Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer of Heavy Metal Contaminants from Animal Feed to Animal Products

Study was conducted to assess the possible transfer of heavy metals from livestock feeds to animal products. In experiment-1, samples (n = 503) collected from conventional farms in Central Greece were analysed for heavy metal Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb), Nickel (Ni) and Chromium (Cr) contents in muscle tissues, livers, kidneys, excremental and basic mixtures in livestock such as cows and sheep. Results showed that transfer of heavy metal contaminants from feed to animal products fluctuated below the permissible risk levels. In experiment 2, samples (n = 440) of feedstuffs used in different feeding regimens and seasons at different livestock farms (sheep (n = 140), dairy cows (n = 180), pigs (n = 120)) were collected. Results showed that relatively high concentration of Cu in the excrements of pigs (155 ± 9.13) and Zn in the excrements of sheep (144.56 ± 5.78) are explained by the use of Cu in the diet of developing piglets and sheep. Ovine showed higher concentration of Ni, Cr, and Pb in their excrements, while, the concentration of heavy metals in forages was under the allowed range to the EU legislation.
Integrating Content and Style in Documents: A Case Study of Patient Information Leaflets

We envisage a novel computer tool for producing technical documentation, in which the author specifies the desired content and style, but the exact wording and layout is determined by the system (including versions in languages the author need not know); a prototype of such a system is being developed in the ICONOCLAST project. Among other things, the system must adapt the wording of the generated document to its punctuation and layout. By studying a corpus of patient information leaflets, we have found many detailed examples of this interaction, some of which are described here. In particular, we focus on ways in which the use of special layout patterns (e.g., vertical lists, boxes) changes the options for wording, sometimes licensing departures from normal conventions of grammar and punctuation.
Efficient retrieval of similar time sequences under time warping

Fast similarity searching in large time sequence databases has typically used Euclidean distance as a dissimilarity metric. However, for several applications, including matching of voice, audio and medical signals (e.g., electrocardiograms), one is required to permit local accelerations and decelerations in the rate of sequences, leading to a popular, field tested dissimilarity metric called the "time warping" distance. From the indexing viewpoint, this metric presents two major challenges: (a) it does not lead to any natural indexable "features", and (b) comparing two sequences requires time quadratic in the sequence length. To address each problem, we propose to use: (a) a modification of the so called "FastMap", to map sequences into points, with little compromise of "recall" (typically zero); and (b) a fast linear test, to help us discard quickly many of the false alarms that FastMap will typically introduce. Using both ideas in cascade, our proposed method achieved up to an order of magnitude speed-up over sequential scanning on both real and synthetic datasets.
Approximating Aggregate Queries about Web Pages via Random Walks

We present a random walk as an eÆcient and accurate approach to approximating certain aggregate queries about web pages. Our method uses a novel random walk to produce an almost uniformly distributed sample of web pages. The walk traverses a dynamically built regular undirected graph. Queries we have estimated using this method include the coverage of search engines, the proportion of pages belonging to . com and other domains, and the average size of web pages. Strong experimental evidence suggests that our walk produces accurate results quickly using very limited resources.
Role and Task of the System Architect

The role of the system architect is described from three viewpoints: deliverables, responsibilities and activities. This description shows the inherent tension in this role: a small set of hard deliverables, covering a fuzzy set of responsibilities, hiding an enormous amount of barely visible day-to-day work. Distribution This article or presentation is written as part of the Gaudi project. The Gaudi project philosophy is to improve by obtaining frequent feedback. Frequent feedback is pursued by an open creation process. This document is published as intermediate or nearly mature version to get feedback. Further distribution is allowed as long as the document remains complete and unchanged. March 6, 2013 status: concept version: 2.0 V4aa IO design, brainstorm, explain Idea think, analyze listen, talk, walk around Blah Blah write, consolidate, browse present, meet, teach, discuss read, review Design S p e c Report test, integrate assist project leader with work breakdown, schedule, risks travel to customer, supplier, conference provide vision and leadership Deliverables of the System Architect Spec Design Report R e p o r t Report Design Design Spec Spec The Role and Task of the System Architect 2 Gerrit Muller version: 2.0 March 6, 2013 RSAdeliverables List of Deliverables Customer and Life-Cycle Needs ( what is needed) System Specification ( what will be realized) Design Specification ( how the system will be realized) Verification Specification ( how the system will be verified) Verification Report (the result of the verification) Feasibility Report (the results of a feasibility study) Roadmap The Role and Task of the System Architect 3 Gerrit Muller version: 2.0 March 6, 2013 RSAdeliverablesSpecific Responsibilities of the System Architect
NRC Publications Archive Archives des publications du CNRC

With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). This problem becomes even more evident in collaborative computing environments. PII may be hidden anywhere within the file system of a computer. As well, in the course of different activities, via collaboration or not, personally identifiable information may migrate from computer to computer. This makes meeting the organizational privacy requirements all the more complex. Our particular interest is to develop technology that would automatically discover workflow across organizational collaborators that would include private data. Since in this context, it is important to understand where and when the private data is discovered, in this paper, we focus on PII discovery, i.e. automatically identifying private data existant in semi-structured and unstructured (free text) documents. The first part of the process involves identifying PII via named entity recognition. The second part determines relationships between those entities based upon a supervised machine learning method. We present test results of our methods using publicly-available data generated from different collaborative activities to provide an assessment of scalability in cooperative computing environment.
NOHIS-Tree: High-Dimensional Index Structure for Similarity Search

— In Content-Based Image Retrieval systems it is important to use an efficient indexing technique in order to perform and accelerate the search in huge databases. The used indexing technique should also support the high dimensions of image features. In this paper we present the hierarchical index NOHIS-tree (Non Overlapping Hierarchical Index Structure) when we scale up to very large databases. We also present a study of the influence of clustering on search time. The performance test results show that NOHIS-tree performs better than SR-tree. Tests also show that NOHIS-tree keeps its performances in high dimensional spaces. We include the performance test that try to determine the number of clusters in NOHIS-tree to have the best search time.
Segmented strain accumulation in the High Himalaya expressed in river channel steepness

We investigate segmentation of High Himalayan strain by cross-orogen structures separating western and eastern obliquely convergent sectors from a central orthogonally convergent sector, and evaluate the relationship be-tween the size of regions accumulating strain, their proximity to the toe of the thrust wedge, and recurrence of M w >7 earthquakes. We present a map of river channel steepness ( k sn )—a proxy for rock-uplift rate over 10 5 yr, for the Himalayan arc—and evaluate the strength of its correlation with Main Hima-layan thrust (MHT) coupling (–0.6), earthquake density (0.6), topography (0.6), lithotectonic units (0.5), and precipitation (–0.3) along 40 profiles spanning the Himalaya from 78°E to 92°E. We interpret the k sn map to be foremost a function of recent strain accumulation. This reveals prominent offsets of hinterland strain accumulation collocated with cross-orogen strike-slip and extensional fault systems. Clusters of high-k sn rivers are located near the boundary be-tween the strongly and weakly coupled portions of the MHT, where fault behavior changes from seismogenic to sliding at the rheologic brittle-to-plastic transition (BPT). We propose that the rate at which major MHT earthquakes repeat is related to four parameters: convergence rate (nearly uniform); spatial dimensions of the high-k sn cluster (proxy for volume of material accumulating strain); the high k sn clusters distance from the toe of thrust wedge (fault surface area over which static friction must be overcome); and the degree of obliquity between India-Asia convergence and the local trend of the orogen (proxy for the magnitude of strain partitioning).
Early Gesture Recognition Method with an accelerometer

An accelerometer is installed in most current mobile phones, such as iPhones, Android-powered devices, and video game controllers for the Wii or PS3, which enables easy and intuitive operations. Therefore, many gesture-based user interfaces that use accelerometers are expected to appear in the future. Gesture recognition systems with an accelerometer generally have to be models constructed with a user's gesture data before use, and they need to recognize any unknown gestures by comparing them with an output of the recognition result and feedback delays since the recognition process generally starts after the gesture has finished, which may cause users to retry gestures and thus degrade the interface usability. We propose an early stages gesture recognition method that sequentially calculates the distance between the input and training data, and outputs recognition results only when one output candidate has a stronger likelihood than the others. Gestures are recognized in the early stages of a given motion without deteriorating the level of accuracy, which improves the interface usability. Our evaluation results indicated that the recognition accuracy approached 1.00 and the recognition results were output 1,000 msec on average before a gesture had finished.
Do Migrant Remittances Reduce Poverty? Micro-Level Evidence from Punjab, Pakistan

This paper provides a poverty profile of households and then investigates the effects of international remittances on poverty incidence and severity in Punjab, Pakistan. Using cross-section data from the latest Multiple Indicator Cluster Survey for Punjab, the disaggregated analysis on the remittance-poverty nexus is examined by districts and urban-rural locales. From the poverty profile for migrant households with remittances and the counterfactual scenario of no remittances, the differences in the poverty reduction effect seem larger for poverty headcount than on the depth of poverty. The same trend holds for the urban-rural locales. This implies that remittances inflow were not really helpful for the poorest of the poor. The regression analysis further reveals that migrant remittances have significantly reduced the level and depth of poverty for households in all districts of Punjab, with the highest probability of being non-poor for rural households in the districts of South Punjab.
A Framework for Planning in Continuous-time Stochastic Domains ( extended abstract )

We propose a framework for policy generation in continuoustime stochastic domains with concurrent actions and events of uncertain duration. We make no assumptions regarding the complexity of the domain dynamics, and our planning algorithm can be used to generate policies for any discrete event system that can be simulated. We use the continuous stochastic logic (CSL) as a formalism for expressing temporally extended probabilistic goals and have developed a probabilistic anytime algorithm for verifying plans in our framework. We also present an efficient procedure for comparing two plans that can be used in a hill-climbing search for a goal-satisfying
Dynamic right-sizing for power-proportional data centers

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of predictably low load. This paper investigates how much can be saved by dynamically ‘right-sizing’ the data center by turning off servers during such periods, and how to achieve that saving via an online algorithm. We prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new ‘lazy’ online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data center workloads and show that significant cost-savings are possible.
Euclidean Invariant Computation of Stochastic Completion

Computations in visual cortex have many features in common with computations in the continuum, even though they are implemented in a discrete network. In particular they are Euclidean invariant: An arbitrary rotation and translation of the input produces an identical transformation of the output. We introduce the notion of a shiftable-twistable wavelet basis and show how it can be used to perform Eu-clidean invariant discrete computations on the continuous space of positions and directions. The particular computation we consider is that of completing the boundaries of partially occluded objects.
Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA

programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. This paper focuses on the implementation of Adaptive Infinite Impulse response (IIR) filter on an FPGA using Modified Particle Swarm Optimization (PSO) Algorithm. The proposed Filter is capable of finding the global optimum solution for system identification problem in less number of iterations. The modified PSO algorithm has been developed and simulated using MATLAB. The result shows the enhanced speed of purposed design in terms of number of iterations it takes to identify the unknown system. The same algorithm has also been realized on various Xilinx FPGA devices and performances have also been analyzed. The area utilization by the proposed design on different FPGA devices has been compared. The results show that proposed filter is consuming very less area in terms of LUTs and Slices to provide enhanced area efficiency.
Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.
Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorders

The primary method for identifying mental disorders automatically has traditionally involved using binary classifiers. These classifiers are trained using behavioral data obtained from an interview setup. In this training process, data from individuals with the specific disorder under consideration are categorized as the positive class, while data from all other participants constitute the negative class. In practice, it is widely recognized that certain mental disorders share similar symptoms, causing the collected behavioral data to encompass a variety of attributes associated with multiple disorders. Consequently, attributes linked to the targeted mental disorder might also be present within the negative class. This data impurity may lead to sub-optimal training of the classifier for a mental disorder of interest. In this study, we investigate this hypothesis in the context of major depressive disorder (MDD) and post-traumatic stress disorder detection (PTSD). The results show that upon removal of such data impurity, MDD and PTSD detection performances are significantly improved.
A Bi-level assessment of Twitter in predicting the results of an election: Delhi Assembly Elections 2020

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate.
A Dynamic Network for Cryptocurrencies*

Cryptocurrencies return cross-predictability yields information on risk propagation and market segmentation. To explore these effects, we build a dynamic network of cryptocurrencies based on the evolution of return cross-predictability and develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent group membership of cryptocurrencies. We show that return cross-predictability and cryptocurrencies’ characteristics, including hashing algorithms and proof types, jointly determine the cryptocurrencies market segmentation. Portfolio analysis reveals that more centered cryptocurrencies in the network earn higher risk premiums. JEL Classification: G11, G12, G41.
Iterative Schedule Optimization for Parallelization in the Polyhedron Model

In high-performance computing, one primary objective is to exploit the performance that the given target hardware can deliver to the fullest. Compilers that have the ability to automatically optimize programs for a specific target hardware can be highly useful in this context. Iterative (or search-based) compilation requires little or no prior knowledge and can adapt more easily to concrete programs and target hardware than static cost models and heuristics. Thereby, iterative compilation helps in situations in which static heuristics do not reflect the combination of input program and target hardware well. Moreover, iterative compilation may enable the derivation of more accurate cost models and heuristics for optimizing compilers. In this context, the polyhedron model is of help as it provides not only a mathematical representation of programs but, more importantly, a uniform representation of complex sequences of program transformations by schedule functions. The latter facilitates the systematic exploration of the set of legal transformations of a given program. Early approaches to purely iterative schedule optimization in the polyhedron model do not limit their search to schedules that preserve program semantics and, thereby, suffer from the need to explore numbers of illegal schedules. More recent research ensures the legality of program transformations but presumes a sequential rather than a parallel execution of the transformed program. Other approaches do not perform a purely iterative optimization. We propose an approach to iterative schedule optimization for parallelization and tiling in the polyhedron model. Our approach targets loop programs that profit from data locality optimization and coarse-grained loop parallelization. The schedule search space can be explored either randomly or by means of a genetic algorithm. To determine a schedule’s profitability, we rely primarily on measuring the transformed code’s execution time. While benchmarking is accurate, it increases the time and resource consumption of program optimization tremendously and can even make it impractical. We address this limitation by proposing to learn surrogate models from schedules generated and evaluated in previous runs of the iterative optimization and to replace benchmarking by performance prediction to the extent possible. Our evaluation on the PolyBench 4.1 benchmark set reveals that, in a given setting, iterative schedule optimization yields significantly higher speedups in the execution of the program to be optimized. Surrogate performance models learned from training data that was generated during previous iterative optimizations can reduce the benchmarking effort without strongly impairing the optimization result. A prerequisite for this approach is a sufficient similarity between the training programs and the program to be optimized.