcting codes can be used to enhance the error resilience of protocols like NewHope, Frodo, or Kyber. For our case study, we focus on the recently introduced NewHope Simple and propose and analyze four different options for error correction: (i) BCH code; (ii) combination of BCH code and additive threshold encoding; (iii) LDPC code; and (iv) combination of BCH and LDPC code. We show that lattice-based cryptography can profit from classical and modern codes by combining BCH and LDPC codes. This way we achieve quasi-error-free communication and an increase of the estimated post-quantum bit-security level by 20.39% and a decrease of the communication overhead by 12.8%.
A Verifiable Shuffle for the GSW Cryptosystem

We provide the first verifiable shuffle specifically for fully homomorphic schemes. A verifiable shuffle is a way to ensure that if a node receives and sends encrypted lists, the content will be the same, even though no adversary can trace individual list items through the node. Shuffles are useful in e-voting, traffic routing and other applications. We build our shuffle on the ideas and techniques of Groth's 2010 shuffle, but make necessary modifications for a less ideal setting where the randomness and ciphertexts admit no group structure. The protocol relies heavily on the properties of the so-called gadget matrices, so we have included a detailed introduction to these.
Towards online detection of driver distraction: Eye-movement simulation based on a combination of vestibulo–ocular reflex and optokinetic reflex models

With the aim of developing a method for evaluating driver distraction based on the difference between simulated and observed eye movements, we integrated a vestibulo–ocular reflex (VOR) model and an optokinetic reflex (OKR) model. On the basis of 2 × 2 factorial design analysis (VOR model only and VOR+OKR model; with and without visual stimulus), we confirmed that the VOR+OKR model showed a better performance than the VOR model, with a smaller mean-square error. We also conducted an experiment that used an n-back task to simulate mental workload while driving. The mean-square error between the simulated and observed eye movement became larger in the presence of mental workload. This result suggests that the VOR+OKR model could be applied in the evaluation of driver distraction.