Reviewerly/exHarmony huggingface dataset:
Kernel Online System for Fast Principal Component Analysis and its Adaptive Learning

An artificial neural system for data compression that sequentially processes linearly nonseparable classes is proposed. The main elements of this system include adjustable radial-basis functions (Epanechnikov’s kernels), an adaptive linear associator learned by a multistep optimal algorithm, and Hebb-Sanger neural network whose nodes are formed by Oja’s neurons. For tuning the modified Oja’s algorithm, additional filtering (in case of noisy data) and tracking (in case of nonstationary data) properties were introduced. The main feature of the proposed system is the ability to work in conditions of significant nonlinearity of the initial data that are sequentially fed to the system and have a non-stationary nature. The effectiveness of the developed approach was confirmed by the experimental results. The proposed kernel online neural system is designed to solve compression and visualization tasks when initial data form linearly nonseparable classes in general problem of Data Stream Mining and Dynamic Data Mining. The main benefit of the proposed approach is high speed and ability to process data whose characteristics are changed in time.
Enhancing E-commerce Recommender System Adaptability with Online Deep Controllable Learning-To-Rank

In the past decade, recommender systems for e-commerce have witnessed significant advancement. Recommendation scenarios can be divided into different type (e.g., pre-, during-, post-purchase, campaign, promotion, bundle) for different user groups or different businesses. For different scenarios, the goals of recommendation are different. This is reflected by the different performance metrics employed. In addition, online promotional campaigns, which attract high traffic volumes, are also a critical factor affecting e-commerce recommender systems. Typically, prior to a promotional campaign, the Add-to-Cart Rate (ACR) is the target of optimization. During the campaign, this changes to Gross Merchandise Volumes (GMV). Immediately after the campaign, it becomes Click Through Rates CTR. Dynamically adapting among these potentially conflicting optimization objectives is an important capability for recommender systems deployed in real-world e-commerce platforms. In this paper, we report our experience designing and deploying the Deep Controllable Learning-To-Rank (DC-LTR) recommender system to address this challenge. It enhances the feedback controller in LTR with multi-objective optimization so as to maximize different objectives under constraints. Its ability to dynamically adapt to changing business objectives has resulted in significant business advantages. Since September 2019, DC-LTR has become a core service enabling adaptive online training and real-time deployment ranking models based on changing business objectives in AliExpress and Lazada. Under both everyday use scenarios and peak load scenarios during large promotional campaigns, DC-LTR has achieved significant improvements in satisfying real-world business objectives.
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods.
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training

This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. Existing message passing-based graph neural networks use edges either for graph traversal and/or selection of message encoding functions. Ignoring the edge semantics could have severe repercussions on the quality of embeddings, especially when dealing with two nodes having multiple relations. Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training. Although existing hard negative sampling techniques can identify challenging negative relationships for optimization, new techniques are required to control false negatives during training as false negatives could corrupt the learning process. To address these issues, first, we propose RelGNN -- a message passing-based heterogeneous graph attention model. In particular, RelGNN generates the states of different relations and leverages them along with the node states to weigh the messages. RelGNN also adopts a self-attention mechanism to balance the importance of attribute features and topological features for generating the final entity embeddings. Second, we introduce a parameter-free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling. ASA reduces the false-negative rate by leveraging positive relationships to effectively guide the identification of true negative samples. Our experimental evaluation demonstrates that RelGNN optimized by ASA for relationship prediction improves state-of-the-art performance across established benchmarks as well as on a real industrial dataset.
SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA PRODUCT REVIEWS

Well timed and profitable analytics over "Social Networks" is now a lead for gain in many industries to increase brand exposure and broaden customer reach by analyzing the reviews and recommending products to consumers.The objective is to extract the unstructured conversation or posts from Twitter and present a methodology for sentiment analysis on Twitter that reviews the products by considering the consumer's sentiments Naive Bayesian Classifier.Samsung smartphones were taken into analysis and the experiment reveals six possible different sentiments: joy, surprise, anger, disgust, fear and sadness in the analysing process of the tweets.From the experimental results, it is observed that 80% of the customers has given positive feedback on the product and 20% has given negative feedback and the level of accuracy of the proposed approach is 90%.
FPGA accelerator for CNN: an exploration of the kernel structured sparsity and hybrid arithmetic computation

The deployment of large-scale deep neural networks on field programmable gate array (FPGA) platforms is severely hindered by the high requirements on computational resources and off-chip data bandwidth. Traditional nonstructured sparsity algorithms can efficiently reduce the nonzero weights of neural network models. However, the nonstructured sparse connections across channels also degrade the degree of computational parallelism and consequently seriously deteriorate the performance of the FPGA accelerator. We propose an FPGA accelerator by exploring the kernel structured sparsity and hybrid arithmetic computation for the convolutional neural network (CNN). On the one hand, we introduce a hardware-friendly kernel pruning method to reduce the number of arithmetic operations of the CNN model. Our proposed method maintains high accuracy (achieving a less than 0.32% accuracy loss) and achieves a high degree of parallelism. On the other hand, we design a specific hybrid arithmetic computation for the FPGA accelerator to speed up the performance of the pruned CNN model. The FPGA accelerator consists of only 64 sets of hybrid 8-bit and 16-bit floating-point units for the convolution operation. Experiments on VGGNet16 demonstrate that the proposed FPGA accelerator achieves a state-of-the-art 5 × convolution operation reduction and a 3 × parameter compression. The proposed FPGA accelerator is able to perform at 13.2 FPS, and the corresponding energy efficiency can be boosted up to 1.9 image / J.
A Study on PUBLIC Lifelong Education Model for Second Life Planning and Support for Middle-Aged Adults

Purpose: The purpose of this study is to propose a Public lifelong education model for the second life plan of middle-aged and elderly people in order to cope with the rapid progress of today s aging society. Public support is needed so that middle-aged adults can design their second life and live successfully according to their own plans. In this study, the purpose is to propose a lifelong education model for the second life design and support for middle-aged adults. Method: The research method of this study used various methods such as literature research, Delphi research, and expert advisory meeting. First, literature research and previous studies related to life redesign and second life of middle-aged adults were analyzed. And, using the components of the middle-aged adult lifelong education model of the expert group extracted from the Delphi survey results. Finally, a lifelong education model was de-veloped to support the second life plan for middle-aged and elderly people by collecting and reflecting the opin-ions of the expert group. Results: As a result of the study, the second life support lifelong education model for middle-aged and elderly people focuses on middle-aged learners, and analyzes the second life plan of middle-aged and elderly through counseling on life course and learning according to the learner s personal career and orientation. It is necessary to develop lifelong education programs that can support this and guide them to participate in such lifelong edu-cation. Conclusion: Supporting the second life plan for middle-aged and elderly people is not something that any individual or institution can do. Therefore, it is necessary to approach the lifelong education model that supports the second life plan of the middle-aged and elderly with a strategy that connects and utilizes existing systems and functions to the maximum extent.
Bio-Inspired Adversarial Attack Against Deep Neural Networks

The paper develops a new adversarial attack against deep neural networks (DNN), based on applying bio-inspired design to moving physical objects. To the best of our knowledge, this is the first work to introduce physical attacks with a moving object. Instead of following the dominating attack strategy in the existing literature, i.e., to introduce minor perturbations to a digital input or a stationary physical object, we show two new successful attack strategies in this paper. We show by superimposing several patterns onto one physical object, a DNN becomes confused and picks one of the patterns to assign a class label. Our experiment with three flapping wing robots demonstrates the possibility of developing an adversarial camouflage to cause a targeted mistake by DNN. We also show certain motion can reduce the dependency among consecutive frames in a video and make an object detector "blind", i.e., not able to detect an object exists in the video. Hence in a successful physical attack against DNN, targeted motion against the system should also be considered.
Forster Decomposition and Learning Halfspaces with Noise

A Forster transform is an operation that turns a distribution into one with good anti-concentration properties. While a Forster transform does not always exist, we show that any distribution can be efficiently decomposed as a disjoint mixture of few distributions for which a Forster transform exists and can be computed efficiently. As the main application of this result, we obtain the first polynomial-time algorithm for distribution-independent PAC learning of halfspaces in the Massart noise model with strongly polynomial sample complexity, i.e., independent of the bit complexity of the examples. Previous algorithms for this learning problem incurred sample complexity scaling polynomially with the bit complexity, even though such a dependence is not information-theoretically necessary.
Nondiscriminatory treatment: A straightforward framework for multi-human parsing

Multi-human parsing aims to segment every body part of every human instance. Nearly all state-of-the-art methods follow the "detection first" or "segmentation first" pipelines. Different from them, we present an end-to-end and box-free pipeline from a new and more human-intuitive perspective. In training time, we directly do instance segmentation on humans and parts. More specifically, we introduce a notion of "indiscriminate objects with categorie" which treats humans and parts without distinction and regards them both as instances with categories. In the mask prediction, each binary mask is obtained by a combination of prototypes shared among all human and part categories. In inference time, we design a brand-new grouping post-processing method that relates each part instance with one single human instance and groups them together to obtain the final human-level parsing result. We name our method as Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP). Experiments show that our network performs superiorly against state-of-the-art methods by a large margin on the MHP v2.0 and PASCAL-Person-Part datasets.
Optimized Polynomial Multiplier Architectures for Post-Quantum KEM Saber.

Saber is one of the four finalists in the ongoing NIST post-quantum cryptography standardization project. A significant portion of Saber's computation time is spent on computing polynomial multiplications in polynomial rings with powers-of-two moduli. We propose several optimization strategies for improving the performance of polynomial multiplier architectures for Saber, targeting different hardware platforms and diverse application goals. We propose two high-speed architectures that exploit the smallness of operand polynomials in Saber and can achieve great performance with a moderate area consumption. We also propose a lightweight multiplier that consumes only 541 LUTs and 301 FFs on a small Artix-7 FPGA.
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability.

As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. In addition, these methods are also computationally inefficient, and require significant hyper-parameter tuning. In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty. We instantiate this framework to obtain Bayesian versions of LIME and KernelSHAP which output credible intervals for the feature importances, capturing the associated uncertainty. The resulting explanations not only enable us to make concrete inferences about their quality (e.g., there is a 95\% chance that the feature importance lies within the given range), but are also highly consistent and stable. We carry out a detailed theoretical analysis that leverages the aforementioned uncertainty to estimate how many perturbations to sample, and how to sample for faster convergence. This work makes the first attempt at addressing several critical issues with popular explanation methods in one shot, thereby generating consistent, stable, and reliable explanations with guarantees in a computationally efficient manner. Experimental evaluation with multiple real world datasets and user studies demonstrate that the efficacy of the proposed framework.
Classification and Localization Consistency Regularized Student-Teacher Network for Semi-supervised Cervical Cell Detection

Cytopathology image analysis gives an important indication of the cervical carcinoma. Automation-assisted diagnosis has received more and more attention because of its high efficiency. Thanks to the development of artificial intelligence, supervised deep learning methods have shown promising results for cervical cell detection task. However, large amounts of labeled data are quite expensive and time-consuming for acquisition. In this paper, we propose a Classification and Localization Consistency Regularized Student-Teacher Network (CLCR-STNet) with online pseudo label mining to leverage both labeled and unlabeled data for semi-supervised cervical cell detection. Both classification and localization consistency regularization are introduced to ensure that the bounding boxes predicted by the student and teacher networks are consistent. Instead of sharing the network parameters with student model, our teacher model is updated using exponential moving average (EMA). Moreover, the teacher network is used to generate high-confidence pseudo labels for unlabeled data to provide student network with more supervised information. The experiment results show that the proposed method outperforms the supervised methods learned using labeled data only.
Working conditions of Nursing professionals in coping with the Covid-19 pandemic/ Condiciones de trabajo de los profesionales de enfermería para afrontar la pandemia del Covid-19/ Condições de trabalho dos profissionais de enfermagem no enfrentamento da pandemia da covid-19

ABSTRACT Objective To trigger a reflection on the current working conditions of Nursing professionals in coping with the Covid-19 pandemic. Method A theoretical-reflective study supported by studies from the Marxist perspective, national and international scientific articles, and official documents from the World Health Organization and the Federal Nursing Council. Results The daily work of Nursing professionals in the face of the Covid-19 pandemic presents unfavorable working conditions in Brazil and worldwide, with emphasis on the deficit of professionals, overload of activities, low pay, and personal protective equipment, often insufficient and inadequate, conditions that can lead to exhaustion, illness and death. Conclusion This study can contribute to raising discussions about the need for improvements in the working conditions of Nursing professionals, especially in pandemic times and the impact on the health of these professionals.
On nondeterminism in combinatorial filters

The problem of combinatorial filter reduction arises from resource optimization in robots; it is one specific way in which automation can help to achieve minimalism, to build better robots. This paper contributes a new definition of filter minimization that is broader than its antecedents, allowing filters (input, output, or both) to be nondeterministic. This changes the problem considerably. Nondeterministic filters may re-use states to obtain more ‘behavior’ per vertex. We show that the gap in size can be significant (larger than polyno-mial), suggesting such cases will generally be more challenging than deterministic problems. Indeed, this is supported by the core complexity result established in this paper: producing nondeterministic minimizers is PSPACE-hard. The hardness separation for minimization existing between deterministic filter and automata, thus, fails to hold for the nondeterministic case.
A Literature Review of Bangla Document Clustering

Document clustering is a machine learning approach to categorize documents into related groups without any definition to the documents prior to the process.It helps to categorize very large chunks of documents into similar categories for making the process of finding a particular document easier.It also helps in retrieval of the data.There has been numerous works in document clustering in other languages but the amount of work in Bangla is still not sufficient.In this paper it has been aimed to evaluate the techniques that have been adopted in clustering Bangla documents.These techniques and their effectiveness has also been compared in contrast to the contemporary methods adopted by researchers around the world on other languages and a vision is proposed on current state of development in Bangla Document Clustering.
A Functional Programming Language with Versions

While modern software development heavily uses versioned packages, programming languages rarely support the concept of versions in their semantics, which makes software updates more bulky and unsafe. This paper proposes a programming language that intrinsically supports versions. The main goals are to design core language features to support multiple versions in one program and establish a proper notion of type safety with those features. The proposed core calculus, called Lambda VL, has versioned values, each containing different values under different versions. We show the construction of the type system as an extension of coeffect calculus by mapping versions to computational resources. The type system guarantees the existence of a valid combination of versions for a program. The calculus enables programming languages to use multiple versions of a package within a program. It will serve as a basis for designing advanced language features like module systems and semantic versioning.
Training analysis of optimization models in machine learning

Machine learning is fast evolving, with numerous theoretical advances and applications in a variety of domains. In reality, most machine learning algorithms are based on optimization issues. This interaction is also explored in the special topic on machine learning and large-scale optimization. Furthermore, machine learning optimization issues have several unique characteristics that are rarely seen in other optimization contexts. Aside from that, the notions of classical optimization vs machine learning will be discussed. Finally, this study will give an outline of these particular aspects of machine learning optimization.
Analisis Frasa Nominal Subordinatif pada Teks Berita Suara.Com “Tak Semuanya Sehat, Sayuran Jenis Ini Justru Picu Tekanan Darah Tinggi”

Subordinate nominal phrases are a collection or consist of two or more words whose two elements are not equal. Some have a position as the superior element or core of the phrase, and who have a position as subordinate elements or are referred to as additional explanatory phrases. This article aims to find out the subordinative nominal phrase contained in the Suara.com news text entitled Not All Healthy, Vegetables of This Type Precisely Trigger High Blood Pressure edition of September 24, 2020, according to the composition. The method used in writing this article is qualitative data analysis method by searching, finding and classifying each subordinative nominal phrasing contained in the Suara.com news text entitled Not All Healthy, Vegetables of This Type Precisely Trigger High Blood Pressure edition of September 24, 2020. The result obtained from this research is the discovery of subordinate nominal phrases in accordance with their respective arrangements. With this research is expected to help the community, especially students to be able to understand and be able to make subordinative nominal phrases in news text sentences or not in the learning of Indonesian language and the world of literacy.