Reviewerly/exHarmony huggingface dataset:
Neural Input Search for Large Scale Recommendation Models

Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embeddings to represent discrete items as continuous vectors, and the vocabulary sizes and embedding dimensions, despite their heavy influence on the model's accuracy, are often manually selected in a heuristical manner.
A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Aspect-based sentiment analysis (ABSA) task is a fine-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the related works merely focus on the subtask of Chinese aspect term polarity inferring and fail to emphasize the research of Chinese-oriented ABSA multi-task learning. Based on the local context focus (LCF) mechanism, this paper firstly proposes a multi-task learning model for Chinese-oriented aspect-based sentiment analysis, namely LCF-ATEPC. Compared with other models, this model equips the capability of extracting aspect term and inferring aspect term polarity synchronously. The experimental results on four Chinese review datasets outperform state-of-the-art performance on the ATE and APC subtask. And by integrating the domain-adapted BERT model, LCF-ATEPC achieves the state-of-the-art performance of ATE and APC in the most commonly used SemEval-2014 task4 Restaurant and Laptop datasets. Moreover, this model is effective to analyze both Chinese and English reviews collaboratively and the experimental results on a multilingual mixed dataset prove its effectiveness.
Coupling chaotic system based on unit transform and its applications in image encryption

Chaotic maps are very important for establishing chaos-based image encryption systems. This paper introduces a coupling chaotic system based on a certain unit transform, which can combine any two 1D chaotic maps to generate a new one with better performance in cryptography applications. The chaotic behavior analysis has verified this coupling system's effectiveness and progress. In particular, we give a specific strategy about selecting an appropriate unit transform function to enhance the chaotic behaviors of generated maps. Besides, a new chaos-based pseudo-random number generator, shorted as CBPRNG, is designed to improve the randomness of chaotic sequences. Moreover, based on CBPRNG, a novel digital image encryption algorithm is introduced, where we design a two-way multi-round transformation network to encrypt the higher bits and lower bits of input images separately. Simulation results and security analysis indicate that the proposed image encryption scheme is competitive with some existing methods.
Parallax Attention for Unsupervised Stereo Correspondence Learning

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume techniques to capture stereo correspondence over large disparities. However, since disparities can vary significantly for stereo cameras with different baselines, focal lengths and resolutions, the fixed maximum disparity used in cost volume techniques hinders them to handle different stereo image pairs with large disparity variations. In this paper, we propose a generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations. Our PAM integrates epipolar constraints with attention mechanism to calculate feature similarities along the epipolar line to capture stereo correspondence. Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks. Moreover, we introduce a new and large-scale dataset named Flickr1024 for stereo image super-resolution. Experimental results show that our PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner. Comparative results show that our PASMnet and PASSRnet achieve the state-of-the-art performance.
LEChain: A blockchain-based lawful evidence management scheme for digital forensics

Lawful evidence management in digital forensics is of paramount importance in police investigations because such evidence is used to convict suspects of crimes. Existing studies have adopted cloud computing to collect evidence and then leveraged blockchain to support the transparency, immutability, and auditability of the evidence. Unfortunately, such studies only rely on a weak security model and do not cover the entire life cycle of the evidence or address the key privacy issues, i.e., witness privacy in evidence collection and juror privacy in court trials. In this work, we propose LEChain, a blockchain-based lawful evidence management scheme to supervise the entire evidence flow and all of the court data (e.g., votes and trial results), extending from evidence collection and access during the police investigation to jury voting in the court trials. Specifically, we utilize short randomizable signatures to anonymously authenticate witnesses’ identities to protect the witness privacy. Then, we leverage fine-grained access control based on ciphertext-policy attribute-based encryption for evidence access. Next, we design a secure voting method to protect juror privacy. In addition, we build a consortium blockchain to record evidence transactions. Finally, we formally analyze the security and privacy of LEChain and evaluate its computational costs and communication overhead by implementing a prototype based on a local Ethereum test network.
Barren Plateaus Preclude Learning Scramblers

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. We prove a no-go theorem for learning an unknown scrambling process with QML, showing that it is highly probable for any variational Ansatz to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from Ansatz-based barren plateaus or no-free-lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information.
IoT enabled cancer prediction system to enhance the authentication and security using cloud computing

In recent days, Internet of Things, Cloud Computing, Deep learning, Machine learning and Artificial Intelligence are considered to be an emerging technologies to solve variety of real world problems. These techniques are importantly applied in various fields such as healthcare systems, transportation systems, agriculture and smart cities to produce fruitful results for number of issues in today's environment. This research work focuses on one such application in the field of IoT together with cloud computing. More number of sensors that are deployed in human body is used to collect patient related data such as deviation in body temperature and others which leads to variation in blood cells that turned to be cancerous cells. Main intention of this work is design a cancer prediction system using Internet of Things upon extracting the details of blood results to test whether it is normal or abnormal. In addition to this, encryption is done on the blood results of cancer affected patient and store it in cloud for quick reference through Internet for the doctor or healthcare nurse to handle the patient data secretly. This research work concentrates on enhancing the health care computations and processing. It provides a framework to enhance the performance of the existing health care industry across the globe. As the entire medical data has to be saved in cloud, the traditional medical treatment limitations can be overcome. Encryption and decryption is done using AES algorithm in order to provide authentication and security in handling cancer patients. The main focus is to handle healthcare data effectively for the patient when they are away from the home town since the needed cancer treatment details are stored in cloud. The task completion time is greatly reduce from 400 to 160 by using VMs. CloudSim gives an adaptable simulation structure that empowers displaying and reproduced results.
Pandemic in a smart city: Singapore’s COVID-19 management through technology &amp; society

On 23 January 2020, Singapore announced its first COVID-19 case, becoming one of the first countries to be affected by the virus outside China. The government acted swiftly, closed its borders, introduced circuit-breaking measures, and deployed public health and medical expertise in tackling the virus. Both technology and human resources were used extensively for contact tracing, quarantining, and pathogenic management. While all these measures helped in a successful containment initially, the second wave of COVID-19 cases emerged at the foreign worker dormitories, affecting thousands of workers. Singapore's approach in tackling the situation shifted rapidly and began to involve civil society organizations and individuals in the fight against the virus. In this paper, we argue that while state-led technologies such asTraceTogether and Safe Entry helped in the techno-governance of bodies on the move, bottom-up digital solutions, and innovative engagement of individuals are equally crucial in building a smart and resilient Singapore.
PPCL: Privacy-preserving collaborative learning for mitigating indirect information leakage

Collaborative learning and related techniques such as federated learning, allow multiple clients to train a model jointly while keeping their datasets at local. Secure Aggregation in most existing works focus on protecting model gradients from the server. However, an dishonest user could still easily get the privacy information from the other users. It remains a challenge to propose an effective solution to prevent information leakage against dishonest users. To tackle this challenge, we propose a novel and effective privacy-preserving collaborative machine learning scheme, targeting at preventing information leakage agains adversaries. Specifically, we first propose a privacy-preserving network transformation method by utilizing Random-Permutation in Software Guard Extensions(SGX), which protects the model parameters from being inferred by a curious server and dishonest clients. Then, we apply Partial-Random Uploading mechanism to mitigate the information inference through visualizations. To further enhance the efficiency, we introduce network pruning operation and employ it to accelerate the convergence of training. We present the formal security analysis to demonstrate that our proposed scheme can preserve privacy while ensuring the convergence and accuracy of secure aggregation. We conduct experiments to show the performance of our solution in terms of accuracy and efficiency. The experimental results show that the proposed scheme is practical.
Comparing pre-trained language models for Spanish hate speech detection

Nowadays, due to the great uncontrolled content posted daily on the Web, there has also been a huge increase in the dissemination of hate speech worldwide. Social media, blogs and community forums are examples where people are freely allowed to communicate. However, freedom of expression is not always respectful since offensive or insulting language is sometimes used. Social media companies often rely on users and content moderators to report on this type of content. Nevertheless, due to the large amount of content generated every day on the Web, automatic systems based on Natural Language Processing techniques are required for identifying abusive language online. To date, most of the systems developed to combat this problem are mainly focused on English content, but this issue is a worldwide concern and therefore other languages such as Spanish are involved. In this paper, we address the task of Spanish hate speech identification on social media and provide a deeper understanding of the capabilities of new techniques based on machine learning. In particular, we compare the performance of Deep Learning methods with recently pre-trained language models based on Transfer Learning as well as with traditional machine learning models. Our main contribution is the achievement of promising results in Spanish by applying multilingual and monolingual pre-trained language models such as BERT, XLM and BETO.
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations. 1
Information Leakage in Embedding Models

Embeddings are functions that map raw input data to low-dimensional vector representations, while preserving important semantic information about the inputs. Pre-training embeddings on a large amount of unlabeled data and fine-tuning them for downstream tasks is now a de facto standard in achieving state of the art learning in many domains.
Linguistic generalization and compositionality in modern artificial neural networks

In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language-processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it offers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.
Quantum Switch for the Quantum Internet: Noiseless Communications Through Noisy Channels

Counter-intuitively, quantum mechanics enables quantum particles to propagate simultaneously among multiple space-time trajectories. Hence, a quantum information carrier can travel through different communication channels in a quantum superposition of different orders, so that the relative time-order of the communication channels becomes indefinite. This is realized by utilizing a quantum device known as quantum switch. In this paper, we investigate, from a communication-engineering perspective, the use of the quantum switch within the quantum teleportation process, one of the key functionalities of the Quantum Internet. Specifically, a theoretical analysis is conducted to quantify the performance gain that can be achieved by employing a quantum switch for the entanglement distribution process within the quantum teleportation with respect to the case of absence of quantum switch. This analysis reveals that, by utilizing the quantum switch, the quantum teleportation is heralded as a noiseless communication process with a probability that, remarkably and counter-intuitively, increases with the noise levels affecting the communication channels considered in the indefinite-order time combination.
Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively. Informed graph-based searches, such as A * , traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so. Anytime sampling-based planners, such as RRT * , traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution. This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT * solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A * , and its approximation improves indefinitely with additional computational time, as in RRT * . It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.
Approximate leave-future-out cross-validation for Bayesian time series models

One of the common goals of time series analysis is to use the observed series to inform predictions for future observations. In the absence of any actual new data to predict, cross-validation can be used to estimate a model's future predictive accuracy, for instance, for the purpose of model comparison or selection. Exact cross-validation for Bayesian models is often computationally expensive, but approximate cross-validation methods have been developed, most notably methods for leave-one-out cross-validation (LOO-CV). If the actual prediction task is to predict the future given the past, LOO-CV provides an overly optimistic estimate because the information from future observations is available to influence predictions of the past. To properly account for the time series structure, we can use leave-future-out cross-validation (LFO-CV). Like exact LOO-CV, exact LFO-CV requires refitting the model many times to different subsets of the data. Using Pareto smoothed importance sampling, we propose a method for approximating exact LFO-CV that drastically reduces the computational costs while also providing informative diagnostics about the quality of the approximation.
Blockchain Based Cloud Computing: Architecture and Research Challenges

Blockchain technology is a distributed ledger with records of data containing all details of the transactions carried out and distributed among the nodes present in the network. All the transactions carried out in the system are confirmed by consensus mechanisms, and the data once stored cannot be altered. Blockchain technology is the necessary technology behind Bitcoin, which is a popular digital Cryptocurrency. “Cloud computing is a practice of using a network of remote servers hosted on the internet to store, manage, and process data, rather than a local server or a personal computer.” It is still facing many challenges like data security, data management, compliance, reliability. In this article, we have mentioned some of the significant challenges faced by the cloud and proposed solutions by integrating it with blockchain technology. We tend to investigate a brief survey on earlier studies focused on blockchain integrating with the cloud to depict their supremacy. In this survey, we have also developed architecture integrating blockchain with cloud revealing the communication between blockchain and cloud.
SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach

Human poses that are rare or unseen in a training set are challenging for a network to predict. Similar to the long-tailed distribution problem in visual recognition, the small number of examples for such poses limits the ability of networks to model them. Interestingly, local pose distributions suffer less from the long-tail problem, i.e., local joint configurations within a rare pose may appear within other poses in the training set, making them less rare. We propose to take advantage of this fact for better generalization to rare and unseen poses. To be specific, our method splits the body into local regions and processes them in separate network branches, utilizing the property that a joint’s position depends mainly on the joints within its local body region. Global coherence is maintained by recombining the global context from the rest of the body into each branch as a low-dimensional vector. With the reduced dimensionality of less relevant body areas, the training set distribution within network branches more closely reflects the statistics of local poses instead of global body poses, without sacrificing information important for joint inference. The proposed split-and-recombine approach, called SRNet, can be easily adapted to both single-image and temporal models, and it leads to appreciable improvements in the prediction of rare and unseen poses.