nils-herrmann/clean_scirepeval huggingface dataset:
Clock delta compression for scalable order-replay of non-deterministic parallel applications

The ability to record and replay program execution helps significantly in debugging non-deterministic MPI applications by reproducing message-receive orders. However, the large amount of data that traditional record-and-reply techniques record precludes its practical applicability to massively parallel applications. In this paper, we propose a new compression algorithm, Clock Delta Compression (CDC), for scalable record and replay of non-deterministic MPI applications. CDC defines a reference order of message receives based on a totally ordered relation using Lamport clocks, and only records the differences between this reference logical-clock order and an observed order. Our evaluation shows that CDC significantly reduces the record data size. For example, when we apply CDC to Monte Carlo particle transport Benchmark (MCB), which represents common non-deterministic communication patterns, CDC reduces the record size by approximately two orders of magnitude compared to traditional techniques and incurs between 13.1% and 25.5% of runtime overhead.
Accuracy of full-arch scans using intraoral scanners

This study aimed to evaluate the accuracy of intraoral scanners in full-arch scans. A representative model with 14 prepared abutments was digitized using an industrial scanner (reference scanner) as well as four intraoral scanners (iTero, CEREC AC Bluecam, Lava C.O.S., and Zfx IntraScan). Datasets obtained from different scans were loaded into 3D evaluation software, superimposed, and compared for accuracy. One-way analysis of variance (ANOVA) was implemented to compute differences within groups (precision) as well as comparisons with the reference scan (trueness). A level of statistical significance of p < 0.05 was set. Mean trueness values ranged from 38 to 332.9 μm. Data analysis yielded statistically significant differences between CEREC AC Bluecam and other scanners as well as between Zfx IntraScan and Lava C.O.S. Mean precision values ranged from 37.9 to 99.1 μm. Statistically significant differences were found between CEREC AC Bluecam and Lava C.O.S., CEREC AC Bluecam and iTero, Zfx Intra Scan and Lava C.O.S., and Zfx Intra Scan and iTero (p < 0.05). Except for one intraoral scanner system, all tested systems showed a comparable level of accuracy for full-arch scans of prepared teeth. Further studies are needed to validate the accuracy of these scanners under clinical conditions. Despite excellent accuracy in single-unit scans having been demonstrated, little is known about the accuracy of intraoral scanners in simultaneous scans of multiple abutments. Although most of the tested scanners showed comparable values, the results suggest that the inaccuracies of the obtained datasets may contribute to inaccuracies in the final restorations.
Review Monte Carlo Ray Tracing in Optical Canopy Reflectance Modelling

Monte Carlo ray tracing in optical canopy reflectance modelling M.I. Disney a , P. Lewis b & P.R.J. North c a Remote Sensing Unit, Department of Geography, University College London, 26 Bedford Way, London, WC1H OAP, UK Email: b Remote Sensing Unit, Department of Geography, University College London, 26 Bedford Way, London, WC1H OAP, UK c Institute of Terrestrial Ecology, Monks Wood, Abbots Ripton, Huntingdon, Cambs., PE17 2LS, UK Version of record first published: 19 Oct 2009.
Ultrasonic networking for E-health applications

Wirelessly networked systems of intra-body sensors and actuators could enable revolutionary applications at the intersection between biomedical science, networking, and control with a strong potential to advance medical treatment of major diseases of our times. Yet, most research to date has focused on communications along the body surface among devices interconnected through traditional electromagnetic radio-frequency (RF) carrier waves; while the underlying root challenge of enabling networked intra-body miniaturized sensors and actuators that communicate through body tissues is substantially unaddressed. The main obstacle to enabling this vision of networked implantable devices is posed by the physical nature of propagation in the human body. The human body is composed primarily (65 percent) of water, a medium through which RF electromagnetic waves do not easily propagate, even at relatively low frequencies. Therefore, in this article we take a different perspective and propose to investigate and study the use of ultrasonic waves to wirelessly internetwork intra-body devices. We discuss the fundamentals of ultrasonic propagation in tissues, and explore important tradeoffs, including the choice of a transmission frequency, transmission power, and transducer size. Then, we discuss future research challenges for ultrasonic networking of intra-body devices at the physical, medium access and network layers of the protocol stack.
Individuals' Internet Security Perceptions and Behaviors: Polycontextual Contrasts Between the United States and China

Dinev and Hu 2007 332 IS professionals and students USA Survey Theory of Planned Behavior Intention to use protective information technologies Furnell et al. 2007 415 UK residents UK Survey NA Safe behavior, knowledge-seeking behavior Lee and Kozar 2005 212 Internet Users USA Survey Theory of Planned Behavior, IT Innovation Adoption of an anti-spyware system Liang and Xue 2009 NA NA Theory building PMT, Cybernetic Process Theory Problem-focused and emotionfocused coping behavior Woon et al. 2005 189 students and faculty Singapore Survey PMT Have enabled/ have not enabled a firewall on home wireless network
Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.
Programming Spiking Neural Networks on Intel’s Loihi

Loihi is Intel’s novel, manycore neuromorphic processor and is the first of its kind to feature a microcode-programmable learning engine that enables on-chip training of spiking neural networks (SNNs). The authors present the Loihi toolchain, which consists of an intuitive Python-based API for specifying SNNs, a compiler and runtime for building and executing SNNs on Loihi, and several target platforms (Loihi silicon, FPGA, and functional simulator). To showcase the toolchain, the authors describe how to build, train, and use a SNN to classify handwritten digits from the MNIST database.
Class Specific TF-IDF Boosting for Short-text Classification: Application to Short-texts Generated During Disasters

Proper formulation of features plays an important role in shorttext classification tasks as the amount of text available is very little. In literature, Term Frequency Inverse Document Frequency (TF-IDF) is commonly used to create feature vectors for such tasks. However, TF-IDF formulation does not utilize the class information available in supervised learning. For classification problems, if it is possible to identify terms that can strongly distinguish among classes, then more weight can be given to those terms during feature construction phase. This may result in improved classifier performance with the incorporation of extra class label related information. We propose a supervised feature construction method to classify tweets, based on the actionable information that might be present, posted during different disaster scenarios. Improved classifier performance for such classification tasks can be helpful in the rescue and relief operations. We used three benchmark datasets containing tweets posted during Nepal and Italy earthquakes in 2015 and 2016 respectively. Experimental results show that the proposed method obtains better classification performance on these benchmark datasets.
Detection of self intersection in synthetic hand pose generators

Synthetic hand pose data has been frequently used in vision based hand gesture recognition. However existing synthetic hand pose generators are not able to detect intersection between various hand parts and can synthesize self intersecting poses. Using such data may lead to learning wrong models. We propose a method to eliminate self intersecting synthetic hand poses by accurately detecting intersections between various hand parts. We model each hand part as a convex hull and calculate pairwise distance between the parts, labeling any pair with a negative distance as intersecting. A hand pose with at least one pair of intersecting parts is labeled as self intersecting. We show experimentally that our method is very accurate and performs better than existing techniques. We also show that it is fast enough for offline data generation.
Wheelchair motion control guide using eye gaze and blinks based on bug 2 algorithm

In this work, we propose a new approach alongside the typical method, to control the motorized wheelchair using EOG signals. The new approach grants the user to look around without restraint, while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair; eye gazing and blinking. The user can still appoint to control the wheelchair using the typical manual method in case the surroundings and obstacle structures do not assist with the new developed auto navigation method. In the new auto navigation approach the microcontroller can attain the goal point direction and distance by calculating the gaze angle of the user. Bug2 algorithm is utilized to navigate the wheelchair in the auto controlling method. Experimental tests show slightly different results than theory, because the bug algorithms cannot continuously update the robot's position data in experiments.
Dimethyltryptamine (DMT): prevalence, user characteristics and abuse liability in a large global sample.

This paper presents original research on prevalence, user characteristics and effect profile of N,N-dimethyltryptamine (DMT), a potent hallucinogenic which acts primarily through the serotonergic system. Data were obtained from the Global Drug Survey (an anonymous online survey of people, many of whom have used drugs) conducted between November and December 2012 with 22,289 responses. Lifetime prevalence of DMT use was 8.9% (n=1980) and past year prevalence use was 5.0% (n=1123). We explored the effect profile of DMT in 472 participants who identified DMT as the last new drug they had tried for the first time and compared it with ratings provided by other respondents on psilocybin (magic mushrooms), LSD and ketamine. DMT was most often smoked and offered a strong, intense, short-lived psychedelic high with relatively few negative effects or "come down". It had a larger proportion of new users compared with the other substances (24%), suggesting its popularity may increase. Overall, DMT seems to have a very desirable effect profile indicating a high abuse liability that maybe offset by a low urge to use more.
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples

Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. We for the first time introduce a new attacking scheme for the attacker and set a practical constraint for white box attack. Under this proposed attacking scheme we present the best defense ever reported against some of the recent strong attacks. It consists of a set of non linear function to process the input data which will make it more robust over adversarial attack. However, we make this processing layer completely hidden from the attacker. Blind pre-processing improves the white box attack accuracy of MNIST from 94.3% to 98.7%. Even with increasing defense when others defenses completely fails, blind preprocessing remains one of the strongest ever reported. Another strength of our defense is that, it eliminates the need for adversarial training as it can significantly increase the MNIST accuracy without adversarial training as well. Additionally, blind pre-processing can also increase the inference accuracy in the face of powerful attack on Cifar-10 and SVHN data set as well without much sacrificing clean data accuracy.
Chapter 4 Personal Ornaments and Symbolism Among the Neanderthals

“The distinctive faculties of Man are visibly expressed in his elevated cranial domeda feature which, though much debased in certain savage races, essentially characterises the human species. But, considering that the Neanderthal skull is eminently simial, both in its general and particular characters, I feel myself constrained to believe that the thoughts and desires which once dwelt within it never soared beyond those of a brute. The Andamaner, it is indisputable, possesses but the dimmest conceptions of the existence of the Creator of the Universe: his ideas on this subject, and on his own moral obligations, place him very little above animals of marked sagacity; nevertheless, viewed in connection with the strictly human conformation of his cranium, they are such as to specifically identify him with Homo sapiens. Psychical endowments of a lower grade than those characterising the Andamaner cannot be conceived to exist: they stand next to brute benightedness. (.) Applying the above argument to the Neanderthal skull, and considering . that it more closely conforms to the brain-case of the Chimpanzee, . there seems no reason to believe otherwise than that similar darkness characterised the being to which the fossil belonged” (King, 1864; .
Detecting Changes in XML documents

We present a diff algorithm for XML data. This work is motivated by the support for change control in the context of the Xyleme project that is investigating dynamic warehouses capable of storing massive volume of XML data. Because of the context, our algorithm has to be very efficient in terms of speed and memory space even at the cost of some loss of “quality”. Also, it considers, besides insertions, deletions and updates (standard in diffs), a move operation on subtrees that is essential in the context of XML. Intuitively, our diff algorithm uses signatures to match (large) subtrees that were left unchanged between the old and new versions. Such exact matchings are then possibly propagated to ancestors and descendants to obtain more matchings. It also uses XML specific information such as ID attributes. We provide a performance analysis of the algorithm. We show that it runs in average in linear time vs. quadratic time for previous algorithms. We present experiments on synthetic data that confirm the analysis. Since this problem is NPhard, the linear time is obtained by trading some quality. We present experiments (again on synthetic data) that show that the output of our algorithm is reasonably close to the “optimal” in terms of quality. Finally we present experiments on a small sample of XML pages found on the Web.
GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach

Location tagging, also known as geotagging or geolocation, is the process of assigning geographical coordinates to input data. In this paper we present an algorithm for location tagging of textual documents. Our approach makes use of previous work in natural language processing by using a state-of-the-art part-of-speech tagger and named entity recognizer to find blocks of text which may refer to locations. A knowledge base (OpenStreatMap) is then used to find a list of possible locations for each of these blocks of text. Finally, one location is chosen for each block of text by assigning distance-based scores to each location and repeatedly selecting the location and block of text with the best score. We tested our geolocation algorithm with Wikipedia articles about topics with a well-defined geographical location that are geotagged by the articles’ authors, where classification approaches have achieved median errors as low as 11 km. However, the maximum accuracy of these approaches is limited by the class size, so future work may not yield significant improvement. Our algorithm tags a location to each block of text that was identified as a possible location reference, meaning a text is typically assigned multiple tags. When we considered only the tag with the highest distancebased score, we achieved a 10th percentile error of 490 metres and median error of 54 kilometres on the Wikipedia dataset we used. When we considered the five location tags with the greatest scores, we found that 50% of articles were assigned at least one tag within 8.5 kilometres of the article’s author-assigned true location. We also tested our approach on a set of Twitter messages that are tagged with the location from which the message was sent. This dataset is more challenging than the geotagged Wikipedia articles, because Twitter texts are shorter, tend to contain unstructured text, and may not contain information about the location from where the message was sent in the first place. Nevertheless, we make some interesting observations about potential use of our geolocation algorithm for this type of input. We explain how we use the Spark framework for data analytics to collect and process our test data. In general, classification-based approaches for location tagging may be reaching their upper limit for accuracy, but our precision-focused approach has high accuracy for some texts and shows significant potential for improvement overall.
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data

The aim of image captioning is to generate captions by machine to describe image contents. Despite many efforts, generating discriminative captions for images remains non-trivial. Most traditional approaches imitate the language structure patterns, thus tend to fall into a stereotype of replicating frequent phrases or sentences and neglect unique aspects of each image. In this work, we propose an image captioning framework with a self-retrieval module as training guidance, which encourages generating discriminative captions. It brings unique advantages: (1) the self-retrieval guidance can act as a metric and an evaluator of caption discriminativeness to assure the quality of generated captions. (2) The correspondence between generated captions and images are naturally incorporated in the generation process without human annotations, and hence our approach could utilize a large amount of unlabeled images to boost captioning performance with no additional annotations. We demonstrate the effectiveness of the proposed retrievalguided method on COCO and Flickr30k captioning datasets, and show its superior captioning performance with more discriminative captions.
Deep Active Learning for Dialogue Generation

We use hamming-diverse Beam Search  to generate K responses at each turn. The beams are diversified by maximizing an objective that consists of a standard sequence likelihood term and a dissimilarity metric between the beams. Beam Search: Likely to produce almost identical beams like “I don’t care!” and “I don’t care.”. Diverse Beam Search: Likely to produce beams like “I don’t care!” and “Who cares?”.
Object proposal on RGB-D images via elastic edge boxes

As a fundamental preprocessing of various multimedia applications, object proposal aims to detect the candidate windows possibly containing arbitrary objects in images with two typical strategies, window scoring and grouping. In this paper, we first analyze the feasibility of improving object proposal performance by integrating window scoring and grouping strategies. Then, we propose a novel object proposal method for RGB-D images, named elastic edge boxes. The initial bounding boxes of candidate object regions are efficiently generated by edge boxes, and further adjusted by grouping the super-pixels within elastic range to obtain more accurate candidate windows. To validate the proposed method, we construct the largest RGB-D image data set NJU1800 for object proposal with balanced object number distribution. The experimental results show that our method can effectively and efficiently generate the candidate windows of object regions and it outperforms the state-of-the-art methods considering both accuracy and efficiency.
Side lobe suppression of Vivaldi antenna using shorting pin structure

This paper was executed in order to design side lobe suppression of Vivaldi antenna using shorting pin structure at S-band frequency. Nowadays, Vivaldi antenna has been used to satellite communications, remote sensing and radio telescope. There are a variety of designs such as tapered slot Vivaldi antenna, antipodal Vivaldi antenna and balanced antipodal Vivaldi antenna. Tapered slot Vivaldi antenna has been selected to design the project. The design of the antenna is start with patch antenna by using basis of transmission line model (TLM) formula to calculate the length and width of the patch. It is shaped into Vivaldi antenna. For the final design, the shorting pin structure will connect the patch on top side and the ground plane to absorb the unbalanced current. The shorting pin will reduce the side lobe. The antenna was simulated by using CST Microwave Studio. Based on the simulation results, the Vivaldi antenna shows a good performance as the return loss less than - 10 dB, voltage standing wave ratio, gain, directivity and radiation pattern was obtained the desired result as for a resonant frequency of 3 GHz. The radiation pattern of Vivaldi antenna with shorting pin structure reducing as compared with the design without shorting pin structure. The final antenna design shows that it has a few main lobe magnitudes where the antenna can focus and direct the energy for transmission or receiving of the energy signal.