nils-herrmann/clean_scirepeval huggingface dataset:
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies

We present the nested Chinese restaurant process (nCRP), a stochastic process that assigns probability distributions to ensembles of infinitely deep, infinitely branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, we present an application to information retrieval in which documents are modeled as paths down a random tree, and the preferential attachment dynamics of the nCRP leads to clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm finds an approximation to a posterior distribution over trees, topics and allocations of words to levels of the tree. We demonstrate this algorithm on collections of scientific abstracts from several journals. This model exemplifies a recent trend in statistical machine learning—the use of Bayesian nonparametric methods to infer distributions on flexible data structures.
Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval

A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.
Bootstrap Learning of Heuristic Functions

We investigate the use of machine learning to create effective heuristics for search algorithms such as IDA* or heuristicsearch planners. Our method aims to generate a strong heuristic from a given weak heuristic h0 through bootstrapping. The “easy” problem instances that can be solved using h0 provide training examples for a learning algorithm that produces a heuristic h1 that is expected to be stronger than h0. If h0 is too weak to solve any of the given instances we use a random walk technique to create a sequence of successively more difficult instances starting with ones that are solvable by h0. The bootstrap process is then repeated using hi in lieu of hi−1 until a sufficiently strong heuristic is produced. We test our method on the 15and 24-sliding tile puzzles, the 17and 24-pancake puzzles, and the 15and 20-blocks world. In every case our method produces a heuristic that allows IDA* to solve randomly generated problem instances extremely quickly with solutions very close to optimal.
E-government success factors: Mapping practical tools to theoretical foundations

Strategies are systematic and long-term approaches to problems. Federal, state, and local governments are investing in the development of strategies to further their e-government goals. These strategies are based on their knowledge of the field and the relevant resources available to them. Governments are communicating these strategies to practitioners through the use of practical guides. The guides provide direction to practitioners as they consider, make a case for, and implement IT initiatives. This article presents an analysis of a selected set of resources government practitioners use to guide their e-government efforts. A selected review of current literature on the challenges to information technology initiatives is used to create a framework for the analysis. A gap analysis examines the extent to which IT-related research is reflected in the practical guides. The resulting analysis is used to identify a set of commonalities across the practical guides and a set of recommendations for future development of practitioner guides and future research into e-government initiatives. D 2005 Elsevier Inc. All rights reserved.
Quantifying the Language of Schizophrenia in Social Media

Analyzing symptoms of schizophrenia has traditionally been challenging given the low prevalence of the condition, affecting around 1% of the U.S. population. We explore potential linguistic markers of schizophrenia using the tweets1 of self-identified schizophrenia sufferers, and describe several natural language processing (NLP) methods to analyze the language of schizophrenia. We examine how these signals compare with the widelyused LIWC categories for understanding mental health (Pennebaker et al., 2007), and provide preliminary evidence of additional linguistic signals that may aid in identifying and getting help to people suffering from schizophrenia.
Effect of body mass and clothing on decomposition of pig carcasses

Carcass mass and carcass clothing are factors of potential high forensic importance. In casework, corpses differ in mass and kind or extent of clothing; hence, a question arises whether methods for post-mortem interval estimation should take these differences into account. Unfortunately, effects of carcass mass and clothing on specific processes in decomposition and related entomological phenomena are unclear. In this article, simultaneous effects of these factors are analysed. The experiment followed a complete factorial block design with four levels of carcass mass (small carcasses 5–15 kg, medium carcasses 15.1–30 kg, medium/large carcasses 35–50 kg, large carcasses 55–70 kg) and two levels of carcass clothing (clothed and unclothed). Pig carcasses (N = 24) were grouped into three blocks, which were separated in time. Generally, carcass mass revealed significant and frequently large effects in almost all analyses, whereas carcass clothing had only minor influence on some phenomena related to the advanced decay. Carcass mass differently affected particular gross processes in decomposition. Putrefaction was more efficient in larger carcasses, which manifested itself through earlier onset and longer duration of bloating. On the other hand, active decay was less efficient in these carcasses, with relatively low average rate, resulting in slower mass loss and later onset of advanced decay. The average rate of active decay showed a significant, logarithmic increase with an increase in carcass mass, but only in these carcasses on which active decay was driven solely by larval blowflies. If a blowfly-driven active decay was followed by active decay driven by larval Necrodes littoralis (Coleoptera: Silphidae), which was regularly found in medium/large and large carcasses, the average rate showed only a slight and insignificant increase with an increase in carcass mass. These results indicate that lower efficiency of active decay in larger carcasses is a consequence of a multi-guild and competition-related pattern of this process. Pattern of mass loss in large and medium/large carcasses was not sigmoidal, but rather exponential. The overall rate of decomposition was strongly, but not linearly, related to carcass mass. In a range of low mass decomposition rate increased with an increase in mass, then at about 30 kg, there was a distinct decrease in rate, and again at about 50 kg, the rate slightly increased. Until about 100 accumulated degree-days larger carcasses gained higher total body scores than smaller carcasses. Afterwards, the pattern was reversed; moreover, differences between classes of carcasses enlarged with the progress of decomposition. In conclusion, current results demonstrate that cadaver mass is a factor of key importance for decomposition, and as such, it should be taken into account by decomposition-related methods for post-mortem interval estimation.
Wikipedia-Based Distributional Semantics for Entity Relatedness

Wikipedia provides an enormous amount of background knowledge to reason about the semantic relatedness between two entities. We propose Wikipedia-based Distributional Semantics for Entity Relatedness (DiSER), which represents the semantics of an entity by its distribution in the high dimensional concept space derived from Wikipedia. DiSER measures the semantic relatedness between two entities by quantifying the distance between the corresponding high-dimensional vectors. DiSER builds the model by taking the annotated entities only, therefore it improves over existing approaches, which do not distinguish between an entity and its surface form. We evaluate the approach on a benchmark that contains the relative entity relatedness scores for 420 entity pairs. Our approach improves the accuracy by 12% on state of the art methods for computing entity relatedness. We also show an evaluation of DiSER in the Entity Disambiguation task on a dataset of 50 sentences with highly ambiguous entity mentions. It shows an improvement of 10% in precision over the best performing methods. In order to provide the resource that can be used to find out all the related entities for a given entity, a graph is constructed, where the nodes represent Wikipedia entities and the relatedness scores are reflected by the edges. Wikipedia contains more than 4.1 millions entities, which required efficient computation of the relatedness scores between the corresponding 17 trillions of entity-pairs.
Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

Histopathological grading of cancer not only offers an insight to the patients' prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in F1 score on more than 450 histopathological images at 40× magnification.
Ingested plastic transfers hazardous chemicals to fish and induces hepatic stress

Plastic debris litters aquatic habitats globally, the majority of which is microscopic (< 1 mm), and is ingested by a large range of species. Risks associated with such small fragments come from the material itself and from chemical pollutants that sorb to it from surrounding water. Hazards associated with the complex mixture of plastic and accumulated pollutants are largely unknown. Here, we show that fish, exposed to a mixture of polyethylene with chemical pollutants sorbed from the marine environment, bioaccumulate these chemical pollutants and suffer liver toxicity and pathology. Fish fed virgin polyethylene fragments also show signs of stress, although less severe than fish fed marine polyethylene fragments. We provide baseline information regarding the bioaccumulation of chemicals and associated health effects from plastic ingestion in fish and demonstrate that future assessments should consider the complex mixture of the plastic material and their associated chemical pollutants.
LineScout Technology Opens the Way to Robotic Inspection and Maintenance of High-Voltage Power Lines

Historically, the inspection and maintenance of high-voltage power lines have been performed by linemen using various traditional means. In recent years, the use of robots appeared as a new and complementary method of performing such tasks, as several initiatives have been explored around the world. Among them is the teleoperated robotic platform called LineScout Technology, developed by Hydro-Québec, which has the capacity to clear most obstacles found on the grid. Since its 2006 introduction in the operations, it is considered by many utilities as the pioneer project in the domain. This paper’s purpose is to present the mobile platform design and its main mechatronics subsystems to support a comprehensive description of the main functions and application modules it offers. This includes sensors and a compact modular arm equipped with tools to repair cables and broken conductor strands. This system has now been used on many occasions to assess the condition of power line infrastructure and some results are presented. Finally, future developments and potential technologies roadmap are briefly discussed.
Drones for smart cities: Issues in cybersecurity, privacy, and public safety

It is expected that drones will take a major role in the connected smart cities of the future. They will be delivering goods and merchandise, serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security of smart cities. However, pervasive use of drones for future smart cities also brings together several technical and societal concerns and challenges that needs to be addressed, including in the areas of cybersecurity, privacy, and public safety. Drones, while can be used for the betterment of the society, can also be used by malicious entities to conduct physical and cyber attacks, and threaten the society. The goal of this survey paper is to review various aspects of drones in future smart cities, relating to cybersecurity, privacy, and public safety. We will also provide representative results on cyber attacks using drones.
TarMAC: Targeted Multi-Agent Communication

We explore a collaborative multi-agent reinforcement learning setting where a team of agents attempts to solve cooperative tasks in partially-observable environments. In this scenario, learning an effective communication protocol is key. We propose a communication architecture that allows for targeted communication, where agents learn both what messages to send and who to send them to, solely from downstream task-specific reward without any communication supervision. Additionally, we introduce a multi-stage communication approach where the agents co-ordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to complex 3D indoor environments. We demonstrate the benefits of targeted as well as multi-stage communication. Moreover, we show that the targeted communication strategies learned by agents are both interpretable and intuitive.
Flowers or a robot army?: encouraging awareness & activity with personal, mobile displays

Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.
LVRT Capability of DFIG-Based WECS Under Asymmetrical Grid Fault Condition

In this paper, the low-voltage ride-through (LVRT) capability of the doubly fed induction generator (DFIG)-based wind energy conversion system in the asymmetrical grid fault situation is analyzed, and the control scheme for the system is proposed to follow the requirements defined by the grid codes. As analyzed in the paper, the control efforts of the negative-sequence current are much higher than that of the positive-sequence current for the DFIG. As a result, the control capability of the DFIG restrained by the dc-link voltage will degenerate for the fault type with higher negative-sequence voltage component and 2φ fault turns out to be the most serious scenario for the LVRT problem. When the fault location is close to the grid connection point, the DFIG may be out of control resulting in non-ride-through zones. In the worst circumstance when LVRT can succeed, the maximal positive-sequence reactive current supplied by the DFIG is around 0.4 pu, which coordinates with the present grid code. Increasing the power rating of the rotor-side converter can improve the LVRT capability of the DFIG but induce additional costs. Based on the analysis, an LVRT scheme for the DFIG is also proposed by taking account of the code requirements and the control capability of the converters. As verified by the simulation and experimental results, the scheme can promise the DFIG to supply the defined positive-sequence reactive current to support the power grid and mitigate the oscillations in the generator torque and dc-link voltage, which improves the reliability of the wind farm and the power system.
Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends

With cloud computing, new services in information technology (IT) emerge from the convergence of business and technology perspectives which furnish users access to IT resources anytime and anywhere using pay-per-use fashion. Therefore, it should supply eminent functioning gain to the user and simultaneously ought to be advantageous for the cloud service provider. To accomplish this goal, many challenges have to be faced, where load balancing is one of them. The optimal selection of a resource for a particular job does not mean that the selected resource persists optimized for the whole execution of the job. The resource overloading/under-loading must be avoided which could be gained by appropriate load balancing mechanisms. However, to the best of our knowledge, despite the importance of load balancing techniques and mechanisms, there is not any comprehensive and systematic review about studying and analyzing its important techniques. Hence, this paper presents a systematic literature review of the existing load balancing techniques proposed so far. Detailed classifications have also been included based on different parameters which are relying upon the analysis of the existing techniques. Also, the advantages and disadvantages associated with several load balancing algorithms have been discussed and the important challenges of these algorithms are addressed so that more efficient load balancing techniques can be developed in future.
Identifying Intention Posts in Discussion Forums

This paper proposes to study the problem of identifying intention posts in online discussion forums. For example, in a discussion forum, a user wrote “I plan to buy a camera,” which indicates a buying intention. This intention can be easily exploited by advertisers. To the best of our knowledge, there is still no reported study of this problem. Our research found that this problem is particularly suited to transfer learning because in different domains, people express the same intention in similar ways. We then propose a new transfer learning method which, unlike a general transfer learning algorithm, exploits several special characteristics of the problem. Experimental results show that the proposed method outperforms several strong baselines, including supervised learning in the target domain and a recent transfer learning method.
Transmission Lines Loaded With Bisymmetric Resonators and Their Application to Angular Displacement and Velocity Sensors

This paper is focused on the analysis of coplanar waveguides (CPWs) loaded with circularly shaped electric-LC (ELC) resonators, the latter consisting of two coplanar loops connected in parallel through a common gap. Specifically, the resonator axis is aligned with the CPW axis, and a dynamic loading with ELC rotation is considered. Since the ELC resonator is bisymmetric, i.e., it exhibits two orthogonal symmetry planes, the angular orientation range is limited to 90°. It is shown that the transmission and reflection coefficients of the structure depend on the angular orientation of the ELC. In particular, the loaded CPW behaves as a transmission line-type (i.e., all-pass) structure for a certain ELC orientation (0°) since the resonator is not excited. However, by rotating the ELC, magnetic coupling to the line arises, and a notch in the transmission coefficient (with orientation dependent depth and bandwidth) appears. This feature is exploited to implement angular displacement sensors by measuring the notch depth in the transmission coefficient. To gain more insight on sensor design, the lumped element equivalent-circuit model for ELC-loaded CPWs with arbitrary ELC orientation is proposed and validated. Based on this approach, a prototype displacement sensor is designed and characterized. It is shown that by introducing additional elements (a circulator and an envelope detector), novel and high precision angular velocity sensors can also be implemented. An angular velocity sensor is thus proposed, characterized, and satisfactorily validated. The proposed solution for angular sensing is robust against environmental variations since it is based on the geometrical alignment/misalignment between the symmetry planes of the coupled elements.
QEx: robust quad mesh extraction

The most popular and actively researched class of quad remeshing techniques is the family of parametrization based quad meshing methods. They all strive to generate an integer-grid map, i.e. a parametrization of the input surface into R2 such that the canonical grid of integer iso-lines forms a quad mesh when mapped back onto the surface in R3. An essential, albeit broadly neglected aspect of these methods is the quad extraction step, i.e. the materialization of an actual quad mesh from the mere "quad texture". Quad (mesh) extraction is often believed to be a trivial matter but quite the opposite is true: numerous special cases, ambiguities induced by numerical inaccuracies and limited solver precision, as well as imperfections in the maps produced by most methods (unless costly countermeasures are taken) pose significant challenges to the quad extractor. We present a method to sanitize a provided parametrization such that it becomes numerically consistent even in a limited precision floating point representation. Based on this we are able to provide a comprehensive and sound description of how to perform quad extraction robustly and without the need for any complex tolerance thresholds or disambiguation rules. On top of that we develop a novel strategy to cope with common local fold-overs in the parametrization. This allows our method, dubbed QEx, to generate all-quadrilateral meshes where otherwise holes, non-quad polygons or no output at all would have been produced. We thus enable the practical use of an entire class of maps that was previously considered defective. Since state of the art quad meshing methods spend a significant share of their run time solely to prevent local fold-overs, using our method it is now possible to obtain quad meshes significantly quicker than before. We also provide libQEx, an open source C++ reference implementation of our method and thus significantly lower the bar to enter the field of quad meshing.