Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

In recent years, object detection and classification has gained so much popularity in different application areas like face detection, self- driving cars, pedestrian detection, security surveillance systems etc. The traditional detection methods like background subtraction, Gaussian Mixture Model (GMM), Support Vector Machine (SVM) have certain drawbacks like overlapping of objects, distortion due to smoke, fog, lightening conditions etc. In this paper, thermal images are used as thermal cameras capture the image by using the heat generated by the objects. Thermal camera images are not influenced by smoke and bad weather conditions which makes them a built-up apparatus in inquiry and safeguards or fire-fighting applications. These days, deep learning techniques are extensively used for detection and classification. In this paper, a comparative analysis has been done by applying Faster region based convolutional neural network on thermal images and visual spectrum images. The experimental results show that thermal camera images are better as compared to visible spectrum images.
Automatic example sentence extraction for a contemporary German dictionary

The integration of illustrative examples into monolingual dictionaries provides an intuitive means for grasping the meaning of a word. Tight space constraints of print media no longer apply with online dictionaries. Thus, the inclusion of examples is obviously a useful complement or substitute for the traditional ways of meaning exemplification. In this article, an approach is presented to automatically extract example sentences from a large German corpus collection. The extraction is done on the basis of the notions of sentence readability and complexity and word usage. The extracted examples are a good pre-selection for further integration into a digitized version of a contemporary German dictionary by lexicographers. A quantitative and qualitative evaluation of the extraction results is presented in the article. The work is related to the dictionary project Digitales Worterbuch der deutschen Sprache (The Digital Dictionary of the German Language, DWDS in short) which integrates multiple dictionary and corpus resources and language statistics on the German language in a digital lexical information system which can be accessed on-line.
Closed-Form Solutions for Fixed-Strike Arithmetic Asian Options

Exact probability density functions were derived for the discretely and continuously monitored arithmetic average prices when asset returns follow a normal distribution. They were calculated by a one-step time convolution to maturity, normalized by Esscher transform. They are of an incomplete-market variance gamma distribution. The derivation led to exact closed-form pricing formulas for the discretely and continuously monitored arithmetic Asian options. Extending this approach to the underlying Lѐvy asset returns processes, the discretely and continuously monitored fixed-strike arithmetic Asian options can be priced by single closed-form integrals.
Enabling efficient online profiling of homogeneous and heterogeneous multicore systems

Using profiling tools is a common way to understand computer systems and software and to achieve the best performance. Profiling becomes more important as computing technology advances and makes it more difficult to intuitively reason about system characteristics. However, newer computing technologies such as multicore and heterogeneous systems require new profiling methods more suited to the specific challenges of such systems. In this dissertation, we focus on important profiling problems in each of three application classes: multithreaded applications, multiprogrammed workloads, and heterogeneous systems. For multithreaded applications, we target reducing the overhead of collecting a trace of application characteristics. Reducing the overhead reduces the impact on thread interleavings in a multithreaded application. We reduce the overhead by buffering gathered profile data in a dynamic binary instrumentation system to decouple collection of profile data from processing of profile data. By controlling the code that is generated to fill the buffer and using a variety of methods to empty the buffer, we reduce the overhead by half compared to the previous best implementation in the system. For multiprogrammed workloads, we focus on profiling temperatures on multicore systems. We first isolate several issues relating to profiling applications and temperatures on such workloads, such as sampling frequency effects, temperature and application representations, and aligning multiple profiles. We then use our profile information to show that linear regression on application characteristics is unsuitable for modeling application temperatures, but that application phase changes can also model temperature changes. We also analyze the effects of temperature spreading amongst cores and the different thermal responses of different cores. Finally, we address collecting a full-system profile on heterogeneous systems. Existing profiling methods focus on each resource in isolation without considering the interactions between multiple resources. We present and build upon a straightforward method for combining profiles on heterogeneous systems that can show how CPU performance relates to GPU performance. We then analyze the potential performance impact of profiling the application. We also extend our method for combining profiles with a data flow pass that helps determine how data computed by the GPU was originally generated on the CPU.
Usability Evaluation : Models , Methods , and Applications

Usability is a recent, even sometimes debated, concept in the human and computer interaction field in which interdisciplinary issues of design engineering and philosophy, cognitive psychology and ergonomics converge. The approach we endorse in order to analyse this interdisciplinary concept is strictly related to the evolution of the evaluation models, methods and applications of usability. At first, we discuss the relation between usability and accessibility as dimensions of the intrasystemic relation between the user and technology, by proposing a definition and a model of evaluation. In the second section, we analyse the mental model’s role in system interaction and the distance between them as an outlook to understand usability problems. In the same section, the measures of usability are presented. In the third section, an application of the integrated model of evaluation is described as a step of an assistive technology assessment process in a centre for technical aid. Finally, in the last section, a brief history of human computer interaction evolution and the consequential development of usability and accessibility concepts and methods are presented, in order to understand that usability and accessibility are, at first, linked to technology changes and then strictly related to the spread of technologies.
System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.
Online Estimation of DSGE Models

This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared with the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.
Field Programmable Neural Array: Artificial Intelligence at the Edge

This paper will present the architecture of an Field Programmable Neural Array (FPNA) for AI applications at the tactical computing edge. This platform combines domain specific accelerators for AI with a reconfiguable interconect to permit any Deep Neural Network to be mapped into the FPNA. The FPNA includes domain specific accelerators that perform inference tasks with higher computing efficiency than CPUs and GPUs, approaching that of ASICs designed specifically for AI applications, and a reconfigurable interconnect providing the flexibility and connectivity of an FPGA. Keywords—artificial intelligence; machine learning; domain specific accelerators; field programmable
Tech-assisted language learning tasks in an EFL setting: use of hand phone recording feature

Technology with its speedy great leaps forward has undeniable impact on every aspect of our life in the new millennium. It has supplied us with different affordances almost daily or more precisely in a matter of hours. Technology and Computer seems to be a break through as for their roles in the Twenty-First century educational system. Examples are numerous, among which CALL, CMC, and Virtual learning spaces come to mind instantly. Amongst the newly developed gadgets of today are the sophisticated smart Hand phones which are far more ahead of a communication tool once designed for. Development of Hand phone as a wide-spread multi-tasking gadget has urged researchers to investigate its effect on every aspect of learning process including language learning. This study attempts to explore the effects of using cell phone audio recording feature, by Iranian EFL learners, on the development of their speaking skills. Thirty-five sophomore students were enrolled in a pre-posttest designed study. Data on their English speaking experience using audio–recording features of their Hand phones were collected. At the end of the semester, the performance of both groups, treatment and control, were observed, evaluated, and analyzed; thereafter procured qualitatively at the next phase. The quantitative outcome lent support to integrating Hand phones as part of the language learning curriculum.
A Secure Personal Identification System

Biometrics, which is sometimes called biometric recognition, refers to the recognition of individuals based on their physiological (e.g., fingerprints, face, retina, iris) and/or behavioral (e.g., gait, signature) characteristics. In this book, an explanation is given of the usage of fingerprints from an integrity point of view, how to improve the captured images by enhancement and feature extraction when it is a poor and low resolution fingerprint image, and how to protect templates in order to enhance the security and privacy issue of the entire authentication system. Some problems with current systems have been identified. In order to solve these difficulties, this study has been divided into three parts. In the first part, an improved preprocessing approach is presented using gamma manipulation and gamma correction techniques to adjust lightness and intensities of the poor image due to enhancing the quality. The second part is to improve the feature extraction technique using Projection Incorporated Subspace based on PCA. The third part is a protection analysis of foremost privacy enhanced technologies including watermark embedding technique with fixed digit encryption.
A Provision For Workflow Scheduling using Round Robin Algorithm in Cloud

: Cloud computing has grown very rapidly and now a days it become a very attractive computing paradigm which main aims to provide the end users with directly transmit the computing power anytime. The data centers of next generation goals are to design them for providing power in the form of virtual services (application logic, hardware, database, user-interface, and so on). It enables the users for deploying and accessing the applications at an efficient cost. In cloud computing, there are three recent fields of developments such as distributed computing, grid computing and parallel computing. Workflow scheduling is a method of discovery the proficient mapping of workflow tasks to the appropriate resources so that the implementation can be accomplished to convince objective criteria. The objectives of scheduling a workflow task are different for different application. Workflow application can be executed in cloud computing environments in utility-based fashion. In this paper author is using improved round robin algorithm in workflow has been implemented for increasing the scalability, less memory consumption.
THREE‐DIMENSIONAL MICROSURGICAL AND TRACTOGRAPHIC ANATOMY OF THE WHITE MATTER OF THE HUMAN BRAIN

OBJECTIVEWe sought to investigate the three-dimensional structure of the white matter of the brain by means of the fiber-dissection technique and diffusion-tensor magnetic resonance imaging to assess the usefulness of the combination of both techniques, compare their results, and review the potential functional role of fiber tracts. METHODSFifteen formalin-fixed human hemispheres were dissected according to Klingler's fiber-dissection technique with the aid of 36 to 340 magnification. Three-dimensional anatomic images were created with the use of specific software. Two hundred patients with neurological symptoms and five healthy volunteers were studied with diffusion-tensor magnetic resonance imaging (3 T) and tractographic reconstruction. RESULTSThe most important association, projection, and commissural fasciculi were identified anatomically and radiologically. Analysis of their localization, configuration, and trajectory was enhanced by the combination of both techniques. Three-dimensional anatomic reconstructions provided a better perception of the spatial relationships among the white matter tracts. Tractographic reconstructions allowed for inspection of the relationships between the tracts as well as between the tracts and the intracerebral lesions. The combination of topographical anatomic studies of human fiber tracts and neuroanatomic research in experimental animals, with data from the clinicoradiological analysis of human white matter lesions and intraoperative subcortical stimulation, aided in establishing the potential functional role of the tracts. CONCLUSIONThe fiber-dissection and diffusion-tensor magnetic resonance imaging techniques are reciprocally enriched not only in their application to the study of the complex intrinsic architecture of the brain, but also in their practical use for diagnosis and surgical planning.
Assessing the Fairness of Intelligent Systems

Nowadays, software systems based on Machine Learning models are ubiquitous, often being used in scenarios that directly affect people’s lives. Consequently, societal and legal concerns arise, namely that decisions supported by the models’ outputs may lead to the unfair treatment of individuals, based on attributes like race, age, or sex. In fact, fairness is one of the properties systems must have to be compliant with current legislation, namely the EU General Data Protection Regulation. The main objective of this work is to assess the fairness of software systems based on Machine Learning models in classification scenarios. Data preparation and pre-processing are key on any Machine Learning pipeline, and their effect on fairness needed to be studied in detail. Thus, we assessed the impact of the encoding of the categorical features, the removal of the sensitive attribute from the training data, as well as sampling methods, such as random undersampling and random oversampling. The influence of the learning algorithm was also considered, with an initial evaluation of Decision Trees and Random Forests. Fairness was measured at different stages of the pipeline to understand the procedures with the most impact on it. Our results show that performing sampling with respect to the true labels and opting for Random Forests over Decision Trees often has a negative effect on fairness. Although removing the sensitive attribute from the training data prevents incurring in direct discrimination, the models are often still able to explore associations between this attribute and the remaining features, with the resulting classifications sometimes even being more unfair than the data. As a result, organisations must be aware of and carefully assess the trade-off between classification performance and fairness.
Performance and Power Optimization for Multi-core Systems using Multi-level Scaling

Author(s): Almatouq, Munirah | Advisor(s): Gaudiot, Jean-Luc | Abstract: Integrating more cores per chip to increase the performance of processors has been trendingfor the past decade. However, this trend cannot be sustained because the reduction in powerconsumption per core has slowed down while the power budget per chip has not increased.Modern processor chips are becoming so power constrained to the point that not all theirdevices can be powered at once - this is often referred to as dark silicon. To maximizeperformance within these power constraints, the system must carefully select the set ofresources to be used.To solve this problem, several power management techniques such as Dynamic Voltage/FrequencyScaling (DVFS), core scaling, and resource scaling have been the subject of active researchand have proven to be effective. However, most of these solutions are sub-optimal becausethey explore only one layer of the architecture. Although considering one layer reduces thecomplexity of the technique, it limits the exploitation of potential improvement in performanceand energy consumption.The problem is an order of magnitude more complex for power constrained multi-core architectures.We need power management systems that can take advantage of dierent scalingtechniques. Many studies have been conducted on scaling with the sole objective of performanceimprovement. Nevertheless, few of them have considered both performance and energy consumption in the optimization process.This dissertation proposes an optimization technique that balances performance and energyconsumption by applying a joint control of core, resource and frequency scaling. Thissystem finds the optimal configuration for a given application and accordingly adapts thearchitecture configuration.The proposed technique consists of three stages: configuration sampling, response surfacemodels to approximate performance and energy consumption, and online optimization usinga genetic algorithm (GA). To evaluate the system, experiments were conducted on asimulated 12 core architecture. Our experiments have shown that the performance couldimprove by 15% on average while achieving energy savings of up to 26%. Using a per-coreconfiguration improves the performance by 25% on average and reduces the energy by 18%.
Dynamic Clustering and a Hybrid Optimization for Roughness Calibration in Water Distribution Model

Water Distribution Networks (WDNs) modelling is an important tool for efficient operation and design, where the hydraulic simulations are used to establish the optimal conditions. However, there are uncertainties in this modelling process, especially on nodal demand and pipe roughness, which can be surpassed through an efficient calibration process. For this, real pressure data of some measurement points are used as reference to adjust these parameters until the simulated pressure meet the real value of monitoring nodes. Due to the high degrees of freedom of the problem the result obtained with this procedure is not capable to represent the reality when the WDN operates with different conditions. To manage this high degree of freedom, this paper proposes a hybrid method to reduce the uncertainty observed in pipes roughness calibration. The method has three main stages: the first uses k-means algorithm to create groups of pipes to be calibrated separately. The algorithm takes the hydraulic and physical features of the pipes, such as Reynolds number, age and material of the pipes to create clusters with similar roughness. On the second stage, an Artificial Neural Network (ANN) is trained for pressure estimation. The ANN uses as input the real pressure obtained by the sensors and estimates the pressure on the remaining nodes. These two stages reduce the degrees of freedom for the final stages, where pipes roughness are obtained through an optimization process, minimizing the difference between simulated and estimated pressure by ANN. Each group defined previously is calibrated using the Particle Swarm Optimization (PSO). As the k-means relies on the pipes roughness to create the clusters, it is associated with the PSO in this stage to create a dynamic clustering process. While the pipes roughness are being optimized in each step of the optimization process, the clusters are redefined to the next iteration. This is an important step because with the changes of the pipes’ roughness, the Reynolds number for each pipe changes. With the dynamic clustering, the pipes can move from one cluster to another, improving the quality of the results. An adaptation of Modena network is proposed and the adapted model included age zones at the network, generating different roughness scenarios, that difficult the calibration process.
Factors Preventing the Use of a Lightweight Design Workflow that is Inspired by the Human Locomotive System

A workflow for the design process of technological products was derived from a model that describes the interplay of lightweight design principles in the human locomotive system. This workflow is not yet ready to be used. In this paper, starting points for new research with the goal to enable the use of the workflow are discussed. Using the interplay of lightweight design principles of the human body in technical applications is approached because it is claimed that the interplay leads to an additional reduction of mass. This was proven for a technological system in a previous study. This study lead to a workflow to consider the interplay of the principles in the design process of the technological system. In this paper, the essential parts of this workflow are described in an abstract diagram as a calculation workflow. Subsequently, inputs and outputs of the workflow are identified. Then, the calculation workflow is integrated into the process of design. Afterwards, it is discussed that tension chording, which is one of the lightweight design principles, needs further investigation, because the interplay of the principles can only be used if the principles themselves are used.
Integration of uncertainty and definition of critical thresholds for CO2 storage risk assessment

The main goal of the thesis is to define how the uncertainty can be accounted for in the process of riskassessment for CO2 storage and to quantify by means of numerical models the scenarii of leakage by lateralmigration and through the caprock. The chosen scenarii are quantified using the system modeling approachfor which ad-hoc predictive numerical models are developed. A probabilistic parametric uncertaintypropagation study using polynomial chaos expansion is performed. Matters of spatial variability are alsodiscussed and a comparison between homogeneous and heterogeneous representations of permeability isprovided.