Reviewerly/exHarmony huggingface dataset:
Quantum‐Memory‐Assisted Entropic Uncertainty Relations

Uncertainty relations take a crucial and fundamental part in the frame of quantum theory, and are bringing on many marvelous applications in the emerging field of quantum information sciences. Especially, as entropy is imposed into the uncertainty principle, entropy-based uncertainty relations lead to a number of applications including quantum key distribution, entanglement witness, quantum steering, quantum metrology, and quantum teleportation. Herein, the history of the development of the uncertainty relations is discussed, especially focusing on the recent progress with regard to quantum-memory-assisted entropic uncertainty relations and dynamical characteristics of the measured uncertainty in some explicit physical systems. The aims are to help deepen the understanding of entropic uncertainty relations and prompt further explorations for versatile applications of the relations on achieving practical quantum tasks.
Byte Pair Encoding is Suboptimal for Language Model Pretraining

The success of pretrained transformer language models (LMs) in natural language processing has led to a wide range of pretraining setups.In particular, these models employ a variety of subword tokenization methods, most notably byte-pair encoding (BPE) (Sennrich et al., 2016;Gage, 1994), the WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling (Kudo, 2018), to segment text.However, to the best of our knowledge, the literature does not contain a direct evaluation of the impact of tokenization on language model pretraining.We analyze differences between BPE and unigram LM tokenization, finding that the latter method recovers subword units that align more closely with morphology and avoids problems stemming from BPE's greedy construction procedure.We then compare the fine-tuned task performance of identical transformer masked language models pretrained with these tokenizations.Across downstream tasks and two languages (English and Japanese), we find that the unigram LM tokenization method matches or outperforms BPE.We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking

In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. The fusion weights are determined by using offline-trained global and local multimodal fusion networks, and then adopted to linearly combine the response maps of RGB and T modalities. Second, when the appearance cue is unreliable, we comprehensively take motion cues, i.e., target and camera motions, into account to make the tracker robust. We further propose a tracker switcher to switch the appearance and motion trackers flexibly. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.
Adaptive Consistency Prior based Deep Network for Image Denoising

Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network. First, by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function into the traditional consistency prior, we propose a novel adaptive consistency prior (ACP). Second, by incorporating the ACP term into the maximum a posteriori framework, a model-based denoising method is proposed. This method is further used to inform the network design, leading to a novel end-to-end trainable and interpretable deep denoising network, called DeamNet. Note that the unfolding process leads to a promising module called dual element-wise attention mechanism (DEAM) module. To the best of our knowledge, both our ACP constraint and DEAM module have not been reported in the previous literature. Extensive experiments verify the superiority of DeamNet on both synthetic and real noisy image datasets.
A comprehensive hierarchical blockchain system for carbon emission trading utilizing blockchain of things and smart contract

Climate change is a major issue that has disastrous implications on the environment, which, if continued, will cause severe consequences. One of the leading causes of climate change is the emissions of harmful gases, particularly CO2. In 1997, the Kyoto protocol was signed by 192 countries, creating a system that monetizes CO2 emissions with an aim to control them. Most countries signed a carbon trading scheme, but the scheme fell short of its goals due to manipulation, lack of integrity, and multiple other challenges. This work presents an improved blockchain-based approach to achieve the objective of monitoring the reduction of carbon emissions. Blockchain's distinct features such as security, immutability, transparency, traceability, and trust, make it a robust and reliable solution for the carbon trading market. Previous Blockchain-based proposals were not comprehensive, practical, applicable, or efficient enough to form an effective solution. This research addresses the current gaps and proposes a comprehensive three-stage hierarchical blockchain framework that employs smart contracts in Blockchain of Things (BoT) to ensure integrity in the system and reach fair trade status that favors the environment over companies' cost reductions and profit-making. The result is an optimized carbon emission trading framework, fully transparent with automated trading and control mechanisms.
Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM). For each of the studied parameters, 144 different (144 × 8 models) linear models (ARIMA) are presented, with the superior model of each parameter being selected based on statistical indices. Moreover, 48 nonlinear models (ORELM) and 48 hybrid models (ARIMA-ORELM) were considered. The use of linear and nonlinear approaches to model the linear and nonlinear terms (respectively) of each time series in the hybrid model increased the efficiency and accuracy of the predictions for all of the time series. The influent wastewater nonlinear TSS model and the effluent COD and BOD models attained the best performance with a high correlation coefficient of 0.95. The use of hybrid models improved the prediction capability of all quality parameters with the best performance being achieved for the effluent BOD model (R2 = 0.99).
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS

We present a learning-based approach to detect and fix a broad range of bugs in Javascript programs. We frame the problem in terms of learning a sequence of graph transformations: given a buggy program modeled by a graph structure, our model makes a sequence of predictions including the position of bug nodes and corresponding graph edits to produce a fix. Unlike previous works that use deep neural networks, our approach targets bugs that are more complex and semantic in nature (i.e.~bugs that require adding or deleting statements to fix). We have realized our approach in a tool called HOPPITY. By training on 338,877 Javascript code change commits on Github, HOPPITY correctly detects and fixes bugs in 9,612 out of 42,365 programs in an end-to-end fashion. Given the bug location and type of the fix, HOPPITY also outperforms the baseline approach by a wide margin.
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction

Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document.Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs.However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well.In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a onestep neural approach which emphasizes interclause modeling to perform end-to-end extraction.It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking.Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.Clause I. Document Encoding II.Inter-Clause Relationship Modeling III.Clause Pair Representation Learning and Ranking Clause Clause Clause Fullyconnected Clause Graph
A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS

Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and other concerned components. In the recent decade, it has been noted that the demand for this emerging technology is gradually increased with cost-effective results. Although this technology offers extraordinary results, but at the same time, it also offers multifarious security perils that need to be handled effectively to preserve the trust among all engaged stakeholders. For this, the literature proposes several authentications and data preservation schemes, but somehow they fail to tackle this issue with effectual results. Keeping in view, these constraints, in this paper, we proposed a lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices. With decentralized authentication, we have depreciated the validation latency among pairing devices followed by improved communication statistics. Moreover, the experimental results were compared with the benchmark models to acknowledge the significance of our model. During the evaluation phase, the proposed model reveals incredible advancement in terms of comparative parameters in comparison with benchmark models.
A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorithm: leveraging availability of pairs of semantically data points and samples, the learner forces the inner product of representations of similar pairs with each other to be higher on average than with negative samples. The current paper uses the term contrastive learning for such algorithms and presents a theoretical framework for analyzing them by introducing latent classes and hypothesizing that semantically similar points are sampled from the same latent class. This framework allows us to show provable guarantees on the performance of the learned representations on the average classification task that is comprised of a subset of the same set of latent classes. Our generalization bound also shows that learned representations can reduce (labeled) sample complexity on downstream tasks. We conduct controlled experiments in both the text and image domains to support the theory.
Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings

This paper presents a complete system for automatic recognition and the diagnosis of electrical insulator strings which efficiently combines different deep learning-based components to build a versatile solution to the automation problem of the power line inspection process. To this aim, the proposed system integrates one component responsible for insulator string segmentation and two components in charge of its diagnosis. The insulator string segmentation component consists of a novel fully convolutional network (FCN) architecture, termed Up-Net, which enhances the capabilities of the state-of-the-art U-Net network by introducing new skip connections at certain levels of the architecture. Furthermore, we propose a second variant of the Up-Net network by training it within a generative adversarial network (GAN) framework. The capabilities of the proposed Up-Net variants are incremented by the application of data augmentation and transfer learning techniques, achieving accurate segmentation of the insulator string elements (i.e., discs and caps). Regarding the insulator string diagnosis, we design a convolutional neural network (CNN) which takes as input the mask generated by the insulator string segmentation component and is capable of identifying the absence of a variable number of discs. The second diagnosis component consists of a novel strategy which integrates a Siamese convolutional neural network (SCNN) designed for modeling the similarity between adjacent discs and allowing the detection of several types of disc defects using the same model. The proposed system has been extensively evaluated in several video sequences from real aerial inspections of high-voltage insulators, showing robust insulator recognition and diagnosis capabilities.
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition

In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing.In particular, we improve differentiable architecture search by removing the softmax-local constraint.Also, we apply differentiable NAS to named entity recognition (NER).It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling.On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines.It achieves a new state-ofthe-art on the NER task.
Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other single and multi-magnification-based architectures by achieving the highest mean intersection-over-union, and can be used to facilitate pathologists' assessments of breast cancer.
Anomaly detection in wireless sensor network using machine learning algorithm

Security in the Wireless Sensor Network(WSNs) is an essential and a challenging task. Anomaly detection is a key challenge to ensure the security in WSN. WSNs are vulnerable to various threats which may cause the node to get damaged and produce faulty measurements. The detection of such anomalous data is required to reduce false alarms. Machine learning algorithm based detection of anomalous data becomes popular now. Most of the current machine anomaly detection algorithms run in a stationary environment and require the entire training data to be kept in the node. In this paper, we formulate an Online Locally Weighted Projection Regression (OLWPR) for anomaly detection in Wireless Sensor Network. Linear Weighted Projection Regression methods are non parametric and the current predictions are performed by local functions that use only the subset of data. So, the computation complexity is low which is one of the requirements in Wireless Sensor Network. The dimensionality reduction in LWPR is done online by Principal Component Analysis (PCA) to handle the irrelevant and redundant data in the input data. After the prediction process, the dynamic threshold value is determined by a dynamic thresholding method to find the deviations of predicted value from the actual sensed value. OLWPR attains the detection rate of 86 percentage and very low error rate of only 16%.
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects.Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences.Advances in machine reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks.Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apurímac Quechua and Haitian Creole.We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods based on multilingual pretraining and zero-shot fine-tuning falls short compared to translation-based transfer.Finally, we propose strategies to adapt multilingual models to out-of-sample resource-lean languages where only a small corpus or a bilingual dictionary is available, and report substantial improvements over the random baseline.The XCOPA dataset is freely available at github.com/cambridgeltl/xcopa.
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data. This research area’s major concern lies in developing an extraordinary fusion scheme that can extract and integrate key information from various modalities. However, previous work is restricted by the lack of leveraging dynamics of independence and correlation between modalities to reach top performance. To mitigate this, we propose the Bi-Bimodal Fusion Network (BBFN), a novel end-to-end network that performs fusion (relevance increment) and separation (difference increment) on pairwise modality representations. The two parts are trained simultaneously such that the combat between them is simulated. The model takes two bimodal pairs as input due to the known information imbalance among modalities. In addition, we leverage a gated control mechanism in the Transformer architecture to further improve the final output. Experimental results on three datasets (CMU-MOSI, CMU-MOSEI, and UR-FUNNY) verifies that our model significantly outperforms the SOTA. The implementation of this work is available at https://github.com/declare-lab/multimodal-deep-learning and https://github.com/declare-lab/BBFN.
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at https://github.com/IDEA-Research/MaskDINO.
Deep Ordinal Hashing With Spatial Attention

Hashing has attracted increasing research attention in recent years due to its high efficiency of computation and storage in image retrieval. Recent works have demonstrated the superiority of simultaneous feature representations and hash functions learning with deep neural networks. However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images. The loss of local spatial structure makes the performance bottleneck of hash functions, therefore limiting its application for accurate similarity retrieval. In this paper, we propose a novel deep ordinal hashing (DOH) method, which learns ordinal representations to generate ranking-based hash codes by leveraging the ranking structure of feature space from both local and global views. In particular, to effectively build the ranking structure, we propose to learn the rank correlation space by exploiting the local spatial information from fully convolutional network and the global semantic information from the convolutional neural network simultaneously. More specifically, an effective spatial attention model is designed to capture the local spatial information by selectively learning well-specified locations closely related to target objects. In such hashing framework, the local spatial and global semantic nature of images is captured in an end-to-end ranking-to-hashing manner. Experimental results conducted on three widely used datasets demonstrate that the proposed DOH method significantly outperforms the state-of-the-art hashing methods.