Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design and Evaluation of a Self-Organizing Superpeer Network

Superpeer architectures exploit the heterogeneity of nodes in a peer-to-peer (P2P) network by assigning additional responsibilities to higher capacity nodes. In the design of a superpeer network for file sharing, several issues have to be addressed: how client peers are related to superpeers, how superpeers locate files, how the load is balanced among the superpeers, and how the system deals with node failures. In this paper, we introduce a self-organizing superpeer network architecture (SOSPNet) that solves these issues in a fully decentralized manner. SOSPNet maintains a superpeer network topology that reflects the semantic similarity of peers sharing content interests. Superpeers maintain semantic caches of pointers to files, which are requested by peers with similar interests. Client peers, on the other hand, dynamically select superpeers offering the best search performance. We show how this simple approach can be employed not only to optimize searching, but also to solve generally difficult problems encountered in P2P architectures such as load balancing and fault tolerance. We evaluate SOSPNet using a model of the semantic structure derived from eight-month traces of two large file-sharing communities. The obtained results indicate that SOSPNet achieves close-to-optimal file search performance, quickly adjusts to changes in the environment (node joins and leaves), survives even catastrophic node failures, and efficiently distributes the system load taking into account superpeer capacities.
Investigating townscape change and management in urban conservation areas: the importance of detailed monitoring of planned alterations

This paper examines some of the planed townscape changes affecting large city centre conservation areas and that have influenced policy through over twenty years of control. Data from local planning authority planning application files are used to identify the development trends in central conservation areas and the difficulties these pose for conservation policies. Detailed examination highlights the significant changes that can occur in central areas at the small scale, and some of the processes of change which remain largely unexamined by planning authorities. Identification of this ‘hidden’ change demonstrates the importance of monitoring at the small scale, and can identify key conservation ‘problems’ affecting these areas, including facadism, erosion of character and the weakness of local policies.
Informal music education: The nature of a young child’s engagement in an individual piano lesson setting

The purpose of this study was to gain insight into the nature of a young child’s engagement in an individual music lesson setting based on principles of informal learning. The informal educational space allowed the child to observe, explore, and interact with a musical environment as a process of enculturation and development (Gordon, 2013; McCloskey, 2012). The setting incorporated the piano, and was considered a readiness experience for later formal piano training. Data collection occurred via video recording, with 24 episodes isolated for close analysis. Richly descriptive narratives of each episode were analyzed by line-by-line transformation regarding the nature of the child’s engagement (Van Manen, 1990). The child’s engagement in the setting was viewed through lenses of lived space, lived body, lived time, lived other (Van Manen, 1990), and lived musicality (Kooistra, 2013). Results include that the child engaged in the setting by expanding the boundaries of the room, using the full range of the room, using his body in rhythmic response, directing the time, including the parent in the lesson, creating pretend contexts, making music (singing, chanting, moving, playing the piano and other instruments), embedding music in his activities, and believing in his musicianship.
Endoscopic submucosal dissection versus endoscopic mucosal resection for type 0-II superficial gastric lesions larger than 20 mm

Background: Endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD) are increasingly used for the treatment of superficial gastrointestinal neoplasia. However, the limits and the indications for each technique are still debated. Our retrospective study aimed to compare these techniques in patients with gastric flat lesions larger than 20 mm without the non-lifting sign. Methods: Between January 2013 and July 2016, a total of 36 patients with early gastric flat lesions larger than 20 mm and without the non-lifting sign were resected by ESD and were followed up by endoscopy. As a control group, 40 EMR cases from our database were matched. En bloc and curative resection were compared between the two groups according to histological assessment, tumor size, recurrence, complication rate, and procedure time. A Kaplan-Meier comparison was performed for both groups with a log-rank test to compare the survival curves; the chi-square test was employed for other parameters. Results: En bloc resection rate and curative resection rate were significantly higher in the ESD group than in the EMR group. Procedure time was significantly longer in the ESD group. No significant differences were found in the recurrence and complication rates, although the former were higher in the EMR group and the latter in the ESD group. Survival curves were similar for both groups. Conclusions: Our retrospective analysis seems to confirm a clear advantage for ESD over EMR in removing early superficial gastric neoplasm. Although ESD has expanded the endoscopic resectability of endoscopic gastric lesions, EMR may still be considered one of the therapeutic options for flat gastric lesions without the non-lifting sign.
Tinnitus: from cortex to cochlea

Het medische woord voor oorsuizen is tinnitus. Tinnitus is het waarnemen van geluid, waar geen bron voor is. Het kan dus alleen worden waargenomen door de persoon zelf en niet door anderen. De meeste volwassenen hebben wel eens last gehad van tijdelijke tinnitus. Tinnitus kan echter ook blijvend zijn. Geschat wordt dat permanente tinnitus bij 8-20% van de mensen in de algemene bevolking voorkomt, waarbij de meeste mensen goed met deze klacht kunnen omgaan. Echter, 1-3% van de mensen met tinnitus heeft zoveel klachten dat ze medische hulp zoeken. De oorzaak van tinnitus is nog niet bekend. Momenteel gaat men ervan uit dat bij tinnitus veranderingen in de centrale hersengebieden, inclusief de hersengebieden die betrokken zijn bij horen, een bepalende rol spelen bij het ontstaan van tinnitus. Het ontstaan wordt waarschijnlijk vooraf gegaan door een vorm van schade aan het gehoor of gehoororgaan. Deze schade zorgt dan voor de veranderingen in het brein. Dit proefschrift beschrijft het onderzoek dat is gedaan naar de oorzaak van tinnitus. Hierbij is extra nadruk gelegd op de zenuwbanen van hersengebieden die bij horen betrokken zijn. De oorzaak van tinnitus is helaas nog steeds niet ontrafeld. Wel is duidelijk geworden dat links-rechts verschillen in het brein die eerder aan tinnitus werden toegeschreven, ook bij gezonde proefpersonen voorkomen. We hebben geen afwijkingen in functioneren kunnen vinden in specifieke zenuwbanen die van de hersenstam naar het oor lopen. Om dit ingewikkelde systeem van zenuwbanen verder te onderzoeken, doen we meerdere suggesties voor nieuwe onderzoeken.
Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series

Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods are compared with a recent hierarchical Dirichlet process hidden Markov model (HDP-HMM) and a baseline standard hidden Markov model, of which the former suffers from poor performance on moderate-dimensional data and sensitivity to parameter settings, while the latter suffers from rapid state transitioning, over-segmentation and poor performance on a segmentation task involving human activity accelerometer data from the UCI Repository. The regularized methods developed here are able to perfectly characterize change of behavior in the human activity data for roughly half of the real-data test cases, with accuracy of 94% and low variation of information. In contrast to the HDP-HMM, our methods provide simple, drop-in replacements for standard hidden Markov model update rules, allowing standard expectation maximization (EM) algorithms to be used for learning.
Structural diversity and tie strength in the purchase of a social networking app

Although people increasingly rely on online services to maintain their relationships, we know relatively little about what drives their use. To address this, I analyze data from a social networking site that started charging its users for an app that populates their e‐mail address books with updated contact information. I find that purchase rates of the app were higher for users with large, structurally diverse networks – which contain several distinct social groups. Moreover, personal ties (i.e., family members and friends) increased purchase rates more than professional ties. I attribute the first effect to the difficulty of obtaining information about a large, diverse social network, which the app reduces, and the second effect to the regularity with which people use information about their personal ties.
Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.
Joint Surgical Gesture and Task Classification with Multi-Task and Multimodal Learning

We propose a novel multi-modal and multi-task architecture for simultaneous low level gesture and surgical task classification in Robot Assisted Surgery (RAS) videos.Our end-to-end architecture is based on the principles of a long short-term memory network (LSTM) that jointly learns temporal dynamics on rich representations of visual and motion features, while simultaneously classifying activities of low-level gestures and surgical tasks. Our experimental results show that our approach is superior compared to an ar- chitecture that classifies the gestures and surgical tasks separately on visual cues and motion cues respectively. We train our model on a fixed random set of 1200 gesture video segments and use the rest 422 for testing. This results in around 42,000 gesture frames sampled for training and 14,500 for testing. For a 6 split experimentation, while the conventional approach reaches an Average Precision (AP) of only 29% (29.13%), our architecture reaches an AP of 51% (50.83%) for 3 tasks and 14 possible gesture labels, resulting in an improvement of 22% (21.7%). Our architecture learns temporal dynamics on rich representations of visual and motion features that compliment each other for classification of low-level gestures and surgical tasks. Its multi-task learning nature makes use of learned joint re- lationships and combinations of shared and task-specific representations. While benchmark studies focus on recognizing gestures that take place under specific tasks, we focus on recognizing common gestures that reoccur across different tasks and settings and significantly perform better compared to conventional architectures.
CRAFT: Complementary Recommendations Using Adversarial Feature Transformer

Traditional approaches for complementary product recommendations rely on behavioral and non-visual data such as customer co-views or co-buys. However, certain domains such as fashion are primarily visual. We propose a framework that harnesses visual cues in an unsupervised manner to learn the distribution of co-occurring complementary items in real world images. Our model learns a non-linear transformation between the two manifolds of source and target complementary item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring object categories, we train a generative transformer network directly on the feature representation space by casting it as an adversarial optimization problem. Such a conditional generative model can produce multiple novel samples of complementary items (in the feature space) for a given query item. The final recommendations are selected from the closest real world examples to the synthesized complementary features. We apply our framework to the task of recommending complementary tops for a given bottom clothing item. The recommendations made by our system are diverse, and are favored by human experts over the baseline approaches.
i3: intelligent, interactive investigation of OLAP data cubes

The goal of the i3(eye cube) project is to enhance multidimensional database products with a suite of advanced operators to automate data analysis tasks that are currently handled through manual exploration. Most OLAP products are rather simplistic and rely heavily on the user's intuition to manually drive the discovery process. Such ad hoc user-driven exploration gets tedious and error-prone as data dimensionality and size increases. We first investigated how and why analysts currently explore the data cube and then automated them using advanced operators that can be invoked interactively like existing simple operators. Our proposed suite of extensions appear in the form of a toolkit attached with a OLAP product. At this demo we will present three such operators: DIFF, RELAX and INFORM with illustrations from real-life datasets.
Open-Source Based Testbed for Multioperator 4G/5G Infrastructure Sharing in Virtual Environments

Fourth-Generation (4G) mobile networks are based on Long-Term Evolution (LTE) technologies and are being deployed worldwide, while research on further evolution towards the Fifth Generation (5G) has been recently initiated. 5G will be featured with advanced network infrastructure sharing capabilities among different operators. Therefore, an open-source implementation of 4G/5G networks with this capability is crucial to enable early research in this area. The main contribution of this paper is the design and implementation of such a 4G/5G open-source testbed to investigate multioperator infrastructure sharing capabilities executed in virtual architectures. The proposed design and implementation enable the virtualization and sharing of some of the components of the LTE architecture. A testbed has been implemented and validated with intensive empirical experiments conducted to validate the suitability of virtualizing LTE components in virtual infrastructures (i.e., infrastructures with multitenancy sharing capabilities). The impact of the proposed technologies can lead to significant saving of both capital and operational costs for mobile telecommunication operators.
Selection in Non-Factoid Question Answering using Convolutional Neural Network

Answer selection in question answering has recently received a considerable amount of attention because the syntactic and semantic facts involved in this task cannot be captured by standard information retrieval models. However, the success of word embedding techniques along the deep learning algorithms in reducing the semantic gap in many natural language processing task indicates that question answering should be another beneficiary of these techniques. As a result, in this project, we want to investigate the approach proposed in [4], which has applied deep learning technique to answer selection task for non-factoid question answering and shown promising results.
Scalable Distributed Nonnegative Matrix Factorization with Block-Wise Updates

Nonnegative Matrix Factorization (NMF) has been applied with great success on a wide range of applications. As NMF is increasingly applied to massive datasets such as web-scale dyadic data, it is desirable to leverage a cluster of machines to store those datasets and to speed up the factorization process. However, it is challenging to efficiently implement NMF in a distributed environment. In this paper, we show that by leveraging a new form of update functions, we can perform local aggregation and fully explore parallelism. Therefore, the new form is much more efficient than the traditional form in distributed implementations. Moreover, under the new form of update functions, we can perform frequent updates and lazy updates, which aim to use the most recently updated data whenever possible and avoid unnecessary computations. As a result, frequent updates and lazy updates are more efficient than their traditional concurrent counterparts. Through a series of experiments on a local cluster as well as the Amazon EC2 cloud, we demonstrate that our implementations with frequent updates or lazy updates are up to two orders of magnitude faster than the existing implementation with the traditional form of update functions.
Spectrofluorimetric Determination of Daunorubicin Using Terbium-Deferasirox as a Fluorescence Probe

A b s t r a c t A spectrofluorimetric method is proposed for the determination of Daunorubicin (DNR) based on its quenching effect on the fluorescence intensity of Tb 3+ - deferasirox (DFX) complex as a fluorescent probe. The excitation and emission wavelengths were 328 and 545 nm, respectively. The optimum conditions for the determination of DNR were investigated considering the effects of various parameters on the quenched fluorescence intensity. In the optimum conditions the decrease of the fluorescence intensity of the system showed a good linear relationship with the concentration of DNR in the range of 15-1100 μg L -1 , with a correlation coefficient 0.998. The detection limit (3s) was 4.3 μg L -1 and the relative standard deviation for four replicate determinations of different concentrations of DNR was in the range of 1.6–4.7%. The procedure was successfully applied to the determination of daunorubicin in spiked urine and serum samples.
Vertical links minimized 3D NoC topology and router-arbiter design

Design of a topology and its router plays a vital role in a 3D Network-on-Chip (3D NoC) architecture. In this paper, we develop a partially vertically connected topology, so called 3D Recursive Network Topology (3D RNT) and using an analytical model, we study the performance of the 3D RNT. Delay per Buffer Size (DBS) and Chip Area per Buffer Size (CABS) are the parameters considered for the performance evaluation. Our experimental results show that the vertical links are cut down upto 75% in 3D RNT compared to that of 3D Fully connected Mesh Topology (3D FMT) at the cost of increasing DBS by 8%, besides 10% lesser CABS is observed in the 3D RNT. Further, a Programmable Prefix router-Arbiter (PPA) is designed for 3D NoC and its performance is analyzed. The results of the experimental analysis indicate that PPA has lesser delay and area (gate count) compared to Round Robin Arbiter (RRA) with prefix network.
Driving Policy Transfer via Modularity and Abstraction

End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches. The key idea is to encapsulate the driving policy such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. We evaluate the presented approach in simulated urban environments and in the real world. In particular, we transfer a driving policy trained in simulation to a 1/5-scale robotic truck that is deployed in a variety of conditions, with no finetuning, on two continents. The supplementary video can be viewed at this https URL
A CLOCK CONTROL STRATEGY BASED CLUSTERING METHOD FOR PEAK POWER AND RMS CURRENT REDUCTION

In VLSI circuits, peak power and RMS current reduction are the challenging tasks. Both power density and peak power are proportional to each other. If these parameters are reduced, it may lead to a timing violation and logic failures. In order to overcome these failures, Clock Control Strategy based Clustering Method (CCSCM) is proposed. In the proposed clustering method, we design to reduce the path delays, to find the inequality paths and to make it equality through a slack values using slack variables. In previous method, benchmark circuits are used which results in finding difficult in a slack delay and RMS current reduction. But in our proposed algorithm, CMOS full adder is developed which make use of finite impulse response (FIR) filter design based on the 180 NM technology. A 180 nm generation logic technology has been developed with high performance 140 nm LGATE transistors, six layers of aluminum interconnects and low-e SiOF dielectrics. The transistors are optimized for a reduced 1.3-1.5 V operation to provide high performance and low power. The interconnects feature high aspect ratio metal lines for low resistance and fluorine doped SiO2 inter-level dielectrics for reduced capacitance. 16 Mbit SRAMs with a 5.59 mm2 6-T cell size have been built on this technology as a yield and reliability test vehicle.
Final Report of Final Year Project HKU-Face : A Large Scale Dataset for Deep Face Recognition

Current research and development of face recognition mainly focus on three perspectives, the scale and purity of facial dataset, the effectiveness of loss functions and employment of effective backbone network structures[2, 6, 9, 13, 19, 34]. The scale and labelling errors of training dataset is usually the problem encountered by many researchers in institutions for face recognition research. This project introduces a dataset filtering and construction procedures for solving the problem with less human effort. Pre-trained face recognition models like Sphereface[19] and Facenet[28] are employed to extract deep features of face images. Then these representational features are used for noisy image filtering. Impressive result of automatic filtering and purity level have been achieved compared with the raw dataset. Except for the self-constructed dataset, another filtered dataset named CASIAVGG merged from CASIA-Webface[40] dataset and VGG[23] Dataset is also researched and analyzed in this project.