nils-herrmann/clean_scirepeval huggingface dataset:
Dermatoscopy of genital warts.

Genital warts may mimic a variety of conditions, thus complicating their diagnosis and treatment. The recognition of early flat lesions presents a diagnostic challenge.



We sought to describe the dermatoscopic features of genital warts, unveiling the possibility of their diagnosis by dermatoscopy.



Dermatoscopic patterns of 61 genital warts from 48 consecutively enrolled male patients were identified with their frequencies being used as main outcome measures.



The lesions were examined dermatoscopically and further classified according to their dermatoscopic pattern. The most frequent finding was an unspecific pattern, which was found in 15/61 (24.6%) lesions; a fingerlike pattern was observed in 7 (11.5%), a mosaic pattern in 6 (9.8%), and a knoblike pattern in 3 (4.9%) cases. In almost half of the lesions, pattern combinations were seen, of which a fingerlike/knoblike pattern was the most common, observed in 11/61 (18.0%) cases. Among the vascular features, glomerular, hairpin/dotted, and glomerular/dotted vessels were the most frequent finding seen in 22 (36.0%), 15 (24.6%), and 10 (16.4%) of the 61 cases, respectively. In 10 (16.4%) lesions no vessels were detected. Hairpin vessels were more often seen in fingerlike (χ(2) = 39.31, P = .000) and glomerular/dotted vessels in knoblike/mosaic (χ(2) = 9.97, P = .008) pattern zones; vessels were frequently missing in unspecified (χ(2) = 8.54, P = .014) areas.



Only male patients were examined.



There is a correlation between dermatoscopic patterns and vascular features reflecting the life stages of genital warts; dermatoscopy may be useful in the diagnosis of early-stage lesions.
Limiting Factors of the Safe Operating Area for Power Devices

This paper gives an overview about different failure mechanisms which limit the safe operating area of power devices. It is demonstrated how the device internal processes can be investigated by means of device simulation. For instance, the electrothermal simulation of high-voltage diode turn-off reveals how a backside filament transforms into a continuous filament connecting the anode and cathode and how this can be accompanied with a transition from avalanche-induced into thermally driven carrier generation. A similar current destabilization may occur during insulated-gate bipolar transistor turn-off with a high turn-off rate, when the channel is closed quickly leading to strong dynamic avalanche. It is explained how the current filamentation depends on substrate resistivity, device thickness, channel width, and switching conditions (gate resistor and overcurrent). Filamentation processes during short-circuit events are discussed, and possible countermeasures are suggested. A mechanism of a periodically emerging and vanishing filament near the edge of the chip is presented. Examples on current destabilizing effects in gate turn-off thyristors, integrated gate-commutated thyristors, and metal-oxide-semiconductor field-effect transistors are given, and limitations of current device simulation are discussed.
Symmetry-Based Semantic Parsing

Semantic parsing maps sentences to formal meaning representations, enabling question answering, natural language interfaces, and many other applications. However, there is no agreement on what the meaning representation should be, and constructing a sufficiently large corpus of sentence-meaning pairs for learning is extremely challenging. In this paper, we argue that both of these problems can be avoided if we adopt a new notion of semantics. For this, we take advantage of symmetry group theory, a highly developed area of mathematics concerned with transformations of a structure that preserve its key properties. We define a symmetry of a sentence as a syntactic transformation that preserves its meaning. Semantically parsing a sentence then consists of inferring its most probable orbit under the language’s symmetry group, i.e., the set of sentences that it can be transformed into by symmetries in the group. The orbit is an implicit representation of a sentence’s meaning that suffices for most applications. Learning a semantic parser consists of discovering likely symmetries of the language (e.g., paraphrases) from a corpus of sentence pairs with the same meaning. Once discovered, symmetries can be composed in a wide variety of ways, potentially resulting in an unprecedented degree of immunity to syntactic variation.
Game-theoretic Analysis of Computation Offloading for Cloudlet-based Mobile Cloud Computing

Mobile cloud computing (MC2) is emerging as a promising computing paradigm which helps alleviate the conflict between resource-constrained mobile devices and resource-consuming mobile applications through computation offloading. In this paper, we analyze the computation offloading problem in cloudlet-based mobile cloud computing. Different from most of the previous works which are either from the perspective of a single user or under the setting of a single wireless access point (AP), we research the computation offloading strategy of multiple users via multiple wireless APs. With the widespread deployment of WLAN, offloading via multiple wireless APs will obtain extensive application. Taking energy consumption and delay (including computing and transmission delay) into account, we present a game-theoretic analysis of the computation offloading problem while mimicking the selfish nature of the individuals. In the case of homogeneous mobile users, conditions of Nash equilibrium are analyzed, and an algorithm that admits a Nash equilibrium is proposed. For heterogeneous users, we prove the existence of Nash equilibrium by introducing the definition of exact potential game and design a distributed computation offloading algorithm to help mobile users choose proper offloading strategies. Numerical extensive simulations have been conducted and results demonstrate that the proposed algorithm can achieve desired system performance.
Effect of a human-type communication robot on cognitive function in elderly women living alone

Considering the high prevalence of dementia, it would be of great value to develop effective tools to improve cognitive function. We examined the effects of a human-type communication robot on cognitive function in elderly women living alone.



In this study, 34 healthy elderly female volunteers living alone were randomized to living with either a communication robot or a control robot at home for 8 weeks. The shape, voice, and motion features of the communication robot resemble those of a 3-year-old boy, while the control robot was not designed to talk or nod. Before living with the robot and 4 and 8 weeks after living with the robot, experiments were conducted to evaluate a variety of cognitive functions as well as saliva cortisol, sleep, and subjective fatigue, motivation, and healing.



The Mini-Mental State Examination score, judgement, and verbal memory function were improved after living with the communication robot; those functions were not altered with the control robot. In addition, the saliva cortisol level was decreased, nocturnal sleeping hours tended to increase, and difficulty in maintaining sleep tended to decrease with the communication robot, although alterations were not shown with the control. The proportions of the participants in whom effects on attenuation of fatigue, enhancement of motivation, and healing could be recognized were higher in the communication robot group relative to the control group.



This study demonstrates that living with a human-type communication robot may be effective for improving cognitive functions in elderly women living alone.
Wafer Level Multi-chip Gang Bonding Using TCNCF

High throughput interconnection technology has been achieved using a multi-chip gang bonding process with an advanced chip on wafer (CoW) test vehicle (TV). The TV had 30 μm of fine-pitch copper pillar (CuP) and the bonding test was performed using non-conductive film (NCF). Therefore, all the steps including NCF lamination, wafer back-grinding and sawing, and thermo-compression bonding (TCB) were accomplished at the wafer level. Firstly, a layer of non-conductive film (NCF) was laminated on top of the wafer prior to back-grind and saw processes. Secondly, eight multi chips of 6x8 mm2 and 4x8 mm2 having 200 μm thickness were attached and aligned serially on the interposer wafer. For micro-bump interconnection, parallel bonding for the eight chips was applied for a few seconds after the serial chip attach process. Visual inspection and measurement confirmed the lateral fillet coverage was less than 110 μm without resin bleed-out and creeping. Destructive and non-destructive analyses were performed to examine solder joint formation & void inspection. Finally, reliability test was performed to confirm stability of the bonding process in a moisture resistance test (MRT) of L2Aa (60'C/60%RH, 120hrs) and L3 (30'C/60RH%, 192hrs) and temperature cycle (T/C) B 1000x. Electrical resistance was measured by probing for micro-bumps that are daisy-chained together. The resistance showed negligible change in upper and lower limit and no electrical failures or delamination occurred after reliability conditioning.
Compact Planar Magic-T Using E-Plane Substrate Integrated Waveguide (SIW) Power Divider and Slotline Transition

A novel planar magic-T using an E-plane substrate integrate waveguide (SIW) power divider and a SIW-slotline transition is proposed in this letter. Due to the metal ground between the two input/output ports, the E-plane SIW power divider has a 180° reverse phase characteristic. A SIW-slotline transition is utilized to realize the H-plane input/output port of the magic-T. Good agreement between the measured and simulated results indicate that the planar magic-T has a fractional bandwidth (FBW) of 18% (13.2-15.8 GHz), and the amplitude and phase imbalances are less than 0.24 dB and 1.5°, respectively.
Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC

Recurrent neural networks (RNNs) provide state-of-the-art accuracy for performing analytics on datasets with sequence (e.g., language model). This paper studied a state-of-the-art RNN variant, Gated Recurrent Unit (GRU). We first proposed memoization optimization to avoid 3 out of the 6 dense matrix vector multiplications (SGEMVs) that are the majority of the computation in GRU. Then, we study the opportunities to accelerate the remaining SGEMVs using FPGAs, in comparison to 14-nm ASIC, GPU, and multi-core CPU. Results show that FPGA provides superior performance/Watt over CPU and GPU because FPGA's on-chip BRAMs, hard DSPs, and reconfigurable fabric allow for efficiently extracting fine-grained parallelisms from small/medium size matrices used by GRU. Moreover, newer FPGAs with more DSPs, on-chip BRAMs, and higher frequency have the potential to narrow the FPGA-ASIC efficiency gap.
Efficient resampling methods for training support vector machines with imbalanced datasets

Random undersampling and oversampling are simple but well-known resampling methods applied to solve the problem of class imbalance. In this paper we show that the random oversampling method can produce better classification results than the random undersampling method, since the oversampling can increase the minority class recognition rate by sacrificing less amount of majority class recognition rate than the undersampling method. However, the random oversampling method would increase the computational cost associated with the SVM training largely due to the addition of new training examples. In this paper we present an investigation carried out to develop efficient resampling methods that can produce comparable classification results to the random oversampling results, but with the use of less amount of data. The main idea of the proposed methods is to first select the most informative data examples located closer to the class boundary region by using the separating hyperplane found by training an SVM model on the original imbalanced dataset, and then use only those examples in resampling. We demonstrate that it would be possible to obtain comparable classification results to the random oversampling results through two sets of efficient resampling methods which use 50% less amount of data and 75% less amount of data, respectively, compared to the sizes of the datasets generated by the random oversampling method.
System Engineering for Smart Cities-Hybrid-Agile Approach in Smart Cities Procurement

A smart city is a growing phenomenon of the last years with a lot of researches as well as implementation activities. A smart city is an interdisciplinary field that requires a high level of cooperation among experts from different fields and a contribution of the latest technologies in order to achieve the best results in six key areas. The six key areas cover economy, environment, mobility, people, living and governance. Following a system development methodology is in general a necessity for a successful implementation of a system or a project. Smart city projects introduce additionally new challenges. There is a need for cooperation across many fields, from technical or economic through legislation to humanitarian, together with sharing of resources. The traditional Systems Engineering methodologies fail with respect to such challenges. This paper provides an overview of the existing Systems Engineering methodologies and their limitations. A new Hybrid-Agile approach is proposed and its advantages with respect to smart city projects are discussed. However, the approach expects changes in our thinking. Customers (typically municipality or governmental organizations) have to become active and engaged in smart city projects. It is demonstrated that a city cannot be smart without smart government.
The use of the Mini-Nutritional Assessment (MNA) tool in elderly orthopaedic patients

To assess the use of the Mini-Nutritional Assessment (MNA) in elderly orthopaedic patients. An observation study assessing the nutritional status of female orthopaedic patients. The orthopaedic wards of the Royal Surrey County Hospital.Subjects: Forty-nine female patients aged 60–103 y; dietary records were obtained for 41 subjects and 36 subjects gave a blood sample for biochemical analysis. MNA questionnaire, anthropometry, plasma albumin, transferrin, C-reactive protein (CRP) levels and dietary analyses. The group as a whole had low mean values for body weight, albumin and transferrin and high CRP levels. In addition, the group had mean energy intakes well below the estimated average requirement (EAR) and mean intakes of vitamin D, magnesium, potassium, selenium and non-starch polysaccharides (NSP) were below the lower reference nutrient intakes (LRNI). The MNA screening section categorized 69% of the patients as requiring a full assessment (scored 11 or below), but for the purposes of the study the MNA was completed on all patients. The MNA assessment categorized 16% of the group as ‘malnourished’ (scored<17 points), 47% as ‘at risk’ (scored 17.5–23.5) and 37% as ‘well nourished’ (scored>23.5). Significant differences were found between the malnourished and well nourished groups for body weight (P<0.001), body mass index (BMI) (P<0.001), demiquet (P<0.001) and mindex (P<0.001). Mean values for energy and nutrient intakes showed a clear stepwise increase across the three groups for all nutrients except sodium, with significant differences for protein (P<0.05), carbohydrate (P<0.05), riboflavin (P<0.05) niacin (P<0.05), pyridoxine (P<0.05), folate (P<0.05), calcium (P<0.05), selenium (P<0.05), iron (P<0.05) and NSP (P<0.05) intakes. Stepwise multiple regression analysis indicated that anthropometric assessments were the most predictive factors in the total MNA score. The sensitivity and specificity of the MNA was assessed in comparison with albumin levels, energy intake and mindex. The sensitivity of the MNA classification of those scoring less than 17 points in comparison with albumin levels, energy intake and mindex varied from 27 to 57% and the specificity was 66–100%. This was compared with the sensitivity and specificity of using a score of less than 23.5 on the MNA to predict malnourished individuals. Using this cut-off the sensitivity ranged from 75 to 100%, but the specificity declined to between 37 and 50%.Conclusions: The results suggest that the MNA is a useful diagnostic tool in the identification of elderly patients at risk from malnutrition and those who are malnourished in this hospital setting. Nestlé Clinical Nutrition, Croydon, Surrey. (2000) 54, 555–562
Open reduction and fixation of medial Moore type II fractures of the tibial plateau by a direct dorsal approach

Moore type II Entire Condyle fractures of the tibia plateau represent a rare and highly unstable fracture pattern that usually results from high impact traumas. Specific recommendations regarding the surgical treatment of these fractures are sparse. We present a series of Moore type II fractures treated by open reduction and internal fixation through a direct dorsal approach. Five patients (3 females, 2 males) with Entire Condyle fractures were retrospectively analyzed after a mean follow-up period of 39 months (range 12–61 months). Patient mean age at the time of operation was 36 years (range 26–43 years). Follow-up included clinical and radiological examination. Furthermore, all patient finished a SF36 and Lysholm knee score questionnaire. Average range of motion was 127/0/1° with all patients reaching full extension at the time of last follow up. Patients reached a mean Lysholm score of 81.2 points (range 61–100 points) and an average SF36 of 82.36 points (range 53.75–98.88 points). One patient sustained deep wound infection after elective implant removal 1 year after the initial surgery. Overall all patients were highly satisfied with the postoperative result. The direct dorsal approach to the tibial plateau represents an adequate method to enable direct fracture exposure, open reduction, and internal fixation in posterior shearing medial Entire Condyle fractures and is especially valuable when also the dorso-lateral plateau is depressed.
Artificial-Intelligence-Based Methodology for Load Disaggregation at Bulk Supply Point

Real-time load composition knowledge will dramatically benefit demand-side management (DSM). Previous works disaggregate the load via either intrusive or nonintrusive load monitoring. However, due to the difficulty in accessing all houses via smart meters at all times and the unavailability of frequently measured high-resolution load signatures at bulk supply points, neither is suitable for frequent or widespread application. This paper employs the artificial intelligence (AI) tool to develop a load disaggregation approach for bulk supply points based on the substation rms measurement without relying on smart meter data, customer surveys, or high-resolution load signatures. Monte Carlo simulation is used to generate the training and validation data. Load compositions obtained by the AI tool are compared with the validation data and used for load characteristics estimation and validation. Probabilistic distributions and confidence levels of different confidence intervals for errors of load compositions and load characteristics are also derived.
Detecting Multipliers of Jihadism on Twitter

Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. This work is aiming at detecting tweeps that are involved in media mujahideen - the supporters of jihadist groups who disseminate propaganda content online. To do this we use a machine learning approach where we make use of two sets of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added. In our experiments we have used the AdaBoost classifier. The results shows that our approach works very well for classifying English tweeps and English tweets but the approach does not perform as well on Arabic data.
Automatic detection of bike-riders without helmet using surveillance videos in real-time

In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach for violation reporting which helps in improving reliability of the proposed approach. In order to evaluate our approach, we have provided a performance comparison of three widely used feature representations namely histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and local binary patterns (LBP) for classification. The experimental results show detection accuracy of 93.80% on the real world surveillance data. It has also been shown that proposed approach is computationally less expensive and performs in real-time with a processing time of 11.58 ms per frame.
Interval-level measurement with visual analogue scales in Internet-based research: VAS Generator.

The present article describes VAS Generator (www.vasgenerator.net), a free Web service for creating a wide range of visual analogue scales that can be used as measurement devices in Web surveys and Web experimentation, as well as for local computerized assessment. A step-by-step example for creating and implementing a visual analogue scale with visual feedback is given.  Generator and the scales it generates work independently of platforms and use the underlying languages HTML and JavaScript. Results from a validation study with 355 participants are reported and show that the scales generated with VAS Generator approximate an interval-scale level. In light of previous research on visual analogue versus categorical (e.g., radio button) scales in Internet-based research, we conclude that categorical scales only reach ordinal-scale level, and thus visual analogue scales are to be preferred whenever possible.
Design and deployment of secure, robust, and resilient SDN controllers

The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google's backbone network, Microsoft's public cloud, and NTT's edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof- the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.