Reviewerly/exHarmony huggingface dataset:
Providing visual analytics guidance through decision support

Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different purposes come specific considerations on how to provide the needed guidance. In this paper, we propose a generic method for making these considerations by framing the guidance problem as a decision problem and applying decision making theory and models toward its solution. This method passes through three stages: (1) identifying decision points; (2) deriving and evaluating alternatives; (3) visualizing the resulting alternatives to support users in comparing them and making their choice. Our method is realized as a set of practical worksheets and illustrated by applying it to a use case of providing guidance among different clustering methods. Finally, we compare our method with existing guidance frameworks to relate and delineate the respective goals and contributions of each.
Task-guided IRL in POMDPs that scales

In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). We address two limitations of existing IRL techniques. First, they require an excessive amount of data due to the information asymmetry between the expert and the learner. Second, most of these IRL techniques require solving the computationally intractable forward problem—computing an optimal policy given a reward function—in POMDPs. The developed algorithm reduces the information asymmetry while increasing the data efficiency by incorporating task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations. Further, the algorithm avoids a common source of algorithmic complexity by building on causal entropy as the measure of the likelihood of the demonstrations as opposed to entropy. Nevertheless, the resulting problem is nonconvex due to the so-called forward problem. We solve the intrinsic nonconvexity of the forward problem in a scalable manner through a sequential linear programming scheme that guarantees to converge to a locally optimal policy. In a series of examples, including experiments in a high-fidelity Unity simulator, we demonstrate that even with a limited amount of data and POMDPs with tens of thousands of states, our algorithm learns reward functions and policies that satisfy the task while inducing similar behavior to the expert by leveraging the provided side information.
PSYCHOLOGICAL FEATURES OF ETHNIC HETEROGENEITY OF MODERN SOCIETY

International scientific conference "Modern scientific developments in pedagogy and psychology" : conference proceedings (November 3–4, 2022. Riga, the Republic of Latvia). Riga, Latvia : "Baltija Publishing", 2022. 204 pages.
Selection of optimal frequencies in multiple-frequency fringe projection profilometry

The selection of frequencies which are in some sense optimal is an open research problem in phase shifting profilometry. We propose a novel method for selecting multiple frequencies for phase shifting profilometry. The frequencies are selected to provide the best possible resistance to phase unwrapping errors when temporal unwrapping is used. The proposed frequency selection method is based on the maximization of the minimal distance between lines of the line-constellation in the wrapped phase space. It is realized as an exhaustive search over the fringe counts, which is more efficient than searching over the frequencies or over the wavelengths. We also propose an efficient and effective method for filtering points for which the phase may be incorrectly unwrapped. The proposed filtering method is based on limiting the distance between the wrapped phase measurement and the line of the line-constellation. We demonstrate the applicability of the proposed frequency selection method through the construction and the experimental validation of several frequency designs which contain only high frequencies, and which are insensitive to large errors in wrapped phase. We also demonstrate the effectiveness of the proposed filtering.
WASTE CLASSIFICATION USING RESNET-152

Waste classification is the process of identifying and separating different types of waste materials based on their characteristics, such as composition, size, and shape. Accurate waste classification is important for efficient waste management and resource recovery, as it allows for the proper disposal or reuse of waste materials. One method for automated waste classification is the use of deep learning algorithms, such as the ResNet-152 model. ResNet-152 is a convolutional neural network (CNN) that has been trained on a large dataset of images and is able to classify objects in images with high accuracy. By applying ResNet-152 to images of waste materials, it is possible to classify the materials into different categories, such as paper, plastic, or metal. This can be done in real-time using computer vision techniques and can significantly improve the efficiency and accuracy of waste classification processes. However, the success of waste classification using ResNet-152 or other CNNs depends on the quality and diversity of the training data, as well as the availability of computational resources and infrastructure. Key Words: Computer Vision, Deep Learning, Transfer Learning, ResNet-152, Waste Management.
MODEL SINGLE EXPONENTIAL SMOOTHING UNTUK MEMPREDIKSI JUMLAH PESERTA DIDIK BARU DI SMPN 1 BATAUGA

&lt;p&gt;&lt;em&gt;Acceptance of new students&lt;/em&gt;&lt;em&gt;, known as &lt;/em&gt;&lt;em&gt;Penerimaan Peserta Didik Baru (PPDB)&lt;/em&gt;&lt;em&gt;, is an annual education unit agenda. Predicting the number of new students is needed in school management planning activities. This study aims to predict the number of new students at SMPN 1 Batauga in 2022. The data used is data from the last 5 years. 2017 to 2021. The Single Exponential Smoothing method is used to make predictions using parameters α=0.1 to α=0.9. From the prediction results, it is obtained that the alpha value has the smallest error value, namely alpha=0.9 and MAPE of 20%. So that the number of students at SMPN 1 Batauga in 2022 is 114 people.&lt;/em&gt;&lt;/p&gt;
Truth‐value relations and logical relations

Abstract After some generalities about connections between functions and relations in Sections 1 and 2 recalls the possibility of taking the semantic values of ‐ary Boolean connectives as ‐ary relations among truth‐values rather than as ‐ary truth functions. Section 3, the bulk of the paper, looks at correlates of these truth‐value relations as applied to formulas, and explores in a preliminary way how their properties are related to the properties of “logical relations” among formulas such as equivalence, implication (entailment) and contrariety (logical incompatibility), concentrating for illustrative purposes on binary logical relations such as those just listed. To avoid an excess of footnotes, some points have been deferred to an Appendix as “Longer Notes”.
Lessons From the Father of Software Engineering

The recent passing of Barry Boehm motivated the opportunity to synthesize lessons from his contributions in software engineering. This article summarizes six lessons that impacted how software is designed, built, and managed.
Learning half-spaces on general infinite spaces equipped with a distance function

For a general infinite distance space X, with no assumptions about the distance function, which need not satisfy the metric axioms, it is not clear what the VC-dimension of the class H of half-spaces in X may be and if there are generalization error bounds for learning H. We define a combinatorial dimension of X to be the independence number of the class of balls in X. We compute it for Euclidean space and for several non-metric distance spaces. Using this dimension, we are able to provide a generalization error bound for learning H over any infinite distance space X.
<scp>AroMa</scp> : Evaluating Deep Learning Systems for Stealthy Integrity Attacks on Multi-tenant Accelerators

Multi-tenant applications have been proliferating in recent years, supported by the emergence of computing-as-service paradigms. Unfortunately, multi-tenancy induces new security vulnerabilities due to spatial or temporal co-location of applications with possibly malicious intent. In this article, we consider a special class of stealthy integrity attacks on multi-tenant deep learning accelerators. One interesting conclusion is that it is possible to perform targeted integrity attacks on kernel weights of deep learning systems such that it remains functional but mis-labels specific categories of input data through standard RowHammer attacks by only changing 0.0009% of the total weights. We develop an automated framework, AroMa , to evaluate the impact of multi-tenancy on security of deep learning accelerators against integrity attacks on memory systems. We present extensive evaluations on AroMa to demonstrate its effectiveness.
Unsupervised Semantic Segmentation Inpainting Network Using a Generative Adversarial Network with Preprocessing

The generative adversarial neural network has shown a novel result in the image generation area. However, applying it to a semantic segmentation inpainting task exhibits instability due to the different data distribution. To solve this problem, we propose an unsupervised semantic segmentation inpainting method using an adversarial deep neural network with a newly introduced preprocessing method and loss function. For stabilizing the adversarial training for semantic segmentation inpainting, we match the probability distribution of the segmentation maps with the developed preprocessing method. In addition, a new cross-entropy total variation loss for the probability map is introduced to improve the segmentation inpainting work by smoothing the segmentation map. The experimental results demonstrate the proposed algorithm’s effectiveness on both synthetic and real datasets.
Socialistic 3D tracking of humans from a mobile robot for a ‘human following robot’ behaviour

Abstract Robotic guides take visitors on a tour of a facility. Such robots must always know the position of the visitor for decision-making. Current tracking algorithms largely assume that the person will be nearly always visible. In the robotic guide application, a person’s visibility could be often lost for prolonged periods, especially when the robot is circumventing a corner or making a sharp turn. In such cases, a person cannot quickly come behind the limited field of view rear camera. We propose a new algorithm that can track people for prolonged times under such conditions. The algorithm is benefitted from an application-level heuristic that the person will be nearly always following the robot, which can be used to guess the motion. The proposed work uses a Particle Filter with a ‘follow-the-robot’ motion model for tracking. The tracking is performed in 3D using a monocular camera. Unlike approaches in the literature, the proposed work observes from a moving base that is especially challenging since a rotation of the robot can cause a large sudden change in the position of the human in the image plane that the approaches in the literature would filter out. Tracking in 3D can resolve such errors. The proposed approach is tested for three different indoor scenarios. The results showcase that the approach is significantly better than the baselines including tracking in the image and projecting in 3D, tracking using a randomized (non-social) motion model, tracking using a Kalman Filter and using LSTM for trajectory prediction.
Zero-Shot Blind Learning for Single-Image Super-Resolution

Deep convolutional neural networks (DCNNs) have manifested significant performance gains for single-image super-resolution (SISR) in the past few years. Most of the existing methods are generally implemented in a fully supervised way using large-scale training samples and only learn the SR models restricted to specific data. Thus, the adaptation of these models to real low-resolution (LR) images captured under uncontrolled imaging conditions usually leads to poor SR results. This study proposes a zero-shot blind SR framework via leveraging the power of deep learning, but without the requirement of the prior training using predefined imaged samples. It is well known that there are two unknown data: the underlying target high-resolution (HR) images and the degradation operations in the imaging procedure hidden in the observed LR images. Taking these in mind, we specifically employed two deep networks for respectively modeling the priors of both the target HR image and its corresponding degradation kernel and designed a degradation block to realize the observation procedure of the LR image. Via formulating the loss function as the approximation error of the observed LR image, we established a completely blind end-to-end zero-shot learning framework for simultaneously predicting the target HR image and the degradation kernel without any external data. In particular, we adopted a multi-scale encoder–decoder subnet to serve as the image prior learning network, a simple fully connected subnet to serve as the kernel prior learning network, and a specific depthwise convolutional block to implement the degradation procedure. We conducted extensive experiments on several benchmark datasets and manifested the great superiority and high generalization of our method over both SOTA supervised and unsupervised SR methods.
Resource Allocation and Scheduling in Modern Cloud Computing

With various types of resources, infrastructures and users participating in the current big data ecosystem at an astonishing speed, resource allocation and scheduling has been identified as one of the key areas needing substantial research for the advancement of system performance.
An intelligent analysis method of security and stability control strategy based on the knowledge graph

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection.
Model-based Resource Management for Fine-grained Services

Brief Biography: Alim Ul Gias is currently a Research Associate at the Centre for Parallel Computing (CPC), University of Westminster. He completed his PhD from Imperial College London in 2022. Before starting his PhD, Alim was a lecturer at Institute of Information Technology (IIT), University of Dhaka (DU). He completed his bachelor's and master's program from the same institute. His current research focuses on different Quality of Service (QoS) aspects of cloud-native applications e.g., microservices. In particular, he aims to address the performance and resource management challenges concenrining the microservices architecture.
How do Students Perceive Guided Writing in Learning Descriptive Text?

The purpose of this study was to investigate how students perceive about guided writing as a means of learning descriptive text. Descriptive quantitative method was employed, and a questionnaire served as the data collection tool. Seventy ninth graders from one school in Central Java made up the study's sample. The questionnaire has 16 questions broken down into 4 categories: the effectiveness of the timing, the easiness of using correct spelling, the support from the teacher, the benefits of guided writing. The data analysis showed that students had a favorable impression of guided writing. It also demonstrated the usefulness of guided writing as a teaching method for writing a descriptive text.
User-based Hierarchical Network of Sina Weibo Emotion Analysis

Emotion analysis on Sina Weibo has a great impetus for government agencies to survey public opinion and enterprises to track market demand. Most of the existing emotion analysis work on Sina Weibo focuses on mining the information contained in a single Weibo, ignoring the problem of inaccurate information extraction caused by the lack of contextual information in Weibo texts. Inspired by humans judging user emotional states from Weibo texts, this article creates a Weibo text five-category emotion classification dataset based on active users and proposes a user-based hierarchical network for Weibo emotion analysis. First, use the multi-head attention mechanism and convolutional neural network set in the information extraction module to analyze a single Weibo text to fully extract the emotional information contained in the text; at the same time, through the moving window set in the relevant information capture module, obtain other Weibo texts posted by the same user within a period, and capture the effective correlation information between Weibo texts; then, the dual text representation obtained above is concatenated, and through the information interaction layer, the relevant information is retrieved again, and the text representation is updated; finally, the classifier output the five-category emotion labels corresponding to each Weibo text. We demonstrate the model’s effectiveness through experiments and analysis in the results.
Dual-Lens HDR using Guided 3D Exposure CNN and Guided Denoising Transformer

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing methods usually treat the HDR imaging problem as an image fusion problem and the HDR result is estimated by fusing the aligned short exposure image and long exposure image. However, the image fusion pipeline depends highly on the image alignment, which is difficult to be perfect. We propose to transfer the dual-lens HDR imaging problem into the disentangled enhancement of exposure correction and denoising for the short exposure image, guided by the long exposure image. In the guided exposure correction module, we make use of the guidance image and 3D color transformation to propose a guided 3D exposure CNN (GEC) to get the rough HDR result from the short exposure image. Then, in the guided denoising module, we make use of the cross-attention mechanism to propose a guided denoising transformer (GDT) to directly use the long exposure image as guidance to denoise the rough HDR result in a pyramid way. And in both modules, we bypass the difficult image alignment processing. Experimental results demonstrate the superiority of our method over the state-of-the-art ones.
Uphill resampling for particle filter and its implementation on graphics processing unit

We introduce a new resampling method, named Uphill, that is free from numerical instability and suitable for parallel implementation on graphics processing unit (GPU). Common resampling algorithms such as Systematic suffer from numerical instability when single precision floating point numbers are used. This is due to cumulative summation over the weights of particles when the weights differ widely or the number of particles is large. The Metropolis and Rejection resampling algorithms do not suffer from numerical instability as they only calculate the ratios of weights pairwise rather than perform collective operations over the weights. They are more suitable for the GPU implementation of the particle filter. However, they undergo non-coalesced global memory access patterns which cause their speed deteriorate rapidly as the number of particles gets large. Uphill also does not suffer from numerical instability but, experiences the same non-coalesced global memory access problem with Metropolis and Rejection. We introduce its faster version named Uphill-Fast which eliminates this problem. We make comparisons of Uphill and Uphill-Fast with the Systematic, Metropolis, Metropolis-C2 and Rejection resampling methods with respect to quality and speed. We also compare them on a highly non-linear system. Uphill-Fast runs faster and attains similar quality, in terms of RMSE, in comparison with Metropolis and Rejection when the number of particles is very large. Uphill-Fast runs with roughly same speed as Metropolis-C2 with better variance and MSE when the number of particles is very large.
Design and analysis of lightweight anonymity protocol for host- and AS-level anonymity

Lightweight anonymity protocols in the network layer are promising in terms of high throughput and low latency. The realistic and weak assumption of local adversaries deletes the need for hop-by-hop encryption of onion routing, and this contributes to high throughput and low latency. Among them, PHI and its successor dPHI are promising due to the fact that path-setup packets for anonymous paths are forwarded according to IP routing. This feature enables easy deployment of the protocol on the Internet infrastructure. However, PHI and dPHI are vulnerable to adversaries such as malicious ASes who compromise servers for path setup, called helpers, and malicious ASes who leverage topological information and routing policies of the IP network. The paper resolves the vulnerabilities in the two steps. In the first step, we design attacks that leverage the vulnerabilities to break anonymity. In the second step, we extend dPHI to mitigate such attacks by adding a new server called a guard. The extended protocol is called gPHI, and gPHI is resilient against adversaries who perform such attacks. This paper designs gPHI and validates its resilience through formal proof and simulation.