Miracle-dz/UniFAR-FTUs huggingface dataset:
The New Economy , eValue and the Impact on User Acceptance of Pervasive ICT

This paper explores issues of eValue creation within communities joined through the Web, and the implications on end user acceptance of pervasive information and communication technology (ICT). We draw on developments in several streams of research on network economics in order to understand the changed logic of value creation on the Web, and its implications on the moderators of user acceptance. The focal point of interest is the adoption-diffusion continuum, from prior use to post-adoptive behavior, within the context of attention getting and attention giving. Hedonic and Self-fulfilling eValue (consumer perspective), as well as Surplus eValue (business perspective) are constructs introduced to aid clarifying when and how value-rational endconsumer action affects attitude toward use. The analysis strives a unified view on user acceptance decision processing, combining the Management Perspective (Cooper & Zmud 1990), the User Perspective (Rogers 1995), the Organizational Perspective (Swanson & Ramiller 2004), with emphasis on the Information Processing perspective (Engel et al. 1995).
Automated question generation and question answering from Turkish texts using text-to-text transformers

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. To reduce the expenses associated with the manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (QG) techniques can be utilized. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using a Turkish QA dataset. To the best of our knowledge, this is the first academic work that attempts to perform automated text-to-text question generation from Turkish texts. Evaluation results show that the proposed multi-task setting achieves state-of-the-art Turkish question answering and question generation performance over TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and pre-trained models are available at this https URL.
Supplementary Material for “Cross-Domain Video Anomaly Detection without Target Domain Adaptation”

K.ColorJitter 0.1,0.1,0.1,0.1 K.RandomAffine degrees=360 K.RandomPerspective distortion scale=0.2 Additional Implementation Details. We implement zxVAD in PyTorch [1]. We resize the input frames to 256×256 and normalize them to the range of [−1,1]. The generator, the discriminator, and the normalcy classifier are trained with the learning rates of 0.0002, 0.00002, and 0.00002, respectively with the Adam [2] optimizer (β1 =0.5,β2 =0.999), following [3]. The generator takes 4 frames as input and outputs one frame. We drop the last sigmoid layer of N (·) as suggested in [4]. We extracted the frames of all TI datasets at 30 frames/sec. The batch size is set as 8. The training iterations for both SHT and UCFC are set as 5000 in all settings of combinations with TI datasets. Unless otherwise specified, we use the default PyTorch parameters. The average training time is ∼2 hours for VAD datasets and ∼24 hours for the experiments involving TI datasets on the Nvidia Titan Xp GPUs.
Tools for Evaluating the Content, Efficacy, and Usability of Mobile Health Apps According to the Consensus-Based Standards for the Selection of Health Measurement Instruments: Systematic Review

Background: There are several mobile health (mHealth) apps in mobile app stores. These apps enter the business-to-customer market with limited controls. Both, apps that users use autonomously and those designed to be recommended by practitioners require an end-user validation to minimize the risk of using apps that are ineffective or harmful. Prior studies have reviewed the most relevant aspects in a tool designed for assessing mHealth app quality, and different options have been developed for this purpose. However, the psychometric properties of the mHealth quality measurement tools, that is, the validity and reliability of
Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

Background Here we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies, with the aim of achieving a high performing algorithm comparable to human screening. Methods We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). Results ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using assigned the inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. Conclusions This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews, , but represents a promising approach to integrating human decisions and automation in systematic review methodology.
Models for Discrete Data

1. Introduction 2. Sampling distributions 3. Logistic regression 4. Log-linear models 5. Coordinate-free models 6. Additional topics Appendix A: Power for chi-squared tests Appendix B: Program for exact tests Appendix C: The hypergeometric distribution References Selected solutions and hints Index Examples index Author index Subject index
Applied Gamification in Self-guided Exhibitions Lessons learned from theory and praxis

This paper contributes to the current understanding of applied digital gamification by providing insights from two design cases from the Danish aqua zoo, the North Sea Oceanarium, concerned with self-facilitated exhibitions. Grounded in a short review of the current state of art, we provide two empirical case examples, concerning a mobile augmented reality design and an Instagram service. Analyzing the design process behind these cases, we identify some of the challenges arising from applying gamification in practice, and whether these insights verify, extents or contradicts current examples of applied gamification research. Specifically, the cases provide insights to the challenge of on-boarding visitors into participating and using the designed products during their visit. In both cases, providing certain incentives for using the app or participating in the Instagram challenge, seemed to activate and engage more visitors and motivate them to participate in the activities as well as downloading the app and preparing for the activities on their own volition prior to their visit. By looking closer at what might have triggered the motivation with the visitors, a connection could be made to gamification and serious games, pertaining to the applied game design arena, in that there is something about extrinsic motivational properties at play, that persuaded the visitors to use the digital experience layers.
Change Management in Business Process Modeling Based on Object Oriented Petri Net

Business Process Modeling (BPM) is the first and most important step in business process management lifecycle. Graph based formalism and rule based formalism are the two most predominant formalisms on which process modeling languages are developed. BPM technology continues to face challenges in coping with dynamic business environments where requirements and goals are constantly changing at the execution time. Graph based formalisms incur problems to react to dynamic changes in Business Process (BP) at the runtime instances. In this research, an adaptive and flexible framework based on the integration between Object Oriented diagramming technique and Petri Net modeling language is proposed in order to support change management techniques for BPM and increase the representation capability for Object Oriented modeling for the dynamic changes in the runtime instances. The proposed framework is applied in a higher education environment to achieve flexible, updatable and dynamic BP. Keywords—Business Process Modeling, Change Management, Graph Based Modeling, Rule Based Modeling, Object Oriented Petri Net.
CONCURRENCY AND COMPUTATION : PRACTICE AND EXPERIENCE Concurrency Computat

Optimizing a given software system to exploit the features of the underlying system has been an area of research for many years. Recently, a number of self-adapting software systems have been designed and developed for various computing environments. In this paper, we discuss the design and implementation of a software system that dynamically adjusts the parallelism of applications executing on computational Grids in accordance with the changing load characteristics of the underlying resources. The migration framework implemented by our software system is aimed at performance-oriented Grid systems and implements tightly coupled policies for both suspension and migration of executing applications. The suspension and migration policies consider both the load changes on systems as well as the remaining execution times of the applications thereby taking into account both system load and application characteristics. The main goal of our migration framework is to improve the response times for individual applications. We also present some results that demonstrate the usefulness of our migration framework. Published in 2005 by John Wiley & Sons, Ltd.
How does CPPI perform against the simplest guarantee strategies

Capital protected structured products are popular with both investors and investment banks. A number of strategies ranging in complexity and cost exist that provide a minimum guaranteed payoff at maturity. In this paper the performance of Constant Proportion Portfolio Insurance (CPPI), a major strategy in the market, is evaluated against two simple strategies: a risk-free and a gapless investment. The CPPI strategy is general, allowing for discrete monitoring of trading ranges, ratchet features and leverage constraints. The risky asset is modelled as an asymmetric GARCH process. The CPPI’s performance against these simple strategies is found to be inferior in the majority of cases and deteriorates further with the inclusion of management fees and costs. Moreover, under various risk adjusted performance ratios the CPPI is dominated by the simple gapless (buy-and-hold) strategy.
Difficulty-Aware Hybrid Search in Peer-to-Peer Networks

By combining an unstructured protocol with a DHT-based index, hybrid Peer-to-Peer (P2P) improves search efficiency in terms of query recall and response time. The key challenge in hybrid search is to estimate the number of peers that can answer a given query. Existing approaches assume that such a number can be directly obtained by computing item popularity. In this work, we show that such an assumption is not always valid, and previous designs cannot distinguish whether items related to a query are distributed in many peers or are in a few peers. To address this issue, we propose QRank, a difficulty-aware hybrid search, which ranks queries by weighting keywords based on term frequency. Using rank values, QRank selects proper search strategies for queries. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that QRank significantly improves the search quality as well as reducing system traffic cost compared with existing approaches.
Thermal hyperalgesia via supraspinal mechanisms in mice lacking glutamate decarboxylase (GAD) 65

γ -amino butyric acid (GABA), which is synthesized by two isoforms of glutamate decarboxylase (GAD), inhibits the transfer of nociceptive signals from primary afferent fibers to the central nervous system. However, the roles of a 65-kDa isoform of GAD (GAD65)-mediated GABA in nociceptive processing are less clear. This study tested whether partial reductions in GABAergic inhibitory tone by GAD65 gene knockout (GAD65 − / − ) would contribute to the regulation of pain threshold in mice. Experiments were performed on male wild-type (WT) mice and GAD65 − / − mice. Acute nociception and inflammatory pain tests were compared between WT mice and GAD65 − / − mice. GABA A receptor-mediated inhibitory postsynaptic currents were also examined using the whole-cell patch clamp method in somatosensory cortical neurons in brain slices. In the hotplate test which reflects supraspinal sensory integration, a significant reduction in the latency was observed for GAD65 − / − mice. Intraperitoneal administration of the GABA transporter 1 inhibitor, NO-711 (C 21 H 22 N 2 O 3 ⋅ HCl), dose-dependently prolonged the latency in both genotypes, suggesting that GABA concentration contributes to acute thermal nociception. However, there was no genotype difference in responses to the tail-immersion test or von Frey test, indicating that spinal reflex and mechanical nociception are kept intact in GAD65 − / − mice. There was no genotype difference in responses to chemical inflammatory nociception (formalin test and carrageenan test). Although properties of the phasic component of IPSCs were similar in both genotypes, tonic inhibition was significantly reduced in GAD65 − / − mice. These results support the hypothesis that GAD65-mediated GABA synthesis plays relatively small but significant roles in nociceptive processing via supraspinal mechanisms.
Using the Chaotic Map in Image Steganography

Recent years have seen more interest from researchers in image encryption using chaotic signals. A new method was proposed for image Steganography in this article in which two chaotic signals for specifying the location of the different parts of the message in the picture. An 80-bit key was used to reach the preliminary measures of the two chaotic signals.One of the advantages of this method is its security which is caused by the chaotic signals and the higher range of the PSNR(42.06)
A Survey of Mobile Crowdsensing Techniques

Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.
BoxRouter: A New Global Router Based on Box Expansion and Progressive ILP

In this paper, we propose a new global router, BoxRouter, powered by the concept of box expansion, progressive integer linear programming (PILP), and adaptive maze routing (AMR). BoxRouter first uses a simple prerouting strategy to predict and capture the most congested region with high fidelity as compared to the final routing. Based on progressive box expansion initiated from the most congested region, BoxRouting is performed with PILP and AMR. Our PILP is shown to be much more efficient than the traditional ILP in terms of speed and quality, and the AMR based on multisource multitarget with bridge model is effective in minimizing the congestion and wirelength. It is followed by an effective postrouting step, which reroutes without rip-up to enhance the routing solution further and obtain smooth tradeoff between wirelength and routability. Our experimental results show that the BoxRouter significantly outperforms the state-of-the-art published global routers, e.g., 91 % better routability than Labyrinth (with 14% less wirelength and 3.3times speedup), 79% better routability than Chi-dispersion router (with similar wirelength and 2times speedup), and 4.2% less wirelength and 16times speedup than a multicommodity flow-based router (with similar routability). Additional enhancement in box expansion and postrouting further improves the result with similar wirelength but much better routability than the latest work in global routing.
South Baltic Guideline for the Application of Dredged Materials, Coal Combustion Products and Geosynthetics in Dike Construction

Dredged materials research has been developing rapidly during the past decade. Although sediments that are taken on shore are generally considered a waste according to European legislation, the materials prove to have a good potential to be recovered in a variety of applications. Anthropogenic materials, such as by-products of coal combustion (CCPs), generally fall under the waste law, too. Therefore, comparable hurdles have to be taken when it comes to the recovery of the materials. The European Waste Framework Directive demands a recovery rate for these secondary materials of 70 % by 2020. This implicates research and development in all fields of sediment management and material recovery. With a growing environmental awareness all around the Baltic Sea, many debates discuss strategies on how to reduce the amount of dredged sediments dumped at sea. In addition, many ideas are being developed how to increase the possibilities for CCP recovery beyond the standard road construction and concrete production applications. Here, it is of vital importance to address the particular characteristics of the different types of materials in an environmental context.
Hierarchical node replication attacks detection in wireless sensors networks

Wireless sensor networks (WSNs) are composed of a large number of low-cost, low-power, and multi-functional sensor nodes that communicate at short distance through wireless links. They are usually deployed in an open and uncontrolled environment where attackers may be present. Due to the use of low-cost materials, hardware components are not tamper-resistant and an adversary could access a sensor's internal state. An adversary can easily capture even a single node and inserts duplicated nodes at any location in the network. If no specific detection mechanisms are established, the attacker could lead many insidious attacks such as subverting data aggregation protocols by injecting false data, revoking legitimate nodes and disconnecting the network if the replicated nodes are judiciously placed at chosen locations. In this paper, we propose a hierarchical distributed algorithm for detecting node replication attacks using a Bloom filter mechanism and a cluster head selection, we also introduce the adequate network replies in case of detection. We also present simulation results for random topologies and show that our algorithm is able to detect replication attacks in all cases.
DR AF T FOREST : Focused Object Retrieval by Exploiting Significant Tag Paths

We present a novel unsupervised technique to extracting the main content of Web pages, which mingles wrapper induction with content-based informativeness analysis. Our approach uses keywords to trace locations of relevant blocks and to identify interesting structural patterns in the form of XPath expressions. The system, Forest, receives as input structurally close Web pages, and produces a ranked list of structural patterns that pinpoint the location of the main content. The main interest of such an approach is that it derives a wrapper that is specific to the content management system (CMS), rather than a single page feature. The most informative pattern that Forest identifies is used to extract the content of interest from the input pages. We extensively evaluate Forest for a diversified dataset of Web pages, and report improved results over state-of-the art techniques in Web article content extraction and other relevant baselines.
AIS Electronic

In consumer and information systems research, it remains unclear how consumers consider smartphone app recommendations in the course of their decision making process that leads to product choices in the physical store. Moreover, it is unclear which type of information smartphone apps should transport to consumers and if there are any customer segmentation criteria for smartphone app design. With respect to the theoretical and managerial importance of recommendation services in the form of smartphone apps we want to shed some light on this topic. Combining literature from the fields of IS and marketing research, we hypothesize that personalized recommendations via smartphone apps can help to boost sales in physical grocery stores. Furthermore, we hypothesize that additional popularity information (in the form of “stars”) does not amplify the positive effect of personalized recommendations. In addition, we assume that the effects of recommendation usage differ for men and women. We conducted a field study with a European grocery retailer to test our hypotheses. Finally, we discuss first implications as well as central limitations of our research and present the next research steps.