nils-herrmann/clean_scirepeval huggingface dataset:
Image transmission and cryptography on the basis of Mobius transform

A new method of image transmission and cryptography on the basis of Mobius transformation is proposed in this paper. Based on the Mobius transformation, the method of modulation and demodulation in Chen-Mobius communication system, which is quite different from the traditional one, is applied in the image transmission and cryptography. To make such a processing, the Chen-Mobius inverse transformed functions act as the “modulation” waveforms and the receiving end is coherently “demodulated” by the often-used digital waveforms. Simulation results are discussed in some detail. It shows that the new application has excellent performances that the digital image signals can be restored from intense noise and encrypted ones.
Evaluation of the Problem Behavior Frequency Scale-Teacher Report Form for assessing behavior in a sample of urban adolescents.

This study evaluated the structure and validity of the Problem Behavior Frequency Scale-Teacher Report Form (PBFS-TR) for assessing students' frequency of specific forms of aggression and victimization, and positive behavior. Analyses were conducted on two waves of data from 727 students from two urban middle schools (Sample 1) who were rated by their teachers on the PBFS-TR and the Social Skills Improvement System (SSIS), and on data collected from 1,740 students from three urban middle schools (Sample 2) for whom data on both the teacher and student report version of the PBFS were obtained. Confirmatory factor analyses supported first-order factors representing 3 forms of aggression (physical, verbal, and relational), 3 forms of victimization (physical, verbal and relational), and 2 forms of positive behavior (prosocial behavior and effective nonviolent behavior), and higher-order factors representing aggression, victimization, and positive behavior. Strong measurement invariance was established over gender, grade, intervention condition, and time. Support for convergent validity was found based on correlations between corresponding scales on the PBFS-TR and teacher ratings on the SSIS in Sample 1. Significant correlations were also found between teacher ratings on the PBFS-TR and student ratings of their behavior on the Problem Behavior Frequency Scale-Adolescent Report (PBFS-AR) and a measure of nonviolent behavioral intentions in Sample 2. Overall the findings provided support for the PBFS-TR and suggested that teachers can provide useful data on students' aggressive and prosocial behavior and victimization experiences within the school setting.
Pareto based artificial bee colony algorithm for multi objective single model assembly line balancing with uncertain task times

Assembly line balancing is significant for efficient and cost effective production of the products and is therefore gaining popularity in recent years. However, several uncertain events in assembly lines might causes variation in the task time and due to these variations there always remains a possibility that completion time of tasks might exceed the predefined cycle time. To hedge against this issue, a single model assembly line balancing problem with uncertain task times and multiple objectives is presented. Current research is aimed to minimize cycle time in addition to maximize the probability that completion time of tasks on stations will not exceed the cycle time and minimize smoothness index simultaneously. A Pareto based artificial bee colony algorithm is proposed to get Pareto solution of the multiple objectives. The proposed algorithm called Pareto based artificial bee colony algorithm (PBABC) introduces some extra steps i.e., sorting of food sources, niche technique and preserve some elitists in the standard artificial bee colony algorithm (ABC) to get Pareto solution. Furthermore, the effective parameters of the proposed algorithm are tuned using Taguchi method. Experiments are performed to solve standard assembly line balancing problems taken from operations research (OR) library. The performance of proposed PBABC algorithm is compared with a famous multi objective optimization algorithm NSGA II, in literature. Computational result shows that proposed PBABC algorithm outperforms NSGA II in terms of the quality of Pareto solutions and computational time.
Keyword spotting for Google assistant using contextual speech recognition

We present a novel keyword spotting (KWS) system that uses contextual automatic speech recognition (ASR). For voice-activated devices, it is common that a KWS system is run on the device in order to quickly detect a trigger phrase (e.g. “Ok Google”). After the trigger phrase is detected, the audio corresponding to the voice command that follows is streamed to the server. The audio is transcribed by the server-side ASR system and semantically processed to generate a response which is sent back to the device. Due to limited resources on the device, the device KWS system might introduce false accepts (FA) and false rejects (FR) that can cause an unsatisfactory user experience. We describe a system that uses server-side contextual ASR and trigger phrase non-terminals to improve overall KWS accuracy. We show that this approach can significantly reduce the FA rate (by 89%) while minimally increasing the FR rate (by 0.2%). Furthermore, we show that this system significantly improves the ASR quality, reducing Word Error Rate (WER) (by 10% to 50% relative), and allows the user to speak seamlessly, without pausing between the trigger phrase and the voice command.
A Hybrid ACO+CP for Balancing Bicycle Sharing Systems

The idea of bike sharing is to provide bikes to the citizens via stations that are located all around the city. At each station, bikes are stored in special racks, such that users can easily pick up or return a bike. However, popular stations are often emptied or filled very quickly, resulting in annoyed users who cannot return or retrieve bikes. To avoid this, the stations must be balanced. Bike sharing systems are balanced by distributing bikes from one station to another by using specific vehicles. Therefore, balancing the system corresponds to finding a tour for each vehicle, including loading and unloading instructions per station such that the resulting system is balanced. Clearly, balancing bike sharing systems is a difficult task, since it requires solving a vehicle routing problem combined with distributing single commodities (bikes) according to the target values at the stations. In the following, we are consistent with the notation introduced in . We consider balancing a bike sharing system with S stations S = {1, . . . , S} and a set of depots D = {S + 1, . . . , S +D}, where each station s ∈ S has a capacity Cs > 0, a number of available bikes bs and the number of target bikes ts that denotes the number of bikes that should be at station s after balancing the system. We use V vehicles V = {1, . . . , V } with capacity cv > 0 and initial load b̂v ≥ 0 that distribute the bikes within maximal t̂v > 0 time units. The travel times between stations (and the depots) is given by a travel time matrix ttu,v ∗Scheduling and Timetabling Group, Department of Electrical, Management and Mechanical Engineering, University of Udine, Via Delle Scienze, 206 33100 Udine, Italy – {luca.digaspero,tommaso.urli}@uniud.it †DTS Mobility Department, Austrian Institute of Technology. Giefinggasse 2, 1210 Vienna, Austria – andrea.rendl@ait.ac.at
Knowledge, attitudes, and practices surrounding breast cancer and screening in female teachers of buraidah, saudi arabia.

Breast cancer is by far the most frequent cancer of women. However the preventive measures for such problem are probably less than expected. The objectives of this study are to assess breast cancer knowledge and attitudes and factors associated with the practice of breast self examination (BSE) among female teachers of Saudi Arabia.



We conducted a cross-sectional survey of teachers working in female schools in Buraidah, Saudi Arabia using a self-administered questionnaire to investigate participants' knowledge about the risk factors of breast cancer, their attitudes and screening behaviors. A sample of 376 female teachers was randomly selected. Participants lived in urban areas, and had an average age of 34.7 ±5.4 years.



More than half of the women showed a limited knowledge level. Among participants, the most frequently reported risk factors were non-breast feeding and the use of female sex hormones. The printed media was the most common source of knowledge. Logistic regression analysis revealed that high income was the most significant predictor of better knowledge level. Knowing a non-relative case with breast cancer and having a high knowledge level were identified as the significant predictors for practicing BSE.



The study points to the insufficient knowledge of female teachers about breast cancer and identified the negative influence of low knowledge on the practice of BSE. Accordingly, relevant educational programs to improve the knowledge level of women regarding breast cancer are needed.
Supervised pattern recognition in food analysis.

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.
Empowering or oppressing? Development and exploration of the Enjoyment of Sexualization Scale.

Sexualization of girls and women in America is rampant and has many negative consequences. Women, however, often report enjoying being sexually admired by men. Given this paradox, it is unclear whether such enjoyment represents an authentic empowerment of women's sexuality or is related to traditional feminine norms and sexist beliefs. In Studies 1 and 2, the authors developed and tested the eight-item Enjoyment of Sexualization Scale (ESS). It had good reliability and was differentiated from related constructs including body surveillance, body shame, self-sexualizing behaviors, and appearance-contingent self-esteem. In Study 3, endorsement of traditional gender norms, endorsement of benevolent sexism, and endorsement of hostile sexism were all positively related to the ESS. Moreover, women who both enjoyed sexualization and engaged in self-objectification reported more negative eating attitudes. Overall, there was little support for positive effects of enjoying sexualization. The extent to which enjoying sexualization actually empowers women or contributes to their oppression is discussed.
Driver yawning detection based on deep convolutional neural learning and robust nose tracking

Driver yawning detection is one of the key technologies used in driver fatigue monitoring systems. Real-time driver yawning detection is a very challenging problem due to the dynamics in driver's movements and lighting conditions. In this paper, we present a yawning detection system that consists of a face detector, a nose detector, a nose tracker and a yawning detector. Deep learning algorithms are developed for detecting driver face area and nose location. A nose tracking algorithm that combines Kalman filter with a dedicated open-source TLD (Track-Learning-Detection) tracker is developed to generate robust tracking results under dynamic driving conditions. Finally a neural network is developed for yawning detection based on the features including nose tracking confidence value, gradient features around corners of mouth and face motion features. Experiments are conducted on real-world driving data, and results show that the deep convolutional networks can generate a satisfactory classification result for detecting driver's face and nose when compared with other pattern classification methods, and the proposed yawning detection system is effective in real-time detection of driver's yawning states.
Metastasis: Dissemination and growth of cancer cells in metastatic sites

Metastases, rather than primary tumours, are responsible for most cancer deaths. To prevent these deaths, improved ways to treat metastatic disease are needed. Blood flow and other mechanical factors influence the delivery of cancer cells to specific organs, whereas molecular interactions between the cancer cells and the new organ influence the probability that the cells will grow there. Inhibition of the growth of metastases in secondary sites offers a promising approach for cancer therapy.
Enabling efficient OS paging for main-memory OLTP databases

Even though main memory is becoming large enough to fit most OLTP databases, it may not always be the best option. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). Therefore, it is more economical to store the coldest records on a fast secondary storage device such as a solid-state disk. However, main-memory DBMS have no knowledge of secondary storage, while traditional disk-based databases, designed for workloads where data resides on HDD, introduce too much overhead for the common case where the working set is memory resident.
 In this paper, we propose a simple and low-overhead technique that enables main-memory databases to efficiently migrate cold data to secondary storage by relying on the OS's virtual memory paging mechanism. We propose to log accesses at the tuple level, process the access traces offline to identify relevant access patterns, and then transparently re-organize the in-memory data structures to reduce paging I/O and improve hit rates. The hot/cold data separation is performed on demand and incrementally through careful memory management, without any change to the underlying data structures. We validate experimentally the data re-organization proposal and show that OS paging can be efficient: a TPC-C database can grow two orders of magnitude larger than the available memory size without a noticeable impact on performance.
Prediction of potential for organ donation after cardiac death in patients in neurocritical state: a prospective observational study

Successful donation of organs after cardiac death (DCD) requires identification of patients who will die within 60 min of withdrawal of life-sustaining treatment (WLST). We aimed to validate a straightforward model to predict the likelihood of death within 60 min of WLST in patients with irreversible brain injury.



In this multicentre, observational study, we prospectively enrolled consecutive comatose patients with irreversible brain injury undergoing WLST at six medical centres in the USA and the Netherlands. We assessed four clinical characteristics (corneal reflex, cough reflex, best motor response, and oxygenation index) as predictor variables, which were selected on the basis of previous findings. We excluded patients who had brain death or were not intubated. The primary endpoint was death within 60 min of WLST. We used univariate and multivariable logistic regression analyses to assess associations with predictor variables. Points attributed to each variable were summed to create a predictive score for cardiac death in patients in neurocritical state (the DCD-N score). We assessed performance of the score using area under the curve analysis.



We included 178 patients, 82 (46%) of whom died within 60 min of WLST. Absent corneal reflexes (odds ratio [OR] 2·67, 95% CI 1·19-6·01; p=0·0173; 1 point), absent cough reflex (4·16, 1·79-9·70; p=0·0009; 2 points), extensor or absent motor responses (2·99, 1·22-7·34; p=0·0168; 1 point), and an oxygenation index score of more than 3·0 (2·31, 1·10-4·88; p=0·0276; 1 point) were predictive of death within 60 min of WLST. 59 of 82 patients who died within 60 min of WLST had DCD-N scores of 3 or more (72% sensitivity), and 75 of 96 of those who did not die within this interval had scores of 0-2 (78% specificity); taking into account the prevalence of death within 60 min in this population, a score of 3 or more was translated into a 74% chance of death within 60 min (positive predictive value) and a score of 0-2 translated into a 77% chance of survival beyond 60 min (negative predictive value).



The DCD-N score can be used to predict potential candidates for DCD in patients with non-survivable brain injury. However, this score needs to be tested specifically in a cohort of potential donors participating in DCD protocols.
An End-To-End Siamese Convolutional Neural Network for Loop Closure Detection in Visual Slam System

Loop closure detection is essential and important in visual simultaneous localization and mapping (SLAM) systems. Most existing methods typically utilize a separate feature extraction part and a similarity metric part. Compared to these methods, an end-to-end network is proposed in this paper to jointly optimize the two parts in a unified framework for further enhancing the interworking between these two parts. First, a two-branch siamese network is designed to learn respective features for each scene of an image pair. Then a hierarchical weighted distance (HWD) layer is proposed to fuse the multi-scale features of each convolutional module and calculate the distance between the image pair. Finally, by using the contrastive loss in the training process, the effective feature representation and similarity metric can be learned simultaneously. Experiments on several open datasets illustrate the superior performance of our approach and demonstrate that the end-to-end network is feasible to conduct the loop closure detection in real time and provides an implementable method for visual SLAM systems.
A Rule-Based Recommender System for Online Discussion Forums

In this paper we present a rule-based personalization framework for encapsulating and combining personalization algorithms known from adaptive hypermedia and recommender systems. We show how this personalization framework can be integrated into existing systems by example of the educational online board Comtella-D, which exploits the framework for recommending relevant discussions to the users. In our evaluations we compare different recommender strategies, investigate usage behavior over time, and show that a small amount of user data is sufficient to generate precise recommendations.
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning

One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Successor Features approach this problem by learning a feature representation that satisfies a temporal constraint. We present an implementation of an approach that decouples the feature representation from the reward function, making it suitable for transferring knowledge between domains. We then assess the advantages and limitations of using Successor Features for transfer.
Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.
Machine learning approach for predicting bumps on road

In today's days, due to increase in number of vehicles the probability of accidents are also increasing. The user should be aware of the road circumstances for safety purpose. Several methods requires installing dedicated hardware in vehicle which are expensive. so we have designed a Smart-phone based method which uses a Accelerometer and GPS sensors to analyze the road conditions. The designed system is called as Bumps Detection System(BDS) which uses Accelerometer for pothole detection and GPS for plotting the location of potholes on Google Map. Drivers will be informed in advance about count of potholes on road. we have assumed some threshold values on z-axis(Experimentally Derived)while designing the system. To justify these threshold values we have used a machine learning approach. The k means clustering algorithm is applied on the training data to build a model. Random forest classifier is used to evaluate this model on the test data for better prediction.
Syllable-based automatic Arabic speech recognition

In this paper, we concentrate on the automatic recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consists from forty-four Egyptian speakers. Experiments show that the recognition rate using syllables outperformed the rate obtained using monophones, triphones and words by 2.68%, 1.19% and 1.79% respectively. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. Moreover, syllable-based recognition has relatively smaller number of used units and runs faster than word-based recognition.  Speech recognition, syllables, Arabic language, HMMs.
Head, modifier, and constraint detection in short texts

Head and modifier detection is an important problem for applications that handle short texts such as search queries, ads keywords, titles, captions, etc. In many cases, short texts such as search queries do not follow grammar rules, and existing approaches for head and modifier detection are coarse-grained, domain specific, and/or require labeling of large amounts of training data. In this paper, we introduce a semantic approach for head and modifier detection. We first obtain a large number of instance level head-modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head and modifier detection. Furthermore, we identify a subset of modifiers that we call constraints. Constraints are usually specific and not negligible as far as the intent of the short text is concerned, while non-constraint modifiers are more subjective. The mechanism we developed has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.