Miracle-dz/UniFAR-FTUs huggingface dataset:
A Secure Collaborative Spectrum Sensing Strategy in Cyber-Physical Systems

As an emerging cross-disciplinary research area, Cyber Physical Systems (CPS) are attracting considerable attention worldwide. It is an essential but challenging requirement to offer secure and trustworthy real-time feedback to CPS users using spectrum sharing wireless networks. This requirement can be satisfied by collaborative spectrum sensing technology in Cognitive radio networks (CRN). Despite its promising benefits, collaborative spectrum sensing introduces new security threats especially internal attacks (i.e. attacks launched by internal nodes) that can degrade the efficiency of spectrum sensing. To tackle this challenge, we propose a new Transferring Reputation mechanism and Dynamic Game model based secure collaborative spectrum sensing strategy (TRDG). More specifically, a location aware transferring reputation mechanism is proposed to resolve the reputation loss problem caused by user mobility. Furthermore, a dynamic game based recommendation incentive strategy (DGRIS) is built to incentive SUs to provide honest information. The simulation experiments show that the TRDG enhances the accuracy of spectrum sensing and defends against the internal attacks effectively without relying on a central authority.
Social Networks, Personalized Advertising, and Privacy Controls

This paper investigates how internet users’ perception of control over their personal information aﬀects how likely they are to click on online advertising. The paper uses data from a randomized ﬁeld experiment that examined the relative eﬀectiveness of personalizing ad copy with posted personal information on a social networking website. The website gave users more control over their personally identiﬁable information in the middle of the ﬁeld test. The website did not change how advertisers used data to target and personalize ads. After the introduction of improved privacy controls, users were twice as likely to click on personalized ads. There was no comparable change in the eﬀectiveness of ads that did not make explicit that they used private information when targeting. The increase in eﬀectiveness was larger for ads that used more unique private information to personalize their message.
Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

Buildings contribute to nearly 30% of global carbon dioxide emissions, making a significant impact on climate change. Despite advanced design methods, such as those based on dynamic simulation tools, a significant discrepancy exists between designed and actual performance. This so-called performance gap occurs as a result of many factors, including the discrepancies between theoretical properties of building materials and properties of the same materials in buildings in use, reflected in the physics properties of the entire building. There are several different ways in which building physics properties and the underlying properties of materials can be established: a co-heating test, which measures the overall heat loss coefficient of the building; a dynamic heating test, which, in addition to the overall heat loss coefficient, also measures the effective thermal capacitance and the time constant of the building; and a simulation of the dynamic heating test with a calibrated simulation model, which establishes the same three properties in a non-disruptive way in comparison with the actual physical tests. This article introduces a method of measuring building physics properties through actual and simulated dynamic heating tests. It gives insights into the properties of building materials in use and it documents significant discrepancies between theoretical and measured properties. It introduces a quality assurance method for building construction and retrofit projects, and it explains the application of results on energy efficiency improvements in building design and control. It calls for re-examination of material properties data and for increased safety margins in order to make significant improvements in building energy efficiency.
Pointing in entertainment-oriented environments: appreciation versus performance

Multimedia applications for consumer entertainment often employ a point-and-select interaction style, borrowed from more task-oriented computer applications. The environment of use and the pointing devices involved are so different, however, that a higher importance should be attributed to the users' appreciation of the pointing device than to its efficiency or any other objective performance measure. We set up an experiment to investigate how appreciation and performance measures relate. The experiment involved six different input devices, two CD-i titles and 16 subjects making both voluntary and prescribed cursor control movements. For the mouse-like pointing devices we obtained a Fitts' Law-like dependence on target width and target distance. This was, however, not replicated for any of the other input devices, mainly owing to a positive influence of cursor constraints. Concerning the relation of the performance measures with the users' appreciation, we found that neither time-to-target nor relative-path-length on its own is a reliable indicator of the users' appreciation. Together, however, they might explain the appreciation scores to a considerable extent.
Modeling Ferroresonance Phenomena in an Underground Distribution System

The objective of this paper is to provide an overview of ferroresonance phenomena, its modeling aspects, and practical experience in recognizing, avoiding, and solving the problem. In particular, we will present symptoms of ferroresonance and personal accounts of engineers who witnessed the situations. An actual case involving an underground cable circuit with blown fuses is presented along with solutions to avoid ferroresonance
AISeL ) All Sprouts Content Sprouts 12-6-2010 Combining the User as Social Actor Model , Institutional Theory and a Theory of Unobtrusive Power to Understand the Acquiescence of Software Developers

In this paper we argue that a combination of a social actor model, Institutional theory, and a model of unobtrusive power can constitute a theoretical framework for understanding how the business client is able to exercise control and subsequent subjugation of developers in the systems development process. Specifically, the paper develops a 3-level theoretical framework grounded in institutional theory that integrates elements of Lamb and Klingâs social actor model and Scottâs 3-pillars framework concentrating on the relationships among systems developers, the business client, the SDM, and the context surrounding its use. The framework is strengthened through the application of a third level â Hardyâs multi-dimensional model of power, offering explanations of political inactivity by developers. In this paper we discuss how all three theory can be combined in a framework for analyzing the power relations between developers and the business client. We apply this theoretical framework in a case study of the deployment of a mandated in-house developed systems development methodology in a large IT department of a major Australian bank Here we will show how, from the perspective of developers, the business client exercise both overt and unobtrusive power over the development process.
Document Security within Institutions Using Image Steganography Technique

Steganography is the art or practice of concealing a message, image, or file within another message, image, or file. This paper looks at how documents within an institution can be concealed using images within the institutions network. This application developed using C programming language is low cost and stand alone and user friendly. The process of encryption is done on the user’s computer before the document is sent over the institutions network to its intended recipient. This process of encrypting and decrypting images makes a hacker or casual user unable to read documents that is not originally intended for such user.
Approximating Nash Equilibria and Dense Subgraphs via an Approximate Version of Carathéodory's Theorem

We present algorithmic applications of an approximate version of Carath\'{e}odory's theorem. The theorem states that given a set of vectors $X$ in $\mathbb{R}^d$, for every vector in the convex hull of $X$ there exists an $\varepsilon$-close (under the $p$-norm distance, for $2\leq p < \infty$) vector that can be expressed as a convex combination of at most $b$ vectors of $X$, where the bound $b$ depends on $\varepsilon$ and the norm $p$ and is independent of the dimension $d$. This theorem can be derived by instantiating Maurey's lemma, early references to which can be found in the work of Pisier (1981) and Carl (1985). However, in this paper we present a self-contained proof of this result. Using this theorem we establish that in a bimatrix game with $ n \times n$ payoff matrices $A, B$, if the number of non-zero entries in any column of $A+B$ is at most $s$ then an $\varepsilon$-Nash equilibrium of the game can be computed in time $n^{O\left(\frac{\log s }{\varepsilon^2}\right)}$. This, in particular, gives us a polynomial-time approximation scheme for Nash equilibrium in games with fixed column sparsity $s$. Moreover, for arbitrary bimatrix games---since $s$ can be at most $n$---the running time of our algorithm matches the best-known upper bound, which was obtained by Lipton, Markakis, and Mehta (2003). The approximate Carath\'{e}odory's theorem also leads to an additive approximation algorithm for the normalized densest $k$-subgraph problem. Given a graph with $n$ vertices and maximum degree $d$, the developed algorithm determines a subgraph with exactly $k$ vertices with normalized density within $\varepsilon$ (in the additive sense) of the optimal in time $n^{O\left( \frac{\log d}{\varepsilon^2}\right)}$. Additionally, we show that a similar approximation result can be achieved for the problem of finding a $k \times k$-bipartite subgraph of maximum normalized density.
Text classification using string kernels

We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences that are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. Experimental comparisons of the performance of the kernel compared with a standard word feature space kernel (Joachims, 1998) show positive results on modestly sized datasets. The case of contiguous subsequences is also considered for comparison with the subsequences kernel with different decay factors. For larger documents and datasets the paper introduces an approximation technique that is shown to deliver good approximations efficiently for large datasets.
Layer Flexible Adaptive Computation Time for Recurrent Neural Networks

Deep recurrent neural networks perform well on sequence data and are the model of choice. It is a daunting task to decide the number of layers, especially considering different computational needs for tasks within a sequence of different difficulties. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Contrary to the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set andWikipedia language modeling. Experimental results show the performance improvement of 8% to 12% and indicate the model’s ability to dynamically change the number of layers.
Validating automated sentiment analysis of online cognitive behavioral therapy patient texts An exploratory study

The accuracy of automated sentiment analysis is domain dependent, and it is unclear how well the technology is applicable to iCBT. This paper presents an empirical study in which automated sentiment analysis by an algorithm for the Dutch language is validated against human judgment. Methods: A total of 493 iCBT user texts were evaluated on overall sentiment and the presence of ﬁve speciﬁc emotions by an algorithm, and by 52 psychology students who evaluated 75 randomly selected texts each, providing about eight human evaluations per text. Inter-rater agreement (IRR) between algorithm and humans, and humans among each other, was analyzed by calculating the intra-class correlation under a numerical interpretation of the data, and Cohen’s kappa, and Krippendorff’s alpha under a categorical interpretation. Results: All analyses indicated moderate agreement between the algorithm and average human judgment with respect to evaluating overall sentiment, and low agreement for the speciﬁc emotions. Somewhat surprisingly, the same was the case for the IRR among human judges, which means that the algorithm performed about as well as a randomly selected human Thus, considering average human judgment as a benchmark for the applicability of automated sentiment analysis, the technique can be considered for practical application. algorithm against a more solid benchmark, on applying the algorithm in an application in which empathic feedback is provided, for example, by an embodied conversational agent, or on improving the algorithm for the iCBT domain with a bottom-up machine learning approach.
The gene regulatory basis of bystander activation in CD8+ T cells

CD8+ T cells are classically recognized as adaptive lymphocytes based on their ability to recognize specific foreign antigens and mount memory responses. However, recent studies indicate that some antigen-inexperienced CD8+ T cells can respond to innate cytokines alone in the absence of cognate T cell receptor stimulation, a phenomenon referred to as bystander activation. Here, we demonstrate that neonatal CD8+ T cells undergo a robust and diverse program of bystander activation, which corresponds to enhanced innate-like protection against unrelated pathogens. Using a multi-omics approach, we found that the ability of neonatal CD8+ T cells to respond to innate cytokines derives from their capacity to undergo rapid chromatin remodeling, resulting in the usage of a distinct set of enhancers and transcription factors typically found in innate-like T cells. We observed that the switch between innate and adaptive functions in the CD8+ T cell compartment is mediated by changes in the abundance of distinct subsets of cells. The innate CD8+ T cell subset that predominates in early life was also present in adult mice and humans. Our findings provide support for the layered immune hypothesis and indicate that the CD8+ T cell compartment is more functionally diverse than previously thought. A multi-omic analysis reveals distinct programs of bystander activation in CD8+ T cells produced at various stages of life. Editor’s summary Although T cells are primarily activated in response to antigen recognition via their T cell receptor (TCR), they can also undergo TCR-independent “bystander” activation in response to certain cytokines. Using single-cell transcriptomics and chromatin accessibility profiling, Watson et al. found that neonatal CD8+ T cells undergo a distinct and robust program of innate-like bystander activation controlled by a balance of Bach2 and AP-1 transcription factor activity. Neonatal CD8+ T cells could protect against bacterial, viral, and parasitic pathogens in a TCR-independent manner. Innate-like CD8+ T cells were also present in both adult mice and humans, indicating that multiple layers of defense may exist within the adult naïve T cell pool. —Claire Olingy
Novel bioinformatics approach to investigate quantitative phenotype-genotype associations in neuroimaging studies

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using the Alzheimer’s Disease Neuroimaging Initiative sample.
The Art of Opinion Mining and Its Application Domains: A Survey

The advent of Web 2.0 and Social media content has stirred much excitement and created abundant opportunities for understanding the opinions of the general public and consumers toward social events, political movements, company strategies marketing campaigns, and product preferences. Individuals, businesses and government can now easily know the general opinion prevailing on a product, company or public policy. This paper critically evaluates existing work, presents an opinion mining framework and exposes new areas of research in opinion mining. Overall item sentiment can be expressed based on its sentiment words in general or by specifically identifying its features and the opinions being expressed about them. This leads us to the motivation of the framework for opinion mining and categorizing current literature in such a manner as to make clear, research opportunities. The freedom offered by the web as a platform for presenting opinions on any subject brings with it many new opportunities.
Translation by Machine of Complex Nominals : Getting it Right

Wepresentamethodfor compositionallytranslatingnoun-noun(NN) compounds, usingaword-level bilingualdictionaryandsyntactictemplatesfor candidategeneration, andcorpusanddictionarystatisticsfor selection.Weproposeasupportvectorlearning-basedmethodemployingtargetlanguagecorpusandbilingual dictionarydata,andevaluateit over a English Japanesemachinetranslationtask. We show theproposed methodto besuperiorto previousmethodsandalsorobustover low-frequency NN compounds.
An Ontology-Based Evaluation of Process Modeling with Petri Nets

Selecting an appropriate business process modeling technique forms an important task within the methodological challenges of business process management and workflow engineering projects. Many of the available techniques have been developed on the basis of Petri nets, which is a popular modeling technique for workflow-oriented applications. Despite the popularity of Petri nets, however, a comprehensive evaluation of this standard notation is still required. Such an evaluation would afford a deeper understanding of the capabilities and shortcomings of this fundamental process modeling technique. This paper presents the first contribution towards a theoretically sound analysis of the representational capabilities of Petri nets. Based on an established methodology, we conducted a representational analysis of Petri nets using a representation model based on the Bunge ontology. Through this work, we identify a number of issues related to the practice of process modeling with Petri nets in contemporary process management initiatives. Our findings contribute to the ongoing revision and extension of process modeling techniques based on Petri nets and as such may lead to more mature solutions to business process modeling and management.
Psychometric Evaluation of Anxiety, Depression, and Sleep Quality after a Mild Traumatic Brain Injury: A Longitudinal Study

Introduction. Over 1 million mild traumatic brain injury (mTBI) cases are reported annually worldwide and may result in cognitive, physical, and emotional deterioration; depression; anxiety; and sleep problems. However, studies on long-term mTBI effects are limited. This study included 440 patients, and regular follow-ups of psychological assessments were performed for 2 years. Four questionnaires, including the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), Beck's anxiety inventory (BAI), and Beck's depression inventory (BDI), were used to evaluate sleep problems, daytime sleepiness, anxiety, and depression, respectively. Results show that BAI and BDI scores considerably improved at the 6th-week, 1st-year, and 2nd-year follow-ups compared to baseline, yet these remained significantly different. In addition, anxiety and depression were prominent symptoms in a select subgroup of patients with poor initial evaluations, which improved over the 2 years. However, the ESS and PSQI scores fluctuated only mildly over the same time span. In conclusion, the mTBI patients showed a gradual improvement of anxiety and depression over the 2 years following injury. While anxiety and depression levels for mTBI patients in general did not return to premorbid status, improvements were observed. Sleep disorders persisted and were consistent with initial levels of distress.
Approximation for minimum triangulation of convex polyhedra

The minimum triangulation of a convex polyhedron is a triangulation that contains the minimum number of tetrahedra over all its possible triangulations. Since finding the minimum triangulation of convex polyhedra was recently shown to be NP-hard, it becomes significant to find algorithms that give good approximation. In this paper, we give a new triangulation algorithm with an improved approximation ratio 2 - &OHgr;(1/√n). We also show that this is best possible for algorithms that only consider the combinatorial structure of the polyhedra.
Persian Printed Numeral Characters Recognition Using Geometrical Central Moments and Fuzzy Min-Max Neural Network

printed numeral characters recognition with emphasis on representation and recognition stages is introduced. For the first time, in Persian optical character recognition, geometrical central moments as character image descriptor and fuzzy min-max neural network for Persian numeral character recognition has been used. Set of different experiments on binary images of regular, translated, rotated and scaled Persian numeral characters has been done and variety of results has been presented. The best result was 99.16% correct recognition demonstrating geometrical central moments and fuzzy min-max neural network are adequate for Persian printed numeral character recognition.
Effectiveness of Machine Learning Techniques for Macula Edema Detection

Macula Edema is an abnormality in the retina seen in patients with prolonged diabetes. If left untreated, it can cause vision loss. Macula Edema is characterized by swelling of macula or proximity of surrogate exudates to the fovea. Ophthalmologists use subjective approach to diagnose Macula Edema and normally perform pupil dilation which causes inconvenience to the patients. Moreover this procedure is time consuming and laborious. Instead of using this conventional method based on surrogates which are exudates, the paper has concentrated on the exclusive features that represent macula swelling. A total of 23 such features are extracted. Support Vector Machine and Random Forest (RF) classifiers are used for detection of Macula Edema for the chosen database. It was found that the RF algorithm performed better with an accuracy of 80.95 % in comparison with SVM at 71.43 %.