nils-herrmann/clean_scirepeval huggingface dataset:
Towards Accurate Object Localization with Smartphones

In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.
Death by hanging while watching violent pornographic videos on the Internet—suicide or accidental autoerotic death?

In deaths by hanging, it may sometimes be difficult to differentiate between autoerotic accident and suicide. Our report deals with a 30-year-old man who was found hanged in the living room of his flat. The deceased was wearing headphones connected to a PC. Within the deceased's view was a computer screen showing the last picture of a video file downloaded from the Internet with the head of an unclothed, allegedly hanged female. The deceased's left hand was inside his trousers in the genital region. The autopsy did not only show findings typical for hanging, but also advanced sarcoidosis, which was known to the victim. Although this basic illness could have been a possible motive for suicide, the circumstances in the presented case pointed more in the direction of an accidental autoerotic death. As far as we know, this is the first description of a death during autoerotic activity in which sexual stimulation was achieved by watching a video file downloaded from the Internet.
Arabic Sentiment Classification: A Hybrid Approach

This paper proposes a sentiment analysis approach for the Arabic language that combines lexicon based and corpus based techniques. The main idea of this approach is to represent the review for the corpus-based approach in the same way it is seen in lexicon-based approach, through replacing the polarity words with their corresponding label Positive 'POS' or Negative 'NEG' in the lexicon, this way the terms that are important but rare can be taken into consideration by the classifier. A comprehensive comparison is conducted using different classifiers, and experimental results showed that the proposed hybrid approach outperforms the corpus-based approach and the highest accuracy reached 96.34% using random forest classifier with 6-fold cross validation.
A framework for automatic heart sound analysis without segmentation

A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.



Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.



The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.



The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the technique of domain randomization, in which the parameters of the simulator-such as lighting, pose, object textures, etc.-are randomized in non-realistic ways to force the neural network to learn the essential features of the object of interest. We explore the importance of these parameters, showing that it is possible to produce a network with compelling performance using only non-artistically-generated synthetic data. With additional fine-tuning on real data, the network yields better performance than using real data alone. This result opens up the possibility of using inexpensive synthetic data for training neural networks while avoiding the need to collect large amounts of hand-annotated real-world data or to generate high-fidelity synthetic worlds-both of which remain bottlenecks for many applications. The approach is evaluated on bounding box detection of cars on the KITTI dataset.
Balanced random forest for imbalanced data streams

Data with highly imbalanced class distributions are common in real life. Machine learning application domains such as e-commerce, risk management, environmental, and health monitoring often suffer from class imbalance since the interesting case occurs rarely. Yet another layer of complexity is added when data arrives as massive streams. In such a setting, it is often of interest that a learning algorithm is updated in an incremental fashion for scalability and model adaptivity reasons while still handling the class imbalance. In this paper, we propose an ensemble algorithm for imbalanced data streams based on the offline balanced random forest idea. We also show on a recent dataset that the algorithm is useful for the buyer prediction problem in large-scale recommender systems.
Art and art in Early Childhood : What Can Young Children Learn from “ a / Art activities ? ”

Justifying art activities in early childhood education seems like a trivial task. Everyone knows that young children love to draw, dip their fingers in paint or squeeze playdough to create images and forms that only those with hardened hearts would find difficult to appreciate. Children seem happier when they have access to art materials and supplies than when they are denied such opportunities, and usually they do not need much invitation to spontaneously take advantage of these offerings. The outcomes of children’s “art play” tend to fascinate adult audiences – and adults: from artistically naïve parents, through psychologists and therapists, to researchers specifically studying artistic development – have long attempted to understand their significance and meaning. Early childhood classrooms are cheerful and require minimal budgets to decorate with the abundance of children’s art. Early childhood parents and teachers also trust, or at least hope, that there are some significant formative benefits to children from their engagement in art activities, such as development of creativity.
Fertility potential after unilateral and bilateral orchidopexy for cryptorchidism

The aim of the study was to compare fertility potential in patients who had been operated upon in childhood because of unilateral or bilateral cryptorchidism. The study covered 68 men (age 25–30 years) with a history of unilateral (49) or bilateral orchidopexy (Mandat et al. ). Fertility potential was estimated with semen analysis (sperm concentration, motility and morphology), testicular volume measurement and hormonal status evaluation [follicle-stimulating hormone (FSH) and inhibin B levels]. Differences were analysed with the nonparametric Mann–Whitney test. The group of subjects with bilateral orchidopexy had significantly decreased sperm concentration (P = 0.047), sperm motility (P = 0.003), inhibin B level (P = 0.036) and testicular volume (P = 0.040), compared to subjects with unilateral orchidopexy. In the group with bilateral orchidopexy, there was a strong negative correlation between inhibin B and FSH levels (P < 0.001, r s = −0.772). Sperm concentration in this group correlated positively with inhibin B level (P = 0.004, r s = 0.627) and negatively with FSH level (P = 0.04, r s = −0.435). The group of subjects with unilateral orchidopexy who had been operated before the age of 8 years had significantly increased inhibin B level (P = 0.006) and testicular volume (P = 0.007) and decreased FSH level (P = 0.01), compared to subjects who had been operated at the age of 8 or later. Men who underwent bilateral orchidopexy in their childhood have appreciably poorer prognosis for fertility compared to men who underwent a unilateral procedure. Our study also confirmed that men who underwent unilateral orchidopexy in their childhood before the age of 8 years have better prognosis for fertility compared to those who were operated later.
Assistive technology in elderly care.

New developments in assistive technology are likely to make an important contribution to the care of elderly people in institutions and at home. Video-monitoring, remote health monitoring, electronic sensors and equipment such as fall detectors, door monitors, bed alerts, pressure mats and smoke and heat alarms can improve older people's safety, security and ability to cope at home. Care at home is often preferable to patients and is usually less expensive for care providers than institutional alternatives.
The Diophantine Equation B 2 X 4 ? Dy 2 = 1

If b and d are given positive integers with b > 1, then we show that the equation of the title possesses at most one solution in positive integers X, Y . Moreover, we give an explicit characterization of this solution, when it exists, in terms of fundamental units of associated quadratic fields. The proof utilizes estimates for linear forms in logarithms of algebraic numbers in conjunction with properties of Pellian equations and the Jacobi symbol and explicit determination of the integer points on certain elliptic curves.
Hop-by-hop adaptive video streaming in content centric network

To guarantee Quality of Experience (QoE) for video streaming services in a future Internet architecture, Content Centric Network (CCN), Dynamic Adaptive Streaming via HTTP (DASH) technology is used to deliver the proper video content according to the network situation. However, CCN enables a host-to-content communication model and has a universal caching design, which seriously decreases the performance of DASH over CCN. In this paper, we propose a hop-by-hop adaptive video streaming scheme (HAVS-CCN) to improve the performance of adaptive video streaming in CCN. HAVS-CCN is simple and applicable to be deployed on DASH over CCN. It directly adjusts video quality and solves network congestion at the bottleneck of transmission path when DASH inaccurately estimates the network throughout. Our scheme optimizes the hop-by-hop content transmission, which achieves video quality adaption and data packet flow control simultaneously. Simulation results, on small-scale networks and large-scale networks, reveal that DASH with HAVS-CCN scheme outperforms the original DASH over CCN, in terms of video playback quality and average delay.
A Sensor Based Anti-Poaching System in Tanzania National Parks

In recent years poaching incidents has been massively increased encompass slaughtering of endangered species in Tanzania and Africa in totality. Different initiatives has been taken world widely including establishment of International Anti-Poaching foundation (IAPF). Tanzania in particular has taken several initiatives on the matter at different time including sending her own military army across the borders of National parks as an attempt to eradicate poaching activities. However poachers are still continue to put a bullet on the heads of these species of monumental importance. The main idea presented in this paper involve employing a modern and a sophisticated technology in which poachers will be left behind and being netted easily there by eliminating Poaching activities. The idea utilize animals themselves with sensors as mobile biological sensors (MBS) mounted with sensor fusion (having visual, infrared camera and GPS) that transmits the location of MBS, access points for wireless communication and a central computer system which classifies animal actions. The system propose three different action of responses, firstly: access points continuously receive data about animals’ location using GPS at certain time intervals and the gathered data is then classified and checked to see if there is a sudden movement (panic) of the animal groups: this action is called animal behavior classification (ABC). The second action can be called visualization where by different image processing techniques of the obtained images surrounding an animal group are performed and therefore provide an ample assistance in understanding what makes sudden movement of the animal group. The last action is to send messages to the game ranger’s cellular phones about the panic of animals and the location through GSM network
Flexible and Creative Chinese Poetry Generation Using Neural Memory

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.
Irving L. Janis' Victims of Groupthink

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Training with automated agents improves people's behavior in negotiation and coordination tasks

There is inconclusive evidence whether practicing tasks with computer agents improves people’s performance on these tasks. This paper studies this question empirically using extensive experiments involving bilateral negotiation and threeplayer coordination tasks played by hundreds of human subjects. We used different training methods for subjects, including practice interactions with other human participants, interacting with agents from the literature, and asking participants to design an automated agent to serve as their proxy in the task. Following training, we compared the performance of subjects when playing state-of-the-art agents from the literature. The results revealed that in the negotiation settings, in most cases, training with computer agents increased people’s performance as compared to interacting with people. In the three player coordination game, training with computer agents increased people’s performance when matched with the state-of-the-art agent. These results demonstrate the efficacy of using computer agents as tools for improving people’s skills when interacting in strategic settings, saving considerable effort and providing better performance than when interacting with human counterparts.
Micro and macro geographical analysis of Y-chromosome lineages in South Iberia.

In this study, we intend to identify the evolutionary footprints of the South Iberian population focusing on the Berber and Arab influence, which has received little attention in the literature. Analysis of the Y-chromosome variation represents a convenient way to assess the genetic contribution of North African populations to the present-day South Iberian genetic pool and could help to reconstruct other demographic events that could have influenced on that region. A total of 26 Y-SNPs and 17 Y-STRs were genotyped in 144 samples from 26 different districts of South Iberia in order to assess the male genetic composition and the level of substructure of male lineages in this area. To obtain a more comprehensive picture of the genetic structure of the South Iberian region as a whole, our data were compared with published data on neighboring populations. Our analyses allow us to confirm the specific impact of the Arab and Berber expansion and dominion of the Peninsula. Nevertheless, our results suggest that this influence is not bigger in Andalusia than in other Iberian populations.
Rage against the virtual machine: hindering dynamic analysis of Android malware

Antivirus companies, mobile application marketplaces, and the security research community, employ techniques based on dynamic code analysis to detect and analyze mobile malware. In this paper, we present a broad range of anti-analysis techniques that malware can employ to evade dynamic analysis in emulated Android environments. Our detection heuristics span three different categories based on (i) static properties, (ii) dynamic sensor information, and (iii) VM-related intricacies of the Android Emulator. To assess the effectiveness of our techniques, we incorporated them in real malware samples and submitted them to publicly available Android dynamic analysis systems, with alarming results. We found all tools and services to be vulnerable to most of our evasion techniques. Even trivial techniques, such as checking the value of the IMEI, are enough to evade some of the existing dynamic analysis frameworks. We propose possible countermeasures to improve the resistance of current dynamic analysis tools against evasion attempts.
Automated malware detection using artifacts in forensic memory images

Malware is one of the greatest and most rapidly growing threats to the digital world. Traditional signature-based detection is no longer adequate to detect new variants and highly targeted malware. Furthermore, dynamic detection is often circumvented with anti-VM and/or anti-debugger techniques. Recently heuristic approaches have been explored to enhance detection accuracy while maintaining the generality of a model to detect unknown malware samples. In this paper, we investigate three feature types extracted from memory images - registry activity, imported libraries, and API function calls. After evaluating the importance of the different features, different machine learning techniques are implemented to compare performances of malware detection using the three feature types, respectively. The highest accuracy achieved was 96%, and was reached using a support vector machine model, fitted on data extracted from registry activity.
Reinforcement Learning Based Approach for Adaptive Release Planning in an Agile Environment

Agile programming involves continually evolving requirements along with a possible change in their business value and an uncertainty in their time of development. This leads to the difficulty in adapting the release plans according to the response of the environment at each iteration step. This paper shows how a machine learning approach can support the release planning process in an agile environment. The objective is to adapt the release plans according to the results of the previous iterations in the present environment . Reinforcement learning technique has been used to learn the release planning process in an environment of various constraints and multiple objectives. The technique has been applied to a case study to show the utility of the method. The simulation results show that the reinforcement technique can be easily integrated into the release planning process. The teams can learn from the previous iterations and incorporate the learning into the release plans
A Multi-Task Learning Formulation for Survival Analysis

Predicting the occurrence of a particular event of interest at future time points is the primary goal of survival analysis. The presence of incomplete observations due to time limitations or loss of data traces is known as censoring which brings unique challenges in this domain and differentiates survival analysis from other standard regression methods. The popularly used survival analysis methods such as Cox proportional hazard model and parametric survival regression suffer from some strict assumptions and hypotheses that are not realistic in most of the real-world applications. To overcome the weaknesses of these two types of methods, in this paper, we reformulate the survival analysis problem as a multi-task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration. We propose an indicator matrix to enable the multi-task learning algorithm to handle censored instances and incorporate some of the important characteristics of survival problems such as non-negative non-increasing list structure into our model through max-heap projection. We employ the L2,1-norm penalty which enables the model to learn a shared representation across related tasks and hence select important features and alleviate over-fitting in high-dimensional feature spaces; thus, reducing the prediction error of each task. To efficiently handle the two non-smooth constraints, in this paper, we propose an optimization method which employs Alternating Direction Method of Multipliers (ADMM) algorithm to solve the proposed multi-task learning problem. We demonstrate the performance of the proposed method using real-world microarray gene expression high-dimensional benchmark datasets and show that our method outperforms state-of-the-art methods.
Finite-time topology identification function projective synchronization of Cohen-Grossberg neural networks with time delays and stochastic disturbance

We studied finite-time function projective synchronization of unknown delayed Cohen-Grossberg neural networks with stochastic disturbance. A hybrid control scheme is proposed to let the drive-response networks synchronize and have a scaling function relation in finite time with topology identification by using the finite-time stability theory. Furthermore, we estimate the high bounds of the synchronization settling time. Finally, the corresponding numerical simulation and its application in secure communication are provided to verify the correctness of the method we proposed.
Using Contextual Speller Techniques and Language Modeling for ESL Error Correction

We present a modular system for detection and correction of errors made by nonnative (English as a Second Language = ESL) writers. We focus on two error types: the incorrect use of determiners and the choice of prepositions. We use a decisiontree approach inspired by contextual spelling systems for detection and correction suggestions, and a large language model trained on the Gigaword corpus to provide additional information to filter out spurious suggestions. We show how this system performs on a corpus of non-native English text and discuss strategies for future enhancements.