nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of Respiratory Pattern From Video Using Selective Ensemble Aggregation

Noncontact estimation of respiratory pattern (RP) and respiratory rate (RR) has multiple applications. Existing methods for RP and RR measurement fall into one of the three categories—1) estimation through nasal air flow measurement, 2) estimation from video-based remote photoplethysmography, and 2) estimation by measurement of motion induced by respiration using motion detectors. However, these methods require specialized sensors, are computationally expensive, and/or critically depend on selection of a region of interest (ROI) for processing. In this paper, a general framework is described for estimating a periodic signal driving noisy linear time-invariant (LTI) channels connected in parallel with unknown dynamics. The method is then applied to derive a computationally inexpensive method for estimating RP using two-dimensional cameras that does not critically depend on ROI. Specifically, RP is estimated by imaging changes in the reflected light caused by respiration-induced motion. Each spatial location in the field of view of the camera is modeled as a noise-corrupted LTI measurement channel with unknown system dynamics, driven by a single generating respiratory signal. Estimation of RP is cast as a blind deconvolution problem and is solved through a method comprising subspace projection and statistical aggregation. Experiments are carried out on 31 healthy human subjects by generating multiple RPs and comparing the proposed estimates with simultaneously acquired ground truth from an impedance pneumograph device. The proposed estimator agrees well with the ground truth in terms of correlation measures, despite variability in clothing pattern, camera angle, and ROI.
Correlation Assessment between Three-Dimensional Facial Soft Tissue Scan and Lateral Cephalometric Radiography in Orthodontic Diagnosis

The aim of the present prospective study was to investigate correlations between 3D facial soft tissue scan and lateral cephalometric radiography measurements.  The study sample comprised 312 subjects of Caucasian ethnic origin. Exclusion criteria were all the craniofacial anomalies, noticeable asymmetries, and previous or current orthodontic treatment. A cephalometric analysis was developed employing 11 soft tissue landmarks and 14 sagittal and 14 vertical angular measurements corresponding to skeletal cephalometric variables. Cephalometric analyses on lateral cephalometric radiographies were performed for all subjects. The measurements were analysed in terms of their reliability and gender-age specific differences. Then, the soft tissue values were analysed for any correlations with lateral cephalometric radiography variables using Pearson correlation coefficient analysis.  Low, medium, and high correlations were found for sagittal and vertical measurements. Sagittal measurements seemed to be more reliable in providing a soft tissue diagnosis than vertical measurements.  Sagittal parameters seemed to be more reliable in providing a soft tissue diagnosis similar to lateral cephalometric radiography. Vertical soft tissue measurements meanwhile showed a little less correlation with the corresponding cephalometric values perhaps due to the low reproducibility of cranial base and mandibular landmarks.
Methods for Developing Trust Models for Intelligent Systems

Our research goals are to understand and model the factors that affect trust in intelligent systems across a variety of application domains. In this chapter, we present two methods that can be used to build models of trust for such systems. The first method is the use of surveys, in which large numbers of people are asked to identify and rank factors that would influence their trust of a particular intelligent system. Results from multiple surveys exploring multiple application domains can be used to build a core model of trust and to identify domain specific factors that are needed to modify the core model to improve its accuracy and usefulness. The second method involves conducting experiments where human subjects use the intelligent system, where a variety of factors can be controlled in the studies to explore different factors. Based upon the results of these human subjects experiments, a trust model can be built. These trust models can be used to create design guidelines, to predict initial trust levels before the start of a system’s use, and to measure the evolution of trust over the use of a system. With increased understanding of how to model trust, we can build systems that will be more accepted and used appropriately by target populations.
Preventing SQL Injection attack using pattern matching algorithm

SQL injection attacks, a class of injection flaw in which specially crafted input strings leads to illegal queries to databases, are one of the topmost threats to web applications. A Number of research prototypes and commercial products that maintain the queries structure in web applications have been developed. But these techniques either fail to address the full scope of the problem or have limitations. Based on our observation that the injected string in a SQL injection attack is interpreted differently on different databases. A characteristic diagnostic feature of SQL injection attacks is that they change the intended structure of queries issued. Pattern matching is a technique that can be used to identify or detect any anomaly packet from a sequential action. Injection attack is a method that can inject any kind of malicious string or anomaly string on the original string. Most of the pattern based techniques are used static analysis and patterns are generated from the attacked statements. In this paper, we proposed a detection and prevention technique for preventing SQL Injection Attack (SQLIA) using Aho–Corasick pattern matching algorithm. In this paper, we proposed an overview of the architecture. In the initial stage evaluation, we consider some sample of standard attack patterns and it shows that the proposed algorithm is works well against the SQL Injection Attack. SQL Injection Attack; Pattern matching; Static Pattern; Dynamic Pattern
The effects of sequential combinations of virtual experience, direct experience, and indirect experience: the moderating roles of need for touch and product involvement

This study explored the effects of sequential combinations of consumer experiences. Four kinds of sequential combinations of consumer experiences were designed: exposing to escapist virtual experience preceding direct experience (VEescapist → DE), exposing to education virtual experience preceding direct experience (VEeducation → DE), exposing to escapist virtual experience preceding indirect experience (VEescapist → IDE), and exposing to education virtual experience preceding indirect experience (VEeducation → IDE). The results indicated that “VEescapist → IDE” produces the highest product knowledge and brand attitude; “VEescapist → DE” produces the lowest perceived risk. Additionally, the moderating roles of need for touch and product involvement also explored. For the high need for touch, “VEescapist → IDE” produces the highest product knowledge and “VEescapist → DE” produces the lowest perceived risk; for the high product involvement, “VEescapist → IDE” produces the highest product knowledge and brand attitude and “VEescapist → DE” produces the lowest perceived risk.
What is and What is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors

Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD). In this paper, we investigate the images that are selected and discarded in constructing a new SOD dataset and find that many similar candidates, complex shape and low objectness are three main attributes of many non-salient objects. Moreover, objects may have diversified attributes that make them salient. As a result, we propose a novel salient object detector by ensembling linear exemplar regressors. We first select reliable foreground and background seeds using the boundary prior and then adopt locally linear embedding (LLE) to conduct manifold-preserving foregroundness propagation. In this manner, a foregroundness map can be generated to roughly pop-out salient objects and suppress non-salient ones with many similar candidates. Moreover, we extract the shape, foregroundness and attention descriptors to characterize the extracted object proposals, and a linear exemplar regressor is trained to encode how to detect salient proposals in a specific image. Finally, various linear exemplar regressors are ensembled to form a single detector that adapts to various scenarios. Extensive experimental results on 5 dataset and the new SOD dataset show that our approach outperforms 9 state-of-art methods.
Augmented LSTM Framework to Construct Medical Self-Diagnosis Android

Given a health-related question (such as "I have a bad stomach ache. What should I do?"), a medical self-diagnosis Android inquires further information from the user, diagnoses the disease, and ultimately recommend best solutions. One practical challenge to build such an Android is to ask correct questions and obtain most relevant information, in order to correctly pinpoint the most likely causes of health conditions. In this paper, we tackle this challenge, named "relevant symptom question generation": Given a limited set of patient described symptoms in the initial question (e.g., "stomach ache"), what are the most critical symptoms to further ask the patient, in order to correctly diagnose their potential problems? We propose an augmented long short-term memory (LSTM) framework, where the network architecture can naturally incorporate the inputs from embedding vectors of patient described symptoms and an initial disease hypothesis given by a predictive model. Then the proposed framework generates the most important symptom questions. The generation process essentially models the conditional probability to observe a new and undisclosed symptom, given a set of symptoms from a patient as well as an initial disease hypothesis. Experimental results show that the proposed model obtains improvements over alternative methods by over 30% (both precision and mean ordinal distance).
Taxi-RS: Taxi-Hunting Recommendation System Based on Taxi GPS Data

Recommender systems are constructed to search the content of interest from overloaded information by acquiring useful knowledge from massive and complex data. Since the amount of information and the complexity of the data structure grow, it has become a more interesting and challenging topic to find an efficient way to process, model, and analyze the information. Due to the Global Positioning System (GPS) data recording the taxi's driving time and location, the GPS-equipped taxi can be regarded as the detector of an urban transport system. This paper proposes a Taxi-hunting Recommendation System (Taxi-RS) processing the large-scale taxi trajectory data, in order to provide passengers with a waiting time to get a taxi ride in a particular location. We formulated the data offline processing system based on HotSpotScan and Preference Trajectory Scan algorithms. We also proposed a new data structure for frequent trajectory graph. Finally, we provided an optimized online querying subsystem to calculate the probability and the waiting time of getting a taxi. Taxi-RS is built based on the real-world trajectory data set generated by 12 000 taxis in one month. Under the condition of guaranteeing the accuracy, the experimental results show that our system can provide more accurate waiting time in a given location compared with a naïve algorithm.
Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks

In this work we present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives state-of-the-art results for both.
ROS Regulation During Abiotic Stress Responses in Crop Plants

Abiotic stresses such as drought, cold, salt and heat cause reduction of plant growth and loss of crop yield worldwide. Reactive oxygen species (ROS) including hydrogen peroxide (H2O2), superoxide anions (O2 (•-)), hydroxyl radical (OH•) and singlet oxygen ((1)O2) are by-products of physiological metabolisms, and are precisely controlled by enzymatic and non-enzymatic antioxidant defense systems. ROS are significantly accumulated under abiotic stress conditions, which cause oxidative damage and eventually resulting in cell death. Recently, ROS have been also recognized as key players in the complex signaling network of plants stress responses. The involvement of ROS in signal transduction implies that there must be coordinated function of regulation networks to maintain ROS at non-toxic levels in a delicate balancing act between ROS production, involving ROS generating enzymes and the unavoidable production of ROS during basic cellular metabolism, and ROS-scavenging pathways. Increasing evidence showed that ROS play crucial roles in abiotic stress responses of crop plants for the activation of stress-response and defense pathways. More importantly, manipulating ROS levels provides an opportunity to enhance stress tolerances of crop plants under a variety of unfavorable environmental conditions. This review presents an overview of current knowledge about homeostasis regulation of ROS in crop plants. In particular, we summarize the essential proteins that are involved in abiotic stress tolerance of crop plants through ROS regulation. Finally, the challenges toward the improvement of abiotic stress tolerance through ROS regulation in crops are discussed.
Chunking during human visuomotor sequence learning

Motor sequence learning is a process whereby a series of elementary movements is re-coded into an efficient representation for the entire sequence. Here we show that human subjects learn a visuomotor sequence by spontaneously chunking the elementary movements, while each chunk acts as a single memory unit. The subjects learned to press a sequence of 10 sets of two buttons through trial and error. By examining the temporal patterns with which subjects performed a visuomotor sequence, we found that the subjects performed the 10 sets as several clusters of sets, which were separated by long time gaps. While the overall performance time decreased by repeating the same sequence, the clusters became clearer and more consistent. The cluster pattern was uncorrelated with the distance of hand movements and was different across subjects who learned the same sequence. We then split a learned sequence into three segments, while preserving or destroying the clusters in the learned sequence, and shuffled the segments. The performance on the shuffled sequence was more accurate and quicker when the clusters in the original sequence were preserved than when they were destroyed. The results suggest that each cluster is processed as a single memory unit, a chunk, and is necessary for efficient sequence processing. A learned visuomotor sequence is hierarchically represented as chunks that contain several elementary movements. We also found that the temporal patterns of sequence performance transferred from the nondominant to dominant hand, but not vice versa. This may suggest a role of the dominant hemisphere in storage of learned chunks. Together with our previous unit-recording and imaging studies that used the same learning paradigm, we predict specific roles of the dominant parietal area, basal ganglia, and presupplementary motor area in the chunking.
Antihyperglycaemic effects of ethanol extracts of Carica papaya and Pandanus amaryfollius leaf in streptozotocin-induced diabetic mice.

Diabetes mellitus is a global disease that is increasing in an alarming rate. The present study was undertaken to study the antidiabetic effect of the ethanol extracts of Carica papaya and Pandanus amaryfollius on streptozotocin-induced diabetic mice. The results of the present study indicated that there was no significant difference in the body weight of the treated groups when compared to diabetic control. Whereas, there was significant (P < 0.05) decrease in the blood glucose level of the plant-treated groups compared to the diabetic control. Histologically the pancreas of the treated groups indicated significant regeneration of the β-cells when compared to the diabetic control. The liver tissues of the treated group indicated a reduction in fatty changes and pyknotic nucleus. The kidney tissues of the treated groups indicated significant recovery in the cuboidal tissue. The results from the phytochemical screening indicated the presence of flavonoids, alkaloids, saponin and tannin in C. papaya and P. amaryfollius. The antidiabetic effect of C. papaya and P. amaryfollius observed in the present study may be due to the presence of these phytochemicals.
Cognitive Internet of Things: A New Paradigm Beyond Connection

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.
Is There a Link Between Executive Compensation and Accounting Fraud ?

This study investigates the association between the structure of executive compensation and accounting fraud. We study 50 firms accused of accounting fraud by the Securities and Exchange Commission (SEC) during the period 1996-2003 as compared to firms not accused of accounting fraud during the same period. We find that the probability of accounting fraud is increasing in the percent of total executive compensation that is stock-based (termed stock-based mix) after controlling for governance characteristics, financial performance, financial distress, firm size, and the likelihood of management wanting to obtain external financing. We find that while the unconditional likelihood of accounting fraud is small, a one standard deviation increase in the proportion of compensation that is stock-based increases the probability of an accounting fraud by approximately 68%. For managers to undertake fraud they must perceive positive benefits from it. We examine the extent to which managerial wealth was overstated prior to the alleged fraud by measuring the decline in managerial wealth once the alleged fraud was made public. We find that the value of managerial stock holdings in firms accused of fraud declined by 49% at the median over the six months following the accusation of fraud. We do not conclude from this evidence that stock-based compensation is inefficient. Rather, the evidence suggests that compensation committees face a trade-off between the positive incentive effects afford by stock-based compensation and the negative effect of increasing the probability of accounting fraud.
Ironic Complicity: Satisfiability Algorithms and Circuit Lower Bounds

I discuss recent progress in developing and exploiting connections between SAT algorithms and circuit lower bounds. The centrepiece of the article is Williams’ proof that NEXP 6⊆ ACC, which proceeds via a new algorithm for ACC-SAT beating brute-force search. His result exploits a formal connection from non-trivial SAT algorithms to circuit lower bounds. I also discuss various connections in the reverse direction, which have led to improved algorithms for k-SAT, Formula-SAT and AC-SAT, among other
A Spellchecker for Dyslexia

Poor spelling is a challenge faced by people with dyslexia throughout their lives. Spellcheckers are therefore a crucial tool for people with dyslexia, but current spellcheckers do not detect real-word errors, which are a common type of errors made by people with dyslexia. Real-word errors are spelling mistakes that result in an unintended but real word, for instance, form instead of from. Nearly 20% of the errors that people with dyslexia make are real-word errors. In this paper, we introduce a system called Real Check that uses a probabilistic language model, a statistical dependency parser and Google n-grams to detect real-world errors. We evaluated Real Check on text written by people with dyslexia, and showed that it detects more of these errors than widely used spellcheckers. In an experiment with 34 people (17 with dyslexia), people with dyslexia corrected sentences more accurately and in less time with Real Check.
Large-volume liposuction and extensive abdominoplasty: a feasible alternative for improving body shape.

Forty-two female patients underwent liposuction of large volumes and extensive abdominoplasty during an 18-month period, with an average follow-up period of 9 months. Fifty-two percent of the patients underwent a third surgical procedure, which was basically aesthetic. The average age was 40 years; weight varied between 51 kg and 113 kg, with only 11 percent of the patients at their ideal weight. The tumescent technique was used for performing liposuction, which was done on the loins, trochanters, lumbar, upper scapular, and upper lateral abdominal regions, avoiding liposuction of the abdominal flap in all cases except for one. The abdominoplasty was extensive with detachment to the xiphoid process without detaching liposuctioned areas. Volumes of 1600 ml to 11,200 ml were obtained through liposuction, with an average of 4230 cc, and minimal blood loss. Fatty dermal tissue was removed through abdominoplasty and it varied between 400 g and 5000 g with an average of 1300 g. Only the two heaviest patients required an autologous blood unit, because they underwent breast reduction at the same time. All the other patients were managed exclusively with intravenous crystalloids solutions. No major complications arose, and only five patients had minor complications. Based on these results, we consider it to be feasible to combine the liposuctioning of large volumes with abdominoplasty to improve body shape, even with the addition of a third surgical procedure. Nevertheless, it is essential to use the tumescent technique for liposuction to reduce bleeding to a minimum and to allow us to perform other procedures.