nils-herrmann/clean_scirepeval huggingface dataset:
Advance Persistent Threat: Botnet

Growth of internet era and corporate sector dealings communication online has introduced crucial security challenges in cyber space. Statistics of recent large scale attacks defined new class of threat to online world, advanced persistent threat (APT) able to impact national security and economic stability of any country. From all APTs, botnet is one of the well-articulated and stealthy attacks to perform cybercrime. Botnet owners and their criminal organizations are continuously developing innovative ways to infect new targets into their networks and exploit them. The concept of botnet refers collection of compromised computers (bots) infected by automated software robots, that interact to accomplish some distributed task which run without human intervention for illegal purposes. They are mostly malicious in nature and allow cyber criminals to control the infected machines remotely without the victim's knowledge. They use various techniques, communication protocols and topologies in different stages of their lifecycle; also specifically they can upgrade their methods at any time. Botnet is global in nature and their target is to steal or destroy valuable information from organizations as well as individuals. In this paper we present real world botnet (APTs) survey.
Stochastic Programming from Modeling Languages

The majority of deterministic mathematical programming problems have a compact formulation in terms of algebraic equations. Therefore they can easily take advantage of the facilities offered by algebraic modeling languages. These tools allow expressing models by using convenient mathematical notation (algebraic equations) and translate the models into a form understandable by the solvers for mathematical programs. Algebraic modeling languages provide facility for the management of a mathematical model and its data, and access different general-purpose solvers. The use of algebraic modeling languages (AMLs) simplifies the process of building the prototype model and in some cases makes it possible to create and maintain even the production version of the model. As presented in other chapters of this book, stochastic programming (SP) is needed when exogenous parameters of the mathematical programming problem are random. Dealing with stochasticities in planning is not an easy task. In a standard scenario-by-scenario analysis, the system is optimized for each scenario separately. Varying the scenario hypotheses we can observe the different optimal responses of the system and delineate the “strong trends” of the future. Indeed, this scenarioby-scenario approach implicitly assumes perfect foresight. The method provides a first-stage decision, which is valid only for the scenario under consideration. Having as many decisions as there are scenarios leaves the decision-maker without a clear recommendation. In stochastic programming the whole set of scenarios is combined into an event tree, which describes the unfolding of uncertainties over the period of planning. The model takes into account the uncertainties characterizing the scenarios through stochastic programming techniques. This adaptive plan is much closer, in spirit, to the way that decision-makers have to deal with uncertain future
3D-LaneNet: end-to-end 3D multiple lane detection

We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with onboard sensing instead of relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. The intranetwork IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-per-column output representation enables our endto-end approach replacing common heuristics such as clustering and outlier rejection. In addition, our approach explicitly handles complex situations such as lane merges and splits. Promising results are shown on a new 3D lane synthetic dataset. For comparison with existing methods, we verify our approach on the image-only tuSimple lane detection benchmark and reach competitive performance.
PRIVACY ISSUES IN KNOWLEDGE DISCOVERY AND DATA MINING

Recent developments in information technology have enabled collection and processing of vast amounts of personal data, such as criminal records, shopping habits, credit and medical history, and driving records. This information is undoubtedly very useful in many areas, including medical research, law enforcement and national security. However, there is an increasing public concern about the individuals' privacy. Privacy is commonly seen as the right of individuals to control information about themselves. The appearance of technology for Knowledge Discovery and Data Mining (KDDM) has revitalized concern about the following general privacy issues: • secondary use of the personal information, • handling misinformation, and • granulated access to personal information. They demonstrate that existing privacy laws and policies are well behind the developments in technology, and no longer offer adequate protection. We also discuss new privacy threats posed KDDM, which includes massive data collection, data warehouses, statistical analysis and deductive learning techniques. KDDM uses vast amounts of data to generate hypotheses and discover general patterns. KDDM poses the following new challenges to privacy.
Statistics and Chemometrics for Analytical Chemistry

Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the statistics and chemometrics for analytical chemistry book. You can open the device and get the book by on-line.
Similarity Measures for Text Document Clustering

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets. A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, cosine similarity, and relative entropy. In this paper, we compare and analyze the effectiveness of these measures in partitional clustering for text document datasets. Our experiments utilize the standard Kmeans algorithm and we report results on seven text document datasets and five distance/similarity measures that have been most commonly used in text clustering.
Compressing deep convolutional neural networks in visual emotion recognition

In this paper, we consider the problem of insufficient runtime and memory-space complexities of deep convolutional neural networks for visual emotion recognition. A survey of recent compression methods and efficient neural networks architectures is provided. We experimentally compare the computational speed and memory consumption during the training and the inference stages of such methods as the weights matrix decomposition, binarization and hashing. It is shown that the most efficient optimization can be achieved with the matrices decomposition and hashing. Finally, we explore the possibility to distill the knowledge from the large neural network, if only large unlabeled sample of facial images is available.
A Fundamental Approach to Cyber Risk Analysis

This article provides a framework for actuaries to think about cyber risk. We propose a differentiated view on cyber versus conventional risk by separating the nature of risk arrival from the target exposed to risk. Our review synthesizes the literature on cyber risk analysis from various disciplines, including computer and network engineering, economics, and actuarial sciences. As a result, we identify possible ways forward to improve rigorous modeling of cyber risk, including its driving factors. This is a prerequisite for establishing a deep and stable market for cyber risk insurance.
Chest pathology identification using deep feature selection with non-medical training

We demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. Convolutional neural networks (CNN) learn higher level image representations. In this work we explore the features extracted from layers of the CNN along with a set of classical features, including GIST and bag-ofwords (BoW). We show results of classification using each feature set as well as fusing among the features. Finally, we perform feature selection on the collection of features to show the most informative feature set for the task. Results of 0.78-0.95 AUC for various pathologies are shown on a dataset of more than 600 radiographs. This study shows the strength and robustness of the CNN features. We conclude that deep learning with large scale nonmedical image databases may be a good substitute, or addition to domain specific representations which are yet to be available for general medical image recognition tasks.
Hybrid VAE: Improving Deep Generative Models using Partial Observations

Deep neural network models trained on large labeled datasets are the state-of-theart in a large variety of computer vision tasks. In many applications, however, labeled data is expensive to obtain or requires a time consuming manual annotation process. In contrast, unlabeled data is often abundant and available in large quantities. We present a principled framework to capitalize on unlabeled data by training deep generative models on both labeled and unlabeled data. We show that such a combination is beneficial because the unlabeled data acts as a data-driven form of regularization, allowing generative models trained on few labeled samples to reach the performance of fully-supervised generative models trained on much larger datasets. We call our method Hybrid VAE (H-VAE) as it contains both the generative and the discriminative parts. We validate H-VAE on three large-scale datasets of different modalities: two face datasets: (MultiPIE, ) and a hand pose dataset (NYU Hand Pose). Our qualitative visualizations further support improvements achieved by using partial observations.
Enhancing student engagement using the flipped classroom.

The flipped classroom is an innovative pedagogical approach that focuses on learner-centered instruction. The purposes of this report were to illustrate how to implement the flipped classroom and to describe students' perceptions of this approach within 2 undergraduate nutrition courses. The template provided enables faculty to design before, during, and after class activities and assessments based on objectives using all levels of Bloom's taxonomy. The majority of the 142 students completing the evaluation preferred the flipped method compared with traditional pedagogical strategies. The process described in the report was successful for both faculty and students.
SysML-Sec: A model driven approach for designing safe and secure systems

Security flaws are open doors to attack embedded systems and must be carefully assessed in order to determine threats to safety and security. Subsequently securing a system, that is, integrating security mechanisms into the system's architecture can itself impact the system's safety, for instance deadlines could be missed due to an increase in computations and communications latencies. SysML-Sec addresses these issues with a model-driven approach that promotes the collaboration between system designers and security experts at all design and development stages, e.g., requirements, attacks, partitioning, design, and validation. A central point of SysML-Sec is its partitioning stage during which safety-related and security-related functions are explored jointly and iteratively with regards to requirements and attacks. Once partitioned, the system is designed in terms of system's functions and security mechanisms, and formally verified from both the safety and the security perspectives. Our paper illustrates the whole methodology with the evaluation of a security mechanism added to an existing automotive system.
Oral Reading Fluency as an Indicator of Reading Competence : A Theoretical , Empirical , and Historical Analysis

The purpose of this article is to consider oral reading fluency as an indicator of overall reading competence. We begin by examining theoretical arguments for supposing that oral reading fluency may reflect overall reading competence. We then summarize several studies substantiating this phenomenon. Next, we provide an historical analysis of the extent to which oral reading fluency has been incorporated into measurement approaches during the past century. We conclude with recommendations about the assessment of oral reading fluency for research and practice.
Learning to generalize 3D spatial relationships

This paper presents an approach to learn meaningful spatial relationships in an unsupervised fashion from the distribution of 3D object poses in the real world. Our approach begins by extracting an over-complete set of features to describe the relative geometry of two objects. Each relationship type is modeled using a relevance-weighted distance over this feature space. This effectively ignores irrelevant feature dimensions. Our algorithm RANSEM for determining subsets of data that share a relationship as well as the model to describe each relationship is based on robust sample-based clustering. This approach combines the search for consistent groups of data with the extraction of models that precisely capture the geometry of those groups. An iterative refinement scheme has shown to be an effective approach for finding concepts of differing degrees of geometric specificity. Our results show that the models learned by our approach correlate strongly with the English labels that have been given by a human annotator to a set of validation data drawn from the NYUv2 real-world Kinect dataset, demonstrating that these concepts can be automatically acquired given sufficient experience. Additionally, the results of our method significantly out-perform K-means, a standard baseline for unsupervised cluster extraction.
The Fourth Industrial Revolution

`introduction Technologies are amplifying each other in a fusion of technologies across the physical digital and biological worlds. We are witnessing profound shifts across all industries market by the emergence of new business models, the disruption of incumbents and the reshaping of production, consumption, transportation and delivery systems. On the social front a paradigm shift is underway in how we work and communicate, as well as how we express, inform, and entertain our self. Decision makers are too often caught in traditional linear (non-disruptive) thinking or too absorbed by immediate concerns to think strategically about the forces of disruption and innovation shaping our future.
The transfer of touch DNA from hands to glass, fabric and wood.

The transfer of DNA from hands to objects by holding or touching has been examined in the past. The main purpose of this study was to examine the variation in the amount of DNA transferred from hands to glass, fabric and wood. The study involved 300 volunteers (100 for glass, 100 for fabric and 100 for wood) 50% of which were male and 50% female. The volunteers held the material for 60s. The DNA was recovered from the objects using a minitape lift, quantified using the Quantifiler kit assay, extracted using a 'Qiagen(®) QIAamp DNA mini kit' and amplified using the AmpFlSTR(®) SGM Plus™ Amplification Kit at 28 cycles. The results show that using ANOVA there was a significant difference (F=8.2, p<0.05) between the three object types in the amount of DNA recovered. In terms of DNA transfer and recovery, wood gave the best yield, followed by fabric and then glass. The likelihood of success of obtaining a profile indicative of the holder was approximately 9% for glass samples, 23% for fabric and 36% for wood. There was no significant difference between the amount of DNA transferred by male or female volunteers. In this study good shedder status, as defined by obtaining useful profiles of 6 or more alleles, is estimated at approximately 22% of the population. The phenomenon of secondary transfer was observed when mixed DNA profiles were obtained but the incidence was low at approximately 10% of the total number of samples. DNA profiles corresponding to more than one person were found on objects which had been touched by only one volunteer. Although secondary transfer is possible the profiles obtained from touched objects are more likely to be as a result of primary transfer rather than a secondary source.
Antioxidant activity and phenolic compounds in selected herbs.

The antioxidant capacities (oxygen radical absorbance capacity, ORAC) and total phenolic contents in extracts of 27 culinary herbs and 12 medicinal herbs were determined. The ORAC values and total phenolic contents for the medicinal herbs ranged from 1.88 to 22.30 micromol of Trolox equivalents (TE)/g of fresh weight and 0.23 to 2.85 mg of gallic acid equivalents (GAE)/g of fresh weight, respectively. Origanum x majoricum, O. vulgare ssp. hirtum, and Poliomintha longiflora have higher ORAC and phenolic contents as compared to other culinary herbs. The ORAC values and total phenolic content for the culinary herbs ranged from 2.35 to 92.18 micromol of TE/g of fresh weight and 0.26 to 17.51 mg of GAE/g of fresh weight, respectively. These also were much higher than values found in the medicinal herbs. The medicinal herbs with the highest ORAC values were Catharanthus roseus, Thymus vulgaris, Hypericum perforatum, and Artemisia annua. A linear relationship existed between ORAC values and total phenolic contents of the medicinal herbs (R = 0.919) and culinary herbs (R = 0.986). High-performance liquid chromatography (HPLC) coupled with diode-array detection was used to identify and quantify the phenolic compounds in selected herbs. Among the identified phenolic compounds, rosmarinic acid was the predominant phenolic compound in Salvia officinalis, Thymus vulgaris, Origanum x majoricum, and P. longiflora, whereas quercetin-3-O-rhamnosyl-(1 --> 2)-rhamnosyl-(1 --> 6)-glucoside and kaempferol-3-O-rhamnosyl-(1 --> 2)-rhamnosyl-(1 --> 6)-glucoside were predominant phenolic compounds in Ginkgo biloba leaves.
Fake News Stance Detection

Social network and online news media are gaining popularity in recent years. Meanwhile, online fake news are becoming widespread. As a result, automating fake news detection is essential to maintain robust online media and social network. In this work, machine learning methods are employed to detect the stance of newspaper headlines on their bodies, which can serve as an important indication of content authenticity. If the newspaper headline is defined to be “unrelated” to their bodies, it indicates a high probability of the news to be “fake”. Specifically, multiple methods are used to extract features relevant to stance detection from a collection of headlines and news article bodies with different stances. These features are then used to train multiple machine learning models including support vector machines, multinomial Naive Bayes, Softmax, and multilayer perceptron. We have demonstrated very high accuracy to detect relevance between the headlines and bodies. This work can be used as a important building block for fake news detection.
Neurobiology of addiction: a neurocircuitry analysis.

Drug addiction represents a dramatic dysregulation of motivational circuits that is caused by a combination of exaggerated incentive salience and habit formation, reward deficits and stress surfeits, and compromised executive function in three stages. The rewarding effects of drugs of abuse, development of incentive salience, and development of drug-seeking habits in the binge/intoxication stage involve changes in dopamine and opioid peptides in the basal ganglia. The increases in negative emotional states and dysphoric and stress-like responses in the withdrawal/negative affect stage involve decreases in the function of the dopamine component of the reward system and recruitment of brain stress neurotransmitters, such as corticotropin-releasing factor and dynorphin, in the neurocircuitry of the extended amygdala. The craving and deficits in executive function in the so-called preoccupation/anticipation stage involve the dysregulation of key afferent projections from the prefrontal cortex and insula, including glutamate, to the basal ganglia and extended amygdala. Molecular genetic studies have identified transduction and transcription factors that act in neurocircuitry associated with the development and maintenance of addiction that might mediate initial vulnerability, maintenance, and relapse associated with addiction.
IMPACT OF HOTEL DISCOUNT STRATEGIES ON CONSUMERS ’ EMOTION AND BEHAVIOR IN THE PRESENCE OF HIGH AND LOW INVOLVEMENT CONSUMERS

While hotels come up with various discount strategies to attract consumers, especially during a recession, both hotels and consumers seem to favor dynamic pricing. Previous studies also indicated that price discounts give consumers not only monetary benefits but also positive emotional responses. The purpose of this study was to investigate how uniform pricing and dynamic pricing influence consumers’ emotion and behavior, identifying the role of their involvement. Results of study suggested that high involvement consumers responded more positively to dynamic pricing than uniform pricing. Moreover, younger and female consumers were more likely to be involved in obtaining a discount, and high involvement consumers showed more positive feelings, and were more likely to tell others, and make repeat purchases from a discount as compared to low involvement consumers.
Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion.

The aim of the present study was to investigate the kinematic and kinetic differences in the execution of vertical jumps between individuals with good and poor ankle dorsiflexion. Fifteen physical education students were assigned to the flexible group (FG), while another 15 were assigned to the inflexible group (IFG). The two groups executed countermovement jumps (CMJ) and drop jumps from a 60 cm height (DJ60). For the CMJ, the FG jumped higher (32.0 ± 4.0 cm vs. 30.2 ± 4.9 cm, P = 0.27) and used a greater range of motion in all leg joints. The IFG jumpers raised their heels off the ground and had a greater horizontal distance between the centre of mass of the trunk and the centre of the hip joint (LCMh 25.6 ± 3.4 cm vs. 30.9 ± 4.3 cm, P < 0.001). In the DJ60 the FG jumped higher (22.4 ± 5.9 cm vs. 19.5 ± 4.6 cm, P = 0.14) with a greater vertical shift of the body centre of mass (BCM) (S = 0.45 ± 0.11 cm vs. 0.36 ± 0.05 cm, P < 0.01) and better joint coordination. The IFG jumpers changed the position of their trunk and heels depending on the jump type. Trainers should reconsider the technical issues of vertical jumps according to the flexibility of the ankle joint.