Reviewerly/exHarmony huggingface dataset:
Optimization of photovoltaic panel tilt angle for short periods of time or multiple reorientations

Photovoltaic installations typically use fixed-mount photovoltaics (PV) panels with a constant orientation throughout the year. However, this does not maximize the energy output since the irradiance received by the panels depends on the sun position and the weather. This paper presents a novel approach to maximize the energy produced by fixed-mount PV panels for short-term and for permanent PV installations. For permanent installations, we considered a multiple-tilt scenario where the panel orientation is modified throughout the year. We developed a bi-layer algorithm to optimize the angles and timing of adjustments. Our method has been implemented in an open-source software, allowing optimal orientations and dates to be calculated for any installation. The optimal dates and the optimal angles have been successfully calculated for PV panels located at Reykjavik (Iceland), Sherbrooke (Canada), Quito (Ecuador), and Brasília (Brazil). We found that two reorientations per year were the most suitable option for all locations, resulting in a 3 % to 4.8 % gain in annual energy production compared to no reorientation. For short-term installations, using optimal orientation can improve energy production, with for instance 13 % improvement for a monthly installation in Brasilia.
Profiling and optimization of Python-based social sciences applications on HPC systems by means of task and data parallelism

The article presents optimization techniques for two Python-based large-scale social sciences applications: SN (Social Network) Simulator and KPM (Kernel Polynomial Method). These applications use MPI technology to transfer data between computing processes, which in the regular implementation leads to load imbalance and performance degradation. To avoid this effect, we propose a 2-stage optimization. In the first step, the order of tasks is changed, and in the second step, the tasks are divided into smaller ones for easier allocation. In addition, we focus on mitigating performance and memory bottlenecks using modern ccNUMA systems with multiple NUMA domains. As part of the performance analysis, the limitations of communication in data traffic between and within the processor were revealed and resolved through appropriate data allocation. Benchmarking was carried out, examining various environments, including vendors of traditional x86-64 and ARM-based processors for HPC.
Deep Corner

Abstract Recent studies have shown promising results on joint learning of local feature detectors and descriptors. To address the lack of ground-truth keypoint supervision, previous methods mainly inject appropriate knowledge about keypoint attributes into the network to facilitate model learning. In this paper, inspired by traditional corner detectors, we develop an end-to-end deep network, named Deep Corner, which adds a local similarity-based keypoint measure into a plain convolutional network. Deep Corner enables finding reliable keypoints and thus benefits the learning of the distinctive descriptors. Moreover, to improve keypoint localization, we first study previous multi-level keypoint detection strategies and then develop a multi-level U-Net architecture, where the similarity of features at multiple levels can be exploited effectively. Finally, to improve the invariance of descriptors, we propose a feature self-transformation operation, which transforms the learned features adaptively according to the specific local information. The experimental results on several tasks and comprehensive ablation studies demonstrate the effectiveness of our method and the involved components.
Pedagogical Practices for Dyslexic Learners in Moroccan EFL Classroom

Dyslexia is a specific learning disability that affects not only students’ first-language literacy skills but also foreign language learning. It is characterized by difficulties with accurate and/or fluent word recognition as well as poor spelling and decoding abilities. It does not indicate a lack of intelligence or idleness, but rather it is a typical condition that influences the manner the brain processes written and spoken language. In order to ensure that dyslexic students successfully acquire the necessary levels of foreign language proficiency, they need additional support. A total of forty-seven teachers took part anonymously in an online study. A questionnaire was used to provide a descriptive account of the training requirements of EFL teachers who face the challenge of teaching dyslexic students within the mainstream classroom in the six largest universities in Morocco. The findings indicated that foreign language teachers often lack adequate understanding of the nature of dyslexia and the challenges it poses in foreign language learning, and they are unfamiliar with the relevant teaching techniques and methods to support dyslexic students’ language learning processes. However, they expressed their interest in some courses that would help them expand their knowledge and skills and be able to teach dyslexic students more effectively and inclusively.
Revisiting Pontoppidan: Sentiment analysis and topic modelling on ‘Eagle's Flight’

Abstract Our aim is to examine the evolution of Henrik Pontoppidan's authorship by comparing the five different editions that exist of his short story ‘Eagle's Flight’ (‘Ørneflugt’), as well as enriching the understanding of the story. We attempt to validate the existing views of ‘Eagle's Flight’ based on classic literary analysis (qualitative part) by approaching them from a different perspective, that of digital text analysis (quantitative part). The digital analysis focuses on sentiment analysis and topic modelling as ways of discovering differences in the same story across different versions. We have chosen this story as it is relatively easy and quite fast to read, allowing the reader to validate (or not) the output of the machine learning algorithms on their own. We test whether these techniques can be applied in a Danish literature context, and how they further contribute to the analysis and understanding of the literature. Our findings suggest that the methods complement each other and point in the same direction, that Pontoppidan has changed his way of writing during the time of the authorship from a Romantic towards a more Pragmatic style. Our approach can be applied to the works of any author.
Teaching Is a Journey: Vulnerability in Our Work as Educators

This department provides a space for current and past PK-12 teachers of mathematics to connect with other teachers of mathematics through their stories that lend personal and professional support.
The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task

The diffusion-improved ant colony optimization (DIACO) algorithm, as introduced in this paper, addresses the slow convergence speed and poor stability of the ant colony optimization (ACO) in obstacle avoidance path planning for quadruped robots. DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. Simultaneously, DIACO adjusts the pheromone concentration left by ants on the path based on the map information and diffuses the pheromone within a specific range following the artificial potential field algorithm. In the global pheromone update, DIACO adjusts the pheromone on both the optimal path and the worst path generated by the current iteration, thereby enhancing the probability of ants finding the optimal path in the subsequent iteration. This paper designs a steering gait based on the tort gait to fulfill the obstacle avoidance task of a quadruped robot. The effectiveness of the DIACO algorithm and steering gait is validated through a simulation environment with obstacles constructed in Adams. The simulation results reveal that DIACO demonstrates improved convergence speed and stability compared to ACO, and the quadruped robot effectively completes the obstacle avoidance task using the path planning provided by DIACO in combination with the steering gait.
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true authorship. Traditionally, the notion of authorship and its accompanying privacy concern is only toward human authors. However, in recent years, due to the explosive advancements in Neural Text Generation (NTG) techniques in NLP, capable of synthesizing human-quality openended texts (so-called "neural texts"), one has to now consider authorships by humans, machines, or their combination. Due to the implications and potential threats of neural texts when used maliciously, it has become critical to understand the limitations of traditional AA/AO solutions and develop novel AA/AO solutions in dealing with neural texts. In this survey, therefore, we make a comprehensive review of recent literature on the attribution and obfuscation of neural text authorship from a Data Mining perspective, and share our view on their limitations and promising research directions.
Image Appeal Revisited: Analysis, New Dataset, and Prediction Models

There are more and more photographic images uploaded to social media platforms such as Instagram, Flickr, or Facebook on a daily basis. At the same time, attention and consumption for such images is high, with image views and liking as one of the success factors for users and driving forces for social media algorithms. Here, "liking" can be assumed to be driven by image appeal and further factors such as who is posting the images and what they may show and reveal about the posting person. It is therefore of high research interest to evaluate the appeal of such images in the context of social media platforms. Such an appeal evaluation may help to improve image quality or could be used as an additional filter criterion to select good images. To analyze image appeal, various datasets have been established over the past years. However, not all datasets contain high-resolution images, are up to date, or include additional data, such as meta-data or social-media-type data such as likes and views. We created our own dataset "AVT-ImageAppeal-Dataset", which includes images from different photo-sharing platforms. The dataset also includes a subset of other state-of-the-art datasets and is extended by social-media-type data, meta-data, and additional images. In this paper, we describe the dataset and a series of laboratory- and crowd-tests we conducted to evaluate image appeal. These tests indicate that there is only a small influence when likes and views are included in the presentation of the images in comparison to when these are not shown, and also the appeal ratings are only a little correlated to likes and views. Furthermore, it is shown that lab and crowd tests are highly similar considering the collected appeal ratings. In addition to the dataset, we also describe various machine learning models for the prediction of image appeal, using only the photo itself as input. The models have a similar or slightly better performance than state-of-the-art models. The evaluation indicates that there is still an improvement in image appeal prediction and furthermore, other aspects, such as the presentation context could be evaluated.
Training Data Generation Using Human Link Model for State Estimation of Care Robot User

The importance of care robots is growing owing to the increasing numbers of the elderly and the shortage of caregivers. For robots to automatically assist the movements of the elderly users, the state of their pose should be estimated. Hence, we proposed a method for estimating the user’s state using candidate positions of the center of gravity of the robot user using a few sensors. In this method, sensor outputs in all states and movements of the user are collected in advance, and a state estimation and abnormality detection model created by machine learning using the relationship between the outputs and state is used as training data. However, it is difficult to collect training data pertaining to elderly people, before use, for this purpose. Therefore, a database of human actions is created for use as the training data. Rich databases of such actions performed sans robots already exist, but not where care robots are used. Conventional methods for simulating human body movements to estimate human states require actual data and expert coordination and do not address detailed state estimation for physical support. Therefore, this study proposed a method for generating training data using a human link model, which enables the creation of a model that can estimate the state of a robot user without requiring the elderly user to stand up and perform other robot-assisted actions before use. Training data is generated using a link model and candidate centers of gravity, a simple method by which the state of the care robot user can be estimated, and physical support for standing, walking, and sitting can be provided. The effectiveness of the state estimation using the link model generated training data is verified off-line using sensor data independently obtained from the actual movements of the robot and user and also through experiments using an actual care robot. The results validated the state estimation since the time error was sufficiently short (0.35-1.85s), and the experiment confirmed that the robot could realize assistive actions with 90% accuracy.
Discrepancy-Aware Meta-Learning for Zero-Shot Face Manipulation Detection

In this paper, we propose a discrepancy-aware meta-learning approach for zero-shot face manipulation detection, which aims to learn a discriminative model maximizing the generalization to unseen face manipulation attacks with the guidance of the discrepancy map. Unlike existing face manipulation detection methods that usually present algorithmic solutions to the known face manipulation attacks, where the same types of attacks are used to train and test the models, we define the detection of face manipulation as a zero-shot problem. We formulate the learning of the model as a meta-learning process and generate zero-shot face manipulation tasks for the model to learn the meta-knowledge shared by diversified attacks. We utilize the discrepancy map to keep the model focused on generalized optimization directions during the meta-learning process. We further incorporate a center loss to better guide the model to explore more effective meta-knowledge. Experimental results on the widely used face manipulation datasets demonstrate that our proposed approach achieves very competitive performance under the zero-shot setting.
Hyperpixels: Flexible 4D Over-Segmentation for Dense and Sparse Light Fields

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users. A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and do not adequately deal with sparse LFs with large occlusions. Furthermore, the spatio-angular LF cues are not fully exploited in the existing methods. In this paper, the concept of hyperpixels is defined and a flexible, automatic, and adaptive representation for both dense and sparse 4D LFs is proposed. Initially, disparity maps are estimated for all views to enhance over-segmentation accuracy and consistency. Afterwards, a modified weighted K -means clustering using robust spatio-angular features is performed in 4D Euclidean space. Experimental results on several dense and sparse 4D LF datasets show competitive and outperforming performance in terms of over-segmentation accuracy, shape regularity and view consistency against state-of-the-art methods.
Unregistered Firms, Financial Access and Innovation

The purpose of this paper was to examine the relationship between a firm starting operation informally and its future innovation and whether this relation is moderated by institutional support (having access to finance from financial institutions to run their business).Data from the World Bank Enterprise Survey on 30 Eastern European and Central Asian countries were analysed using probit regression analysis. The findings show that there is a positive significant relationship between firms that start operation informally and the firm’s innovation and that such effect persists overtime. The study found that this relationship is stronger if the firms can gain access to finance to expand their business activities. Finally, the results show that such a relationship is based on the type of innovation being pursued by the firm. By examining the moderation effect of access to finance on starting a business informally, the study provides an alternative explanation to policymakers on how to deal with informal firms to benefit from their contribution to growth.
Addressing Wicked Problems and Deep Uncertainties in Risk Analysis

How can decision analysts and risk analysts help to improve policy and decision-making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. This chapter discusses constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model-based methods, such as the paradigm of identifying a single “best-fitting” model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.
Storyboarding a Serious Game Environment for Evaluating the Impact of Empathetic AI for Sonar Operators

Sonar operators work under high pressure and stressful conditions, handling large volumes of data that they must quickly perceive and interpret in order to make high-stakes decisions. Artificial Intelligence (AI) has the potential to aid them by processing large amounts of information and highlighting the critical data amongst the “noise” for the sonar operators to focus on. In order for such an AI to be effective, we need to build a cooperative human-AI team.
From Polynomial Invariants to Linear Loops

Loop invariants are software properties that hold before and after every iteration of a loop. As such, invariants provide inductive arguments that are key in automating the verification of program loops. The problem of generating loop invariants; in particular, invariants described by polynomial relations (so called polynomial invariants), is therefore one of the hardest problems in software verification. In this paper we advocate an alternative solution to invariant generation. Rather than inferring invariants from loops, we synthesise loops from invariants. As such, we generate loops that satisfy a given set of polynomials; in other words, our synthesised loops are correct by construction.
“There is a bit of grace missing”: Understanding non-use of mobile robotic telepresence in a global technology company

Mobile robotic telepresence technology may be a potential solution to hybrid communication as it allows remote attendees embodied movement in space. Yet, there is little exploration into how its affordances fit with the practices and needs of workplaces. This paper presents findings from interviews conducted following a discontinued deployment of telepresence robots at the offices of a global technology company. The findings indicate that in this case 1) The knowledge workers were equipped to manage hybrid work, 2) the robots offered limited perceived value, and 3) the robots were a poor fit to the knowledge workers’ physically distributed workflows. Drawing workflows and non-use literature, we explore how features of the technology failed to align with the office’s work practices and needs, and discuss the implications for evaluating the low use of robotic telepresence.
Order bounds for C2-finite sequences

A sequence is called C-finite if it satisfies a linear recurrence with constant coefficients. We study sequences which satisfy a linear recurrence with C-finite coefficients. Recently, it was shown that such C2-finite sequences satisfy similar closure properties as C-finite sequences. In particular, they form a difference ring.
Algebra-Based Loop Analysis

Automating loop analysis, and in particular synthesizing loop invariants, is a central challenge in the computer-aided verification of programs with loops, with applications in compiler optimization, probabilistic programming and IT security. While this challenge is in general undecidable, several techniques have emerged to automatically summarize the functional behaviour of software loops, thus providing inductive loop invariants that may prevent programmers from introducing errors while making changes in their code. In this tutorial, we show that novel combinations of methods from computer algebra, algorithmic combinatorics and static loop analysis provide powerful workhorses to derive (all) polynomial loop invariants, synthesize affine loops from invariants, and infer quantitative properties over the value distributions of probabilistic loop variables.
Lightweight recurrent cross-modal encoder for video question answering

A video question answering task essentially boils down to how to fuse the information between text and video effectively to predict an answer. Most works employ a transformer encoder as a cross-modal encoder to fuse both modalities by leveraging the full self-attention mechanism. Due to the high computational cost of the self-attention and the high dimensional data of video, they either have to settle for: (1) only training the cross-modal encoder on offline-extracted video and text features or (2) training the cross-modal encoder with the video and text feature extractor, but only using sparsely-sampled video frames. Training only from offline-extracted features suffers from the disconnection between the extracted features and the data of the downstream task because the video and text feature extractors are trained independently on different domains, e.g., action recognition for the video feature extractor and semantic classification for the text feature extractor. Training using sparsely-sampled video frames might suffer from information loss if the video contains very rich information or has many frames. To alleviate those issues, we propose Lightweight Recurrent Cross-modal Encoder (LRCE) that replaces the self-attention operation with a single learnable special token to summarize the text and video features. As a result, our model incurs a significantly lower computational cost. Additionally, we perform a novel multi-segment sampling which sparsely samples the video frames from different segments of the video to provide more fine-grained information. Through extensive experiments on three VideoQA datasets, we demonstrate the LRCE achieves significant performance gains compared to previous works. The code of our proposed method is available at https://github.com/Sejong-VLI/VQA-LRCE-KBS-2023.