Miracle-dz/UniFAR-FTUs huggingface dataset:
MyRun: balancing design for reflection, recounting and openness in a museum-based participatory platform

Cultural organisations are increasingly looking towards using digital technologies to supplement, augment and extend visitors' experiences of exhibits and museums. In this paper, we describe the design and evaluation of MyRun, a 'participatory platform' for a museum. Our goal with MyRun was to use experience-centered design principles of reflecting, recounting and openness as a basis for engaging visitors in sharing stories about experiences related to a nationally significant cultural event. We undertook a qualitative evaluation of the system based upon observations of its use, the contributions visitors made to the platform, and interviews with 10 visitors. We discuss how visitors approached MyRun, contributed and browsed stories, and the challenges associated with the expectations visitors and curators placed on cultural exhibits. We close by identifying a series of design opportunities for future participatory platforms in museum settings.
A Friend Recommender System for Social Networks by Life Style Extraction Using Probabilistic Method -"Friendtome"

Every day we are overwhelmed with many choices and options, simultaneously recommendation systems have gained popularity in providing suggestions. Today every web application has its own recommendation system. Whereas, Recommendation systems for social networks are different from other kinds of system, since the item here are rational human beings rather than goods. Hence, the ‘Social’ factor has to be accounted for when making a recommendation. We considered one of the most popular social Networking sites that is Facebook as it offers impressive features. Here, we are mainly focusing on recommending friend with similar interest which is different among all the existing ones where Facebook uses social graph a friend of friend approach to recommend friend which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. And Netflix, Foursquare which all focus on recommending items. Hence we proposed framework Friendtome, a novel semantic based friend recommendation system for social networks. In this paper, a social network is formally represented and taking text mining as a perspective, we have proposed a framework that will recommend friend using an efficient Algorithm. Here, we have analyzed the structure of Facebook and considering the activities of individuals got some values & computed the score of each individual based on which we have, analyzed and computed to show the percentage of similarity of life styles between users, and recommends friends to users if their life styles have high similarity.
vProbe: Scheduling Virtual Machines on NUMA Systems

With the development of multi-core platforms andcloud computing, Non-Uniform Memory Access (NUMA) architecturehas been dominant in cloud data centers in recentyears. However, NUMA architecture is not well supported invirtualized environments. Because of the semantic gap introducedby the virtualization layer, hypervisors know little aboutthe characteristics of applications running in virtual machines (VMs). More importantly, in order to guarantee hypervisors' applicability, load balance strategies of virtual CPU (VCPU) schedulers do not consider the memory access characteristicsof applications running in VMs, which probably introducessignificant shared resource contention and unnecessary remotememory accesses. In this paper, we propose a NUMA-aware VCPU schedulerbased on Xen, named vProbe, to improve the performanceof memory-intensive applications while maintaining the transparencyof the virtualization layer in NUMA-based servers. It collects performance monitoring units (PMU) data for eachVCPU and analyzes their memory access characteristics. Then, according to the memory access characteristics of each VCPU, it periodically reassigns all memory-intensive VCPUs to eachNUMA node evenly while preferentially allocating them to theirlocal nodes, which aims to alleviate shared resource contentionand reduce unnecessary remote memory accesses. Moreover, when a physical CPU (PCPU) becomes idle, it preferentiallysteals a VCPU from the run queues of PCPUs in the local nodeto this PCPU, which helps to maintain balanced last-level cache (LLC) contention and reduce extra remote memory accesses. Our evaluation shows that vProbe can significantly improve theperformance of memory-intensive applications (e.g., up to 45.2% performance improvement compared with the Credit scheduler) while introducing negligible overheads.
Comparative Analysis of Software Libraries for Public Key Cryptography

Software implementations of public key cryptosystems require efficient realization of operations on large integers and elements of the Galois Field. Multiple libraries implementing such operations exist both commercially and in the public domain, in this paper, we perform comparison of eight libraries: CLN, CryptoPP, GNU MP, LiDIA, MIRACL, NTL, OpenSSL and PIOLOGIE, using performance and support of public key primitive operations. The performance of all libraries is ranked based on the measurements performed according to a methodology that takes into account the performance and relative use of primitive cryptographic operations. The performance results shows that GNU MP has the best performance for operations on large integers, OpenSSL has the best performance for operations on elliptic curves over prime fields and LiDIA and MIRACL have the best performance for operations on elliptic curves over binary fields. CryptoPP leads in terms of support for cryptographic primitives and schemes, but is the slowest of all investigated libraries.
Organizational volatility and its effects on software defects

The key premise of an organization is to allow more efficient production, including production of high quality software. To achieve that, an organization defines roles and reporting relationships. Therefore, changes in organization's structure are likely to affect product's quality. We propose and investigate a relationship between developer-centric measures of organizational change and the probability of customer-reported defects in the context of a large software project. We find that the proximity to an organizational change is significantly associated with reductions in software quality. We also replicate results of several prior studies of software quality supporting findings that code, change, and developer characteristics affect fault-proneness. In contrast to prior studies we find that distributed development decreases quality. Furthermore, recent departures from an organization were associated with increased probability of customer-reported defects, thus demonstrating that in the observed context the organizational change reduces product quality.
Autism, Context/Noncontext Information Processing, and Atypical Development

Autism has been attributed to a deficit in contextual information processing. Attempts to understand autism in terms of such a defect, however, do not include more recent computational work upon context. This work has identified that context information processing depends upon the extraction and use of the information hidden in higher-order (or indirect) associations. Higher-order associations underlie the cognition of context rather than that of situations. This paper starts by examining the differences between higher-order and first-order (or direct) associations. Higher-order associations link entities not directly (as with first-order ones) but indirectly through all the connections they have via other entities. Extracting this information requires the processing of past episodes as a totality. As a result, this extraction depends upon specialised extraction processes separate from cognition. This information is then consolidated. Due to this difference, the extraction/consolidation of higher-order information can be impaired whilst cognition remains intact. Although not directly impaired, cognition will be indirectly impaired by knock on effects such as cognition compensating for absent higher-order information with information extracted from first-order associations. This paper discusses the implications of this for the inflexible, literal/immediate, and inappropriate information processing of autistic individuals.
Affect Recognition for Interactive Companions

Affect sensitivity is an important requirement for artificial companions to be capable of engaging in social interaction with human users. This paper provides a general overview of some of the issues arising from the design of an affect recognition framework for artificial companions. Limitations and challenges are discussed with respect to other capabilities of companions and real world scenarios for affect sensitive human-companion interaction.
The Significance of Job Satisfaction in Modern Organizations

This chapter explains the relationship between job satisfaction and organizational constructs in modern organizations; job satisfaction, job performance, and adaptability; job satisfaction and negative organizational issues; and the importance of job satisfaction in the health care industry. Job satisfaction is an attitude that employees have about their work and job-related activities. Job satisfaction is important from the perspective of maintaining employees within the organization. High job satisfaction effectively leads to the improved organizational productivity, decreased employee turnover, and reduced job stress in modern organizations. Job satisfaction leads to a positive ambience at the workplace and is essential to ensure the higher revenues for the organization. Organizations should create the systematic management and leadership strategies to increase the high levels of job satisfaction of their employees. When employees are satisfied with their jobs, they will energetically deliver the higher levels of job performance.
A Survey on Preserving Privacy towards Location Proof

 Abstract— Location proof of a particular person relies on his/her mobile device position. One of the valuable features of the location proofs tells about accessing the location based services (LBS) by using mobile device. Location privacy is mandatory for every user to keep their location confidential . Every user needs to maintain the privacy level according to their spatial and temporal region. In this paper, we have presented a survey about the various techniques that are well suited to preserve location privacy and location proofs.
FAULT-TOLERANT DESIGN OF BATCHER–BANYAN CLASS ATM SWITCHES

Many proposed packet switching systems for high-speed networks have employed the Batcher–banyan fabric due to its modular structure, self-routeing functionality, and distributed conflict-resolving mechanism. In this paper, we present a systematic fault-tolerant design for the Batcher–banyan class of switches. Our first concern is the development of an on-line error detection mechanism which is the prerequisite of any effective fault tolerant operation. Based on the technique developed by Davis, we propose a general fault-testing technique and verify it for a variety of unique-path self-routeing networks. Furthermore, we extend this method to Batcher sorting networks and construct a fault-tolerant Batcher sorter. Lastly, after contemplating the technique constraints and system performance, we deploy the above techniques into different levels of the Batcher–banyan networks and achieve a fault-tolerant version of the StarBurst switch, a member of the Batcher–banyan class of switches.
Image retrieval with rotation invariance

The enormous increase in the image database sizes, as well as its vast development in various applications, retrieval of images based on its content is an important research area with application to digital libraries and multimedia databases. Content Based Image Retrieval (CBIR) is one of the methods which use low level features like color, texture and shape information to retrieve the images. Effective texture feature is an essential component in any content based image retrieval systems in which Curvelet captures the texture features more efficiently than other spectral features like Gabor and wavelet. This paper describes an approach to achieve a rotation invariant CBIR by using Curvelet texture feature and make them into different segments of similar groups to improve the efficiency of retrieval. Combine all these features into a feature vector and retrieve the similar images based on similarity check. This approach gives better results even for the images found in different orientations.
Communication patterns of some Afrikaans speaking Coloureds and Whites in the Cape Peninsula

INTERGROUP relations are of the utmost importance in South Africa where these re- lationships have seriously deteriorated dur- ing the past decade. In this study the focus Is on the communication patterns of Afri- kaans speaking Coloureds and Whites In the Cape Peninsula. These two groups share important facets of a common culture and this aspect should be a major impetus for positive communication between them. This approach proved to be rather simplis- tic and therefore attention is given to the social identity theory and the contact hypothesis to explain the data obtained in the pilot study. Although there are some similarities between the two population groups there are also some differences in the communication patterns. Communica- tion between the two groups takes place primarily in the context of the work situa- tion. Because of the relatively small number of respondants involved in the study no generalized conclusions could be made. It seems, however, that the relationships of different groups of people in a multicultural society will not improve as long as certain measures exist in the society to treat them as totally different entities with different needs and aspirations. This aspect was dis- cussed in depth in the theoretical founda- tion of the study.
Self-reported symptoms of anxiety and depression in chronic stroke patients with and without aphasia

ABSTRACT Background: About half of the stroke population suffers from emotional difficulties, such as anxiety and depression post-stroke. Acquiring aphasia is seen to increase the risk of developing symptoms of emotional difficulties such as anxiety and depression. Aims: The aim of the present study was to investigate self-reported emotional difficulties in chronic stroke patients with and without aphasia 1 year post-stroke. Further, we aimed to investigate the relationship between aphasia severity and self-reported symptoms of post-stroke depression and anxiety. We expected to find that patients with aphasia reported more symptoms of anxiety and depression than patients without aphasia. Furthermore, we expected to find that aphasia severity was associated with self-reported symptoms of anxiety and depression. Methods & Procedures: The Norwegian Basic Aphasia Assessment (NBAA) was used to assess aphasia severity. The Hospital Anxiety and Depression Scale (HADS) was administered to all patients to assess self-reported symptoms of anxiety and depression. To investigate group differences we used the patients’ scores on the subscales communication, ability to carry out daily activities, and ability to participate in social/leisure activities from the Stroke Impact Scale (SIS), the modified Rankin Scale (mRS), and the Mini Mental State Examination (MMSE). Outcomes & Results: There were no significant differences between the groups regarding self-reported symptoms of anxiety and depression. We found a significant correlation between aphasia severity and symptoms of depression, indicating that patients with more aphasic difficulties reported more symptoms of depression. Specifically, the subscale repetition and reading comprehension from the NBAA correlated with the HADS total score. Furthermore, we found significant correlations between the patients’ performance on reading comprehension, repetition, and reading out loud and the HADS depression subscale. There was a significant difference between the groups on the SIS communication scores and the patients’ scores on the MMSE, whereas the aphasia group had more self-reported communication difficulties and a lower level of cognitive functioning 1 year post-stroke. However, on the SIS daily activities the non-aphasia group scored significantly lower. Finally, the groups did not differ on functional disability as measured by the mRS at admission to the hospital. Conclusions: Although there were no significant differences between the aphasic and non-aphasic groups as to the degree of self-reported symptoms of anxiety and depression, patients with aphasia reported symptoms of anxiety and depression that seemed to be associated with specific language problems, as the ability to repeat, and to read and comprehend words and sentences.
Exomic Sequencing of Four Rare Central Nervous System Tumor Types

A heterogeneous population of uncommon neoplasms of the central nervous system (CNS) cause significant morbidity and mortality. To explore their genetic origins, we sequenced the exomes of 12 pleomorphic xanthoastrocytomas (PXA), 17 non-brainstem pediatric glioblastomas (PGBM), 8 intracranial ependymomas (IEP) and 8 spinal cord ependymomas (SCEP). Analysis of the mutational spectra revealed that the predominant single base pair substitution was a C:G>T:A transition in each of the four tumor types. Our data confirm the critical roles of several known driver genes within CNS neoplasms, including TP53 and ATRX in PGBM, and NF2 in SCEPs. Additionally, we show that activating BRAF mutations play a central role in both low and high grade glial tumors. Furthermore, alterations in genes coding for members of the mammalian target of rapamycin (mTOR) pathway were observed in 33% of PXA. Our study supports the hypothesis that pathologically similar tumors arising in different age groups and from different compartments may represent distinct disease processes with varied genetic composition.
Parallelization of Face Detection Engine

Video processing is computationally intensive and often has accompanying real-time or super-real-time requirements. For example, video tagging and surveillance systems need to robustly analyze video and automatically recognize the faces in real time. The semiconductor industry has shifted from increasing clock speeds to a strategy of growth through increasing core counts. This shift from single core to multi-core presents a major challenge to application developers to exploit sufficient parallelism in performance-sensitive applications. This give rise to a new computation paradigm for developing more advance algorithms. In this paper, we present a method to efficiently parallelize face detection which can be extended to any object detection algorithms for SMP architectures. We also show that a well-designed parallel code of face detection algorithm will result in a performance gain in excess of 2X on dual core systems.
Soft data analysis within a decision support system

This paper presents a work achieved within a decision support system. The aim is to support an operator in his/her task of analyzing soft data to monitor and anticipate a geopolitical crisis. The incoming data is filtered and analyzed so as to retain only the relevant events. Therefore, the system extracts the relevant events from the incoming soft data and produces synthetic descriptions of these events. The approach is divided into three steps. First the domain of interest is modeled with an ontology. It describes the events that are relevant to monitor, as well as geographical and political concepts and issues. Then, the system extracts the relevant information items from texts, based on linguistic patterns. Finally, the extracted items are fused to deduce synthetic events. The fusion process is based on conceptual graphs. The approach is illustrated using a collection of news and newspaper articles that deal with Ivory Coast.
A Remark on the Restricted Isometry Property in Orthogonal Matching Pursuit

This paper demonstrates that if the restricted isometry constant δ<i>K</i>+1 of the measurement matrix A satisfies [δ<i>K</i>+1 <; 1 √K+1] then a greedy algorithm called Orthogonal Matching Pursuit (OMP) can recover every K-sparse signal x in K iterations from Ax. By contrast, a matrix is also constructed with the restricted isometry constant [δ<i>K</i>+1 = 1 √K] such that OMP can not recover some K-sparse signal x in K iterations. This result positively verifies the conjecture given by Dai and Milenkovic in 2009.
Human Amniotic Fluid Glycoproteins Expressing Sialyl Lewis Carbohydrate Antigens Stimulate Progesterone Production in Human Trophoblasts in vitro

Background: Progesterone is thought to mediate immune modulator effects by regulating uterine responsiveness. The aim of the study was to clarify the effect of transferrin and glycodelin A (former name PP14) as sialyl Lewis X-expressing glycoproteins on the release of progesterone by trophoblast cells in vitro. Methods: Cytotrophoblast cells were prepared from human term placentas by standard dispersion of villous tissue followed by a Percoll gradient centrifugation step. Trophoblasts were incubated with varying concentrations (50–300 µg/ml) of human amniotic fluid- and serum-transferrin as well as with glycodelin A. Culture supernatants were assayed for progesterone, human chorionic gonadotropin (hCG) and cortisol by enzyme immunometric methods. Results: The release of progesterone is increased in amniotic fluid transferrin- and glycodelin A-treated trophoblast cell cultures compared to untreated trophoblast cells. There is no relation between transferrin and the hCG or cortisol production of trophoblast cells. Conclusion: The results suggest that sialyl Lewis carbohydrate antigen-expressing amniotic fluid glycoproteins modulate the endocrine function of trophoblasts in culture by upregulating progesterone production.
Graph-Based Friend Recommendation in Social Networks Using Artificial Bee Colony

Friend recommendation is a fundamental problem in online social networks, which aims to recommend new links for each user. In this paper, a new methodology based on graph topology and artificial bee colony is proposed to effective friend recommendation in social networks. In proposed approach, a sub-graph of network is composed by the study user and all the other connected users separately by three degree of separation from the root user. The proposed recommendation system computes four parameters within the generated sub-graph, and suggests the new links for the root user. Artificial bee colony is applied to optimize the relative importance of the weights of each parameter. To verify the proposed methodology, we chose a graph with 1000 members from YouTube. We considered the 20% of all links within the network graph to learning the system using artificial bee colony algorithm. These links were removed from the graph, and a data was generated by using all candidate nodes within the resulted graph, to be a recommend. Then, the generated data were divided into training set and evaluation set. Obtained results demonstrated the robustness of proposed approach with a 36% return rate.
Effective Region Based Segmentation Technique for High Resolution Aerial Imagery

Due to tremendous progress in spatial resolution in an imagery obtained from space born platforms such as Ikonos, World-view1, IRS series, methods of segment based image analysis for generating and updating geographical information are becoming more and more important. Detailed information about the objects including their shadows is now possible to extract from remote sensing data. In this paper, we discuss the effectiveness of edge guided region growing over simple region growing segmentation technique for high resolution aerial imagery. This paper is an attempt to develop an unsupervisory algorithm for automated software to segment aerial images. Experimental results demonstrate promising performance achievements compared to other existing segmentation methods for the same. Furthermore, the simplicity of this method is an attractive feature for real-time applications