nils-herrmann/clean_scirepeval huggingface dataset:
Duality Theorems for Finite Structures (Characterising Gaps and Good Characterisations)

We provide a correspondence between the subjects of duality and density in classes of finite relational structures. The purpose of duality is to characterise the structures C that do not admit a homomorphism into a given target B by the existence of a homomorphism from a structure A into C. Density is the order-theoretic property of containing no covers (or ‘gaps’). We show that the covers in the skeleton of a category of finite relational models correspond naturally to certain instances of duality statements, and we characterise these covers.
Refining Bounding-Box Regression for Object Localization

For the last several years, convolutional neural network (CNN) based object detection systems have used a regression technique to predict improved object bounding boxes based on an initial proposal using low-level image features extracted from the CNN. In spite of its prevalence, there is little critical analysis of bounding-box regression or in-depth performance evaluation. This thesis surveys an array of techniques and parameter settings in order to further optimize bounding-box regression and provide guidance for its implementation. I refute a claim regarding training procedure, and demonstrate the effectiveness of using principal component analysis to handle unwieldy numbers of features produced by very deep CNNs.
Battery choice and management for new-generation electric vehicles

Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending on cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a high capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa.
Handbook of computer vision algorithms in image algebra

Thus the Gestalt domain with its three operations forms a general algebra. J. N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, 2nd ed. (1072), Computational Techniques and Algorithms for Image Processing (S. (1047), Universal Algebra and Coalgebra (Klaus Denecke, Shelly L. Wismath), World (986), Handbook of Mathematical Models in Computer Vision, (N. Paragios, (985), Numerical Optimization, second edition (Jorge Nocedal, Stephen J.
Design for Manufacturing With Emerging Nanolithography

In this paper, we survey key design for manufacturing issues for extreme scaling with emerging nanolithography technologies, including double/multiple patterning lithography, extreme ultraviolet lithography, and electron-beam lithography. These nanolithography and nanopatterning technologies have different manufacturing processes and their unique challenges to very large scale integration (VLSI) physical design, mask synthesis, and so on. It is essential to have close VLSI design and underlying process technology co-optimization to achieve high product quality (power/performance, etc.) and yield while making future scaling cost-effective and worthwhile. Recent results and examples will be discussed to show the enablement and effectiveness of such design and process integration, including lithography model/analysis, mask synthesis, and lithography friendly physical design.
A Novel Distributed Fog-Based Networked Architecture to Preserve Energy in Fog Data Centers

The distinguishing feature of the Fog Computing (FC) paradigm is that FC spreads communication and computing resources over the wireless access network, so as to provide resource augmentation to resource and energy-limited wireless (possibly mobile) devices. Since FC would lead to substantial reductions in energy consumption and access latency, it will play a key role in the realization of the Fog of Everything (FoE) paradigm. The core challenge of the resulting FoE paradigm is tomaterialize the seamless convergence of three distinct disciplines, namely, broadband mobile communication, cloud computing, and Internet of Everything (IoE). In this paper, we present a new IoE architecture for FC in order to implement the resulting FoE technological platform. Then, we elaborate the related Quality of Service (QoS) requirements to be satisfied by the underlying FoE technological platform. Furthermore, in order to corroborate the conclusion that advancements in the envisioned architecture description, we present: (i) the proposed energy-aware algorithm adopt Fog data center; and, (ii) the obtained numerical performance, for a real-world case study that shows that our approach saves energy consumption impressively in theFog data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm.
Large-scale Simple Question Answering with Memory Networks

Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering ; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions. To this end, we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks. We conduct our study within the framework of Memory Networks (Weston et al., 2015) because this perspective allows us to eventually scale up to more complex reasoning, and show that Memory Networks can be successfully trained to achieve excellent performance .
Self-Esteem , Daily Internet Use and Social Media Addiction as Predictors of Depression among Turkish Adolescents

In this study, direct and indirect effects of self-esteem, daily internet use and social media addiction to depression levels of adolescents have been investigated by testing a model. This descriptive study was conducted with 1130 students aged between 12 and 18 who are enrolled at different schools in southern region of Aegean. In order to collect data, “Children's Depression Inventory”, “Rosenberg Self-esteem Scale” and “Social Media Addiction Scale” have been used. In order to test the hypotheses Pearson's correlation and structural equation modeling were performed. The findings revealed that self-esteem and social media addiction predict %20 of the daily internet use. Furthermore, while depression was associated with self-esteem and daily internet use directly, social media addiction was affecting depression indirectly. Tested model was able to predict %28 of the depression among adolescents.
Using multi-dimensional in-depth long-term case studies for information visualization evaluation

Information visualization is meant to support the analysis and comprehension of (often large) datasets through techniques intended to show/enhance features, patterns, clusters and trends, not always visible even when using a graphical representation. During the development of information visualization techniques the designer has to take into account the users' tasks to choose the graphical metaphor as well as the interactive methods to be provided. Testing and evaluating the usability of information visualization techniques are still a research question, and methodologies based on real or experimental users often yield significant results. To be comprehensive, however, experiments with users must rely on a set of tasks that covers the situations a real user will face when using the visualization tool. The present work reports and discusses the results of three case studies conducted as Multi-dimensional In-depth Long-term Case studies. The case studies were carried out to investigate MILCs-based usability evaluation methods for visualization tools.
Measures of Presortedness and Optimal Sorting Algorithms

The concept of presortedness and its use in sorting are studied. Natural ways to measure presortedness are given and some general properties necessary for a measure are proposed. A concept of a sorting algorithm optimal with respect to a measure of presortedness is defined, and examples of such algorithms are given. A new insertion sort algorithm is shown to be optimal with respect to three natural measures. The problem of finding an optimal algorithm for an arbitrary measure is studied, and partial results are proven.
Jsvee & Kelmu: Creating and Tailoring Program Animations for Computing Education

To learn to program, a novice programmer must understand the dynamic, runtime aspect of program code, a so-called notional machine. Understanding the machine can be easier when it is represented graphically, and tools have been developed to this end. However, these tools typically support only one programming language and do not work in a web browser. In this article, we present the functionality and technical implementation of the two visualization tools. First, the language-agnostic and extensible Jsvee library helps educators visualize notional machines and create expression-level program animations for online course materials. Second, the Kelmu toolkit can be used by ebook authors to augment automatically generated animations, for instance by adding annotations such as textual explanations and arrows. Both of these libraries have been used in introductory programming courses, and there is preliminary evidence that students find the animations useful.
A novel network coded parallel transmission framework for high-speed Ethernet

Parallel transmission, as defined in high-speed Ethernet standards, enables to use less expensive optoelectronics and offers backwards compatibility with legacy Optical Transport Network (OTN) infrastructure. However, optimal parallel transmission does not scale to large networks, as it requires computationally expensive optimal multipath routing algorithms to minimize differential delay, and thus the required buffer size to ensure frame synchronization. In this paper, we propose a novel parallel transmission framework for high-speed Ethernet, which we refer to as network coded parallel transmission, capable of effective buffer management and frame synchronization without the need for complex multipath algorithms. We show that using network coding can reduce the delay caused by packet reordering at the receiver, thus requiring a smaller overall buffer size, while improving the network throughput. We design the framework in full compliance with high-speed Ethernet standards specified in IEEE802.3ba and present detailed schemes including encoding, data structure of coded parallel transmission, buffer management and decoding at the receiver side. The proposed network coded parallel transmission framework is simple to implement and presents a potential major breakthrough in the system design of future high-speed Ethernet.
Covfefe: A Computer Vision Approach For Estimating Force Exertion

Cumulative exposure to repetitive and forceful activities may lead to musculoskeletal injuries which not only reduce workers’ efficiency and productivity, but also affect their quality of life. Thus, widely accessible techniques for reliable detection of unsafe muscle force exertion levels for human activity is necessary for their well-being. However, measurement of force exertion levels is challenging and the existing techniques pose a great challenge as they are either intrusive, interfere with humanmachine interface, and/or subjective in the nature, thus are not scalable for all workers. In this work, we use face videos and the photoplethysmography (PPG) signals to classify force exertion levels of 0%, 50%, and 100% (representing rest, moderate effort, and high effort), thus providing a non-intrusive and scalable approach. Efficient feature extraction approaches have been investigated, including standard deviation of the movement of different landmarks of the face, distances between peaks and troughs in the PPG signals. We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos. Based on the data collected from 20 subjects, features extracted from the face videos give 90% accuracy in classification among the 100% and the combination of 0% and 50% datasets. Further combining the PPG signals provide 81.7% accuracy. The approach is also shown to be robust to the correctly identify force level when the person is talking, even though such datasets are not included in the training.
An Equivalence of Fully Connected Layer and Convolutional Layer

This article demonstrates that convolutional operation can be converted to matrix multiplication, which has the same calculation way with fully connected layer. The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend. To be concise and to make the article more readable, we only consider the linear case. It can be extended to the non-linear case easily through plugging in a non-linear encapsulation to the values like this σ(x) denoted as x′.
Mondor disease: Thrombosis of the dorsal vein of the penis

Superficial vein thrombosis is a rare pathology that was first described by Mordor, although his description of phlebitis was observed exclusively at the thoracic wall. In 1955, Braun-Falco described penile thrombosis and later superficial penile vein thrombosis was first reported by Helm and Hodge. Mondor's disease of the penis is a rare entity with a reported incidence of 1.39%. It is described most of the time as a self-limited disease however it causes great morbidity to the patient who suffers from it. The pathogenesis of Mondor's disease is unknown. Its diagnosis is based on clinical signs such as a cordlike induration on the dorsal face of the penis, and imaging studies, doppler ultrasound is the instrument of choice. Treatment is primarily symptomatic but some cases may require surgical management however an accurate diagnostic resolves almost every case. We will describe the symptoms, diagnosis, and treatment of superficial thrombophlebitis of the dorsal vein of the penis.
Toward Socially Intelligent Service Robots

In the Intelligent Robotics Laboratory (IRL) at Vanderbilt University we seek to develop service robots with a high level of social intelligence and interactivity. In order to achieve this goal, we have identified two main issues for research. The first issue is how to achieve a high level of interaction between the human and the robot. This has lead to the formulation of our philosophy of Human Directed Local Autonomy (HuDL), a guiding principle for research, design, and implementation of service robots. The motivation for integrating humans into a service robot system is to take advantage of human intelligence and skill. Human intelligence can be used to interpret robot sensor data, eliminating computationally expensive and possibly error-prone automated analyses. Human skill is a valuable resource for trajectory and path planning as well as for simplifying the search process. In this paper we present our plans for integrating humans into a service robot system. We present our paradigm for human/robot interaction, HuDL. The second issue is the general problem of system integration, with a specific focus on integrating humans into the service robotic system. This work has lead to the development of the Intelligent Machine Architecture (IMA), a novel software architecture that has been specifically designed to simplify the integration of the many diverse algorithms, sensors, and actuators necessary for socially intelligent service robots. Our testbed system is described, and some example applications of HuDL for aids to the physically disabled are given. An evaluation of the effectiveness of the IMA is also presented.
Progranulin and TDP-43: Mechanistic Links and Future Directions

Loss-of-function mutations in the multifunctional growth factor progranulin (GRN) cause frontotemporal lobar degeneration (FTLD) with TDP-43 protein accumulation. Nuclear TDP-43 protein with key roles in RNA metabolism is also aggregated in amyotrophic lateral sclerosis (ALS), suggesting that ALS and FTLD constitute a broad disease continuum. However, the fact that mutations in GRN are associated with FTLD, while mutations in TDP-43 cause a preferential loss of motor neurons resulting in ALS-end of the disease spectrum, suggests involvement of both cell-autonomous and non-autonomous mechanisms. Studies on animal models and in vitro studies have been instrumental in understanding the link between GRN and TDP-43 and also their role in neurodegeneration. For instance, in mouse models, allelic deficiencies of Grn do not recapitulate human pathology of TDP-43 brain accumulations, but embryonic neurons derived from these mice do show abnormal TDP-43 accumulation after additional cellular challenges, suggesting that TDP-43 changes observed in GRN mutation carriers might also relate to stress. Recent results have shown that the dual action of GRN in growth modulation and inflammation could be due to its negative regulation of TNF-α signaling. In addition, GRN also interacts with sortilin and is endocytosed, thereby regulating its own levels and possibly also modulating the turnover of other proteins including that of TDP-43. Accumulating evidence suggests that TDP-43 abnormal cellular aggregation causes a possible gain of function, also suggested by recently constructed mouse models of TDP-43 proteinopathy; however, it would be inconvincible that sequestration of physiological TDP-43 within cellular aggregates observed in patients would be innocuous for disease pathogenesis. This review discusses some of these data on the possible link between GRN and TDP-43 as well as mechanisms involved in TDP-43-led neurodegeneration. Continued multitiered efforts on genetic, cell biological, and animal modeling approaches would prove crucial in finding a cure for GRN-related diseases.
Exergames Encouraging Exploration of Hemineglected Space in Stroke Patients With Visuospatial Neglect: A Feasibility Study

Use of exergames can complement conventional therapy and increase the amount and intensity of visuospatial neglect (VSN) training. A series of 9 exergames-games based on therapeutic principles-aimed at improving exploration of the neglected space for patients with VSN symptoms poststroke was developed and tested for its feasibility.



The goal was to determine the feasibility of the exergames with minimal supervision in terms of (1) implementation of the intervention, including adherence, attrition and safety, and (2) limited efficacy testing, aiming to document possible effects on VSN symptoms in a case series of patients early poststroke.



A total of 7 patients attended the 3-week exergames training program on a daily basis. Adherence of the patients was documented in a training diary. For attrition, the number of participants lost during the intervention was registered. Any adverse events related to the exergames intervention were noted to document safety. Changes in cognitive and spatial exploration skills were measured with the Zürich Maxi Mental Status Inventory and the Neglect Test. Additionally, we developed an Eye Tracker Neglect Test (ETNT) using an infrared camera to detect and measure neglect symptoms pre- and postintervention.



The median was 14 out of 15 (93%) attended sessions, indicating that the adherence to the exergames training sessions was high. There were no adverse events and no drop-outs during the exergame intervention. The individual cognitive and spatial exploration skills slightly improved postintervention (P=.06 to P=.98) and continued improving at follow-up (P=.04 to P=.92) in 5 out of 7 (71%) patients. Calibration of the ETNT was rather error prone. The ETNT showed a trend for a slight median group improvement from 15 to 16 total located targets (+6%).



The high adherence rate and absence of adverse events showed that these exergames were feasible and safe for the participants. The results of the amount of exergames use is promising for future applications and warrants further investigations-for example, in the home setting of patients to augment training frequency and intensity. The preliminary results indicate the potential of these exergames to cause improvements in cognitive and spatial exploration skills over the course of training for stroke patients with VSN symptoms. Thus, these exergames are proposed as a motivating training tool to complement usual care. The ETNT showed to be a promising assessment for quantifying spatial exploration skills. However, further adaptations are needed, especially regarding calibration issues, before its use can be justified in a larger study sample.
Challenges in Secure Software Evolution - The Role of Software Architecture

Achieving quality properties for software systems and maintaining them during evolution is challenging. Especially, security properties often degrade during software evolution. This is often not noticed and can lead to monetary loss and serious damage to the company’s image. Approaches for maintaining security properties exist but fail to exploit the knowledge of the architectural design phase. This results in high effort and slow reactions on evolutionary changes. In this paper, we describe five key challenges in maintaining security properties during software evolution and show how architecture supports mastering them.