nils-herrmann/clean_scirepeval huggingface dataset:
Positional asphyxia or diabetic ketoacidosis? A case report.

We describe an autopsy case in which a patient with diabetic ketoacidosis (DKA) was found in a head-down position. A female in her late 70s was found dead in her home in a supine position on the kitchen floor. The upper part of her body was hanging down over the edge of the kitchen floor to the backyard through the open window. External examination revealed congestion of the head and upper region of the face and neck. There were numerous petechiae on the superior palpebral conjunctivae and upper part of the oral mucosa. On internal examination, extensive hemorrhages in the subcutaneous fat tissues and muscles were observed at the upper part of the neck, although there were no external injuries on the neck. Histopathological examination revealed that hemorrhages were accompanied with infiltration of polymorphonuclear leukocytes both within and around the hemorrhages on the neck skin. Nodular glomerulosclerosis and many fat droplets in the cytoplasm of proximal tubule cells were found in the kidney. Postmortem blood analysis showed acetone (204.2 μg/ml), HbA1c (10.8%), acetoacetate (<2.0 μmol/l), 3-hydroxybutyrate (11,844 μmol/l), blood urea nitrogen (128.9 mg/dl), and creatinine (3.11 mg/dl). The glucose and acetone levels in the urine were 876.7 mg/dl and 201.4 μg/ml, respectively, suggesting that she suffered severe DKA. However, since hemorrhage of the neck could have developed only when she was still alive, asphyxia should have arisen antemortem. Based on these findings, we concluded that the direct cause of her death is positional asphyxia, which was resulted from DKA. It is difficult to diagnose the cause of death when the victim is in an unusual posture. To confirm a suspicion of positional asphyxia, photographs of the undisturbed scene are useful in addition to a precise autopsy and accurate examinations.
High-Gain Reconfigurable Sectoral Antenna Using an Active Cylindrical FSS Structure

A novel design of a high-gain reconfigurable sectoral antenna using an active cylindrical frequency selective surface (FSS) structure is presented. The FSS structure consists of metallic discontinuous strips with PIN diodes in their discontinuities, and it is placed cylindrically around an omnidirectional electromagnetically coupled coaxial dipole (ECCD) array. The cylindrical FSS structure is divided into two semi-cylinders. By controlling the state of diodes in each semi-cylinder, a directive radiation pattern is obtained that can be swept in the entire azimuth plane. The effect of the diode-state configuration and the radius of the cylindrical structure are carefully studied to obtain an optimum sectoral radiation pattern. In addition, a solution for increasing the matching bandwidth of the antenna is also proposed. An experimental prototype was fabricated, and the measured results show a beamwidth of 20° in elevation and 70° in the azimuth plane at 2.1 GHz with a gain of 13 dBi. With these features, the proposed antenna is suitable for base-station applications in wireless communication systems.
The regulation of explicit and implicit race bias: the role of motivations to respond without prejudice.

Three studies examined the moderating role of motivations to respond without prejudice (e.g., internal and external) in expressions of explicit and implicit race bias. In all studies, participants reported their explicit attitudes toward Blacks. Implicit measures consisted of a sequential priming task (Study 1) and the Implicit Association Test (Studies 2 and 3). Study 3 used a cognitive busyness manipulation to preclude effects of controlled processing on implicit responses. In each study, explicit race bias was moderated by internal motivation to respond without prejudice, whereas implicit race bias was moderated by the interaction of internal and external motivation to respond without prejudice. Specifically, high internal, low external participants exhibited lower levels of implicit race bias than did all other participants. Implications for the development of effective self-regulation of race bias are discussed.
Trending topic prediction on social network

The fast information sharing on social network services generates more than thousands of topics every day. It is extremely important for business organizations and administrative decision makers to learn the popularity of these topics as quickly as possible. In this paper, we propose a prediction mode based on SVM with features of three subsets: quantity specific features, quality and user specific features which supplement each other. Furthermore, we divide topic data into time slices which is used as a unit of feature construction. Our findings suggest that the capability of our prediction model outperforms previous methods and also reveals that subsets of features play different role in the prediction of trending topics.
Positive Definite Kernels in Machine Learning

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions {k(x, ·), x ∈ X} associated with a kernel k defined on a space X . We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain datatypes such as images, graphs or speech segments.  This report is a draft. I apologize in advance for the numerous mistakes, typos, and not always well written parts it contains. Comments and suggestions will be highly appreciated.
The cyber threat landscape: Challenges and future research directions

Cyber threats are becoming more sophisticated with the blending of once distinct types of attack into more damaging forms. Increased variety and volume of attacks is inevitable given the desire of financially and criminally-motivated actors to obtain personal and confidential information, as highlighted in this paper. We describe how the Routine Activity Theory can be applied to mitigate these risks by reducing the opportunities for cyber crime to occur, making cyber crime more difficult to commit and by increasing the risks of detection and punishment associated with committing cyber crime. Potential research questions are also identified. a 2011
How Data Breaches Ruin Firm Reputation on Social Media! - Insights from a Sentiment-based Event Study

Data breach events are heavily discussed in social media. Data breaches, which imply the loss of personal sensitive data, have negative consequences on the affected firms such as loss of market value, loss of customers and reputational damage. In the digital era, wherein ensuring information security is extremely demanding and the dissemination of information occurs at a very high speed, protecting corporate reputation has become a challenging task. While several studies have provided empirical evidence of the financial consequences of data breaches, little attention has been dedicated to the link between data breaches and reputational risk. To address this research gap, we have measured the reputational effect of data breaches based on social media content by applying a novel approach, the sentiment-based event study. The empirical results provide strong evidence that data breach events deteriorate the reputation of companies involved in such incidents.
Prediction of return in online shopping

Mail order business gains popularity day by day. One major problem for the retailers is the high return rates. The incurred cost of returns forces companies to take measures to reduce the number of returns without affecting the customer satisfaction. The aim of this study is to predict whether a purchase results with a return or not based on historical purchase data using machine learning techniques. The data consist of various information about the purchase, customer and the items. Major contribution of this study is to show that using external information to generate features which would otherwise be impossible to extract from data directly improves prediction accuracy significantly.
Action observation activates premotor and parietal areas in a somatotopic manner: an fMRI study.

Functional magnetic resonance imaging (fMRI) was used to localize brain areas that were active during the observation of actions made by another individual. Object- and non-object-related actions made with different effectors (mouth, hand and foot) were presented. Observation of both object- and non-object-related actions determined a somatotopically organized activation of premotor cortex. The somatotopic pattern was similar to that of the classical motor cortex homunculus. During the observation of object-related actions, an activation, also somatotopically organized, was additionally found in the posterior parietal lobe. Thus, when individuals observe an action, an internal replica of that action is automatically generated in their premotor cortex. In the case of object-related actions, a further object-related analysis is performed in the parietal lobe, as if the subjects were indeed using those objects. These results bring the previous concept of an action observation/execution matching system (mirror system) into a broader perspective: this system is not restricted to the ventral premotor cortex, but involves several somatotopically organized motor circuits.
Proofs of Space-Time and Rational Proofs of Storage

We introduce a new cryptographic primitive: Proofs of SpaceTime (PoSTs) and construct a practical protocol for implementing these proofs. A PoST allows a prover to convince a verifier that she spent a “spacetime” resource (storing data—space—over a period of time). Formally, we define the PoST resource as a linear tradeoff between CPU work and space-time (under reasonable cost assumptions, a rational user will prefer to use the lower-cost space-time resource over CPU work). Compared to a proof-of-work, a PoST requires less energy use, as the “difficulty” can be increased by extending the time period over which data is stored without increasing computation costs. Our definition is very similar to “Proofs of Space” [ePrint 2013/796, 2013/805] but, unlike the previous definitions, takes into account amortization attacks and storage duration. Moreover, our protocol uses a very different (and simpler) technique, making use of the fact that we explicitly allow a space-time tradeoff.
Easy Questions First? A Case Study on Curriculum Learning for Question Answering

Cognitive science researchers have emphasized the importance of ordering a complex task into a sequence of easy to hard problems. Such an ordering provides an easier path to learning and increases the speed of acquisition of the task compared to conventional learning. Recent works in machine learning have explored a curriculum learning approach called selfpaced learning which orders data samples on the easiness scale so that easy samples can be introduced to the learning algorithm first and harder samples can be introduced successively. We introduce a number of heuristics that improve upon selfpaced learning. Then, we argue that incorporating easy, yet, a diverse set of samples can further improve learning. We compare these curriculum learning proposals in the context of four non-convex models for QA and show that they lead to real improvements in each of them.
VIPS: a Vision-based Page Segmentation Algorithm

A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results.
Resources , Capabilities and Entrepreneurial Perceptions

We review and develop a subjectivist theory of entrepreneurship that focuses on individuals, their knowledge, resources and skills, and the processes of discovery and creativity, which constitute the heart of entrepreneurship. First, we establish the fundamental importance of subjectivity in entrepreneurial discovery and creativity. Second, we build on Penrose (1959) to elaborate how entrepreneurs’ perceptions and personal knowledge shape a firm’s subjective productive opportunity set. Third, we explain that entrepreneurial perceptions and knowledge partly originate from entrepreneurs’ experiences in specific business settings such as the firm, the management team, and the industry. Fourth, we highlight the causal connections between subjectivity in entrepreneurship and observed heterogeneity in firm-level economic performance. Lastly, we suggest directions for further advancing a subjectivist resource-based approach to future entrepreneurship research.
What are the Fashion Trends in New York?

Fashion is a reflection of the society of a period. Given that New York City is one of the world's fashion capitals, understanding its change in fashion becomes a way to know the society and the times. To keep up with fashion trends, it is important to know what's " in" and what's "out" for a season. Though the fashion trends have been analyzed by fashion designers and fashion analysts for a long time, this issue has been ignored in multimedia science. In this paper, we present a novel algorithm that automatically discovers visual style elements representing fashion trends for a certain season. The visual style elements are discovered based on the stylistic coherent and unique characteristics. The experimental results demonstrate the effectiveness of our proposed method through a large number of catwalk show videos.
Distributed Denial-of-Service (DDoS) Threat in Collaborative Environment - A Survey on DDoS Attack Tools and Traceback Mechanisms

Collaborative applications are feasible nowadays and are becoming more popular due to the advancement in internetworking technology. The typical collaborative applications, in India include the Space research, Military applications, Higher learning in Universities and Satellite campuses, State and Central government sponsored projects, e-governance, e-healthcare systems, etc. In such applications, computing resources for a particular institution/organization spread across districts and states and communication is achieved through internetworking. Therefore the computing and communication resources must be protected against security attacks as any compromise on these resources would jeopardize the entire application/mission. Collaborative environment is prone for various threats, of which Distributed Denial of Service (DDoS) attacks are of major concern. DDoS attack prevents legitimate access to critical resources. A survey by Arbor networks reveals that approximately 1,200 DDoS attacks occur per day. As the DDoS attack is coordinated, the defense for the same has to be a collaborative one. To counter DDoS attacks in a collaborative environment, all the routers need to work collaboratively by exchanging their caveat messages with their neighbors. This paper analyses the security measures in a collaborative environment, identifles the popular DDoS attack tools, and surveys the existing traceback mechanisms to trace the real attacker.
Risk assessment method for cyber security of cyber physical systems

Cyber security is one of the most important risks for all types of cyber-physical systems (CPS). To evaluate the cyber security risk of CPS, a quantitative hierarchized assessment model consists of attack severity, attack success probability and attack consequence is proposed, which can assess the risk caused by an ongoing attack at host level and system level. Then the definitions and calculation methods of the three indexes are discussed in detail. Finally, this paper gives the risk assessment algorithm which describes the steps of implementation. Numerical example shows that the model can response to the attack timely and obtain the system security risk change curve. So that it can help users response to the risk timely. The risk change curve can also be used to predict the risk for the future time.
Trust in Online Shopping: The Korean Student Experience

E-commerce has become an important part of business. In South Korea, the market size of online shopping malls was 13,460 billion Korean Won in 2006, and this figure keeps growing. Thus, gaining loyal customers has become a rising concern. In this study, we adopted Lewicki and Bunker's three different types of trust, namely, calculus-based trust, knowledge-based trust, and identification-based trust, in order to investigate their hierarchical relationships in e-commerce and their impacts on customer satisfaction and loyalty. A total of 104 responses from university students were analyzed to test the proposed model and its hypotheses using PLS. The results showed that hierarchical relationships between different types of trust exist in the online environment, and among them, knowledge-based trust has the strongest impact on customer satisfaction. This finding implied that practitioners should focus on developing an appropriate online strategy in terms of how to build up trust-based relationships with online customers.
Analysis of Representations for Domain Adaptation

Discriminative learning methods for classification perfor m well when training and test data are drawn from the same distribution. In many situa tions, though, we have labeled training data for a sourcedomain, and we wish to learn a classifier which performs well on atargetdomain with a different distribution. Under what conditions can we adapt a classifier trained on the source dom ain for use in the target domain? Intuitively, a good feature representation is a crucial factor in the success of domain adaptation. We formalize this intuition t heoretically with a generalization bound for domain adaption. Our theory illus trates the tradeoffs inherent in designing a representation for domain adaptation nd gives a new justification for a recently proposed model. It also points toward a promising new model for domain adaptation: one which explicitly minimizes the d ifference between the source and target domains, while at the same time maximizing the margin of the training set.
Online Learning of Weighted Relational Rules for Complex Event Recognition

Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time.
Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.
Biomedical Image Processing

medical fields has led to the automated processing ofpictorial data. Here, a device called the cytocomputer searches for genetic mutations. A computer revolution has occurred not only in technical fields but also in medicine, where vast amounts of information must be processed quickly and accurately. Nowhere is the need for image processing techniques more apparent than in clinical diagnosis or mass screening applications where data take the form of digital images. New high-resolution scanning techniques such as computed tomography, nuclear magnetic resonance, po-sitron emission tomography, and digital radiography produce images containing immense amounts of relevant information for medical analysis. But as these scanning techniques become more vital to clinical diagnosis, the work for specialists who must visually examine the resultant images increases. In many cases, quantitative data in the form of measurements and counts are needed to supplement nonimage patient data, and the manual extraction of these data is a time-consuming and costly step in an otherwise automated process. Furthermore, subtle variants of shade and shape can be the earliest clues to a diagnosis, placing the additional burden of complete thoroughness on the examining specialist. For the last five years, the University of Michigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing. System designers have incorporated the requirements of extracting a verifiable answer from an image in a reasonable time into an integrated approach to hardware and software design. The system includes a parallel pipelined image processor, called a cytocomputer, and a high-level language specifically created for image processing, C-3PL, the cytocomputer parallel picture processing language. These studies have involved a great many people from both the medical and engineering communities and have highlighted the interdisciplinary aspects of biomedical image processing. The methods have been tested in anatomy, developmental biology, nuclear medicine, car-diology, and transplant rejection. The general consensus is that quantification by automated image analysis not only increases diagnostic accuracy but also provides significant data not obtainable from qualitative analysis alone. One study in particular, on which descriptions in this article are based, involves a joint effort by the University of Michigan's human genetics and electrical and computer engineering departments and is supported by a grant from the National Cancer Institute. Basically, automated image analysis is being applied via sophisticated biochemical and computer techniques to derive …