Reviewerly/exHarmony huggingface dataset:
BERT-QE: Contextualized Query Expansion for Document Re-ranking

Query expansion aims to mitigate the mismatch between the language used in a query and in a document.However, query expansion methods can suffer from introducing non-relevant information when expanding the query.To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion.In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.
Nonlocal advantage of quantum coherence in a dephasing channel with memory*

We investigate nonlocal advantage of quantum coherence (NAQC) in a correlated dephasing channel modeled by the multimode bosonic reservoir. We obtain analytically the dephasing and memory factors of this channel for the reservoir having a Lorentzian spectral density, and analyze how they affect the NAQC defined by the l 1 norm and relative entropy. It is shown that the memory effects of this channel on NAQC are state-dependent, and they suppress noticeably the rapid decay of NAQC for the family of input Bell-like states with one excitation. For the given transmission time of each qubit, we also obtain the regions of the dephasing and memory factors during which there is NAQC in the output states.
Contrastive Learning with Hard Negative Samples

We consider the question: how can you sample good negative examples for contrastive learning? We argue that, as with metric learning, learning contrastive representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge toward using hard negatives is that contrastive methods must remain unsupervised, making it infeasible to adopt existing negative sampling strategies that use label information. In response, we develop a new class of unsupervised methods for selecting hard negative samples where the user can control the amount of hardness. A limiting case of this sampling results in a representation that tightly clusters each class, and pushes different classes as far apart as possible. The proposed method improves downstream performance across multiple modalities, requires only few additional lines of code to implement, and introduces no computational overhead.
Tight finite-key security for twin-field quantum key distribution

Abstract Quantum key distribution (QKD) offers a reliable solution to communication problems that require long-term data security. For its widespread use, however, the rate and reach of QKD systems must be improved. Twin-field (TF) QKD is a step forward toward this direction, with early demonstrations suggesting it can beat the current rate-versus-distance records. A recently introduced variant of TF-QKD is particularly suited for experimental implementation, and has been shown to offer a higher key rate than other variants in the asymptotic regime, where users exchange an infinite number of signals. Here, we extend the security of this protocol to the finite-key regime, showing that it can overcome the fundamental bounds on point-to-point QKD with ~10 10 transmitted signals. In many practical regimes of interest, our analysis offers higher key rates than those of alternative variants. Moreover, some of the techniques we develop are applicable to the finite-key analysis of other QKD protocols.
Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques

Breast cancer is one the most critical disease and suffered many people around the world.The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease.In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data.In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer.Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection.The model performance evaluation metrics have been used for model performance evaluation.The BC data sets have been used for testing of the proposed method.The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms.The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy.We have been employed McNemar's statistical test for performance comparison of our different models.Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods.Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer.In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution

Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks.Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications.Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked.In this work, we present invisible backdoors that are activated by a learnable combination of word substitution.We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections.The results raise a serious alarm to the security of NLP models, which requires further research to be resolved.
3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data

Alzheimer's disease (AD) is a neurodegenerative disease that afflicts millions of people worldwide. Early detection of AD is critical, as drug trials show a promising advantage to those patients with early diagnoses. In this study, magnetic resonance imaging (MRI) datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and The Open Access Series of Imaging Studies are used. Our method for performing the classification of AD is to combine a set of shearlet-based descriptors with deep features. A major challenge in classifying such MRI datasets is the high dimensionality of feature vectors because of the large number of slices of each MRI sample. Given the volumetric nature of the MRI data, we propose using the 3D shearlet transform (3D-ST), but we obtain the average of all directionalities, which reduces the dimensionality. On the other hand, we propose to leverage the capabilities of convolutional neural networks (CNN) to learn feature maps from stacked MRI slices, which generate a very compact feature vector for each MRI sample. The 3D-ST and CNN feature vectors are combined for the classification of AD. After the concatenation of the feature vectors, they are used to train a classifier. Alternatively, a custom CNN model is utilized, in which the descriptors are further processed end to end to obtain the classification model. Our experimental results show that the fusion of shearlet-based descriptors and deep features improves classification performance, especially on the ADNI dataset.
A bibliometric review of cryptocurrencies: how have they grown?

Abstract With the development of new technologies, some concepts become relevant in the economic area, as is the case with cryptocurrencies, in general, or Bitcoin and Ethereum, in particular. Due to the impact of these tools, a detailed bibliometric study that allows us to obtain all information about cryptocurrencies must be conducted. This study will help scientific production by specifying the development and lines of related research that have been followed and are currently being followed. We have used Tableau, R (Bibliometrix R Package), and VOSviewer software to analyze the information. These have been combined to create and review unified metadata from the Web of Science (WoS) and Scopus databases. The bibliometric analysis shows 771 articles on the WoS database and 648 articles on Scopus published between 2010 and early 2019. They present the most relevant articles, research areas, countries, institutions, authors, journals, and trends during the last few years. In conclusion, the number of publications has grown in the last 3 years. The analysis shows the evolution of blockchain technology used in this type of cryptocurrency. The review of this period marks a possible end to the historical part of cryptocurrencies, thereby opening the current topic to its multiple applications.
Multi-region Nonuniform Brightness Correction Algorithm Based on L-Channel Gamma Transform

Nonuniform brightness distribution of collimator reticle images leads to the recognition difficulty of reticle grids and coordinate scales on images and affects the digitalization of reticle coordinate system. The article proposes an image quality improvement algorithm that operates on L channel of LAB color space to perform brightness correction. It designs four typical image modules depending on representative brightness features. Corresponding brightness correction models for all typical image modules are constructed by designing the new correction functions based on nonlinear gamma transform. A concept of brightness similarity is defined for automatic matching between typical image modules and the image region to be processed. On this basis, the algorithm can automatically adjust brightness correction mode to ensure the brightness of each pixel tends to be uniform, which is reflected in a visible improvement in the color distortion of the image. The algorithm can effectively reduce the complexity of model processing and adaptively adjust brightness correction intensity according to the pixel brightness value. It enables more accurate corrections with good results.
TransCenter: Transformers With Dense Representations for Multiple-Object Tracking

Transformers have proven superior performance for a wide variety of tasks since they were introduced. In recent years, they have drawn attention from the vision community in tasks such as image classification and object detection. Despite this wave, an accurate and efficient multiple-object tracking (MOT) method based on transformers is yet to be designed. We argue that the direct application of a transformer architecture with quadratic complexity and insufficient noise-initialized sparse queries - is not optimal for MOT. We propose TransCenter, a transformer-based MOT architecture with dense representations for accurately tracking all the objects while keeping a reasonable runtime. Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN). On one hand, the dense image-related detection queries allow us to infer targets' locations globally and robustly through dense heatmap outputs. On the other hand, the set of sparse tracking queries efficiently interacts with image features in our TransCenter Decoder to associate object positions through time. As a result, TransCenterexhibits remarkable performance improvements and outperforms by a large margin the current state-of-the-art methods in two standard MOT benchmarks with two tracking settings (public/private). TransCenter is also proven efficient and accurate by an extensive ablation study and, comparisons to more naive alternatives and concurrent works. The code is made publicly available at https://github.com/yihongxu/transcenter.
Digital Watermarking Technique for Text Document Protection Using Data Mining Analysis

In the current era, information security is on its top priority for all organizations.The individuals, government officials, and military with the rapid development of Internet technologies like the Internet of Things (IoT), big data, and cloud computing facing data security problems.As the massive rate of data growth, it is a challenging task for the researchers, that how to manage the vast amount of data safely and effectively while designing smart cities.It has been quite easy to produce an illegal copy of digital contents.The verification of digital content is one of the major issues because digital contents are generated daily and shared via the internet.The limited techniques are available for document copyright protection.However, most of the existing techniques produce distortion during watermark insertion or lack of capacity.In the said perspective, a digital watermarking technique is proposed for document copyright protection and ownership verification with the help of data mining.The techniques of data mining are applied to find suitable properties from the document for embedding watermark.The proposed model provides copyright protection to text documents on local and cloud computing paradigm.For the evaluation of the proposed technique, 20 different text documents are used to perform many attacks such as formatting, insertion, and deletion attacks.The proposed technique attained a high-level of imperceptibility where peak signal to noise ratio (PSNR) values are between 64.67% and 71.03%, and similarity (SIM) percentage is between 99.92% and 99.99%.The proposed technique is robust and resists from formatting attacks and capacity of the proposed technique is also improved as compared to the previous techniques.
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit

We consider learning two layer neural networks using stochastic gradient descent. The mean-field description of this learning dynamics approximates the evolution of the network weights by an evolution in the space of probability distributions in $R^D$ (where $D$ is the number of parameters associated to each neuron). This evolution can be defined through a partial differential equation or, equivalently, as the gradient flow in the Wasserstein space of probability distributions. Earlier work shows that (under some regularity assumptions), the mean field description is accurate as soon as the number of hidden units is much larger than the dimension $D$. In this paper we establish stronger and more general approximation guarantees. First of all, we show that the number of hidden units only needs to be larger than a quantity dependent on the regularity properties of the data, and independent of the dimensions. Next, we generalize this analysis to the case of unbounded activation functions, which was not covered by earlier bounds. We extend our results to noisy stochastic gradient descent. Finally, we show that kernel ridge regression can be recovered as a special limit of the mean field analysis.
Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.
Blockchain in general management and economics: a systematic literature review

Purpose This paper aims to provide a systematic review of the literature addressing blockchain technology (BT) in general management and economics (GME). Design/methodology/approach A systematic literature review methodology is used to collect and analyze the literature. Findings Three clusters of research are identified, namely, law, economy and innovation. The use of BT in law areas fosters the registration of intellectual property (IP) rights, although conflict between some of BT applications and regulatory frameworks is present. Research on cryptocurrencies is of high interest to the economy today. In the innovation cluster, BT improves security, traceability and transparency in operations over supply chains. However, BT has many technical imperfections that hinder its wide adoption. Furthermore, the need for the full commitment of all business actors complicates its implementation. Research on BT is still at a nascent stage. Conceptual papers dominate the literature (18 theories are discussed). Eight main future research directions are described. For example, the relationship between the use of BT in supply chains and competitive advantage is not established; and quality standards for BT platforms are not developed. Research limitations/implications The review is restricted to academic journals in the fields of GME, which limits the extent of the conclusions. Originality/value The paper synthesizes 47 studies published in academic journals, avoiding misleading claims and inaccurate information insofar as possible; and provides a spectrum of descriptive statistics and qualitative meta-synthesis analysis of the current literature.
Detecting numerical bugs in neural network architectures

Detecting bugs in deep learning software at the architecture level provides additional benefits that detecting bugs at the model level does not provide. This paper makes the first attempt to conduct static analysis for detecting numerical bugs at the architecture level. We propose a static analysis approach for detecting numerical bugs in neural architectures based on abstract interpretation. Our approach mainly comprises two kinds of abstraction techniques, i.e., one for tensors and one for numerical values. Moreover, to scale up while maintaining adequate detection precision, we propose two abstraction techniques: tensor partitioning and (elementwise) affine relation analysis to abstract tensors and numerical values, respectively. We realize the combination scheme of tensor partitioning and affine relation analysis (together with interval analysis) as DEBAR, and evaluate it on two datasets: neural architectures with known bugs (collected from existing studies) and real-world neural architectures. The evaluation results show that DEBAR outperforms other tensor and numerical abstraction techniques on accuracy without losing scalability. DEBAR successfully detects all known numerical bugs with no false positives within 1.7–2.3 seconds per architecture. On the real-world architectures, DEBAR reports 529 warnings within 2.6–135.4 seconds per architecture, where 299 warnings are true positives.
Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning

For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud gives rise to new security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control mechanism using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of the authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over the existing approaches.
Learning Deep Binary Descriptor with Multi-Quantization

In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual analysis. Existing learning-based binary descriptors such as compact binary face descriptor (CBFD) and DeepBit utilize the rigid sign function for binarization despite of data distributions, which usually suffer from severe quantization loss. In order to address the limitation, we propose a deep multi-quantization network to learn a data-dependent binarization in an unsupervised manner. More specifically, we design a K-Autoencoders (KAEs) network to jointly learn the parameters of feature extractor and the binarization functions under a deep learning framework, so that discriminative binary descriptors can be obtained with a fine-grained multi-quantization. As DBD-MQ simply allocates the same number of quantizers to each real-valued feature dimension ignoring the elementwise diversity of informativeness, we further propose a deep competitive binary descriptor with multi-quantization (DCBD-MQ) method to learn optimal allocation of bits with the fixed binary length in a competitive manner, where informative dimensions gain more bits for complete representation. Moreover, we present a similarity-aware binary encoding strategy based on the earth mover's distance of Autoencoders, so that elements that are quantized into similar Autoencoders will have smaller Hamming distances. Extensive experimental results on six widely-used datasets show that our DBD-MQ and DCBD-MQ outperform most state-of-the-art unsupervised binary descriptors.