nils-herrmann/clean_scirepeval huggingface dataset:
An Automated Methodology for Worker Path Generation and Safety Assessment in Construction Projects

Collisions between automated moving equipment and human workers in job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess project plans in terms of hazards before their execution. Our methodology has the following steps: 1) several potential plans are extracted from an initial activity graph; 2) plans are translated from a high-level activity graph to a discrete-event simulation model; 3) trajectories and safety policies are generated that avoid static and moving obstacles using existing motion planning algorithms; 4) safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment; and 5) managerial implications are provided to select an acceptable plan with the aid of a sensitivity analysis of different factors (cost, resources, and deadlines) that affect the safety of a plan. Finally, we present illustrative case study examples to demonstrate the usefulness of our model.Note to Practitioners—Currently, construction project planning does not explicitly consider safety due to a lack of automated tools that can identify a plan’s safety level before its execution. This paper proposes an automated construction safety assessment tool which is able to evaluate the alternate construction plans and help to choose considering safety, cost, and deadlines. Our methodology uses discrete-event modeling along with motion planning to simulate the motions of workers and equipment, which account for most of the hazards in construction sites. Our method is capable of generating safe motion trajectories and coordination policies for both humans and machines to minimize the number of collisions. We also provide safety heatmaps as a spatiotemporal visual display of construction site to identify risky zones inside the environment throughout the entire timeline of the project. Additionally, a detailed sensitivity analysis helps to choose among plans in terms of safety, cost, and deadlines.
Peek’s harpoon: a new device for the treatment of laparoscopic sacropexy. Preliminary study in unembalmed cadavers

The aim of the study is to facilitate the suture on the sacral promontory for laparoscopic sacrocolpopexy. We hypothesised that a new method of sacral anchorage using a biosynthetic material, the polyether ether ketone (PEEK) harpoon, might be adequate because of its tensile strength, might reduce complications owing to its well-known biocompatibility, and might shorten the duration of surgery. We verified the feasibility of insertion and quantified the stress resistance of the harpoons placed in the promontory in nine fresh cadavers, using four stress tests in each case. Mean values were analysed and compared using the Wilcoxon and Fisher’s exact tests. The harpoon resists for at least 30 s against a pulling force of 1 N, 5 N and 10 N. Maximum tensile strength is 21 N for the harpoon and 32 N for the suture. Harpoons broke in 6 % and threads in 22 % of cases. Harpoons detached owing to ligament rupture in 64 % of the cases. Regarding failures of the whole complex, the failure involves the harpoon in 92 % of cases and the thread in 56 %. The four possible placements of the harpoon in the promontory were equally safe in terms of resistance to traction. The PEEK harpoon can be easily anchored in the promontory. Thread is more resistant to traction than the harpoon, but the latter makes the surgical technique easier. Any of the four locations tested is feasible for anchoring the device.
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Supplementary Material

In this supplementary document, we present additional results to complement the paper. First, we provide the detailed configurations and parameters of the generator and discriminator in the proposed Generative Adversarial Network. Second, we present the qualitative comparisons with the state-ofthe-art CNN-based optical flow methods. The complete results and source code are publicly available on http://vllab.ucmerced.edu/wlai24/semiFlowGAN.
Accessory navicular bone incidence in Chinese patients: a retrospective analysis of X-rays following trauma or progressive pain onset

Optimal treatment of symptomatic accessory navicular bones, generally asymptomatic ‘extra’ ossicles in the front interior ankle, remains debated. Incidence and type of accessory navicular bones in Chinese patients were examined as a basis for improving diagnostic and treatment standards. Accessory navicular bones were retrospectively examined in 1,625 (790 men and 835 women) patients with trauma-induced or progressive symptomatic ankle pain grouped by gender and age from August 2011 to May 2012. Anterior–posterior/oblique X-ray images; presence; type; affected side; modified Coughlin’s classification types 1, 2A, 2B, and 3; and subgroups a–c were recorded. Accessory navicular bones were found in 329 (20.2 %) patients (143 men and 186 women; mean age, 47.24 ± 18.34, ranging 14–96 years). Patients aged 51–60 exhibited most accessory navicular bones (29.7 %), with risk slightly higher in women and generally increasing from minimal 10.9 % at ages 11–20 to age 51 and thereafter declining to 0.4 % by age 90. The incidence was 41.6 % for Type 1 (Type 1a: 9.1 %, Type 1b: 15.5 %, and Type 1c: 19.4 %), 36.8 % for Type 2 (Type 2Aa: 2.1 %, Type 2Ab: 13.7 %, Type 2Ac: 5.1 %, Type 2Ba: 2.1 %, 2Bb: 2.1 %, and 2Bc: 11.6 %), and 21.6 % for Type 3 (Type 3a: 4.5 %, Type 3b: 14 %, and Type 3c: 3.0 %). Approximately one-fifth (20.3 %) of ankle pain patients exhibited accessory navicular bones, with Type 2 most common and middle-aged patients most commonly affected. Thus, accessory navicular bones may be less rare than previously thought, underlying treatable symptomatic conditions of foot pain and deformity.
Mining feature-opinion pairs and their reliability scores from web opinion sources

Due to proliferation of Web 2.0, there is an exponential growth in user generated contents in the form of customer reviews on the Web, containing precious information useful for both customers and manufacturers. However, most of the contents are stored in either unstructured or semi-structured format due to which distillation of knowledge from this huge repository is a challenging task. In this paper, we propose a text mining approach to mine product features, opinions and their reliability scores from Web opinion sources. A rule-based system is implemented, which applies linguistic and semantic analysis of texts to mine feature-opinion pairs that have sentence-level co-occurrence in review documents. The extracted feature-opinion pairs and source documents are modeled using a bipartite graph structure. Considering feature-opinion pairs as hubs and source documents as authorities, Hyperlink-Induced Topic Search (HITS) algorithm is applied to generate reliability score for each feature-opinion pair with respect to the underlying corpus. The efficacy of the proposed system is established through experimentation over customer reviews on different models of electronic products.
Classification in Networked Data: A Toolkit and a Univariate Case Study

This paper presents NetKit, a modular toolkit for classifica tion in networked data, and a case-study of its application to a collection of networked data sets use d in prior machine learning research. Networked data are relational data where entities are inter connected, and this paper considers the common case where entities whose labels are to be estimated a re linked to entities for which the label is known. NetKit is based on a three-component framewo rk, comprising a local classifier, a relational classifier, and a collective inference procedur . Various existing relational learning algorithms can be instantiated with appropriate choices for the se three components and new relational learning algorithms can be composed by new combinations of c omponents. The case study demonstrates how the toolkit facilitates comparison of differen t learning methods (which so far has been lacking in machine learning research). It also shows how the modular framework allows analysis of subcomponents, to assess which, whether, and when partic ul components contribute to superior performance. The case study focuses on the simple but im portant special case of univariate network classification, for which the only information avai lable is the structure of class linkage in the network (i.e., only links and some class labels are avail ble). To our knowledge, no work previously has evaluated systematically the power of class-li nkage alone for classification in machine learning benchmark data sets. The results demonstrate clea rly th t simple network-classification models perform remarkably well—well enough that they shoul d be used regularly as baseline classifiers for studies of relational learning for networked dat a. The results also show that there are a small number of component combinations that excel, and that different components are preferable in different situations, for example when few versus many la be s are known.
Service and Utility Oriented Distributed Computing Systems: Challenges and Opportunities for Modeling and Simulation Communities

Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as Data Centers and Grids. We present various case studies on the use of GridSim in modeling and simulation of Business Grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.
Building an Ontology of Boardgame Mechanics based on the BoardGameGeek Database and the MDA Framework

This work intends to build a Game Mechanics Ontology based on the mechanics category presented in BoardGameGeek.com vis à vis the formal concepts from the MDA framework. The 51 concepts presented in BoardGameGeek (BGG) as game mechanics are analyzed and arranged in a systemic way in order to build a domain sub-ontology in which the root concept is the mechanics as defined in MDA. The relations between the terms were built from its available descriptions as well as from the authors’ previous experiences. Our purpose is to show that a set of terms commonly accepted by players can lead us to better understand how players perceive the games components that are closer to the designer. The ontology proposed in this paper is not exhaustive. The intent of this work is to supply a tool to game designers, scholars, and others that see game artifacts as study objects or are interested in creating games. However, although it can be used as a starting point for games construction or study, the proposed Game Mechanics Ontology should be seen as the seed of a domain ontology encompassing game mechanics in general.
Cuckoo: A Computation Offloading Framework for Smartphones

Offloading computation from smartphones to remote cloud resources has recently been rediscovered as a technique to enhance the performance of smartphone applications, while reducing the energy usage. In this paper we present the first practical implementation of this idea for Android: the Cuckoo framework, which simplifies the development of smartphone applications that benefit from computation offloading and provides a dynamic runtime system, that can, at runtime, decide whether a part of an application will be executed locally or remotely. We evaluate the framework using two real life applications.
Mycosis fungoides: a dermatological masquerader.

Mycosis fungoides (MF), a low-grade lymphoproliferative disorder, is the most common type of cutaneous T-cell lymphoma. Typically, neoplastic T cells localize to the skin and produce patches, plaques, tumours or erythroderma. Diagnosis of MF can be difficult due to highly variable presentations and the sometimes nonspecific nature of histological findings. Molecular biology has improved the diagnostic accuracy. Nevertheless, clinical experience is of substantial importance as MF can resemble a wide variety of skin diseases. We performed a literature review and found that MF can mimic >50 different clinical entities. We present a structured framework of clinical variations of classical, unusual and distinct forms of MF. Distinct subforms such as ichthyotic MF, adnexotropic (including syringotropic and folliculotropic) MF, MF with follicular mucinosis, granulomatous MF with granulomatous slack skin and papuloerythroderma of Ofuji are delineated in more detail.
A novel resolver-to-360/spl deg/ linearized converter

A novel and simple resolver-to-dc converter is presented. It is shown that by appropriate processing of the sine and cosine resolver signals, the proposed converter may produce an output voltage proportional to the shaft angle. A dedicated compensation method is applied to produce an almost perfectly linear output. This enables determination of the angle with reasonable accuracy without a processor and/or a look-up table. The tests carried out under various operating conditions are satisfactory and in good agreement with theory. This paper gives the theoretical analysis, the computer simulation, the full circuit details, and experimental results of the proposed scheme.
Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

The application of machine learning for the detection of malicious network traffic has been well researched over the past several decades; it is particularly appealing when the traffic is encrypted because traditional pattern-matching approaches cannot be used. Unfortunately, the promise of machine learning has been slow to materialize in the network security domain. In this paper, we highlight two primary reasons why this is the case: inaccurate ground truth and a highly non-stationary data distribution. To demonstrate and understand the effect that these pitfalls have on popular machine learning algorithms, we design and carry out experiments that show how six common algorithms perform when confronted with real network data. With our experimental results, we identify the situations in which certain classes of algorithms underperform on the task of encrypted malware traffic classification. We offer concrete recommendations for practitioners given the real-world constraints outlined. From an algorithmic perspective, we find that the random forest ensemble method outperformed competing methods. More importantly, feature engineering was decisive; we found that iterating on the initial feature set, and including features suggested by domain experts, had a much greater impact on the performance of the classification system. For example, linear regression using the more expressive feature set easily outperformed the random forest method using a standard network traffic representation on all criteria considered. Our analysis is based on millions of TLS encrypted sessions collected over 12 months from a commercial malware sandbox and two geographically distinct, large enterprise networks.
Realistic virtual grasping

We present a physically-based approach to grasping and manipulation of virtual objects that produces visually realistic results, addresses the problem of visual interpenetration of hand and object models, and performs force rendering for force-feedback gloves in a single framework. Our approach couples tracked hand configuration to a simulation-controlled articulated hand model using a system of linear and torsional spring-dampers. We discuss an implementation of our approach that uses a widely-available simulation tool for collision detection and response. We illustrate the resulting behavior of the virtual hand model and of grasped objects, and we show that the simulation rate is sufficient for control of current force-feedback glove designs. We also present a prototype of a system we are developing to support natural whole-hand interactions in a desktop-sized workspace.
A Decomposition Approach for the Scheduling of a Steel Plant Production

In this paper we present a decomposition strategy for solving large scheduling problems using mathematical programming methods. Instead of formulating one huge and unsolvable MILP problem, we propose a decomposition scheme that generates smaller programs that can often be solved to global optimality. The original problem is split into subproblems in a natural way using the special features of steel making and avoiding the need for expressing the highly complex rules as explicit constraints. We present a small illustrative example problem, and several real-world problems to demonstrate the capabilities of the proposed strategy, and the fact that the solutions typically lie within 1-3% of the global optimum.
Cloud service placement via subgraph matching

Fast service placement, finding a set of nodes with enough free capacity of computation, storage, and network connectivity, is a routine task in daily cloud administration. In this work, we formulate this as a subgraph matching problem. Different from the traditional setting, including approximate and probabilistic graphs, subgraph matching on data-center networks has two unique properties. (1) Node/edge labels representing vacant CPU cycles and network bandwidth change rapidly, while the network topology varies little. (2) There is a partial order on node/edge labels. Basically, one needs to place service in nodes with enough free capacity. Existing graph indexing techniques have not considered very frequent label updates, and none of them supports partial order on numeric labels. Therefore, we resort to a new graph index framework, Gradin, to address both challenges. Gradin encodes subgraphs into multi-dimensional vectors and organizes them with indices such that it can efficiently search the matches of a query's subgraphs and combine them to form a full match. In particular, we analyze how the index parameters affect update and search performance with theoretical results. Moreover, a revised pruning algorithm is introduced to reduce unnecessary search during the combination of partial matches. Using both real and synthetic datasets, we demonstrate that Gradin outperforms the baseline approaches up to 10 times.
Mir-34: A New Weapon Against Cancer?

The microRNA(miRNA)-34a is a key regulator of tumor suppression. It controls the expression of a plethora of target proteins involved in cell cycle, differentiation and apoptosis, and antagonizes processes that are necessary for basic cancer cell viability as well as cancer stemness, metastasis, and chemoresistance. In this review, we focus on the molecular mechanisms of miR-34a-mediated tumor suppression, giving emphasis on the main miR-34a targets, as well as on the principal regulators involved in the modulation of this miRNA. Moreover, we shed light on the miR-34a role in modulating responsiveness to chemotherapy and on the phytonutrients-mediated regulation of miR-34a expression and activity in cancer cells. Given the broad anti-oncogenic activity of miR-34a, we also discuss the substantial benefits of a new therapeutic concept based on nanotechnology delivery of miRNA mimics. In fact, the replacement of oncosuppressor miRNAs provides an effective strategy against tumor heterogeneity and the selective RNA-based delivery systems seems to be an excellent platform for a safe and effective targeting of the tumor.
Measuring Young’s modulus of a wooden bar using longitudinal vibration without measuring its weight

This study aimed at developing a simple measuring method to determine Young’s modulus of a wooden bar by measuring its longitudinal vibration without measuring its weight. Before and after attaching an iron piece to a wooden bar, a free–free longitudinal vibration test was performed to obtain Young’s modulus. Young’s modulus was calculated by substituting the ratio of the resonance frequencies of a wooden rectangular bar with and without attached iron piece into the frequency equation. The calculated results resembled the experimental values without the iron piece.
First report of human parechovirus type 3 infection in a pregnant woman.

Human parechovirus type 3 (HPeV3) can cause serious conditions in neonates, such as sepsis and encephalitis, but data for adults are lacking. The case of a pregnant woman with HPeV3 infection is reported herein. A 28-year-old woman at 36 weeks of pregnancy was admitted because of myalgia and muscle weakness. Her grip strength was 6.0kg for her right hand and 2.5kg for her left hand. The patient's symptoms, probably due to fasciitis and not myositis, improved gradually with conservative treatment, however labor pains with genital bleeding developed unexpectedly 3 days after admission. An obstetric consultation was obtained and a cesarean section was performed, with no complications. A real-time PCR assay for the detection of viral genomic ribonucleic acid against HPeV showed positive results for pharyngeal swabs, feces, and blood, and negative results for the placenta, umbilical cord, umbilical cord blood, amniotic fluid, and breast milk. The HPeV3 was genotyped by sequencing of the VP1 region. The woman made a full recovery and was discharged with her infant in a stable condition.
Show and Recall: Learning What Makes Videos Memorable

With the explosion of video content on the Internet, there is a need for research on methods for video analysis which take human cognition into account. One such cognitive measure is memorability, or the ability to recall visual content after watching it. Prior research has looked into image memorability and shown that it is intrinsic to visual content, but the problem of modeling video memorability has not been addressed sufficiently. In this work, we develop a prediction model for video memorability, including complexities of video content in it. Detailed feature analysis reveals that the proposed method correlates well with existing findings on memorability. We also describe a novel experiment of predicting video sub-shot memorability and show that our approach improves over current memorability methods in this task. Experiments on standard datasets demonstrate that the proposed metric can achieve results on par or better than the state-of-the art methods for video summarization.