Reviewerly/exHarmony huggingface dataset:
Locality-Aware Channel-Wise Dropout for Occluded Face Recognition

Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the training images with artificial occlusions has been proved as a useful approach. However, these artificial occlusions are commonly generated by adding a black rectangle or several object templates including sunglasses, scarfs and phones, which cannot well simulate the realistic occlusions. In this paper, based on the argument that the occlusion essentially damages a group of neurons, we propose a novel and elegant occlusion-simulation method via dropping the activations of a group of neurons in some elaborately selected channel. Specifically, we first employ a spatial regularization to encourage each feature channel to respond to local and different face regions. Then, the locality-aware channel-wise dropout (LCD) is designed to simulate occlusions by dropping out a few feature channels. The proposed LCD can encourage its succeeding layers to minimize the intra-class feature variance caused by occlusions, thus leading to improved robustness against occlusion. In addition, we design an auxiliary spatial attention module by learning a channel-wise attention vector to reweight the feature channels, which improves the contributions of non-occluded regions. Extensive experiments on various benchmarks show that the proposed method outperforms state-of-the-art methods with a remarkable improvement.
ZOCB and ZOTR: Tweakable Blockcipher Modes for Authenticated Encryption with Full Absorption

We define ZOCB and ZOTR for nonce-based authenticated encryption with associated data, and analyze their provable security. These schemes use a tweakable blockcipher (TBC) as the underlying primitive, and fully utilize its input to process a plaintext and associated data (AD). This property is commonly referred to as full absorption, and this has been explored for schemes based on a permutation or a pseudorandom function (PRF). Our schemes improve the efficiency of TBC-based counterparts of OCB and OTR called OCB3 (Krovetz and Rogaway, FSE 2011) and OTR (Minematsu, EUROCRYPT 2014). Specifically, ΘCB3 and OTR have an independent part to process AD, and our schemes integrate this process into the encryption part of a plaintext by using the tweak input of the TBC. Up to a certain length of AD, ZOCB and ZOTR completely eliminate the independent process for it. Even for longer AD, our schemes process it efficiently by fully using the tweak input of the TBC. For this purpose, based on previous tweak extension schemes for TBCs, we introduce a scheme called XTX*. To our knowledge, ZOCB and ZOTR are the first efficiency improvement of ΘCB3 and OTR in terms of the number of TBC calls. Compared to Sponge-based and PRF-based schemes, ZOCB and ZOTR allow fully parallel computation of the underlying primitive, and have a unique design feature that an authentication tag is independent of a part of AD. We present experimental results illustrating the practical efficiency gain and clarifying the efficiency cost for it with a concrete instantiation. The results show that for long input data, our schemes have gains, while we have efficiency loss for short input data.
Blockchain Technology and Its Applications: Case Studies

Blockchain technology is expected to bring about exciting developments across a variety of fields, such as politics, economy, culture, industry, and business model.Blockchain is no doubt one key infrastructure technology to lead the Fourth Industrial Revolution.This paper is a general overview of the main features of blockchain technology.Three types of blockchains (public, private, and consortium) will be presented within a discussion of their major case studies.In addition, the latest trends and developments in blockchain technology will be briefly reviewed.The terminology of blockchain infrastructure technology has the potential to foster technological innovation across various industrial sectors as well as improve mutual harmony and understanding of complex structures and diverse phenomena in human society, especially antagonism, confrontation, and ideological conflict.With such progress in social innovation, blockchain technology will result in a fairer and more transparent human society.Greater attention will also be given to a blockchain-based mobile voting system that will enable the realization of direct democracy in a representative democracy.Furthermore, this paper offers solutions to a trilemma of problems (scalability, security, decentralization) in blockchain.
Cloud Computing Modeling and Simulation using CloudSim Environment

Cloud computing is a major technology in the development of internet services, and it delivers software, infrastructure and platform. It enables the client to offer-based services in a pay-per-use concept. So, it offers a less expensive and easy way of managing things. In this paper, we introduce a formal definition of CloudSim simulator, including its architecture, reasons for adopting for modeling and simulation, pros and cons. Moreover, CloudSim versions and how to implement the cloud environment using CloudSim. Further, we demonstrate that cloudlet scheduler policy TimeShared, SpaceShared and DynamicWorkload approach for VM scheduler TimeShared policy are compared on the bases of some performance parameters in term of average turnaround time, throughput, total execution time and total simulation time. These parameters outperform in DynamicWorkload cloudlet scheduler policy than TimeShared and SpaceShared approach for TimeShared VM scheduler policy. This work is anticipated to the beginner of the field to choose CloudSim simulation platform and suitable approaches for cloud computing.
Aspect Based Sentiment Analysis using POS Tagging and TFIDF

Social media content on the internet is increasing day by day. Since media knowledge helps people in making decisions, web based businesses give their clients an opportunity to express their opinions about items available on the web in the form of surveys and reviews. Sentiment analysis can be used on product reviews or tweets, comments, blogs to infer individual’s feelings or attitudes. Here Aspect Based Sentiment Analysis is used to extract most interesting aspect of a particular product from unlabeled text. We have developed two models for aspect/feature extraction.Model1 uses POS tagging whereas Model2 utilizes TFIDF .In Model 1 we start with noun phrase algorithm and extend it to adjectives and adverbs to extract all the aspect terms. In model2 after data preprocessing TDIDF technique is used. The relative importances of the aspects are calculated and the most important positive, negative and neutral aspects are presented to the user. Naïve Bayes, Support Vector machine, Decision Tree, KNN were used to classify the sentiment polarity of the generated aspects.
U.S. Geological Survey 21st-Century science strategy 2020–2030

First posted January 19, 2021 For additional information, contact: Send email to ask@usgs.govU.S. Geological Survey12201 Sunrise Valley DriveReston, VA 20192 Today’s Earth system challenges are far more complex and urgent than those that existed in 1879 when the USGS was established. Society’s greatest challenges are directly or indirectly linked to major areas of USGS science. Increased pressures on natural resources continue with consequences for national security, food and water availability, natural disasters, human health, and biodiversity loss. As we look forward 10, 20, and 30 years, our mission will be more important than ever before. A broad but coherent view is required for stewardship of the Nation’s land, water, mineral, energy, and ecosystem resources, which involves complex tradeoffs among multiple, often competing objectives. Increasingly, resource managers and decision makers need “the whole USGS”:integrated multidisciplinary Earth and biological science data,geospatial tools,predictive models,decision-support tools, andthe expertise to interpret them.This Science Strategy defines a vision and mission for how we will continue to evolve USGS Science to address these Earth system challenges.
Study of Literary Images of Gifted Characters In Optional Activities As a Means to Develop Capable and Talented Youth

The studies of the life and work of the writers of the second half of the 19th Century, such as N.S. Leskov and F.M. Dostoevsky allow considering the problem of giftedness in the literary aspect. The authors of the article identify the charactersâ€™ inborn, professional and acquired giftedness in the reviewed literary works, and consider features of the images of the gifted characters. The practical significance of the study is in providing teachers with methods of work during elective classes on studying the problem of giftedness. The results of the authorsâ€™ experimental work prove that this form of studies contributes to the development of schoolchildrenâ€™s interest in literature, and stimulates their research activities.
TEDT, a Leakage-Resist AEAD Mode for High Physical Security Applications

We propose TEDT, a new Authenticated Encryption with Associated Data (AEAD) mode leveraging Tweakable Block Ciphers (TBCs). TEDT provides the following features: (i) It offers full leakage-resistance, that is, it limits the exploitability of physical leakages via side-channel attacks, even if these leakages happen during every message encryption and decryption operation. Moreover, the leakage integrity bound is asymptotically optimal in the multi-user setting. (ii) It offers nonce misuse-resilience, that is, the repetition of nonces does not impact the security of ciphertexts produced with fresh nonces. (iii) It can be implemented with a remarkably low energy cost when strong resistance to side-channel attacks is needed, supports online encryption and handles static and incremental associated data efficiently. Concretely, TEDT encourages so-called leveled implementations, in which two TBCs are implemented: the first one needs strong and energy demanding protections against side-channel attacks but is used in a limited way, while the other only requires weak and energy-efficient protections and performs the bulk of the computation. As a result, TEDT leads to more energy-efficient implementations compared to traditional AEAD schemes, whose side-channel security requires to uniformly protect every (T)BC execution.
Multi-Images Recognition of Breast Cancer Histopathological via Probabilistic Neural Network Approach

This paper suggests an approach for automated diagnosis of different types of breast carcinoma histopathological images via machine learning algorithms.The approach starts by extracting some features then use the nondominated genetic algorithm II (NSGA-II) algorithm and Ant Colony Optimization (ACO) to select the best features.Additionally, two classifiers including probabilistic neural network (PNN) and multi-class support vector machine (MSVM) is proposed to determine the type of breast cancer.These histopathological images database consider as a multi-classification task that used 848 BreakHis images with 400× magnification factors.The data divided into 160 samples for the testing phase and 688 samples for the training phase.The experiment results evaluated by four parameters including accuracy, sensitivity, specificity, and precision.For the NSGA-II algorithm, these outcomes refer to the maximum accuracy obtained through the combined features set with the accuracy being 100% by the training dataset and 82.5% by the testing dataset for the PNN classifier.
Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers.The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP.The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized.The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds.In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP.The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators.While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators.In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms.According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances.Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances.Therefore, the HAPSO algorithm is more robust than the APSO algorithm.
Improved Heuristics for Short Linear Programs

In this article, we propose new heuristics for minimising the amount of XOR gates required to compute a system of linear equations in GF(2). We first revisit the well known Boyar-Peralta strategy and argue that a proper randomisation process during the selection phases can lead to great improvements. We then propose new selection criteria and explain their rationale. Our new methods outperform state-of-the-art algorithms such as Paar or Boyar-Peralta (or open synthesis tools such as Yosys) when tested on random matrices with various densities. They can be applied to matrices of reasonable sizes (up to about 32 × 32). Notably, we provide a new implementation record for the matrix underlying the MixColumns function of the AES block cipher, requiring only 94 XORs.
Improving English pronunciation via automatic speech recognition technology

This study presents a research study on applying automatic speech recognition (ASR) technology in English pronunciation correction. We also discuss the relationship between ELF/ESL learners' self-improvement and English teacher's classroom teaching. The results show that ASR technology can help Chinese English learners improve their English pronunciation. The research aims to provide a new and practical way for ELF/ESL learners to improve English pronunciation as well as provide a new mode of English pronunciation teaching for relevant educators.
Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology Image Segmentation

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and labor-intensive. As a subset of weakly supervised learning, Multiple Instance Learning (MIL) has been proven to be effective in segmentation. However, there is a lack of related information between instances in MIL, which limits the further improvement of segmentation performance. In this paper, we propose a novel weakly supervised method for pixel-level segmentation in histopathology images, which introduces Transformer into the MIL framework to capture global or long-range dependencies. The multi-head self-attention in the Transformer establishes the relationship between instances, which solves the shortcoming that instances are independent of each other in MIL. In addition, deep supervision is introduced to overcome the limitation of annotations in weakly supervised methods and make the better utilization of hierarchical information. The state-of-the-art results on the colon cancer dataset demonstrate the superiority of the proposed method compared with other weakly supervised methods. It is worth believing that there is a potential of our approach for various applications in medical images.
Quantum image edge detection based on eight-direction Sobel operator for NEQR

Quantum Sobel edge detection (QSED) is a kind of algorithm for image edge detection using quantum mechanism, which can solve the real-time problem encountered by classical algorithms. However, the existing QSED algorithms only consider two- or four-direction Sobel operator, which leads to a certain loss of edge detail information in some high-definition images. In this paper, a novel QSED algorithm based on eight-direction Sobel operator is proposed, which not only reduces the loss of edge information, but also simultaneously calculates eight directions' gradient values of all pixel in a quantum image. In addition, the concrete quantum circuits, which consist of gradient calculation, non-maximum suppression, double threshold detection and edge tracking units, are designed in details. For a 2^n x 2^n image with q gray scale, the complexity of our algorithm can be reduced to O(n^2 + q^2), which is lower than other existing classical or quantum algorithms. And the simulation experiment demonstrates that our algorithm can detect more edge information, especially diagonal edges, than the two- and four-direction QSED algorithms.
Fine-grained Image Captioning with CLIP Reward

Modern image captioning models are usually trained with text similarity objectives.However, since reference captions in public datasets often describe the most salient common objects, models trained with the text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others.Towards more descriptive and distinctive caption generation, we propose to use CLIP, a multimodal encoder trained on huge image-text pairs from the web, to calculate multi-modal similarity and use it as a reward function.We also propose a simple finetuning strategy of CLIP text encoder to improve grammar that does not require extra text annotation.This completely eliminates the need for reference captions during the reward computation.To comprehensively evaluate descriptive captions, we introduce FineCapEval, a new dataset for caption evaluation with fine-grained criteria: overall, background, object, relations.In our experiments on text-to-image retrieval and FineCapEval, the proposed CLIP-guided model generates more distinctive captions than the CIDEroptimized model.We also show that our unsupervised grammar finetuning of the CLIP text encoder alleviates the degeneration problem of the naive CLIP reward.Lastly, we show human analysis where the annotators strongly prefer CLIP reward to CIDEr and MLE objectives on diverse criteria. 1
Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopes

We study the verification problem for closed-loop dynamical systems with neural-network controllers (NNCS). This problem is commonly reduced to computing the set of reachable states. When considering dynamical systems and neural networks in isolation, there exist precise approaches for that task based on set representations respectively called Taylor models and zonotopes. However, the combination of these approaches to NNCS is non-trivial because, when converting between the set representations, dependency information gets lost in each control cycle and the accumulated approximation error quickly renders the result useless. We present an algorithm to chain approaches based on Taylor models and zonotopes, yielding a precise reachability algorithm for NNCS. Because the algorithm only acts at the interface of the isolated approaches, it is applicable to general dynamical systems and neural networks and can benefit from future advances in these areas. Our implementation delivers state-of-the-art performance and is the first to successfully analyze all benchmark problems of an annual reachability competition for NNCS.
Recent Advances in Retrieval-Augmented Text Generation

Recently retrieval-augmented text generation has achieved state-of-the-art performance in many NLP tasks and has attracted increasing attention of the NLP and IR community, this tutorial thereby aims to present recent advances in retrieval-augmented text generation comprehensively and comparatively. It firstly highlights the generic paradigm of retrieval-augmented text generation, then reviews notable works for different text generation tasks including dialogue generation, machine translation, and other generation tasks, and finally points out some limitations and shortcomings to facilitate future research.
Emerging Immersive Communication Systems: Overview, Taxonomy, and Good Practices for QoE Assessment

Several technological and scientific advances have been achieved recently in the fields of immersive systems (e.g., 360-degree/multiview video systems, augmented/mixed/virtual reality systems, immersive audio-haptic systems, etc.), which are offering new possibilities to applications and services in different communication domains, such as entertainment, virtual conferencing, working meetings, social relations, healthcare, and industry. Users of these immersive technologies can explore and experience the stimuli in a more interactive and personalized way than previous technologies (e.g., 2D video). Thus, considering the new technological challenges related to these systems and the new perceptual dimensions and interaction behaviors involved, a deep understanding of the users’ Quality of Experience (QoE) is required to satisfy their demands and expectations. In this sense, it is essential to foster the research on evaluating the QoE of immersive communication systems, since this will provide useful outcomes to optimize them and to identify the factors that can deteriorate the user experience. With this aim, subjective tests are usually performed following standard methodologies (e.g., ITU recommendations), which are designed for specific technologies and services. Although numerous user studies have been already published, there are no recommendations or standards that define common testing methodologies to be applied to evaluate immersive communication systems, such as those developed for images and video. Taking this into account, a revision of the QoE evaluation methods designed for previous technologies is required to develop robust and reliable methodologies for immersive communication systems. Thus, the objective of this paper is to provide an overview of existing immersive communication systems and related user studies, which can help on the definition of basic guidelines and testing methodologies to be used when performing user tests of immersive communication systems, such as 360-degree video-based telepresence, avatar-based social VR, cooperative AR, etc.
Quantum Zeno and Anti-Zeno Probes of Noise Correlations in Photon Polarization

We experimentally demonstrate, for the first time, noise diagnostics by repeated quantum measurements, establishing the ability of a single photon subjected to random polarization noise to diagnose non-Markovian temporal correlations of such a noise process. Both the noise spectrum and temporal correlations are diagnosed by probing the photon with frequent (partially) selective polarization measurements. We show that noise with positive temporal correlations corresponds to our single photon undergoing a dynamical regime enabled by the quantum Zeno effect (QZE), whereas noise characterized by negative (anti) correlations corresponds to regimes associated with the anti-Zeno effect (AZE). This is the first step toward a novel noise spectroscopy based on QZE and AZE in single-photon state probing able to extract information on the noise while protecting the probe state, a conceptual paradigm shift with respect to traditional interferometric measurements.