Miracle-dz/UniFAR-FTUs huggingface dataset:
Passion and nostalgia in generational media experiences

One component in the generational experience strongly related to media is the intimate and often passionate relation that is developed towards media technologies and content from one’s formative youth period: musical genres and stars, as well as reproduction technologies such as the vinyl record, music cassette tapes, comics and other now dead media forms. Passion, however, is a dialectic concept that not only refers to the joyful desire and intense emotional engagement of cherished objects but also includes its dialectic opposite in the form of pain and suffering. This passion, it is argued in the article, is activated by the nostalgic relationships to past media experiences, the bittersweet remembrances of media habits connected to earlier life phases of one’s own. Taking its point of departure in generational theory of Mannheim and others, this article analyses a series of focus group interviews with Swedish and Estonian media users tentatively belonging to four different generations. Based on the analysis of these interviews, it is suggested that passion and nostalgia are produced, first, in relation to old technologies, second, in relation to childhood memories and, third, at the limits of shared intergenerational experience, that is, at the moment when one realises that one’s own experiences of past media forms cannot be shared by younger generations, and especially one’s own children.
Online lecture recordings and lecture attendance: Investigating student preferences in a large first year psychology course

While blended learning has been around for sometime, the interplay between lecture recordings, lecture attendance and grades needs further examination particularly for large cohorts of over 1000 students in 500 seat lecture theatres. This paper reports on such an investigation with a cohort of 1450 first year psychology students’ who indicated whether they frequently attended lectures or not. The division helped ascertain differences and similarities in preferences for using online recordings. Overall, non-frequent attendees were more likely not to use lecture recordings (48.1%) to make up a missed lecture than frequent attendees (34.3%). Surprisingly, in the last week of semester, 29% of students reported not yet accessing lecture recordings. Students had the intention to use lecture recordings as they envisaged these to be helpful for learning and commented that they would be adversely affected if recordings were not available. In fact, students are passionate about lecture recordings. Analytics show that after Lecture 7, each lecture recording attracted 600 or less unique visits (hits) supporting the finding that most students make strategic use of learning resources available within the blended learning environment.
On Chip Analysis of CNS Lymphoma in Cerebrospinal Fluid

Molecular profiling of central nervous system lymphomas in cerebrospinal fluid (CSF) samples can be challenging due to the paucicellular and limited nature of the samples. Presented herein is a microfluidic platform for complete CSF lymphoid cell analysis, including single cell capture in sub-nanoliter traps, and molecular and chemotherapeutic response profiling via on-chip imaging, all in less than one hour. The system can detect scant lymphoma cells and quantitate their kappa/lambda immunoglobulin light chain restriction patterns. The approach can be further customized for measurement of additional biomarkers, such as those for differential diagnosis of lymphoma subtypes or for prognosis, as well as for imaging exposure to experimental drugs.
Applicability of Inter Project Validation for Determination of Change Prone Classes

research in the field of defect and change proneness prediction of software has gained a lot of momentum over the past few years. Indeed, effective prediction models can help software practitioners in detecting the change prone modules of a software, allowing them to optimize the resources used for software testing. However, the development of the prediction models used to determine change prone classes are dependent on the availability of historical data from the concerned software. This can pose a challenge in the development of effective change prediction models. The aim of this paper is to address this limitation by using the data from models based on similar projects to predict the change prone classes of the concerned software. This inter project technique can facilitate the development of generalized models which can be used to ascertain change prone classes for multiple software projects. It would also lead to optimization of critical time and resources in the testing and maintenance phases. This work evaluates the effectiveness of statistical and machine learning techniques for developing such models using receiver operating characteristic analysis. The observations of the study indicate varied results for the different techniques used.
Relationships form so quickly that you won't cherish them: Mobile Dating Apps and the Culture of Instantaneous Relationships

Mobile dating apps with geolocative function have gained popularity for fostering social, romantic and sexual connections between proximate strangers. Focusing on the experience of social time, this paper sheds light on users' experience on two popular gay mobile dating apps, namely Grindr and Jack'd. Based on in-depth interviews and focus-group discussions with 74 young gay men in Hong Kong, this paper identifies that the tempo and sequence produced by the specific affordances of apps are important to understanding users' experience. Specifically, accelerated tempo of interactions facilitated by constant connectivity, ubiquitous computing, geolocative function, and the apps' messaging system was seen to entail instantaneous and ephemeral relationships. The interface design, which foregrounds profile photos and backgrounds textual self-descriptions, structures the sequence of browsing and screening in a way that prioritizes physical appearance. This design feature was perceived to privilege users seeking casual hook-ups. These findings suggest that the temporality of browsing and exchange on apps is incongruous with the temporal norms prescribing formation of friendship and long-term romance. The violation of these normative expectations affects the perceived quality and satisfaction of app use, resulting in users' frustrations.
Similarity Degree-Based Mobility Pattern Aware Routing in DTNs

Delay-tolerant networks (DTN) have the capacity to interconnect devices in roomy regions unlike traditional networks. Based on common sense, the more similar the mobility pattern of nodes, the more stable their relative positions, which results in a connection between them with a longer duration In this paper, a novel routing scheme using the similarity degree of mobility pattern is proposed to optimize routing performance. In addition, a comprehensive metric called similarity degree is applied to scale the degree of similarity of the mobility pattern which includes deviation of the intermediate node’s speed direction from that of the destination, and distance from the intermediate node to the destination. A message is forwarded to the next hop node with the largest similarity degree.
NTCIR-10 MedNLP Task Baseline System

Natural language processing (NLP) technology that handles clinical, medical and health records has been drawn much attention, because such kinds of records potentially could be rich clinical resources. This paper describes an NLP system that extracts two kinds of information from clinical documents in Japanese, which was developed as a baseline system in the NTCIR-10 MedNLP Pilot Task. Since our system consists of only open source tools and resources, it can be freely used by anyone. The experimental results showed reasonably good performances in both of two subtasks in the NTCIR-10 MedNLP Pilot Task; (1) de-identification task (precision: 86.10%, recall:74.54%, F-measure 79.9%) and (2) complaint and diagnosis task (precision: 87.37%, recall: 71.86%, F-measure 78.86%). These results have demonstrated the basic feasibility of our simple system.
Silicon-Based Plasmonics for On-Chip Photonics

Silicon-based photonic devices dissipate substantially less power and provide a significantly greater information bandwidth than electronic components. Unfortunately, large-scale integration of photonic devices has been limited by their large, wavelength-scale size and the weak optical response of Si. Surface plasmons may overcome these two limitations. Combining the high localization of electronic waves with the propagation properties of optical waves, plasmons can achieve extremely small mode wavelengths and large local electromagnetic field intensities. Si-based plasmonics has the potential to not only reduce the size of photonic components to deeply subwavelength scales, but also to enhance the emission, detection, and manipulation of optical signals in Si. In this paper, we discuss recent advances in Si-based plasmonics, including subwavelength interconnects, modulators, and emission sources. From scales spanning slab waveguides to single nanocrystals, we show that Si-based plasmonics can enable optical functionality competitive in size and speed with contemporary electronic components.
Eco-Sign: a load-based traffic light control system for environmental protection with vehicular communications

The Eco-Sign system is a traffic light control system for minimizing greenhouse gases emitted by idling vehicles at intersections. Eco-Sign provides the following features: (i) it can notify vehicles to turn on/off their engines based on expected waiting time for green lights at intersections, (ii) it can dynamically adjust traffic light timing to minimize the number of vehicles stopping at an intersection based on vehicle arrival and departure rates, and (iii) it is a fully distributed system in the sense that each intersection can learn its local traffic condition and optimize its traffic sign setting to prevent congestions and thus traffic jams. Eco-Sign thus demonstrates a new traffic light control system for environmental protection.
Trace gas emission in chambers : A non-steady-state diffusion model

Non-steady-state (NSS) chambers are widely used to measure trace gas emissions from the Earth’s surface to the atmosphere. Unfortunately, traditional interpretations of time-dependent chamber concentrations often systematically underestimate predeployment exchange rates because they do not accurately represent the fundamental physics of diffusive soil gas transport that follows chamber deployment. To address this issue, we formally derived a time-dependent diffusion model applicable to NSS chamber observations and evaluated its performance using simulated chamber headspace CO2 concentration data generated by an independent, three-dimensional, numerical diffusion model. Using nonlinear regression to estimate the model parameters, we compared the performance of the non-steady-state diffusive flux estimator (NDFE) to that of the linear, quadratic, and steady-state diffusion models that are widely cited in the literature, determined its sensitivity to violation of the primary assumptions on which it is based, and addressed some of the practicalities of its application. In sharp contrast to the other models, NDFE proved an accurate and robust estimator of trace gas emissions across a wide range of soil, chamber design, and deployment scenarios. N
Extraction Algorithms for Hierarchical Header Structures from Spreadsheets

Spreadsheets are widely used to manage statistical data, and a massive amount of valuable spreadsheets are now public as open data. Spreadsheet headers may contain hierarchical structures, and values in spreadsheets are associated with not only headers at the same column or row but also those corresponding to their ancestors. When integrating spreadsheets with other datasets, it is necessary to extract hierarchical header structures and associate this information with values. In this paper, we propose three algorithms to extract hierarchical structures from spreadsheets based on the relation of values. Since our algorithms are focused only on values, they can be applied to heterogeneous spreadsheets such that the structures of row or column headers are not explicitly in
A coplanar multi-band bow-tie antenna based on split ring resonator

In this paper we propose a new design of multiband coplanar antenna based on split ring resonator (SRR), which can be used for several applications. Into the top layer of antenna substrate, two gaps with the shape of a bow-tie rounded are printed symmetrically. Then, we investigate the properties of SRR to improve the performances of the antenna. The proposed antenna covers many frequency namely 1.93 GHz to 2.12 GHz, 2.63 GHz to 2.7 GHz and 3.26 GHz to 3.53 GHz. Its performances are analyzed in terms of reflection coefficient S11, gain and radiation patterns.
Increased Vaspin Levels Are Associated with Beneficial Metabolic Outcome Pre- and Post-Bariatric Surgery

Purpose Vaspin (visceral-adipose-tissue-derived-serine-protease-inhibitor) is a recently identified adipokine with putative insulin-sensitizing properties. Plasma vaspin responses to surgery-induced weight loss are sparse and contradictory. Design and Participants We evaluated changes in vaspin levels and relationship to post-operative outcomes in men (n = 22) and women (n = 55) undergoing biliopancreatic-diversion/duodenal-switch bariatric surgery. Body composition and plasma parameters were measured at baseline, acutely (1 and 5 days) and medium-term (6 and 12 months) post-surgery. Results Fasting preoperative vaspin concentrations were comparable in men vs women. The distribution was biphasic (both men and women) with a nadir of 2.5 ng/ml. Subjects were divided into high (≥2.5 ng/mL, HI-group) and low (<2.5 ng/mL, LO-group) vaspin level. Both groups had comparable sex distribution, age and BMI, but the HI-vaspin group had lower insulin, HOMA, and triglyceride and higher HDL-cholesterol, acylation stimulating protein (ASP) and IL-6 levels (all p<0.05). Post-operatively, both groups decreased BMI comparably over 12 months; the HI-vaspin group maintained high vaspin levels, while the LO-vaspin group gradually increased their levels with weight loss over 12 months. The HI-vaspin group maintained a better glucose, insulin, HOMA, fructosamine, HDL-cholesterol, and triglyceride profile throughout. The HI-vaspin group also had higher gamma-glutamyltransferase and ASP profiles. Finally, baseline vaspin level inversely correlated significantly with baseline and 12-month insulin, HOMA, triglyceride and positively correlated with HDL and ASP. Twelve-month vaspin also correlated similarly, including an inverse correlation with BMI. Conclusion Globally, this study supports the concept of vaspin as a beneficial adipokine in obesity, which may potentially lead to possible therapeutic targets.
Multi-View Video Compression — Exploiting Inter-Image Similarities —

Advances in display and camera technology enable new applications for 3D scene communication. 3DTV is among the most important of these applications; it strives to create a realistic 3D depth impression of the observed scene [1]. Typically, multiple video cameras are used to simultaneously acquire various viewpoints of the scene. The resulting data are often referred to as multi-view video. As the potential degree of 3D realism improves with the camera density around the scene, a vast amount of multi-view video data needs to be stored or transmitted for 3DTV. Multi-view video data is also expected to consume a large portion of the bandwidth available in the Internet of the future. This will include point-to-point communication as well as multicast scenarios. Multimedia distribution via sophisticated content delivery networks and flexible peer-to-peer networks will enable multi-view-video-on-demand as well as live broadcasts. Due to the vast raw bit-rate of multi-view video, efficient compression techniques are essential for 3D scene communication [2]. As the video data originate from the same scene, the inherent similarities of the multi-view imagery are exploited for efficient compression. These similarities can be classified into two types. First, interview similarity is observed between adjacent camera views. Second, temporal similarity is noticed between temporally successive images of each video. Temporal similarities can be exploited with motion compensation techniques that are well known from single-view video compression. Extending that idea, disparity compensation techniques make use of interview similarities for multi-view video compression. When designing compression schemes for multi-view video data, several constraints shape their architecture. In a communication scenario, multi-view video representations should be robust against unreliable transmission. Further, it is desirable that these representations are highly flexible such that subsets of the original data can be accessed easily at various levels of image quality; the level of user interactivity that can be supported by a particular multi-view video representation will be an important consideration for on-demand applications. Finally, the overall trade-off between the quality of the reconstructed views and the bit-rate of its representation will be of high interest when processing the vast amount of data. In the rest of the paper, we will first discuss the importance of exploiting inter-image similarities in multi-view video compression. We then introduce the basic approaches to multi-view video compression. DRAFT IEEE SIGNAL PROCESSING MAGAZINE 2 to multiple views. Another class of algorithms uses adaptive subband decomposition within and across video sequences from different …
CNN-based Estimation of Abdominal Circumference from Ultrasound images

The obstetrics and gynecology ultrasound diagnosis is routinely used to check fetal biometry, and due to its time-consuming routine process, there has been great demand of automatic estimation. Automated analysis of ultrasound images is complicated because ultrasound images are patient-specific, operator-dependent, and machine specific. Among fetal biometry, abdominal circumference (AC) is more difficult to make accurate measurement automatically because abdomen has low contrast against surroundings, non-uniform contrast and irregular shape compared to other parameters. This paper proposes a framework for estimation of the fetal AC from 2D ultrasound data by a specially designed convolutional neural network (CNN) which takes account of doctors' decision process, anatomical structure, and the characteristics of ultrasound image. The proposed framework uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and the Hough transform for the measurement of the AC. We tested the proposed method using clinical ultrasound data acquired from 10 pregnant women. Experimental results showed that, with relatively small training samples, the proposed CNN provided sufficient classification results for AC estimation through the Hough transform. This framework showed good performance on most cases and even for ultrasound images deteriorated by shadowing artifacts. However, for oversized fetus cases, when amniotic fluid is not seen or abdominal area was distorted, it could not estimate correct AC.
FACTS Control for Large Power Systems Incorporating Security Aspects

SUMMARY Flexible AC Transmission Systems (FACTS) are able to control power flows and/or voltages in a power grid providing opportunities to improve system security. In steady state, this corresponds as most important tasks to avoiding overloaded lines, enhancing voltage profile and increasing transfer capacities. In order to benefit from FACTS devices, an appropriate control is crucial. Especially, if the number of FACTS devices in a grid increases and the distances between them decrease, a local control might not be sufficient any more. Mutual influences among different devices may arise and render coordination indispensable. In this paper, a control strategy based on Optimal Power Flow is proposed. The objective is to improve security in steady-state, which implies to avoid overloaded lines and to keep bus voltages close to a pre-defined reference value. As FACTS devices have only influence on a limited area in their vicinity, sensitivity analysis with respect to the settings of the device is used to identify the area of influence. Only buses and lines included in the area of influence are then taken into account in the process of determining the optimal settings for the FACTS device. Consequently, the proposed control is also applicable to large and therefore realistic grids. When the number of devices in a grid increases, it becomes probable that the areas of influence of two or more devices overlap, which results in the mentioned mutual influences. In order to coordinate the devices, an iterative algorithm is applied, where the controllers sequentially determine the settings of the corresponding FACTS devices taking the effects of the other devices into account. Simulation results are given for the IEEE 57 bus grid for the limited control of a single FACTS device as well as for the multi-area control. KEYWORDS FACTS, sensitivity analysis, multi-area control, Optimal Power Flow, congestion management.
Relationships between Sleep Behaviors and Unintentional Injury in Southern Chinese School-Aged Children: A Population-Based Study

The purpose of this study is to explore the relationships between sleep behaviors and injury occurrence among Chinese school-aged children. Data were collected with self-administered questionnaires of a cross-sectional survey which covered the school-aged children from southeastern Chinese urban and rural areas in April 2010. Information was collected on unintentional injury in the past year, sleep duration, napping and daytime fatigue, sleeping pill use, and social-demographic variables. Multivariable logistic regression analyses, controlling for confounding factors, were conducted to assess sleep-related variables that were associated with injuries. Students who slept for less than 8 h had a 30% increased risk of injury (OR: 1.30; 95%CI: 1.01–1.69) compared with those who slept for 8–9 h. Lack of napping, snoring and use of sleeping pills were significantly associated with injury. Among different genders, the slight difference in sleep behaviors predicted the occurrence of injury. Rural children displayed more sleep behaviors associated with injury than urban children. The sleep behaviors of primary school students were more negatively correlated with injury occurrence than junior/senior high school children. Consideration should be given to the prevention of problematic sleep behaviors as a potential risk factor in order to decrease injury rates and promote the health of school-aged children.
PITCH DETECTION ALGORITHM EVALUATION FRAMEWORK

When implementing an existing or designing a brand new pitch detection algorithm (PDA) there is a need to evaluate it and maybe compare it to another one. We could make some comparisons or rough estimates based on graphical output, but this approach is ineffective and definitely inconvenient for larger file-bases. In this paper design and implementation of PDA speech-oriented evaluation framework is presented. It is based on existing ECESS PMA/PDA manually pitch marked reference database. The database consists of four-channel recordings in various environments with varying SNR and thus is suitable for testing robustness of pitch detection algorithms. Standard evaluation criteria widely used by many authors in the field are discussed and few improvements are suggested in this area. Finally results of comparing several PDA algorithms are presented and further idea of using the best one in a real-time punctuation detector is presented too.