Reviewerly/exHarmony huggingface dataset:
Experiencias de cinco madres mexicanas con la enseñanza de las matemáticas a distancia de sus hijos e hijas

During these unique times caused by COVID-19, there are few opportunities for parents to share their opinions. This paper focuses on the experiences of five mothers of Mexican background with their children’s online learning during the 2020-21 school year in Tucson, AZ, United States. Three themes stand out from our conversations with the mothers: the uncertainty about their role in the online teaching modality, the tension with the parents’ work and activities in relation to this new modality, and the changes in how their children are learning mathematics. The pandemic allowed the mothers to have a closer look at their children’s instruction. We suggest taking advantage of this extraordinary situation to promote greater communication in the future between home and school.
Symmetric Cryptographic Framework for Network Security

In this rapidly developing digital environment, a single cryptographic algorithm becomes inefficient and incapable to hold the secrecy of data. A symmetric cryptographic framework is proposed which provides the platform for using the existing and future algorithms in a secured manner. In this research paper, totally six algorithms have been taken into the framework, two algorithms for text, three algorithms for image, and one algorithm for video. The algorithms are grouped into the proposed symmetric encryption framework which provides better network security for the adopted environment. Cryptanalysis and brute force attack have been done to assess the strength of the algorithms incorporated in the framework. Character repetition frequency and brute force attack are analyzed for text encryption algorithms. Mean values, Entropy measure, Differential attack and brute force attack are analyzed and used to assess the reliability of the image and video encryption algorithms. The framework is designed in such a way to adopt the existing and future algorithms. The proposed framework provides a bridge to achieve quality, upgradability, maintainability, and longer usability in applied applications..
Clustering algorithm for mixed attributes data based on glowworm swarm optimisation algorithm and K-prototypes algorithm

The main purpose of this research is to improve the clustering accuracy of mixed attributes data. Therefore, glowworm swarm optimisation (GSO) algorithm is introduced into K-prototypes algorithm to...
Computational Intelligence-based Evaluation of a 3-DOF Robotic-arm Forward Kinematics

Robotic manipulator- forward Kinematics involves the assurance of end-effector arrangements from connecting joint boundaries. The traditional mathematical calculation of controller forward -Kinematics is monotonous and tedious. Accordingly, it is important to execute a strategy that precisely performs forward energy while wiping out the disadvantages of the mathematical calculation technique. Versatile Neuro-Fuzzy Inference System (ANFIS) is a computational knowledge strategy that has been effectively executed for expectation purposes in assorted logical orders. This present examination's essential goal was to evaluate the productivity of ANFIS in foreseeing 3-levels of opportunity automated controller Cartesian directions from connecting joint boundaries. A speculative 3-level of opportunity automated controller has been considered in this investigation. Model preparing information has been obtained by mathematical forward kinematics calculation of the controller's end effector arrangements. Nine datasets have been utilized for model preparing, while five datasets have been utilized for model testing or approval. The ANFIS model's precision has been surveyed by figuring the Mean outright Percentage Error (MAPE) between the real and anticipated end-effector Cartesian directions. Because of Mean Absolute Percentage Error (MAPE), the created ANFIS model has forecast correctness’s of 63.35% and 80.07% in foreseeing x-directions and y-organizes, separately. Accordingly, ANFIS can be dependably executed as a commendable substitute for the customary arithmetical calculation method in anticipating controller Cartesian directions. It is suggested that the precision of other computational knowledge methods like Particle Swarm Optimization (PSO) and Support Vector Machines (SVM) be evaluated.
Education in Times of Crisis. Dialogues Around the Human

This article is the result of reflections on the place of education in times of pandemic from the role of the student. The conversation is situated in educational symmetries and asymmetries in these times of tensions, crises, and social, educational, economic, and political changes. It is then observed that education has changed and will continue to change from the physical space, relationships, teaching methods, curricula, and communications, which invites us to focus on the fragility of the human. Thus, we propose to address four moments: the first one considers the relationship between society and school; the second inquires about the place of the human in education; the third invites us to reflect on education in times of pandemic, as a topic to reconsider; and the fourth addresses a questioning on the type of school we should start talking about. We then reflect on knowledge, practices, and discourses that have been concurring in the current change of era, and on the tensions and dynamics that affect the educational act and, with it, the human experience.
Bridging the Informal and Formal Learning Spaces with WhatsApp

WhatsApp is the most popular mobile instant messaging (IM) app in the global south. Hence, its use in informal and formal learning spaces has significant potential and is worthy of investigation. This study explored how University faculty and students in Namibia and India used WhatsApp for learning support and to bridge the gap between formal and informal learning. From a total of 182 participants, results reveal that WhatsApp has the potential to engage users in an informal and formal learning support and delivery environment. Learning designers and those responsible for professional development need to take note of this instant messaging app and experiment with various culturally contextual learning design models to support learning.
Intuitive and subtle motion-anticipatory auditory cues reduce motion sickness in self-driving cars

Motion sickness may be a factor detrimental to the experience of self-driving cars. This study investigates whether auditory displays can support in lowering passengers' feeling of motion sickness ...
End-to-end SLA management in federated clouds

Cloud federation is a concept that collectively combines segregated cloud services to create an extended pool of resources for clouds to competently deliver their promised level of services. This p...
Evaluation and selection of cloud deployment models using fuzzy combinative distance-based assessment in an academic institute

Cloud computing (CC) is an innovative technology that is completely transforming the way of individuals to collect, share and approach their data files. Although, CC technology provides many benefi...
ANDROID-BASED APPLICATION OF LPPM STMIK PRINGSEWU ARCHIVES TO IMPROVE DATA INTEGRATION OF LECTURER PERFORMANCE

In the era of globalization, advances in information and communication technology affect human activities which have undergone many changes and developments. The development of science in the field of technology today has created new applications with more efficient technology productivity and costs. The rapid development of technology today has affected the work in various organizations. In order to support the performance in an organization, it is not only required the quality of reliable human resources but also required appropriate technological advances. All of this aims to provide convenience for technology users in carrying out their work. Applications that are built can be used via mobile phones or laptops based on browsers to display information in the form of text and images which are accessed using the internet. This application development uses waterfall model information system design and research flow uses Fishbone Mapping Chart model. The development of an Android-based LPPM STMIK Pringsewu Archive Data Application will be integrated with Lecturer Data so that it will facilitate monitoring and archiving Lecturer Data and increasing the Lecturer Performance Index at STMIK Pringsewu. Integrated data focuses on activities related to research and community service.
Gaze-based Multimodal Meaning Recovery for Noisy / Complex Environments

Reference resolution is an important problem that has enormous practical implications in daily life, for example in recovering the intended meaning in communication when the environment is noisy (acoustic noise in the spoken channel, or clutter / occlusion in the visual world). Recent literature indicates that cross-modal processing of all the contributive modalities improves the reference resolution in such settings. In this paper, we investigate the contribution of the eye-tracking methodology, a substantial but underrepresented component of face-to-face communication in NLP systems, to recover the meaning in noisy settings. We integrate gaze features into state-of-the-art language models and test the model on data where parts of the sentences are masked, mimicking noise in the acoustic channel. The results indicate that eye movements can compensate for the missing information in the situation and support communication when language and visual modality fail.
A study on recommender system considering diversity of items based on LDA

With the rapid development of information technology, a recommender system making use of users' behaviour data, such as browsing history or ratings for items, is now one of the important tools for ...
An analytical model of website relationships based on browsing history embedding considerations of page transitions

In recent years, obtaining a large amount of information and receiving various services through websites has become possible. Consequently, web browsing activities are increasing and numerous compa...
Intelligent Task Reminder and Messaging Application: (I-REM)

Now-a-days Google is used to ask questions related to studies , to set an alarm, location and many more, but when it comes to a person who is layman to this era of technology its tuff to perform such activity so observantly, for example elderly people, handicapped etc, Our finding is a spark of idea which eventually reaches to an interesting question what if there is an application which has the capability to store a reminder using voice processing engine and when the date arrives it prompts the content of the reminder via Text to Speech engine. Besides this, the application is built to send a message set for a particular time and a particular date. Alongside the user can cancel a scheduled message as well, The application can also track the users who will be using it, the application comprise Global Positioning System which will be able to broadcast or show timeline of the user’s location. Thus, the idea of I- REM (Intelligent Reminder) can become a new future, and this study of ours can become a fruitful pedagogy for novel users and scholars.
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory

Schr\odinger Bridge (SB) is an optimal transport problem that has receivedincreasing attention in deep generative modeling for its mathematicalflexibility compared to the Scored-based Generative Model (SGM). However, itremains unclear whether the optimization principle of SB relates to the moderntraining of deep generative models, which often rely on constructingparameterized log-likelihood objectives.This raises questions on thesuitability of SB models as a principled alternative for generativeapplications. In this work, we present a novel computational framework forlikelihood training of SB models grounded on Forward-Backward StochasticDifferential Equations Theory -- a mathematical methodology appeared instochastic optimal control that transforms the optimality condition of SB intoa set of SDEs. Crucially, these SDEs can be used to construct the likelihoodobjectives for SB that, surprisingly, generalizes the ones for SGM as specialcases. This leads to a new optimization principle that inherits the same SBoptimality yet without losing applications of modern generative trainingtechniques, and we show that the resulting training algorithm achievescomparable results on generating realistic images on MNIST, CelebA, andCIFAR10.
Modified silhouette based segmentation outperforming in the presence of intensity inhomogeneity in the hyperspectral images

In the real world the intensity inhomogeneity occurs presenting considerable challenges in the field of segmentation of images. In the arena of computer vision and processing of images, the method ...
Speech Toxicity Analysis: A New Spoken Language Processing Task.

Toxic speech, also known as hate speech, is regarded as one of the crucial issues plaguing online social media today. Most recent work on toxic speech detection is constrained to the modality of text with no existing work on toxicity detection from spoken utterances. In this paper, we propose a new Spoken Language Processing task of detecting toxicity from spoken speech. We introduce DeToxy, the first publicly available toxicity annotated dataset for English speech, sourced from various openly available speech databases, consisting of over 2 million utterances. Finally, we also provide analysis on how a spoken speech corpus annotated for toxicity can help facilitate the development of E2E models which better capture various prosodic cues in speech, thereby boosting toxicity classification on spoken utterances.
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models

The continuous growth in both size and training data for modern Deep Neural Networks (DNNs) models has led to training tasks taking days or even months. Distributed training is a solution to reduce training time by splitting the task across multiple NPUs (e.g., GPU/TPU). However, distributed training adds communication overhead between the NPUs in order to synchronize the gradients and/or activation, depending on the parallelization strategy. In today's datacenters, for training at scale, NPUs are connected through multi-dimensional interconnection links with different bandwidth and latency. Hence, keeping all network dimensions busy and maximizing the network BW is a challenging task in such a hybrid network environment, as this work identifies. We propose Themis, a novel collective scheduling scheme that dynamically schedules collectives (divided into chunks) to balance the communication loads across all dimensions, further improving the network BW utilization. Our results show that on average, Themis can improve the network BW utilization of single All-Reduce by 1.88x (2.92x max), and improve the end-to-end training iteration performance of real workloads such as ResNet-50, GNMT, DLRM, and Transformer- 1T by 1.49x (1.96x max), 1.41x (1.81x max), 1.42x (1.80x max), and 1.35x (1.78x max), respectively.
A machine learning based predictive model for time-series modelling and analysis

Time series modelling and forecasting is an essential field of supervised machine learning because of its appreciated contributions into numerous research and real-life applications including the c...
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning

Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult. To solve this problem, it is important to be able to discriminate targets through semantic understanding. In this paper, we propose goal-aware cross-entropy (GACE) loss, that can be utilized in a self-supervised way using auto-labeled goal states alongside reinforcement learning. Based on the loss, we then devise goal-discriminative attention networks (GDAN) which utilize the goal-relevant information to focus on the given instruction. We evaluate the proposed methods on visual navigation and robot arm manipulation tasks with multi-target environments and show that GDAN outperforms the state-of-the-art methods in terms of task success ratio, sample efficiency, and generalization. Additionally, qualitative analyses demonstrate that our proposed method can help the agent become aware of and focus on the given instruction clearly, promoting goal-directed behavior.
Monitoramento da saúde dos trabalhadores da SESAB frente à pandemia da COVID-19

Infection with SARS-CoV-2 is a health crisis worldwide, requiring countries to develop a set of strategies to promote and protect the population. Not unlike, the involvement by health workers raised the need for protective and preventive measures in order to prevent the spread of the virus in the workplace. In this sense, we intend to report the process of monitoring actions for the prevention and surveillance of workers 'health based on the restructuring of DGTES' work processes. This is a descriptive, qualitative study, with quantitative elements of the experience report type that portrays the process of reorganizing the board in response to the organizational needs and interventions installed by the pandemic. In view of this, DGTES started to work with 4 monitoring and advisory teams, totaling 16 workers responsible for the elaboration, dissemination, implementation and monitoring of intervention strategies, control and protection of workers' health, among them, Contingency Plan for Workers, 06 technical notes, 02 technical manuals for the implantation of the testing centers, implantation and support to 76 testing centers and 29 newsletters. The monitoring of suspected and confirmed cases of COVID-19 among health workers made it possible to know the profile of illness of these professionals, the local incidences and conditions of vulnerability, giving rise to promotion and protection strategies, as well as breaking the chain of transmission of the new coronavirus , thus reducing absenteeism and unfavorable outcomes among FTS, demonstrating the relevance of the worker's health network and teamwork in times of crisis.
Predicting the amount of files required to fix a bug

This paper proposes a classifier that can predict the amount of files required to fix a bug. A newly incoming bug can be classified into one of the three classes (categories): Small; Medium; or Lar...
Analysis of Sharpe Ratio with Ultra High Dimensions: Residual Based Nodewise Regression in Factor Models.

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio. Since the nodewise regression is not feasible due to the unknown nature of idiosyncratic errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix of errors which is consistent even when number of assets, p, exceeds the time span of the portfolio, n. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors and p>n. We show that: (1) with p>n, the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios; and (2) with p>n, the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (3) with p<<n, the maximum-out-of-sample Sharpe ratio estimator is consistent.
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models?

Explaining how important each input feature is to a classifier's decision is critical in high-stake applications. An underlying principle behind dozens of explanation methods is to take the prediction difference between before-and-after an input feature (here, a token) is removed as its attribution - the individual treatment effect in causal inference. A recent method called Input Marginalization (IM) (Kim et al., 2020) uses BERT to replace a token - i.e. simulating the do(.) operator - yielding more plausible counterfactuals. However, our rigorous evaluation using five metrics and on three datasets found IM explanations to be consistently more biased, less accurate, and less plausible than those derived from simply deleting a word.
A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction

Despite decades of research, understanding human manipulation activities is,and has always been, one of the most attractive and challenging research topicsin computer vision and robotics. Recognition and prediction of observed humanmanipulation actions have their roots in the applications related to, forinstance, human-robot interaction and robot learning from demonstration. Thecurrent research trend heavily relies on advanced convolutional neural networksto process the structured Euclidean data, such as RGB camera images. Thesenetworks, however, come with immense computational complexity to be able toprocess high dimensional raw data. Different from the related works, we here introduce a deep graph autoencoderto jointly learn recognition and prediction of manipulation tasks from symbolicscene graphs, instead of relying on the structured Euclidean data. Our networkhas a variational autoencoder structure with two branches: one for identifyingthe input graph type and one for predicting the future graphs. The input of theproposed network is a set of semantic graphs which store the spatial relationsbetween subjects and objects in the scene. The network output is a label setrepresenting the detected and predicted class types. We benchmark our new modelagainst different state-of-the-art methods on two different datasets, MANIACand MSRC-9, and show that our proposed model can achieve better performance. Wealso release our source code https://github.com/gamzeakyol/GNet.
Narrow passage RRT*: a new variant of RRT*

Rapidly exploring random tree star (RRT*) has been widely used for optimal path planning for the reason that can solve high degrees of freedom problems. However, this method has many limitations su...
Confidence-Aware Active Feedback for Efficient Instance Search

Relevance feedback is widely used in instance search (INS) tasks to further refine imperfect ranking results, but it often comes with low interaction efficiency. Active learning (AL) technique has achieved great success in improving annotation efficiency in classification tasks. However, considering irrelevant samples' diversity and class imbalance in INS tasks, existing AL methods cannot always select the most suitable feedback candidates for INS problems. In addition, they are often too computationally complex to be applied in interactive INS scenario. To address the above problems, we propose a confidence-aware active feedback (CAAF) method that can efficiently select the most valuable feedback candidates to improve the re-ranking performance. Specifically, inspired by the explicit sample difficulty modeling in self-paced learning, we utilize a pairwise manifold ranking loss to evaluate the ranking confidence of each unlabeled sample, and formulate the INS process as a confidence-weighted manifold ranking problem. Furthermore, we introduce an approximate optimization scheme to simplify the solution from QP problems with constraints to closed-form expressions, and selects only the top-K samples in the initial ranking list for INS, so that CAAF is able to handle large-scale INS tasks in a short period of time. Extensive experiments on both image and video INS tasks demonstrate the effectiveness of the proposed CAAF method. In particular, CAAF outperforms the first-place record in the public large-scale video INS evaluation of TRECVID 2021.