Reviewerly/exHarmony huggingface dataset:
SuperCloud Lite in the Cloud - lightweight, secure, self-service, on-demand mechanisms for creating customizable research computing environments

We describe and examine an automation for deploying on-demand, OAuth2 secured virtual machine instances. Our approach does not require any expert security and web service knowledge to create a secure instance. The approach allows non-experts to launch web-accessible virtual machine services that are automatically secured through OAuth2 authentication, an authentication standard widely employed in academic and enterprise environments. We demonstrate the approach through an example of creating secure commercial cloud instances of the MIT SuperCloud modern research computing oriented software stack. A small example of a use case is examined and compared with native MIT SuperCloud experience as a preliminary evaluation. The example illustrates several useful features. It retains OAuth2 security guarantees and leverages a simple OAuth2 proxy architecture that in turn employs simple DNS based service limits to manage access to the proxy service. The system has the potential to provide a default secure environment in which access is, in theory, limited to a narrow trust circle. It leverages WebSockets to provide a pure browser enabled, zero install base service. For the user, it is entirely self-service so that a non-expert, non-privileged user can launch instances, while supporting access to a familiar environment on a broad selection of hardware, including high-end GPUs and isolated bare-metal resources. The environment includes pre-configured browser based desktop GUI and notebook configurations. It can provide the option of end-user privileged access to the VM for flexible customization. It integrates with a simplified cost-monitoring and machine management framework that provides visibility to commercial cloud charges and some budget guard rails, and supports instance stop, restart, and pausing features to allow intermittent use and cost reduction.
On number of substitutions of vector space over finite field with affine approximations with given accuracy

Nonlinear mapping of a vector space over a finite field in vector space over the same field and, in particular, the non-linearity the permutation space is defined in terms of the Hamming distance to sets of affine mappings. For permutations of an n-dimensional vector space over a field of q elements, an upper bound for the number permutations with non-linearity, not higher than a given value r, where 0 ≤ r &lt; q&lt;sup&gt;n&lt;/sup&gt; - q&lt;sup&gt;n-1&lt;/sup&gt;, and for r &lt; (q&lt;sup&gt;n&lt;/sup&gt; - q&lt;sup&gt;n-1&lt;/sup&gt;)/2, the exact the value of the specified number, which allows us to draw conclusions about distribution of nonlinearity on the set of substitutions.
Using Information and Communication Technology to Enforce Non-Payment of Water Bills: The Case of Water Supply and Sanitation Authorities in Tanzania

Sanitation and having access to clean water are human rights. To serve everyone, water and sanitation service providers must be able to run and maintain a profit. However, this capability is frequently jeopardized by missed or late water bill payments. The ability of utilities to provide appropriate service is directly impacted by non-payment, which also hinders the fulfillment of human right regarding to water and sanitation services. In this study, a mechanism for enforcing prompt payment of water bills which utilize the Electricity Prepaid Metering System is designed. In order to put the designed mechanism into practice, a prototype application has been created. Through in-person interviews with system administrators, data about the Electricity Prepaid Metering System and Water Management Information System was gathered. The proposed enforcement mechanism was designed with the use of a Design Science Research technique. The findings indicate that people who owe unpaid water bills are automatically denied access to electricity services
Museum in Absentia the Staging of the Image

In our epoch, the advent of the digital, following the paradigm of technical reproducibility on which Walter Benjamin reflected, has become an integral part of both traditional and virtual exhibition spaces, significantly modifying the forms of representation, communication and fruition of cultural heritage. Thanks to user-centered strategies, institutions, companies and the many places of knowledge have conveyed through websites and social networks, to meet the renewed needs of consumers. Now more than ever, museums have invested in the transformation of their identity image and in the educational component, configuring themselves as cultural attractors and disseminators at different levels of education and, above all, at the service of a diffusion without geographical boundaries. The use of ICTs has fostered the search for new forms of learning and a different way of interacting with the contents of knowledge. In particular, the virtual museum aims to transmit culture through the digital image that becomes the synthesis of a visual and textual language, easily accessible to everyone. Following the current pandemic, the present paper aims to reflect on the inevitable metamorphosis of the museum institution, in which the role of the digital image becomes fundamental for the scientific and educational scientific and educational dissemination of culture. The current strategies of edutainment are based on the fact that «the human predisposition to enjoy the very act of looking drives us to be moved» [17, p. 4].
Spectral Dual-Channel Encoding for Image Dehazing

In recent years, deep learning-based dehazing models have presented a momentum of dramatic growth. Unfortunately, most deep learning-based approaches heavily rely on synthetically hazed images for model training, which makes these methods brittle to restore hazy images taken from real-world scenes, due to the sample distribution discrepancy between synthetic and realistic images. Although some attempts have been made to overcome this difficulty by augmenting image spatial features with spectral features, the power of the spectral features still remains underutilized. In this paper, we propose the Spectral Dual-Channel Encoding (SDCE) framework for high-quality image dehazing, by unleashing the power of spectral feature encoding. We argue that hazes impose more adverse impacts on high-frequency image features (e.g., outlines and textures) than low-frequency features (e.g., colors), with theoretical and empirical justifications. To better restore hazed high- and low-frequency features, we decompose the hazed images into high- and low-frequency feature components with spectral dual-channel encoding and respectively design effective neural network architectures to recover hazed images on the two feature components. To be specific, we recover the low-frequency feature components with an encoder-decoder, while we specially design a high-frequency aggregation component (HFAC) to recover hazed images on high-frequency feature components, by referring to neighboring feature distributions. We conduct extensive experiments on four real-world image dehazing benchmarks. The experimental results show that our proposed SDCE framework outperforms the state-of-the-art baselines significantly, with an average 4.4% improvement in PSNR and an average 7.7% gain in SSIM.
Reimagining Cognitive Visualizations: Designers’ Leading Edge &amp; Innovative Power

This paper challenges the assumption that virtual environments must mimic the physical world when studying bias and phobias and presents evidence to support that designers must join psychologists and engineers in developing virtual environments for mental health and wellbeing. Even when aware of their biases, humans rarely admit them because they can unveil upsetting truths. When investigating discrimination in lab experiments, study participants are aware of being directly observed, which causes them to act in ways contrary to their typical behavior. Cognitive studies have demonstrated that Virtual Reality (VR) can be an ideal tool to study human perceptions in controlled environments where it is challenging to enact that situation in real life. The study of human behavior through VR simulations is not new to social psychologists. The lack of knowledge of the technological tools and reliance on external firms to create photo-realistic VR environments have delivered less than ideal results. Little attention has been given to VR worlds’ design and artistic implications when exploring mental health and wellbeing. In this paper, case studies on non-photorealistic representation in VR demonstrate that designers’ spatial and visual strategies can facilitate the achievement of the sense of presence and immersion inside immersive simulations for mental health and wellbeing.
Hierarchical Color Encoding for Progressive Image Transmission in Underwater Environments

This letter presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Progressive Hierarchical Image Encoding</i> (PHIE) approach aimed at progressively encoding underwater images and, thus, helping in transmitting them in unreliable, low bandwidth, subaquatic communication channels. The proposal splits an image into several, very small, fragments that can be transmitted separately. The receiver can reconstruct the image after receiving the first fragment and improve the reconstruction quality with each received fragment up to a perfect, lossless, reconstruction. An experimental comparative study using real underwater imagery and showing the benefits and drawbacks of the proposed approach is also presented.
KEMIM: Knowledge-Enhanced User Multi-Interest Modeling for Recommender Systems

Researchers typically leverage side information, such as social networks or the knowledge graph, to overcome the sparsity and cold start problem in collaborative filtering. To tackle the limitations of existing user interest modeling, we propose a knowledge-enhanced user multi-interest modeling for recommender systems (KEMIM). First, we utilize the user-item historical interaction as the knowledge graph’s head entity to create a user’s explicit interests and leverage the relationship path to expand the user’s potential interests through connections in the knowledge graph. Second, considering the diversity of a user’s interests, we adopt an attention mechanism to learn the user’s attention to each historical interaction and each potential interest. Third, we combine the user’s attribute features with interests to solve the cold start problem effectively. With the knowledge graph’s structural data, KEMIM could describe the features of users at a fine granularity and provide explainable recommendation results to users. In this study, we conduct an in-depth empirical evaluation across three open datasets for two different recommendation tasks: Click-Through rate (CTR) prediction and Top-K recommendation. The experimental findings demonstrate that KEMIM outperforms several state-of-the-art baselines.
A QoS-Aware Clustering Based Multi-Layer Model for Web Service Selection

The rapid proliferation of new web services over the last decade has led to an increase in functionally identical services, making the service selection system more challenging. In this work, we propose a web service selection model consisting of two layers, which we call CPSky. The upper layer, called the Prefilter layer, filters and allows only potent services to participate in the selection process. Pruning and clustering based on the quality of service form the basis of prefiltering. The bottom layer, called the Selection layer, uses the proposed Skyline-Plus approach to select the appropriate web service. The existing skyline technique always generates the same set of skyline services and does not consider the end-user requested QoS. Additionally, the skyline results in a non-dominated set of services without any ordering of services. To address these issues, we propose a modified skyline, which we call Skyline-Plus. The Selection layer also identifies replaceable web services using the Pearson correlation coefficient. The proposed approach's efficacy is validated through experimental evaluation on a real-world dataset using four performance evaluation parameters. The experimental results show that the proposed approach performs better than existing similar approaches in terms of efficiency and end-user satisfaction.
Passive RFID-Based Control Interface Integrated Into Clothing and Furniture

This paper presents an early prototype of a passive ultra-high frequency (UHF) radiofrequency identification (RFID) -based control interface. By using two types of gestures, hand tap and body movement, these tags are used as furniture-and clothing-integrated inputs. An extra tag, called a gesture initiator, is integrated into the system to prevent unintentional tag detection. After initiating the gesture tag, the user has a specific time to activate any input tag; otherwise, no output is triggered, and the user must reactivate the gesture initiator tag. Both gestures work by making a specific input tag non-detectable to an RFID reader. The prototype system was tested in an office environment in two test setups, furniture and clothing setups, where two testers successfully triggered applications-turning a light-emitting diode (LED) on or off, making a phone call, and sending a message-by using the two gestures. Next, extensive system reliability evaluation and improvement was carried out in versatile practical scenarios, where a tag can be accidentally blocked by the user or by someone else who passes by between the tag and the reader. The final results of this study suggest that the use of a gesture initiator tag significantly improves system reliability. This system can be integrated into clothing or furniture to control applications remotely and make a quick call or message without requiring a mobile phone at hand.
K-FL: Kalman Filter-Based Clustering Federated Learning Method

Federated learning is a distributed machine learning framework that enables a large number of devices to cooperatively train a model without data sharing. However, because federated learning trains a model using non-independent and identically distributed (non-IID) data stored at local devices, the weight divergence causes a performance loss. This paper focuses on solving the non-IID problems and proposes Kalman filter-based clustering federated learning method called K-FL to get performance gain by providing a specific model with low variance to the device. To the best of our knowledge, it is the first clustering federated learning method that can train a model requiring fewer communication rounds under the premise that non-IID environment without any prior knowledge and an initial value set by the user. From simulations, we demonstrate that the proposed K-FL can train a model much faster, requiring fewer communication rounds than FedAvg and LG-FedAvg when testing neural networks using the MNIST, FMNIST, and CIFAR-10 datasets. As a numerical result, it is shown that the accuracy is improved in all datasets while the computational time cost is reduced by 1.43×, 1.67×, and 1.63× compared to FedAvg, respectively.
Pay Attention via Quantization: Enhancing Explainability of Neural Networks via Quantized Activation

Modern deep learning algorithms comprise highly complex artificial neural networks, making it extremely difficult for humans to track their inference processes. As the social implementation of deep learning progresses, the human and economic losses caused by inference errors are becoming increasingly problematic, making it necessary to develop methods to explain the basis for the decisions of deep learning algorithms. Although an attention mechanism-based method to visualize the regions that contribute to steering angle prediction in an automated driving task has been proposed, its explanatory capability is low. In this paper, we focus on the fact that the importance of each bit in the activation value of a network is biased (i.e., the sign and exponent bits are weighted more heavily than the mantissa bits), which has been overlooked in previous studies. Specifically, this paper quantizes network activations, encouraging important information to be aggregated to the sign bit. Further, we introduce an attention mechanism restricted to the sign bit to improve the explanatory power. Our numerical experiment using the Udacity dataset revealed that the proposed method achieves a 1.14× higher area under curve (AUC) in terms of the deletion metric.
Advanced Hierarchical Topic Labeling for Short Text

Hierarchical Topic Modeling is the probabilistic approach for discovering latent topics distributed hierarchically among the documents. The distributed topics are represented with the respective topic terms. An unambiguous conclusion from the topic term distribution is a challenge for readers. The hierarchical topic labeling eases the challenge by facilitating an individual, appropriate label for each topic at every level. In this work, we propose a BERT-embedding inspired methodology for labeling hierarchical topics in short text corpora. The short texts have gained significant popularity on multiple platforms in diverse domains. The limited information available in the short text makes it difficult to deal with. In our work, we have used three diverse short text datasets that include both structured and unstructured instances. Such diversity ensures the broad application scope of this work. Considering the relevancy factor of the labels, the proposed methodology has been compared against both automatic and human annotators. Our proposed methodology outperformed the benchmark with an average score of 0.4185, 49.50, and 49.16 for cosine similarity, exact match, and partial match, respectively.
A Framework for Mapping DRL Algorithms With Prioritized Replay Buffer Onto Heterogeneous Platforms

Despite the recent success of Deep Reinforcement Learning (DRL) in self-driving cars, robotics and surveillance, training DRL agents takes tremendous amount of time and computation resources. In this article, we aim to accelerate DRL with Prioritized Replay Buffer due to its state-of-the-art performance on various benchmarks. The computation primitives of DRL with Prioritized Replay Buffer include environment emulation, neural network inference, sampling from Prioritized Replay Buffer, updating Prioritized Replay Buffer and neural network training. The speed of running these primitives varies for various DRL algorithms such as Deep Q Network and Deep Deterministic Policy Gradient. This makes a fixed mapping of DRL algorithms inefficient. In this work, we propose a framework for mapping DRL algorithms onto heterogeneous platforms consisting of a multi-core CPU, a GPU and a FPGA. First, we develop specific accelerators for each primitive on CPU, FPGA and GPU. Second, we relax the data dependency between priority update and sampling performed in the Prioritized Replay Buffer. By doing so, the latency caused by data transfer between GPU, FPGA and CPU can be completely hidden without sacrificing the rewards achieved by agents learned using the target DRL algorithms. Finally, given a DRL algorithm specification, our design space exploration automatically chooses the optimal mapping of various primitives based on an analytical performance model. On widely used benchmark environments, our experimental results demonstrate up to 997.3× improvement in training throughput compared with baseline mappings on the same heterogeneous platform. Compared with the state-of-the-art distributed Reinforcement Learning framework RLlib, we achieve 1.06 <inline-formula><tex-math notation="LaTeX">$\times \sim$</tex-math></inline-formula> 1005× improvement in training throughput.
Human Pose Estimation Using Thermal Images

This study addresses the human pose estimation problem on thermal images using Convolutional Neural Networks and Vision Transformer architectures. To do this, eight human pose estimation methods designed for visible images were extended to be applied in the thermal domain. Due to the lack of large, representative datasets containing labeled thermal images, this extension requires transfer learning between the visible and the thermal domain, and a database for fine-tuning the networks in the thermal domain. Thus, it is proposed to train the networks using a grayscale version of the COCO dataset, and then fine-tune them in the thermal domain. Fine-tuning is carried out using the new UCH-Thermal-Pose database presented in this work. This database includes 600 thermal images for training, 200 for validation, and 104 for testing, all of them fully labeled. Moreover, in the paper, a comparative study of the eight extended deep-based methods for human pose detection is carried out. The UCH-Thermal-Pose database and the source code of all the methods compared are made available for future studies.
THE USE OF VIDEO MEDIA TO IMPROVE STUDENTS’ PRONUNCIATION

This study was conducted to investigate the use of video media to improve students’ pronunciation in the second grade of a junior high school. Thus its specific objective was to find out: The effectiveness of video in teaching students pronunciation of of /ʒ/ /tʃ/ /dʒ/ /θ/ /ð/. The research method used in this study was quasi-experimental and it was conducted in one junior high school in Cikajang. The data in this study were collected by using pre-test and post-test. They were then analyzed by using the Mann-Whitney t-test to see if there is significant difference in the mean of both groups’ score and the dependent t-test to see if the use of video media significantly affects students’ pronunciation. The findings showed that use video media is effective in improving students’ score in pronunciation post-test score of experimental group, M=13.06, of control group M=7,73 the result of the dependent t-test: t(30) = - 18.351 &lt; 0.05 with tcrit (30) = 2.042. The obtained value of tcritis proven to be greater than tcrit (-18.351 &gt; 2.042) and this result is significant at α level 0.05. In addition, this technique is very helpful as it becomes in teaching pronunciation.
A differentially private distributed optimization method for constrained optimization

We study a convex optimization problem with coupled inequality constraints over a multi-agent network while protecting the privacy of agents. Using the dual decomposition method, we first design a fully distributed dual algorithm. In the process of information exchange, dual (price) variables are injected artificially with the Laplace noise to preserve the privacy of local objective functions and constraint functions held by individual agents. Then we show that our algorithm can preserve the $ \epsilon $-differentially privacy by choosing an appropriate parameter of the Laplace noise. We also prove the convergence of the algorithm to the optimal solutions of primal and dual variables under the assumption of the decaying step-size. In addition, the trade-off between the level of privacy and the precision of the algorithm is discussed. Finally, several numerical studies are performed to validate the efficacy of the proposed algorithm.