nils-herrmann/clean_scirepeval huggingface dataset:
Didmoad ( Wolfram ) Syndrome : Case Report

Wolfram syndrome, also known by the mnemonic DIDMOAD (diabetes insipidus, diabetes mellitus, optic atrophy and deafness) is a rare progressive neurodegenerative disorder. This syndrome is further divided to WFS1 and WFS2 based on the different genetic molecular basis and clinical features. In this report, we described a known case of Wolfram syndrome requiring anesthesia for cochlear implantation. Moreover, a brief review of molecular genetics and anesthetic considerations are presented.
A Survey on Uncertainty Management in Data Integration

In the last few years, uncertainty management has come to be recognized as a fundamental aspect of data integration. It is now accepted that it may not be possible to remove uncertainty generated during data integration processes and that uncertainty in itself may represent a source of relevant information. Several issues, such as the aggregation of uncertain mappings and the querying of uncertain mediated schemata, have been addressed by applying well-known uncertainty management theories. However, several problems lie unresolved. This article sketches an initial picture of this highly active research area; it details existing works in the light of a homogeneous framework, and identifies and discusses the leading issues awaiting solutions.
KSU Rich Arabic Speech Database

Arabic is one of the major languages in the world. Unfortunately not so much research in Arabic speaker recognition has been done. One main reason for this lack of research is the unavailability of rich Arabic speech databases. In this paper, we present a rich and comprehensive Arabic speech database that we developed for the Arabic speaker / speech recognition research and/or applications. The database is rich in different aspects: (a) it has 752 speakers; (b) the speakers are from different ethnic groups: Saudis, Arabs, and non-Arabs; (c) utterances are both read text and spontaneous; (d) scripts are of different dimensions, such as, isolated words, digits, phonetically rich words, sentences, phonetically balanced sentences, paragraphs, etc.; (e) different sets of microphones with medium and high quality; (f) telephony and non-telephony speech; (g) three different recording environments: office, sound proof room, and cafeteria; (h) three different sessions, where the recording sessions are scheduled at least with 2 weeks interval. Because of the richness of this database, it can be used in many Arabic, and non-Arabic, speech processing researches, such as speaker / speech recognition, speech analysis, accent identification, ethnic groups / nationality recognition, etc. The richness of the database makes it a valuable resource for research in Arabic speech processing in particular and for research in speech processing in general. The database was carefully manually verified. The manual verification was complemented with automatic verification. Validation was performed on a subset of the database where the recognition rate reached 100% for Saudi speakers and 96% for non-Saudi speakers by using a system with 12 Mel frequency Cepstral coefficients, and 32 Gaussian mixtures.
Group skyline computation

Given a multi-dimensional dataset of tuples, skyline computation returns a subset of tuples that are not dominated by any other tuples when all dimensions are considered together. Conventional skyline computation, however, is inadequate to answer various queries that need to analyze not just individual tuples of a dataset but also their combinations. In this paper, we study group skyline computation which is based on the notion of dominance relation between groups of the same number of tuples. It determines the dominance relation between two groups by comparing their aggregate values such as sums or averages of elements of individual dimensions, and identifies a set of skyline groups that are not dominated by any other groups. We investigate properties of group skyline computation and develop a group skyline algorithm GDynamic which is equivalent to a dynamic algorithm that fills a table of skyline groups. Experimental results show that GDynamic is a practical group skyline algorithm.
Experimental switching frequency limits of 15 kV SiC N-IGBT module

This paper presents extensive experimental switching characteristics of a state-of-the-art 15 kV SiC N-IGBT (0.32 cm2 active area) up to 10 kV, 10 A and 175°C. The influence of the thermal resistance of the module package, cooling mechanism, and the increased energy loss with temperature are investigated for determining the switching frequency limits of the IGBT. Detailed FEM analysis is conducted for extracting the thermal resistance of each layer in the 15 kV module from the IGBT junction to the base plate, and then down to the ambient. Using this thermal information and the experimental switching data, the inductive switching frequency limits are analytically evaluated for liquid and air cooling cases with 660 W/cm2 and 550 W/cm2 power dissipation densities respectively, considering 150°C as maximum junction temperature. The air cooling power dissipation density of the 15 kV IGBT is experimentally validated using a dc-dc boost converter at 10 kV, 6.4 kW output and 550 W/cm2 under steady state operating conditions. The gate resistances used for the entire experiments are RG(ON) = 20 Ω and RG(OFF) = 10 Ω.
Polymer Biodegradation and Biodegradable Polymers – a Review

Synthetic polymers are important in many branches of industry, for example in the packaging industry. However, they have an undesirable influence on the environment and cause problems with waste deposition and utilization. Thus, there is a tendency to substitute such polymers with polymers that undergo biodegradable processes. Increasing interest in applying polymers based on natural materials such as starch has been
Design of a large-scale storage-class RRAM system

Resistive Random Access Memory (RRAM) is a promising next generation non-volatile memory (NVM) technology, thanks to its performance potential, endurance and ease-of-integration with standard silicon CMOS processes. While prior work has evaluated RRAM as a replacement for DRAM or even cache memory, to our knowledge there is no prior study that has investigated whether RRAM could be a viable NAND flash replacement in building large-scale storage-class memory systems. Motivated by this observation, our paper first discusses and quantifies the main problems associated with RRAM that prevent it from replacing NAND flash. The main solution we propose, "slab-based memory access with local/global bitlines," enables dense RRAM islands but can also cause performance related problems. To compensate for the latter, we also propose exploiting internal resource parallelism in RRAM and employing optimized data movement interfaces. Our extensive experimental evaluation using a cycle-level NVM simulator and real workloads under diverse computing domains indicate that the proposed architecture can provide 2.95 ~ 8.28 times better bandwidth and 66% ~ 88% shorter latency as compared to the conventional NAND flash, and improve the system-level performance of our workloads by 5x, with a storage capacity similar to that of the state-of-the-art NAND flash.
Occlusion invariant face recognition using selective local non-negative matrix factorization basis images

Please cite this article in press as: H.J. Oh Vis. Comput. (2008), doi:10.1016/j.imavis In this paper, we propose a novel occlusion invariant face recognition algorithm based on Selective Local Non-negative Matrix Factorization (S-LNMF) technique. The proposed algorithm is composed of two phases; the occlusion detection phase and the selective LNMF-based recognition phase. We use a local approach to effectively detect partial occlusions in an input face image. A face image is first divided into a finite number of disjointed local patches, and then each patch is represented by PCA (Principal Component Analysis), obtained by corresponding occlusion-free patches of training images. And the 1-NN threshold classifier is used for occlusion detection for each patch in the corresponding PCA space. In the recognition phase, by employing the LNMF-based face representation, we exclusively use the LNMF bases of occlusion-free image patches for face recognition. Euclidean nearest neighbor rule is applied for the matching. We have performed experiments on AR face database that includes many occluded face images by sunglasses and scarves. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the S-LNMF method shows superior performance to other conventional approaches.
On the requirements for successful GPS spoofing attacks

An increasing number of wireless applications rely on GPS signals for localization, navigation, and time synchronization. However, civilian GPS signals are known to be susceptible to spoofing attacks which make GPS receivers in range believe that they reside at locations different than their real physical locations. In this paper, we investigate the requirements for successful GPS spoofing attacks on individuals and groups of victims with civilian or military GPS receivers. In particular, we are interested in identifying from which locations and with which precision the attacker needs to generate its signals in order to successfully spoof the receivers. We will show, for example, that any number of receivers can easily be spoofed to one arbitrary location; however, the attacker is restricted to only few transmission locations when spoofing a group of receivers while preserving their constellation. In addition, we investigate the practical aspects of a satellite-lock takeover, in which a victim receives spoofed signals after first being locked on to legitimate GPS signals. Using a civilian GPS signal generator, we perform a set of experiments and find the minimal precision of the attacker's spoofing signals required for covert satellite-lock takeover.
Real-time DDoS attack detection for Cisco IOS using NetFlow

Flow-based DDoS attack detection is typically performed by analysis applications that are installed on or close to a flow collector. Although this approach allows for easy deployment, it makes detection far from real-time and susceptible to DDoS attacks for the following reasons. First, the fact that the flow export process is timeout-based and that flow collectors typically provide data to analysis applications in chunks, can result in detection delays in the order of several minutes. Second, by the nature of flow export, attack traffic may be amplified by the flow export process if the original packets are small enough and are part of small flows. We have shown in a previous work how to perform DDoS attack detection on a flow exporter instead of a flow collector, i.e., close to the data source and in a real-time fashion, which however required access to a fully-extendible flow monitoring infrastructure. In this work, we investigate whether it is possible to operate the same detection system on a widely deployed networking platform: Cisco IOS. Since our ultimate goal is to identify besides the presence of an attack also attackers and targets, we rely on NetFlow. In this context, we present our DDoS attack detection prototype that has shown to generate a constant load on the underlying platform - even under attacks - underlining that DDoS attack detection can be performed on a Cisco Catalyst 6500 in production networks, if enough spare capacity is available.
Related Fact Checks

The emergence of “Fake News” and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new “Related Fact Checks” service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a “Related Fact Checks” service, and present results from an evaluation of an implementation.
Bilateral areolar sebaceous hyperplasia in a post-menopausal woman.

299 normal: 40.0-75.0%) and normocytic anaemia (Hgb: 104 g/L; normal: 115-150 g/L). Acinetobacter junii and Flavobacterium meningosepticum were cultured from the pus of the sinus tracts, and blood cultures were negative. We further sequenced the -secretase gene of the patient. After informed consent was obtained, we collected EDTA anti-coagulated venous blood samples from the patient and his parents. The study was approved by the Institutional Ethical Committee of Tianjin Medical University General Hospital and was conducted according to the Declaration of Helsinki principles. Genomic DNA was extracted from peripheral blood lymphocytes by standard procedures using Flexi Gene DNA kits (QIAGEN GmbH, Hilden, Germany) and was diluted to working concentrations of 15-20 ng/ L for polymerase chain reaction (PCR). All exons and exonintron boundaries of PSENEN, PSEN1, NCSTN and APH1 genes were amplified by PCR. The DNA products were sequenced directly after purification using an ABI PRISM® 3730 automated sequencer (Applied Biosystems, Foster City, CA, USA). Sequencing revealed a guanine deletion mutation (c.66delG) in PSENEN in the patient (figure 1B), but not in his parents. After 14 days of treatment, the skin lesions were markedly lessened and the leukocyte and neutrophil counts returned to normal level. Haemoglobin increased to 114 g/L. AI is now considered a disease of follicular occlusion rather than an inflammatory or infectious process of the apocrine glands . Recently, the genetic aetiology of the disease has been unravelled. The -secretase subunit genes (PSEN1, PSENEN and NCSTN) were found to harbour pathogenic mutations leading to AI. Here, we report a sporadic case of AI and our sequencing revealed a guanine deletion mutation (c.66delG) in PSENEN. This mutation is known to cause a frameshift mutation in the protein . Up to now, and to our knowledge, only four sporadic cases have been reported and all the mutations occurred in NCSTN: c.632C>G, c.553G>A, c.996+7G>A, and c.1101+10A>G, whereas 19 mutations were reported in familial AI which were scattered among the NCSTN, PSENEN and PSEN1 genes. PSEN1 gene mutation has not been reported in Caucasian patients, while missense, nonsense, frameshift and splice site mutations are reported in PSENEN or NCSTN in both Chinese and Caucasian patients [3, 5, 6]. Our case shows that PSENEN c.66delG gene mutation can lead to both familial and sporadic AI, and may be a mutation hot spot in AI.
The Wireless Shark: Identifying WiFi Devices Based on Probe Fingerprints

Due to the broadcast nature of WiFi communication anyone with suitable hardware is able to monitor surrounding traffic. However, a WiFi device is able to listen to only one channel at any given time. The simple solution for capturing traffic across multiple channels involves channel hopping, which as a side effect reduces dwell time per channel. Hence monitoring with channel hopping does not produce a comprehensive view of the traffic across all channels at a given time.
 In this paper we present an inexpensive multi-channel WiFi capturing system (dubbed the wireless shark") and evaluate its performance in terms of traffic cap- turing efficiency. Our results confirm and quantify the intuition that the performance is directly related to the number of WiFi adapters being used for listening. As a second contribution of the paper we use the wireless shark to observe the behavior of 14 different mobile devices, both in controlled and normal office environments. In our measurements, we focus on the probe traffic that the devices send when they attempt to discover available WiFi networks. Our results expose some distinct characteristics in various mobile devices' probing behavior.
Massive hiatal hernia: a review.

Massive hiatal hernia remains a significant challenge in the field of esophageal surgery. The literature of the last 50 years shows that this is still a very controversial topic with a significant number of unanswered questions regarding its management. First, definitions used may create confusion between a large sliding hernia and a massive one. Once diagnosed using an accepted definition, the natural evolution of the condition remains speculative, namely for those patients who are asymptomatic when initially seen. Should these patients be offered a surgical repair and why. Second, independently of the surgical approach selected when a repair is offered the high recurrence rate reported suggests an imperfect solution at present. Three important aspects associated with these hernias raise unanswered questions: Is there an association with reflux disease and should this be a consideration when treatment is offered? The high reported failure rate with standard repairs may suggest a shortening of the esophagus, and if so, is an elongation gastroplasty necessary to improve results? Does an enlarged esophageal hiatus make this a disease of the diaphragm requiring a special technical approach? Third, as this condition is so frequently seen in old age patients, what is the best approach for the octogenarian, the nonagenarian? What is the ideal management of the patient with an exaggerated body mass index? These questions prompted us to aim at reviewing the contributions from the medical and surgical literature in order to establish a basic knowledge on this topic. This should allow a synthesis of information sufficient to plan prospective studies aiming at clarifying the unanswered questions raised by this benign condition with potentially lethal consequences.
Resonant bits: controlling digital musical instruments with resonance and the ideomotor effect

Resonant Bits proposes giving digital information resonant dynamic properties, requiring skill and concerted e↵ort for interaction. This paper applies resonant interaction to musical control, exploring musical instruments that are controlled through both purposeful and subconscious resonance. We detail three exploratory prototypes, the first two illustrating the use of resonant gestures and the third focusing on the detection and use of the ideomotor (subconscious micro-movement) e↵ect.
The urban sustainable development goal : Indicators , complexity and the politics of measuring cities

Article history: Received 8 July 2016 Received in revised form 15 November 2016 Accepted 29 December 2016 Available online 25 January 2017 As part of the post-2015 United Nations sustainable development agenda, the world has its first urban sustainable development goal (USDG) “to make cities and human settlements inclusive, safe, resilient and sustainable”. This paper provides an overview of the USDG and explores some of the difficulties around using this goal as a tool for improving cities. We argue that challenges emerge around selecting the indicators in the first place and also around the practical use of these indicators once selected. Three main practical problems of indicator use include 1) the poor availability of standardized, open and comparable data 2) the lack of strong data collection institutions at the city scale to support monitoring for the USDG and 3) “localization” the uptake and context specific application of the goal by diverse actors in widely different cities. Adding to the complexity, the USDG conversation is taking place at the same time as the proliferation of a bewildering array of indicator systems at different scales. Prompted by technological change, debates on the “data revolution” and “smart city” also have direct bearing on the USDG. We argue that despite these many complexities and challenges, the USDG framework has the potential to encourage and guide needed reforms in our cities but only if anchored in local institutions and initiatives informed by open, inclusive and contextually sensitive data collection and monitoring.
An advanced musculoskeletal humanoid Kojiro

We have been promoting a project of musculoskeletal humanoids. The project aims at the long-term goal of human-symbiotic robots as well as the mid-term goal of necessary design and control concepts for musculoskeletal robots. This paper presents the concepts and aim of the project and also shows the outline of our latest results about development of new musculoskeletal humanoid Kojiro, which is the succeeding version of our previous Kotaro.
Giraphx: Parallel Yet Serializable Large-Scale Graph Processing

Bulk Synchronous Parallelism (BSP) provides a good model for parallel processing of many large-scale graph applications, however it is unsuitable/inefficient for graph applications that require coordination, such as graph-coloring, subcoloring, and clustering. To address this problem, we present an efficient modification to the BSP model to implement serializability (sequential consistency) without reducing the highlyparallel nature of BSP. Our modification bypasses the message queues in BSP and reads directly from the worker’s memory for the internal vertex executions. To ensure serializability, coordination is performed— implemented via dining philosophers or token ring— only for border vertices partitioned across workers. We implement our modifications to BSP on Giraph, an open-source clone of Google’s Pregel. We show through a graph-coloring application that our modified framework, Giraphx, provides much better performance than implementing the application using dining-philosophers over Giraph. In fact, Giraphx outperforms Giraph even for embarrassingly parallel applications that do not require coordination, e.g., PageRank.
Predicting Preterm Birth Based on Maternal and Fetal Data

Preterm birth is “a birth which takes place after at least 20, but less than 37, completed weeks of gestation. This includes both live births, and stillbirths” . Preterm birth may cause problems such as perinatal mortality, serious neonatal morbidity and moderate to severe childhood disability. Between 6-10% of all births in Western countries are preterm and preterm deaths are the cause for more than two-third of all perinatal deaths [9]. While the recent advances in neonatal medicine has greatly increase the chance of survival of infants born after 20 weeks of gestation, these infants still frequently suffer from lifelong handicaps, and their care can exceed a million dollars during the first year of life [5 as cited in 6]. As a first step for preventing preterm birth, decision support tools are needed to help doctors predict preterm birth [6].
A Dependency-Based Neural Network for Relation Classification

Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.