nils-herrmann/clean_scirepeval huggingface dataset:
Hi, How Can I Help You?: Automating Enterprise IT Support Help Desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain, the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.
A Novel Approach for Edge Detection of Images

Edge detection is a problem of fundamental importance in image analysis. Many approaches for edge detection have already revealed more are waiting to be. But edge detection using K-means algorithm is the most heuristic and unique approach. In this paper, we have proposed an algorithmic technique to detect the edge of any kind of true gray scale images considering the artificial features of the image as the feature set which is fed to K-Means algorithm for clustering the image and there to detect clearly the edges of the objects present in the considered image. The artificial features, which we have considered here, are mean, standard deviation, entropy and busyness of pixel intensity values.
Three-Dimensional Memristor-Based Crossbar Architecture for Capsule Network Implementation

Although the Capsule Network (CapsNet) has a better proven performance for the recognition of overlapping digits than Convolutional Neural Networks (CNNs), a large number of matrix-vector multiplications between lower-level and higher-level capsules impede efficient implementation of the CapsNet on conventional hardware platforms. Since three-dimensional (3-D) memristor crossbars provide a compact and parallel hardware implementation of neural networks, this paper provides an architecture design to accelerate convolutional and matrix operations of the CapsNet. By using 3-D memristor crossbars, the PrimaryCaps, DigitCaps, and convolutional layers of a CapsNet perform the matrix-vector multiplications in a highly parallel way. Simulations are conducted to recognize digits from the USPS database and to analyse the work efficiency of the proposed circuits. The proposed design provides a new approach to implement the CapsNet on memristor-based circuits.
Learning for Efficient Supervised Query Expansion via Two-stage Feature Selection

Query expansion (QE) is a well known technique to improve retrieval effectiveness, which expands original queries with extra terms that are predicted to be relevant. A recent trend in the literature is Supervised Query Expansion (SQE), where supervised learning is introduced to better select expansion terms. However, an important but neglected issue for SQE is its efficiency, as applying SQE in retrieval can be much more time-consuming than applying Unsupervised Query Expansion (UQE) algorithms. In this paper, we point out that the cost of SQE mainly comes from term feature extraction, and propose a Two-stage Feature Selection framework (TFS) to address this problem. The first stage is adaptive expansion decision, which determines if a query is suitable for SQE or not. For unsuitable queries, SQE is skipped and no term features are extracted at all, which reduces the most time cost. For those suitable queries, the second stage is cost constrained feature selection, which chooses a subset of effective yet inexpensive features for supervised learning. Extensive experiments on four corpora (including three academic and one industry corpus) show that our TFS framework can substantially reduce the time cost for SQE, while maintaining its effectiveness.
Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities

Delineating infarcted tissue in ischemic stroke lesions is crucial to determine the extend of damage and optimal treatment for this life-threatening condition. However, this problem remains challenging due to high variability of ischemic strokes’ location and shape. Recently, fully-convolutional neural networks (CNN), in particular those based on U-Net , have led to improved performances for this task [7]. In this work, we propose a novel architecture that improves standard U-Net based methods in three important ways. First, instead of combining the available image modalities at the input, each of them is processed in a different path to better exploit their unique information. Moreover, the network is densely-connected (i.e., each layer is connected to all following layers), both within each path and across different paths, similar to HyperDenseNet [11]. This gives our model the freedom to learn the scale at which modalities should be processed and combined. Finally, inspired by the Inception architecture , we improve standard U-Net modules by extending inception modules with two convolutional blocks with dilated convolutions of different scale. This helps handling the variability in lesion sizes. We split the 93 stroke datasets into training and validation sets containing 83 and 9 examples respectively. Our network was trained on a NVidia TITAN XP GPU with 16 GBs RAM, using ADAM as optimizer and a learning rate of 1×10−5 during 200 epochs. Training took around 5 hours and segmentation of a whole volume took between 0.2 and 2 seconds, as average. The performance on the test set obtained by our method is compared to several baselines, to demonstrate the effectiveness of our architecture, and to a state-of-art architecture that employs factorized dilated convolutions, i.e., ERFNet [26].
Reconfigurable Codesign of STT-MRAM Under Process Variations in Deeply Scaled Technology

Recently, spin-transfer torque magnetic random access memory (STT-MRAM) has been considered as a promising universal memory candidate for future memory and computing systems, thanks to its nonvolatility, high speed, low power, good endurance, and scalability. However, as technology scales down, STT-MRAM suffers from serious process variations and thermal fluctuations, which greatly degrade the performance and stability of STT-MRAM. In general, the optimization and robustness of STT-MRAM under process variations often require a hybrid design flow and multilevel codesign strategies. In this paper, we quantitatively analyze the impacts of process variations and thermal fluctuations on the STT-MRAM performances from physics, technology, and circuit design point of views. Based on the analyses, we found that readability is becoming the newest challenge for deeply scaled STT-MRAM due to the conflict between sensing margin and read disturbance. To deal with this problem, a novel reconfigurable design strategy from device, circuit, and architecture codesign perspective is then presented. Finally, a conceptual hybrid magnetic/CMOS design flow is also proposed for STT-MRAM in deeply scaled technology nodes.
Convolutional neural networks (CNN) based human fall detection on Body Sensor Networks (BSN) sensor data

According to the World Health Organization, around 28–35% of people aged 65 and older fall each year. This number increases to around 32–42% for people over 70 years old. For this reason, this research targets the exploration of the role of Convolutional Neural Networks(CNN) in human fall detection. There are a number of current solutions related to fall detection; however, remain low detection accuracy. Although CNN has proven a powerful technique for image recognition problems, and the CNN library in Matlab was designed to work with either images or matrices, this research explored how to apply CNN to streaming sensor data, collected from Body Sensor Networks (BSN), in order to improve the fall detection accuracy. The idea of this research is that given the stream data sets as input, we converted them into images before applying CNN. The final accuracy result achieved is, to the best of our knowledge, the highest compared to other proposed methods: 92.3%.
JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

Balanced graph partitioning is a well known NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems including the optimal storage of large sets of graph-structured data over several hosts-a key problem in today's Cloud infrastructure. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because they typically involve frequent global operations over the entire graph. In this paper, we propose a fully distributed algorithm, called JA-BE-JA, that uses local search and simulated annealing techniques for graph partitioning. The algorithm is massively parallel: there is no central coordination, each node is processed independently, and only the direct neighbors of the node, and a small subset of random nodes in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We perform a thorough experimental analysis, which shows that the minimal edge-cut value achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks JA-BEJA outperforms METIS, which makes JA-BE-JA-a bottom-up, self-organizing algorithm-a highly competitive practical solution for graph partitioning.
A 65nm CMOS DCXO system for generating 38.4MHz and a real time clock from a single crystal in 0.09mm2

An integrated digitally-controlled crystal oscillator (DCXO) is presented that generates both 38.4 MHz and also a 32.768 kHz real time clock (RTC) from a single 38.4 MHz crystal. The DCXO can startup independently and transition seamlessly in and out of software control. The tuning range is 280 ppm with 2 ppb/step and guaranteed monotonicity. The phase noise is -135 dBc/Hz at 1kHz offset and -146 dBc/Hz at 10 kHz offset. The current consumption is 5 mA from a 1.4 V supply in full power mode and 234 μA in low power mode, including the LDO and all clock buffers. The DCXO is implemented in standard 65 nm digital CMOS with a die area of 0.09mm2.
Streaming Recommender Systems

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.
Fetus in Fetu in the Scrotal Sac: Case Report and Literature Review

Fetus in fetu (FIF) is a rare congenital anomaly. The most common site at which FIF occurs is the retroperitoneum. The mechanisms underlying the development of FIF have not been fully elucidated. The monozygotic twin theory postulates that FIF results from the unequal division of the totipotent cells of the blastocyst. However, the monozygotic twin theory does not explain all cases of FIF.Herein, we describe the clinical characteristics of a 20-day-old infant with scrotal sac swelling. Ultrasonography and computed tomography revealed the presence of a mass consistent with a FIF rather than a teratoma. Surgical removal and a subsequent pathological evaluation demonstrated that the anencephalic fetus exhibited limb buds adjacent to a palpable vertebral column, supporting the diagnosis of FIF. The infant had an uneventful recovery and was discharged on the fifth postoperative day. In the present report, the pathogenesis, presentation, diagnosis, and management of FIF, as well as new concepts emerging in this area of research, are discussed.Although the majority of cases of FIF may be diagnosed preoperatively, FIF should be distinguished from teratoma because the latter has substantial malignant potential. The recommended treatment for FIF is complete resection. To confirm the diagnosis of FIF, pathological examination, karyotyping, serologic marker assessment, and DNA restriction site mapping should be performed after removing the mass. Although FIF is thought to be a benign disorder, follow-up is necessary as a precaution against malignant recurrence, which has been described once.
Male circumcision, penile human papillomavirus infection, and cervical cancer in female partners.

It is uncertain whether male circumcision reduces the risks of penile human papillomavirus (HPV) infection in the man and of cervical cancer in his female partner.



We pooled data on 1913 couples enrolled in one of seven case-control studies of cervical carcinoma in situ and cervical cancer in five countries. Circumcision status was self-reported, and the accuracy of the data was confirmed by physical examination at three study sites. The presence or absence of penile HPV DNA was assessed by a polymerase-chain-reaction assay in 1520 men and yielded a valid result in the case of 1139 men (74.9 percent).



Penile HPV was detected in 166 of the 847 uncircumcised men (19.6 percent) and in 16 of the 292 circumcised men (5.5 percent). After adjustment for age at first intercourse, lifetime number of sexual partners, and other potential confounders, circumcised men were less likely than uncircumcised men to have HPV infection (odds ratio, 0.37; 95 percent confidence interval, 0.16 to 0.85). Monogamous women whose male partners had six or more sexual partners and were circumcised had a lower risk of cervical cancer than women whose partners were uncircumcised (adjusted odds ratio, 0.42; 95 percent confidence interval, 0.23 to 0.79). Results were similar in the subgroup of men in whom circumcision was confirmed by medical examination.



Male circumcision is associated with a reduced risk of penile HPV infection and, in the case of men with a history of multiple sexual partners, a reduced risk of cervical cancer in their current female partners.
OntoNotes: A Unified Relational Semantic Representation

The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.
Terra Firma-Forme Dermatosis.

A 12-year-old girl with a history of alopecia areata and vitiligo presented with an asymptomatic brownish dirt-like lesion on the left postauricular skin of approximately 3 years of duration. The patient and her mother tried to clean the “dirt” with water and soap without success. There was no history of rapid weight gain. She had no history of an inflammatory dermatosis in the affected area. Physical examination revealed a dirt-like brownish plaque on the left postauricular skin . Rubbing of the lesion with a 70% isopropyl alcohol-soaked gauze pad and pressure resulted in complete disappearance of the lesion . A diagnosis of terra firma-forme dermatosis was, thus, confirmed. Terra firma-forme dermatosis is characterized by an asymptomatic brownish-black, dirt-like patch/plaque. Affected individuals often have normal hygiene habits. Characteristically, the lesion cannot be removed by conventional washing with soap and water but can be removed by wiping with isopropyl alcohol while applying some pressure. Terra firmaforme dermatosis is most frequently seen in prepubertal children and adolescents. It is believed that the condition results from delayed maturation of keratinocytes with incomplete development of keratin squames, and retention of keratinocytes and melanin within the epidermis. Sites of predilection include the neck, followed by the ankles and trunk. The main differential diagnoses are dermatosis neglecta and acanthosis nigricans. Dermatosis neglecta typically affects individuals of any age with neglected hygiene. The lesions can be removed with normal washing with soap and water as well as with alcohol swab or cotton ball. The lesion of acanthosis nigricans consists of dark, velvety thickening of the skin usually on the nape and sides of the neck. The condition is most commonly associated with obesity. The hyperpigmentation or “dirt” cannot be removed either by normal washing with soap and water or alcohol swab or cotton ball.
A visualization methodology for characterization of network scans

Many methods have been developed for monitoring network traffic, both using visualization and statistics. Most of these methods focus on the detection of suspicious or malicious activities. But what they often fail to do refine and exercise measures that contribute to the characterization of such activities and their sources, once they are detected. In particular, many tools exist that detect network scans or visualize them at a high level, but not very many tools exist that are capable of categorizing and analyzing network scans. This paper presents a means of facilitating the process of characterization by using visualization and statistics techniques to analyze the patterns found in the timing of network scans through a method of continuous improvement in measures that serve to separate the components of interest in the characterization so the user can control separately for the effects of attack tool employed, performance characteristics of the attack platform, and the effects of network routing in the arrival patterns of hostile probes. The end result is a system that allows large numbers of network scans to be rapidly compared and subsequently identified.
Identifying and Classifying Processes (Traditional and Soft Factors) that Support COTS Component Selection: A Case Study

COTS-Based Systems (CBS) development focuses on building large software systems by integrating previously existing software components. CBS success depends on successful evaluation and selection of Commercial-Off-TheShelf (COTS) software components to fit customer requirements. Literature shows that successful selection of offthe-shelf systems to fit customer requirements remains problematic. This paper presents the outcome of a study aimed at using a social-technical approach to identify and classify processes (including traditional and soft factors) that support COTS software selection. The identified factors and lessons learnt from case study assisted in elaborating and further development of Social-Technical Approach to COTS Evaluation framework (STACE).
A Dialogue in Natural Language

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.
SybilGuard: Defending Against Sybil Attacks via Social Networks

Peer-to-peer and other decentralized,distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack,a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system,the malicious user is able to "out vote" the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks.Our protocol is based on the "social network "among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small "cut" in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create.We show the effectiveness of SybilGuard both analytically and experimentally.
Optimizing Search Engine Result using Intelligent Model

Many search engine users face problems while retrieving their required Information. For example, a user may find it is difficult to retrieve sufficient relevant information because he use too few keywords to search or the user is inexperienced and do not search using proper keywords and the search engine is not able to receive the user real meaning through his given keywords. Also, due to the recent improvements of search engines and the rapid growth of the web, the search engines return a huge number of web pages, and then the user may take long time to look at all of these pages to find his needed information. The problem of obtaining relevant results in web searching has been tackled by several approaches. Although very effective techniques are currently used by the most popular search engines, but no a priori knowledge on the user&apos;s desires beside the search keywords is available. In this paper, we present an approach for optimizing the search engine results using artificial intelligence techniques such as document clustering and genetic algorithm to provide the user with the most relevant pages to the search query. The proposed method uses the Meta-data that is coming from the user preferences or the search engine query log files. These data is important to find the most related information to the user while searching the web. Finally, the method