Reviewerly/exHarmony huggingface dataset:
Decoherence-free subspace and entanglement sudden death of multi-photon polarization states in fiber channels

Abstract The construction of quantum networks requires long-distance teleportation of multi-qubit entangled states. Here, we investigate the entanglement dynamics of GHZ and W states in fiber channels. In a fiber channel, the two most important phenomena that affect polarization entanglement are polarization mode dispersion (PMD) and polarization-dependent loss (PDL). We theoretically characterize how PMD and PDL vectors affect three-qubit states. In particular, upon quantifying the entanglement at the output states using concurrence and entanglement witnesses, we reveal the occurrence of entanglement sudden death and the appearance of decoherence-free subspaces in tripartite systems. Finally, we explore the evolution of GHZ and W state with an arbitrary number of photons in a fiber network and discuss the decoherence mechanism of the 4-party cluster state.
Virtual reality consumer product injuries: An analysis of national emergency department data

The growing popularity of virtual reality devices and increasingly widespread distribution of VR products into the home exposes users to risk of bodily harm. Safety features are integrated into the devices themselves, but the burden of cautious use rests upon the end user. The purpose of this study is to quantify and describe the array of injuries and demographics effected by the burgeoning VR industry to inform and encourage mitigation strategies.The National Electronic Injury Surveillance System (NEISS) data was used to examine a nationwide sample of emergency department records from 2013 - 2021. Inverse probability sample weights for cases were applied to arrive at national estimates. NEISS data included consumer product injuries, patient age, sex, race and ethnicity, drug and alcohol involvement, diagnoses, injury descriptions, and emergency department disposition.The first VR-related injury was reported in the NEISS data in 2017, and injuries were estimated to number 125. Incidents of VR-related injuries amplified as increased VR units sold, and by 2021, there was a 352% increase in VR injuries totaling a weighted estimate of 1,336 ED visits. The most common VR-related injury diagnosis is fracture (30.3%), followed by laceration (18.6%), contusion (13.9%), other (11.8%), and strain / sprain (10.0%). VR-related injuries involve the hand (12.1%), face (11.5%), finger (10.6%), and knee (9.0%), head (7.0%) and upper trunk (7.0%). Patients age 0-5 most commonly experienced injuries to the face (62.3%). Injuries in patients 6-18 were mostly to the hand (22.3%) and face (12.8%). Patients 19-54 experienced primarily injuries to the knee (15.3%), finger (13.5%), and wrist (13.3%). Patients aged 55 and older disproportionately experienced injuries in the upper trunk (49.1%) and upper arm (25.2%).This is the first study to describe the incidence, demographics and characteristics of injuries from VR device use. Sales of home VR units continue to increase annually and the rapid increase in VR consumer injuries is being managed by emergency departments across the country. An understanding of these injuries will inform VR manufacturers, application developers, and users to promote safe product development and operation.
Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings

Nowadays, online reviews and ratings are the most valuable source of word-of-mouth, voice-of-customer, and feedback, also customers can make purchasing decisions on what to buy, where to buy, and what to select. Genuine online reviews are becoming popular, but unfortunately, we have an issue that might only sometimes be unbiased or accurate. Because most of the reviews are fake reviews and ratings, these could mislead innocent customers and highly influence customers' purchasing decisions in the wrong manner. This paper's primary goal is to accurately detect fake reviews and what is the main difference between them. The secondary goal is to detect fake ratings and actual ratings-based reviews across the online platform, especially Amazon datasets. The Paper proposes two novel deep-learning Hybrid techniques: CNN-LSTM for detecting fake online reviews, and LSTM-RNN for detecting fake ratings in the e-commerce domain. Both Hybrid models can outperform and achieve better performance with the most advanced word embedding techniques, Glove, and One hot encoding techniques. As per the experimental results, the first technique efficiently detects fake online reviews with the highest prediction accuracy. The second hybrid model is better than the existing models that detect fake online ratings with the most excellent precision of 93.8%. The experimental research efficiently revealed that the CNN-LSTM and LSTM-RNN methods are more efficient and practicable and might be better suited for optimal results and maximizing the efficiency of fake online review detection.
ClapAuth: A Gesture-Based User-Friendly Authentication Scheme to Access a Secure Infrastructure

In this paper we propose a gesture-based user-friendly smartwatch-based user authentication scheme called ClapAuth to authenticate the users to gain physical access to a secure infrastructure. In ClapAuth users are authenticated by performing clapping actions, while wearing their smartwatch in one hand. ClapAuth, while users perform clapping gestures, profiles them by collecting data from their smartwatches' built-in accelerometer and gyroscope sensors. We have evaluated the proposed scheme on a publicly available dataset by using state-of-the-art n-class machine learning classifiers, namely Random Forest (RF), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN). KNN outperformed other two classifiers and attained $$93.3\%$$ TAR at the cost of $$0.22\%$$ FAR. ClapAuth could be widely accepted as it utilizes users' familiarity with a common action, such as clapping, and users are not required to remember any secret code or gesture.
Evaluation of neotectonic activity using watershed geomorphic analysis: A case study in the west of Dokan Lake, Kurdistan Region, Iraq

The Iraqi Kurdistan Region's landscapes are the product of a complex merging of the effects of the collision between the Arabian and Eurasian plates and various geomorphic processes. A study of the Khrmallan drainage basin in the west of Dokan Lake, based on a morphotectonic study is a significant contribution to our understanding of the Neotectonic activity in the High Folded Zone. This study investigated an integrated method of detail morphotectonic mapping and geomorphic indices' analysis for determining the signal of Neotectonic activity, using the digital elevation model (DEM) and satellite images. The detailed morphotectonic map, along with extensive field data, revealed considerable variation in the relief and morphology of the study area, and eight morphotectonic zones were recognized. The presence of a high anomalous value in stream length gradient (SL) ranges from 19 to 769, increasing the channel sinuosity index (SI) reaching 1.5, and the tendency of basin shifting through the transverse topographic index (T) ranging from 0.02 to 0.5, conclude that the study area is tectonically active. The strong relationship between the growth of the Khalakan anticline and the activation of faulting is concurrent with the collision of the Arabian and Eurasian plates. A hypothesis of an antecedent can be applied to the Khrmallan valley.
A Novel Method of Traffic Rule Compliance Detection Using Morphological Operations at Toll Booths

Vehicle collisions cause fatalities and disability for individuals all over the world (s). In India, vehicle accidents caused by infractions of traffic laws claim more lives than naxal violence or natural calamities. However, people may drive while disregarding traffic laws, and doing so risks their lives. The main causes of traffic accidents are often careless lane switching and the usage of mobile devices. Additionally, these fatal traffic accidents' financial burden on the affected individuals and the government is significant. The government is investing a lot of money to spread awareness, motivate people to obey traffic laws, and organise many awareness campaigns to inform people to obey the law and save lives. Traffic cops now manually discover violations of traffic laws. The activity is time-consuming and chaotic, and there is a chance that the traffic police would operate dishonestly, which might result in more traffic rule violations. There has been a great deal of investigation into traffic management systems over the last 20 years but at the expense of infrastructure requirements. Sensorbased approaches have been utilised to monitor these breaches. This research describes how machine vision-based feature extraction utilising classification and matching algorithms on Raspberry Pi hardware may be used to identify violations of traffic rules on roads and highways. An inexpensive Raspberry Pi-based system based on feature extraction for monitoring and detecting infractions of traffic lanes and other rules. SIFT, SURF, ORB, KAZE, AKAZE, BRISK, and ROOTSIFT were also used to discover rule violations.
A Novel Outlier-Robust Filter with Gaussian Multi-scale Mixture Model

In many practical applications, the prior statistics of process and measurement noises are inaccurate, and the heavy-tailed distributed noises are prone to occur due to outliers. To achieve state estimation under this situation, a new outlier-robust Kalman filter with Gaussian multi-scale mixture model (GMSMM-ORKF) is proposed. First, a GMSMM is presented to model the posterior probability density functions. Then, the hierarchical prior models on the mixture probability vectors and scale parameters are built. Furthermore, employing variational Bayesian inference, a GMSMM-ORKF is derived. Finally, the superiority of the filter is illustrated by the simulation and real data.
Exploring the Promise of Augmented Reality for Dual Language Vocabulary Learning Among Bilingual Children

There is increasing evidence supporting the efficacy of augmented reality (AR) education applications, yet comparatively few reports exploring AR for early childhood. Augmented reality is uniquely suited to language instruction and the early elementary context, thus has potential to support the growing number of English language learners (ELLs) in the United States. The current study describes the early stages in the development of a dual language AR application for teaching vocabulary to ELLs. We present iterative design data and a proof-of-prototype user study including child and parent acceptance data. Findings indicate that there is potential for AR to provide English vocabulary instruction with L1 supports for children.
A New Pairing-Free Certificateless Signcryption Scheme

Signcryption is a cryptographic primitive which provides unforgeability and confidentiality for digital communications. Many signcryption schemes have been constructed in the literature for secure communication between smart objects. But, many of these existing schemes are not secure and inefficient for resource constrained applications like WSNs, Mobile computing, VANETs and IoT applications. To enrich the security and efficiency issues, in this paper, we propose a new signcryption scheme in certificateless based framework and prove its security under the CDHP and ECDLP assumptions. The efficiency analysis indicates that our scheme is more efficient than other existing signcryption schemes and is well suitable for resource-constrained applications.
Investigating how Agile Software Practitioners Repay Technical Debt in Software Projects

Context: The adoption of agile methodologies can bring benefits to software projects, but such methodologies are also prone to technical debt (TD) accumulation. Knowing the practices used for repaying debt items and practice avoidance reasons (PARs) considered for explaining their non-repayment can aid agile practitioners to improve their capacity to manage TD. Aims: To investigate how agile software practitioners repay TD items in software projects. Method: We analyze a data set composed of 179 answers collected through a globally distributed family of industrial surveys on TD. Results: Agile practitioners tend to not repay debt items, mainly for those items that happen very often. The main PARs that explain the non-repayment are focusing on short-term goals, lack of organizational interest, and cost. Conversely, the repayment practices code refactoring, investing effort on TD repayment activities, and investing effort on testing activities are the more commonly used practices for eliminating debt items. We organize all this information in TD repayment boards. Conclusion: TD repayment in agile software projects is still far from ideal. The set of practices and PARs, and the boards can support practitioners in sharing their experiences in TD repayment.
Feature flow regularization: Improving structured sparsity in deep neural networks

Pruning is a model compression method that removes redundant parameters in deep neural networks (DNNs) while maintaining accuracy. Most available filter pruning methods require complex treatments such as iterative pruning, features statistics/ranking, or additional optimization designs in the training process. In this paper, we propose a simple and effective regularization strategy from a new perspective of evolution of features, which we call feature flow regularization (FFR), for improving structured sparsity and filter pruning in DNNs. Specifically, FFR imposes controls on the gradient and curvature of feature flow along the neural network, which implicitly increases the sparsity of the parameters. The principle behind FFR is that coherent and smooth evolution of features will lead to an efficient network that avoids redundant parameters. The high structured sparsity obtained from FFR enables us to prune filters effectively. Experiments with VGGNets, ResNets on CIFAR-10/100, and Tiny ImageNet datasets demonstrate that FFR can significantly improve both unstructured and structured sparsity. Our pruning results in terms of reduction of parameters and FLOPs are comparable to or even better than those of state-of-the-art pruning methods.
Seeing Through Money: Democracy, Data Governance, and the Digital Dollar

Financial technology (fintech) companies partner with U.S. government agencies to provide services to millions of people via thousands of programs. While doing so, they continuously collect data to sort, store, score, share, and sell. Most policymakers, scholars, and industry stakeholders agree this model makes the financial system more inclusive, safe, and accountable, thereby “democratizing finance.” Yet standard operating procedure exacerbates identity fraud, punishment of poor people (especially impoverished people of color), and mass surveillance of the general public without the knowledge, much less consent, of the governed.We can reconstruct this system. The government is now exploring “Digital Dollars” — money native to government computer systems. Today, commercial banks and fintech companies channel nearly all payments between the government and the public. Some experts argue government agencies should use the Digital Dollar system to simply improve this interface. Other experts argue agencies should bypass private partners and issue Digital Dollars directly to individuals, households, and businesses (perhaps by also offering public bank accounts or digital wallets). However, because of the background laws of digital privacy, both visions would engender unintended social harm.I take up the challenge of digital privacy and data governance within the Digital Dollar payments system. I first examine the laws and technology that enable the government to engage in mass financial data collection. I then identify the material harms of privacy invasion from the perspectives of consumer law, poverty law, and other bodies of law already defining the lives of precisely the people experts aim to include in a Digital Dollar system.To mitigate harm, I offer a modular proposal for the U.S. Postal Service to issue new devices for using Digital Dollars. These “smartcards,” similar in size to existing debit cards, would not make payments over the internet. Instead, they would store money on the card hardware. People would use the cards for everyday small-dollar transactions, but the cards would not be capable of generating data that companies and agencies could abuse. By upgrading existing technology, we can build a truly universal and safe “public fintech” system for our exciting digital future.
Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning

Data subsampling is an established machine learning pre-processing technique to reduce bias in datasets. However, subsampling can lead to the removal of crucial information from the data and thereby decrease performance. Multiple different subsampling strategies have been proposed, and benchmarking is necessary to identify the best strategy for a specific machine learning task. Instead, we propose to use active machine learning as an autonomous and adaptive data subsampling strategy. We show that active learning-based subsampling can lead to better molecular machine learning performance when compared to both training models on the complete training data and 19 state-of-the-art subsampling strategies. We find that active learning is robust to errors in the data, highlighting the utility of this approach for low quality datasets. Taken together, we here describe a new, adaptive machine learning pre-processing approach and provide novel insights into the behavior and robustness of active machine learning for molecular sciences.
Evaluation of Incremental Entity Extraction with Background Knowledge and Entity Linking

Named entity extraction is a crucial task to support the population of Knowledge Bases (KBs) from documents written in natural language. However, in many application domains, these documents must be collected and processed incrementally to update the KB as more data are ingested. In some cases, quality concerns may even require human validation mechanisms along the process. While very recent work in the NLP community has discussed the importance of evaluating and benchmarking continuous entity extraction, it has proposed methods and datasets that avoid Named Entity Linking (NEL) as a component of the extraction process. In this paper, we advocate for batch-based incremental entity extraction methods that can exploit NEL with a background KB, detect mentions of entities that are not present in the KB yet (NIL mentions), and update the KB with the novel entities. Based on this assumption, we present a methodology to evaluate NEL-based incremental entity extraction, which can be applied to a “static” dataset for evaluating NEL into a dataset for evaluating incremental entity extraction. We apply this methodology to an existing benchmark for evaluating NEL algorithms, and evaluate an incremental extraction pipeline that orchestrates different strong state-of-the-art and baseline algorithms for the tasks involved in the extraction process, namely, NEL, NIL prediction, and NIL clustering. In presenting our experiments, we demonstrate the increased difficulty of the information extraction task in incremental settings and discuss the strengths of the available solutions as well as open challenges.
Exploring the impact of literal transformations within Knowledge Graphs for Link Prediction

Knowledge Graphs are relevant for many applications, but are inherently incomplete. Thus, Link Prediction methods have been proposed to infer new triples in order to complete a given Knowledge Graph. Many Link Prediction methods ignore literals, in spite of the fact that literals can express important information about entities not encoded in relations between entities. The existing methods that do incorporate literal information (e. g., LiteralE) introduce complex architectures by modifying the model or the loss-function. In our research paper, we propose a new approach that relies on graph transformations to transform a graph in such a way that existing Link Prediction methods can leverage the literal information. In particular, we define three transformations and evaluate them in comparison to state-of-the-art approaches. In most cases, the additional triples generated by our transformations lead to a performance increase and even state-of-the-art performance can be reached when comparing against LiteralE. It turned out that even a reductionistic transformation is able to archive comparable results like current, more complex, state-of-the-art approaches which incorporate literals.
Motor “laziness” constrains fixation selection in real-world tasks

A bstract People coordinate their eye, head, and body movements to gather information from a dynamic environment while maximizing reward and minimizing biomechanical and energetic costs. Such natural behavior is not possible in a laboratory setting where the head and body are usually restrained and the tasks and stimuli used often lack ecological validity. Therefore, it’s unclear to what extent principles of fixation selection derived from lab studies, such as inhibition-of-return (IOR), apply in a real-world setting. To address this gap, participants performed nine real-world tasks, including driving, grocery shopping, and building a lego set, while wearing a mobile eye tracker (169 recordings; 26.6 hours). Surprisingly, spatial and temporal IOR were absent in all tasks. Instead, participants most often returned to what they just viewed, and saccade latencies were shorter preceding return than forward saccades. We hypothesized that participants minimize the time their eyes spend in an eccentric position to conserve eye and head motor effort. Correspondingly, we observed center biases in the distributions of fixation location and duration, relative to the head’s orientation. A model that generates scanpaths by randomly sampling these distributions reproduced the spatial and temporal return phenomena seen in the data, including distinct 3-fixation sequences for forward versus return saccades. The amount of the orbit used in each task traded off with fixation duration, as if both incur costs in the same space. Conservation of effort (“laziness”) explains all these behaviors, demonstrating that motor costs shape how people extract and act on relevant visual information from the environment. Significance Statement Humans display remarkably precise yet flexible control of eye and body movements, allowing for a wide range of activities. However, most studies of gaze behavior use the same setup: a head-restrained participant views small images on a computer. Such lab studies find that people avoid looking at the same thing twice, and hesitate in cases when they do. We had people perform nine everyday activities while wearing glasses with embedded eye tracking, and surprisingly found that they did the opposite, often returning to what they just viewed and expediting these “return” eye movements over others. A tendency to keep the eyes centered in the head, which we speculate helps to conserve motor effort, explained these behaviors for all tasks.
Quantum Annealing for Neural Network optimization problems: a new approach via Tensor Network simulations

Quantum Annealing (QA) is one of the most promising frameworks for quantum optimization. Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron. We show that the adiabatic time evolution of QA can be efficiently represented as a suitable Tensor Network. This representation allows for simple classical simulations, well-beyond small sizes amenable to exact diagonalization techniques. We show that the optimized state, expressed as a Matrix Product State (MPS), can be recast into a Quantum Circuit, whose depth scales only linearly with the system size and quadratically with the MPS bond dimension. This may represent a valuable starting point allowing for further circuit optimization on near-term quantum devices.