Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery and Identification of AKAPs

-Kinase Anchoring Proteins (AKAPs) orchestrate and synchronize cellular events by tethering the cAMP-dependent protein kinase (PKA) and other signaling enzymes to organelles and membranes. The control of kinases and phosphatases that are held in proximity to activators, effectors, and substrates favors the rapid dissemination of information from one cellular location to the next. This article charts the inception of the PKA-anchoring hypothesis, the characterization of AKAPs and their nomenclature, and the physiological roles of context-specific AKAP signaling complexes.
Time and sampling frequency offset correction in audio watermarking

In the first part of this paper an audio watermarking system, operating in log-spectrum domain, is presented. Special measures are taken to make this system robust to symbol time offset. In the second part algorithms for sampling frequency offset and symbol time offset estimation and correction are proposed. These algorithms are based on inaudible pilot tones insertion and assure robust watermark transmission at 40 bits/s.
Now I am alone:a corpus stylistic approach to Shakespearian soliloquies

A popular interest in Shakespeare has been matched in recent years by an increasing number of computer-assisted analyses of the plays. Although not without their critics, corpus stylistic studies have offered scope and reliability in the study of literary texts, particularly through key word analyses. In this paper, I show how Wmatrix, a web-based corpus processing environment (Rayson, 2003, 2007), in conjunction with other corpus tools, can systematically extend such key analyses from words, to parts of speech and semantic fields. By so doing, a greater understanding of linguistic aspects of an author’s literary output may be achieved. This study is based on a key word, grammatical category and semantic field analysis of soliloquies and asides in 12 Shakespeare plays. An investigation of the linguistic characteristics of soliloquies/asides as opposed to dialogic speech reveals the overuse of the interjection O and words related to the body. Comparisons of soliloquies across genres tend to match intuitive assumptions. Finally, soliloquies written in the later period (1596-1606) tend to have a far greater proportion of ‘the (noun) of (noun phrase)’ structures. The paper ends by suggesting that more empirical work of this nature is needed to underpin qualitative literary judgements.
Mining user interests from social media by fusing textual and visual features

In this paper, we propose a framework that fuses textual and visual features of user generated social media data to mine the distribution of user interests. The proposed framework consists of three steps: feature extraction, model training, and user interest mining. We choose boards from popular users on Pinterest to collect training and test data. For each pin we extract the term-document matrices as textual features, bag of visual words as low-level visual features, and attributes as mid-level visual features. Representative features are then selected for training topic models using discriminative latent Dirichlet allocation (DLDA). In performance evaluation, pins collected from popular users are used to evaluate the classification accuracy and pins collected from other common users are used to evaluate the recommendation performance. Our experimental results show the efficacy of the proposed method.
Visionary Marketing for the 21 st Century : Facing the Challenge 1487 Competitor Orientation of Small Organisations

Abstract Marketing has been identified as one of the main reasons for small business success or failure. Although an increasing number of empirical studies are devoted to small organisations, most marketing research has focused on large organisations. Current literature on small organisations is mostly concerned with the characteristics and limitations of small organisations or their growth and development stages. This study examines the way small businesses manage competition. The results of a large survey of small New Zealand service and goods firms suggest that the form of competitor orientation in small organisations differs from the classical competitor orientation model found in studies of larger organisations.
Performances of pair designing on software evolution: a controlled experiment

Evolving software design is a critical activity for many reasons: inadequate documentation, loss of strategic knowledge due to personnel's turnover, intrinsic complexity of software design. As a consequence, performances in the evolution tasks may deteriorate. Some experiments demonstrate that pair programming is able to reduce the time spent for accomplishing the tasks while increasing the overall work quality. Pair programming is usually applied to coding; the term 'pair designing' refers to applying the practice of working in pairs to the design phase. Because of the asymmetry existing between coding and designing, pair designing could not replicate all the benefits of pair programming. This paper discusses a controlled experiment for understanding which are the effects of pair designing when evolving software design
Time-Delay Nonlinear System Modelling via Delayed Neural Networks

In this paper, nonlinear systems on-line identification via delayed dynamic neural networks is studied. Dynamic series-parallel neural network model with time delay is presented and the stability conditions are derived using Lyapunov-Krasovskii approach. The conditions for passivity, asymptotic stability are established in some senses. All the results are described by linear matrix inequality (LMI). We conclude that the gradient algorithm for weight adjustment is stable and robust to any bounded uncertainties
Role of oxygen consumption in hypoxia protection by translation factor depletion

SUMMARY The reduction of protein synthesis has been associated with resistance to hypoxic cell death. Which components of the translation machinery control hypoxic sensitivity and the precise mechanism has not been systematically investigated, although a reduction in oxygen consumption has been widely assumed to be the mechanism. Using genetic reagents in Caenorhabditis elegans, we examined the effect on organismal survival after hypoxia of knockdown of 10 factors functioning at the three principal steps in translation. Reduction-of-function of all 10 translation factors significantly increased hypoxic survival to varying degrees, not fully accounted for by the level of translational suppression. Measurement of oxygen consumption showed that strong hypoxia resistance was possible without a significant decrease in oxygen consumption. Hypoxic sensitivity had no correlation with lifespan or reactive oxygen species sensitivity, two phenotypes associated with reduced translation. Resistance to tunicamycin, which produces misfolded protein toxicity, was the only phenotype that significantly correlated with hypoxic sensitivity. Translation factor knockdown was also hypoxia protective for mouse primary neurons. These data show that translation factor knockdown is hypoxia protective in both C. elegans and mouse neurons and that oxygen consumption does not necessarily determine survival; rather, mitigation of misfolded protein toxicity is more strongly associated with hypoxic protection.
Spatial query processing in an object-oriented database system

DBMSs must offer spatial query processing capabilities to meet the needs of applications such as cartography, geographic information processing and CAD. Many data structures and algorithms that process grid representations of spatial data have appeared in the literature. We unify much of this work by identifying common principles and distilling them into a small set of constructs. (Published data structures and algorithms can be derived as special cases.) We show how these constructs can be supported with only minor modifications to current DBMS implementations. The ideas are demonstrated in the context of the range query problem. Analytical and experimental evidence indicates that performance of the derived solution is very good (e.g., comparable to performance of the kd tree.)
Analysis and Tracing of Applications Based on Software Transactional Memory on Multicore Architectures

Transactional Memory (TM) is a new programming paradigm that offers an alternative to traditional lock-based concurrency mechanisms. It offers a higher-level programming interface and promises to greatly simplify the development of correct concurrent applications on multicore architectures. However, simplicity often comes with an important performance deterioration and given the variety of TM implementations it is still a challenge to know what kind of applications can really take advantage of TM. In order to gain some insight on these issues, helping developers to understand and improve the performance of TM applications, we propose a generic approach for collecting and tracing relevant information about transactions. Our solution can be applied to different Software Transactional Memory (STM) libraries and applications as it does not modify neither the target application nor the STM library source codes. We show that the collected information can be helpful in order to comprehend the performance of TM applications.
Depth Filtering in 3D Reconstruction of Indoor Scenes Based on Kinect

Recently due to the appearance of low-cost 3D camera, such as Microsoft Kinect, 3D reconstruction plays an increasingly important role in computer vision fields, as well as image processing and pattern recognition. But owing to the physical shadow and the underlying generating mechanisms, the range map from Kinect contains a large number of noises. In this work, a new 3D reconstruction workflow aiming at the above problem is presented, including hole-filling, coordinate transformation from 2D to 3D, and 3D depth de-noising algorithm. A 3D indoor scene reconstruction example is presented to demonstrate effectiveness of the proposed workflow. The experiments results illustrate that the provided method can reconstruct an indoor scene with less memory cost but good performance.
Detection of multiple, co-occurring actions in video frames

We propose a systematic frame work for the automatic detection of multiple human actions within the same frame in realistic and diverse video settings. One of the major challenges is the process of recognizing and understanding of human actions from videos with large variations resulting from camera motions, changes in human appearance, pose changes, scale changes and back ground clutter etc. In this paper 8 different human actions, occurring independently as well as co-occurring with one of the other seven, are detected. The detected human actions are classified based on the clustering of spatio-temporal features and classified using the multi-channel nonlinear SVM. Applying this method to publicly available data has shown promising results.
FPGA implementation of a minutiae extraction fingerprint algorithm

Fingerprint recognition is one of the most common techniques used for biometric identification. Currently fingerprint technology is suitable to recognize users with high accuracy and low execution times using microprocessors able to solve algorithms with high-computational cost. However, the microprocessorpsilas cost could make the use of fingerprint biometric conditional on specific applications. This paper presents the implementation of a whole minutiae extraction fingerprint algorithm using a Spartan-3 FPGA, as an appropriate solution for portable devices and for the low-cost consumer market. The internal architecture of the proposed embedded system is based on a soft-core microprocessor and several dedicated coprocessors designed in order to accelerate the resolution of the algorithm. Experimental results show as minutiae of fingerprint are obtained in 988 ms when an image of 256 times 256 pixels is analyzed.
Longitudinal Relations Between ADHD Symptoms and Substance Use Across the Transition to College and Evaluation of Promotive and Protective Factors

LONGITUDINAL RELATIONS BETWEEN ADHD SYMPTOMS AND SUBSTANCE USE ACROSS THE TRANSITION TO COLLEGE AND EVALUATION OF PROMOTIVE AND PROTECTIVE FACTORS By Melissa R. Dvorsky, M.S. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Joshua M. Langberg, Ph.D. Associated Professor Department of Psychology The prevalence of substance use and related problems increases during adolescence and peaks in young adulthood with substantial increases during the transition from high school to college (Johnston et al., 2018). However, these increases are not universal for all students (White et al., 2006), and there is substantial variation in rates of substance use during the first year of college (Borasri et al., 2007; Frisher et al., 2007). It is important to identify the individual and environmental factors that serve as risk factors for substance use as well as factors that may promote or protect against use during the high school to college transition. This prospective longitudinal investigated the impact of risk, promotive, and protective factors on the substance use outcomes of 150 high school seniors transitioning to college. The prevalence of alcohol and marijuana use substantially increased from high school to the first semester of college. Results indicate that ADHD symptoms at the end of high school predicted residualized change in alcohol and marijuana use during the first semester of college (i.e., controlling for the autoregressive effect of use during college). For alcohol use (but not marijuana), ADHD symptoms continued to predict subsequent use across the first year of college (from fall to spring of the first year). Promotive models revealed that adaptive social perceptions predicted decreased alcohol and marijuana use, and academic motivation predicted decreased alcohol use, after controlling for the role of ADHD symptoms. Adaptive social perceptions about each substance was protective against future alcohol and marijuana use both before and after the transition to college demonstrated by significant interaction effect with ADHD symptoms, after controlling for the direct risk and promotive effects in the model. These interactions illustrated that adolescents with elevated ADHD symptoms who have high friend disapproval of substances may experience resilience with respect to substance use outcomes. Academic motivation demonstrated promotive (direct) effects for reducing the risk for alcohol use but protective effects were not found. Future research should seek to elucidate more specific mechanisms through which youth and adolescents with elevated ADHD are protected against the high risk for substance use problems.
Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in predicting the fatigue life of composite materials. Additionally, immune algorithms (IA) are used to select three parameters of SVR models. An experimental data set from a laboratory was employed to depict the feasibility of developed SVRIA (support vector regression with immune alforithms) approach in predicting fatigue life of composite materials. Empirical results indicate that the SVRIA is a valid way in predicting fatigue life of composite materials.
Embla - Data Dependence Profiling for Parallel Programming

With the proliferation of multicore processors, there is an urgent need for tools and methodologies supporting parallelization of existing applications. In this paper, we present a novel tool for aiding programmers in parallelizing programs. The tool, Embla, is based on the Valgrind framework, and allows the user to discover the data dependences in a sequential program, thereby exposing opportunities for parallelization. Embla performs an off-line dynamic analysis, and records dependences as they arise during program execution. It reports an optimistic view of parallelizable sequences, and ignores dependences that do not arise during execution. Moreover, since the tool instruments the machine code of the program, it is largely language independent. Since Embla finds the dependencies that occur for particular executions, the confidence one would assign to its results depend on whether different executions yield different (bad) or largely the same (good) dependencies. We present a preliminary investigation into this issue using 84 different inputs to the SPEC CPU2006 benchmark 403.gcc. The results indicate that there is a strong correlation between coverage and finding dependencies; executing the entire program is likely to reveal all dependencies.
Mesh-based image retargeting with spectral graph filtering

We propose a calculation method of deformed image pixel positions for mesh-based image retargeting. Image retargeting is a sophisticated image resizing method which yields resized images with acceptable quality even if we resize the image into different aspect ratio from the original one. It often employs a mesh-based approach, where pixels are nodes of a graph and relationships between pixels are represented as its edges. In this paper, we reformulate a pixel position deformation of image retargeting as a spectral graph filtering with a graph signal processing-based approach. We validate our method through some image retargeting examples with an appropriately designed filter kernels in the graph spectral domain.
Effect of bio-inspired multi-stage regulations for diagnostic molecular automata

Bio-inspired natural computing is expected to bring new solutions to various problems confronting us. We already presented a novel concept for transcriptome diagnosis using transcription-based molecular automata. Here we introduce new multi-stage regulations to this diagnostic system in order to reduce the background noise and increase the sensitivity. We use two explicit detector probes with a stem and a memory loop at the pre-transcriptional stage, and one implicit binary aptameric sensor at the post-transcriptional stage. In addition to this we adopt the error control element to improve the detection ability. Consequently we found that the noise was reduced to one-forty-fifth and the sensitivity increased five-fold by the regulation using the combination of several strategies. Basically the living organisms control themselves by complex and dexterous manners. The incorporation of bio-inspired redundant strategies bring the all-too-simple and unstable artificial systems close to the robust and reliable ones.
Teaching Introduction to Psychology: Promoting Student Learning Using Digital Storytelling and Community Engagement.

This paper demonstrates how partnering with digital storytelling initiatives, like the StoryCorps project, can yield fruitful service-learning opportunities, while also supporting innovative approaches for teaching students from a range of disciplines who are enrolled in Introduction to Psychology (PSYC 1101). While various pedagogical tools for teaching psychology exist, StoryCorps provides a unique opportunity to expose students to a large range of people and related stories to which they may otherwise not have been exposed. This paper outlines a model for how a partnership with a local digital storytelling initiative (StoryCorps) can be integrated into a community engaged course and facilitate course activities reflecting an applied, multi-faceted learning environment. Preliminary data comparing student outcomes in the service-learning course to those in the traditional PSYC 1101 course are also discussed.
Circuit design in nanoscale FDSOI technologies

Planar fully-depleted SOI technology with ultra-thin body and buried oxide presents a platform for an energy-efficient design in deeply scaled technologies without major changes in the bulk-CMOS design infrastructure. Good control of short-channel effects with thin transistor body offers a possibility to reduce the supply voltage. Thin buried oxide provides threshold tuning via body bias. Overall design optimality is achieved through sensitivity-based optimization by selecting optimal supplies and thresholds.
Users’ Perspectives About the Potential Usefulness of Online Storylines to Communicate River Research to a Multi-disciplinary Audience

ABSTRACT Effective communication practices are needed to support adaptive, collaborative and integrative environmental management. We propose the use of online storylines to communicate scientific outcomes in a way that is captivating, easily understood and accessible. Based on 20 interviews, we identified important attributes for the storyline content, structure and user interaction. We designed a storyline example about stakeholders’ perceptions of a river management intervention with consequences on the landscape. The intended audience consists of multi-disciplinary researchers and practitioners that could consider or apply research solutions outside their field of expertise in river management. We introduced the example in a workshop with 14 participants from research and practice. Our findings highlight the importance of including narrative elements via images, interactive figures and timelines to illustrate the research context. Moreover, storylines should explicitly state benefits as well as the limitations of the river research and include a glossary to clarify specific terms.
Role thinking: Standing in other people’s shoes to forecast decisions in conflicts

Better forecasts of decisions in conflict situations, such as occur in business, politics, and war, can help protagonists achieve better outcomes. It is common advice to “stand in the other person’s shoes” when involved in a conflict, a procedure we refer to as “role thinking. ” We tested this advice in order to assess the extent to which it can improve accuracy. Improvement in accuracy is important because prior research found that unaided judgment produced forecasts that were little better than guessing. We obtained 101 role-thinking forecasts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices) of the decisions that would be made in nine diverse conflicts. The accuracy of the forecasts from the novices was 33% and of those from the experts 31%. The accuracy of the role-thinking forecasts was little different from chance, which was 28%. In contrast, when we asked groups of participants to each act as if they were in the shoes one of the protagonists, accuracy was 60%.
Preserving Data in Cloud Computing

Cloud computing is basically virtual pool of resources and it provides these resources to its users via internet as services who use them as when needed basis. On demands of user’s data confidentiality and privacy the data is stored in cloud server as encrypted. Ideally, to maintain the security of user’s data queries should be processing over encrypted data. There are different schemes recommended to support this issue. One of them is Ordered Preserving encryption (OPE) which allows comparison operations to be directly executed over encrypted data. This paper surveys various encryption functions provided to support querying over encrypted data, especially Order Preserving Encryption.