nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 2 Bacteria for Plant Growth Promotion and Disease Management

Soil is an excellent niche of growth of many microorganisms: protozoa, fungi, viruses, and bacteria. Some microorganisms are able to colonize soil surrounding plant roots, the rhizosphere, making them come under the influence of plant roots (Hiltner 1904; Kennedy 2005). These bacteria are named rhizobacteria. Rhizobacteria are rhizosphere competent bacteria able to multiply and colonize plant roots at all stages of plant growth, in the presence of a competing microflora (Antoun and Kloepper 2001) where they are in contact with other microorganisms. This condition is wildly encountered in natural, non-autoclaved soils. Generally, interactions between plants and microorganisms can be classified as pathogenic, saprophytic, and beneficial (Lynch 1990). Beneficial interactions involve plant growth promoting rhizobacteria (PGPR), generally refers to a group of soil and rhizosphere free-living bacteria colonizing roots in a competitive environment and exerting a beneficial effect on plant growth (Kloepper and Schroth 1978; Lazarovits and Nowak 1997; Kloepper et al. 1989; Kloepper 2003; Bakker et al. 2007). However, numerous researchers tend to enlarge this restrictive definition of rhizobacteria as any root-colonizing bacteria and consider endophytic bacteria in symbiotic association: Rhizobia with legumes and the actinomycete Frankia associated with some phanerogams as PGPR genera. Among PGPRs are representatives of the following genera: Acinetobacter, Agrobacterium, Arthrobacter, Azoarcus, Azospirillum, Azotobacter, Bacillus, Burkholderia, Enterobacter, Klebsiella, Pseudomonas, Rhizobium, Serratia, and Thiobacillus. Some of these genera such as Azoarcus spp., Herbaspirillum, and Burkholderia include endophytic species.
Software Vulnerability Analysis Using CPE and CVE

In this paper, we analyze the Common Platform Enumeration (CPE) dictionary and the Common Vulnerabilities and Exposures (CVE) feeds. These repositories are widely used in Vulnerability Management Systems (VMSs) to check for known vulnerabilities in software products. The analysis shows, among other issues, a lack of synchronization between both datasets that can lead to incorrect results output by VMSs relying on those datasets. To deal with these problems, we developed a method that recommends to a user a prioritized list of CPE identifiers for a given software product. The user can then assign (and, if necessary, adapt) the most suitable CPE identifier to the software so that regular (e.g., daily) checks can find known vulnerabilities for this software in the CVE feeds. Our evaluation of this method shows that this interaction is indeed necessary because a fully automated CPE assignment is prone to errors due to the CPE and CVE shortcomings. We implemented an open-source VMS that employs the proposed method and published it on GitHub.
Simple square-root extractor using op amps

This paper presents the simple and inexpensive method to implement a square-root extractor for voltage input signal. The proposed extractor is based on the use of two operational amplifiers (op amps) as only active elements. The proposed technique employs the op amp supply-current sensing to achieve an inherently quadratic characteristic. The low-output distortion in output signal can be achieved. Experimental results verifying the characteristic of the proposed circuit are also included.
Single-Feed Dual-Layer Dual-Band E-Shaped and U-Slot Patch Antenna for Wireless Communication Application

A new design for single-feed dual-layer dual-band patch antenna with linear polarization is presented in this letter. The dual-band performance is achieved by E-shaped and U-slot patches. The proposed bands of the antenna are WLAN (2.40-2.4835 GHz) and WiMAX (3.40-3.61 GHz) bands. The fundamental modes of the two bands are TM01 mode, and the impedance bandwidths ( ) of 26.9% and 7.1% are achieved at central frequencies of 2.60 and 3.50 GHz. The peak gains of two different bands are 7.1 and 7.4 dBi, and good band isolation is achieved between the two bands. The advantages of the antenna are simple structure, wideband performance at low band, and high gains.
Recurrent Network Models of Sequence Generation and Memory

Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and match cellular resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced-choice task. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures.
Feature Incay for Representation Regularization

Softmax loss is widely used in deep neural networks for multi-class classification, where each class is represented by a weight vector, a sample is represented as a feature vector, and the feature vector has the largest projection on the weight vector of the correct category when the model correctly classifies a sample. To ensure generalization, weight decay that shrinks the weight norm is often used as regularizer. Different from traditional learning algorithms where features are fixed and only weights are tunable, features are also tunable as representation learning in deep learning. Thus, we propose feature incay to also regularize representation learning, which favors feature vectors with large norm when the samples can be correctly classified. With the feature incay, feature vectors are further pushed away from the origin along the direction of their corresponding weight vectors, which achieves better inter-class separability. In addition, the proposed feature incay encourages intra-class compactness along the directions of weight vectors by increasing the small feature norm faster than the large ones. Empirical results on MNIST, CIFAR10 and CIFAR100 demonstrate feature incay can improve the generalization ability.
Interpretable Credit Application Predictions With Counterfactual Explanations

We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness, counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret, especially when a high number of features are involved in the explanation. Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.
Autonomous vehicles

The first car was invented in 1870 by Siegfried Marcus (Guarnieri, 2011). Actually it was just a wagon with an engine but without a steering wheel and without brakes. Instead, it was controlled by the legs of the driver. Converting traditional vehicles into autonomous vehicles was not just one step. The first step was just 28 years after the invention of cars that is to say 1898. This step's concept was moving a vehicle by a remote controller (Nikola, 1898). Since this first step and as computers have been becoming advanced and sophisticated, many functions of modern vehicles have been converted to be entirely automatic with no need of even remote controlling. Changing gears was one of the first actions that could be done automatically without an involvement of the driver (Anthony, 1908), so such cars got the title of "automatic cars"; however, nowadays there are vehicles that can completely travel by themselves although they are not yet allowed to travel on public roads in most of the world. Such vehicles are called "autonomous vehicles" or "driverless cars".
Infants' early ability to segment the conversational speech signal predicts later language development: a retrospective analysis.

Two studies examined relationships between infants' early speech processing performance and later language and cognitive outcomes. Study 1 found that performance on speech segmentation tasks before 12 months of age related to expressive vocabulary at 24 months. However, performance on other tasks was not related to 2-year vocabulary. Study 2 assessed linguistic and cognitive skills at 4-6 years of age for children who had participated in segmentation studies as infants. Children who had been able to segment words from fluent speech scored higher on language measures, but not general IQ, as preschoolers. Results suggest that speech segmentation ability is an important prerequisite for successful language development, and they offer potential for developing measures to detect language impairment at an earlier age.
Advanced Persistent Threat Detection System

The Advanced Persistent Threat has quickly risen as a top-level concern for organizations of all types and sizes. Under today's security paradigm, determined attackers will eventually find their way into their target’s network, often employing social engineering tactics, phishing techniques and backdoor exploits to steal credentials and obtain access. Persistent intrusions target key users within organizations to gain access to trade secrets, intellectual property, computer source code, and any other valuable information available. In order to combat APTs, it is imperative that organizations should know what is going on within their internal networks to fill in the gaps left by perimeter security solutions. The APT detection system enables organizations to have a defence-indepth methodology. The APT system designed has a combination of modules like IDS, IPS and UTM, SIEM working together as a grid and correlate rules with each other for complete defence. The firewall provides gateway level protection against attacks. The intrusion detection system detects any sort of anomaly behaviour and threat signatures. Intrusion prevention system detects and prevents vulnerability exploits in the network. In short, the advanced persistent system designed is an incorporation of all security modules working together as a grid to provide a secure defence system as it detects low and slow attacks which do not generate usual alarms and responds real quick to the attack.
Using SDLC Methodology to Implement HIT

HIT failure rate has been estimated to be as high as 50 – 70 % and is considered a major barrier to adoption of IT by the healthcare industry. Factors like staff resistance to change and non-compliance, inadequate management, policies and procedures, and technical failures emerge as primary reasons for HIT implementation failure (Kaplan & Harris-Salamone, 2009). Strategies like interdisciplinary collaboration, open communication, staff training and support, and strong leadership could mitigate risk of failure due to above listed factors. Technological assessment of workflow and decision-making processes, and application of cognitive and human factor engineering principles are important for developing a system that would meet organizational and user needs.
Iris Image Classification Based on Hierarchical Visual Codebook

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.
Attribute And-Or Grammar for Joint Parsing of Human Pose, Parts and Attributes

This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and articulation of body parts, and account for the correlations between poses and attributes. The A-AOG model is an amalgamation of three traditional grammar formulations: (i) Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii) Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii) Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style. The parse graph outputs human detection, pose estimation, and attribute prediction simultaneously, which are intuitive and interpretable. We conduct experiments on two tasks on two datasets, and experimental results demonstrate the advantage of joint modeling in comparison with computing poses and attributes independently. Furthermore, our model obtains better performance over existing methods for both pose estimation and attribute prediction tasks.
Understanding how consumers categorise nutritional labels: A consumer derived typology for front-of-pack nutrition labelling

Significant ongoing debate exists amongst stakeholders as to the best front-of-pack labelling approach and emerging evidence suggests that the plethora of schemes may cause confusion for the consumer. To gain a better understanding of the relevant psychological phenomena and consumer perspectives surrounding FoP labelling schemes and their optimal development a Multiple Sort Procedure study involving free sorting of a range of nutritional labels presented on cards was performed in four countries (n=60). The underlying structure of the qualitative data generated was explored using Multiple Scalogram Analysis. Elicitation of categorisations from consumers has the potential to provide a very important perspective in this arena and results demonstrated that the amount of information contained within a nutrition label has high salience for consumers, as does the health utility of the label although a dichotomy exists in the affective evaluation of the labels containing varying degrees of information aggregation. Classification of exiting front-of-pack labelling systems on a proposed dimension of 'directiveness' leads to a better understanding of why some schemes may be more effective than others in particular situations or for particular consumers. Based on this research an enhanced hypothetical front-of-pack labelling scheme which combines both directive and non-directive elements is proposed.
Improved Face Recognition Rate Using HOG Features and SVM Classifier

A novel face recognition algorithm is presented in this paper. Histogram of Oriented Gradient features are extracted both for the test image and also for the training images and given to the Support Vector Machine classifier. The detailed steps of HOG feature extraction and the classification using SVM is presented. The algorithm is compared with the Eigen feature based face recognition algorithm. The proposed algorithm and PCA are verified using 8 different datasets. Results show that in all the face datasets the proposed algorithm shows higher face recognition rate when compared with the traditional Eigen feature based face recognition algorithm. There is an improvement of 8.75% face recognition rate when compared with PCA based face recognition algorithm. The experiment is conducted on ORL database with 2 face images for testing and 8 face images for training for each person. Three performance curves namely CMC, EPC and ROC are considered. The curves show that the proposed algorithm outperforms when compared with PCA algorithm. IndexTerms: Facial features, Histogram of Oriented Gradients, Support Vector Machine, Principle Component Analysis.
Combining RGB and Depth Images for Indoor Scene Classification Using Deep Learning

There has been significant progress in recognition of outdoor scenes but indoor scene recognition is still an challenge. This is due to the high appearance fluctuation of indoor situations. With the recent developments in indoor and mobile robotics, identifying the indoor scenes has gained importance. Many approaches have been proposed to detect scenes using object detection and geotags. In contrast, the proposal of this paper uses the convolutional neural network which has gained importance with advancement in machine learning methodologies. Our method has higher efficiency than the existing models as we try to classify the environment as a whole rather than using object identification for the same. We test this approach on our dataset which consists of RGB and also depth images of common locations present in academic environments such as class rooms, labs etc. The proposed approach performs better than previous ones with accuracy up to 98%.
A Tutorial on Energy-Based Learning

Energy-Based Models (EBMs) capture dependencies between v ariables by associating a scalar energy to each configuration of the variab les. Inference consists in clamping the value of observed variables and finding config urations of the remaining variables that minimize the energy. Learning consi sts in finding an energy function in which observed configurations of the variables a re given lower energies than unobserved ones. The EBM approach provides a common the re ical framework for many learning models, including traditional discr minative and generative approaches, as well as graph-transformer networks, co nditi nal random fields, maximum margin Markov networks, and several manifold learn ing methods. Probabilistic models must be properly normalized, which so metimes requires evaluating intractable integrals over the space of all poss ible variable configurations. Since EBMs have no requirement for proper normalizat ion, his problem is naturally circumvented. EBMs can be viewed as a form of non-p robabilistic factor graphs, and they provide considerably more flexibility in th e design of architectures and training criteria than probabilistic approaches .
Intelligent Resource Management in Blockchain-Based Cloud Datacenters

Nowadays, more and more companies migrate business from their own servers to the cloud. With the influx of computational requests, datacenters consume tremendous energy every day, attracting great attention in the energy efficiency dilemma. In this paper, we investigate the energy-aware resource management problem in cloud datacenters, where green energy with unpredictable capacity is connected. Via proposing a robust blockchain-based decentralized resource management framework, we save the energy consumed by the request scheduler. Moreover, we propose a reinforcement learning method embedded in a smart contract to further minimize the energy cost. Because the reinforcement learning method is informed from the historical knowledge, it relies on no request arrival and energy supply. Experimental results on Google cluster traces and real-world electricity price show that our approach is able to reduce the datacenters cost significantly compared with other benchmark algorithms.
PAWE: Polysemy Aware Word Embeddings

Word embedding models learn a distributed vectorial representation for words, which can be used as the basis for (deep) learning models to solve a variety of natural language processing tasks. One of the main disadvantages of current word embedding models is that they learn a single representation for each word in a metric space, as a result of which they cannot appropriately model polysemous words. In this work, we develop a new word embedding model that can accurately represent such words by automatically learning multiple representations for each word, whilst remaining computationally efficient. Without any supervision, our model learns multiple, complementary embeddings that all capture different semantic structure. We demonstrate the potential merits of our model by training it on large text corpora, and evaluating it on word similarity tasks. Our proposed embedding model is competitive with the state of the art and can easily scale to large corpora due to its computational simplicity.
Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning

Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.