nils-herrmann/clean_scirepeval huggingface dataset:
Aspect-Oriented Business Process Management

Separation of concerns has long been considered an effective and efficient strategy to deal with complexity in information systems. One sort of concern, like security and privacy, crosses over other concerns in a system. Such concerns are called cross-cutting concerns. As a result, the realization of these concerns is scattered through the whole system, which makes their management difficult. Aspect Orientation is a paradigm in information systems which aims to modularize cross-cutting concerns. This paradigm is well researched in the programming area, where many aspect-oriented programming languages have been developed, e.g., AspectJ. It has also been investigated in other areas, such as requirement engineering and service composition. In the Business Process Management (BPM) area, Aspect Oriented Business Process Modeling aims to specify how this modularization technique can support encapsulating crosscutting concerns in process models. However, it is not clear how these models should be supported in the whole BPM lifecycle. In addition, the support for designing these models has only been limited to imperative process models that support rigid business processes. Neither has it been investigated how this modularization technique can be supported through declarative or hybrid models to support the separation of cross-cutting concerns for flexible business processes. Therefore, this thesis investigates how aspect orientation can be supported over the whole BPM lifecycle using imperative aspect-oriented business process models. It also investigates how declarative and hybrid aspect-oriented business process models can support the separation of cross-cutting concerns in the BPM area. This thesis has been carried out following the design science framework, and the result is presented as a set of artifacts (in the form of constructs, models, methods, and instantiations) and empirical findings. The artifacts support modeling, analysis, implementation/configuration, enactment, monitoring, adjustment, and mining cross-cutting concerns while supporting business processes using Business Process Management Systems. Thus, it covers the support for the management of these concerns over the whole BPM lifecycle. The use of these artifacts and their application shows that they can reduce the complexity of process models by separating different concerns.
Trust Evaluation Model Based on Fuzzy Logic for C2C E-Commerce

With the development of E-commerce, especially the rapid growth of C2C market, transaction trust plays a more and more role. At present ,there is some shortcomings at the on-line reputation mechanism. Based on the analysis of the factors affecting the transactions in C2C E- commerce, this paper firstly built a hierarchical trust evaluation index system.Then established a trust evaluation model for C2C E- commerce based on fuzzy sets theory.Finally this evaluation model was tested in an example, and it was proved that the model could be used to evaluate the trust status of C2C trading platform objectively. The model proposed in this paper can be not only used in C2C E-commerce but also applied in other autonomous trust management.
Automatic number plate recognition system by character position method

Automatic number plate recognition (ANPR) is an important image processing technology used to recognise number plates of vehicles. Plate localisation and character recognition are two stages of ANPR. In this paper, a methodology has been proposed to develop robust ANPR system. A new algorithm has been proposed for number plate localisation which is based on character positioning method. Character recognition is done with support vector machine in which feature vector is calculated by recursive sub-divisions of character image. The problem of similar shape characters has been solved by syntactic analysis of number plate format for a particular geographical region. The system has been tested on 419 sample images from various countries with various variations in viewing angles, illuminations and distances. Experimental results show that the proposed system detects number plates and recognise characters successfully. The overall success rate of plate localisation is 97.21% and recognition of number is 95.06%.
Kinematic Design to Improve Ergonomics in Human Machine Interaction

This paper introduces a novel kinematic design paradigm for ergonomic human machine interaction. Goals for optimal design are formulated generically and applied to the mechanical design of an upper-arm exoskeleton. A nine degree-of-freedom (DOF) model of the human arm kinematics is presented and used to develop, test, and optimize the kinematic structure of an human arm interfacing exoskeleton. The resulting device can interact with an unprecedented portion of the natural limb workspace, including motions in the shoulder-girdle, shoulder, elbow, and the wrist. The exoskeleton does not require alignment to the human joint axes, yet is able to actuate each DOF of our redundant limb unambiguously and without reaching into singularities. The device is comfortable to wear and does not create residual forces if misalignments exist. Implemented in a rehabilitation robot, the design features of the exoskeleton could enable longer lasting training sessions, training of fully natural tasks such as activities of daily living and shorter dress-on and dress-off times. Results from inter-subject experiments with a prototype are presented, that verify usability over the entire workspace of the human arm, including shoulder and shoulder girdle
Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering

Normally exports of goods and products are transactions encouraged by the governments of countries. Typically these incentives are promoted by tax exemptions or lower tax collections. However, exports fraud may occur with objectives not related to tax evasion, for example money laundering. This article presents the results obtained in implementing the unsupervised Deep Learning model to classify Brazilian exporters regarding the possibility of committing fraud in exports. Assuming that the vast majority of exporters have explanatory features of their export volume which interrelate in a standard way, we used the AutoEncoder to detect anomalous situations with regards to the data pattern. The databases used in this work come from exports of goods and products that occurred in Brazil in 2014, provided by the Secretariat of Federal Revenue of Brazil. From attributes that characterize export companies, the model was able to detect anomalies in at least twenty exporters.
Pathologies of Neural Models Make Interpretation Difficult

One way to interpret neural model predictions is to highlight the most important input features—for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word’s importance is determined by either input perturbation—measuring the decrease in model confidence when that word is removed—or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and do not match the words interpretation methods deem important. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.
SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19.
2 . Analysis and diagnosis of dissolved gases in transformer oil

Power transformers are one of the most critical devices in power systems. It is responsible for voltage conversion, power distribution and transmission, and provides power services. Therefore, the normal operation of the transformer is an important guarantee for the safe, reliable, high quality and economical operation of the power system. It is necessary to minimize and reduce the occurrence of transformer failure and accident. The on-line monitoring and fault diagnosis of power equipment is not only the prerequisite for realizing the predictive maintenance of equipment, but also the key to ensure the safe operation of equipment. Although the analysis of dissolved gas in transformer oil is an important means of transformer insulation monitoring, the coexistence of two kinds of faults, such as discharge and overheat, can lead to a lower positive rate of diagnosis. In this paper, we use the basic particle swarm optimization algorithm to optimize the BP neural network DGA method, select the typical oil in the oil as a neural network input, and then use the trained particle swarm algorithm to optimize the neural network for transformer fault type diagnosis. The results show that the method has a good classification effect, which can solve the problem of difficult to distinguish the faults of the transformer when the discharge and overheat coexist. The positive rate of fault diagnosis is high.
Autonomous Quadcopter Videographer

In recent years, the interest in quadcopters as a robotics platform for autonomous photography has increased. This is due to their small size and mobility, which allow them to reach places that are difficult or even impossible for humans. This thesis focuses on the design of an autonomous quadcopter videographer, i.e. a quadcopter capable of capturing good footage of a specific subject. In order to obtain this footage, the system needs to choose appropriate vantage points and control the quadcopter. Skilled human videographers can easily spot good filming locations where the subject and its actions can be seen clearly in the resulting video footage, but translating this knowledge to a robot can be complex. We present an autonomous system implemented on a commercially available quadcopter that achieves this using only the monocular information and an accelerometer. Our system has two vantage point selection strategies: 1) a reactive approach, which moves the robot to a fixed location with respect to the human and 2) the combination of the reactive approach and a POMDP planner that considers the target’s movement intentions. We compare the behavior of these two approaches under different target movement scenarios. The results show that the POMDP planner obtains more stable footage with less quadcopter motion.
A Novel Nonparallel Plane Proximal SVM for Imbalance Data Classification

The research of imbalance data classification is the hot point in the field of data mining. Conventional classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. This paper pays close attention to the uniqueness of uneven data distribution in imbalance classification problems. Without change the original imbalance training data, this paper indicated the advantages of proximal classifier for imbalance data classification. In order to improve the accuracy of classification, this paper proposed a new model named LSNPPC, based the classical proximal SVM models which find two nonparallel planes for data classification. The LS-NPPC model is applied to six UCI datasets and one real application. The results indicate the effectiveness of the proposed model for imbalanced data classification problems.
Social competence intervention for youth with Asperger Syndrome and high-functioning autism: an initial investigation.

Individuals with high functioning autism (HFA) or Asperger Syndrome (AS) exhibit difficulties in the knowledge or correct performance of social skills. This subgroup's social difficulties appear to be associated with deficits in three social cognition processes: theory of mind, emotion recognition and executive functioning. The current study outlines the development and initial administration of the group-based Social Competence Intervention (SCI), which targeted these deficits using cognitive behavioral principles. Across 27 students age 11-14 with a HFA/AS diagnosis, results indicated significant improvement on parent reports of social skills and executive functioning. Participants evidenced significant growth on direct assessments measuring facial expression recognition, theory of mind and problem solving. SCI appears promising, however, larger samples and application in naturalistic settings are warranted.
Coherent and Noncoherent Dictionaries for Action Recognition

In this letter, we propose sparsity-based coherent and noncoherent dictionaries for action recognition. First, the input data are divided into different clusters and the number of clusters depends on the number of action categories. Within each cluster, we seek data items of each action category. If the number of data items exceeds threshold in any action category, these items are labeled as coherent. In a similar way, all coherent data items from different clusters form a coherent group of each action category, and data that are not part of the coherent group belong to noncoherent group of each action category. These coherent and noncoherent groups are learned using K-singular value decomposition dictionary learning. Since the coherent group has more similarity among data, only few atoms need to be learned. In the noncoherent group, there is a high variability among the data items. So, we propose an orthogonal-projection-based selection to get optimal dictionary in order to retain maximum variance in the data. Finally, the obtained dictionary atoms of both groups in each action category are combined and then updated using the limited Broyden–Fletcher–Goldfarb–Shanno optimization algorithm. The experiments are conducted on challenging datasets HMDB51 and UCF50 with action bank features and achieve comparable result using this state-of-the-art feature.
Fully Differential Difference Amplifier (fdda) Based Active Filter 3.1 Differential Difference Amplifier (dda)

This chapter presents the realization of second order active low pass, high pass and band pass filter using fully differential difference amplifier (FDDA). The fully differential difference amplifier is a balanced output differential difference amplifier. It provides low output distortion and high output voltage swing as compared to the DDA. The filters realized with FDDA possess attractive features that do not exist in both traditional (discrete) and modern fully integrated Op-amp circuits. However the frequency range of operation of FDDA is same as that made of the DDA or Op-Amp. The proposed filters possess orthogonality between the cutoff/central frequency and the quality factor. In view of the orthogonality property, the proposed circuits have wide applications in the instrumentation, control systems and signal processing. All the filter realizations have low sensitivity to parameter variations. The first two sections of this chapter present the implementation of DDA and FDDA. Subsequent sections are devoted to the proposed realization filters and main results. The Differential Difference Amplifier (DDA) is an emerging CMOS analog building block. It has been used in a number of applications such as instrumentation amplifier, continuous time filter, implementation of fully differential switch MOS capacitor circuit, common mode detection circuit, telephone line adaption circuit, biomedical applications, floating resistors, sample and hold circuits and MEMs. . Differential Difference Amplifier is an extension of the conventional operational amplifier. An operational amplifier employs only one differential input, whereas two differential inputs in DDA. The schematic diagram of DDA is shown in Figure 3.1. It is a five terminal device with four input terminals named as V pp , V pn , V np , and V nn and the output terminal denoted by V 0 .
Sleep- and circadian rhythm-associated pathways as therapeutic targets in bipolar disorder.

Disruptions in sleep and circadian rhythms are observed in individuals with bipolar disorders (BD), both during acute mood episodes and remission. Such abnormalities may relate to dysfunction of the molecular circadian clock and could offer a target for new drugs.



This review focuses on clinical, actigraphic, biochemical and genetic biomarkers of BDs, as well as animal and cellular models, and highlights that sleep and circadian rhythm disturbances are closely linked to the susceptibility to BDs and vulnerability to mood relapses. As lithium is likely to act as a synchronizer and stabilizer of circadian rhythms, we will review pharmacogenetic studies testing circadian gene polymorphisms and prophylactic response to lithium. Interventions such as sleep deprivation, light therapy and psychological therapies may also target sleep and circadian disruptions in BDs efficiently for treatment and prevention of bipolar depression.



We suggest that future research should clarify the associations between sleep and circadian rhythm disturbances and alterations of the molecular clock in order to identify critical targets within the circadian pathway. The investigation of such targets using human cellular models or animal models combined with 'omics' approaches are crucial steps for new drug development.
Automatic pothole and speed breaker detection using android system

Recent studies have shown that road surface monitoring is essential for the municipal corporations as well as for travelers for choosing the best road possible. Such schemes provide the comfort and security to the vehicle travelers. This paper presents a vibration based approach for automatic detection of potholes and speed breakers along with their co-ordinates. In this approach, a database is maintained for each road, which is made available to the public with the help of global database or through a portal. Potholes and speed breakers are detected along with their severity using android's built-in accelerometer. The results of the proposed approach are tested over a 4 km flat road and compared to manual inspection of pothole and speed breakers on the same considered road. The accuracy of the proposed approach came out to be 93.75% for detection of potholes and speed breakers. This approach is cost efficient and very effective for road surface monitoring.
Intrinsic and extrinsic factors in skin ageing: a review.

As the proportion of the ageing population in industrialized countries continues to increase, the dermatological concerns of the aged grow in medical importance. Intrinsic structural changes occur as a natural consequence of ageing and are genetically determined. The rate of ageing is significantly different among different populations, as well as among different anatomical sites even within a single individual. The intrinsic rate of skin ageing in any individual can also be dramatically influenced by personal and environmental factors, particularly the amount of exposure to ultraviolet light. Photodamage, which considerably accelerates the visible ageing of skin, also greatly increases the risk of cutaneous neoplasms. As the population ages, dermatological focus must shift from ameliorating the cosmetic consequences of skin ageing to decreasing the genuine morbidity associated with problems of the ageing skin. A better understanding of both the intrinsic and extrinsic influences on the ageing of the skin, as well as distinguishing the retractable aspects of cutaneous ageing (primarily hormonal and lifestyle influences) from the irretractable (primarily intrinsic ageing), is crucial to this endeavour.
Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information

Despite the enormous popularity of Online Social Networking sites (OSNs; e.g., Facebook and Myspace), little research in psychology has been done on them. Two studies examining how personality is reflected in OSNs revealed several connections between the Big Five personality traits and self-reported Facebook-related behaviors and observable profile information. For example, extraversion predicted not only frequency of Facebook usage (Study 1), but also engagement in the site, with extraverts (vs. introverts) showing traces of higher levels of Facebook activity (Study 2). As in offline contexts, extraverts seek out virtual social engagement, which leaves behind a behavioral residue in the form of friends lists and picture postings. Results suggest that, rather than escaping from or compensating for their offline personality, OSN users appear to extend their offline personalities into the domains of OSNs.
A Knowledge-Based Topic Modeling Approach for Automatic Topic Labeling

Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines.  Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality of generated labels for each topic. Our work is different in comparison with the previous efforts in this area, where topics are usually represented with a batch of selected words from topics. We have highlighted some aspects of our approach including: 1) we have incorporated ontology concepts with statistical topic modeling in a unified framework, where each topic is a multinomial probability distribution over the concepts and each concept is represented as a distribution over words; and 2) a topic labeling model according to the meaning of the concepts of the ontology included in the learned topics. The best topic labels are selected with respect to the semantic similarity of the concepts and their ontological categorizations. We demonstrate the effectiveness of considering ontological concepts as richer aspects between topics and words by comprehensive experiments on two different data sets. In another word, representing topics via ontological concepts shows an effective way for generating descriptive and representative labels for the discovered topics. Topic modeling; topic labeling; statistical learning; ontologies; linked open data