Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Minimize Computational and Communicational Overhead in Cloud

Cloud computing has been envisioned as the next generation information technology architecture for enterprises. Cloud Computing moves the data on the cloud storage servers maintained by service providers, which deprive the user of their control of the physical possession of data, even though they are the owners of the data. This unique feature however has raised many new security challenges which have not been well understood. Efficient verification techniques are of significant importance for cloud customers to validate data integrity and availability are not applicable on the outsourced data without having a local copy of the data. Downloading all outsourced data in order to validate its integrity is impractical for the excessive I/O cost and the high communication overhead across the network Therefore efficient techniques are needed for this. In this paper, we introduce a framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates on the stored data.
Hardware Compilation from an RTL to a Storage Logic Array Target

This paper treats the automatic translation of register transfer level (RTL) descriptions of digital systems to VLSI realization. The target technology is the storage logic array or SLA. The approach is aimed at applications where the emphasis is on reducing engineering effort and design turnaround time rather than maximizing chip area utilization. The paper develops a mapping between the register transfer language, AHPL, and the SLA. It is shown that each primitive explicitly appearing in an AHPL description can be mapped into an area of real estate in an SLA realization. A detailed development of some of the algorithms is presented. The entire process has been successfully implemented and applied to a set of examples. This is accomplished by developing a final stage for an already existing three-stage multi-application compiler for AHPL. Layout and routing are shown to be a single optimization process if the hardware target is an SLA.
Secure self-certified COTS

With the advent and the rising popularity of networks, Internet, intranets and distributed systems, security is becoming one of the major concerns in IT research. An increasing number of approaches have been proposed to ensure the safety and security of programs. Among those approaches, certified code seems to be the most promising. Unfortunately, as of today, most of the research on certified code have focused on simple type safety and memory safety, rather than security issues. We therefore propose to extend this approach to the security aspects of a program. Our intention is to use such an approach as an efficient and realistic solution to the problem of malicious code detection in COTS. In this paper, we present our progress in defining and implementing a certifying compiler that produces a secure self-certified code that can be used to ensure both safety and security of the code.
Se p 20 00 Canonical approach to 2 D induced gravity

Using canonical method the Liouville theory has been obtained as a grav-itational Wess-Zumino action of the Polyakov string. From this approach it is clear that the form of the Liouville action is the consequence of the bosonic representation of the Virasoro algebra, and that the coefficient in front of the action is proportional to the central charge and measures the quantum braking of the classical symmetrie.
MegaPipe: A New Programming Interface for Scalable Network I/O

We present MegaPipe, a new API for efficient, scalable network I/O for message-oriented workloads. The design of MegaPipe centers around the abstraction of a channel - a per-core, bidirectional pipe between the kernel and user space, used to exchange both I/O requests and event notifications. On top of the channel abstraction, we introduce three key concepts of MegaPipe: partitioning, lightweight socket (lwsocket), and batching.We implement MegaPipe in Linux and adapt memcached and nginx. Our results show that, by embracing a clean-slate design approach, MegaPipe is able to exploit new opportunities for improved performance and ease of programmability. In microbenchmarks on an 8-core server with 64 B messages, MegaPipe outperforms baseline Linux between 29% (for long connections) and 582% (for short connections). MegaPipe improves the performance of a modified version of memcached between 15% and 320%. For a workload based on real-world HTTP traces, MegaPipe boosts the throughput of nginx by 75%.
Ontology-Guided Image Interpretation for GEOBIA of High Spatial Resolution Remote Sense Imagery: A Coastal Area Case Study

Image interpretation is a major topic in the remote sensing community. With the increasing acquisition of high spatial resolution (HSR) remotely sensed images, incorporating geographic object-based image analysis (GEOBIA) is becoming an important sub-discipline for improving remote sensing applications. The idea of integrating the human ability to understand images inspires research related to introducing expert knowledge into image object–based interpretation. The relevant work involved three parts: (1) identification and formalization of domain knowledge; (2) image segmentation and feature extraction; and (3) matching image objects with geographic concepts. This paper presents a novel way that combines multi-scaled segmented image objects with geographic concepts to express context in an ontology-guided image interpretation. Spectral features and geometric features of a single object are extracted after segmentation and topological relationships are also used in the interpretation. Web ontology language–query language (OWL-QL) formalize domain knowledge. Then the interpretation matching procedure is implemented by the OWL-QL query-answering. Compared with a supervised classification, which does not consider context, the proposed method validates two HSR images of coastal areas in China. Both the number of interpreted classes increased (19 classes over 10 classes in Case 1 and 12 classes over seven in Case 2), and the overall accuracy improved (0.77 over 0.55 in Case 1 and 0.86 over 0.65 in Case 2). The additional context of the image objects improved accuracy during image classification. The proposed approach shows the pivotal role of ontology for knowledge-guided interpretation.
The state of play: how commensurate are BTECs and A-levels in sport and physical education

Abstract The somewhat forever-changing landscape of education in England has recently challenged the post-16 sector in new ways, with funding and a knowledge-based curriculum just some of the new initiatives institutions must acclimatise to. Sport provision, a generic term used in this paper to encompass level three sport and physical education programmes, is not in limbo, but certainly faces challenges. This research focused on exploring the current ‘state of play’ of post-16 sport provision. Thirteen semi-structured interviews were carried out on teachers focusing solely on the Advanced (A)-level and Business and Technology Education Council (BTEC) national routes. Interviews were recorded and transcribed verbatim. The data were analysed using the six phases outlined by Braun and Clarke in the form of a thematic analysis. The main findings were that the content is perceived to be challenging due to the depth and detail of knowledge required on topic areas investigated on the A-level route, in contrast to the breadth, related to the number of units of study on the BTEC national programmes. Practical knowledge also emerged as a key theme, with results indicating a diminished importance of the practical aspects of both programmes, and conflicting views were evident on how practical knowledge should be assessed, with some preferring the grading of practical performance only and others preferring wider aspects of performance to feature in the grade awarded. Finally, post-16 options in sport and PE were perceived to be ‘a positive thing’ but the caveat with this is the concerns regarding programme symmetry and how commensurate respective programmes are.
Diurnal variation and radiative influence of Martian water ice clouds

We have identified regions in the Martian tropics with anomalously warm nighttime surface temperatures. The seasonal evolution of these anomalies is strongly correlated with the waxing and waning of the tropical cloud belt that is most prominent during Northern Hemisphere summer. We attribute the anomalies to enhanced downward infrared radiation from water ice clouds. The close agreement with spatial maps of atmospheric extinction derived from Mars Orbiter Laser Altimeter radiometry strongly supports this interpretation. We show that a Mars general circulation model simulation with radiatively active water ice clouds is able to reasonably match the observed spatial pattern and amplitude of the surface temperature anomaly. The nighttime clouds are most prominent in the Tharsis and Arabia regions and are thicker (optical depth ∼ 1) and more extensive than daytime clouds. Our cloud retrievals are the first to spatially map the nighttime clouds and provide an estimate of their thermal influence.
Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.
A sentiment classification method for Chinese document

For the great demand of analyzing and mining user's opinion, this paper presents a novel sentiment classification method for Chinese document. Firstly, the subjective documents are pre-processed and the sentiment features are extracted; then it calculates the basic polarity of the sentiment features based on a sentiment word dictionary, meanwhile dynamic sentiment word's polarity is judged and polarity adjustment is carried out according to the context information; finally, the overall polarity of document is determined by all sentiment words in it. Experiment of comparing with BaseLine and SVM shows the effectiveness of the proposed method.
Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Memetic Algorithms with Variable-depth Search to Overcome Local Optima Memetic Algorithms with Variable-depth Search to Overcome Local Optima

Variable-depth search (VDS) is well-known as Lin-Kernighan strategy for the TSP and Kernighan-Lin for graph partitioning. The basic idea is to make a sequence of local moves and to freeze all moved combinatorial objects to prevent the search from looping. VDS stops when no further local move is possible and returns a best found solution. We analyze memetic algorithms with VDS for three binary combina-torial problems: Mincut, Knapsack, and Maxsat. More precisely, we focus on simply structured problem instances containing local optima that are very hard to overcome. Many common trajectory-based algorithms fail to find a global optimum: the (1+1) EA, iterated local search, and simulated annealing need exponential time with high probability. However, memetic algorithms using VDS easily manage to find a global optimum in expected polynomial time. These results strengthen the usefulness of memetic algorithms with VDS from a theoretical perspective.
The Internet of Things Resource Management Challenge

Caused by the proliferation of the (IoT) and its related application domains such as Building Automation or E-Health, users face a continuously increasing amount of heterogeneous sensors and devices deployed to their environment. As a result, a large variety of protocols, data formats and physical sensing resources needs to be managed in order to gain benefit from the deployed devices. This raises the question how the resources provided by the devices can be efficiently managed and provisioned. Related concepts like on-demand provisioning, elasticity, or resource pooling and sharing are already known from the Cloud Computing domain. This paper presents the Device Cloud approach, which aims at mitigating the IoT resource management issues by applying Cloud Computing concepts to the IoT domain. Similar to the Pay-as-you-Go paradigm, the Device Cloud allows users to allocate devices from a shared resource pool on-demand. Sensors and devices are not just integrated with the Cloud by being enabled to utilize Cloud services. Instead, the physical (IoT) resources become an integral part of the Cloud resource pool and are shared and provisioned like regular Infrastructure as a Service (IaaS) Cloud resources.
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction

In the era of big data, optimizing large scale machine learning problems becomes a challenging task and draws significant attention. Asynchronous optimization algorithms come out as a promising solution. Recently, decoupled asynchronous proximal stochastic gradient descent (DAP-SGD) is proposed to minimize a composite function. It is claimed to be able to off-loads the computation bottleneck from server to workers by allowing workers to evaluate the proximal operators, therefore, server just need to do element-wise operations. However, it still suffers from slow convergence rate because of the variance of stochastic gradient is nonzero. In this paper, we propose a faster method, decoupled asynchronous proximal stochastic variance reduced gradient descent method (DAP-SVRG). We prove that our method has linear convergence for strongly convex problem. Large-scale experiments are also conducted in this paper, and results demonstrate our theoretical analysis.
Adaptive Design of Sampling Directions in Diffusion Tensor MRI and Validation on Human Brain Images

Diffusion tensor reconstruction is made possible through the acquisition of several diffusion weighted images, each corresponding to a given sampling direction in the Q-space. In this study, we address the question of sampling efficiency, and show that in case we have some prior knowledge on the diffusion characteristics, we may be able to adapt the sampling directions for better reconstruction of the diffusion tensor. The prior is a tensor distribution function, estimated over a given region of interest, possibly on several subjects. We formulate an energy related to error on tensor reconstruction, and calculate analytical gradient expression for efficient minimization. We validate our approach on a set of 5199 tensors taken within the corpus callosum of the human brain, and show improvement by an order of 10% on the MSE of the reconstructed tensor.
Creating configurations for testing radiation portal algorithms using factor covering combinatorial designs

Most ports in the United States now inspect a large number of goods for radioactive cargo to address the potential smuggling of illicit nuclear material for terrorism. The U.S. Department of Homeland Security (DHS) has sponsored research to develop systems for detection of illicit nuclear materials at the ports. We present a systematic approach for testing the efficacy of different algorithms used in portals to identify Special Nuclear Materials (SNM) in presence of a number of other confounding factors including background radiation and naturally occurring radiation material (NORM). Algorithms are sensitive to several factors, and for realistic situations, the number of factors is large and exhaustive testing using full factorial designs is impractical. Our method, instead, extends factor covering combinatorial design to create a smaller number of configurations to cover all pairwise or 3-way factor combinations. Further, our method leverages the inherent hierarchy amongst the factors to iteratively develop a more nuanced model using a notion called seed designs. This paper presents representative factors and their values which impacts the performance of the detection algorithms, provides details of the new testing method, and describes how the approach can be used to generate various configurations for evaluating the performance of various detection algorithms.
Energy-Efficient Data Gathering in Query Processing of Sensor Networks

Networked sensor systems of large scale are becoming available in the foreseeable future and provide economical and practical solutions for data collection and environment monitoring. A high-level abstraction of sensor network is to model it as a distributed database system. Query thus can be utilized to gather data from the system. Queries for sensor networks need to be carefully designed, in order to minimize the power consumption and maximize the lifetime. This paper presents a novel method for energy-efficient data gathering in wireless sensor networks. By taking advantage of the delay constraint specified with a query, the method can find the optimal combinations of sending sensor data from nodes to sink, with or without performing data processing. System models for energy and time consumptions of both computation and communication are created. Problem is formalized and algorithm is described in detail. Finally, performance of the proposed method is evaluated. It shows that the proposed method achieves better performance than without performing the optimization.
Compound Wiretap Channels

This paper considers the compound wiretap channel, which generalizes Wyner's wiretap model to allow the channels to the (legitimate) receiver and to the eavesdropper to take a number of possible states. No matter which states occur, the transmitter guarantees that the receiver decodes its message and that the eavesdropper is kept in full ignorance about the message. The compound wiretap channel can also be viewed as a multicast channel with multiple eavesdroppers, in which the transmitter sends information to all receivers and keeps the information secret from all eavesdroppers. For the discrete memoryless channel, lower and upper bounds on the secrecy capacity are derived. The secrecy capacity is established for the degraded channel and the semideterministic channel with one receiver. The parallel Gaussian channel is further studied. The secrecy capacity and the secrecy degree of freedom () are derived for the degraded case with one receiver. Schemes to achieve the for the case with two receivers and two eavesdroppers are constructed to demonstrate the necessity of a prefix channel in encoder design. Finally, the multi-antenna (i.e., MIMO) compound wiretap channel is studied. The secrecy capacity is established for the degraded case and an achievable is given for the general case.
Genetic Diversity and Drug Resistance Mutations in HIV-1 from Untreated Patients in Niamey, Niger

The objective of the study was to estimate the prevalence of transmitted resistance to antiretroviral of HIV-1 circulating in Niger. We collected plasmas from 96 drug-naive patients followed up in the main HIV/AIDS Care Center of Niamey, the capital city of Niger. After RNA extraction and retrotranscription to proviral DNA, nested PCR was performed to amplify PR (codons 1–99) and RT (codons 1–240) fragments for sequencing. Sequences were analysed for phylogeny, then for resistance-associated mutations according to IAS-USA and Stanford's lists of mutations. We characterized six HIV-1 genetic variants: CRF02-AG (56.3%), CRF30_0206 (15.6%), subtype G (15.6%), CRF06_cpx (9.4%), CRF11_cpx (2.1%), and CRF01_AE (1%). About 8.3% of HIV strains had at least 1 resistance mutation: 4 strains with at least 1 mutation to NRTI, 5 for NNRTI, and 1 for PI, respectiveley 4.2%, 5.2%, and 1.0%. These preliminary results gave enough information for the need of instauring HIV drug resistance national surveillance.
Bosonization and Strongly Correlated Systems

This volume provides a detailed account of bosonization. The first part of the book examines the technical aspects of bosonization including one-dimensional fermions, the Gaussian model, the structure of Hilbert space in conformal theories, Bose-Einstein condensation in two dimensions, non-Abelian bosonization, and the Ising and WZNW models. The second part presents applications of the bosonization technique to realistic models including the Tomonaga-Luttinger liquid, spin liquids in one dimension and the spin-1/2 Heisenberg chain with alternative exchange. The third part addresses the problems of quantum impurities. Chapters cover potential scattering, the X-ray edge problem, impurities in Tomonaga-Luttinger liquids and the multi-channel Kondo problem.
Effects of Ethanol and NAP on Cerebellar Expression of the Neural Cell Adhesion Molecule L1

The neural cell adhesion molecule L1 is critical for brain development and plays a role in learning and memory in the adult. Ethanol inhibits L1-mediated cell adhesion and neurite outgrowth in cerebellar granule neurons (CGNs), and these actions might underlie the cerebellar dysmorphology of fetal alcohol spectrum disorders. The peptide NAP potently blocks ethanol inhibition of L1 adhesion and prevents ethanol teratogenesis. We used quantitative RT-PCR and Western blotting of extracts of cerebellar slices, CGNs, and astrocytes from postnatal day 7 (PD7) rats to investigate whether ethanol and NAP act in part by regulating the expression of L1. Treatment of cerebellar slices with 20 mM ethanol, 10−12 M NAP, or both for 4 hours, 24 hours, and 10 days did not significantly affect L1 mRNA and protein levels. Similar treatment for 4 or 24 hours did not regulate L1 expression in primary cultures of CGNs and astrocytes, the predominant cerebellar cell types. Because ethanol also damages the adult cerebellum, we studied the effects of chronic ethanol exposure in adult rats. One year of binge drinking did not alter L1 gene and protein expression in extracts from whole cerebellum. Thus, ethanol does not alter L1 expression in the developing or adult cerebellum; more likely, ethanol disrupts L1 function by modifying its conformation and signaling. Likewise, NAP antagonizes the actions of ethanol without altering L1 expression.
Relative protein quantitation with post translational modifications in mass spectrometry based proteomics

Mass spectrometry has become the tool of choice for most investigations in proteomics. Identification of proteins from complex mixtures has long been achieved and is now routinely used in countless high throughput studies. Quantitation by mass spectrometry is comparably newer and many different strategies have been proposed. One such strategy quantitates the difference in protein expression level among samples via extracted ion chromatograms, or spectral counts or a combination thereof. Another strategy involves mass modifications of the analytes in one or more of the samples under investigation. MSMAG has been developed as an extension to 2DB and it has been shown that it can aid in quantitation of data from experiments employing label-free quantitation. Recently, it has been extended to allow for analysis of data based on labelling strategies. This also makes it possible to quickly visualize and investigate inherent mass differences as presented by post translational modifications.