nils-herrmann/clean_scirepeval huggingface dataset:
Bat Algorithm and Cuckoo Search: A Tutorial

Nature-inspired metaheuristic algorithms have attracted much attention in the last decade, and new algorithms have emerged almost every year with a vast, ever-expanding literature. In this chapter, we briefly review two latest metaheuristics: bat algorithm and cuckoo search for global optimization. Bat algorithm was proposed by Xin-She Yang in 2010, inspired by the echolocation of microbats, while cuckoo search was developed by Xin-She Yang and Suash Deb in 2009, inspired by the brood parasitism of some cuckoo species. Both algorithms have shown superiority over many other metaheuristics over a wide range of applications.
An Approach to Evaluate Data Trustworthiness Based on Data Provenance

Because of the increased need of data sharing among multiple organizations like governmental organizations, financial corporations and medical hospitals, it is critical to ensure data integrity so that effective decisions can be made based on these data. An important component of any solution for assessing data integrity is represented by techniques and tools to evaluate the trustworthiness of data provenance. However, few efforts have been devoted to investigate approaches for assessing how trusted the data are, based in turn on an assessment of the data sources and intermediaries. To bridge this gap, we propose a data provenance trust model which takes into account various factors that may affect the trustworthiness and, based on these factors, assigns trust scores to both data and data providers. Such trust scores represent key information based on which data users may decide whether to use the data and for what purposes.
Affective state detection via facial expression analysis within a human-computer interaction context

The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.
Applying Stochastic Integer Programming to Optimization of Resource Scheduling in Cloud Computing

Resource scheduling based on SLA(Service Level Agreement) in cloud computing is NP-hard problem. There is no efficient method to solve it. This paper proposes an optimal model for the problem and give an algorithm by applying stochastic integer programming technique. Applying Gröbner bases theory for solving the stochastic integer programming problem and the experimental results of the implementation are also presented.
Malware behaviour analysis

Several malware analysis techniques suppose that the disassembled code of a piece of malware is available, which is however not always possible. This paper proposes a flexible and automated approach to extract malware behaviour by observing all the system function calls performed in a virtualized execution environment. Similarities and distances between malware behaviours are computed which allows to classify malware behaviours. The main features of our approach reside in coupling a sequence alignment method to compute similarities and leverage the Hellinger distance to compute associated distances. We also show how the accuracy of the classification process can be improved using a phylogenetic tree. Such a tree shows common functionalities and evolution of malware. This is relevant when dealing with obfuscated malware variants that have often similar behaviour. The phylogenetic trees were assessed using known antivirus results and only a few malware behaviours were wrongly classified.
A Survey of Resource Management in Multi-Tier Web Applications

Web applications are mostly designed with multiple tiers for flexibility and software reusability. It is difficult to model the behavior of multi-tier Web applications due to the fact that the workload is dynamic and unpredictable and the resource demand in each tier is different. Those features also cause the task of resource allocation for multi-tier Web applications very challenging. In order to meet service level agreements (SLAs) with minimal resource costs, Web service providers should dynamically allocate appropriate resources to each tier. This is particularly important to minimize the monetary cost in the pay-as-you-go cloud computing environments. Recently, a number of rule and model based approaches have been proposed for resource provisioning in cloud computing. In this survey, we identify challenges of the resource allocation problem and conduct a comparative review on those rule and model based approaches for resource allocation in multi-tier Web sites. Given the analysis on their advantages and limitations, we outline research directions to further improve the effectiveness of resource management in multi-tier Web applications.
Marijuana Use in the Elderly: Implications and Considerations.

This article reviews the literature on the use of marijuana in the elderly. Pharmacists play an important role in the management of medications including drug use of potentially illegal drugs, including marijuana. The use of both recreational and medical marijuana has grown exponentially in the general population, including in older adults. As of 2017, marijuana for medical use is legal in 26 states and the District of Columbia.



PubMed and Internet search using the following terms: marijuana, cannabis, delta-9-tetrhydrocannabinol (THC), cannabidiol, cannabinoid, elderly, geriatric, and pharmacology. Findings are based on data collected from older adults (65 years of age and older) through August 2016.



Because of the lack of research and funding, reputable literature on the impact of marijuana on older adults is scarce. The available evidence suggests that elderly individuals should be cautious when consuming marijuana, especially those who have certain comorbid conditions.



The geriatric population has a higher likelihood of having multiple comorbidities and is subject to polypharmacy. Marijuana use, medicinal or recreational, complicates the picture with additive central nervous system side effects.



This article reviews the growing information on marijuana use and discusses issues to consider and cautions in usage that can apply to day-to-day clinical practice and geriatric care. The role of the pharmacist in educating patients, caregivers, and health care providers is expanding with the growing number of states that have legalized medical marijuana (26 states and the District of Columbia, as of 2017). Important education points including drug-drug interactions, drug-disease interactions, and signs and symptoms of acute overdose should be considered.



With this review, pharmacists will be informed on recommendations on the use of marijuana in the older adult. Monitoring of therapy, as well as adverse effects, will be reviewed, including some legal issues and challenges.
ECG Biometric Recognition: A Comparative Analysis

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.
A Context-Based Detection Framework for Advanced Persistent Threats

Besides a large set of malware categories such as worms and Trojan horses, Advanced Persistent Threat (APT) is another more sophisticated attack entity emerging in the cyber threats environment. In this paper we propose a model of the APT detection problem as well as a methodology to implement it on a generic organization network. From our knowledge, the proposed method is the first to address the problem of modeling an APT and to provide a possible detection framework.
Enhancing financial performance with social media: An impression management perspective

The growing plethora of social media outlets have sparked both opportunity and concern in how organizations manage their corporate image. While previous research has examined the various problems associated with negative, word-of-mouth transference of information occurring simultaneously throughout many networks in social media, this paper seeks to address social media usage in impression management (IM). Specifically, we seek to answer two questions: Do IM direct-assertive strategies in social media impact a firm’s financial performance? And which social media strategies impact a firm’s financial performance? To analyze these questions in depth, we use text mining to collect and analyze text from a variety of social network platforms, including blogs, forums, and corporate websites, to assess how such IM strategies impact financial performance. Our results provide text mining validation that social media have a 1 Corresponding author positive impact on IM. We also provide further understanding of how social media strengthens organizations’ communication with various internal and external stakeholders. Lastly, we provide future research ideas concerning social media’s usage in IM.
Open-Loop Precision Grasping With Underactuated Hands Inspired by a Human Manipulation Strategy

In this paper, we demonstrate an underactuated finger design and grasping method for precision grasping and manipulation of small objects. Taking inspiration from the human grasping strategy for picking up objects from a flat surface, we introduce the flip-and-pinch task, in which the hand picks up a thin object by flipping it into a stable configuration between two fingers. Despite the fact that finger motions are not fully constrained by the hand actuators, we demonstrate that the hand and fingers can interact with the table surface to produce a set of constraints that result in a repeatable quasi-static motion trajectory. Even when utilizing only open-loop kinematic playback, this approach is shown to be robust to variation in object size and hand position. Variation of up to 20° in orientation and 10 mm in hand height still result in experimental success rates of 80% or higher. These results suggest that the advantages of underactuated, adaptive robot hands can be carried over from basic grasping tasks to more dexterous tasks.
Bayesian stackelberg games and their application for security at Los Angeles international airport

Many multiagent settings are appropriately modeled as Stackelberg games [Fudenberg and Tirole 1991; Paruchuri et al. 2007], where a leader commits to a strategy first, and then a follower selfishly optimizes its own reward, considering the strategy chosen by the leader. Stackelberg games are commonly used to model attacker-defender scenarios in security domains [Brown et al. 2006] as well as in patrolling [Paruchuri et al. 2007; Paruchuri et al. 2008]. For example, security personnel patrolling an infrastructure commit to a patrolling strategy first, before their adversaries act taking this committed strategy into account. Indeed, Stackelberg games are being used at the Los Angeles International Airport to schedule security checkpoints and canine patrols [Murr 2007; Paruchuri et al. 2008; Pita et al. 2008a]. They could potentially be used in network routing, pricing in transportation systems and many other situations [Korilis et al. 1997; Cardinal et al. 2005]. Although the follower in a Stackelberg game is allowed to observe the leader’s strategy before choosing its own strategy, there is often an advantage for the leader over the case where both players must choose their moves simultaneously. To see the advantage of being the leader in a Stackelberg game, consider the game with the payoff as shown in Table I. The leader is the row player and the follower is the column player. The only pure-strategy Nash equilibrium for this game is when the leader plays a and the follower plays c which gives the leader a payoff of 2. However, if the leader commits to a mixed strategy of playing a and b with equal (0.5) probability, then the follower will play d, leading to an expected payoff for the leader of 3.5.
A case study of cooperative learning and communication pedagogy : Does working in teams make a difference ?

Cooperative learning has increasingly become a popular form of active pedagogy employed in academic institutions. This case study explores the relationship between cooperative learning and academic performance in higher education, specifically in the field of communication. Findings from a questionnaire administered to undergraduate students in a communication research course indicate that involvement in cooperative learning is a strong predictor of a student’s academic performance. A significant positive relationship was found between the degree to which grades are important to a student and his or her active participation in cooperative learning. Further, the importance of grades and sense of achievement are strong predictors of performance on readiness assessment tests.
The immediate effect of kinesiology taping on muscular imbalance in the lateral flexors of the neck in infants: a randomized masked study.

To investigate the immediate effect of kinesiology taping (KT) on muscular imbalance in the lateral flexors of the neck.



Randomized controlled trial.



Twenty-nine infants with congenital muscular torticollis and muscular imbalance in the lateral flexors of the neck were chosen consecutively. In addition, 5 healthy infants with no signs of muscular imbalance in the neck were tested.



The infants were randomly allocated to either an intervention group or a control group. The intervention group had kinesiology taping applied on the affected side using the muscle-relaxing technique. The healthy infants were tested both with and without kinesiology taping. The evaluator was blinded to whether the infants were or were not taped.



There was a significant difference in the change of Muscle Function Scale (MFS) scores between the groups (P < .0001). In the intervention group, there were significantly lower scores on the affected side that had been taped (P < .0001) and also significantly higher scores on the unaffected side (P = .01). There were no significant differences in the control group. For the healthy infants, with no imbalance in the lateral flexors of the neck, there were no changes to the MFS scores regardless of whether the kinesiology tape was applied.



For infants with congenital muscular torticollis, kinesiology taping applied on the affected side had an immediate effect on the MFS scores for the muscular imbalance in the lateral flexors of the neck.
An Efficient Micropayment System Based on Probabilistic Polling

Existing software proposals for electronic payments can be divided into “on-line” schemes that require participation of a trusted party (the bank) in every transaction and are secure against overspending, and the “off-line” schemes that do not require a third party and guarantee only that overspending is detected when vendors submit their transaction records to the bank (usually at the end of the day). We propose a new hybrid scheme that combines the advantages of both of the above traditional design strategies. It allows for control of overspending at a cost of only a modest increase in communication compared to the off-line schemes. Our protocol is based on probabilistic polling. During each transaction, with some small probability, the vendor forwards information about this transaction to the bank. This enables the bank to maintain an accurate approximation of a customer’s spending. The frequency of polling messages is related to the monetary value of transactions and the amount of overspending the bank is willing to risk. The probabilistic polling model creates a natural spectrum bridging the existing on-line and off-line electronic commerce models. For transactions of high monetary value, the cost of polling approaches that of the on-line schemes, but for micropayments, the cost of polling is a small increase over the traffic incurred by the off-line schemes.
DGASensor: Fast Detection for DGA-Based Malwares

DNS protocol has been used by many malwares for command-and-control (C&C). To improve the resiliency of C&C communication, Domain Generation Algorithm (DGA) has been utilized by recent malwares such as Locky, Conficker and Zeus. Many detection systems have been introduced for DGA-based botnets detection. However, such botnets detection approaches suffer from several limitations, for instance, requiring a group of DGA domains, period behaviors, the presence of multiple bots, and so forth. It is very hard for them to detect an individually running DGA-based malware which leave only a few traces. In this paper, we develop DGASensor to detect DGA-based malwares immediately by identifying a single DGA domain using lexical evidence. First, DGASensor automatically analyzes the lexical patterns of the most popular domains listed in Alexa top 100,000, and then extracts two templates, namely distribution template and structure template. Second, the above two templates, pronounceable attributes, and some frequently used properties like entropy and length, are used to extract features from a single domain. Third, we train our classifier using a non-DGA dataset consisting of domains obtained from Alexa rank and a DGA dataset generated by known DGAs. At last, we provide a short word filter to decrease the false positive rate. We implement a prototype system and evaluate it using the above training dataset with 10-fold cross validation. Moreover, a set of real world DNS traffic collected from a recursive DNS server is used to measure real world performance of our system. The results show that DGASensor detects DGA domains with accuracy 93% in our training dataset and is able to identify a variety of malwares in the real world dataset with an extremely high processing capability.
Cross-lingual Name Tagging and Linking for 282 Languages

The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating “silver-standard” annotations by transferring annotations from English to other languages through crosslingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from crosslingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data. All the data sets, resources and systems for 282 languages are made publicly available as a new benchmark 1.
Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview

Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding
Hybrid recreation by reverse breeding in Arabidopsis thaliana

Hybrid crop varieties are traditionally produced by selecting and crossing parental lines to evaluate hybrid performance. Reverse breeding allows doing the opposite: selecting uncharacterized heterozygotes and generating parental lines from them. With these, the selected heterozygotes can be recreated as F1 hybrids, greatly increasing the number of hybrids that can be screened in breeding programs. Key to reverse breeding is the suppression of meiotic crossovers in a hybrid plant to ensure the transmission of nonrecombinant chromosomes to haploid gametes. These gametes are subsequently regenerated as doubled-haploid (DH) offspring. Each DH carries combinations of its parental chromosomes, and complementing pairs can be crossed to reconstitute the initial hybrid. Achiasmatic meiosis and haploid generation result in uncommon phenotypes among offspring owing to chromosome number variation. We describe how these features can be dealt with during a reverse-breeding experiment, which can be completed in six generations (∼1 year).
Research on deep neural network's hidden layers in phoneme recognition

In spite of the great success of the deep neural network (DNN) in speech processing, it is still unclear what kind of underlying mechanisms are involved in this achievement. This preliminary study attempts to find an answer by investigating the functions of DNN's hidden layers in representing speech articulations. Two sets of experiments are performed on the hidden layers in speech recognition. The layer removing experiment is conducted on the English TIMIT database, and the layer replacing experiment is to substitute a layer in an English DNN by the corresponding layer in a Japanese DNN. It is found that the different layers seem to be responsible for different phoneme groups according to the place of articulation. The lower layers are responsible for the back vowels, and the higher layers are responsible for the front vowels. The second layer (i.e. the first hidden layer) of the seven-layer network has major responsibility for more than half of the consonants with the constriction located in the front of the vocal tract, while the other consonants rely on the middle and higher layers. The layer replacing experiment demonstrated that the above relation was language independent. It is necessary to design elaborate studies to discover more details in the future.