Miracle-dz/UniFAR-FTUs huggingface dataset:
Arbitrary Viewpoint Visualization for Teleoperation of Disaster Response Robots

In this work, we propose an arbitrary viewpoint visualization system for disaster response robots. Robot operation from safety areas using teleoperation is highly desirable. To provide sufficient information, it is common to employ multiple displays, however, this often reduces operability. To avoid reduced operability we integrate all acquired images into a single bird eye view. The system is also capable of moving this viewpoint to any arbitrary position for ease of operation, and to overlay a computer-generated image of the robot that is synchronized to match the real robot pose onto the generated view when necessary. Experiments confirmed pose synchronization, generation of views from arbitrary positions, as well as improved operability.
Prevalence of Depression , Anxiety and Stress (by DASS Scoring System ) among Medical Students in Islamabad, Pakistan

Objective: To determine the prevalence of depression, anxiety and stress (by DASS scoring system) among purposive sample of medical students in Islamabad, Pakistan. Study Design: Cross - sectional survey (June, 2014 to November, 2014) Sampling Technique : Purposive sampling (non - probability) Methods: The questionnaire used in this study consisted of three components: A socio demographic questionnaire that required each student to pro vide their age, gender and year of study, as well as marks obtained as mean % scoring i n the professional examinations and DASS scale (the Depression, Anxiety, and Stress Scale). Procedure: Sixty - six medical students were the participants who were attendin g private and public medical universities in Pakistan. Following the granting of ethical approval from the university and medical college to conduct the study, medical students from first year to fifth year were contacted
インタラクションデザイン = Interaction design

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Image dequantization for hyperspectral lossy compression with convolutional neural networks

Signiﬁcant work has been devoted to methods based on predictive coding for onboard compression of hyperspectral images. This is supported by the new CCSDS 123.0-B-2 recommendation for lossless and near-lossless compression. While lossless compression can achieve high throughput, it can only achieve limited compression ratios. The introduction of a quantizer and local decoder in the prediction loop allows to implement lossy compression with good rate-performance. However, the need to have a locally decoded version of a causal neighborhood of the current pixel under coding is a signiﬁcant limiting factor in the throughput such encoder can achieve. In this work, we study the rate-distortion performance of a signiﬁcantly simpler and faster onboard compressor based on prequantizing the pixels of the hyperspectral image and applying a lossless compressor (such as the lossless CCSDS CCSDS 123.0-B-2) to the quantized pixels. While this is suboptimal in terms of rate-distortion performance compared to having an in-loop quantizer, we compensate the lower quality with an on-ground post-processor based on modeling the distortion residual with a convolutional neural network. The task of the neural network is to learn the statistics of the quantization error and apply a dequantization model to restore the image.
A Multilevel Terrain Rendering Method Based on Dynamic Stitching Strips

High-quality terrain rendering has been the focus of many visualization applications over recent decades. Many terrain rendering methods use the strategy of Level of Detail (LOD) to create adaptive terrain models, but the transition between different levels is usually not handled well, which may cause popping artefacts that seriously affect the reality of the terrain model. In recent years, many researchers have tried using modern Graphics Processing Unit (GPU) to complete heavy rendering tasks. By leveraging the great power of GPU, high quality terrain models with rich details can be rendered in real time, although the problem of popping artefacts still persists. In this study, we propose a real-time terrain rendering method with GPU tessellation that can effectively reduce the popping artefacts. Coupled with a view-dependent updating scheme, a multilevel terrain representation based on the flexible Dynamic Stitching Strip (DSS) is developed. During rendering, the main part of the terrain model is tessellated into appropriate levels using GPU tessellation. DSSs, generated in parallel, can seamlessly make the terrain transitions between different levels much smoother. Experiments demonstrate that the proposed method can meet the requirements of real-time rendering and achieve a better visual quality compared with other methods.
Service users’ construction of relapse experiences in psychosis : a grounded theory approach and research portfolio.

Pt. 1. Service Based Evaluation. Effective communication within and between services is important in the development of multi-disciplinary collaboration, effective service delivery and satisfactory patient treatment. The primary aim of this study was to audit assessment letter content across three therapy disciplines within Ayrshire and Arran Consulting and Clinical Psychology Services (CCPS). A secondary aim was to audit content within the three therapy disciplines. The overall aim was to ascertain service implications based on the current letter standard. A random sample of sixty letters were audited, twenty written by Clinical Psychologists, twenty by Counselling Psychologists and a further twenty by CBT Psychotherapists. The results suggested overall content inclusion was of a high standard. However, there were some inclusion gaps in specific information pertaining to the themes of case complexity, reason for seeking assistance, previous psychiatric and medical history, maintaining factors and estimated treatment length. It was concluded that training should be conducted across the service to further develop communication within these areas.
A Provenance-based Approach Towards Impact Assessment of Schema Changes in a Data Warehouse Environment

Graph-based solutions are receiving significant attention recently for two reasons: (1) their ability to capture relationships as first class elements in interconnected domains, and (2) their inherent resemblance to real-world scenarios such as social networks. In our work, we investigate the use of graph databases towards two schema management challenges: (a) impact assessment of schema evolution in a data warehouse environment, and (b) schema mapping and integration. We leverage the explicit capture of relationships to address these challenges. Data warehouses are a schema-rich, multi-layered environment consisting of many inter-related artifacts. The artifacts include operational, reconciled and warehouse schemas connected by ETL mappings and a set of queries expressed against the schemas. If a user seeks to make a schema change at any level in the architecture, he or she may not be aware of the other artifacts potentially impacted by the change. Previous approaches to data warehouse schema evolution have focused on proposing algorithms for propagating the impact of the change to the related artifacts in an automated manner. While these contributions ease the task for the user by providing a programmatic way of adapting related artifacts, they do not provide support for detailing the potential impact. We focus on defining and implementing a graph-based model for impact assessment and explanation. Impact assessment involves identification of the artifacts that depend
Feature Selection on Pregnancy Risk Classification Using C5.0 Method

The maternal mortality rate in Indonesia is still relatively high. This is caused by several factors, including the ignorance of pregnant women about the risk status of pregnancy. Several methods are proposed for early detection of the risk of a mother's pregnancy. However, no one has highlighted what features are most influential in the process of classifying the risk of pregnancy. In this research, we use data of pregnant women in one of the health centers in Malang, Indonesia, as a dataset. The dataset has 107 features, therefore, feature selection is needed for the classification process. We propose to use the C5.0 method to select important features while classifying dataset into low, high, and very high risk of pregnancy. C5.0 was chosen because this method has a better pruning algorithm and requires relatively smaller memory compared to C4.5. Another classification method (SVM, Naive Bayes, and Nearest Neighbor) is then used to compare the accuracy values between datasets that use all features with datasets that only use the selected features. The test results show that feature selection can increase accuracy by up to 5%.
Analysis of review helpfulness based on consumer perspective

When consumers make purchase decisions, they generally refer to the reviews generated by other consumers who have already purchased similar products in order to get more information. Online transaction platforms provide a highly convenient channel for consumers to generate and retrieve product reviews. In addition, consumers can also vote reviews perceived to be helpful in making their decision. However, due to diverse characteristics, consumers can have different preferences on products and reviews. Their voting behavior can be influenced by reviews and existing review votes. To explore the influence mechanism of the reviewer, the review, and the existing votes on review helpfulness, we propose three hypotheses based on the consumer perspective and perform statistical tests to verify these hypotheses with real review data from Amazon. Our empirical study indicates that review helpfulness has significant correlation and trend with reviewers, review valance, and review votes. In this paper, we also discuss the implications of our findings on consumer preference and review helpfulness.
Sponsoring the Arts: Melic Perspectives

This paper explores the melic poets’ take on art and its sponsors. Since much has been written on the relationship of epinician poets with their patrons, this paper broadens the focus of enquiry to include other melic genres and, in addition to the verbal, to look at the visual arts as well, i.e. melic representations of communities that sponsor songs and of communities or individuals that sponsor other art-forms such as sculpture, architecture, and precious objects. Taking as starting point Xenophon’s depiction of Simonides in Hiero, I discuss epigrams XXVII and XXVIII Page and relevant testimonia that show Simonides’ keen interest in Athenian dithyrambic contests; Bacchylides’ Ode 19, probably composed for the Great Dionysia; Pindar’s Pythian 7, Paean 8, and fragment 3 in conjunction with Homeric Hymn to Apollo 281-99, Herodotus 1.31, Cicero, De oratore 2. 86. 352-353, [Plutarch] Consolatio ad Apollonium, and Pausanias – all of which offer precious insights into Pindar’s views on sponsoring monumental sculpture and architecture; and Bacchylides’ description of the golden tripods that Hieron offered to Apollo in Ode3. On the basis of this evidence I argue that whatever the nature and the range of remuneration of poets and artists may have been, melic rhetoric shows that it was the relationship of poets, artists and their sponsors with the gods that was ultimately at stake. This is why both the poetry and the traditions about Simonides, Pindar and Bacchylides privilege the divine favour that poets, artists and patrons alike either obtained or were hoping to obtain by offering masterpieces to the gods.
DREADLOCKS: Efficient Deadlock Detection for STM

We present DREADLOCKS, an efficient new shared-memory spin lock that actively detects deadlocks. Instead of spinning on a Boolean value, each thread spins on the lock owner’s per-thread digest, a compact representation of a portion of the locks’ waits-for graph. Digests can be implemented either as bit vectors (for small numbers of threads) or as Bloom filters (for larger numbers of threads). Updates to digests are propagated dynamically as locks are acquired and released. DREADLOCKS can be applied to any spin lock algorithm that allows threads to time out. Preliminary experimental results show that DREADLOCKS outperform timeouts under many circumstances, and almost never do worse.
Feature extraction and classification of blood cells for an automated differential blood count system

The differential blood counter system we developed is an attempt to automate the task performed manually by experts in routine. Feature extraction and classification are two important components of our automated system. In this paper, classification of blood cells using various approaches including neural network based classifiers and support vector machine are presented together with the features used in the classification.
EARLY CHILDHOOD MENTAL HEALTH: A PUBLIC HEALTH APPROACH

Undiagnosed and untreated child mental health problems create a public health emergency in the United States (US). In the US the human suffering, burden of disability, and economic costs associated with mental illness are immense. Mental illness is the second leading cause of disease burden, directly resulting in substantial lost productivity. Behavioral health and prevention research highlight the effectiveness of interventions that reduce risk factors and enhance protective factors associated with mental illness. This focus on prevention and resilience reflects the relatively recent public health approach to mental health. The emphasis on promotion, prevention, and early intervention in a public health approach is especially relevant for young children. Likewise, the social-ecological perspective often used in public health interventions mirrors the multi-tiered influences on young children's social and emotional wellbeing. Finally, child development research shows young children can recover from early detection and early intervention for a mental health problem. One in five children and youth is estimated to experience symptoms of psychiatric disorder each year. Many of the mental health problems diagnosed in school aged children and teenagers originate in early childhood, but there are long delays before diagnosis and treatment are received. Approximately 10% of children experience mental problems to the point of impairment, yet less than 20% of those in need receive treatment. This unmet need in the population most likely to benefit from prevention and early treatment creates a problem of public health significance. Currently, there is little research linking public health approaches to mental health services for children birth to age five. This paper discusses the need for, and relevance of, a public health approach to young children's mental health, and makes recommendations for implementation of that approach.
Dynamic Convolutional Neural Network For Image Super-resolution

Single image super-resolution (SISR) is a technique that reconstructs high resolution image from single low resolution image. Dynamic Convolutional Neural Network (DCNN) is used here for the reconstruction of high resolution image from single low resolution image. It takes low resolution image as input and produce high resolution image as output for dynamic up-scaling factor 2, 3, and 4. The dynamic convolutional neural network directly learns an end-to-end mapping between low resolution and high resolution images. The CNN trained simultaneously with images up-scaled by factors 2, 3, and 4 to make it dynamic. The system is then tested for the input images with up-scaling factors 2, 3 and 4. The dynamically trained CNN performs well for all three up-scaling factors. The performance of network is measured by PSNR, WPSNR, SSIM, MSSSIM, and also by perceptual.Journal of Advanced College of Engineering and Management, Vol. 3, 2017, Page: 1-10
A hardware implementation of intelligent ECG classifier

Purpose – The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach – The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded int.
EdgeSense: Edge-Mediated Spatial- Temporal Crowdsensing

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called <italic>Edge-Mediated Spatial-Temporal Crowdsensing</italic>. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, <italic>EdgeSense</italic> works on top of a secured peer-to-peer network consisted of participants and propose a novel <italic>Decentralized Spatial-Temporal Crowdsensing</italic> framework based on <italic>Parallelized Stochastic Gradient Descent</italic>. To approximate the sensing data in each part of the target area in each sensing cycle, <italic>EdgeSense</italic> uses the local sensor data in participants’ mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the <italic>EdgeSense</italic> on the real-world data sets (temperature <xref ref-type="bibr" rid="ref1">[1]</xref> and PM2.5 <xref ref-type="bibr" rid="ref2">[2]</xref> data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism.
Efficient Feature Selection by Reducing Redundant Features From High Dimensional Data Using Clustering

Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, in-creasing learning accuracy, and improving result comprehensibility. However, the re-cent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. Based on this criteria Redundancy feature reduction algorithm is proposed. The RFR (Redundancy Feature Reduction) algorithms works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of RFR has a high probability of producing a subset of useful and independent features. The real-world high-dimensional image, microarray, and text data are given as input to the algorithm. To ensure efficiency we adopt the efficient minimum-spanning tree clustering method. Finally this algorithm will be able produce good set of feature subset from high dimensional data sets using clustering method.
The Prism of Children's Plurilingualism: A Multi-site Inquiry with Children as Co-researchers across English and French Schools

Through this multi-site study of children’s plurilingualism, I asked what we would discover about plurilingualism by positioning children as co-ethnographers of their plurilingual lives and what teaching might look like if it were based on a plurilingual rather than a monolingual paradigm. During a 4-month collaboration with four different English and French school models in Toronto, Canada and one bilingual school in Montpellier, France, children documented their plurilingual and pluricultural experiences: they took photos of their literacy practices at school and at home and classified them by theme to analyze the linguistic landscape around them and to reflect collaboratively on language policies that manage different spaces. Based on reflexive techniques such as drawing, creative writing and collage, students represented their plurilingualism by creating a series of individual and collective "identity texts" (Cummins & Early, 2011). Creative arts-informed methods were used to allow children to express their views without being limited to the language of instruction (Molinié, 2009; Auger, 2010). In addition, these reflexive techniques scaffolded students’ engagement as co-researchers by enabling them to creatively co-construct new knowledge and represent their perspectives.
Securing Cloud Data Using ABE Under Key Exposure For Outsourced Decryption

: The intruders acquire the keys through backdoor, bribe, or coercion. Once the key is exposed, the only way to protect the data is by limiting access to the cipher text. The most common and strongest algorithms are already defined, the security lies into keys. Although the key is exposed we can still secure our data by dividing and distributing the cipher blocks on the multiple administrative domains so that it becomes very difficult for the intruder to gain access, assuming that intruder cannot compromise all the cipher blocks stored on the server. In the existing scheme, the data is stored on a single server so the attacker can easily get access to the user's confidential data by acquiring a key. In this paper, we propose a method i.e. dubbed Fortress method initially known as Bastion method which guarantees data security even if the key is Exposed, unless and until the attacker gets access to all the cipher blocks. Fortress method gives less than 5% overhead compared to existing secure encryption methods. We are additionally using the reverse circle cipher algorithm and ABE(attribute-based encryption) with outsourced decryption to increase efficiency and reduce decryption cost increased by ABE.
Editorial: Die aktuelle FQS-Schwerpunktausgabe "Doing Biographical Research". FQS als Beispiel für vier Jahre Open Access in den deutschen Sozialwissenschaften

Zusammenfassung: In FQS 4(3) – "Doing Biographical Research" – beschaftigen sich Wissenschaftler(innen) aus unterschiedlichen disziplinaren und nationalen Blickwinkeln mit dem Interview mit einer in Deutschland lebenden turkischen Arbeitsmigrantin. Neben diesen unmittelbar dem Themenschwerpunkt zugehorigen Aufsatzen enthalt FQS 4(3) acht Einzelbeitrage, neun Literaturbesprechungen und zwei Tagungsberichte von Sozialwissenschaftler(inne)n aus insgesamt acht Landern und sieben Disziplinen. Damit wurden seit Erscheinen der ersten Schwerpunktausgabe im Januar 2000 in FQS beinahe 450 Artikel veroffentlicht.