Miracle-dz/UniFAR-FTUs huggingface dataset:
RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center

Cloud data centers and virtualization are being highly considered for enterprises and industries. However, elastic fine-grained resource provision while ensuring performance and SLA guarantees for applications requires careful consideration of important and extremely challenging tradeoffs. In this paper, we present RPPS (Cloud Resource Prediction and Provisioning scheme), a scheme that automatically predict future demand and perform proactive resource provisioning for cloud applications. RPPS employs the ARIMA model to predict the workloads in the future, combines both coarse-grained and fine-grained resource scaling under different situations, and adopts a VM-complementary migration strategy. RPPS can resolve predictive resource provisioning problem when enterprises confront demand fluctuations in cloud data center. We evaluate a prototype of RPPS with traces collected by ourselves using typical CPU intensive applications and as well as workloads from a real data center. The results show that it not only has high prediction accuracy (about 90% match in most time) but also scales the resource well.
SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes’ positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes’ positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes’ relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.
Two prosodies, two languages: infant bilingual strategies in Portuguese and Swedish

The assessment of early bilingual proficiency often involves analysis of the child’s productive lexicon at different stages in acquisition. This approach implies two assumptions: that bilingual awareness is measured by lexical production, and that the child has attained a level in development compatible with the production of equivalent lexical items in each language, before any so-called language ‘differentiation’ becomes apparent. This paper discusses these assumptions, in the light of the linguistic production of three Portuguese-Swedish bilingual siblings between the ages of 0;7 and 1;9, and argues that the children’s ability to produce distinctive prosodic and phonetic patterns associated with each language at this early stage in linguistic development provides clear evidence of bilingual awareness before and at the very outset of the one-word stage.
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.
Metabolomic changes in the rat retina after optic nerve crush.

PURPOSE To identify metabolic pathways and metabolites affected by optic nerve crush that can act as predictors of the disease or therapeutic targets. METHODS The left optic nerve of adult rats was intraorbitally crushed and retinas were dissected 24 hours or 14 days after the lesion (n = 10 per group). Metabolic profiling analysis was carried out by Metabolon, Inc. A total of 195 metabolites were unambiguously detected. Data were normalized and the regulated metabolites were identified after comparing the different conditions. Metabolite concentration changes were analyzed using single and multivariate statistical analysis to detect discriminatory metabolites. Functional clustering and meta-analysis of the regulated metabolites was run through the Metacore platform. RESULTS Comparison of 24 hours versus control, 14 days versus control samples, and 24 hours versus 14 days identified 9, 19, and 32 regulated metabolites, respectively. Single and multivariate analysis identified a total of 27 and 36 metabolites to discriminate between control and 14 days and between 24 hours and 14 days, respectively. Enrichment analysis showed alterations in the amino acid, carbohydrate, and lipid metabolism, which were further linked to translation, oxidative stress, energy (glucose and tricarboxylic acid cycle), and apoptosis through ceramide pathways. CONCLUSIONS Our analysis differentiates a set of metabolites that clearly discriminate control and early-injury samples from late-injury samples. These metabolites could have potential use as diagnostic molecules.
Automation in Mixed-Signal Design: Challenges and Solutions in the Wake of the Nano Era

The use of CMOS nanometer technologies at 65 nm and below will pose serious challenges on the design of mixed-signal integrated systems in the very near future. Rising design complexities, tightening time-to-market constraints, leakage power, increasing technology tolerances, and reducing supply voltages are key challenges that designers face. Novel types of devices, new process materials and new reliability issues are next on the horizon. We discuss new design methodologies and EDA tools that are being or need to be developed to address the problems of designing such mixed-signal integrated systems
Homological aspects of semidualizing modules

We investigate the notion of the $C$-projective dimension of a module, where $C$ is a semidualizing module. When $C=R$, this recovers the standard projective dimension. We show that three natural definitions of finite $C$-projective dimension agree, and investigate the relationship between relative cohomology modules and absolute cohomology modules in this setting. Finally, we prove several results that demonstrate the deep connections between modules of finite projective dimension and modules of finite $C$-projective dimension. In parallel, we develop the dual theory for injective dimension and $C$-injective dimension.
Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization

Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems. Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used because of the prohibitive cost. In this paper, we propose a comprehensive parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures. We also propose an insight of a GPU-based implementation of this algorithm.
Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraints

We consider synthesis of control policies that maximize the probability of satisfying given temporal logic specifications in unknown, stochastic environments. We model the interaction between the system and its environment as a Markov decision process (MDP) with initially unknown transition probabilities. The solution we develop builds on the so-called model-based probably approximately correct Markov decision process (PAC-MDP) methodology. The algorithm attains an $\varepsilon$-approximately optimal policy with probability $1-\delta$ using samples (i.e. observations), time and space that grow polynomially with the size of the MDP, the size of the automaton expressing the temporal logic specification, $\frac{1}{\varepsilon}$, $\frac{1}{\delta}$ and a finite time horizon. In this approach, the system maintains a model of the initially unknown MDP, and constructs a product MDP based on its learned model and the specification automaton that expresses the temporal logic constraints. During execution, the policy is iteratively updated using observation of the transitions taken by the system. The iteration terminates in finitely many steps. With high probability, the resulting policy is such that, for any state, the difference between the probability of satisfying the specification under this policy and the optimal one is within a predefined bound.
Immigrant Performance and Selective Immigration Policy: A European Perspective

The European Union aims at a stronger participation by its population in work to foster growth and welfare. There are concerns about the attachment of immigrants to the labour force, and discussions about the necessary policy responses. Integrated labour and migration policies are needed. The employment chances of the low-skilled are limited. Whereas Europe could benefit from a substantive immigration policy that imposes selection criteria that are more in line with economic needs, the substantial immigration into the European Union follows largely non-economic motives. This paper discusses the economic rationale of a selective immigration policy and provides empirical evidence about the adverse effects of current selection mechanisms.
The use of the Web Qualitative Data Analysis (webQDA) software to maximize the potential of qualitative analysis

Although there is a gradual positive societal change, manifested in greater gender equality, the repercussion of gender role alterations in leadership positions is slow. The purpose of this paper is to present the innovative use of WebQDA qualitative software within a national tourism study. Exploring the practicalities of completing qualitative methodology as a team of researchers, we contribute to theory development. WebQDA software was useful in accelerating coding and analysis of data, captured from seven focus groups. Since the various stages of qualitative research were carried out by several researchers in the research group, this paper questions the implications of this for knowledge production. The systematization of information, required by WebQDA, helped overcome difficulties to synthesize the various interpretations. Thus it was possible to generate more ideas and less researcher bias.
Positioning algorithms for wireless sensor networks

Position information is often one of the key requirements for a wireless sensor network (WSN) to function as intended. Due to drawbacks in using GPS or having to manually setting sensor positions, extracting the position information by means of the network itself has been extensively studied in the literature. In this approach, it is commonly assumed that there are a few sensor nodes with known positions, called reference nodes, and some type of measurements are taken between different nodes. During the last decade, a number of positioning algorithms have been proposed for the positioning problem. Evaluating the performance of various algorithms based on practical data is an important step in classifying different positioning methods. Paper A studies the performance of different approaches through real range measurements made by two ultra-wideband devices. The main result of Paper A is that no positioning algorithm uniformly shows the best performance in different situations. Paper B considers the positioning problem using received signal strength measurements when the channel parameters (transmit power and path-loss exponent) are unknown in sensor nodes. Assuming fixed values for the channel parameters, the positioning problem is formulated as finding a point in the intersection of a number of halfplanes (in a 2D network). The well-known approach projection onto convex sets (POCS) is employed to solve the problem. The proposed method gives a good coarse estimate for the positioning problem. In the literature, it has been shown that cooperation between reference nodes can improve the position estimate. To cope with difficulty in solving the optimal estimator in this type of network, which are mainly due to nonlinearity and nonconvexity issues, a two-step linear estimator is proposed in Paper C to solve the positioning problem. In the first step, a coarse estimate is obtained, and it is refined in the second step. For sufficiently high signal-to-noise-ratios, the proposed estimator attains the optimal performance; i.e., it attains the Cramer-Rao lower bound. Finally, Paper D formulates the positioning problem as a convex feasibility problem (CFP) for both non-cooperative and cooperative networks. To solve a CFP, two methods based on POCS and outer-approximation are employed (two geometric solutions). The properties of POCS for non-cooperative networks are reviewed and an upper-bound on the position error of POCS is proposed. Simulation results show that the positioning algorithms based on geometric solutions are more robust against non-line-of-sight compared to proposed statistical approaches such as nonlinear least squares.
A non-local MRF model for heritage architectural image completion

MRF models have shown state-of-the-art performance for many computer vision tasks. In this work, we propose a non-local MRF model for image completion problem. The goal of image completion is to fill user specified "target" region with patches of "source" regions in a way that is visually plausible to an observer. We represent the patches in the target region of the image as random variables in an MRF, and introduce a novel energy function on these variables. Each variable takes a label from a label set which is a collection of patches of the source region. The quality of the image completion is determined by the value of the energy function. The non-locality in the MRF is achieved through long range pairwise potentials. These long range pairwise potentials are defined to capture the inherent repeating patterns present in heritage architectural images. We minimize this energy function using Belief Propagation to obtain globally optimal image completion. We have tested our method on a wide variety of images and shown superior performance over previously published results for this task.
Similarity detection techniques for mobile platform artifacts

OF THE DISSERTATION Similarity Detection Techniques for Mobile Platform Artifacts by Amruta Gokhale Dissertation Director: Dr. Vinod Ganapathy There has been a tremendous increase in availability and use of mobile apps in recent years. Two dominant mobile platform providers, Apple and Google, have over 1.4 million applications (apps) each in their app markets as of May 2015. Such huge number of available apps gives rise to problems in managing app repositories. Also, a peculiar set of challenges is faced by the large community of mobile app developers. This dissertation describes novel solutions to two problems in the space of mobile apps. The first problem is that of fragmentation of mobile app development across multiple platforms. To accelerate the native development of same app across multiple platforms, mobile app developers need tools to better navigate across different platforms. The second problem is that of keeping app repositories free of plagiarized apps. It is in the best interest of users, developers as well as app repository owners to get rid of plagiarized apps in the app markets, which often carry malicious software with them, and thus pose a threat. Every mobile app interacts with the platform, taking advantage of the functionalities exposed by the platform. The behavior of an app can be described at a high level in terms of its interaction with the underlying platform. By monitoring the app and collecting such interaction in the form of API method invocations, we can record its high level behavior. We use this observation to build two new systems. The first system provides assistance in cross-platform mobile app development. The second system detects plagiarized mobile apps in app repositories. The
Security in Context: Analysis and Refinement of Software Architectures

Security analysis methods can provide correct yet meaningless results if the assumptions underlying the model do not conform to reality. We present an approach to analyze the security of software-intensive system architectures that focusses on making these underlying assumptions explicit, so that they can be taken into account. Starting from an Alloy model of a software architecture, a set of constraints is elicited by leveraging model relaxation techniques. These constraints form a minimal but sufficient condition that the system must meet in order to realise its security requirements. As the approach starts from the minimal guarantees that the system environment offers, it does not depend on an explicit attacker model and can take arbitrary attacker behaviour into account. As it is iterative, it is possible to constructively integrate the approach in a secure software development life cycle. Our results are illustrated by means of a case study.
Vision-aided navigation for aircrafts based on road junction detection

In this paper, a vision-aided navigation method for aircraft is proposed. The road network is detected from ground scenes provided by down-looking camera. The road junctions are extracted and referenced. Based on the principle of camera calibration, the position of the aircraft can be estimated by the road junction matching pairs in image and digital map. It is then employed to fix the accumulative error of INS. In order to improve the matching efficiency, the property of road junction and the orientation and the height output of INS are used to reduce the searching space. The simulation results have verified its feasibility.
Integrated Bias Circuits of RF CMOS Cascode Power Amplifier for Linearity Enhancement

This paper presents a highly linear differential cascode CMOS power amplifier (PA) with gate bias circuits in Common Source (CS) and Common Gate (CG) amplifiers. The proposed Class-D bias circuit at the gate of a CS amplifier injects a reshaped envelope signal only when the envelope signal is above a certain threshold voltage. This improves the linearity of the PA without significantly degrading the efficiency in a high-power region. In addition, the proposed bias circuit at the gate of a CG amplifier controls the second-order nonlinear components to improve the linearity and to reduce the sideband (IMD or ACLR) asymmetry, simultaneously. A single-stage PA including the bias circuits was fabricated using a 0.18-μ m CMOS process, with an integrated passive device (IPD) transmission line transformer (TLT). With a 3.5 V supply, the measurements show that 26.8 dBm with 43.3% PAE at -37 dBc ACLR (5 MHz offset) and 27.8 dBm with 45.8% PAE at -33 dBc ACLR (5 MHz offset) at 1.85 GHz under 3GPP WCDMA test without digital pre-distortions.
On Measuring the Impact of Human Actions in the Machine Learning of a Board Game's Playing Policies

We investigate systematically the impact of human intervention in the training of computer players in a strategy board game. In that game, computer players utilise reinforcement learning with neural networks for evolving their playing strategies and demonstrate a slow learning speed. Human intervention can significantly enhance learning performance, but carry-ing it out systematically seems to be more of a problem of an integrated game development environment as opposed to automatic evolutionary learning.
On-board velocity estimation and closed-loop control of a quadrotor UAV based on optical flow

Robot vision became a field of increasing importance in micro aerial vehicle robotics with the availability of small and light hardware. While most approaches rely on external ground stations because of the need of high computational power, we will present a full autonomous setup using only on-board hardware. Our work is based on the continuous homography constraint to recover ego-motion from optical flow. Thus we are able to provide an efficient fall back routine for any kind of UAV (Unmanned Aerial Vehicles) since we rely solely on a monocular camera and on on-board computation. In particular, we devised two variants of the classical continuous 4-point algorithm and provided an extensive experimental evaluation against a known ground truth. The results show that our approach is able to recover the ego-motion of a flying UAV in realistic conditions and by only relying on the limited on-board computational power. Furthermore, we exploited the velocity estimation for closing the loop and controlling the motion of the UAV online.
RSenter: Tool for Topics and Terms Extraction from Unstructured Data Debris

There is enormous volume of user generated content (data) today in open source repositories, online social networks, and so on that enterprises can feed on to enhance product and services delivery. Apart from the open source data, enterprises are also generating a lot of data in-house since modern business requirements are shifting from paper-base to digital records. The major setback however is that, the data is unstructured in the sense that it is in heterogeneous formats (different file types including multimedia files), it is schema less, and it is scattered on multiple sources. This condition makes knowledge discovery (a.k.a. data mining) very challenging. Previous studies have proposed the hierarchical clustering methodology since it enhances human readability and provides clear dependency structure through topics, term and document organization. But, the methodology can be resource intensive and time consuming. Our work investigates the methodology and proposes a tool called RSenter that searches based on parallelization, random walk (or linear search), pessimistic search, and optimistic search in order to generate the hierarchical structure in real time within a search space. Currently, RSenter can search through NoSQL databases and HTML documents and traverse through all the links that are connected to that HTML to the nth depth, extracting the entire user specified elements (topics and terms). Further, the tool can search through an entire repository and organize the files in a hierarchical structure regardless of the file formats.