nils-herrmann/clean_scirepeval huggingface dataset:
A HIERARCHICAL INTRUSION DETECTION ARCHITECTURE FOR WIRELESS SENSOR NETWORKS

Networks protection against different types of attacks is one of most important posed issue into the network and information security application domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed architectures and guide lines to protect Wireless Sensor Networks (WSNs) against different types of intrusions; but any one of them do not has a comprehensive view to this problem and they are usually designed and implemented in single-purpose; but, the proposed design in this paper tries to has been a comprehensive view to this issue by presenting a complete and comprehensive Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure; i.e., it is designed and applicable, in one or two levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical and clustering structure, clusters' overlapping and using hierarchical routing protocol such as LEACH, but along with minor changes. Finally, the proposed idea has been verified by designing a questionnaire, representing it to some (about 50 people) experts and then, analyzing and evaluating its acquired results.
A novel method of fingerprint minutiae extraction based on Gabor phase

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.
Semiotic schemas: A framework for grounding language in action and perception

A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent’s physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.
ChAirGest: a challenge for multimodal mid-air gesture recognition for close HCI

In this paper, we present a research oriented open challenge focusing on multimodal gesture spotting and recognition from continuous sequences in the context of close human-computer interaction. We contextually outline the added value of the proposed challenge by presenting most recent and popular challenges and corpora available in the field. Then we present the procedures for data collection, corpus creation and the tools that have been developed for participants. Finally we introduce a novel single performance metric that has been developed to quantitatively evaluate the spotting and recognition task with multiple sensors.
Internet of Things: Infrastructure, Architecture, Security and Privacy

Internet of Things (IoT) is one of the emerging technologies of this century and its various aspects, such as the Infrastructure, Security, Architecture and Privacy, play an important role in shaping the future of the digitalised world. Internet of Things devices are connected through sensors which have significant impacts on the data and its security. In this research, we used IoT five layered architecture of the Internet of Things to address the security and private issues of IoT enabled services and applications. Furthermore, a detailed survey on Internet of Things infrastructure, architecture, security, and privacy of the heterogeneous objects were presented. The paper identifies the major challenge in the field of IoT; one of them is to secure the data while accessing the objects through sensing machines. This research advocates the importance of securing the IoT ecosystem at each layer resulting in an enhanced overall security of the connected devices as well as the data generated. Thus, this paper put forwards a security model to be utilised by the researchers, manufacturers and developers of IoT devices, applications and services.
Diagnostic value of serum procalcitonin in patients with chronic renal insufficiency: a systematic review and meta-analysis.

The diagnostic value of procalcitonin (PCT) for patients with renal impairment is unclear.



We searched multiple databases for studies published through December 2011 that evaluated the diagnostic performance of PCT among patients with renal impairment and suspected systemic bacterial infection. We summarized test performance characteristics with the use of forest plots, hierarchical summary receiver operating characteristic (HSROC) curves, and bivariate random effects models.



Our search identified 201 citations, of which seven diagnostic studies evaluated 803 patients and 255 bacterial infection episodes. HSROC-bivariate pooled sensitivity estimates were 73% [95% confidence interval (95% CI) 54-86%] for PCT tests and 78% (95% CI 52-92%) for CRP tests. Pooled specificity estimates were higher for both PCT and CRP tests [PCT, 88% (95% CI 79-93%); CRP, 84% (95% CI, 52-96%)]. The positive likelihood ratio for PCT [likelihood (LR)+ 6.02, 95% CI 3.16-11.47] was sufficiently high to be qualified as a rule-in diagnostic tool, while the negative likelihood ratio was not low enough to be used as a rule-out diagnostic tool (LR- 0.31, 95% CI 0.17-0.57). There was no consistent evidence that PCT was more accurate than CRP test for the diagnosis of systemic infection among patients with renal impairment.



Both PCT and CRP tests have poor sensitivity but acceptable specificity in diagnosing bacterial infection among patients with renal impairment. Given the poor negative likelihood ratio, its role as a rule-out test is questionable.
Early onset scoliosis: the value of serial risser casts.

Treatment of early onset scoliosis (EOS) is challenging. In many cases, bracing will not be effective and growing rod surgery may be inappropriate. Serial, Risser casts may be an effective intermediate method of treatment.



We studied 20 consecutive patients with EOS who received serial Risser casts under general anesthesia between 1999 and 2011. Analyses included diagnosis, sex, age at initial cast application, major curve severity, initial curve correction, curve magnitude at the time of treatment change or latest follow-up for those still in casts, number of casts per patient, the type of subsequent treatment, and any complications.



There were 8 patients with idiopathic scoliosis, 6 patients with neuromuscular scoliosis, 5 patients with syndromic scoliosis, and 1 patient with skeletal dysplasia. Fifteen patients were female and 5 were male. The mean age at first cast was 3.8±2.3 years (range, 1 to 8 y), and the mean major curve magnitude was 74±18 degrees (range, 40 to 118 degrees). After initial cast application, the major curve measured 46±14 degrees (range, 25 to 79 degrees). At treatment change or latest follow-up for those still in casts, the major curve measured 53±24 degrees (range, 13 to 112 degrees). The mean time in casts was 16.9±9.1 months (range, 4 to 35 mo). The mean number of casts per patient was 4.7±2.2 casts (range, 1 to 9 casts). At the time of this study, 7 patients had undergone growing rod surgery, 6 patients were still undergoing casting, 5 returned to bracing, and 2 have been lost to follow-up. Four patients had minor complications: 2 patients each with superficial skin irritation and cast intolerance.



Serial Risser casting is a safe and effective intermediate treatment for EOS. It can stabilize relatively large curves in young children and allows the child to reach a more suitable age for other forms of treatment, such as growing rods.
MaskRNN: Instance Level Video Object Segmentation

Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance — a binary segmentation net providing a mask and a localization net providing a bounding box. Due to the recurrent component and the localization component, our method is able to take advantage of long-term temporal structures of the video data as well as rejecting outliers. We validate the proposed algorithm on three challenging benchmark datasets, the DAVIS-2016 dataset, the DAVIS-2017 dataset, and the Segtrack v2 dataset, achieving state-of-the-art performance on all of them.
A Multi Slot Patch Antenna for 4 G MIMO Communications

A compact two element MIMO (Multiple Input Multiple Output) system is proposed using a multi slot patch antenna employing polarization diversity. The proposed MIMO system offers improved bandwidth, return loss and isolation characteristics. The system resonates at 5.35GHz and 5.81GHz frequencies for VSWR≤2, which can be used for 4G & WiMAX applications. The simulation results of return loss, mutual coupling, correlation coefficient and gain are presented. The channel capacities for the developed system are also estimated for indoor propagation.
Characterizing usage of explicit hate expressions in social media

Social media platforms provide an inexpensive communication medium that allows anyone to publish content and anyone interested in the content can obtain it. However, this same potential of social media provide space for discourses that are harmful to certain groups of people. Examples of these discourses include bullying, offensive content, and hate speech. Out of these discourses hate speech is rapidly recognized as a serious problem by authorities of many countries. In this paper, we provide the first of a kind systematic large-scale measurement and analysis study of explicit expressions of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech, the sensitivity of hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.
Ulcerated midline nodule of the hard palate.

A 14-year-old boy was referred to the Oral Medicine Clinic, School of Dentistry, Universidade Federal de Minas Gerais, for evaluation of a red nodular lesion of the palate. The lesion, which measured 10 10 5 mm, was located in the midline of the hard palate and was firm to palpation. A focal area of ulceration was noted in the center of the lesion . The patient reported that the lesion had been present for 2 months and was somewhat painful, presumably secondary to the ulceration. No lymph nodes were palpable. The patient’s medical history was otherwise noncontributory. No osseous alterations were noted on occlusal radiograph  or computerized tomography scanning.
Trends in business process analysis - from verification to process mining

Business process analysis ranges from model verification at design-time to the monitoring of processes at runtime. Much progress has been achieved in process verificatio n. Today we are able to verify the entire reference model of SAP without any problems. Moreover, more and more pr ocesses leave their “trail” in the form of event logs. This makes it interesting to apply process minin g to these logs. Interestingly, practical applications of process mining reveal that reality is often quite differe nt from the idealized models, also referred to as “PowerPoint reality”. Future process-aware information s ystems will need to provide full support of the entire life-cycle of business processes. Recent results in busine s process analysis show that this is indeed possible, e.g., the possibilities offered by process mining tools suc h as ProM are breathtaking both from a scientific and practical perspective.
Natural Solution to FraCaS Entailment Problems

Reasoning over several premises is not a common feature of RTE systems as it usually requires deep semantic analysis. On the other hand, FraCaS is a collection of entailment problems consisting of multiple premises and covering semantically challenging phenomena. We employ the tableau theorem prover for natural language to solve the FraCaS problems in a natural way. The expressiveness of a type theory, the transparency of natural logic and the schematic nature of tableau inference rules make it easy to model challenging semantic phenomena. The efficiency of theorem proving also becomes challenging when reasoning over several premises. After adapting to the dataset, the prover demonstrates state-of-the-art competence over certain sections of FraCaS.
An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks

Point-of-interest (POI) recommendation is an important service to Location-Based Social Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of POI recommender systems have been proposed, but there is still a lack of systematical comparison thereof. In this paper, we provide an allaround evaluation of 12 state-of-the-art POI recommendation models. From the evaluation, we obtain several important findings, based on which we can better understand and utilize POI recommendation models in various scenarios. We anticipate this work to provide readers with an overall picture of the cutting-edge research on POI recommendation.
Herpes Simplex Virus and Varicella-Zoster Virus.

The most common specimens from immunocompromised patients that are analyzed for detection of herpes simplex virus (HSV) or varicella-zoster virus (VZV) are from skin lesions. Many types of assays are applicable to these samples, but some, such as virus isolation and direct fluorescent antibody testing, are useful only in the early phases of the lesions. In contrast, nucleic acid (NA) detection methods, which generally have superior sensitivity and specificity, can be applied to skin lesions at any stage of progression. NA methods are also the best choice, and sometimes the only choice, for detecting HSV or VZV in blood, cerebrospinal fluid, aqueous or vitreous humor, and from mucosal surfaces. NA methods provide the best performance when reliability and speed (within 24 hours) are considered together. They readily distinguish the type of HSV detected or the source of VZV detected (wild type or vaccine strain). Nucleic acid detection methods are constantly being improved with respect to speed and ease of performance. Broader applications are under study, such as the use of quantitative results of viral load for prognosis and to assess the efficacy of antiviral therapy.
Challenges and Pitfalls of Partitioning Blockchains

Blockchain has received much attention in recent years. This immense popularity has raised a number of concerns, scalability of blockchain systems being a common one. In this paper, we seek to understand how Ethereum, a well-established blockchain system, would respond to sharding. Sharding is a prevalent technique to increase the scalability of distributed systems. To understand how sharding would affect Ethereum, we model Ethereum blockchain as a graph and evaluate five methods to partition the graph. We assess methods using three metrics: the balance among shards, the number of transactions that would involve multiple shards, and the amount of data that would be relocated across shards upon repartitioning of the graph.
Securing Embedded User Interfaces: Android and Beyond

Web and smartphone applications commonly embed third-party user interfaces like advertisements and social media widgets. However, this capability comes with security implications, both for the embedded interfaces and the host page or application. While browsers have evolved over time to address many of these issues, mobile systems like Android — which do not yet support true cross-application interface embedding — present an opportunity to redesign support for secure embedded user interfaces from scratch. In this paper, we explore the requirements for a system to support secure embedded user interfaces by systematically analyzing existing systems like browsers, smartphones, and research systems. We describe our experience modifying Android to support secure interface embedding and evaluate our implementation using case studies that rely on embedded interfaces, such as advertisement libraries, Facebook social plugins (e.g., the “Like” button), and access control gadgets. We provide concrete techniques and reflect on lessons learned for secure embedded user interfaces.
Demand response targeting using big data analytics

The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of shortfall in renewable production. It then becomes important to be able to target the right customers among the large population, since each enrollment has a cost. The availability of high resolution information about each consumers demand consumption can significantly change how such targeting is done. This paper develops a methodology for large scale targeting that combines data analytics and a scalable selection procedure. We propose an efficient customer selection method via stochastic knapsack problem solving and a simple response modeling in one example DR program. To cope with computation issues coming from the large size of data set, we design a novel approximate algorithm.
Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice

Senescent cells (SCs) accumulate with age and after genotoxic stress, such as total-body irradiation (TBI). Clearance of SCs in a progeroid mouse model using a transgenic approach delays several age-associated disorders, suggesting that SCs play a causative role in certain age-related pathologies. Thus, a 'senolytic' pharmacological agent that can selectively kill SCs holds promise for rejuvenating tissue stem cells and extending health span. To test this idea, we screened a collection of compounds and identified ABT263 (a specific inhibitor of the anti-apoptotic proteins BCL-2 and BCL-xL) as a potent senolytic drug. We show that ABT263 selectively kills SCs in culture in a cell type– and species-independent manner by inducing apoptosis. Oral administration of ABT263 to either sublethally irradiated or normally aged mice effectively depleted SCs, including senescent bone marrow hematopoietic stem cells (HSCs) and senescent muscle stem cells (MuSCs). Notably, this depletion mitigated TBI-induced premature aging of the hematopoietic system and rejuvenated the aged HSCs and MuSCs in normally aged mice. Our results demonstrate that selective clearance of SCs by a pharmacological agent is beneficial in part through its rejuvenation of aged tissue stem cells. Thus, senolytic drugs may represent a new class of radiation mitigators and anti-aging agents.
Slick: Secure Middleboxes using Shielded Execution

Cloud computing oers the economies of scale for computational resources with the ease of management, elasticity, and fault tolerance. To take advantage of these benets, many enterprises are contemplating to outsource the middlebox processing services in the cloud. However, middleboxes that process condential and private data cannot be securely deployed in the untrusted environment of the (edge) cloud. To securely outsource middleboxes to the cloud, the stateof-the-art systems advocate network processing over the encrypted trac. Unfortunately, these systems support only restrictive middlebox functionalities, and incur prohibitively high overheads due to the complex computations involved over the encrypted trac. is motivated the design of Slick—a secure middlebox framework for deploying high-performance Network Functions (NFs) on untrusted commodity servers. Slick exposes a generic interface based on Click to design and implement a wide-range of NFs using its out-of-the box elements and C++ extensions. Slick leverages Scone (a shielded execution framework based on Intel SGX) and Intel DPDK to securely process condential data at line rate. More specically, Slick provides hardware-assisted memory protection, and conguration and aestation service for seamless and veriable deployment of middleboxes. We have also added several new features for commonly required functionalities: new specialized Click elements for secure packet processing, secure shared memory packet transfer for NFs chaining, secure state persistence, an ecient on-NIC timer for SGX enclaves, and memory safety against DPDK-specic Iago aacks. Furthermore, we have implemented several SGX-specic optimizations in Slick. Our evaluation shows that Slick achieves near-native throughput and latency.
MobiByte: An Application Development Model for Mobile Cloud Computing

Mobile cloud computing presents an effective solution to overcome smartphone constraints, such as limited computational power, storage, and energy. As the traditional mobile application development models do not support computation offloading, mobile cloud computing requires novel application development models that can facilitate the development of cloud enabled mobile applications. This paper presents a mobile cloud application development model, named MobiByte, to enhance mobile device applications’ performance, energy efficiency, and execution support. MobiByte is a context-aware application model that uses multiple data offloading techniques to support a wide range of applications. The proposed model is validated using prototype applications and detailed results are presented. Moreover, MobiByte is compared with the most recent application models with a conclusion that it outperforms the existing application models in many aspects like energy efficiency, performance, generality, context awareness, and privacy.
A Voting-Based System for Ethical Decision Making

We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.