Miracle-dz/UniFAR-FTUs huggingface dataset:
An agent architecture for intelligent information assistance

Human users trying to plan and accomplish information-dependent goals in highly dynamic environments with prevalent uncertainty must consult various types of information sources in their decision-making processes while the information requirements change as they plan and re-plan. When the users must make time-critical decisions in informationintensive tasks they become cognitively overloaded not only by the planning activities but also by the information-gathering activities at various points in the planning process. We have developed the ANTicipatory Information and Planning Agent (ANTIPA) to manage information adaptively in order to mitigate user cognitive overload. To this end, the agent brings information to the user as a result of user requests but most crucially, it proactively predicts the user’s prospective information needs by recognizing the user’s plan; pre-fetches information that is likely to be used in the future; and offers the information when it is relevant to the current or future planning decisions. This paper describes a fully implemented agent of the ANTIPA architecture using a decision-theoretic user model, and reports preliminary user study results.
Why Aristotle didn ’ t get his flu shot : Experimental measures of Prudence and Prevention ∗

Prudence preferences have been shown to influence precautionary savings, asset allocation, and optimal prevention levels. In this paper, we will collect data from an experimental setting to measure prudence non-parametrically over both gains and losses. We find that 53.1% of individuals are prudent and 15.0% are imprudent. These preferences are constant regardless of the individual’s level of risk aversion. Contrary to previous work in the literature, our results demonstrate that estimating prudence using parametric assumptions on the utility function often incorrectly categorizes risk lovers as imprudent. We also provide suggestive evidence that individuals classified as prudent are more likely to choose lower levels of prevention.
Life in the slow lane; biogeochemistry of biodegraded petroleum containing reservoirs and implications for energy recovery and carbon management

Our understanding of the processes underlying the formation of heavy oil has been transformed in the last decade. The process was once thought to be driven by oxygen delivered to deep petroleum reservoirs by meteoric water. This paradigm has been replaced by a view that the process is anaerobic and frequently associated with methanogenic hydrocarbon degradation. The thermal history of a reservoir exerts a fundamental control on the occurrence of biodegraded petroleum, and microbial activity is focused at the base of the oil column in the oil water transition zone, that represents a hotspot in the petroleum reservoir biome. Here we present a synthesis of new and existing microbiological, geochemical, and biogeochemical data that expands our view of the processes that regulate deep life in petroleum reservoir ecosystems and highlights interactions of a range of biotic and abiotic factors that determine whether petroleum is likely to be biodegraded in situ, with important consequences for oil exploration and production. Specifically we propose that the salinity of reservoir formation waters exerts a key control on the occurrence of biodegraded heavy oil reservoirs and introduce the concept of palaeopickling. We also evaluate the interaction between temperature and salinity to explain the occurrence of non-degraded oil in reservoirs where the temperature has not reached the 80–90°C required for palaeopasteurization. In addition we evaluate several hypotheses that might explain the occurrence of organisms conventionally considered to be aerobic, in nominally anoxic petroleum reservoir habitats. Finally we discuss the role of microbial processes for energy recovery as we make the transition from fossil fuel reliance, and how these fit within the broader socioeconomic landscape of energy futures.
Queen ' s University Belfast-Research Portal Speaker Recognition In Noisy Conditions With Limited Training Data

In this paper we present a novel method for performing speaker recognition with very limited training data and in the presence of background noise. Similarity-based speaker recognition is considered so that speaker models can be created with limited training speech data. The proposed similarity is a form of cosine similarity used as a distance measure between speech feature vectors. Each speech frame is modelled using subband features, and into this framework, multicondition training and optimal feature selection are introduced, making the system capable of performing speaker recognition in the presence of realistic, time-varying noise, which is unknown during training. Speaker identification experiments were carried out using the SPIDRE database. The performance of the proposed new system for noise compensation is compared to that of an oracle model; the speaker identification accuracy for clean speech by the new system trained with limited training data is compared to that of a GMM trained with several minutes of speech. Both comparisons have demonstrated the effectiveness of the new model. Finally, experiments were carried out to test the new model for speaker identification given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.
Device-orientation is more engaging than drag (at least in mobile computing)

Does device-orientation-based panning on mobile devices facilitate engagement? 20 users were asked to pan panoramas by turning around and changing the direction of the device, and by swiping with the finger on the touchscreen. The participants were also asked to rate how engaging they found it on the User Engagement Scale. It turned out that device-orientation-based panning was more engaging than drag-based panning. Moving your body to navigate information can pull you into an affective loop.
Symmetric tiling of closed surfaces: visualization of regular maps

A regular map is a tiling of a closed surface into faces, bounded by edges that join pairs of vertices, such that these elements exhibit a maximal symmetry. For genus 0 and 1 (spheres and tori) it is well known how to generate and present regular maps, the Platonic solids are a familiar example. We present a method for the generation of space models of regular maps for genus 2 and higher. The method is based on a generalization of the method for tori. Shapes with the proper genus are derived from regular maps by tubification: edges are replaced by tubes. Tessellations are produced using group theory and hyperbolic geometry. The main results are a generic procedure to produce such tilings, and a collection of intriguing shapes and images. Furthermore, we show how to produce shapes of genus 2 and higher with a highly regular structure.
Map-based adaptive foothold planning for unstructured terrain walking

This paper presents an adaptive foothold planning method for a hexapod walking robot. A local terrain map acquired with an inexpensive structured light sensor is exploited as the information source for the planning algorithm, which uses a polynomial-based approximation method to create a decision surface. The robot learns from simulations, therefore no a priori knowledge is required. The results show that the method is general enough to work on various types of terrain. The planned footholds enable the robot to walk more stable, avoiding slippages and fall-downs.
Mammogram Edge Detection Using Hybrid Soft Computing Methods

Image segmentation is a crucial step in a wide range of method image processing systems. It is useful in visualization of the different objects present in the image. In spite of the several methods available in the literature, image segmentation still a challenging problem in most of image processing applications. The challenge comes from the fuzziness of image objects and the overlapping of the different regions. Detection of edges in an image is a very important step towards understanding image features. There are large numbers of edge detection operators available, each designed to be sensitive to certain types of edges. The Quality of edge detection can be measured from several criteria objectively. Some criteria are proposed in terms of mathematical measurement, some of them are based on application and implementation requirements. Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. This can be used specifically for enhancing the tumor area in mammographic images. Different methods are available for edge detection like Roberts, Sobel, Prewitt, Canny, Log edge operators. In this paper a novel algorithms for edge detection has been proposed for mammographic images. Breast boundary, pectoral region and tumor location can be seen clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.
Instruction-level parallelism from execution interlock collapsing

An innovative technique has been developed that permits the collapsing of execution interlocks between integer ALU operations as well as between address generation operations, allowing parallel execution of two instructions, having true dependencies, in a single cycle. Given that the proposed scheme has been shown not to increase the machine cycle time, it potentially provides an attractive means for increasing the instruction--level parallelism. Preliminary results show that within the basic blocks, the geometric mean of the speedup from this new design technique is up to 10% in the integer SPEC Benchmarks. The geometric mean of the speedup including floating point benchmarks is up to 6%. The results also suggest that depending on the application environment this new design may be used as an alternative to the relatively more expensive out--of--order instruction issue approach.
An implementation of random walk algorithm to detect brain cancer in 2-d MRI

Brain is the control unit of a human body and is a highly specialized organ. It controls all the body functions. There are many diseases that could affect the normal functioning of brain and one of them is brain cancer. Some kinds of tumors are the prime cause of brain cancer. A brain tumor takes up space within the skull and can interfere with normal brain activity. It can increase pressure in the brain, shift the brain or push it against the skull, and/or invade and damage nerves and healthy brain tissue. Here random walk method is used to segment brain tissues for detection of cancerous cells.
Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology

Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. The presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using pattern recognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are
A Three-Phase Wireless-Power-Transfer System for Online Electric Vehicles With Reduction of Leakage Magnetic Fields

Wireless charging of electric vehicles based on wireless power transfer (WPT) has become increasingly popular in recent years. However, a leakage electromagnetic field (EMF) that affects other electronic devices or the human body is inevitably generated from the coils of a WPT system, and requirements regarding the suppression of the leakage EMF are increasing. In this paper, we propose a novel three-phase power line in a WPT system for the reduction of the leakage EMF. The proposed structure employs six power lines to reduce the leakage magnetic flux. This structure is compared with conventional power line structures with respect to induced voltage and magnetic field distribution. We also present analytical solutions of the EMF for the proposed and conventional power line structures. These solutions are then compared with numerical solutions using 3-D finite-element analysis and good agreement is demonstrated. The results of the numerical analysis indicate that the proposed power line can significantly reduce the leakage magnetic field from a WPT system. For verification, the proposed and conventional power lines are implemented and the EMFs are measured. A strong correlation between the numerical and experimental results is exhibited.
A Generic Approach to Video Buffer Modeling Using Discrete-Time Analysis

The large share of traffic in the Internet generated by video streaming services puts high loads on access and aggregation networks, resulting in high costs for the content delivery infrastructure. To reduce the bandwidth consumed while maintaining a high playback quality, video players use policies that control and limit the buffer level by using thresholds for pausing and continuing the video download. This allows shaping the bandwidth consumed by video streams and limiting the traffic wasted in case of playback abortion. Especially in mobile scenarios, where the throughput can be highly variant, the buffer policy can have a high impact on the probability of interruptions during video playback. To find the optimal setting for the buffer policy in each network condition, the relationship between the parameters of the buffer policy, the network throughput dynamics, and the corresponding video playback behavior needs to be understood. To this end, we model the video buffer as GI/GI/1 queue with pq-policy using discrete-time analysis. By studying the stochastic properties of the buffer-level distribution, we are able to accurately evaluate the impact of network and video bitrate dynamics on the video playback quality based on the buffer policy. We find a fundamental relationship between the bandwidth variation and the expected interarrival time of segments, meaning that overproportionately more bandwidth is necessary to prevent stalling events for high bandwidth variation. The proposed model further allows to optimize the trade-off between the traffic wasted in case of video abortion and video streaming quality experienced by the user.
Overexpression of DOC-1R Inhibits Cell Cycle G1/S Transition by Repressing CDK2 Expression and Activation

DOC-1R (deleted in oral cancer-1 related) is a novel putative tumor suppressor. This study investigated DOC-1R antitumor activity and the underlying molecular mechanisms. Cell phenotypes were assessed using flow cytometry, BrdU incorporation and CDK2 kinase assays in DOC-1R overexpressing HeLa cells. In addition, RT-PCR and Western blot assays were used to detect underlying molecular changes in these cells. The interaction between DOC-1R and CDK2 proteins was assayed by GST pull-down and immunoprecipitation-Western blot assays. The data showed that DOC-1R overexpression inhibited G1/S phase transition, DNA replication and suppressed CDK2 activity. Molecularly, DOC-1R inhibited CDK2 expression at the mRNA and protein levels, and there were decreased levels of G1-phase cyclins (cyclin D1 and E) and elevated levels of p21, p27, and p53 proteins. Meanwhile, DOC-1R associated with CDK2 and inhibited CDK2 activation by obstructing its association with cyclin E and A. In conclusion, the antitumor effects of DOC-1R may be mediated by negatively regulating G1 phase progression and G1/S transition through inhibiting CDK2 expression and activation.
Blogging Zhanaozen: hegemonic discourse and authoritarian resilience in Kazakhstan

ABSTRACT Post-Soviet authoritarian regimes – particularly in Central Asia – have proved highly resilient since independence. Existing explanations for regime longevity should be augmented by consideration of non-material, discursive sources of political legitimacy. A robust authoritarian regime requires the production and circulation of a hegemonic discourse that is internalized by influential social groups. This type of dominant discourse has emerged in Kazakhstan, making it difficult for political opponents to promote alternative political imaginaries and mobilize popular support. State control over media is challenged by Internet-based platforms, but in Kazakhstan social media and blogging have also offered an opportunity for the regime to reproduce its own hegemonic discourse. This article uses a discourse analysis of posts by bloggers in the aftermath of a violent conflict in Zhanaozen in Kazakhstan in 2011 to demonstrate how central elements in the state discourse are reproduced online, even by independent bloggers, suggesting that an official discourse has the ability to maintain its hegemonic status despite widespread use of blogs and social media.
Issues in the design and specification of class libraries

The design and specification of an extensible class library presents a difficult challenge: because extensibility comes from allowing the user to override parts of the implementation, more of the internal structure must be exposed to the user than in a typical procedure library. This raises issues in both how the library is designed and how its specification is written. Specification of the CLOS Metaobject Protocol required a combination of new and existing techniques to address these issues. We present those techniques, and discuss their relation to the underlying issues.
Hurst Exponent and Trading Signals Derived from Market Time Series

In this contribution, we investigate whether it is possible to use chaotic properties of time series in forecasting. Time series of market data have components of white noise without any trend, and they have components of brown noise containing trends. We constructed a new technical indicator MH (Moving Hurst) based on Hurst exponent that describes chaotic properties of time series. Further, we stated and proved a hypothesis that this indicator can bring more profit than the very well known indicator MACD (Moving Averages Convergence Divergence) that is based on moving averages of time series values. In our experiments, we tested and evaluated our proposal using hypothesis testing. We argue that Hurst exponent can be used as an indicator of technical analysis under considerations discussed in our paper.
Conditions on input vectors for consensus solvability in asynchronous distributed systems

This paper introduces and explores a new condition based approach to solve the consensus problem in asynchronous systems. The approach consists of identifying sets of input vectors, called conditions, for which it is possible to design a protocol solving consensus despite the occurrence of up to f process crashes. The first main result is a generic consensus protocol for the shared memory model. It always guarantees agreement, and also termination (at least) when the inputs satisfy the condition with which the protocol has been instantiated, or when there are no crashes. Then, two particular realistic conditions are defined, and proved to be maximal. The second main result is a characterization of the conditions that allow to solve consensus. The consensus protocol works for any such condition. An efficient version of the protocol is then designed for the message passing model that works when f\geq, and it is shown that no such protocol exists when f\geq n/2. It is also shown how the protocol's safety can be traded for its liveness.
Trust, reciprocity, and contract enforcement : experiments on satisfaction guaranteed

Theorists and policy analysts have convincingly argued that greater trust makes a more e¢cient society by reducing the need for costly contracts. At the same time, some experiments have suggested that reciprocity is a potent substitute for law when compliance with contracts is imperfectly enforced. This paper examines these issues within the context of a common trust-building contract device: satisfaction guaranteed. We …nd that this mechanism does indeed build trust and improve e¢ciency, but only if it is externally enforced. Paradoxically, only one side of the transaction needs
Two-Dimensional Inductive Power Transfer System for Mobile Robots Using Evenly Displaced Multiple Pickups

The inductive power transfer system for mobile robots, which has the single wire layer of easily fabricated power floor structure and the evenly displaced multiple pickup structure for receiving uniform power, is proposed. Due to its simple structure, a wide-area power floor of 3.52 m2 (1.6 m ×2.2 m) could be fabricated as a prototype. Three pickups with evenly displaced angle and space are adopted, considering the limited bottom area of the free-moving mobile robot. The size and position of each pickup are appropriately selected for the given subwinding size of the power floor and magnetic pole arrangement. Experiments for the prototype show that the proposed multiple pickup structure lowers the spatial output power variation and that enough output power of 10 W can be obtained for the mobile robots.
Olanzapine-Induced Hyperphagia and Weight Gain Associate with Orexigenic Hypothalamic Neuropeptide Signaling without Concomitant AMPK Phosphorylation

The success of antipsychotic drug treatment in patients with schizophrenia is limited by the propensity of these drugs to induce hyperphagia, weight gain and other metabolic disturbances, particularly evident for olanzapine and clozapine. However, the molecular mechanisms involved in antipsychotic-induced hyperphagia remain unclear. Here, we investigate the effect of olanzapine administration on the regulation of hypothalamic mechanisms controlling food intake, namely neuropeptide expression and AMP-activated protein kinase (AMPK) phosphorylation in rats. Our results show that subchronic exposure to olanzapine upregulates neuropeptide Y (NPY) and agouti related protein (AgRP) and downregulates proopiomelanocortin (POMC) in the arcuate nucleus of the hypothalamus (ARC). This effect was evident both in rats fed ad libitum and in pair-fed rats. Of note, despite weight gain and increased expression of orexigenic neuropeptides, subchronic administration of olanzapine decreased AMPK phosphorylation levels. This reduction in AMPK was not observed after acute administration of either olanzapine or clozapine. Overall, our data suggest that olanzapine-induced hyperphagia is mediated through appropriate changes in hypothalamic neuropeptides, and that this effect does not require concomitant AMPK activation. Our data shed new light on the hypothalamic mechanism underlying antipsychotic-induced hyperphagia and weight gain, and provide the basis for alternative targets to control energy balance.