nils-herrmann/clean_scirepeval huggingface dataset:
Multicommodity Flows in Planar Undirected Graphs and Shortest Paths

This paper deals with the multicommodity flow problems for two classes of planar undirected graphs. The first class C<subscrpt>12</subscrpt> consists of graphs in which each source-sink pair is located on one of two specified face boundaries. The second class C<subscrpt>01</subscrpt> consists of graphs in which some of the source-sink pairs are located on a specified face boundary and all the other pairs share a common sink located on the boundary. We show that the multicommodity flow problem for a graph in C<subscrpt>12</subscrpt> (resp. C<subscrpt>01</subscrpt>) can be reduced to the shortest path problem for an undirected (resp. a directed) graph obtained from the dual of the original undirected graph.
HOGgles: Visualizing Object Detection Features

We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
Conditional Privacy Preserving Security Protocol for NFC Applications

NFC (Near field Communication) is a short-range wireless communication technology whose VGF technology distance is around 4 inches, and it operates in the 13.56MHz frequency band at a speed of 106Kbps to 424Kbps. The combination of NFC with smart devices resulted in widening the range of NFC, which includes data exchange, service discovery, connection, e-payment, and ticketing. It is expected to replace credit cards in electronic payment, especially. According to based payment services is expected to increase by 11.3 times from $316 million in 2010 to $3.572 billion in 2015, and Juniper research predicted that the global NFC payment market size would be increased to $180 billion in 2017. To use NFC in electronic payment, security is a prerequisite to be addressed. Presently, NFC security standards define data exchange format, tag types, and security protocols, centering on NFC forum. It is expressly stipulated in the NFC security standards that key agreement is required for secret communications between users. In the process of key agreement, both users should exchange their public keys. The public key is received from CA (Certificate Authority), and it uses a fixed value until reissued. Malicious internal attackers can create profiles of users through the acquisition of public keys of other users in the process of key agreement. If NFC is used in e-payment in this way, the privacy of users can be infringed through profiles created by attackers. Suppose Alice purchases items such as cloths, food, and medicine several times at a supermarket, the supermarket can get information about her tastes, preferences, and health conditions. The collected information can help her to purchase products more efficiently, but it may contain information that nobody wants to announce to others such as his or her health conditions.
Automatic construction of a context-aware sentiment lexicon: an optimization approach

The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people's sentiments toward different topics. In most sentiment analysis applications, the sentiment lexicon plays a central role. However, it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain. Even worse, in the same domain the same word may indicate different polarities with respect to different aspects. For example, in a laptop review, "large" is negative for the battery aspect while being positive for the screen aspect. In this paper, we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection. We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon. Experiments on two data sets (hotel reviews and customer feedback surveys on printers) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context. In further quantitative evaluation, our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard. In addition, using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task.
Prospects for Mobile Health in Pakistan and Other Developing Countries

Pakistan is a developing country with more than half of its population located in rural areas. These areas neither have sufficient health care facilities nor a strong infrastructure that can address the health needs of the people. The expansion of Information and Communication Technology (ICT) around the globe has set up an unprecedented opportunity for delivery of healthcare facilities and infrastructure in these rural areas of Pakistan as well as in other developing countries. Mobile Health (mHealth)—the provision of health care services through mobile telephony—will revolutionize the way health care is delivered. From messaging campaigns to remote monitoring, mobile technology will impact every aspect of health systems. This paper highlights the growth of ICT sector and status of health care facilities in the developing countries, and explores prospects of mHealth as a transformer for health systems and service delivery especially in the remote rural areas.
Study of breast implant rupture: MRI versus surgical findings

This study evaluated the role of breast magnetic resonance (MR) imaging in the selective study breast implant integrity. We retrospectively analysed the signs of breast implant rupture observed at breast MR examinations of 157 implants and determined the sensitivity and specificity of the technique in diagnosing implant rupture by comparing MR data with findings at surgical explantation. The linguine and the salad-oil signs were statistically the most significant signs for diagnosing intracapsular rupture; the presence of siliconomas/seromas outside the capsule and/or in the axillary lymph nodes calls for immediate explantation. In agreement with previous reports, we found a close correlation between imaging signs and findings at explantation. Breast MR imaging can be considered the gold standard in the study of breast implants. Scopo del nostro lavoro è stato quello di valutare il ruolo della risonanza magnetica (RM) mammaria nello studio selettivo dell’integrità degli impianti protesici. è stata eseguita una valutazione retrospettiva dei segni di rottura documentati all’esame RM effettuati su 157 protesi mammarie, al fine di stabilire la sensibilità e specificità nella diagnosi di rottura protesica, confrontando tali dati RM con i reperti riscontrati in sala operatoria dopo la rimozione della protesi stessa. Il linguine sign e il salad-oil sign sono risultati i segni statisticamente più significativi nella diagnosi di rottura protesica intracapsulare; la presenza di siliconomi/sieromi extracapsulari e/o nei linfonodi ascellari impone l’immediato intervento chirurgico di rimozione della protesi rotta. I dati ottenuti dimostrano, in accordo con la letteratura, una corrispondenza tra i segni dell’imaging e i reperti chirurgici, confermando il ruolo di gold standard della RM nello studio delle protesi mammarie.
Mitigating arcing defect at pad etch

This paper is to present method to mitigate arcing defect encountered at pad etch. The problem was detected during wafer disposition due to equipment alarm. Based on observation, this burnt- like defect material, is observed to have inhibited the wafer surface and exposing the top metal line. The inclination was observed mainly during main etch step. The wafer is believed to have encountered plasma instability during transition from Main Etch (ME) to Over Etch (OE) step. However, this is only detected during backside helium leak alarm. This arcing defect was caused by several factors, of which were related to recipe, wafer condition, processing tool and product design. The approach taken was to mitigate these issues where recipe optimization and tighter equipment parameter control were implemented. The design of experiment was presented to find the optimal setting for backside helium flow and chucking voltage. Apart from that, chamber mix run also plays an important role.
Killing Two Birds with One Stone: Malicious Domain Detection with High Accuracy and Coverage

Inference based techniques are one of the major approaches to analyze DNS data and detecting malicious domains. The key idea of inference techniques is to first define associations between domains based on features extracted from DNS data. Then, an inference algorithm is deployed to infer potential malicious domains based on their direct/indirect associations with known malicious ones. The way associations are defined is key to the effectiveness of an inference technique. It is desirable to be both accurate (i.e., avoid falsely associating domains with no meaningful connections) and with good coverage (i.e., identify all associations between domains with meaningful connections). Due to the limited scope of information provided by DNS data, it becomes a challenge to design an association scheme that achieves both high accuracy and good coverage. In this paper, we propose a new association scheme to identify domains controlled by the same entity. Our key idea is an indepth analysis of active DNS data to accurately separate public IPs from dedicated ones, which enables us to build high-quality associations between domains. Our scheme avoids the pitfall of naive approaches that rely on weak “co-IP” relationship of domains (i.e., two domains are resolved to the same IP) that results in low detection accuracy, and, meanwhile, identifies many meaningful connections between domains that are discarded by existing state-of-the-art approaches. Our experimental results show that the proposed association scheme not only significantly improves the domain coverage compared to existing approaches but also achieves better detection accuracy. Existing path-based inference algorithm is specifically designed for DNS data analysis. It is effective but computationally expensive. To further demonstrate the strength of our domain association scheme as well as improving inference efficiency, we investigate the effectiveness of combining our association scheme with the generic belief propagation algorithm. Through comprehensive experiments, we show that this approach offers significant efficiency and scalability improvement with only minor negative impact of detection accuracy, which suggests that such a combination could offer a good tradeoff for malicious domain detection in practice.
Optimal power coefficient for load balancing and reactive power compensation In DFIG-WTS

Doubly-Fed Induction Generators (DFIG) are widely used nowadays in grid-connected Wind Turbine Systems (WTS). The typical control strategy for WTS is the maximum power coefficient tracking method. However, this method limits desirable ancillary services from WTS, such as power quality improvement in the network. Therefore this paper derives the optimal reference power coefficient of the WTS that can enhance the participation of the DFIG in the load balancing and reactive power support to the grid. The unbalanced power compensation is performed only by the Grid Side Converter (GSC), while a proper reactive power share between the GSC and the rotor side converter is used to achieve optimal active power injection. The performance of the proposed optimization approach, based on the capability curve of the DFIG-GSC in steady state, is compared with the typical maximum power point tracking method. The results were obtained by simulating a 2-MW DFIG-WTS in MATLAB/Simulink.
Scheduling of plug-in electric vehicle battery charging with price prediction

This paper proposes a reinforcement learning algorithm that solves the problem of scheduling the charging of a plug-in electric vehicle's (PEV) battery. The algorithm is employed in the demand side management of smart grids. The goal of the algorithm is to minimize the charging cost of the consumer over long term time horizon. The PEV battery charging problem is modeled as a Markov decision process (MDP) with unknown transition probabilities. A Sarsa reinforcement learning method with eligibility traces is proposed for learning the pricing patterns and solving the charging problem. The model uses true day-ahead prices for the current day and predicted prices for the next day. Simulation results using true pricing data demonstrate the cost savings to the consumer.
Near-regular texture analysis and manipulation

A near-regular texture deviates geometrically and photometrically from a regular congruent tiling. Although near-regular textures are ubiquitous in the man-made and natural world, they present computational challenges for state of the art texture analysis and synthesis algorithms. Using regular tiling as our anchor point, and with user-assisted lattice extraction, we can explicitly model the deformation of a near-regular texture with respect to geometry, lighting and color. We treat a deformation field both as a function that acts on a texture and as a texture that is acted upon, and develop a multi-modal framework where each deformation field is subject to analysis, synthesis and manipulation. Using this formalization, we are able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.
Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices

Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the increasing interest in the literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. This paper aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. We used the systematic literature review method for reviewing the peer-reviewed papers on continuous practices published between 2004 and June 1, 2016. We applied the thematic analysis method for analyzing the data extracted from reviewing 69 papers selected using predefined criteria. We have identified 30 approaches and associated tools, which facilitate the implementation of continuous practices in the following ways: 1) reducing build and test time in continuous integration (CI); 2) increasing visibility and awareness on build and test results in CI; 3) supporting (semi-) automated continuous testing; 4) detecting violations, flaws, and faults in CI; 5) addressing security and scalability issues in deployment pipeline; and 6) improving dependability and reliability of deployment process. We have also determined a list of critical factors, such as testing (effort and time), team awareness and transparency, good design principles, customer, highly skilled and motivated team, application domain, and appropriate infrastructure that should be carefully considered when introducing continuous practices in a given organization. The majority of the reviewed papers were validation (34.7%) and evaluation (36.2%) research types. This paper also reveals that continuous practices have been successfully applied to both greenfield and maintenance projects. Continuous practices have become an important area of software engineering research and practice. While the reported approaches, tools, and practices are addressing a wide range of challenges, there are several challenges and gaps, which require future research work for improving the capturing and reporting of contextual information in the studies reporting different aspects of continuous practices; gaining a deep understanding of how software-intensive systems should be (re-) architected to support continuous practices; and addressing the lack of knowledge and tools for engineering processes of designing and running secure deployment pipelines.
Dermoscopic Findings in CutaneousMetastases

C utaneous metastases rarely develop in patients having cancer with solid tumors. The reported incidence of cutaneous metastases from a known primary malignancy ranges from 0.6% to 9%, usually appearing 2 to 3 years after the initial diagnosis.1-11 Skin metastases may represent the first sign of extranodal disease in 7.6% of patients with a primary oncologic  Cutaneous metastases may also be the first sign of recurrent disease after treatment, with 75% of patients also having visceral metastases.2 Infrequently, cutaneous metastases may be seen as the primary manifestation of an undiagnosed malignancy.12 Prompt recognition of such tumors can be of great significance, affecting prognosis and management. The initial presentation of cutaneous metastases is frequently subtle and may be overlooked without proper index of suspicion, appearing as multiple or single nodules, plaques, and ulcers, in decreasing order of frequency. Commonly, a painless, mobile, erythematous papule is initially noted, which may enlarge to an inflammatory nodule over  Such lesions may be misdiagnosed as cysts, lipomas, fibromas, or appendageal tumors. Clinical features of cutaneous metastases rarely provide information regarding the primary tumor, although the location of the tumor may be helpful because cutaneous metastases typically manifest in the same geographic region as the initial cancer. The most common primary tumors seen with cutaneous metastases are melanoma, breast, and squamous cell carcinoma of the head and neck.1 Cutaneous metastases are often firm, because of dermal or lymphatic involvement, or erythematous. These features may help rule out some nonvascular entities in the differential diagnosis (eg, cysts and fibromas). The presence of pigment most commonly correlates with cutaneous metastases from melanoma. Given the limited body of knowledge regarding distinct clinical findings, we sought to better elucidate the dermoscopic patterns of cutaneous metastases, with the goal of using this diagnostic tool to help identify these lesions. We describe 20 outpatients with biopsy-proven cutaneous metastases secondary to various underlying primary malignancies. Their clinical presentation is reviewed, emphasizing the dermoscopic findings, as well as the histopathologic correlation.
Liveness Detection Using Gaze Collinearity

This paper presents a liveness detection method based on tracking the gaze of the user of a face recognition system using a single camera. The user is required to follow a visual animation of a moving object on a display screen while his/her gaze is measured. The visual stimulus is designed to direct the gaze of the user to sets of collinear points on the screen. Features based on the measured collinearity of the observed gaze are then used to discriminate between live attempts at responding to this challenge and those conducted by "impostors" holding photographs and attempting to follow the stimulus. An initial set of experiments is reported that indicates the effectiveness of the proposed method in detecting this class of spoofing attacks.
A Large-Scale Evaluation of Automated Unit Test Generation Using EvoSuite

Research on software testing produces many innovative automated techniques, but because software testing is by necessity incomplete and approximate, any new technique faces the challenge of an empirical assessment. In the past, we have demonstrated scientific advance in automated unit test generation with the EVOSUITE tool by evaluating it on manually selected open-source projects or examples that represent a particular problem addressed by the underlying technique. However, demonstrating scientific advance is not necessarily the same as demonstrating practical value; even if VOSUITE worked well on the software projects we selected for evaluation, it might not scale up to the complexity of real systems. Ideally, one would use large “real-world” software systems to minimize the threats to external validity when evaluating research tools. However, neither choosing such software systems nor applying research prototypes to them are trivial tasks.
 In this article we present the results of a large experiment in unit test generation using the VOSUITE tool on 100 randomly chosen open-source projects, the 10 most popular open-source projects according to the SourceForge Web site, seven industrial projects, and 11 automatically generated software projects. The study confirms that VOSUITE can achieve good levels of branch coverage (on average, 71% per class) in practice. However, the study also exemplifies how the choice of software systems for an empirical study can influence the results of the experiments, which can serve to inform researchers to make more conscious choices in the selection of software system subjects. Furthermore, our experiments demonstrate how practical limitations interfere with scientific advances, branch coverage on an unbiased sample is affected by predominant environmental dependencies. The surprisingly large effect of such practical engineering problems in unit testing will hopefully lead to a larger appreciation of work in this area, thus supporting transfer of knowledge from software testing research to practice.
A Framework for Multi-Robot Coalition Formation

Task allocation is a fundamental problem that any multirobot system must address. Numerous multi-robot task allocation schemes have been proposed over the past decade. A vast majority of these schemes address the problem of assigning a single robot to each task. However as the complexity of multi-robot tasks increases, often situations arise where multiple robot teams need to be assigned to a set of tasks. This problem, also known as the coalition formation problem has received relatively little attention in the multi-robot community. This paper provides a generic, task independent framework for solutions to this problem for a variety task environments. In particular, the paper introduces RACHNA, a novel auction based coalition formation system for dynamic task environments. This is an extension to our previous work which proposed a static multi-robot coalition formation algorithm based on a popular heuristic from the Distributed Artificial Intelligence