nils-herrmann/clean_scirepeval huggingface dataset:
A Hybrid Approach to Rumour Detection in Microblogging Platforms

Microblogging platforms facilitate fast and frequent communication among very large numbers of users. Rumours, especially in times of crisis, tend to spread quickly, causing confusion, and impair people's ability to make decisions. Hence, it is of utmost importance to automatically detect a rumour as soon as possible. Using the PHEME dataset that contains rumours and non-rumours pertaining to five major events, we have developed a rumour detection system that classifies posts from Twitter, a popular microblogging website. We have first analyzed and ranked a number of content-based and user-based features. Some content-based features are derived using natural language processing techniques. We then trained multiple machine learning models (Naive Bayes, Random Forests and Support Vector Machines) using different combinations of the features. Finally, we compared the performance of these models. The performance of the models on one such event resulted in 78% accuracy.
An Improved Pairwise Exchange Heuristic for the Dynamic Plant Layout Problem 1 . 0

Much of the research in facility layout has focused on static layouts where the material handling flow is assumed to be constant during the planning horizon. But in today’s market-based, dynamic environment, layout rearrangement may be required during the planning horizon to maintain layout effectiveness. A few algorithms have been proposed to solve this problem. They include dynamic programming and pair-wise exchange. In this paper we propose an improved dynamic pair-wise exchange heuristic based on a previous method published in this journal. Tests show that the proposed method is effective and efficient.
Porokeratotic eccrine ostial and dermal duct nevus: A noteworthy presentation

We herein present a case of PEODDN that had unique clinical and histopathological findings. Our patient was a 30-year-old healthy lady who presented with multiple, asymptomatic, elevated skin lesions over her left upper limb. Small lesions appeared three years ago over her left index finger and gradually extended to the forearm. There was no seasonal variation. Cutaneous examination demonstrated multiple well-circumscribed, hyperpigmented, verrucous papules on the dorsum of the left index finger extending up to the extensor aspect of mid forearm. Some of the lesions were discrete while others coalesced to form plaques in a linear configuration. Of note, many of these papules showed a central pit . Hair, nails, and mucosae were within normal limits; however, the nail of the index finger showed multiple shallow pits and a single longitudinal groove. Family history and systemic examination were not contributory. Linear porokeratosis, linear lichen planus, linear verrucous epidermal nevus, and linear Darier’s disease were considered in the list of clinical differentials. A complete blood cell count with differential analysis and a comprehensive biochemistry panel did not reveal any abnormality. Histopathology of a representative skin lesion from the forearm showed hyperkeratosis, acanthosis, and papillomatosis. Cornoid lamella overlying a dilated eccrine duct ostium was visualized. An interesting finding was the presence of focal lichenoid infiltrate in the upper dermis [Figure 2a and b]. Based on the clinical and histopathological findings, our case was diagnosed as PEODDN along with a unique histological finding of a lichenoid infiltrate.
Community Specific Temporal Topic Discovery from Social Media

Studying temporal dynamics of topics in social media is very useful to understand online user behaviors. Most of the existing work on this subject usually monitors the global trends, ignoring variation among communities. Since users from different communities tend to have varying tastes and interests, capturing communitylevel temporal change can improve the understanding and management of social content. Additionally, it can further facilitate the applications such as community discovery, temporal prediction and online marketing. However, this kind of extraction becomes challenging due to the intricate interactions between community and topic, and intractable computational complexity. In this paper, we take a unified solution towards the communitylevel topic dynamic extraction. A probabilistic model, CosTot (Community Specific Topics-over-Time) is proposed to uncover the hidden topics and communities, as well as capture community-specific temporal dynamics. Specifically, CosTot considers text, time, and network information simultaneously, and well discovers the interactions between community and topic over time. We then discuss the approximate inference implementation to enable scalable computation of model parameters, especially for large social data. Based on this, the application layer support for multi-scale temporal analysis and community exploration is also investigated. We conduct extensive experimental studies on a large real microblog dataset, and demonstrate the superiority of proposed model on tasks of time stamp prediction, link prediction and topic perplexity.
Idiopathic penile mondors' disease: A case report

A 34-year old man was admitted to us with a mild painful cord-like induration on the left dorsal side of the penis extending from corona of the glans to the base of the penis. The patient was treated with anti-inflammatory and anticoagulan agents. Cord-like-induration resolved and recanalized in 4th week, and venous flow was detected in Doppler examination.
A novel algorithm for community detection and influence ranking in social networks

Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail how the node's influence is distributed through the network. Similarity in this influence space defines a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Our approach not only differentiates the influence ranking but also effectively finds communities in both undirected and directed networks, and incorporates these two important tasks into one integrated framework. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks.
TILDE: A Temporally Invariant Learned DEtector

We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on untrained standard datasets.
Cross-Site Scripting Attacks in Social Network APIs

Nowadays, it is becoming more popular that RESTful APIs are used by web developers to enhance the functionality of websites. However, this might raise potential XSS attack threats. Unlike traditional XSS attacks, XSS attacks in this scenario may take advantage of more characteristics of RESTful APIs. RESTful APIs are common in social networks. Consequently, in this paper, we took social networks as motivating examples to illustrate XSS attacks in
Minimizing stand-by leakage power in static CMOS circuits

In this paper we concern ourselves with the problem of minimizing leakage power in CMOS circuits consisting of AOI (and-or-invert) gates as they operate in stand-by mode or an idle mode waiting for other circuits to complete their operation. It is known that leakage power due to subthreshold leakage current in transistors in the OFF state is dependent on the input vector applied. Therefore, we try to compute an input vector that can be applied to the circuit in stand-by mode so that the power loss due to sub-threshold leakage current is the minimum possible. We employ a integer linear programming (ILP) approach to solve the problem of minimizing leakage by first obtaining a good lower bound (estimate) on the minimum leakage power and then rounding the solution to actually obtain an input vector that causes low leakage. The chief advantage of this technique as opposed to others in the literature is that it invariably provides us with a good idea about the quality of the input vector found.
OSINT-based Data-driven Cybersecurity Discovey

This work was supported by EC through funding of DiSIEM project, ref. G.A. no 700692, and by FCT through funding of LASIGE Research Unit, ref. UID/CEC/00408/2013 A wealth of OSINT is published daily to the Internet ... and yet, companies do not take advantage from it. Security feeds, hackers, security analysts, researchers,, users, and others post their experiences and findings. Scraping such data can provide valuable and much needed security awareness. Twitter is a natural data aggregator, since it provides visibility to any kind of content.
Reducing bandwidth requirement for delivering video over wide area networks with proxy server

Due to the high bandwidth requirement and rate variability of compressed video, delivering video across wide area networks (WANs) is a challenging issue. Proxy servers have been used to reduce network congestion and improve client access time on the Internet by caching passing data. In this paper, we investigate ways to store or stage partial video in proxy servers to reduce the network bandwidth requirement over WAN. A client needs to access a portion of the video from a proxy server over a local area network (LAN) and the rest from a central server across a WAN. Therefore, client buffer requirement and video synchronization are to be considered. We study the tradeoffs between client buffer, storage requirement on the proxy server, and bandwidth requirement over WAN. Given a video delivery rate for the WAN, we propose several frame staging selection algorithms to determine the video frames to be stored in the proxy server. A scheme called chunk algorithm, which partitions a video into different segments (chunks of frames) with alternating chunks stored in the proxy server, is shown to offer the best tradeoff. We also investigate an efficient way to utilize client buffer when the combination of video streams from WAN and LAN is considered.
Automatic Classification of Questions into Bloom's Cognitive Levels Using Support Vector Machines

In recent years, E-learning has increasingly become a promising technology in educational institutions. Among numerous components of E-learning systems, question bank is a primordial component. Question bank is a repository of questions that assists students and instructors in the educational process. In question bank, questions are annotated, stored and retrieved based on predefined criteria such as bloom's cognitive levels. Definitely, for question bank management, the automatic classification of questions according to Bloom's cognitive levels is of particular benefit. This paper explores the effectiveness of support vector machines (SVMs), in tackling the problem of question classification into Bloom's cognitive levels. To do so, a dataset of pre-classified questions has been collected. Each question is processed through removal of punctuations and stop words, tokenization, stemming, term weighting and length normalization. SVMs classifiers, namely linear kernel, have been built and evaluated on approximately 70% and 30% of the dataset respectively, using SVM-Light software package. The obtained preliminary results show a satisfactory effectiveness of SVMs with respect to classification accuracy and precision. However, due to the small size of the current dataset, the results of the classifiers' recall and F-measure suggest a need for further experiments with larger dataset to obtain conclusive results.
Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning

We propose a novel method capable of retrieving clips from untrimmed videos based on natural language queries. This cross-modal retrieval task plays a key role in visual-semantic understanding, and requires localizing clips in time and computing their similarity to the query sentence. Current methods generate sentence and video embeddings and then compare them using a late fusion approach, but this ignores the word order in queries and prevents more fine-grained comparisons. Motivated by the need for fine-grained multi-modal feature fusion, we propose a novel early fusion embedding approach that combines video and language information at the word level. Furthermore, we use the inverse task of dense video captioning as a side-task to improve the learned embedding. Our full model combines these components with an efficient proposal pipeline that performs accurate localization of potential video clips. We present a comprehensive experimental validation on two large-scale text-to-clip datasets (Charades-STA and DiDeMo) and attain state-ofthe-art retrieval results with our model.
Hybrid DC Circuit Breaker and Fault Current Limiter With Optional Interruption Capability

Since most of renewable energy resources are dc type, dc microgrids find more attention in deregulated power systems for improvement of reliability and efficiency of the system. In dc microgrids, there is no zero-crossing in current and voltage; so the circuit breaking and fault current limiting are serious concerns. Conventional thyristor-based dc fault current limiters are attractive solutions for tackling this problem. However, they impose permanent interruption on the system in case of temporary fault. In this paper, a novel hybrid circuit breaker (HCB) is proposed to improve power quality of the system by adopting different limiting behaviors for permanent and temporary faults. The proposed HCB transfers load power through a mechanical circuit breaker (MCB) under normal condition. Upon fault occurrence in both sides of the HCB, the fault current is transferred from the MCB to a current limiting path incorporating a zero voltage switching (ZVS) transition mechanism. The HCB limits the fault current for a predefined duration and then interrupts the faulty feeder. In case of temporary fault, the current is returned to the MCB in ZVS. The HCB operation principle is discussed in different operation modes. Capabilities of the proposed HCB are tested and verified by different experiments carried out on a prototype.
Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases.

Two of the most important prognostic indicators for breast cancer are tumor size and extent of axillary lymph node involvement. Data on 24,740 cases recorded in the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute were used to evaluate the breast cancer survival experience in a representative sample of women from the United States. Actuarial (life table) methods were used to investigate the 5-year relative survival rates in cases with known operative/pathologic axillary lymph node status and primary tumor diameter. Survival rates varied from 45.5% for tumor diameters equal to or greater than 5 cm with positive axillary nodes to 96.3% for tumors less than 2 cm and with no involved nodes. The relation between tumor size and lymph node status was investigated in detail. Tumor diameter and lymph node status were found to act as independent but additive prognostic indicators. As tumor size increased, survival decreased regardless of lymph node status; and as lymph node involvement increased, survival status also decreased regardless of tumor size. A linear relation was found between tumor diameter and the percent of cases with positive lymph node involvement. The results of our analyses suggest that disease progression to distant sites does not occur exclusively via the axillary lymph nodes, but rather that lymph node status serves as an indicator of the tumor's ability to spread.
The CQL continuous query language: semantic foundations and query execution

CQL, a continuous query language, is supported by the STREAM prototype data stream management system (DSMS) at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and stored relations. We begin by presenting an abstract semantics that relies only on “black-box” mappings among streams and relations. From these mappings we define a precise and general interpretation for continuous queries. CQL is an instantiation of our abstract semantics using SQL to map from relations to relations, window specifications derived from SQL-99 to map from streams to relations, and three new operators to map from relations to streams. Most of the CQL language is operational in the STREAM system. We present the structure of CQL's query execution plans as well as details of the most important components: operators, interoperator queues, synopses, and sharing of components among multiple operators and queries. Examples throughout the paper are drawn from the Linear Road benchmark recently proposed for DSMSs. We also curate a public repository of data stream applications that includes a wide variety of queries expressed in CQL. The relative ease of capturing these applications in CQL is one indicator that the language contains an appropriate set of constructs for data stream processing.
Development of an Electronic Health Record Application using a Multiple View Service Oriented Architecture

Service-Oriented Architecture has been widely adopted in several domains in past years with the purpose of developing distributed applications. Within the health domain, integration of legacy systems by means of web services has been applied in order to develop complex applications. However, few approaches in this area treat complexity by delineating a software architecture to which applications must conform. In most cases in the literature, SOA applications in the health domain are documented using only one or two architecture views. This paper proposes a multiple view Service Oriented Architecture which is the basis for development of an Electronic Health Record (EHR) application. In order to develop the EHR, new requirements as well as current functionalities obtained from integrating legacy systems by means of web services were considered in a cohesive approach. Four architecture views, Scenarios, Business Process, Implementation and Logical View are presented. Each architecture view addresses one specific concern that organizes important concepts, facilitates understanding the system, and improves possibilities of communication between stakeholders. As a result, important software principles such as separation of concerns, component-based development and modularity are considered for development and integration of legacy systems in order to develop the EHR application to be deployed in a public hospital.
Multimodal Recurrent Model with Attention for Automated Radiology Report Generation

Radiologists routinely examine medical images such as XRay, CT, or MRI and write reports summarizing their descriptive findings and conclusive impressions. A computer-aided radiology report generation system can lighten the workload for radiologists considerably and assist them in decision making. Although the rapid development of deep learning technology makes the generation of a single conclusive sentence possible, results produced by existing methods are not sufficiently reliable due to the complexity of medical images. Furthermore, generating detailed paragraph descriptions for medical images remains a challenging problem. To tackle this problem, we propose a novel generative model which generates a complete radiology report automatically. The proposed model incorporates the Convolutional Neural Networks (CNNs) with the Long Short-Term Memory (LSTM) in a recurrent way. It is capable of not only generating high-level conclusive impressions, but also generating detailed descriptive findings sentence by sentence to support the conclusion. Furthermore, our multimodal model combines the encoding of the image and one generated sentence to construct an attention input to guide the generation of the next sentence, and henceforth maintains coherence among generated sentences. Experimental results on the publicly available Indiana U. Chest X-rays from the Open-i image collection show that our proposed recurrent attention model achieves significant improvements over baseline models according to multiple evaluation metrics.
Cataract Surgery in Eyes with Low Corneal Astigmatism: Implantation of the Acrysof IQ Toric SN6AT2 Intraocular Lens

To assess the refractive and visual outcomes following cataract surgery and implantation of the AcrySof IQ Toric SN6AT2 intraolcular lens (IOL) (Alcon Laboratories, Inc., Fort Worth, TX, USA) in patients with low corneal astigmatism.



This study is a retrospective, consecutive, single surgeon series of 98 eyes of 88 patients (with low preoperative corneal astigmatism) undergoing cataract surgery and implantation of the AcrySof IQ Toric SN6AT2 IOL. Postoperative measurements were obtained 1-month postsurgery. Main outcome measures were monocular distance visual acuity and residual refractive astigmatism.



Mean preoperative corneal astigmatic power vector (APV) was 0.38±0.09 D. Following surgery and implantation of the toric IOL, mean postoperative refractive APV was 0.13±0.10 D. Mean postoperative distance uncorrected visual acuity was 0.08±0.09 logMAR. Postoperative spherical equivalent refraction (SER) resulted in a mean of -0.23±0.22 D, with 96% of eyes falling within 0.50 D of the target SER.



The AcrySof IQ Toric SN6AT2 IOL is a safe and effective option for eyes undergoing cataract surgery with low levels of preoperative corneal astigmatism.
Zero-voltage-switching PWM three-level converter with current-doubler-rectifier

This paper proposes a zero-voltage-switching (ZVS) pulse-width modulation three-level converter with current-doubler-rectifier, which achieves ZVS for all the switches under wide load range and in a wide line range. The rectifier diodes commutate naturally, therefore no oscillation occurs. The determination of the output filter inductance and the blocking capacitor is discussed in details. The experimental results are presented to verify the operation principle of the proposed converter.
Botnet Detection in the Internet of Things using Deep Learning Approaches

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request.