nils-herrmann/clean_scirepeval huggingface dataset:
Individual differences in spatial text processing : High spatial ability can compensate for spatial working memory interference

a r t i c l e i n f o The present study investigates the relation between spatial ability and visuo-spatial and verbal working memory in spatial text processing. In two experiments, participants listened to a spatial text (Experiments 1 and 2) and a non-spatial text (Experiment 1), at the same time performing a spatial or a verbal concurrent task, or no secondary task. To understand how individuals who differ in spatial ability process spatial text during dual task performance, spatial individual differences were analyzed. The tasks administered were the Vandenberg and Kuse [Vandenberg, S. G., & Kuse, A. R. (1978). Mental rotation, a group test of three-dimensional spatial visualization. Perceptual and Motor Skills, 47, 599-604.] mental rotation test (MRT) and a reading comprehension task (RCT). Individuals with high (HMR) and low (LMR) mental rotation differed in MRT scores but had similar RCT performance. Results showed that the HMR group, in contrast with LMR counterparts, preserved good spatial text recall even when a spatial concurrent task was performed; however, Experiment 2 revealed a modification of spatial concurrent task performance in LMR as well in HMR group. Overall, results suggest that HMR individuals have more spatial resources than LMR individuals, allowing them to compensate for spatial working memory interference, but only to a limited extent, given that the processing of spatial information is still mediated by VSWM. The present research investigates how spatial ability modulates the processing of environmental information acquired through spatial description. This represents a very common real-life experience in which the environment is indirectly acquired. The spatial mental representation emerging from processing descriptions is a mental model (Johnson-Laird, 1983). Several studies have demonstrated that spatial mental models reflect the spatial properties of descriptions, maintaining relations between objects (e. One way to approach the question of the characteristics of spatial mental representation is to analyze individual differences. A number of studies have noted the importance of visuo-spatial abilities in spatial mental representation also derived from spatial description (see Section 1.1). More recently, the role of some cognitive aspects such as working memory (WM) in spatial descriptions processing was considered (see Section 1.2). The role of WM in spatial text processing is analyzed according to the Baddeley model (Baddeley, 1986; Baddeley & Hitch, 1974). This is a theoretical framework where visuo-spatial abilities can be engaged through a specific role. For this reason, the approach in the present research is …
A Comparison of BPMN and UML 2 . 0 Activity Diagrams

Interest in evaluating Business Process Modeling Languages has widespread, in part due to the increase of the numb er of languages available for this purpose. Several works on the evaluation of BP MLs are available. Their evaluation are mainly based on perspectives centere d in modeling experts. In this paper, we address the readability perspective of tw o BPMLs (UML 2.0 and BPMN) for people not familiar with process modelin g. The UML can be tailored for purposes beyond softwar e modeling and offers Activity Diagrams for business process modeling. BP MN was designed for modeling business process and has a primary goal of being understandable by all business stakeholders. We compared undergraduates ( freshmen) understanding of business process modeled in BPMN and UML 2.0 Activi ty Diagrams. Our results are interesting, since we were able to find that th ese two languages do not have significant differences, despite BPMN readability d esign goals.
A feasibility study of mobile phone text messaging to support education and management of type 2 diabetes in Iraq.

We undertook a feasibility study to evaluate feasibility and utility of short message services (SMSs) to support Iraqi adults with newly diagnosed type 2 diabetes.



Fifty patients from a teaching hospital clinic in Basrah in the first year after diagnosis were recruited to receive weekly SMSs relating to diabetes self-management over 29 weeks. Numbers of messages received, acceptability, cost, effect on glycated hemoglobin (HbA1c), and diabetes knowledge were documented.



Forty-two patients completed the study, receiving an average 22 of 28 messages. Mean knowledge score rose from 8.6 (SD 1.5) at baseline to 9.9 (SD 1.4) 6 months after receipt of SMSs (P=0.002). Baseline and 6-month knowledge scores correlated (r=0.297, P=0.049). Mean baseline HbA1c was 79 mmol/mol (SD 14 mmol/mol) (9.3% [SD 1.3%]) and decreased to 70 mmol/mol (SD 13 mmol/mol) (8.6% [SD 1.2%]) (P=0.001) 6 months after the SMS intervention. Baseline and 6-month values were correlated (r=0.898, P=0.001). Age, gender, and educational level showed no association with changes in HbA1c or knowledge score. Changes in knowledge score were correlated with postintervention HbA1c (r=-0.341, P=0.027). All patients were satisfied with text messages and wished the service to be continued after the study. The cost of SMSs was €0.065 per message.



This study demonstrates SMSs are acceptable, cost-effective, and feasible in supporting diabetes care in the challenging, resource-poor environment of modern-day Iraq. This study is the first in Iraq to demonstrate similar benefits of this technology on diabetes education and management to those seen from its use in better-resourced parts of the world. A randomized controlled trial is needed to assess precise benefits on self-care and knowledge.
A Study of Reinforcement Learning in Multi-Agent Systems

Reinforcement learning has recently gained popularity due to its many successful applications in various fields. In this project reinforcement learning is implemented in a simple warehouse situation where robots have to learn to interact with each other while performing specific tasks. The aim is to study whether reinforcement learning can be used to train multiple agents. Two different methods have been used to achieve this aim, Q-learning and deep Q-learning. Due to practical constraints, this paper cannot provide a comprehensive review of real life robot interactions. Both methods are tested on single-agent and multi-agent models in Python computer simulations. The results show that the deep Q-learning model performed better in the multiagent simulations than the Q-learning model and it was proven that agents can learn to perform their tasks to some degree. Although, the outcome of this project cannot yet be considered sufficient for moving the simulation into reallife, it was concluded that reinforcement learning and deep learning methods can be seen as suitable for modelling warehouse robots and their interactions.
RELATIONSHIP BETWEEN JOB SATISFACTION AND TURNOVER INTENTION : An Empirical Study on the IT Firms in Palestine

Studies investigating the relationship between job satisfaction and turnover intention are abundant. Yet, this relationship has not been fully addressed in the IT field particularly in the developing countries. Moving from this point, this study aims at further probe this area by evaluating the levels of job satisfaction and turnover intention among a sample of IT employees in the Palestinian IT firms. Then, it attempts to examine the sources of job satisfaction and the causes of turnover intention among those employees. The findings show job security, work conditions, pay and benefits, work nature, coworkers, career advancement, supervision and management were all significantly correlated with overall job satisfaction. Only job security, pay, and coworkers were able to significantly influence turnover intention. Implications of the findings and future research directions are discussed
Large-Scale Phenotype-Based Antiepileptic Drug Screening in a Zebrafish Model of Dravet Syndrome1,2,3

Mutations in a voltage-gated sodium channel (SCN1A) result in Dravet Syndrome (DS), a catastrophic childhood epilepsy. Zebrafish with a mutation in scn1Lab recapitulate salient phenotypes associated with DS, including seizures, early fatality, and resistance to antiepileptic drugs. To discover new drug candidates for the treatment of DS, we screened a chemical library of ∼1000 compounds and identified 4 compounds that rescued the behavioral seizure component, including 1 compound (dimethadione) that suppressed associated electrographic seizure activity. Fenfluramine, but not huperzine A, also showed antiepileptic activity in our zebrafish assays. The effectiveness of compounds that block neuronal calcium current (dimethadione) or enhance serotonin signaling (fenfluramine) in our zebrafish model suggests that these may be important therapeutic targets in patients with DS. Over 150 compounds resulting in fatality were also identified. We conclude that the combination of behavioral and electrophysiological assays provide a convenient, sensitive, and rapid basis for phenotype-based drug screening in zebrafish mimicking a genetic form of epilepsy.
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.
Addictive use of social networking sites can be explained by the interaction of Internet use expectancies, Internet literacy, and psychopathological symptoms

Most people use the Internet in a functional way to achieve certain goals and needs. However, there is an increasing number of people who experience negative consequences like loss of control and distress based on an excessive use of the Internet and its specific online applications. Some approaches postulate similarities with behavioral addictions as well as substance dependencies. They differentiate between a generalized and a specific Internet addiction, such as the pathological use of social networking sites (SIA-SNS). Prior studies particularly identified the use of applications, personal characteristics, and psychopathological symptoms as significant predictors for the development and maintenance of this phenomenon. So far, it remains unclear how psychopathological symptoms like depression and social anxiety interact with individual expectancies of Internet use and capabilities of handling the Internet, summarized as Internet literacy.



The current study (N = 334) investigated the interaction of these components in a structural equation model.



The results indicate that the effects of depression and social anxiety on SIA-SNS were mediated by Internet use expectancies and self-regulation.



Thus, Internet use expectancies seem to be crucial for SIA-SNS, which is in line with prior models.



SNS use may be reinforced by experienced gratification and relief from negative feelings. Individual competences in handling the Internet may be preventive for the development of SIA-SNS.
Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require intelligent algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a deep learning (DL) architecture with model predictive control (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.
Hierarchical Recurrent Neural Networks for Long-Term Dependencies

We have already shown that extracting long-term dependencies from sequential data is difficult, both for determimstic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencIes are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for HMMs and IOHMMs.
Aoristic analysis: the spatial interpretation of unspecific temporal events

Temporal limitations of GIS databases are never more apparent than when the time of a change to any spatial object is unknown. This paper examines an unusual type of spatiotemporal imprecision where an event occurs at a known location but at an unknown time. Aoristic analysis can provide a temporal weight and give an indication of the probability that the event occurred within a de ned period. Visualisation of temporal weights can be enhanced by modi cations to existing surface generation algorithms and a temporal intensity surface can be created. An example from burglaries in Central Nottingham (UK) shows that aoristic analysis can smooth irregularities arising from poor database interrogation, and provide an alternative conceptualisation of space and time that is both comprehensible and meaningful.
Wavelet Transform Based Steganography Technique to Hide Audio Signals in Image

Information security is one of the most important factors to be considered when secret information has to be communicated between two parties. Cryptography and steganography are the two techniques used for this purpose. Cryptography scrambles the information, but it reveals the existence of the information. Steganography hides the actual existence of the information so that anyone else other than the sender and the recipient cannot recognize the transmission. In steganography the secret information to be communicated is hidden in some other carrier in such a way that the secret information is invisible. In this paper an image steganography technique is proposed to hide audio signal in image in the transform domain using wavelet transform. The audio signal in any format (MP3 or WAV or any other type) is encrypted and carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analyzed for different attacks. It is found that the technique is robust and it can withstand the attacks. The quality of the stego image is measured by Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Universal Image Quality Index (UIQI). The quality of extracted secret audio signal is measured by Signal to Noise Ratio (SNR), Squared Pearson Correlation Coefficient (SPCC). The results show good values for these metrics.
Motivation through the Design of Work : Test of a Theory ]

A model is proposed that specifies the conditions under which individuals will become internally motivated to perform effectively on their jobs. The model focuses on the interaction among three classes of variables: (a) the psychological states of employees that must be present for internally motivated work behavior to develop; (b) the characteristics of jobs that can create these psychological states; and (c) the attributes of individuals that determine how positively a person will respond to a complex and challenging job. The model was tested for 658 employees who work on 62 different jobs in seven organizations, and results support its validity. A number of special features of the model are discussed (including its use as a basis for the diagnosis of jobs and the evaluation of job redesign projects), and the model is compared to other theories of job design.
STPA-SafeSec: Safety and security analysis for cyber-physical systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF

In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Networks, Long Short Term Memory and Conditional Random Fields. Extensive experiments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96% on macro averaged F1, which improves the state-of-the-art neural networkbased method by 2.82% and outperforms the Top-1 system in the shared task by 1.77%. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29% on overall F1 and gains the best performance on the Good and Bad categories.
Chinese Knowledge Base Question Answering by Attention-Based Multi-Granularity Model

Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity extraction stage, an entity extraction model is built to locate topic entities in questions. The Levenshtein Ratio entity linker is proposed to conduct effective entity linking. All the relevant subject-predicate-object (SPO) triples to topic entity are searched from the knowledge base as candidates. In relation selection, an attention-based multi-granularity interaction model (ABMGIM) is proposed. Two main contributions are as follows. First, a multi-granularity approach for text embedding is proposed. A nested character-level and word-level approach is used to concatenate the pre-trained embedding of a character with corresponding embedding on word-level. Second, we apply a hierarchical matching model for question representation in relation selection tasks, and attention mechanisms are imported for a fine-grained alignment between characters for relation selection. Experimental results show that our model achieves a competitive performance on the public dataset, which demonstrates its effectiveness.
An Unsupervised Method for Detection of XSS Attack

Cross-site scripting (XSS) is a code injection attack that allows an attacker to execute malicious script in another user’s browser. Once the attacker gains control over the Website vulnerable to XSS attack, it can perform actions like cookie-stealing, malware-spreading, session-hijacking and malicious redirection. Malicious JavaScripts are the most conventional ways of performing XSS attacks. Although several approaches have been proposed, XSS is still a live problem since it is very easy to implement, but di cult to detect. In this paper, we propose an e↵ective approach for XSS attack detection. Our method focuses on balancing the load between client and the server. Our method performs an initial checking in the client side for vulnerability using divergence measure. If the suspicion level exceeds beyond a threshold value, then the request is discarded. Otherwise, it is forwarded to the proxy for further processing. In our approach we introduce an attribute clustering method supported by rank aggregation technique to detect confounded JavaScripts. The approach is validated using real life data.
Joint prediction of depths, normals and surface curvature from RGB images using CNNs

Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals. This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel.