Reviewerly/exHarmony huggingface dataset:
Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization

Neural network visualization techniques mark image locations by their relevancy to the network's classification. Existing methods are effective in highlighting the regions that affect the resulting classification the most. However, as we show, these methods are limited in their ability to identify the support for alternative classifications, an effect we name the saliency bias hypothesis. In this work, we integrate two lines of research: gradient-based methods and attribution-based methods, and develop an algorithm that provides per-class explainability. The algorithm back-projects the per pixel local influence, in a manner that is guided by the local attributions, while correcting for salient features that would otherwise bias the explanation. In an extensive battery of experiments, we demonstrate the ability of our methods to class-specific visualization, and not just the predicted label. Remarkably, the method obtains state of the art results in benchmarks that are commonly applied to gradient-based methods as well as in those that are employed mostly for evaluating attribution methods. Using a new unsupervised procedure, our method is also successful in demonstrating that self-supervised methods learn semantic information. Our code is available at: https://github.com/shirgur/AGFVisualization.
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks

In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals that the maximum node degree and spectral norm of the weights govern the generalization bounds of both models. We also show that our bound for GCNs is a natural generalization of the results developed in arXiv:1707.09564v2 [cs.LG] for fully-connected and convolutional neural networks. For message passing GNNs, our PAC-Bayes bound improves over the Rademacher complexity based bound in arXiv:2002.06157v1 [cs.LG], showing a tighter dependency on the maximum node degree and the maximum hidden dimension. The key ingredients of our proofs are a perturbation analysis of GNNs and the generalization of PAC-Bayes analysis to non-homogeneous GNNs. We perform an empirical study on several real-world graph datasets and verify that our PAC-Bayes bound is tighter than others.
Fine-Grained Network Analysis for Modern Software Ecosystems

Modern software development is increasingly dependent on components, libraries, and frameworks coming from third-party vendors or open-source suppliers and made available through a number of platforms (or forges ). This way of writing software puts an emphasis on reuse and on composition, commoditizing the services that modern applications require. On the other hand, bugs and vulnerabilities in a single library living in one such ecosystem can affect, directly or by transitivity, a huge number of other libraries and applications. Currently, only product-level information on library dependencies is used to contain this kind of danger, but this knowledge often reveals itself too imprecise to lead to effective (and possibly automated) handling policies. We will discuss how fine-grained function-level dependencies can greatly improve reliability and reduce the impact of vulnerabilities on the whole software ecosystem.
Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases

The use of machine learning (ML) and deep learning (DL) methods in hematology includes diagnostic, prognostic, and therapeutic applications. This increase is due to the improved access to ML and DL tools and the expansion of medical data. The utilization of ML remains limited in clinical practice, with some disciplines further along in their adoption, such as radiology and histopathology. In this review, we discuss the current uses of ML in diagnosis in the field of hematology, including image-recognition, laboratory, and genomics-based diagnosis. Additionally, we provide an introduction to the fields of ML and DL, highlighting current trends, limitations, and possible areas of improvement.
Schrödinger’s Pirate: How to Trace a Quantum Decoder

We explore the problem of traitor tracing where the pirate decoder can contain a quantum state. Our main results include:
Serverless Isn't Server-Less

Serverless computing in the cloud, or functions as a service (FaaS), poses new and unique systems design challenges. Serverless offers improved programmability for customers, yet at the cost of increased design complexity for cloud providers. One such challenge is effective and consistent resource management for serverless platforms, the implications of which we explore in this paper.
Learning to Recombine and Resample Data For Compositional Generalization

Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data—particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. We describe R&R, a learned data augmentation scheme that enables a large category of compositional generalizations without appeal to latent symbolic structure. R&R has two components: recombination of original training examples via a prototype-based generative model and resampling of generated examples to encourage extrapolation. Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems—instruction following (SCAN) and morphological analysis (SIGMORPHON 2018)—where R&R enables learning of new constructions and tenses from as few as eight initial examples.
Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach

A distributed database model can be effectively optimized through using query optimization. In such a model, the optimizer attempts to identify the most efficient join order, which minimizes the overall cost of the query plan. Successful query processing largely relies on the methodology implemented by the query optimizer. Many researches are concerned with the fact that query processing is considered an NP-hard problem especially when the query becomes bigger. Regarding large queries, it has been found that heuristic methods cannot cover all search spaces and may lead to falling in a local minimum. This paper examines how quantum-inspired ant colony algorithm, a hybrid strategy of probabilistic algorithms, can be devised to improve the cost of query joins in distributed databases. Quantum computing has the ability to diversify and expand, and thus covering large query search spaces. This enables the selection of the best trails, which speeds up convergence and helps avoid falling into a local optimum. With such a strategy, the algorithm aims to identify an optimal join order to reduce the total execution time. Experimental results show that the proposed quantum-inspired ant colony offers a faster convergence with better outcome when compared with the classic model.
Multi-objective simulated annealing for hyper-parameter optimization in convolutional neural networks

In this study, we model a CNN hyper-parameter optimization problem as a bi-criteria optimization problem, where the first objective being the classification accuracy and the second objective being the computational complexity which is measured in terms of the number of floating point operations. For this bi-criteria optimization problem, we develop a Multi-Objective Simulated Annealing (MOSA) algorithm for obtaining high-quality solutions in terms of both objectives. CIFAR-10 is selected as the benchmark dataset, and the MOSA trade-off fronts obtained for this dataset are compared to the fronts generated by a single-objective Simulated Annealing (SA) algorithm with respect to several front evaluation metrics such as generational distance, spacing and spread. The comparison results suggest that the MOSA algorithm is able to search the objective space more effectively than the SA method. For each of these methods, some front solutions are selected for longer training in order to see their actual performance on the original test set. Again, the results state that the MOSA performs better than the SA under multi-objective setting. The performance of the MOSA configurations are also compared to other search generated and human designed state-of-the-art architectures. It is shown that the network configurations generated by the MOSA are not dominated by those architectures, and the proposed method can be of great use when the computational complexity is as important as the test accuracy.
English Grammar Error Correction Algorithm Based on Classification Model

English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar error correction algorithm, elaborated the development background, current status, and future challenges of the classification model, introduced the methods and principles of feature extraction method and dynamic residual structure, constructed a basic model for English grammar error correction based on the classification model, analyzed the classification model and translation model of English grammar error correction, proposed the English grammar error correction algorithm based on the classification model, performed the analyses of the model architecture and model optimizer of the grammar error correction algorithm, and finally conducted a simulation experiment and its result analysis. The study results show that, with the continuous increase of training samples and the continuous progress of learning process, the proposed English grammar error correction algorithm based on the classification model will continue to increase its classification accuracy, further refine its recognition rules, and gradually improve correction efficiency, thereby reducing processing time, saving storage space, and streamlining processing flow. The study results of this paper provide a certain reference for the further research on English grammar error correction algorithm based on the classification model.
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors

Knowledge distillation, in which a student model is trained to mimic a teacher model, has been proved as an effective technique for model compression and model accuracy boosting. However, most knowledge distillation methods, designed for image classification, have failed on more challenging tasks, such as object detection. In this paper, we suggest that the failure of knowledge distillation on object detection is mainly caused by two reasons: (1) the imbalance between pixels of foreground and background and (2) lack of distillation on the relation between different pixels. Observing the above reasons, we propose attention-guided distillation and non-local distillation to address the two problems, respectively. Attention-guided distillation is proposed to find the crucial pixels of foreground objects with attention mechanism and then make the students take more effort to learn their features. Non-local distillation is proposed to enable students to learn not only the feature of an individual pixel but also the relation between different pixels captured by non-local modules. Experiments show that our methods achieve excellent AP improvements on both one-stage and two-stage, both anchor-based and anchor-free detectors. For example, Faster RCNN101 with our distillation achieves 43.9 AP on COCO2017, which is 4.1 higher than the baseline. Codes will be released on Github.
Toward Next Generation of Blockchain Using Improvized Bitcoin-NG

The Bitcoin-Next Generation (NG) is a new blockchain protocol that is designed to scale the efficiency in terms of throughput and delay. Bitcoin-NG is a Byzantine fault-tolerant blockchain protocol robust to extreme churn and shares the same trust model as Bitcoin. Researchers are trying to extend their applications by integrating them with existing technologies, such as the Internet of Things (IoT). However, due to high computational power and high consensus delay, current blockchain protocols, such as Bitcoin, are not suitable for working with lightweight IoT devices. Moreover, they are not energy-efficient as the mining of blocks requires dedicated mining machines that consume electricity. However, protocols, such as Bitcoin-NG, are designed to reduce the consensus delay, but they still need high computational power and high energy. This article proposes a new mechanism for blockchain to reduce the consensus delay, reduce energy consumption, and increase the throughput by introducing a new leader election scheme in the blockchain.
Understanding the Dark Web

This chapter presents the main differences of the surface web, Deep Web and Dark Web as well as their dependences. It further discusses the nature of the Dark Web and the structure of the most prominent darknets, namely, Tor, I2P and Freenet, and provides technical information regarding the technologies behind these darknets. In addition, it discusses the effects police actions on the surface web can have on the Dark Web, the “dilemma” of usage that anonymity technologies present,as well as the challenges LEAs face while trying to prevent and fight against crime and terrorism in the Dark Web.
Speech Gender Classification Using Bidirectional Long Short Term Memory

Gender classification based on voice is crucial for speech recognition, which can be applied to various applications. It is generally developed using conventional machine learning and deep learning approaches. In this research, a gender classification model based on speech is developed using Bidirectional Long Short-Term Memory (BLSTM). The Mel Frequency Cepstral Coefficient (MFCC) is exploited to extract the features to train the BLSTM. Evaluation using a low dataset of 1,000 utterances, 500 males and 500 females, for five runs shows that the model is accurately capable of classifying the gender of the speakers. With a train-test split portion of 80:20, the model obtains an average accuracy of 86.7%, where the highest and the lowest accuracy are 90.5% and 81.0%, respectively. Reducing the portion decreases its performance. It is still stable for the 50:50 train-test split.
SOMHunter V2 at Video Browser Showdown 2021

This paper presents an enhanced version of an interactive video retrieval tool SOMHunter that won Video Browser Showdown 2020. The presented enhancements focus on improving text querying capabilities since the text search model plays a crucial part in successful searches. Hence, we introduce the ability to specify multiple text queries with further positional specification so users can better describe positional relationships of the objects. Moreover, a possibility to further specify text queries with an example image is introduced as well as consequent changes to the user interface of the tool.
Efficient and Fine-Grained Signature for IIoT With Resistance to Key Exposure

Attribute-based signature (ABS) can provide fine-grained and anonymous authentication for the Industrial Internet of Things (IIoT). However, key exposure and the computational performance bottleneck of resource-constrained devices raise a challenge to the IIoT-oriented ABS schemes. To confront the above challenge, this article proposes an intrusion-resilient server-aided ABS (IR-SA-ABS) scheme. This scheme designs to periodically update the signing key with the assistance of a helper device, and the signing key is refreshed for multiple rounds aperiodically within each time period under the supervision of the helper device. In this way, the system remains secure even if both the helper device and the signing device are compromised. During this process, we significantly reduce the computational overheads of resource-constrained devices by delegating all of the key update and refresh operations and most of the signature generation and verification operations to a powerful server. Subsequently, we present the rigorous security proof of IR-SA-ABS. The comparison and experiment demonstrate the efficiency and practicality of IR-SA-ABS in the IIoT scenario.
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, such information removal is undesirable. On the other hand, recent strategies suggest to randomly cut and mix patches and their labels among training images, to enjoy the advantages of regional dropout without having any pointless pixel in the augmented images. We argue that the random selection of the patch may not necessarily represent any information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation. Therefore, we propose SaliencyMix that carefully selects a representative image patch with the help of a saliency map and mixes this indicative patch with the target image that leads the model to learn more appropriate feature representation. SaliencyMix achieves the best known top-1 error of 21.26% and 20.09% for ResNet-50 and ResNet-101 architectures on ImageNet classification, respectively and also improves the model robustness against adversarial perturbations. Furthermore, SaliencyMix trained model helps to improve the object detection performance.
BEmoD: Development of Bengali Emotion Dataset for Classifying Expressions of Emotion in Texts

Recently, emotion detection in language has increased attention to NLP researchers due to the massive availability of people’s expressions, opinions, and emotions through comments on the Web 2.0 platforms. It is a very challenging task to develop an automatic sentiment analysis system in Bengali due to the scarcity of resources and the unavailability of standard corpora. Therefore, the development of a standard dataset is a prerequisite to analyze emotional expressions in Bengali texts. This paper presents an emotional dataset (hereafter called ‘BEmoD’) for analysis of emotion in Bengali texts and describes its development process, including data crawling, pre-processing, labeling, and verification. BEmoD contains 5200 texts, which are labeled into six basic emotional categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with a Cohen’s $$\kappa $$ score of 0.920 shows the agreement among annotators. The evaluation analysis also shows the distribution of emotion words that follow Zipf’s law.
Exact and approximate continuous-variable gate decompositions

We gather and examine in detail gate decomposition techniques for continuous-variable quantum computers and also introduce some new techniques which expand on these methods. Both exact and approximate decomposition methods are studied and gate counts are compared for some common operations. While each having distinct advantages, we find that exact decompositions have lower gate counts whereas approximate techniques can cover decompositions for all continuous-variable operations but require significant circuit depth for a modest precision.
E-MOC: An Efficient Secret Sharing Model for Multimedia on Cloud

In this multimedia era, the security of the multimedia content over Cloud becomes a crucial research area. There are several modes of communication available for transmitting the information over the Cloud from one place to another place around the globe. Such communication networks are open to all, where anyone can access very easily. To abate the crimes, there are so many techniques evolved, such as RSA, DES, Elgamal, etc. However, these techniques seem limited to provide security to the user’s data. This work highlights a novel multimedia encryption model over Cloud, where the key is generated dynamically without involving the user by making the use of the original information. It creates difficulty for an attacker to break the cipher in a reasonable time, and there is no chance for an attacker to guess the key because the key is independent of the user’s behavior.
Sign Language Recognition Using ResNet50 Deep Neural Network Architecture

Communication is a barrier between the deaf-mute community and the rest of the society. Sign Language is used for communication among such people who cannot speak and listen. The automation of sign language recognition has gained researchers attention in the last few years. Many complex and costly hardware systems have already been developed to assist the purpose. However, we propose to use deep learning approach for automated sign language recognition. We devised a novel 2-level ResNet50 based Deep Neural Network Architecture to classify fingerspelled words. The dataset used is the standard American Sign Language Hand gesture dataset by [1]. The dataset was first augmented using various augmentation techniques. In our 2-level ResNet50 based approach the Level 1 model classifies the input image into one of the 4 sets. After an image is classified into one of the sets it is provided as an input to the corresponding second level model for predicting the actual class of the image. Our approach yields an accuracy of 99.03% on 12,048 test images.
Implementation of algorithms in pattern &amp; impression evidence: A responsible and practical roadmap

Over the years, scientific and legal scholars have called for the implementation of algorithms (e.g., statistical methods) in forensic science to provide an empirical foundation to experts' subjective conclusions. Despite the proliferation of numerous approaches, the practitioner community has been reluctant to apply them operationally. Reactions have ranged from passive skepticism to outright opposition, often in favor of traditional experience and expertise as a sufficient basis for conclusions. In this paper, we explore why practitioners are generally in opposition to algorithmic interventions and how their concerns might be overcome. We accomplish this by considering issues concerning human—algorithm interactions in both real world domains and laboratory studies as well as issues concerning the litigation of algorithms in the American legal system. Taking into account those issues, we propose a strategy for approaching the implementation of algorithms, and the different ways algorithms can be implemented, in a responsible and practical manner.