Reviewerly/exHarmony huggingface dataset:
A Study on the Quality Characteristics of Material Management Using Kano Model and Timko Coefficients

Purpose In cases of inventory management at small and medium-sized manufacturing companies, it is difficult to distinguish priorities of on-site inventory management, and the activities to identify and improve inventory management problems are not clear. To solve these problems, we suggest the methodology to present the quality characteristics that need improvement first. Methods We propose the quality characteristics that need improvement by applying the methodology. We define the quality attributes of inventory management and classify the quality characteristics through a survey applying Kano model. Using the results of the survey, Timko’s customer satisfaction coefficient is calculated to improve the quality of inventory management. Results The proposed model is validated through a case study of a shipbuilding equipment manufacturing company. The model identified and classified 11 attributes of the quality of inventory management. The reliability of these attributes are verified by the Cronbach’s alpha (α). Conclusion The proposed model contributes to identify key attributes that affect the manufacturer''s inventory management. The model can also provide priorities of the attributes that need to be improved first. Therefore, this research can help to clearly identify and progress the improvement activities that are really needed in practical manufacturing companies.
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families

Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning directed acyclic graphs, greedy algorithms are popular despite their worst-case exponential runtime. In practice, however, they are very efficient. We provide new insight into this phenomenon by studying a general greedy score-based algorithm for learning DAGs. Unlike edge-greedy algorithms such as the popular GES and hill-climbing algorithms, our approach is vertex-greedy and requires at most a polynomial number of score evaluations. We then show how recent polynomial-time algorithms for learning DAG models are a special case of this algorithm, thereby illustrating how these order-based algorithms can be rigourously interpreted as score-based algorithms. This observation suggests new score functions and optimality conditions based on the duality between Bregman divergences and exponential families, which we explore in detail. Explicit sample and computational complexity bounds are derived. Finally, we provide extensive experiments suggesting that this algorithm indeed optimizes the score in a variety of settings.
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

In the standard Markov decision process formalism, users specify tasks by writing down a reward function. However, in many scenarios, the user is unable to describe the task in words or numbers, but can readily provide examples of what the world would look like if the task were solved. Motivated by this observation, we derive a control algorithm from first principles that aims to visit states that have a high probability of leading to successful outcomes, given only examples of successful outcome states. Prior work has approached similar problem settings in a two-stage process, first learning an auxiliary reward function and then optimizing this reward function using another reinforcement learning algorithm. In contrast, we derive a method based on recursive classification that eschews auxiliary reward functions and instead directly learns a value function from transitions and successful outcomes. Our method therefore requires fewer hyperparameters to tune and lines of code to debug. We show that our method satisfies a new data-driven Bellman equation, where examples take the place of the typical reward function term. Experiments show that our approach outperforms prior methods that learn explicit reward functions.
Private learning implies quantum stability

Learning an unknown $n$-qubit quantum state $\rho$ is a fundamental challenge in quantum computing. Information-theoretically, it is known that tomography requires exponential in $n$ many copies of $\rho$ to estimate it up to trace distance. Motivated by computational learning theory, Aaronson et al. introduced many (weaker) learning models: the PAC model of learning states (Proceedings of Royal Society A'07), shadow tomography (STOC'18) for learning shadows of a state, a model that also requires learners to be differentially private (STOC'19) and the online model of learning states (NeurIPS'18). In these models it was shown that an unknown state can be learned approximately using linear-in-$n$ many copies of rho. But is there any relationship between these models? In this paper we prove a sequence of (information-theoretic) implications from differentially-private PAC learning, to communication complexity, to online learning and then to quantum stability. Our main result generalizes the recent work of Bun, Livni and Moran (Journal of the ACM'21) who showed that finite Littlestone dimension (of Boolean-valued concept classes) implies PAC learnability in the (approximate) differentially private (DP) setting. We first consider their work in the real-valued setting and further extend their techniques to the setting of learning quantum states. Key to our results is our generic quantum online learner, Robust Standard Optimal Algorithm (RSOA), which is robust to adversarial imprecision. We then show information-theoretic implications between DP learning quantum states in the PAC model, learnability of quantum states in the one-way communication model, online learning of quantum states, quantum stability (which is our conceptual contribution), various combinatorial parameters and give further applications to gentle shadow tomography and noisy quantum state learning.
Pirouette: Higher-Order Typed Functional Choreographies.

We present Pirouette, a language for typed higher-order functional choreographic programming. Pirouette offers programmers the ability to write a centralized functional program and compile it via endpoint projection into programs for each node in a distributed system. Moreover, Pirouette is defined generically over a (local) language of messages, and lifts guarantees about the message type system to its own. Message type soundness also guarantees deadlock freedom. All of our results are verified in Coq.
A Feasible Way to Enhance CBIR using Histogram of Oriented Gradients Method

Content Based Image Retrieval (CBIR) is a creating pattern in Digital Image Processing (DIP) for seeking and recovering the inquiry picture from extensive variety of databases. CBIR structure contain distinctive stages to think and match the highlights and discover the photos from the extensive scale picture databases dependent on visual substance, for example, Color, Shape and Texture as indicated by the client's advantage. To supply the sensible response to the customer inquiry, CBIR gives some keep running of work. Another strategy is arranged in this paper for shading picture ordering by building up the simplicity of SVM framework. A New strategy is proposed on this paper for hue photo ordering by means of misusing the effortlessness of the HOG technique. In this calculation, presently we have proposed a blend of shading, shape and surface highlights. In this methodology, the past work is improved to accomplish better accuracy. In this paper we propose HOG to upgrade picture quality and associated partition system to enhance result than base work.
Asynchronous Stochastic Optimization Robust to Arbitrary Delays

We consider stochastic optimization with delayed gradients where, at each time step $t$, the algorithm makes an update using a stale stochastic gradient from step $t - d_t$ for some arbitrary delay $d_t$. This setting abstracts asynchronous distributed optimization where a central server receives gradient updates computed by worker machines. These machines can experience computation and communication loads that might vary significantly over time. In the general non-convex smooth optimization setting, we give a simple and efficient algorithm that requires $O( \sigma^2/\epsilon^4 + \tau/\epsilon^2 )$ steps for finding an $\epsilon$-stationary point $x$, where $\tau$ is the \emph{average} delay $\smash{\frac{1}{T}\sum_{t=1}^T d_t}$ and $\sigma^2$ is the variance of the stochastic gradients. This improves over previous work, which showed that stochastic gradient decent achieves the same rate but with respect to the \emph{maximal} delay $\max_{t} d_t$, that can be significantly larger than the average delay especially in heterogeneous distributed systems. Our experiments demonstrate the efficacy and robustness of our algorithm in cases where the delay distribution is skewed or heavy-tailed.
Security flaw in Amazon's Ring Neighbors app exposed home addresses, users' locations

Amazon-owned Ring has suffered another privacy miscue, after a flaw in its Neighbors app exposed the exact locations and home addresses of some of its users.
Variational Inference for Continuous-Time Switching Dynamical Systems

Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jump process modulating a subordinated diffusion process. We provide the exact evolution equations for the prior and posterior marginal densities, the direct solutions of which are however computationally intractable. Therefore, we develop a new continuous-time variational inference algorithm, combining a Gaussian process approximation on the diffusion level with posterior inference for Markov jump processes. By minimizing the path-wise Kullback-Leibler divergence we obtain (i) Bayesian latent state estimates for arbitrary points on the real axis and(ii) point estimates of unknown system parameters, utilizing variational expectation maximization. We extensively evaluate our algorithm under the model assumption and for real-world examples.
Amazon workers, community members protest outside Jeff Bezos' NYC home for better COVID-19 protections

The workers are calling for state lawmakers to pass the?New York Health and Essential Rights Act, otherwise known as the NY HERO Act. The legislation would mandate the Departments of Labor and Health to implement minimum standards for workplace safety, enforceable through significant fines. Regulations would include?protocols on testing, PPE, social distancing, hand hygiene, disinfection?and engineering controls.
Block Chain and Edge Computing Base Design Secured Framework for Tender

Purpose: The objective of this paper is to create a secured website for government tender using block chain technologyMethodology: block chain is implemented for all login form, tenders information, agent’s information and users’ information and it will record all the information done by the user or agent so it is difficult to hack or cheat the system. The time stamp and block chain is implemented for all the historiesFindings: The Motive of the tender project is to provide a more secure framework for the government and avoid cor-ruption, security issues, and the main disadvantage is tender process is fully online, and the public does not have an idea about tender and what is happening inside the process. In order to avoid all those things we have created one new website and totally we are having five modules admin, agent, public, blockchain, cryp-tographic techniques. With the help of all these things, we will be implementing the government tender web-site with rules and regulations given by the government.Originality/value: AES Algorithm: To generate ciphertext, the Rijndael program employs a SP internet with many rounds. The numerical of circular is determined by the clue measurements.
PolyViT: Co-training Vision Transformers on Images, Videos and Audio

Can we train a single transformer model capable of processing multiple modalities and datasets, whilst sharing almost all of its learnable parameters? We present PolyViT, a model trained on image, audio and video which answers this question. By co-training different tasks on a single modality, we are able to improve the accuracy of each individual task and achieve state-of-the-art results on 5 standard video- and audio-classification datasets. Co-training PolyViT on multiple modalities and tasks leads to a model that is even more parameter-efficient, and learns representations that generalize across multiple domains. Moreover, we show that co-training is simple and practical to implement, as we do not need to tune hyperparameters for each combination of datasets, but can simply adapt those from standard, single-task training.
Forward and Inverse Kinematics Control of a Collaborative 6-DOF Robot for an Augmented Reality Educational System

This research work describes the mathematical framework and development procedure for the implementation of the direct and inverse kinematics equations of a six degrees of freedom cobot under the Unity platform, which is used to generate the augmented reality version of the cobot; In the same way, the Vuforia library is also used to create the multiplatform application that can run on conventional computers or any portable electronic devices whit the Android system. Kinematic solutions for the techman® 6 DOF robot were not found in the literature, the direct and inverse kinematic control of the robot was achieved with the presented solution. The developed application was validated and verified for its intended use, that is educational and distance learning of robotics and virtual and augmented reality technologies.
Primary and secondary aspects of musical gestures in live coding performance

Embodied interaction in live coding performance has been reported and is particularly present in dance performance. Here, we review studies from music phychology and perception along with studies from live coding and human-computer interaction. We examined how the live coder may experience primary aspects of musical gestures and we discuss how music listening and musical imagery may trigger mental models of gestural unfoldings. Disciplinary gaps between music psychology and the live coding community are discussed.
Arabic Optical Character Recognition (OCR) Technology at Qatar National Library

Optical Character Recognition (OCR) is the practice of extracting text from images. The process itself is becoming popular in terms of usage and research, as it spans multiple areas of science, including image processing, machine learning, information retrieval, and...
Residential Plot Record Management System for Windhoek City

In today’s society efficiency is what we yearn for in all aspects of our daily lives with ever changing technology at our disposal. The current manual approach that is being used by the City of Windhoek (CoW) is very tedious and creates a lot of paperwork. A qualitative research design was applied to represent a smaller population of the CoWs for data collection and analysis. The study investigated how the CoW advertises, processes and distributes residential plots amongst its residents, to help curb the unevenness of plot allocation and speed up the entire application approval process. The system allows applicants to apply for residential plots online. Once a user applies, the system checks how many residential plots/houses are owned by an individual. Depending on the threshold put in place by the CoW, applicants will be automatically put on a waiting list or given priority.
The Impact on Applying Numbered Head Together (NHT) as Teaching Strategy to Increase the Students’ Reading Comprehension

The purpose of this study was to determine the impact on applying the Numbered Head Together (NHT) strategy on eighth-grade students' reading comprehension of recount material at SMPN 10 Kota Serang. For the study, the researchers conducted quantitative research with a true experimental design. The participants in this study were eighth grade students of SMPN 10 Kota Serang. Cluster random sampling was used to choose the sample for this study. There were 30 students in each of the two classes participated in this study: VIII B as the experimental class and VIII G as the control class. The experimental class was instructed using the NHT strategy, while the control group was instructed using the lecturing technique. The data were gathered by pre- and post-tests, and the hypothesis was tested by using the t-test procedure. The t-test calculation revealed that t-count (9.22) was higher than t-table (2.00) with a significance of 0.05. This indicates that the alternative hypothesis (Ha) has been accepted. It can be determined that applying the NHT strategy had a substantial impact on students' reading comprehension of recount text in eighth grade at SMPN 10 Kota Serang.
Grey wolf optimization algorithm for hierarchical document clustering

In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as Rand, Purity, F-Measure, and entropy, indicates that the proposed M-GW O algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on Purity metric, 75% better than all algorithms using F-Measure metric, and 100% based on entropy metric).
Text Counterfactuals via Latent Optimization and Shapley-Guided Search

We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model’s prediction. White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input. Optimization-based approaches become difficult in the language domain due to the discrete nature of text. We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations. We additionally use Shapley values to estimate the combinatoric effect of multiple changes. We then use these estimates to guide a beam search for the final counterfactual text. We achieve favorable performance compared to recent white-box and black-box baselines using human and automatic evaluations. Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.
LibGuides: Information Literacy for Children: Kindergarten

This is a resource for teachers and librarians to find information on teaching Information Literacy to children.
A Study on the Impact of COVID-19 SMS on Preventive Behavior-Focused on the Confirmed Patients Notification SMS-

Background/Objectives: The Republic of Korea is still a good example of k-prevention which SMS played a big role in real time. Therefore, the effect of SMS and covid 19 preventive behavior was studied.Methods/Statistical analysis: The survey respondents were focused on university students in the Chungnam region of Korea. The number of survey respondents was 135, 53 male students, 39.3%, and 82 female students, 60.7%. Looking at the distribution by grade, the fourth year was the most with 66 students and 48.9%, followed by the third year with 29 students and 21.5%, and the second year with 22 students and 16.3%.Findings: Looking at the table3 shaded areas, the CFA result of AVE.5 or less and CR.7 or less, perceived sensitivity and perceived disability, did not secure concentration validity. -As a result of CFA, the main concepts for this study were found to be inappropriate for analyzing the research model. Accordingly, analysis is performed using regression analysis rather than structural equations. The diagonal matrix for each factor in italics is the variance extraction index. The correlation between subjective norm and behavioral intention was highest (r=.703), perceived benefit (r=.655), perceived seriousness (r=.493), perceived sensitivity (r=.491) It also shows a significant relationship with the back. However, there was no significant relationship between perceived disability and behavioral intention. As a result of conducting multiple regression analysis of the concepts constituting the health belief model as an independent variable with behavioral intention as a dependent variable, self-efficacy (β=.439, p<.001) and perceived benefit (β=.321, p) Only<.001) appears to have a significant effect.Improvements/Applications: The SMS is helpful in preventing one's own health that the perceived benefit affects the preventive behavior. Therefore, the local governments that send the SMS is helpful in the position of the recipient.
Deep learning for computer vision in the art domain

In recent years, computer vision algorithms based on machine learning have seen rapid development.In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes.Nowadays other fields such as the field of cultural heritage, where an abundance of data is available, also get into the focus of research.In the line of current research endeavours, we collaborated with the Getty Research Institute which provided us with a challenging dataset, containing images of paintings and drawings.In this technical report, we present the results of the seminar ``Deep Learning for Computer Vision''.In this seminar, students of the Hasso Plattner Institute evaluated state-of-the-art approaches for image classification, object detection and image recognition on the dataset of the Getty Research Institute.The main challenge when applying modern computer vision methods to the available data is the availability of annotated training data, as the dataset provided by the Getty Research Institute does not contain a sufficient amount of annotated samples for the training of deep neural networks.However, throughout the report we show that it is possible to achieve satisfying to very good results, when using further publicly available datasets, such as the WikiArt dataset, for the training of machine learning models.
Safe Path Recommender based on Crime Statistics using Distributed Database

This research aims to implement safe path using crime statistics of Delhi Ncr on a Google map through the combined use of distributed database concepts as well as using some machine learning algorithms. The application can be deployed as a web application and it is crucial for prediction and depiction of safe route using the information entered by the user in the source and destination fields.