Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Sketching Bounds for Exp-concave Stochastic Minimization

We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension. For common eigendecay patterns of the population covariance matrix, this quantity is significantly smaller than the ambient dimension. Our results reveal interesting connections to sketching results in numerical linear algebra. In particular, our statistical analysis highlights a novel and natural relationship between algorithmic stability of empirical risk minimization and ridge leverage scores, which play significant role in sketching-based methods. Our main computational result is a fast implementation of a sketch-to-precondition approach in the context of exp-concave empirical risk minimization.
A Business Model founded on Bayes' Decision Procedure for Video-Call Billing from the Estimated End-to-End QOS

This paper proposes a business model founded on Bayes decision test for video-call billing. This model is based on the end-to-end Quality of Service (QoS) obtained exploiting the tracing watermarking procedure recently introduced in literature. In particular, we employ the watermark embedded in each transmitted video-sequence to monitor the QoS of the communication link and then to adequately fare the user depending on the real received video-quality. We need to know only the MSE of the received watermark to estimate the end-to-end QoS of the communication link. Numerical results are conducted to characterize the effectiveness of the proposed method, using two different business scenarios, and confirm the validity of such approach. Therefore, the network operator is able to implement an adaptive billing strategy, depending on the effective received quality and maximizing the profit.
Exploring Embodied Learning for Early Childhood Education

This PhD project is multidisciplinary, at the intersection of Early Childhood Education, Design, and Human-Computer Interaction. The PhD goal aim at developing new approaches for early childhood education focusing on the role of body in the learning process. We leverage digital tangible and interactive technologies to develop specific scenarios that are evaluated in preschools. Our work proposes novel paradigms and uses of digital technologies currently not found in typical preschool environments.
Online News Veracity Assessment Using Emotional Weight

Trillions of data are being created every day on the Internet due to the growing number of social platforms on the World Wide Web (WWW). Processed data when given in context makes information of any knowledge. However, irresponsible use of the data or misinterpretation of data could be the reasons for false information dissemination. Many researchers from various fields, such as computer science and social science, draw their focus on assessing the veracity of information. There are many techniques to perceive this topic, for instance, social network behaviour, and semantic analysis. The common practice is using semantic analysis approach, where the syntactic structure is analysed and polarity of the texts is determined. In this paper, we approach the veracity assessment by using emotion analysis. We identified emotional states conveyed in news content and calculated the weight of each state in each news content. Contrary to popular belief, our finding showed that emotional, or affective states conveyed in false news are varied - positive and negative states. The distinct feature is the weight of the states in news content. Using multi-layer perceptron, we classified the news and achieved 90% accuracy with our collected dataset and 85% using LIAR dataset.
Multi-Class Bitcoin-Enabled Service Identification Based on Transaction History Summarization

In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.
Mobile data offloading: how much can WiFi deliver?

This is a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited about 100 iPhone users from a metropolitan area and collected statistics on their WiFi connectivity during about a two and half week period in February 2010. We find that a user is in WiFi coverage for 70% of the time on average and the distributions of WiFi connection and disconnection times have a strong heavy-tail tendency with means around 2 hours and 40 minutes, respectively. Using the acquired traces, we run trace-driven simulation to measure offloading efficiency under diverse conditions e.g. traffic types, deadlines and WiFi deployment scenarios. The results indicate that if users can tolerate a two hour delay in data transfer (e.g, video and image up-loads), the network can offload 70% of the total 3G data traffic on average. We also develop a theoretical framework that permits an analytical study of the average performance of offloading. This tool is useful for network providers to obtain a rough estimate on the average performance of offloading for a given inputWiFi deployment condition.
A High-Efficient Low-Cost Converter for Capacitive Wireless Power Transfer Systems

Growth of the Internet of Things (IoT) spurs need for new ways of delivering power. Wireless power transfer (WPT) has come into the spotlight from both academia and industry as a promising way to power the IoT devices. As one of the well-known WPT techniques, the capacitive power transfer (CPT) has the merit of low electromagnetic radiation and amenability of combined power and data transfer over a capacitive interface. However, applying the CPT to the IoT devices is still challenging in reality. One of the major issues is due to the small capacitance of the capacitive interface, which results in low efficiency of the power transfer. To tackle this problem, we present a new step-up single-switch quasi-resonant (SSQR) converter for the CPT system. To enhance the CPT efficiency, the proposed converter is designed to operate at low frequency and drive small current into the capacitive interfaces. In addition, by eliminating resistor-capacitor-diode (RCD) snubber in the converter, we reduce the implementation cost of the CPT system. Based on intensive experimental work with a CPT system prototype that supports maximum 50 W (100 V/0.5 A) power transfer, we demonstrate the functional correctness of the converter that achieves up to 93% efficiency.
A Preliminary Research of Prediction Markets Based on Blockchain Powered Smart Contracts

Prediction markets are markets where participants trade contracts whose payoffs are tied to a future event, thereby yielding prices that can be interpreted as market aggregated forecasts. Past studies have shown that the prediction markets can provide accurate forecasts, sometimes better than sophisticated statistical tools. Due to their advantages, prediction markets have been widely used in the prediction of elections, project management, product quality, and impact of events. However, prediction markets also have some limitations, e.g., poor anonymity and limited market liquidity. In this paper, we propose to apply blockchain powered smart contracts to the prediction markets. First, we give a comprehensive overview on the prediction markets, including their theoretical basis, classification and applications. Second, we present how to design prediction markets based on smart contracts. Then, the algorithm of contracts implementation is proposed. Finally, in order to verify the effectiveness of the algorithm, an intra-enterprise prediction market is built based on a private blockchain. The experimental results show that the market can make accurate prediction for a particular event. In addition, the autonomy, self-sufficiency, and decentralization characteristics of blockchain make the prediction markets more efficient and robust.
Trust Management in Fog/Edge Computing by Means of Blockchain Technologies

Trust management is extremely important for sensor networks, and the emerging vision of the Internet of Things (IoT) does not represent an exception. In fact, it allows to realize a dynamic access control needed to cope with internal attacks conducted by compromised nodes, which are likely to occur in real world. However, trust management implies a considerable consumption of energy, due to the amount of messages exchanged to collect reputation scores. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes a suitable trust management for the IoT by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such a technology is described, and a qualitative assessment of its protection degree is provided.
High-Precision Indoor Localization Based on RFID and Stepscan Floor Tiles

Location tracking is of paramount importance to many applications such as healthcare, retail and navigation. Outdoor tracking can be easily implemented using the Global Positioning System (GPS). However, indoor tracking has been a challenging problem to tackle because GPS requires the line of sight to the satellites and therefore it does not work well in indoor environments. In this paper, a high-precision indoor tracking scheme, SRK, is proposed to identify, locate, and track a person in an indoor environment at a low cost. Technically, SRK consists of three components: Stepscan floor tiles, RFID, and Kalman-filter-based prediction. The performance of SRK is investigated through extensive simulations. Our simulation results indicate that SRK increases the tracking capability by at least 24%. Furthermore, SRK helps to reduce the cost of indoor tracking significantly.
A Hierarchical Attentive Deep Neural Network Model for Semantic Music Annotation Integrating Multiple Music Representations

Automatically assigning a group of appropriate semantic tags to one music piece provides an effective way for people to efficiently utilize the massive and ever increasing on-line and off-line music data. In this paper, we propose a novel content-based automatic music annotation model that hierarchically combines attentive convolutional networks and recurrent networks for music representation learning, structure modelling and tag prediction. The model first exploits two separate attentive convolutional networks composed of multiple gated linear units (GLUs) to learn effective representations from both 1-D raw waveform signals and 2-D Mel-spectrogram of the music, which better captures informative features of the music for the annotation task than exploiting any single representation channel. The model then exploits bidirectional Long Short-Term Memory (LSTM) networks to depict the time-varying structures embedded in the description sequences of the music, and further introduces a dual-state LSTM network to encode temporal correlations between two representation channels, which effectively enriches the descriptions of the music. Finally, the model adaptively aggregates music descriptions generated at every time step with a self-attentive multi-weighting mechanism for music tag prediction. The proposed model achieves state-of-the-art results on the public MagnaTagATune music dataset, demonstrating its effectiveness on music annotation.
Fast Screen Space Curvature Estimation on GPU

Curvature is an important geometric property in computer graphics that provides information about the behavior of object surfaces. The exact curvature can only be calculated for a limited set of surfaces description. Most of the time, we deal with triangles, point sets or some other discrete representation of the surface. For those, curvature computation is problematic. Moreover, most of existing algorithms were developed for static geometry and can be slow for interactive modeling. This paper proposes a screen space method which estimates the mean and Gaussian curvature at interactive rates. The algorithm uses positions and normals to estimate the curvature from the second fundamental form matrix. Using the screen space has advantages over the classical approach: low-poly geometry can be used and additional detail can be added with normal and bump maps. The screen space curvature can be easily added to existing rendering pipelines. The proposed algorithm was tested on several models and it outperforms current state-of-the-art GPU approaches.
Secure Fair and Efficient Data Trading Without Third Party Using Blockchain

With the development of information and communication technology, the amount of data in the world is increasing dramatically every day. Now data has been recognized as a valuable asset, and monetization from valuable data becomes an urgent need. In light of this, many data trading platforms have emerged in recent years. Existing data trading schemes, however, generally rely on a third party to trade data. These schemes suffer from complex transaction process, high transaction cost, and possibly unfair exchange. In this paper, we propose two secure, fair and efficient data trading schemes that do not rely on any third party using blockchain. The first scheme achieves direct raw data exchange for large amount of data, while the second scheme achieves data statistics trading. We implement both our schemes with smart contracts, and conduct comprehensive experiments to evaluate their performance. The experiment results show that they are highly efficient in trading data: the first scheme can trade 100,000 data records within 1.51s, while the second scheme can process a statistic over a data set of 1,024 records within 1.99s.
A Sharing Bike Scheduling Optimization Algorithm Based on Two-Dimensional Dynamic Model and Improved Genetic Algorithm

Bike sharing are now one of the most hot items in the financial market, which result in the research on bicycle sharing scheduling risen in recent years. However, the study of bicycle sharing scheduling in China now only remains in the static scheduling stage, just using single objective function of route planning, ignoring the actual geographical conditions and other issues. In this context, this paper proposes a dynamic scheduling model for bicycle sharing with an improved genetic algorithm to reduce the number of routes, which could increase the number of users, and improve the overall efficiency. In addition the scheduling model can be adjusted basing on actual conditions and the urban environment. The experimental results show that this model can greatly increase the number of users and improve the level of income.
Analyzing Financial Smart Contracts for Blockchain

The recent advancement in Blockchain technology and cryp-tocurrencies like Bitcoin and Ethereum has captured interest of many researchers in academia and industry. Since its advent, the Blockchain was inherently supposed to be used in combination with cryptocurrencies but currently this technology is being used in other areas as well. One particular area based on the distributed ledger technology that has grabbed the attention of many technologist and financial marketers is “Smart Contracts”. Based on a cryptocurrency framework known as Ethereum, these smart contracts can be used for different applications such as Option Pricing, Currency Exchange, Revenue Management System, Crowd-funding and Peer-to-Peer networking. In our current effort, we have designed a smart contract and demonstrate that this smart contract could be used to take various possible positions in an European style option. We have analyzed the contract for potential security vulnerabilities when implemented in Blockchain. These option based smart contracts could be used for collateral contract services among finance industries.
Collective Influence Maximization

The omnipresence of cascading process in complex phenomena makes the identification of a small set of influential units, which is widely believed to trigger the outbreak, always an crucial issue in network science. Formulated as Influence maximization (IM) in 2003, this NP-hard problem has received a multitude of heuristic solutions with diverse angles. However, these methods are often unable to provide reliable solutions, due to the lack of an exact metric for evaluating units' contributions on cascading. In this paper, we address IM from optimal percolation and evaluate units based on the collective influence (CI), a novel metric on structural cohesive power that reflects the contributions of each unit's neighborhood on shaping collective dynamics of units over whole network. We reveal that, under probabilistic diffusion model, the structural influence power (CI value) of each node is a weighted cumulation of the diffusion probabilities from neighbors within certain hop. With the newly formulated metric CI, we propose a novel IM algorithm which chooses seeds with the largest CI values.
Determination of oestrogen concentrations in bovine plasma by a recombinant oestrogen receptor-reporter gene yeast bioassay.

A recombinant cell yeast bioassay (RCBA) was applied to the generic measurement of bovine plasma oestrogen concentration. Samples were prepared by diethyl ether extraction of plasma following addition of [3H]17 beta-oestradiol as internal standard; organic and aqueous phases were separated by freezing (recovery 97.1 +/- 0.7%) and dried extract reconstituted in culture medium (recovery 31.4 +/- 4.5%). Plasma oestrogen concentrations were measured by incubation of extracts with yeast containing a stable human oestrogen receptor (hER) and a reporter construct comprising an hER response element regulating beta-galactosidase expression. The linearity of response for the analysis of spiked plasma samples using the RCBA, following corrections, is described by y = 0.8994x - 0.111 (r2 = 0.9776, P < 0.0001). Inter-assay variation for endogenous oestrogen was 11.5% for > 1 pg ml-1. Plasma oestrogen concentrations for intact (n = 5) and castrated (n = 3) males were < 0.5 pg ml-1, and 3.7 +/- 2.6 pg ml-1 for luteal phase females (n = 10). Analysis by RCBA of sequential samples from heifers during the reproductive cycle failed to detect the pre-ovulatory increase in plasma 17 beta-oestradiol as determined by radioimmunoassay (RIA) (maximal concentrations 2.09 +/- 2.1 pg ml-1 and 32.6 +/- 14.6 pg ml-1, respectively). Interestingly, when samples were hydrolysed using Helix pomatia glucuronidase the RCBA gave concentrations (29.5 +/- 8.9 pg ml-1) not significantly different to those obtained by RIA. These preliminary findings suggest that a substantial proportion of plasma oestrogen during the pre-ovulatory period may be conjugated. These data indicate the potential of the RCBA to measure biologically active and physiological levels of plasma oestrogens in cattle. One potentially valuable application of this generic oestrogen assay could be in surveillance programmes to detect illegal use of anabolic oestrogens in live-stock where the identity of the analyte may be unknown.
Network Analysis Based on 2/4G Common Coverage Research in Rural Area

This is a new era of Mobile Internet in China, the fierce competition among domestic telecom carriers focus on the rural market of 4G network. But taking Yun-Gui Plateau as an example, the rural mountains and deep valleys, ravines complex topography and highly dispersed population distribution characteristics, greatly increased the construction difficulty of 4G network in the last mile. In this paper, based on the analysis of a great number of historical data on production, we propose a Timing Advance(TA) partition statistics & center distance calculation algorithm to overcome the lack of traditional algorithm, and design a set of formulas and coefficients to calculate and quantify the coverage degree of 4G signals under 2G coverage. Practice proves that this method can detect the problems of the present network, and guide the planning and construction of Long Term Evolution(LTE) network comprehensively and accurately.
Patch-Wise Normalization for Pose-Invariant Face Recognition from Single Sample

One of the knotty problems in face recognition is that the accuracy drops a lot in the case of recognizing faces captured in varieties of poses. In this paper, a patch-wise normalization method for non-frontal face images is proposed which is practical, effective, and can work well in continuous poses. Firstly, the correlation between 3D face model and the input 2D face image is constructed by the five facial landmarks detected from the input image. And then, a grid of key-points which are manually labelled on the 3D model are mapped onto the probe face image. Finally, for a pair of patches located at the positions which have the same semantic meaning in face images, a warp is estimated by calculating the homography matrix. The reconstructed frontal face image is obtained by stitching all patches sequentially. Recognition experiments on the FERET database proves that the proposed method is effective.
Sparse record and replay with controlled scheduling

Modern applications include many sources of nondeterminism, e.g. due to concurrency, signals, and system calls that interact with the external environment. Finding and reproducing bugs in the presence of this nondeterminism has been the subject of much prior work in three main areas: (1) controlled concurrency-testing, where a custom scheduler replaces the OS scheduler to find subtle bugs; (2) record and replay, where sources of nondeterminism are captured and logged so that a failing execution can be replayed for debugging purposes; and (3) dynamic analysis for the detection of data races. We present a dynamic analysis tool for C++ applications, tsan11rec, which brings these strands of work together by integrating controlled concurrency testing and record and replay into the tsan11 framework for C++11 data race detection. Our novel twist on record and replay is a sparse approach, where the sources of nondeterminism to record can be configured per application. We show that our approach is effective at finding subtle concurrency bugs in small applications; is competitive in terms of performance with the state-of-the-art record and replay tool rr on larger applications; succeeds (due to our sparse approach) in replaying the I/O-intensive Zandronum and QuakeSpasm video games, which are out of scope for rr; but (due to limitations of our sparse approach) cannot faithfully replay applications where memory layout nondeterminism significantly affects application behaviour.