Reviewerly/exHarmony huggingface dataset:
Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization

In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GWO-WD algorithm is presented to solve global optimization problems. First, a modified position-updating equation formulated using the proposed strategy is employed to obtain additional information and improved global solutions. Then, several of the worst individuals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorithm is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulation results of the proposed algorithm are compared with those of the standard GWO algorithm, three GWO variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.
Multiples Over Models

In this article, we describe our efforts to retrace and reimagine period tracking technology—or, mobile applications designed to support the documentation and quantification of menstrual cycle data. In their current form, these systems often encourage those who menstruate to extract intimate information about the body (e.g., consistency or color of menstrual flow, physical and emotional symptoms), while promising to predict fertility and offer insight into managing one's period. In doing so, these technologies subtly dictate the forms of knowledge and types of relationships menstruators are expected to establish with their bodies (i.e., transactional or instrumentalized). Through historical analysis and a series of participatory experiments, we offer a vision for menstrual sensemaking that expands on these forms of interaction and ways of knowing to emphasize multiplicity and dimensionality rather than models, predictability, or a user's relation to averages or norms.
SKINNY-AEAD and SKINNY-Hash

We present the family of authenticated encryption schemes SKINNY-AEAD and the family of hashing schemes SKINNY-Hash. All of the schemes employ a member of the SKINNY family of tweakable block ciphers, which was presented at CRYPTO 2016, as the underlying primitive. In particular, for authenticated encryption, we show how to instantiate members of SKINNY in the Deoxys-I-like ΘCB3 framework to fulfill the submission requirements of the NIST lightweight cryptography standardization process. For hashing, we use SKINNY to build a function with larger internal state and employ it in a sponge construction. To highlight the extensive amount of third-party analysis that SKINNY obtained since its publication, we briefly survey the existing cryptanalysis results for SKINNY-128-256 and SKINNY-128-384 as of February 2020. In the last part of the paper, we provide a variety of ASIC implementations of our schemes and propose new simple SKINNY-AEAD and SKINNY-Hash variants with a reduced number of rounds while maintaining a very comfortable security margin. &#x0D; https://csrc.nist.gov/Projects/Lightweight-Cryptography
Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.
Generating Label Cohesive and Well-Formed Adversarial Claims

Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.
AssembleNet++: Assembling Modality Representations via Attention Connections

We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy attention in order to better learn the importance of features at each convolutional block of the network. A new network component named peer-attention is introduced, which dynamically learns the attention weights using another block or input modality. Even without pre-training, our models outperform the previous work on standard public activity recognition datasets with continuous videos, establishing new state-of-the-art. We also confirm that our findings of having neural connections from the object modality and the use of peer-attention is generally applicable for different existing architectures, improving their performances. We name our model explicitly as AssembleNet++. The code will be available at: https://sites.google.com/corp/view/assemblenet/ .
Text-to-Text Pre-Training for Data-to-Text Tasks

We study the pre-train + fine-tune strategy for data-to-text tasks. Fine-tuning T5 achieves state-of-the-art results on the WebNLG, MultiWoz and ToTTo benchmarks. Such transfer learning enables training of fully end-to-end models that do not rely on any intermediate planning steps, delexicalization or copy mechanisms. T5 pre-training also enables stronger generalization, as evidenced by large improvements on out-of-domain test sets. We hope our work serves as a useful baseline for future research, as pre-training becomes ever more prevalent for data-to-text tasks.
Can We Automate Scientific Reviewing?

The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications. At the same time, the review of each paper is a laborious process that must be carried out by subject matter experts. Thus, providing high-quality reviews of this growing number of papers is a significant challenge. In this work, we ask the question “can we automate scientific reviewing? ”, discussing the possibility of using natural language processing (NLP) models to generate peer reviews for scientific papers. Because it is non-trivial to define what a “good” review is in the first place, we first discuss possible evaluation metrics that could be used to judge success in this task. We then focus on the machine learning domain and collect a dataset of papers in the domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers as input and generate reviews as output. Comprehensive experimental results on the test set show that while system-generated reviews are comprehensive, touching upon more aspects of the paper than human-written reviews, the generated texts are less constructive and less factual than human-written reviews for all aspects except the explanation of the core ideas of the papers, which are largely factually correct. Given these results, we pose eight challenges in the pursuit of a good review generation system together with potential solutions, which, hopefully, will inspire more future research in this direction. We make relevant resource publicly available for use by future research: https://github. com/neulab/ReviewAdvisor. In addition, while our conclusion is that the technology is not yet ready for use in high-stakes review settings we provide a system demo, ReviewAdvisor (http://review.nlpedia.ai/), showing the current capabilities and failings of state-of-the-art NLP models at this task (see demo screenshot in A.2). A review of this paper written by the system proposed in this paper can be found in A.1.
Many-Body Quantum Teleportation via Operator Spreading in the Traversable Wormhole Protocol

By leveraging shared entanglement between a pair of qubits, one can teleport a quantum state from one particle to another. Recent advances have uncovered an intrinsically many-body generalization of quantum teleportation, with an elegant and surprising connection to gravity. In particular, the teleportation of quantum information relies on many-body dynamics, which originate from strongly interacting systems that are holographically dual to gravity; from the gravitational perspective, such quantum teleportation can be understood as the transmission of information through a traversable wormhole. Here, we propose and analyze a new mechanism for many-body quantum teleportation—dubbed peaked-size teleportation. Intriguingly, peaked-size teleportation utilizes precisely the same type of quantum circuit as traversable wormhole teleportation yet has a completely distinct microscopic origin: It relies upon the spreading of local operators under generic thermalizing dynamics and not gravitational physics. We demonstrate the ubiquity of peaked-size teleportation, both analytically and numerically, across a diverse landscape of physical systems, including random unitary circuits, the Sachdev-Ye-Kitaev model (at high temperatures), one-dimensional spin chains, and a bulk theory of gravity with stringy corrections. Our results pave the way toward using many-body quantum teleportation as a powerful experimental tool for (i) characterizing the size distributions of operators in strongly correlated systems and (ii) distinguishing between generic and intrinsically gravitational scrambling dynamics. To this end, we provide a detailed experimental blueprint for realizing many-body quantum teleportation in both trapped ions and Rydberg atom arrays; effects of decoherence and experimental imperfections are analyzed.5 MoreReceived 23 January 2021Accepted 24 May 2022DOI:https://doi.org/10.1103/PhysRevX.12.031013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum correlations in quantum informationQuantum protocolsQuantum teleportationPhysical SystemsRydberg atoms & moleculesTrapped ionsTechniquesSachdev-Ye-Kitaev modelSpin chainsQuantum InformationAtomic, Molecular & OpticalGravitation, Cosmology & AstrophysicsParticles & FieldsStatistical Physics
Flexible and high-accuracy method for uni-directional structured light system calibration

This paper presents a novel method that can accurately calibrate structured light system requiring only one directional structured patterns. This method leverages the existing flexible camera calibration to extract each calibration pose for three-dimensional coordinate determination for each pixel, and then establish pixel-wise relationship between each coordinate and phase. The phase artifacts caused by high-contrast calibration target was addressed to eliminate its impact on the calibration. Comparing with the traditional structured light system calibration method that requires two directional fringe patterns, our method requires only one single directional fringe patterns yet achieves 6.6 times higher accuracy for flat surface measurements.
Probing the entropic uncertainty bound and quantum correlations in a quantum dot system

Abstract The uncertainty principle is one of the most comprehensive and fundamental concepts in quantum theory and it states that it is not possible to simultaneously measure two incompatible observables with high accuracy. In particular, we show that the entropic uncertainty relation that uses quantum memory is one of the most useful entropic uncertainty relations. In this work, we consider a quantum dot system as a solid-state system which is in the thermal regime, and we then probe the quantum correlations and the quantum-memory-assisted entropic uncertainty bound in this type of system. Interestingly, our results show that there are some system parameters that can be adjusted to suppress the entropic uncertainty bound and improve quantum correlations.
Multi-Tier CloudVR

The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client’s field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.
Wav2KWS: Transfer Learning From Speech Representations for Keyword Spotting

With the expanding development of on-device artificial intelligence, voice-enabled devices such as smart speakers, wearables, and other on-device or edge processing systems have been proposed. However, building or obtaining large training datasets that are essential for robust keyword spotting (KWS) remains cumbersome. To address this problem, we propose a deep neural network that can rapidly establish a high-performance KWS system from arbitrary keyword instruction sets. We use an encoder pretrained with a large-scale speech corpus as the backbone network and then design an effective transfer network for KWS. To demonstrate the feasibility of the proposed network, various experiments were conducted on Google Speech Command Datasets V1 and V2. In addition, to verify the applicability of the network for different languages, we conducted experiments using three different Korean speech command datasets. The proposed network outperforms state-of-the-art deep neural networks in both experiments. Furthermore, the proposed network can understand real human voice even when trained with synthetic text-to-speech data.
Improving the Performance of Deep Neural Networks Using Two Proposed Activation Functions

In artificial neural networks, activation functions play a significant role in the learning process.Choosing the proper activation function is a major factor in achieving a successful learning performance.Many activation functions are sufficient universal approximators, but their performance is lacking.Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks.However, the learning process involves many challenges, such as saturation, dying, and exploding/vanishing the gradient problems.The contribution of this work resides in several axes.First, we introduce two novel activation functions: absolute linear units and inverse polynomial linear units.Both activation functions are augmented by an adjustable parameter that controls the slope of the gradient.Second, we present a comprehensive study and a taxonomy of various types of activation functions.Third, we conduct a broad range of experiments on several deep neural architecture models with consideration of network type and depth.Fourth, we evaluate the proposed activation functions' performance in image and text classification tasks.For this purpose, several public benchmark datasets are utilized to evaluate and compare the performance of the proposed functions with that of a group of common activation functions.Finally, we deeply analyze the impact of several common activation functions on deep network architectures.Results reveal that the proposed functions outperform most of the popular activation functions in several benchmarks.The statistical study of the overall experiments on both classification categories indicates that the proposed activation functions are robust and superior among all the competitive activation functions in terms of average accuracy.
Lower Perplexity is Not Always Human-Like

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models.However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community.In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages.Specifically, we re-examine an established generalization -the lower perplexity a language model has, the more human-like the language model isin Japanese with typologically different structures from English.Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like.Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density.Overall, our results suggest that a crosslingual evaluation will be necessary to construct human-like computational models.
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection

Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.In real-world applications, ED typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem.To tackle the issue of low sample diversity in few-shot ED, we propose a novel knowledge-based fewshot event detection method which uses a definition-based encoder to introduce external event knowledge as the knowledge prior of event types.Furthermore, as external knowledge typically provides limited and imperfect coverage of event types, we introduce an adaptive knowledge-enhanced Bayesian metalearning method to dynamically adjust the knowledge prior of event types.Experiments show our method consistently and substantially outperforms a number of baselines by at least 15 absolute F 1 points under the same fewshot settings.
An empirical study toward dealing with noise and class imbalance issues in software defect prediction

The quality of the defect datasets is a critical issue in the domain of software defect prediction (SDP). These datasets are obtained through the mining of software repositories. Recent studies claim over the quality of the defect dataset. It is because of inconsistency between bug/clean fix keyword in fault reports and the corresponding link in the change management logs. Class Imbalance (CI) problem is also a big challenging issue in SDP models. The defect prediction method trained using noisy and imbalanced data leads to inconsistent and unsatisfactory results. Combined analysis over noisy instances and CI problem needs to be required. To the best of our knowledge, there are insufficient studies that have been done over such aspects. In this paper, we deal with the impact of noise and CI problem on five baseline SDP models; we manually added the various noise level (0–80%) and identified its impact on the performance of those SDP models. Moreover, we further provide guidelines for the possible range of tolerable noise for baseline models. We have also suggested the SDP model, which has the highest noise tolerable ability and outperforms over other classical methods. The True Positive Rate (TPR) and False Positive Rate (FPR) values of the baseline models reduce between 20–30% after adding 10–40% noisy instances. Similarly, the ROC (Receiver Operating Characteristics) values of SDP models reduce to 40–50%. The suggested model leads to avoid noise between 40–60% as compared to other traditional models.
Cognitive maps of social features enable flexible inference in social networks

Significance How do people learn the large, complex web of social relations around them? We test how people use information about social features (such as being part of the same club or sharing hobbies) to fill in gaps in their knowledge of friendships and to make inferences about unobserved friendships in the social network. We find the ability to infer friendships depends on a simple but inflexible heuristic that infers friendship when two people share the same features, and a more complex but flexible cognitive map that encodes relationships between features rather than between people. Our results reveal that cognitive maps play a powerful role in shaping how people represent and reason about relationships in a social network.