nils-herrmann/clean_scirepeval huggingface dataset:
Internet addiction in adolescents: Prevalence and risk factors

As new media are becoming daily fare, Internet addiction appears as a potential problem in adolescents. From the reported negative consequences, it appears that Internet addiction can have a variety of detrimental outcomes for young people that may require professional intervention. Researchers have now identified a number of activities and personality traits associated with Internet addiction. This study aimed to synthesise previous findings by (i) assessing the prevalence of potential Internet addiction in a large sample of adolescents, and (ii) investigating the interactions between personality traits and the usage of particular Internet applications as risk factors for Internet addiction. A total of 3,105 adolescents in the Netherlands filled out a self-report questionnaire including the Compulsive Internet Use Scale and the Quick Big Five Scale. Results indicate that 3.7% of the sample were classified as potentially being addicted to the Internet. The use of online gaming and social applications (online social networking sites and Twitter) increased the risk for Internet addiction, whereas agreeableness and resourcefulness appeared as protective factors in high frequency online gamers. The findings support the inclusion of ‘Internet addiction’ in the DSM-V. Vulnerability and resilience appear as significant aspects that require consideration in
MANAGEMENT CONTROL SYSTEMS AND STRATEGY: A CRITICAL REVIEW*

This paper reviews research that studies the relationship between management control systems (MCS) and business strategy. Empirical research studies that use contingency approaches and case study applications are examin ed focusing on specific aspects of MCS and their relationship with strategy. These aspects include cost control orientation, performance evaluation and reward systems, the effect of resource sharing, the role of MCS in influencing strategic change and the choice of interactive and diagnostic controls. More contemporary approaches to the relationship between performance measurement systems and strategy are also considered. It is concluded that our knowledge of the relationship between MCS and strategy is limited, providing considerable scope for further research. A series of future research questions is presented.@ 1997 Elsevier  In recent years there has been a growing interest in the relationship between management control systems (MCS) and strategy. It has been suggested that the MCS should be tailored explicitly to support the strategy of the business to lead to competitive advantage and superior performance (Dent, 1990; Samson et al., 1991; Simons, 1987a, 1990). Also, there is evidence that high organizational performance may result from a matching of an organization’s environment, strategy and internal structures and systems (Govindarajan & Gupta, 1985; Govindarajan, 198@). Strategy was not used explicitly as a variable in MCS research until the 1980s. This is surprising considering the field of business strategy or business policy has become increasingly important since it emerged in the 1950s (see Chandler, 1962). Much of the empirical research in this area follows a contingency approach and involves a search for systematic relationships between specific elements of the MCS and the particular strategy of the organization (Simons, 1987a; Merchant, 1985b; Govindarajan 81 Gupta, 1985). Case studies have also been undertaken to investigate the role of the MCS in supporting and influencing the strategic processes within organizations (Simons, 1990; Roberts, 1990; Archer & Otley, 1991). The focus has been primarily on business strategy at the senior management level of the organization. However, since the mid198Os, in the operations management literature there has been a growing interest in researching the way that manufacturing strategies can be used to gain competitive advantage (Buffa, 1984; Schonberger, 1986; Hayes et al., 1988). Normative studies and single case studies have explored the relationship between MCS and *This paper has benefited from comments of seminar participants at Lancaster University, University of Adelaide and the University of Tasmania, and participants at the European Accounting Association meeting held in Finland in 1993. I am also grateful for the help provided by Anthony Hopwood and the anonymous referees.
Toward a syndrome model of addiction: multiple expressions, common etiology.

It is common for clinicians, researchers, and public policymakers to describe certain drugs or objects (e.g., games of chance) as “addictive,” tacitly implying that the cause of addiction resides in the properties of drugs or other objects. Conventional wisdom encourages this view by treating different excessive behaviors, such as alcohol dependence and pathological gambling, as distinct disorders. Evidence supporting a broader conceptualization of addiction is emerging. For example, neurobiological research suggests that addictive disorders might not be independent:2 each outwardly unique addiction disorder might be a distinctive expression of the same underlying addiction syndrome. Recent research pertaining to excessive eating, gambling, sexual behaviors, and shopping also suggests that the existing focus on addictive substances does not adequately capture the origin, nature, and processes of addiction. The current view of separate addictions is similar to the view espoused during the early days of AIDS diagnosis, when rare diseases were not
Thinking Like a Vertex: A Survey of Vertex-Centric Frameworks for Large-Scale Distributed Graph Processing

The vertex-centric programming model is an established computational paradigm recently incorporated into distributed processing frameworks to address challenges in large-scale graph processing. Billion-node graphs that exceed the memory capacity of commodity machines are not well supported by popular Big Data tools like MapReduce, which are notoriously poor performing for iterative graph algorithms such as PageRank. In response, a new type of framework challenges one to “think like a vertex” (TLAV) and implements user-defined programs from the perspective of a vertex rather than a graph. Such an approach improves locality, demonstrates linear scalability, and provides a natural way to express and compute many iterative graph algorithms. These frameworks are simple to program and widely applicable but, like an operating system, are composed of several intricate, interdependent components, of which a thorough understanding is necessary in order to elicit top performance at scale. To this end, the first comprehensive survey of TLAV frameworks is presented. In this survey, the vertex-centric approach to graph processing is overviewed, TLAV frameworks are deconstructed into four main components and respectively analyzed, and TLAV implementations are reviewed and categorized.
Supervised domain adaptation for I-vector based speaker recognition

In this paper, we present a comprehensive study on supervised domain adaptation of PLDA based i-vector speaker recognition systems. After describing the system parameters subject to adaptation, we study the impact of their adaptation on recognition performance. Using the recently designed domain adaptation challenge, we observe that the adaptation of the PLDA parameters (i.e. across-class and within-class co variances) produces the largest gains. Nonetheless, length-normalization is also important; whereas using an indomani UBM and T matrix is not crucial. For the PLDA adaptation, we compare four approaches. Three of them are proposed in this work, and a fourth one was previously published. Overall, the four techniques are successful at leveraging varying amounts of labeled in-domain data and their performance is quite similar. However, our approaches are less involved, and two of them are applicable to a larger class of models (low-rank across-class).
A Job Recommender System Based on User Clustering

In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main challenge lies on the design of recommendation strategies since different job applicants may have different characteristics. To address the aforementioned challenge, we develop an online JRS, iHR, which groups users into different clusters and employs different recommendation approaches for different user clusters. As a result, iHR has the capability of choosing the appropriate recommendation approaches according to users’ characteristics. Empirical results demonstrate the effectiveness of the proposed system.
Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis

In this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment analysis aims to automatically classify its sentiment/opinion (such as thumbs up or thumbs down) based on the textural information. Despite the success of feature engineering in many previous studies, the hand-coded features do not well capture the semantics of texts. In this research, we argue that learning sentiment-specific semantic representations of documents is crucial for document-level sentiment analysis. We decompose the document semantics into four cascaded constitutes: (1) word representation, (2) sentence structure, (3) sentence composition and (4) document composition. Specifically, we learn sentiment-specific word representations, which simultaneously encode the contexts of words and the sentiment supervisions of texts into the continuous representation space. According to the principle of compositionality, we learn sentiment-specific sentence structures and sentence-level composition functions to produce the representation of each sentence based on the representations of the words it contains. The semantic representations of documents are obtained through document composition, which leverages the sentiment-sensitive discourse relations and sentence representations.
Community detection in incomplete information networks

With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.
MAT: A Multimodal Attentive Translator for Image Captioning

In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional neural network (CNN) feature, we propose to represent the input image as a sequence of detected objects which feeds as the source sequence of the RNN model. In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model. To represent the image in a sequential way, we extract the objects features in the image and arrange them in a order using convolutional neural networks. To further leverage the visual information from the encoded objects, a sequential attention layer is introduced to selectively attend to the objects that are related to generate corresponding words in the sentences. Extensive experiments are conducted to validate the proposed approach on popular benchmark dataset, i.e., MS COCO, and the proposed model surpasses the state-of-the-art methods in all metrics following the dataset splits of previous work. The proposed approach is also evaluated by the evaluation server of MS COCO captioning challenge, and achieves very competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).
Handwriting Analysis of Human Behaviour Based on Neural Network

Graphology or Handwriting analysis is a scientific method of identifying, evaluating and understanding of anyone personality through the strokes and pattern revealed by handwriting. Handwriting reveals the true personality including emotional outlay, honesty, fears and defenses and etc. Handwriting stroke reflects the written trace of each individual's rhythm and Style. The image split into two areas: the signature based on three features and application form of letters digit area. In this research performance evaluation is done by calculating mean square error using Back Propagation Neural Network (BPNN).Human behaviour is analyzed on the basis of signature by using neural
Dynamic smile visualization and quantification: part 1. Evolution of the concept and dynamic records for smile capture.

The "art of the smile" lies in the clinician's ability to recognize the positive elements of beauty in each patient and to create a strategy to enhance the attributes that fall outside the parameters of the prevailing esthetic concept. New technologies have enhanced our ability to see our patients more dynamically and facilitated the quantification and communication of newer concepts of function and appearance. In a 2-part article, we present a comprehensive methodology for recording, assessing, and planning treatment of the smile in 4 dimensions. In part 1, we discuss the evolution of smile analysis and review the dynamic records needed. In part 2, we will review smile analysis and treatment strategies and present a brief case report.
Localization of a stable neural correlate of associative memory.

Do learning and retrieval of a memory activate the same neurons? Does the number of reactivated neurons correlate with memory strength? We developed a transgenic mouse that enables the long-lasting genetic tagging of c-fos-active neurons. We found neurons in the basolateral amygdala that are activated during Pavlovian fear conditioning and are reactivated during memory retrieval. The number of reactivated neurons correlated positively with the behavioral expression of the fear memory, indicating a stable neural correlate of associative memory. The ability to manipulate these neurons genetically should allow a more precise dissection of the molecular mechanisms of memory encoding within a distributed neuronal network.
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison

Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.
A Guided Search Genetic Algorithm for the University Course Timetabling Problem

The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strategy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem.
A Usability Checklist for the Usability Evaluation of Mobile Phone User Interface

In the last decade, the research of the usability of mobile phones has been a newly evolving area with few established methodologies and realistic practices that ensure capturing usability in evaluation. Thus, there exists growing demand to explore appropriate evaluation methodologies that evaluate the usability of mobile phones quickly as well as comprehensively. This study aims to develop a task-based usability checklist based on heuristic evaluations in views of mobile phone user interface (UI) practitioners. A hierarchical structure of UI design elements and usability principles related to mobile phones were developed and then utilized to develop the checklist. To demonstrate the practical effectiveness of the proposed checklist, comparative experiments were conducted on the usability checklist and usability testing. The majority of usability problems found by usability testing and additional problems were discovered by the proposed checklist. It is expected that the usability checklist proposed in this study could be used quickly and efficiently by usability practitioners to evaluate the mobile phone UI in the middle of the mobile phone development process.
Flow Based Security for IoT Devices Using an SDN Gateway

With near exponential growth predicted in the number of Internet of Things (IoT) based devices within networked systems there is need of a means of providing their flexible and secure integration. Software Defined Networking (SDN) is a concept that allows for the centralised control and configuration of network devices, and also provides opportunities for the dynamic control of network traffic. This paper proposes the use of an SDN gateway as a distributed means of monitoring the traffic originating from and directed to IoT based devices. This gateway can then both detect anomalous behaviour and perform an appropriate response (blocking, forwarding, or applying Quality of Service). Initial results demonstrate that, while the addition of the attack detection functionality has an impact on the number of flow installations possible per second, it can successfully detect and block TCP and ICMP flood based attacks.
Lean Sensing: Exploiting Contextual Information for Most Energy-Efficient Sensing

Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.
Solving the 5G Mobile Antenna Puzzle: Assessing Future Directions for the 5G Mobile Antenna Paradigm Shift

Advances in antenna technologies for cellular hand-held devices have been synchronous with the evolution of mobile phones over nearly 40 years. Having gone through four major wireless evolutions , , starting with the analog-based first generation to the current fourth-generation (4G) mobile broadband, technologies from manufacturers and their wireless network capacities today are advancing at unprecedented rates to meet our unrelenting service demands. These ever-growing demands, driven by exponential growth in wireless data usage around the globe , have gone hand in hand with major technological milestones achieved by the antenna design community. For instance, realizing the theory regarding the physical limitation of antennas [4]-[6] was paramount to the elimination of external antennas for mobile phones in the 1990s. This achievement triggered a variety of revolutionary mobile phone designs and the creation of new wireless services, establishing the current cycle of cellular advances and advances in mobile antenna technologies.
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing

Nonparametric two sample testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. We refer to the most common settings as mean difference alternatives (MDA), for testing differences only in first moments, and general difference alternatives (GDA), which is about testing for any difference in distributions. A large number of test statistics have been proposed for both these settings. This paper connects three classes of statistics high dimensional variants of Hotelling’s t-test, statistics based on Reproducing Kernel Hilbert Spaces, and energy statistics based on pairwise distances. We ask the following question how much statistical power do popular kernel and distance based tests for GDA have when the unknown distributions differ in their means, compared to specialized tests for MDA? To answer this, we formally characterize the power of popular tests for GDA like the Maximum Mean Discrepancy with the Gaussian kernel (gMMD) and bandwidth-dependent variants of the Energy Distance with the Euclidean norm (eED) in the high-dimensional MDA regime. We prove several interesting properties relating these classes of tests under MDA, which include (a) eED and gMMD have asymptotically equal power; furthermore they also enjoy a free lunch because, while they are additionally consistent for GDA, they have the same power as specialized high-dimensional t-tests for MDA. All these tests are asymptotically optimal (including matching constants) for MDA under spherical covariances, according to simple lower bounds. (b) The power of gMMD is independent of the kernel bandwidth, as long as it is larger than the choice made by the median heuristic.