Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Disk Authenticated Data Structures for Verifying Data Integrity on Outsourced File Storage

Several companies now provide cost-effective, indefinitely scalable file storage in the cloud. However, the problem of verifying the integrity of data on untrusted storage sites is largely unanswered. Examining Amazon’s Simple Storage Service (S3) as a case study [1], we note that there are no mechanisms incorporated into S3 that would allow a user to detect changes in stored files, due to data corruption or malicious tampering. Naive solutions, such as retaining a copy of each file for comparison, or simply maintaining checksums of each stored file, do not scale well: when dealing with very large amounts of data, even keeping a checksum for each file becomes space intensive, especially for users who seek to outsource their storage needs. In this paper, we examine prior work in developing scalable solutions to the outsourced data integrity problem. Using insights from these works, we propose a refinement of previous techniques that would allow a completely clientside implementation that provides authenticity guarantees at minimal performance and financial cost. Notably, this solution does not require the use of an external authentication server. The paper is structured as follows. In Section 2, we review the authenticated skip list data structure, explain how it can be used for data integrity applications, and review more recent advancements. In Section 3, we present a high-level overview of our new system. In Section 4, we describe our implementation in more detail. In Section 5, we provide initial performance measurements to support the viability of this system and assess the cost of implementation. Finally, in Section 6, we present a roadmap for future work.
Stratifying cancer patients based on multiple kernel learning and dimensionality reduction

In the cancer research, a number of stratification methods have been successfully applied and have assisted in the treatment process. Currently, various data types related to the cancer patients have been measured and collected. This fact leads to a great need of data integration for obtaining more comprehensive cancer study. Most of the previous work is based on a single data type and employed a tailor-made method for a specific data type. In this paper, we have proposed an efficient approach, using multiple kernel learning methods, to better stratify cancer patients. We integrated the three most related-cancer data, including gene expression, DNA methylation, and miRNA expression. The model has combined multiple kernel learning methods and dimensionality reduction. The achieved results demonstrated that our integrative model was more accurate than the ones based on single data type. Our work holds a great promise to contribute to theoretical cancer research and effectively support the prevention and prognosis.
Real-time million-synapse simulation of rat barrel cortex

Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising 50,000 neurons and 50 million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step toward tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.
Analyzing gait with spatiotemporal surfaces

Human motions generate characteristic spatiotemporal patterns. We have developed a set of techniques for analyzing the patterns generated by people walking across the field of view. After change detection, the XYT pattern can be fit with a smooth spatiotemporal surface. This surface is approximately periodic, reflecting the periodicity of the gait. The surface can be expressed as a combination of a standard parameterized surface-the canonical walk-and a deviation surface that is specific to the individual walk. <<ETX>>
Compensating Biases and a Noteworthy Success in the CMIP5 Representation of Antarctic Sea Ice Processes

Coupled Model Intercomparison Project phase 5 (CMIP5) climate models simulate a wide range of historical sea ice areas. Even models with areas close to observed values may contain compensating errors, affecting reliability of their projections. This study focuses on the seasonal cycle of sea ice, including analysis of model concentration budgets. Many models have insufficient autumn ice growth, leading to large winter biases. A subset of models accurately represent sea ice evolution year‐round. However, comparing their winter ice concentration budget to observations reveals a range of behaviors. At least one model has an accurate ice budget, which is only possible due to realistic ice drifts. The CMIP5 generation of model physics and resolution is therefore structurally capable of accurately representing processes in Antarctic sea ice. This implies that substantially improved projections of Antarctic dense ocean water formation and ice sheet melting are possible with appropriate subsetting of existing climate models.
Asset Pricing Under Endogenous Expectations in an Artificial Stock Market

We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create. And we explore the implications of this theory computationally using our Santa Fe artificial stock market. Asset markets, we argue, have a recursive nature in that agents' expectations are formed on the basis of their anticipations of other agents' expectations, which precludes expectations being formed by deductive means. Instead traders continually hypothesize---continually explore---expectational models, buy or sell on the basis of those that perform best, and confirm or discard these according to their performance. Thus individual beliefs or expectations become endogenous to the market, and constantly compete within an ecology of others' beliefs or expectations. The ecology of beliefs coevolves over time. Computer experiments with this endogenous-expectations market explain one of the more striking puzzles in finance: that market traders often believe in such concepts as technical trading, "market psychology," and bandwagon effects, while academic theorists believe in market efficiency and a lack of speculative opportunities. Both views, we show, are correct, but within different regimes. Within a regime where investors explore alternative expectational models at a low rate, the market settles into the rational-expectations equilibrium of the efficient-market literature. Within a regime where the rate of exploration of alternative expectations is higher, the market self-organizes into a complex pattern. It acquires a rich psychology, technical trading emerges, temporary bubbles and crashes occur, and asset prices and trading volume show statistical features---in particular, GARCH behavior---characteristic of actual market data.
SocialNLP 2014 The Second Workshop on Natural Language Processing for Social Media in conjunction with COLING-2014

In ironic texts what is literally said is usually negated, and in absence of an explicit negation marker. This makes social computing quite challenging. Detecting irony is very much important for NLP tasks such as polarity classification, sentiment analysis, opinion mining, or reputation analysis. There is a growing interest from the research community in investigating the impact of irony on polarity classification and sentiment analysis. A tasks will be organised at SemEval in 2015 on Sentiment Analysis of Figurative Language in Twitter (http://alt.qcri.org/semeval2015/task11). What are the linguistic patterns that users employ in social media in order to try to be ironic in just maybe 140 characters? Linguistic devices that go beyond positive or negative polarity such as ambiguity, incongruity, unexpectedness and emotional contexts have an important role as triggers of irony. In the talk I will describe how irony is employed in social media texts (Twitter, Amazon, Facebook etc.) and what are the recent stateof-the-art attempts for its automatic detection. At the end of the talk, I will address also the even more challenging and fine-grained problem of distinguishing among irony, sarcasm and satire: e.g. If you find it hard to laugh at yourself, I would be happy to do it for you. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Page numbers and proceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/
Lidar Measurements of Pollution Above the Canadian Oil Sands

ii Dedication . v Acknowledgments . vi Table of
Applications of low-rank compressed seismic data to full-waveform inversion and extended image volumes

Conventional oil and gas fields are increasingly difficult to explore and image, resulting in the call for more complex wave-equation-based inversion algorithms that require dense long-offset samplings. Consequently, there is an exponential growth in the size of data volumes and prohibitive demands on computational resources. We have developed a method to compress and process seismic data directly in a low-rank tensor format, which drastically reduces the amount of storage required to represent the data. Seismic data exhibit a low-rank structure in a particular transform domain, which can be exploited to compress the dense data in one extremely storage-efficient tensor format when the data are fully sampled or can be interpolated when the data have missing entries. In either case, once our data are represented in the compressed tensor form, we have developed an algorithm to extract source or receiver gathers directly from the compressed parameters. This extraction process can be done on the fly directly on the compressed data, and it does not require scanning through the entire data set to form shot gathers. We apply this shot-extraction technique in the context of stochastic full-waveform inversion as well as forming full subsurface image gathers through probing techniques and reveal the minor differences between using the full and compressed data, while drastically reducing the total memory costs.
Intelligent emotion recognition system using brain signals (EEG)

Human emotion is a complex and psycho physiological state of mind which can be expressed as positive or negative reactions to external and internal stimuli. Typical communication channels that indicate emotions are voice and facial expressions. People who are paralyzed or have other severe movement disorders have no way of expressing their emotions thereby forming a wide communication rift between them and the outside world. Communication through eye tracking is one of the alternative ways of giving such disabled patients to interact with the outside world. This paper investigates the possibility of recognizing emotions using signal processing of Electroencephalography using discrete wavelet transform and feeding appropriate values to an adaptive neuro fuzzy inference system for classification. The system enables severely disabled as well as able users to interact with the system using eye movement in order to respond to detected emotion. The solution can be used to detect emotions of motor disabled people and provision a means of communication; also it is a learning tool for trainee neurologists. The prototype was built using Matlab successfully and it was evaluated by experts and the intended users very creditably stating it as a useful software for disabled people.
Sequence discriminative training for low-rank deep neural networks

Deep neural networks (DNNs) have proven very successful for automatic speech recognition but the number of parameters tends to be large, leading to high computational cost. To reduce the size of a DNN model, low-rank approximations of weight matrices, computed using singular value decomposition (SVD), have previously been applied. Previous studies only focused on clean speech, whereas the additional variability in noisy speech could make model reduction difficult. Thus we investigate the effectiveness of this SVD method on noisy reverberated speech. Furthermore, we combine the low-rank approximation with sequence discriminative training, which further improved the performance of the DNN, even though the original DNN was constructed using a discriminative criterion. We also investigated the effect of the order of application of the low-rank and sequence discriminative training. Our experiments show that low rank approximation is effective for noisy speech and the most effective combination of discriminative training with model reduction is to apply the low rank approximation to the base model first and then to perform discriminative training on the low-rank model. This low-rank discriminatively trained model outperformed the full discriminatively trained model.
Data gathering mechanism with local sink in geographic routing for wireless sensor networks

Most existing geographic routing protocols on sensor networks concentrates on finding ways to guarantee data forwarding from the source to the destination, and not many protocols have been done on gathering and aggregating data of sources in a local and adjacent region. However, data generated from the sources in the region are often redundant and highly correlated. Accordingly, gathering and aggregating data from the region in the sensor networks is important and necessary to save the energy and wireless resources of sensor nodes. We introduce the concept of a local sink to address this issue in geographic routing. The local sink is a sensor node in the region, in which the sensor node is temporarily selected by a global sink for gathering and aggregating data from sources in the region and delivering the aggregated data to the global sink. We next design a Single Local Sink Model for determining optimal location of single local sink. Because the buffer size of a local sink is limited and the deadline of data is constrained, single local sink is capable of carrying out many sources in a large-scale local and adjacent region. Hence, we also extend the Single Local Sink Model to a Multiple Local Sinks Model. We next propose a data gathering mechanism that gathers data in the region through the local sink and delivers the aggregated data to the global sink. Simulation results show that the proposed mechanism is more efficient in terms of the energy consumption, the data delivery ratio, and the deadline miss ratio than the existing mechanisms.
Robust Geometry Estimation Using the Generalized Voronoi Covariance Measure

The Voronoi Covariance Measure of a compact set K of R^d is a tensor-valued measure that encodes geometric information on K and which is known to be resilient to Hausdorff noise but sensitive to outliers. In this article, we generalize this notion to any distance-like function delta and define the delta-VCM. We show that the delta-VCM is resilient to Hausdorff noise and to outliers, thus providing a tool to estimate robustly normals from a point cloud approximation. We present experiments showing the robustness of our approach for normal and curvature estimation and sharp feature detection.
Land suitability analysis for decision-making in cassava (Manihot Spp.) cultivation in southern part of Adamawa State, Nigeria

This study assessed the physical land suitability for cassava cultivation in southern part of Adamawa State using Multi-criteria evaluation and GIS technique. Within the study area, the production of cassava is mainly for food and there are only little opportunities for its commercial development. Therefore it is necessary to carry out land suitability analysis in order to provide information on the study area that would guide in sustaining long term production of cassava on commercial basis. The environmental variables examined were Mean Annual rainfall, Mean Temperature, Length of rainfall, Relief and Soil. The primary data were sourced by means of field survey to obtain the coordinates of the current cassava growing areas for mapping. The five factor maps were reclassified based on environmental requirement of cassava crop in the IDRISI Taiga environment and different weights were assigned to each factor to represent their relative importance using the pair-wise comparison matrix. The result, which is explicitly revealed on a single map, indicates that the areas that are suitable for cassava cultivation constitutes 65.92% while those that are not suitable amounts to 34.08%. Similarly, the current cassava growing areas were mapped and the areas identified are Dissol, Mbulo, Maitani, Farang, Timdore and Wadore which fell within the suitable category. The study therefore recommends that cassava production should be encouraged among farmers so as to utilize the proportion of land area found to be suitable for cassava production in Southern Adamawa State. Keywords: Land suitability, Cassava cultivation, Multi-criteria evaluation, Pair-wise Comparison Matrix, IDRISITaiga
Visual force sensing with flexible nanowire buckling springs

A calibrated method of force sensing is demonstrated in which the buckled shape of a long flexible metallic nanowire, referred to as a ‘nanoneedle’, is interpreted to determine the applied force. An individual needle of 157 nm diameter by 15.6 µm length is grown on an atomic force microscope (AFM) cantilever with a desired orientation (by the method of Yazdanpanah et al 2005 J. Appl. Phys. 98 073510). Using a nanomanipulator the needle is buckled in the chamber of a scanning electron microscope (SEM) and the buckled shapes are recorded in SEM images. Force is determined as a function of deflection for an assumed elastic modulus by fitting the shapes using the generalized elastica model (De Bona and Zelenika 1997 Proc. Inst. Mech. Eng. C 211 509–17). In this calibration the elastic modulus (68.3 GPa) was determined using an auxiliary AFM measurement, with the needle in the same orientation as in the SEM. Following this calibration the needle was used as a sensor in a different orientation than the AFM coordinates to deflect a suspended PLLA polymer fiber from which the elastic modulus (2.96 GPa) was determined. The practical value of the sensing method does depend on the reliability and ruggedness of the needle. In this study the same needle remained rigidly secured to the AFM cantilever throughout the entire SEM/AFM calibration procedure and the characterization of the nanofiber.
New developments in EPOS tools for configuring and generating embedded systems

Embedded systems usually run dedicated applications in highly restricted environments. This paper describes our approach to configure and generate embedded systems and a tool to assist this process that is being used with EPOS (Embedded and Parallel Operating System), an OS developed using AOSD. This tool receives a high level specification of the application and builds the necessary computational support for it. This paper describes some improvements to the tool that are enabling this process to be done automatically. Co-design, design space exploration and partitioning techniques were modeled as independent components, which allow to modify the implementation technique used in each step of this process and to use it to compose a new tool without changing the whole developing chain. Our main contribution is in the development of highly portable and application-oriented embedded systems using AOSD in a semi-automatized process guided by our tool.
Delay-Minimal Transmission for Energy Constrained Wireless Communications

We investigate the problem of minimizing the overall transmission delay of data packets in a single-user wireless communication system, where the transmitter has a fixed amount of energy to transmit all of the data packets. We consider two different scenarios. In the first scenario, we assume that packets arrive randomly at the transmitter. We propose two different approaches to solve this problem. First, we develop an iterative algorithm that allocates the total energy of the transmitter to its individual packets, in a way to minimize the total delay. As a second approach, we develop a dynamic programming formulation for the problem. In the second scenario, we assume that all of the packets have already arrived before the transmission starts. In this situation, the cost function has a fixed form, and is convex and differentiable. In this scenario, the iterative algorithm we develop is guaranteed to converge to the unique global optimal solution.
Bioelectrochemical stimulation of petroleum hydrocarbon degradation in saline soil using U‐tube microbial fuel cells

Bioremediation is a cost‐effective and eco‐friendly approach to decontaminate soils polluted by petroleum hydrocarbons. However, this technique usually requires a long time due to the slow degradation rate by bacteria. By applying U‐tube microbial fuel cells (MFCs) designed here, the degradation rate of petroleum hydrocarbons close to the anode (<1 cm) was enhanced by 120% from 6.9 ± 2.5% to 15.2 ± 0.6% with simultaneous 125 ± 7 C of charge output (0.85 ± 0.05 mW/m2, 1 kΩ) in the tested period (25 days). Hydrocarbon fingerprint analysis showed that the degradation rate of both alkanes and polycyclic aromatic hydrocarbons (PAHs) was accelerated. The decrease of initial water content from 33% to 28% and 23% resulted in a decrease on charge output and hydrocarbon degradation rate, which could be attributed to the increase of internal resistance. A salt accumulation was observed in each reactor due to the evaporation of water from the air‐cathode, possibly inhibited the activity of exoelectrogenic bacteria (EB) and resulted in the elimination of the current at the end of the tested period. The number of hydrocarbon degradation bacteria (HDB) in soil close to the anode increased by nearly two orders of magnitude in the MFC assisted system (373 ± 56 × 103 CFU/g‐soil) than that in the disconnected control (8 ± 2 × 103 CFU/g‐soil), providing a solid evidence for in situ biostimulation of HDB growth by colonization of EB in the same system. Biotechnol. Bioeng. 2012; 109:426–433. © 2011 Wiley Periodicals, Inc.