nils-herrmann/clean_scirepeval huggingface dataset:
Crust: A Bounded Verifier for Rust (N)

Rust is a modern systems language that provides guaranteed memory safety through static analysis. However, Rust includes an escape hatch in the form of "unsafe code," which the compiler assumes to be memory safe and to preserve crucial pointer aliasing invariants. Unsafe code appears in many data structure implementations and other essential libraries, and bugs in this code can lead to memory safety violations in parts of the program that the compiler otherwise proved safe. We present CRUST, a tool combining exhaustive test generation and bounded model checking to detect memory safety errors, as well as violations of Rust's pointer aliasing invariants within unsafe library code. CRUST requires no programmer annotations, only an indication of the modules to check. We evaluate CRUSTon data structures from the Rust standard library. It detects memory safety bugs that arose during the library's development and remained undetected for several months.
Update on dermoscopy of Spitz/Reed naevi and management guidelines by the International Dermoscopy Society.

Spitzoid lesions represent a challenging and controversial group of tumours, in terms of clinical recognition, biological behaviour and management strategies. Although Spitz naevi are considered benign tumours, their clinical and dermoscopic morphological overlap with spitzoid melanoma renders the management of spitzoid lesions particularly difficult. The controversy deepens because of the existence of tumours that cannot be safely histopathologically diagnosed as naevi or melanomas (atypical Spitz tumours). The dual objective of the present study was to provide an updated classification on dermoscopy of Spitz naevi, and management recommendations of spitzoid-looking lesions based on a consensus among experts in the field. After a detailed search of the literature for eligible studies, a data synthesis was performed from 15 studies on dermoscopy of Spitz naevi. Dermoscopically, Spitz naevi are typified by three main patterns: starburst pattern (51%), a pattern of regularly distributed dotted vessels (19%) and globular pattern with reticular depigmentation (17%). A consensus-based algorithm for the management of spitzoid lesions is proposed. According to it, dermoscopically asymmetric lesions with spitzoid features (both flat/raised and nodular) should be excised to rule out melanoma. Dermoscopically symmetric spitzoid nodules should also be excised or closely monitored, irrespective of age, to rule out atypical Spitz tumours. Dermoscopically symmetric, flat spitzoid lesions should be managed according to the age of the patient. Finally, the histopathological diagnosis of atypical Spitz tumour should warrant wide excision but not a sentinel lymph-node biopsy.
Customisation of Paillier homomorphic encryption for efficient binary biometric feature vector matching

Computing the Hamming weight between binary biometric feature vectors in a homomorphic encryption domain can be rather inefficient due to the required bit-wise encryption. A biometric matching technique more efficient than the Goldwasser-Mlcali approach is proposed based on exploiting Paillier's capability of encrypting messages larger than one bit at a time. The efficiency is documented in an iris identification context.
Comparison of TSCS regression and neural network models for panel data forecasting: debt policy

Empirical studies of variations in debt ratios across firms have analyzed important determinants of capital structure using statistical models. Researchers, however, rarely employ nonlinear models to examine the determinants and make little effort to identify a superior prediction model among competing ones. This paper reviews the time-series cross-sectional (TSCS) regression and the predictive abilities of neural network (NN) utilizing panel data concerning debt ratio of high-tech industries in Taiwan. We built models with these two methods using the same set of measurements as determinants of debt ratio and compared the forecasting performance of five models, namely, three TSCS regression models and two NN models. Models built with neural network obtained the lowest mean square error and mean absolute error. These results reveal that the relationships between debt ratio and determinants are nonlinear and that NNs are more competent in modeling and forecasting the test panel data. We conclude that NN models can be used to solve panel data analysis and forecasting problems.
Optimum FIR Digital Filter Implementations for Decimation , Interpolation , and Narrow-Band Filtering

In this paper a general theory of multistage decimators and interpolators for sampling rate reduction and sampling rate increase is presented. A set of curves and the necessary relations for optimally designing multistage decimators is also given. It is shown that the processes of decimation and interpolation are duals and therefore the same set of design curves applies to both problems. Further, it is shown that highly efficient implementations of narrow-band finite impulse response (FIR) fiiters can be obtained by cascading the processes of decimation and interpolation. Examples show that the efficiencies obtained are comparable to those of recursive elliptic filter designs.
Experimental stability study of an Octorotor using an intelligent controller

In this paper, the attitude stabilization problem of an Octorotor with coaxial motors is studied. To this end, the new method of intelligent adaptive control is presented. The designed controller which includes fuzzy and PID controllers, is completed by resistant adaptive function of approximate external disturbance and changing in the dynamic model. In fact, the regulation factor of PID controller is done by the fuzzy logic system. At first, the Fuzzy-PID and PID controllers are simulated in MATLAB/Simulink. Then, the Fuzzy-PID controller is implemented on the Octorotor with coaxial motors as online auto-tuning. Also, LabVIEW software has been used for tests and the performance analysis of the controllers. All of this experimental operation is done in indoor environment in the presence of wind as disturbance in the hovering operation. All of these operations are real-time and telemetry wireless is done by network connection between the robot and ground station in the LABVIEW software. Finally, the controller efficiency and results are studied.
Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs

In this work we research the role of body dynamics in the complexity of kinematic patterns in a quadruped robot with compliant legs. Two gait patterns, lateral sequence walk and trot, along with leg length control patterns of different complexity were implemented in a modular, feed-forward locomotion controller. The controller was tested on a small, quadruped robot with compliant, segmented leg design, and led to self-stable and self-stabilizing robot locomotion. In-air stepping and on-ground locomotion leg kinematics were recorded, and the number and shapes of motion primitives accounting for 95% of the variance of kinematic leg data were extracted. This revealed that kinematic patterns resulting from feed-forward control had a lower complexity (in-air stepping, 2-3 primitives) than kinematic patterns from on-ground locomotion (νm4 primitives), although both experiments applied identical motor patterns. The complexity of on-ground kinematic patterns had increased, through ground contact and mechanical entrainment. The complexity of observed kinematic on-ground data matches those reported from level-ground locomotion data of legged animals. Results indicate that a very low complexity of modular, rhythmic, feed-forward motor control is sufficient for level-ground locomotion in combination with passive compliant legged hardware.
Model Reference Adaptive System Sensorless Vector Control of an Induction Motor Using a Novel Fractional Order PI Controller Adaptation Mechanism

Recently, speed sensorless control of Induction Motor (IM) drives received great attention to avoid the different problems associated with direct speed sensors. Among different rotor speed estimation techniques, Model Reference Adaptive System (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. In this paper a novel adaptation mechanism is proposed which replaces normally used conventional Proportional-Integral (PI) controller in MRAS adaptation mechanism by a Fractional Order PI (FOPI) controller. The performance of two adaptation mechanism controllers has been verified through simulation results using MATLAB/SIMULINK software. It is seen that the performance of the induction motor has improved when FOPI controller is used in place of classical PI controller.
VLSI architecture and chip for combined invisible robust and fragile watermarking

Research in digital watermarking is mature. Several software implementations of watermarking algorithms are described in the literature, but few attempts have been made to describe hardware implementations. The ultimate objective of the research presented in this paper was to develop low-power, highperformance, real-time, reliable and secure watermarking systems, which can be achieved through hardware implementations. In this paper, we discuss the development of a very-large-scale integration architecture for a high-performance watermarking chip that can perform both invisible robust and invisible fragile image watermarking in the spatial domain. We prototyped the watermarking chip in two ways: (i) by using a Xilinx field-programmable gate array and (ii) by building a custom integrated circuit. To the best of our knowledge, this prototype is the first watermarking chip with both invisible robust and invisible fragile watermarking capabilities.
Awareness and Adoption of Open Source Software among LIS Professionals of Engineering Colleges of Odisha

To provide a general context for library implementations of open source software (OSS), the purpose of this paper is to assess and evaluate the awareness and adoption of OSS by the LIS professionals working in various engineering colleges of Odisha. The study is based on survey method and questionnaire technique was used for collection data from the respondents. The study finds that although the LIS professionals of engineering colleges of Odisha have knowledge on OSS, their uses in libraries are in budding stage. Suggests that for the widespread use of OSS in engineering college libraries of Odisha, a cooperative and participatory organisational system, positive attitude of authorities and LIS professionals, proper training provision for LIS professionals need to be developed.
Screening for adolescent depression: comparison of the Kutcher Adolescent Depression Scale with the Beck depression inventory.

Self-report instruments commonly used to assess depression in adolescents have limited or unknown reliability and validity in this age group. We describe a new self-report scale, the Kutcher Adolescent Depression Scale (KADS), designed specifically to diagnose and assess the severity of adolescent depression. This report compares the diagnostic validity of the full 16-item instrument, brief versions of it, and the Beck Depression Inventory (BDI) against the criteria for major depressive episode (MDE) from the Mini International Neuropsychiatric Interview (MINI). Some 309 of 1,712 grade 7 to grade 12 students who completed the BDI had scores that exceeded 15. All were invited for further assessment, of whom 161 agreed to assessment by the KADS, the BDI again, and a MINI diagnostic interview for MDE. Receiver operating characteristic (ROC) curve analysis was used to determine which KADS items best identified subjects experiencing an MDE. Further ROC curve analyses established that the overall diagnostic ability of a six-item subscale of the KADS was at least as good as that of the BDI and was better than that of the full-length KADS. Used with a cutoff score of 6, the six-item KADS achieved sensitivity and specificity rates of 92% and 71%, respectively-a combination not achieved by other self-report instruments. The six-item KADS may prove to be an efficient and effective means of ruling out MDE in adolescents.
CRISPR-Cpf1 assisted genome editing of Corynebacterium glutamicum

Corynebacterium glutamicum is an important industrial metabolite producer that is difficult to genetically engineer. Although the Streptococcus pyogenes (Sp) CRISPR-Cas9 system has been adapted for genome editing of multiple bacteria, it cannot be introduced into C. glutamicum. Here we report a Francisella novicida (Fn) CRISPR-Cpf1-based genome-editing method for C. glutamicum. CRISPR-Cpf1, combined with single-stranded DNA (ssDNA) recombineering, precisely introduces small changes into the bacterial genome at efficiencies of 86-100%. Large gene deletions and insertions are also obtained using an all-in-one plasmid consisting of FnCpf1, CRISPR RNA, and homologous arms. The two CRISPR-Cpf1-assisted systems enable N iterative rounds of genome editing in 3N+4 or 3N+2 days. A proof-of-concept, codon saturation mutagenesis at G149 of γ-glutamyl kinase relieves L-proline inhibition using Cpf1-assisted ssDNA recombineering. Thus, CRISPR-Cpf1-based genome editing provides a highly efficient tool for genetic engineering of Corynebacterium and other bacteria that cannot utilize the Sp CRISPR-Cas9 system.
A Convolutional Neural Network Approach for 2 D / 3 D Medical Image Registration

We present a Convolutional Neural Network (CNN) regression based framework for 2-D/3-D medical image registration, which directly estimates the transformation parameters from image features extracted from the DRR and the X-ray images using learned hierarchical regressors. Our framework consists of learning and application stages. In the learning stage, CNN regressors are trained using supervised machine learning to reveal the correlation between the transformation parameters and the image features. In the application stage, CNN regressors are applied on extracted image features in a hierarchical manner to estimate the transformation parameters. Our experiment results demonstrate that the proposed method can achieve real-time 2-D/3-D registration with very high (i.e., sub-milliliter) accuracy.
A comparison of four prompt modes for route finding for community travellers with severe cognitive impairments.

Navigational skills are fundamental to community travel and, hence, personal independence and are often disrupted in people with cognitive impairments. Navigation devices are being developed that can support community navigation by delivering directional information. Selecting an effective mode to provide route-prompts is a critical design issue. This study evaluated the differential effects on pedestrian route finding using different modes of prompting delivered via a handheld electronic device for travellers with severe cognitive impairments.



A within-subject comparison study was used to evaluate potential differences in route navigation performance when travellers received directions using four different prompt modes: (1) aerial map image, (2) point of view map image, (3) text based instructions/no image and (4) audio direction/no image.



Twenty travellers with severe cognitive impairments due to acquired brain injury walked four equivalent routes using four different prompting modes delivered via a wrist-worn navigation device. Navigation scores were computed that captured accuracy and confidence during navigation.



Results of the repeated measures Analysis of Variance suggested that participants performed best when given prompts via speech-based audio directions. The majority of the participants also preferred this prompting mode. Findings are interpreted in the context of cognitive resource allocation theory.
AISeL ) All Sprouts Content Sprouts 11-20-2008 Proposing the Hedonic Affect Model ( HAM ) to Explain how Stimuli and Performance Expectations Predict Affect in Individual and Group Hedonic Systems Use

Hedonic systems represent a multibillion-dollar industry and play an important role in how people recreate, socialize, and even conduct business. A key goal of hedonic system design is to promote positive affectâa variable known to influence cognitive beliefs, trust, disclosure, adoption, and purchase intentions. Yet, little research has identified or explained how stimuli from design features lead to positive affect in hedonic systems. This article introduces a new theoretical model, the Hedonic Affect Model (HAM), which is a comprehensive and generalizable model explaining the causes of positive and negative affect in a hedonic software context. HAM outlines three stages that provide an explanation of how stimuli lead to positive affect in hedonic contexts. In stage 1, HAM specifies group and individual interaction inputs that are likely to play a role in users' hedonic evaluations of a system. Stage 2 explains how the interaction inputs and intrinsic motivation influence hedonic performance perceptions. Stage 3 explains how performance expectations and perceived performance lead to a positive disconfirmation and influence users' affect.
Large-Vocabulary Speech Recognition Algorithms

P roviding the computer with a natural interface, including the ability to understand human speech, has been a research goal for almost 40 years. Speech recognition research started with an attempt to decode isolated words from a small vocabulary. As time progressed, the research community began working on large-vocabulary and continuousspeech tasks. Practical versions of such systems have become moderately usable and commercially successful only in the past few years, however. Even now, these commercial applications either restrict the vocabulary to a few thousand words, in the case of banking or airline reservation systems, or require high-bandwidth, high-feedback situations such as dictation, which requires modifying the user’s speech to minimize recognition errors. Early attempts at speech recognition tried to apply expert knowledge about speech production and perception processes, but researchers found that such knowledge was inadequate for capturing the complexities of continuous speech. To date, statistical modeling techniques trained from hundreds of hours of speech have provided most speech recognition advancements. Speech researchers have combined these modeling techniques with the massive increase in available computing power over the past several years to explore complex models with hundreds of thousands of parameters. Multisite speech recognition research cooperation and competition, supported through government agencies such as DARPA, have also fueled advancements in this field. In addition to participating in government-sponsored competitions, industrial labs, universities, and other companies have fostered rapid advances in speech recognition technology by sharing data and algorithms . A by-product of these cooperative efforts has been that most successful systems share roughly the same architecture and algorithms because each site immediately copies other sites’ successful algorithms. To enable next-generation applications such as speech recognition over cellular phones, transcription of call center interactions, and recognition of broadcast news, researchers continue to work on the advanced speech recognition (ASR) problem. An understanding of today’s ASR systems architecture provides a basis for exploring the recent advances motivated by next-generation applications.
A salt-induced butA gene of Tetragenococcus halophilus confers salt tolerance to Escherichia coli by heterologous expression of its dual copies

Accumulating glycine betaine through the ButA transport system from an exogenous supply is a survival strategy employed by Tetragenococcus halophilus, a moderate halophilic lactic acid bacterium with crucial role in flavor formation of high-salt food fermentation, to achieve cellular protection. In this study, we firstly confirmed that butA expression was up-regulated under salt stress conditions by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Subsequently, we discovered that recombinant Escherichia coli MKH13 strains with single- and double-copy butA complete expression box(es) showed typical growth curves while they differed in their salt adaption and tolerance. Meanwhile, high-performance liquid chromatography (HPLC) experiments confirmed results obtained from growth curves. In summary, our results indicated that regulation of butA expression was salt-induced and double-copy butA cassettes entrusted a higher ability of salt adaption and tolerance to E. coli MKH13, which implied the potential of muti-copies of butA gene in the genetic modification of T. halophilus for improvement of salt tolerance and better industrial application.
Biomarkers of human exposure to pesticides.

For centuries, several hundred pesticides have been used to control insects. These pesticides differ greatly in their mode of action, uptake by the body, metabolism, elimination from the body, and toxicity to humans. Potential exposure from the environment can be estimated by environmental monitoring. Actual exposure (uptake) is measured by the biological monitoring of human tissues and body fluids. Biomarkers are used to detect the effects of pesticides before adverse clinical health effects occur. Pesticides and their metabolites are measured in biological samples, serum, fat, urine, blood, or breast milk by the usual analytical techniques. Biochemical responses to environmental chemicals provide a measure of toxic effect. A widely used biochemical biomarker, cholinesterase depression, measures exposure to organophosphorus insecticides. Techniques that measure DNA damage (e.g., detection of DNA adducts) provide a powerful tool in measuring environmental effects. Adducts to hemoglobin have been detected with several pesticides. Determination of chromosomal aberration rates in cultured lymphocytes is an established method of monitoring populations occupationally or environmentally exposed to known or suspected mutagenic-carcinogenic agents. There are several studies on the cytogenetic effects of work with pesticide formulations. The majority of these studies report increases in the frequency of chromosomal aberrations and/or sister chromatid exchanges among the exposed workers. Biomarkers will have a major impact on the study of environmental risk factors. The basic aim of scientists exploring these issues is to determine the nature and consequences of genetic change or variation, with the ultimate purpose of predicting or preventing disease.
Fast Feature Fool: A data independent approach to universal adversarial perturbations

State-of-the-art object recognition Convolutional Neural Networks (CNNs) are shown to be fooled by image agnostic perturbations, called universal adversarial perturbations. It is also observed that these perturbations generalize across multiple networks trained on the same target data. However, these algorithms require training data on which the CNNs were trained and compute adversarial perturbations via complex optimization. The fooling performance of these approaches is directly proportional to the amount of available training data. This makes them unsuitable for practical attacks since its unreasonable for an attacker to have access to the training data. In this paper, for the first time, we propose a novel data independent approach to generate image agnostic perturbations for a range of CNNs trained for object recognition. We further show that these perturbations are transferable across multiple network architectures trained either on same or different data. In the absence of data, our method generates universal perturbations efficiently via fooling the features learned at multiple layers thereby causing CNNs to misclassify. Experiments demonstrate impressive fooling rates and surprising transferability for the proposed universal perturbations generated without any training data.