Reviewerly/exHarmony huggingface dataset:
PIXAL: Anomaly Reasoning with Visual Analytics

Anomaly detection remains an open challenge in many application areas. While there are a number of available machine learning algorithms for detecting anomalies, analysts are frequently asked to take additional steps in reasoning about the root cause of the anomalies and form actionable hypotheses that can be communicated to business stakeholders. Without the appropriate tools, this reasoning process is time-consuming, tedious, and potentially error-prone. In this paper we present PIXAL, a visual analytics system developed following an iterative design process with professional analysts responsible for anomaly detection. PIXAL is designed to fill gaps in existing tools commonly used by analysts to reason with and make sense of anomalies. PIXAL consists of three components: (1) an algorithm that finds patterns by aggregating multiple anomalous data points using first-order predicates, (2) a visualization tool that allows the analyst to build trust in the algorithmically-generated predicates by performing comparative and counterfactual analyses, and (3) a visualization tool that helps the analyst generate and validate hypotheses by exploring which features in the data most explain the anomalies. Finally, we present the results of a qualitative observational study with professional analysts. These results of the study indicate that PIXAL facilitates the anomaly reasoning process, allowing analysts to make sense of anomalies and generate hypotheses that are meaningful and actionable to business stakeholders.
A graphical representation of binary linear codes

A binary $[n,k]$-linear code $\mathcal{C}$ is a $k$-dimensional subspace of $\mathbb{F}_2^n$. For $\boldsymbol{x}\in \mathbb{F}_2^n$, the set $\boldsymbol{x}+\mathcal{C}$ is a coset of $\mathcal{C}$. In this work we study a partial ordering on the set of cosets of a binary linear code $\mathcal{C}$ of length $n$ and we construct a graph using the orphan structure of this code.
What You See is What You Classify: Black Box Attributions

An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature of such networks. Most existing approaches find such attributions either using activations and gradients or by repeatedly perturbing the input. We instead address this challenge by training a second deep network, the Explainer, to predict attributions for a pre-trained black-box classifier, the Explanandum. These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest. Our approach produces sharper and more boundary-precise masks when compared to the saliency maps generated by other methods. Moreover, unlike most existing approaches, ours is capable of directly generating very distinct class-specific masks in a single forward pass. This makes the proposed method very efficient during inference. We show that our attributions are superior to established methods both visually and quantitatively with respect to the PASCAL VOC-2007 and Microsoft COCO-2014 datasets.
Co-design of Embodied Neural Intelligence via Constrained Evolution

We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our main inspiration comes from evolution, which has led to wide variability and adaptation in Nature and has the potential to significantly improve design and behavior simultaneously. Our method takes an input agent with optional simple constraints such as leg parts that should not evolve or allowed ranges of changes. It uses physics-based simulation to determine its locomotion and finds a behavior policy for the input design, later used as a baseline for comparison. The agent is then randomly modified within the allowed ranges creating a new generation of several hundred agents. The generation is trained by transferring the previous policy, which significantly speeds up the training. The best-performing agents are selected, and a new generation is formed using their crossover and mutations. The next generations are then trained until satisfactory results are reached. We show a wide variety of evolved agents, and our results show that even with only 10% of changes, the overall performance of the evolved agents improves 50%. If more significant changes to the initial design are allowed, our experiments' performance improves even more to 150%. Contrary to related work, our co-design works on a single GPU and provides satisfactory results by training thousands of agents within one hour.
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation

In the field of Earth Observation (EO), Continual Learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these algorithms assume that data is (a) coming from a single source, and (b) fully labeled. Real-world EO datasets are instead characterized by a large heterogeneity (e.g., coming from aerial, satellite, or drone scenarios), and for the most part they are unlabeled, meaning they can be fully exploited only through the emerging Self-Supervised Learning (SSL) paradigm. For these reasons, in this paper we propose a new algorithm for merging SSL and CL for remote sensing applications, that we call Continual Barlow Twins (CBT). It combines the advantages of one of the simplest self-supervision techniques, i.e., Barlow Twins, with the Elastic Weight Consolidation method to avoid catastrophic forgetting. In addition, for the first time we evaluate SSL methods on a highly heterogeneous EO dataset, showing the effectiveness of these strategies on a novel combination of three almost non-overlapping domains datasets (airborne Potsdam dataset, satellite US3D dataset, and drone UAVid dataset), on a crucial downstream task in EO, i.e., semantic segmentation. Encouraging results show the superiority of SSL in this setting, and the effectiveness of creating an incremental effective pretrained feature extractor, based on ResNet50, without the need of relying on the complete availability of all the data, with a valuable saving of time and resources.
Spectral Analysis of Non-unitary Two-phase Quantum Walks in One Dimension

It is recently shown by Asahara-Funakawa-Seki-Tanaka that existing index theory for chirally symmetric (discrete-time) quantum walks can be extended to the setting of non-unitary quantum walks. More precisely, they consider a certain non-unitary variant of the two-phase split-step quantum walk as a concrete one-dimensional example, and give a complete classification of the associated index in their study. Note, however, that it remains uncertain whether or not their index gives a lower bound for the number of so-called topologically protected bound states unlike the setting of unitary quantum walks. In fact, the spectrum of a non-unitary operator can be any subset of the complex plane, and so the definition of such bound states is ambiguous in the non-unitary case. The purpose of the present article is to show that the simple use of transfer matrices naturally allows us to obtain an explicit formula for a topologically bound state associated with the non-unitary split-step quantum walk model mentioned above.
A Practical Evaluation of Remote Work Issues and the SolarWinds Breach Using the ISO/IEC 27001 Cybersecurity Framework and the ISO/IEC 27002 Guidelines

This article outlines the ISO/IEC 27001 framework and the ISO/IEC 27002 guidelines, focusing on their application to two cybersecurity issues. In light of the Covid-19 pandemic, remote work has become commonplace. The factors regarding remote work have led organizations to address the cybersecurity vulnerabilities associated with the activity. ISO/IEC 27001 is one such framework that can effectively mitigate the effects of a cyber-attack. The SolarWinds breach is another example that is discussed in this article. The piece demonstrates that had SolarWinds Corp. implemented the ISO/IEC 27001 framework, the effects of the breach could have been significantly mitigated. The result is that the ISO/IEC 27001 framework is an effective mechanism for alleviating the negative consequences of a cyber-attack.
BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework

Federated learning (FL) enables collaborative training of machine learning models without sharing training data. Traditional FL heavily relies on a trusted centralized server. Although decentralized FL eliminates the central dependence, it may worsen the other inherit problems faced by FL such as poisoning attacks and data representation leakage due to insufficient restrictions on the behavior of participants, and heavy communication cost, especially in fully decentralized scenarios, i.e., peer-to-peer (P2P) settings. In this paper, we propose a blockchain-based fully decentralized P2P framework for FL, called BlockDFL. It takes blockchain as the foundation, leveraging the proposed PBFT-based voting mechanism and two-layer scoring mechanism to coordinate FL among peer participants without mutual trust, while effectively defending against poisoning attacks. Gradient compression is introduced to lowering communication cost and prevent data from being reconstructed from transmitted model updates. Extensive experiments conducted on two real-world datasets exhibit that BlockDFL obtains competitive accuracy compared to centralized FL and can defend poisoning attacks while achieving efficiency and scalability. Especially when the proportion of malicious participants is as high as 40%, BlockDFL can still preserve the accuracy of FL, outperforming existing fully decentralized P2P FL frameworks based on blockchain.
A Survey of Sentiment Analysis in the Arabic Language

What the others think?" is an essential question for individuals, companies, and governments.All need to know the public opinions to make their decisions wisely.In the last decade, sentiment analysis and opinion mining have become one of the growing research areas.This paper presents the current state of sentiment analysis and opinion mining research.In particular, researches those deals with the Arabic language.We tried to cover the techniques and methods in sentiment analysis and the challenges in the field.We described the leading methods and approaches that have been introduced in the literature for Arabic Sentiment analysis and opinion mining.The main contributions of this paper include the sophisticated categorizations of a large number of recent articles about Arabic Sentiment analysis.These articles are categorized according to their contributions in the various sentiment analysis techniques.
Development of a liquefaction damage assessment system based only on seismic records

A new artificial neural network (ANN) system was developed for assessing the damage risk due to liquefaction during an earthquake, based only on seismic records.Compared to conventional technology that requires information about the soil properties and constitution, there is a great advantage in being able to estimate damage from seismic records alone.Further, if smalland medium-level intensity earthquake records can be used to determine the risk of liquefaction in advance, preventive measures can be taken to avoid it.Here, we introduce the results of applying this system to the liquefaction damage observed during the 2011 Great East Japan Disaster in the Kanto area of Japan.In addition, we have made the system available to the public via the Internet, so we will also discuss how to use it.
Indonesian Language Teaching and Education Among The Millennial Generation

Researchers are researching how and what factors can help millennials learn Indonesian correctly and adequately. In the end, Indonesian is not only taught in classes but also understood and loved and becomes an identity for millennial teenagers. This research will be carried out using a qualitative approach as the method. The data of this research were obtained through various results of previous studies and studies that are still related and related to this research. The millennial learning model requires teachers to have personality, social, educational, and professional competencies related to the linguistic field. Students are complex individuals about physical, emotional, spiritual, moral, cultural, social, and intellectual development. The proper steps include the correct use of media in educating the millennial generation through electronic tools and the internet. There are many challenges in educating the millennial generation, such as maintaining millennial youth’s values and positive characteristics for education. Finally, educators also need to develop the attitude of these millennials in their critical attitude through the provision of materials and the use of appropriate media.
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model

High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models. Although synchronous AR training is designed to have higher training efficiency, asynchronous PS training would be a better choice for training speed when there are stragglers (slow workers) in the shared cluster, especially under limited computing resources. An ideal way to take full advantage of these two training modes is to switch between them upon the cluster status. However, switching training modes often requires tuning hyper-parameters, which is extremely time- and resource-consuming. We find two obstacles to a tuning-free approach: the different distribution of the gradient values and the stale gradients from the stragglers. This paper proposes Global Batch gradients Aggregation (GBA) over PS, which aggregates and applies gradients with the same global batch size as the synchronous training. A token-control process is implemented to assemble the gradients and decay the gradients with severe staleness. We provide the convergence analysis to reveal that GBA has comparable convergence properties with the synchronous training, and demonstrate the robustness of GBA the recommendation models against the gradient staleness. Experiments on three industrial-scale recommendation tasks show that GBA is an effective tuning-free approach for switching. Compared to the state-of-the-art derived asynchronous training, GBA achieves up to 0.2% improvement on the AUC metric, which is significant for the recommendation models. Meanwhile, under the strained hardware resource, GBA speeds up at least 2.4x compared to synchronous training.
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation

Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the number of training points) remains a hurdle in applying GPs to large data. We present a fast posterior mean prediction algorithm called FastMuyGPs to address this shortcoming. FastMuyGPs is based upon the MuyGPs hyperparameter estimation algorithm and utilizes a combination of leave-one-out cross-validation, batching, nearest neighbors sparsification, and precomputation to provide scalable, fast GP prediction. We demonstrate several benchmarks wherein FastMuyGPs prediction attains superior accuracy and competitive or superior runtime to both deep neural networks and state-of-the-art scalable GP algorithms.
THE DEVELOPMENT OF E-MODULES BASED ON THE SCIENTIFIC APPROACH TO THE ADJUSTMENT OF LIVING CREATURES TO THEIR ENVIRONMENT

This study aims to find out how to create a science e-module, develop a science e-module, the feasibility of developing a science e-module, and improve student learning outcomes by using a science e-module based on a scientific approach to the adaptation of living things to their environment. Research Methods Research and Development. The research sample was carried out in class V SD YPKP 2 Sentani with a total of 25 students. The results showed that: 1) The steps of making e-modules as teaching materials through seven components of e-modules, namely the formulation of teaching objectives, instructions for using e-modules, activity sheets, student worksheets, evaluation sheets, navigation menus and other features. interactive features. 2) The feasibility of the science module based on a scientific approach on the adaptation of living things to their environment which consists of material validation components, media validation, teacher responses from YPKP 2 Sentani Elementary School teachers, and YPKP 2 Sentani Elementary School students' responses are very good, with an average module eligibility IPA is 92.82% with a very suitable category to be used as teaching material. The increase in student learning outcomes with the n-Gain test obtained an average of 0.6 in the medium category.
Markedness in Visual Semantic AI

We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or ethnicity. Given the option to label an image as "a photo of a person" or to select a label denoting race or ethnicity, CLIP chooses the "person" label 47.9% of the time for White individuals, compared with 5.0% or less for individuals who are Black, East Asian, Southeast Asian, Indian, or Latino or Hispanic. The model is more likely to rank the unmarked "person" label higher than labels denoting gender for Male individuals (26.7% of the time) vs. Female individuals (15.2% of the time). Age affects whether an individual is marked by the model: Female individuals under the age of 20 are more likely than Male individuals to be marked with a gender label, but less likely to be marked with an age label, while Female individuals over the age of 40 are more likely to be marked based on age than Male individuals. We also examine the self-similarity (mean pairwise cosine similarity) for each social group, where higher self-similarity denotes greater attention directed by CLIP to the shared characteristics (age, race, or gender) of the social group. As age increases, the self-similarity of representations of Female individuals increases at a higher rate than for Male individuals, with the disparity most pronounced at the "more than 70" age range. All ten of the most self-similar social groups are individuals under the age of 10 or over the age of 70, and six of the ten are Female individuals. Existing biases of self-similarity and markedness between Male and Female gender groups are further exacerbated when the groups compared are individuals who are White and Male and individuals who are Black and Female. Results indicate that CLIP reflects the biases of the language and society which produced its training data.
Rate of Peripheral Blood Smear Examination Service Provision and its Barriers among Public Hospitals in Southern Ethiopia: A Mixed-Methods Study

Introduction: Peripheral blood smear examination is a vital hematological test for diagnosis and monitoring of blood disorders. Despite its substantial role in the management of communicable and non-communicable diseases, its utilization is low. We aimed to assess the rate of peripheral blood smear examination service provision and its barriers among public hospitals in southern Ethiopia. Method: A cross-sectional study was conducted from January 01 to March 31, 2019, in five public hospitals of Southern Ethiopia. Socio-demographic data was collected from patients and healthcare providers. Peripheral blood smears were prepared using capillary blood collected from 423 patients with abnormal complete blood cell count. The smears were stained with Wright’s stain and examined under microscope by 1000X magnification to detect and identify morphological abnormality in the blood cells. A key informant interview was done with healthcare providers. Health facilities were assessed using a standard checklist. The quantitative data were analyzed by SPSS version 20 software. The qualitative data were transcribed, categorized, and thematically analyzed. The results of descriptive analyzes are presented in tables and figures. Result: The rate of provision of peripheral blood smear examination service was 11.6%. Nearly 90% of the eligible patients did not receive this service. Relatively better rate of service provision was observed among hospitals with essential resources for the service, and those participating in Hematology external quality assurance scheme. Lack of training, shortage of laboratory supplies, and inadequate supportive supervision were identified as barriers to regularly provide peripheral blood smear examination service. Conclusion: Rate of peripheral blood smear examination service provision is low in the area. A large proportion of eligible patients missed the deserved service. Adequate laboratory supplies, training, and continued supportive supervision should be considered to improve the provision of this vital service.
Explore Website

Think about the times you have thought of visiting a place and try to plan your travel and schedule it. It was always a hard task to do it by yourself, so most of us usually try to depend on travel groups or organizations. Explore is a website aimed at the travelers out there, the basic aim of the project is to bring out underrated places and spots around and nearby the place you are in or deciding to visit. Explore is also a social media for travelers where they can upload pictures of places they visited with the location Keywords: Travel, Web application, Nearby location tracker
FL Games: A Federated Learning Framework for Distribution Shifts

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, which can yield to catastrophic generalization on data from a different client, which represents a new domain. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL GAMES effectively resolves this challenge and exhibits smooth performance curves. Further, FL GAMES scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL GAMES achieves high out-of-distribution performance on various benchmarks.
Strategies for Terminology Translation in Interpretation of Amusement Facilities Installation Engineering

The domestic amusement equipment industry is developing rapidly. In the production and installation interpretation activities of imported amusement facilities, the translation of terms is very important. Combined with the author’s interpreting practice, the whole process of interpreting practice is studied and analyzed, and the corresponding terminology translation methods are proposed from the three links of pre-translation, translation and post-translation. The author analyzes the three major techniques of preparation before interpretation, the “four methods” during interpretation, and the summary after interpretation in the interpretation task of the amusement facility industry, forming a technical closed loop.