Miracle-dz/UniFAR-FTUs huggingface dataset:
Caching scheme based on user clustering and user requests prediction in small cells

In order to cope with the relentless data tsunami brought by the growth of mobile video service, this paper proposes a two-stage caching scheme. Considering the characteristic of the ultra-dense network that many users may be in the overlapping coverage of multiple small cells, we cluster those users with k-medoids clustering algorithm and the users of the same cluster access to the same matching small cell in the first phase. In the second phase, a cache algorithm is proposed to make cache decision of video files on the basis of predicting the future requests of the users in the same small cell by collaborative filtering algorithm. At last, the simulation results show that the gain of the proposed caching scheme over the existing caching schemes in the cache hit rate and the average delay.
Analysis of Circuit Aging on Accuracy Degradation of Deep Neural Network Accelerator

Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate the deployment of deep learning in portable and smart wearable devices. To overcome the fundamental challenges of power and resource limitation, application-specific integrated circuit accelerators have emerged to compact the model and use lower precision arithmetic to increase the throughput of computation with reduced power consumption. Although very high energy efficiency has been achieved by removing redundant weights, compressing data and even sacrificing timing margin, such trend in hardware acceleration that pushes the deep learning systems to the error threshold can be disastrous for the tasks they performed due to failure or degraded performance of circuit components. Concerned by the lack of attention on the evolving unreliability effects in artificial intelligent accelerators implemented by the continuously scaled CMOS technology, this paper is the first to evaluate the effect of circuit aging on performance degradation of deep learning accelerator. Our findings indicate that DNN system running at their peak throughput rate can experience up to 84% accuracy drop after a year of aging and the accumulation of errors aggravates with the depth of learning. It is also found that relaxation of throughput rate can slow down the loss of classification accuracy considerably.
Extended-State-Observer-Based Chattering Free Sliding Mode Control for Nonlinear Systems With Mismatched Disturbance

This paper presents a novel chattering free sliding mode control method for a class of nonlinear systems in the presence of matched disturbance and mismatched disturbance based on an extended state observer. First, a nonlinear extended state observer based on the hyperbolic tangent function is constructed to estimate the mismatched disturbance. And the second order extended state observer error system is proved to be bounded stable by using Lyapunov stability theory. Then a novel chattering free sliding mode surface integrated with the disturbance estimation is designed for this system even with mismatched disturbance. The theoretical analysis indicates that the system states can be stabilized to a neighborhood of the equilibrium point under the proposed controller. Compared with the traditional chattering free sliding mode control method, the presented control strategy can expose satisfactory control performance with chattering reduction and mismatched disturbance suppression. Finally, simulation results demonstrate the effectiveness of the proposed method.
Toward Online Testing of Federated and Heterogeneous Distributed Systems

Making distributed systems reliable is notoriously difficult. It is even more difficult to achieve high reliability for federated and heterogeneous systems, i.e., those that are operated by multiple administrative entities and have numerous inter-operable implementations. A prime example of such a system is the Internet's inter-domain routing, today based on BGP. We argue that system reliability should be improved by proactively identifying potential faults using an online testing functionality. We propose DiCE, an approach that continuously and automatically explores the system behavior, to check whether the system deviates from its desired behavior. DiCE orchestrates the exploration of relevant system behaviors by subjecting system nodes to many possible inputs that exercise node actions. DiCE starts exploring from current, live system state, and operates in isolation from the deployed system. We describe our experience in integrating DiCE with an opensource BGP router. We evaluate the prototype's ability to quickly detect origin misconfiguration, a recurring operator mistake that causes Internet-wide outages. We also quantify DiCE's overhead and find it to have marginal impact on system performance.
Bridging the LAPPS Grid and CLARIN

The Andrew K. Mellon Foundation has funded a project to create a “trust network” between the Language Applications (LAPPS) Grid, a major framework for composing pipelines of natural language processing (NLP) tools, and the WebLicht workﬂow engine hosted by the CLARIN-D Center in T¨ubingen. The project also includes integration of NLP services available from the LINDAT/CLARIN Center in Prague. The goal is to allow users on one side of the bridge to gain appropriately authenticated access to the other and enable seamless communication among tools and resources in both frameworks. The resulting “meta-framework” provides users across the globe with access to an unprecedented array of language processing facilities that cover multiple languages, tasks, and applications, all of which are fully interoperable.
Dynamic Optimization for Web Page Based on User's Estimated Browsing Intention

When a web designer designs a web page, they decide the order and layout of the elements for the user. Multiple users who use the same web page have varying purposes. The order and layout decided by the designer is not necessarily effective for all users' intentions. The purpose of this study is to estimate the mental model of the user visiting a web page and to change the functionality of the web browser to optimize the order and layout of the web components in real time, based on the estimation result. In this paper, we propose a feature-modeling of the page elements (the basis for realizing this function) and an optimization method using a simple mental model.
Playing games with reinforcement learning via perceiving orientation and exploring diversity

The reinforcement learning can guide the agents to perform optimally under various complex environments. Although reinforcement learning has brought breakthrough for many domains, they are constrained by two bottlenecks: extremely delayed reward signal and the trade-off between diversity and speed. In this paper, we propose a novel framework to alleviate those two bottlenecks. For the delayed reward, we introduce a new term, named the orientation perception term, to calculate the award for each state. For a series of actions successfully leading to the target state, this term takes a difference to each state and assigns award to all states on the pathway, rather than only offers award to the target state. This mechanism allows the learning algorithm to percept the orientation information by distinguishing different states. For the trade-off between diversity and speed, we integrate the curriculum learning into the exploration process and propose the diversity exploration scheme. In the beginning, this scheme is prone to exploring the unexecuted action so as to discover the optimal action series. With the learning process carrying on, the scheme gradually relays more on the acquired knowledge and reduces the random probability. Such randomicity to certainty diversity exploration scheme guides the learning scheme to achieve proper balance between strategy diversity and convergency speed. We name the complete framework OpDe Reinforcement Learning and prove the algorithm convergence. Experiments on a standard platform demonstrate the effectiveness of the complete framework.
Pressurized hot water extraction of bioactive compounds from artichoke by‐products

Artichoke by‐products are a suitable source of health‐promoting ingredients for the production of dietary supplements and food additives. A pressurized hot water extraction (PHWE) was developed to recover caffeoylquinic acids (CQAs) and flavone glycosides (FLs) from agro‐industrial artichoke by‐products. The main factors influencing PHWE efficiency and CQA isomerization (temperature, numbers of cycles, modifier, and extraction time) were carefully studied and optimized by response surface design. The proposed PHWE procedure provides an exhaustive extraction of CQAs and FLs (recoveries: 93–105% and 90–105%) from artichoke external bracts and leaves of different cultivars (p > 0.05), without significant formation of artefacts generated by high temperatures. PHWE extracts showed CQA and FL levels (14–37 mg/g and 3–19 mg/g, respectively) comparable to commercial products and marked antioxidative effects (EC50 11–83 μg/mL) by cellular antioxidant activity assay in human hepatocarcinoma HepG2 cells. These results proved that PHWE is an excellent green technique to recover bioactive compounds from artichoke agro‐industrial residues.
Within- and trans-generational plasticity: seed germination responses to light quantity and quality

The ability to predict potential competitive environments allows plants to adjust to conditions that could limit growth and survival. Here we show that seeds integrate information from light environments experienced by mother plants and by seed themselves. Seeds responded to distinct components of the light environment: light wavelength, which predicts the presence of neighbours, and light quantity, which indicates resource abundance. Responses to the maternal environment were stronger than responses to the seed environment, suggesting that the integration of environmental information across generations may have different predictive value depending on the generation that experienced the cue.
Pronunciation Dictionaries for the Alsatian Dialects to Analyze Spelling and Phonetic Variation

This article presents new pronunciation dictionaries for the under-resourced Alsatian dialects, spoken in north-eastern France. These dictionaries are compared with existing phonetic transcriptions of Alsatian, German and French in order to analyze the relationship between speech and writing. The Alsatian dialects do not have a standardized spelling system, despite a literary history that goes back to the beginning of the 19th century. As a consequence, writers often use their own spelling systems, more or less based on German and often with some specifically French characters. But none of these systems can be seen as fully canonical. In this paper, we present the findings of an analysis of the spelling systems used in four different Alsatian datasets, including three newly transcribed lexicons, and describe how they differ by taking the phonetic transcriptions into account. We also detail experiments with a grapheme-to-phoneme (G2P) system trained on manually transcribed data and show that the combination of both spelling and phonetic variation presents specific challenges.
Improved Map Generation by Addition of Gaussian Noise for Indoor SLAM using ROS

Rao-Blackwellized Particle Filter (RBPF) is used in this paper to solve the Simultaneous Localization and Mapping (SLAM) problem. RBPF algorithm uses particle filter where each particle carries an individual map of the environment. With the usage of Robot Operating System (ROS), GMapping package was used as a basis for map generation and SLAM. To improve the map generation, Gaussian noise was introduced to the data from laser range finder and also the odometry from the robot Pioneer P3AT’s pose. The introduced algorithm was successful in decreasing the uncertainty as well as increased the knowledge of each particle in the estimation of the robot’s pose, proven through practical experiment. Exploration experiments were also carried out to test the performance of P3AT based on our proposed method.
The Late Embryogenesis Abundant Protein Family in Cassava (Manihot esculenta Crantz): Genome-Wide Characterization and Expression during Abiotic Stress

Late embryogenesis abundant (LEA) proteins, as a highly diverse group of polypeptides, play an important role in plant adaptation to abiotic stress; however, LEAs from cassava have not been studied in cassava. In this study, 26 LEA members were genome-wide identified from cassava, which were clustered into seven subfamily according to evolutionary relationship, protein motif, and gene structure analyses. Chromosomal location and duplication event analyses suggested that 26 MeLEAs distributed in 10 chromosomes and 11 MeLEA paralogues were subjected to purifying selection. Transcriptomic analysis showed the expression profiles of MeLEAs in different tissues of stem, leaves, and storage roots of three accessions. Comparative transcriptomic analysis revealed that the function of MeLEAs in response to drought may be differentiated in different accessions. Compared with the wild subspecies W14, more MeLEA genes were activated in cultivated varieties Arg7 and SC124 after drought treatment. Several MeLEA genes showed induction under various stresses and related signaling treatments. Taken together, this study demonstrates the transcriptional control of MeLEAs in tissue development and the responses to abiotic stress in cassava and identifies candidate genes for improving crop resistance to abiotic stress.
Energy Supply and Demand Side Management in Industrial Microgrid Context. (Gestion de la production et de la demande d'énergie dans un contexte de Microgrid Industriel)

Energy Supply and Demand Side Management in Industrial Microgrid Context Due to increased energy costs and environmental concerns such as elevated carbon footprints, centralized power generation systems are restructuring themselves to reap benefits of distributed generation in order to meet the ever growing energy demands. Microgrids are considered as a possible solution to deploy distributed generation which includes Distributed Energy Resources (DERs) (e.g., solar, wind, battery, etc). In this thesis, we are interested in addressing energy management challenges in an industrial microgrid where energy loads consist of industrial processes. Our plan of attack is to divide the microgrid energy management into supply and demand sides. In supply side, the challenges include modeling of power generations and smoothing out fluctuations of the DERs. To model power generations, we propose a model based on service curve concepts of Network Calculus (NC). Using this mathematical tool, we determine a minimum amount of power the DERs can generate and aggregating them will give us total power production in the microgrid. After that, if there is an imbalance between energy supply and demand, we put forward different strategies to minimize energy procurement costs. Based on real power consumption data of an industrial site located in France, significant cost savings can be achieved by adopting the strategies. In this thesis, we also study how to mitigate the effects of power fluctuations of DERs in conjunction with Energy Storage Systems (ESSs). For this purpose, we propose a Gaussian-based smoothing algorithm and compare it with state-of-the-art smoothing algorithms. We found out that the proposed algorithm uses less battery size for smoothing purposes when compared to other algorithms. To this end, we are also interested in investigating effects of allowable range of fluctuations on battery sizes. In demand side, the aim is to reduce energy costs through Demand Side Management (DSM) approaches such as Demand Response (DR) and Energy Efficiency (EE). As industrial processes are power-hungry consumers, a small power consumption reduction using the DSM approaches could translate into crucial savings. This thesis focuses on DR approach that can leverage time varying electricity prices to move energy demands from peak to off-peak hours. To attain this goal, we rely on a queuing theory-based model to characterize temporal behaviors (arrival and departure of jobs) of a manufacturing system. After defining job arrival and departure processes, an effective utilization function
Quantum thermodynamics

Quantum thermodynamics is an emerging research field aiming to extend standard thermodynamics and non-equilibrium statistical physics to ensembles of sizes well below the thermodynamic limit, in non-equilibrium situations and with the full inclusion of quantum effects. Fuelled by experimental advances and the potential of future nanoscale applications, this research effort is pursued by scientists with different backgrounds, including statistical physics, many-body theory, mesoscopic physics and quantum information theory, who bring various tools and methods to the field. A multitude of theoretical questions are being addressed ranging from issues of thermalisation of quantum systems and various definitions of ‘work’ to the efficiency and power of quantum engines. This overview provides a perspective on a selection of these current trends accessible to postgraduate students and researchers alike.
Quantitative characterization of turbidity by radiative transfer based reflectance imaging.

A new and noncontact approach of multispectral reflectance imaging has been developed to inversely determine the absorption coefficient of μ a , the scattering coefficient of μs and the anisotropy factor g of a turbid target from one measured reflectance image. The incident beam was profiled with a diffuse reflectance standard for deriving both measured and calculated reflectance images. A GPU implemented Monte Carlo code was developed to determine the parameters with a conjugate gradient descent algorithm and the existence of unique solutions was shown. We noninvasively determined embedded region thickness in heterogeneous targets and estimated in vivo optical parameters of nevi from 4 patients between 500 and 950nm for melanoma diagnosis to demonstrate the potentials of quantitative reflectance imaging.
Role of Small GTPases and its Interacting Molecules in Abiotic Stress Tolerance of Legumes

Grain legumes play a major role in nutritional security, comprising of economically significant food and fodder crops. Being cultivated in marginal lands during lean season, the productivity of these crops is being affected by varying abiotic and biotic stress conditions. Legumes are sensitive crops to abiotic stress conditions. To overcome stress conditions, crops need major adaptive mechanisms which are highly specific to the species. Legumes are having their own mechanism of changing their plant stature, anatomical structures, cytoplasmic streaming, pH flux and stress signalling where myosins and rabGTPases, were found to play a major role. These adaptations arise out of signal perception and response by the plants. Stress signals are sensed by plants and transduced to activate downstream targets through intricate signaling network and crosstalk, in which transcription factors and signaltransducing GTPases play key roles. There is increasing evidence demonstrating that small GTPase proteins are involved in mediation of numerous physiological processes, ranging from pollen growth and root hair development in response to abiotic stress. Diverse functions of plant small GTPases under different conditions were detailed in this manuscript. This further will help in exploitation of genetic variation among these smaller proteins which could facilitate in identifying tolerant germplasm resources.
Optimal mix of renewable power generation in the MENA region as a basis for an efficient electricity supply to europe

Renewable Energy sources are gaining importance in the Middle East and North Africa (MENA) region. The purpose of this study is to quantify the optimal mix of renewable power generation in the MENA region, taking Morocco as a case study. Based on hourly meteorological data and load data, a 100% solar-plus-wind only scenario for Morocco is investigated. For the optimal mix analyses, a mismatch energy modelling approach is adopted with the objective to minimise the required storage capacities. For a hypothetical Moroccan energy supply system which is entirely based on renewable energy sources, our results show that the minimum storage capacity is achieved at a share of 63% solar and 37% wind power generations.
Why do marketing partnerships end ? Evidence from global sport sponsorships

Sponsorship of sport, arts, and entertainment-related organizations has become an essential part of the marketing mix for brands. On a global basis, $57.5 billion was allocated towards sponsorship in 2015, an increase of 4.1% from 2014 (IEG, 2016). From the perspective of the sponsoring firm, research has consistently shown that investing in sponsorship can positively affect brand awareness (Levin, Joiner & Cameron, 2001), brand image (Gwinner & Eaton, 1999), brand loyalty (Levin, Beasley, & Gamble, 2004), and a firm’s financial performance (Mazodier & Rezaee, 2013). At the same time, many sport organizations in particular rely on sponsorship as an essential funding mechanism. For example, in Formula One (F1) Racing, more than 70% of the operating budgets of teams are generated via corporate sponsorship (Jensen & Cobbs, 2014). In another example, more than 34% of the revenue generated by the International Olympic Committee (IOC) during the 200812 quadrennial resulted from sponsorship (IOC, 2015). Despite sponsorship’s effectiveness as an international marketing communications platform for global brands and importance to sponsored organizations, the dynamics of sponsor-property relationships have been afforded scant attention. Therefore, this research utilizes a survival analysis modeling approach (i.e., Jensen, 2016) to better understand factors and conditions that may jeopardize what is intended to be a long-term, multiyear relationship.
Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling

Behavioral and mental health are pressing issues worldwide. Counseling is emerging as a core treatment for a variety of mental and behavioral health disorders. Seeking to improve the understanding of counseling practice, researchers have started to explore Natural Language Processing approaches to analyze the nature of counseling interactions by studying aspects such as mirroring, empathy, and reﬂective listening. A challenging aspect of this task is the lack of psychotherapy corpora. In this paper, we introduce a new dataset of high-quality and low-quality counseling conversations collected from public web sources. We present a detailed description of the dataset collection process, including preprocessing, transcription, and the annotation of two counseling micro-skills: reﬂective listening and questions. We show that the obtained dataset can be used to build text-based classiﬁers able to predict the overall quality of a counseling conversation and provide insights into the linguistic differences between low-quality and high-quality counseling.