Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum Decision Support Framework for Cognitive Radio Networks

The exponential increase of mobile devices and the wide availability of bandwidth-hungry applications have created an eruption in mobile data traffic. Such extraordinary evolution in wireless data usage cause a severe capacity shortage in wireless mobile networks and presents substantial challenges to cellular operators and telecommunication regulatory authorities. Operators consider various technologies to improve their infrastructure, such as upgrading their entire network to LTE, taking advantage of existing available spectrum, or leveraging new spectrum opportunities such as the newly vacated TV band. However, such network designs do not facilitate robustness in spectrum usage. Cognitive Radio Network offers a capable solution for assuaging this problem. In mobile networks, the wireless spectrum bands are also used by the secondary users in the absence of the licensed users. Spectrum decision is to be performed by secondary users while catering for the inconsistent behavior of fluctuating nature of spectrum slots and diverse service requirements of various wireless applications, secondary users have to adopt, aiming at optimizing the transmission performance of SUs. A SU has to sense multiple target spectrum slots in the shortest possible time before deciding to select and occupy the most suitable to its QoS requirements idle slot for its transmission. Spectrum decision process selects the most suited slot from these available slots for opportunistic use by secondary users. A support framework for CRNs has been proposed, which is called Spectrum decision Support Framework (SDSF). SDSF offers an intelligent spectrum decision scheme that first senses the idle slots and then enables SUs to swiftly occupy them effectively. SDSF integrates various spectrum decision techniques and takes into account various spectrum slot characterization parameters. A scientific support framework has been developed for SUs in the CRN which includes spectrum slot
Code Similarity Detection using AST and Textual Information

In the teaching process of computer language courses, a large amount of programming experimental content needs to be supplemented. Students sometimes copy codes from each other, which seriously reduces the teaching quality of computer language courses and makes it difficult to improve students' programming abilities. To solve this problem, this paper proposes a novel code similarity detection algorithm based on code text and AST. By removing comments, blank characters, and other "cleaning" processes from the code text, the normalized code text is obtained. Then, word segmentation, word frequency statistics, weight calculation, and other operations are carried out. The code fingerprint is obtained by using the Simhash algorithm. According to the specification of computer language grammar, lexical analysis and syntax analysis are conducted to extract the AST (abstract syntax tree), and redundant information is eliminated. According to the Zhang-Shasha algorithm, the AST edit distance is calculated and then compared to the AST. Finally, the similar degree between the text similarity and AST similarity is calculated. In order to verify the effectiveness of this method, taking Python code as an example, the code on the open source programming platform and LeetCode is used to build the test data set according to the common code plagiarism method. Experimental results show that this method is capable at detecting several common means of plagiarism, and low similarity can be obtained for the experimental detection of unrelated codes and non-plagiarized codes. Therefore, we believe that this algorithm can effectively be used for the code similarity detection of experimental code in computer language courses.
A Career Track Recommender System for Senior High School Students using Fuzzy Logic

Rule-based algorithms are mostly used in recommender systems but still, cannot address the issue of uncertainty in decision making particularly to senior high school students in choosing the right career track because of numerous influential factors that may affect their decisions. That is why a career track recommender system using fuzzy logic has been developed to address this issue. In this paper, the significant factors that is most influential to the decision of the students as best attributes were determined using feature selection filtering techniques and used as crisp inputs. The result shows that the developed fuzzy model performs a high predictive accuracy based on the computed mean absolute error (MAE) and root-mean-square error (RMSE) scores which decreases from the training, to the validation and test sets. The recommendation returns the best possible result based on the computed normalized discounted cumulative gain (nDCG) which is 0.948 from the desired to the actual preference of students which is almost near to 1.0. With these, the developed recommender system is highly recommended as perceived by the users in terms of usability, maintainability and portability.
A multivariate sparse deconvolution algorithm for multi echo fMRI.

This thesis presents a novel algorithm for the deconvolution of multi echo fMRI data with no prior information on the timings of the neuronal events. Based on previous work on the field, a new signal model is proposed in order to take the processing from a voxelwise analysis to an entire brain one. Di↵erent proximal operators have been studied for solving the optimisation problem present in the deconvolution, since it is an ill-posed inverse problem, and a novel method based on the stability selection procedure has been suggested to answer to the choice of the regularization parameter dilemma. The method takes advantage of the area under the curve (AUC) of the stability paths to avoid the selection of a single regularization parameter. An optimal approach for the thresholding of AUC timeseries is studied and di↵erent debiasing methods for removing the sparsity in prolonged events are presented. The results demonstrate that the MvMESPFM algorithm provides promising results when estimating neuronal-related events even on noisy data. Subject to being thoroughly tested on experimental data, testing conducted on simulated signals suggests that the tool could eventually be introduced to the processing pipelines of di↵erent research lines regarding fMRI data analysis.
Building Abstractions for Staged DSLs in Performance-Oriented Program Generators

diss . eth no. 26058 B U I L D I N G A B S T R A C T I O N S F O R S TA G E D D S L S I N P E R F O R M A N C E O R I E N T E D P R O G R A M G E N E R AT O R S A dissertation submitted to attain the degree of doctor of sciences of eth zurich (Dr. sc. ETH Zurich)
Ranking Users in Social Networks with Motif-Based PageRank

PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which represent first-order relations between two connected nodes. It ignores higher-order relations that may exist between nodes. In this article, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order relations into the conventional PageRank computation. Motifs are subgraphs consisting of a small number of nodes. We use motifs to capture higher-order relations between nodes in a network and introduce two methods, one linear and one non-linear, to combine first-order and higher-order relations in PageRank computation. We conduct extensive experiments on three real-world networks, namely, DBLP, Epinions, and Ciao. We study different types of motifs, including 3-node simple and anchor motifs, 4-node and 5-node motifs. Besides using single motif, we also run MPR with ensemble of multiple motifs. We also design a learning task to evaluate the abilities of authority prediction with motif-based features. All experimental results demonstrate that MPR can significantly improve the performance of user ranking in social networks compared to the baseline methods.
Development of remotely operated vehicle underwater robot

Nowadays, Remotely Operated Vehicle (ROV) robot has been widely used in industry especially in oil and gas sector. It has been used to do a task inside the sea water environment besides has a capability to perform a deep sea rescue operation and recover objects from the ocean floor. Development on the design of the ROV underwater robot is done to increase the performance of the robot in the ocean. This project will discuss the steps from designing the ROV until its prototype construction. There are several steps need to be followed in order to produce efficient mechanical structure or design of the ROV underwater robot. In this project it consist three preliminary designs and one final design. The final design is produced through evaluation process of three preliminary designs of ROV. Computational fluid dynamic (CFD) software is used in order to analyse and identify the drag coefficient of the ROV underwater robot structure. Other than that, other software and calculation is used to determine the behaviour of the robot inside the water. This thesis also will provide the overview of the process in designing, constructing and testing the ROV with respect to mechanical part only. The main material used for this ROV is Aluminium square hollow and Acrylonitrile butadiene styrene (ABS). Entire joint and holder used in this ROV is custom made in order to maintain the originality of the ROV design. All the steps are carefully conducted in order to design and construct an effective structure for underwater robot in term of its drag coefficient and stability performance.
A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors

The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the other model is TLBO (Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data. In this work, some spatial information used for analysis is the Normalized Difference Vegetation Index, Standard Precipitation Index and Vegetation Condition Index. TLBO finds some best feature value set in the data that represents the specific yield of the crop. So, these selected feature valued set is passed in the Error Back Propagation Neural Network for learning. Here, the training was done in such a way that all set of features were utilized in pair with their yield value as output. For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India. The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.
Configuration in smart service systems: A practice‐based inquiry

In this paper, we develop a new perspective on configuration processes inherent to smart service systems that brings human actors to the theoretical foreground, is informed by practice theory, and is grounded in an intervention study of smart locating systems used in home care of persons with dementia. Maintaining control of the location of persons with dementia is a major source of caregiver burden, and assistive technologies such as smart locating systems hold the promise of enhancing quality of life and reducing caregiver burden. Drawing from research on smart service systems, organizational configuration research, and practice theory, we articulate a conceptual model of configuring in practice to explain how practice elements and the capabilities of smart technologies become mutually configured. Our analysis of study participants' experiences with the locating systems identified three distinctive configurations, characterized as enhanced smartness, degraded smartness, and fragile smartness, which developed through configuring processes. The configuring in practice model we develop here helps explain why unexpected and anticipated configurations develop and thus the conditions that facilitate or inhibit value cocreation in smart service systems.
Face recognition system using Viola Jones, histograms of oriented gradients and multi-class support vector machine

This study aims to determine the best distance to install a CCTV camera in identifying a person’s face in the passenger inspection area at the airport, which can be developed for suspect detection systems. The training data used is in the form of an image with five different angles per person, while the testing data is in the form of video. The initial stage conducted is face detection based on local features (a pair of eyes, nose, and mouth) in the input data using the Viola-Jones method. The face detection results process with pre-processing, feature extraction, and classification. In the pre-processing stage, the Brightness Enhancement (BE), Grayscale and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are used to improve the quality of the detected face. Furthermore, the feature extraction and classification stages use the Histogram of Oriented Gradient (HOG) and the Multi-class Support Vector Machine (MSVM) methods, respectively. The result shows that the best accuracy obtained is 86.76% for a CCTV camera distance of 300 cm and a height of 250 cm.
Research on the construction of the spiral evolutionary design methodology for a product service system based on existing products

A product service system composed of products, services, hardware and software support, and stakeholders enhances the added value and competitiveness of products through resource integration. To reduce the input in servitization and improve feasibility, manufacturers tend to construct a product service system based on existing products to meet the demands of customers, instead of constructing a completely new product service system. Therefore, this article presents a spiral evolutionary design method to construct a product service system based on existing products. With the ultimate goal of the system evolution being the maximization of the use of existing products while satisfying the demands of customers, the system performance will be spirally upgraded under the guidance of the system evolution chain through the operation of the system symbiotic ring. The construction of a mobile fire extinguishing product service system based on existing fire-fighting equipment was used as an example to illustrate the feasibility of this methodology. Finally, a feasibility evaluation system was established to evaluate and compare the previous product service system methodologies with the methodology in this article.
Multiple Delay Estimation for Collision Resolution in Non-Orthogonal Random Access

In machine-type communications (MTC), contention-based random access is employed to support a number of MTC devices with a limited number of resource blocks (RBs). Since multiple active devices may transmit signals in the same RB or channel, the collision caused by the presence of multiple signals is inevitable and the detection of collision becomes important. Furthermore, if the arrival time and the number of multiple signals can be estimated, successive interference cancellation (SIC) can be employed in time domain to improve the throughput. In this paper, we focus on the estimation of round-trip delays (RTD) of multiple signals in non-orthogonal random access (NORA) based on the maximum likelihood (ML) criterion. Since the computational complexity of the ML approach is high, we propose a low-complexity approach based on variational inference, which is widely used in machine learning. We also show that the number of signals can be reliably estimated from the estimated RTDs.
Anomaly Detection Algorithm Based On Electric Equipment

Traditional methods for detecting data anomalies of power equipment fail to fully mine the data characteristics. And it has shortcomings such as complex calculation, poor flexibility and low accuracy. To solve the problem of abnormal fault detection of electric equipment, the abnormal detection algorithm based on statistical analysis and machine learning is used in the paper. The reconstruction method based on Long Short Term Memory (LSTM) time sequence is proposed for time series data abnormal detection. Experiments show that the new method is effective in detecting and correcting abnormal data, which reduces the detection time and improves the accuracy of state estimation results. There are huge differences within the positive samples in anomaly detection, so several methods are studied in the paper. One-class SVM algorithm is often used in novelty detection and isolation forest algorithm is used in outlier detection. Machine learning methods which is based on statistical analysis will play an increasingly important role in the fields of equipment monitoring and predictive maintenance, safety of electric equipment.
Assessment of movement of the Belex15 index using linear regression and neural network

The stock market remains a focus of attention for researchers. So far, various technical and statistical methods and models have been developed and used to show different results, but none with great success. In this paper the author used two approaches to estimate price movement in stock market: simple linear regression and backpropagation neural network.The aim of this paper is to estimate the value of the Belex15 stock exchange index and to analyze its movement using two aforementioned approaches.
A 1.8 V 0.918 ppm/°C CMOS bandgap voltage reference with curvature-compensated

A new precise current mode bandgap voltage reference with TlnT compensation of non-linear temperature is proposed. By using the voltage difference of two transistors working in different current states, the nonlinear compensation term of TlnT is generated. At the same time, the optimal high-precision reference voltage source is obtained by using the method of piecewise compensation. This design with 0. 18μm CMOS process is achieved an ultra-low temperature coefficient (TC) of 0.918 ppm/°C from −40 °C to 125 °C. The minimum required supply voltage is 1.8V and the current consumption is 23 μA at 25 °C. And the line regulation performance is 0.0055%/V at 25 °C with a supply voltage range of 1.8V to 3.6V. key words: bandgap voltage reference, TlnT, temperature coefficient Classification: Integrated circuits
What learning analytics can learn from students as partners

ABSTRACT The growing practice of students as partners (SaP) has sparked numerous conversations in higher education about the roles students do and should play in shaping the future. SaP scholars contend that by engaging with students in meaningful partnership, underpinned by values such reciprocity, students can have deeper and more meaningful learning experiences. Similar in this goal, the field of learning analytics also strives to create deeper and more meaningful learning experiences for students, however, the current approach differs. Students and other stakeholders such as teachers are rarely part of the process in learning analytics, and this divide, and lack of communication, between designers and users, has led to several critical issues in the area. Therefore, this paper will discuss three ongoing issues within learning analytics and draw on SaP values and guiding principles, such as reciprocity, emphasis on the process, and shared responsibility to question the current way learning analytics perceives students. By arguing for a change in perspective and the adoption of SaP in learning analytics, we will further add on growing literature about the importance of universities to become transparent and collaborative communities.
Optimal SF Allocation in LoRaWAN Considering Physical Capture and Imperfect Orthogonality

We propose a theoretical framework for maximizing the LoRaWAN capacity in terms of the number of end nodes, when they all have the same traffic generation process. The model optimally allocates the spreading factor to the nodes so that attenuation and collisions are optimized. We use an accurate propagation model considering Rayleigh channel, and we take into account physical capture and imperfect SF orthogonality while guaranteeing a given transmission success probability to each served node in the network. Numerical results show the effectiveness of our SF allocation policy. Our framework also quantifies the maximum capacity of single cell networks and the gain induced by multiplying the gateways on the covered area. We finally evaluate the impact of physical capture and imperfect SF orthogonality on the SF allocation and network performances.
Optimal Control in Wasserstein Spaces

A wealth of mathematical tools allowing to model and analyse multi-agent systems has been brought forth as a consequence of recent developments in optimal transport theory. In this thesis, we extend for the first time several of these concepts to the framework of control theory. The first result presented in this manuscript is the generalization of the Pontryagin Maximum Principle, both in the absence and presence of constraints, to optimal control problems of multi-agent systems studied in the so-called mean-field approximation framework. The proof of this result relies on the generalization of techniques from geometric control theory to the setting of the Riemannian structure of the Wasserstein spaces. Subsequently, we investigate sufficient conditions for the Lipschitz-in-space regularity of mean-field optimal control. These results are generally crucial for ensuring the correspondence between the microscopic multi-agent systems and their macroscopic approximations. We obtain them by combining a mean-field approximation argument with an existence result for Lipschitz optimal feedbacks formulated for microscopic multi-agent models. Later on, we focus our attention on alignment models. We propose a convergence analysis based on Lyapunov-type arguments for cooperative systems subject to random communication failures. We further propose a sparse control strategy which allows to stir weakly-cooperative systems towards a state of almost-alignment. We finally present a result of sub-Riemannian geometry, in which we complete the classification of the generic singularities of the conjugate locus for contact distributions in dimension 3. This result is based on transversality arguments applied to the jets of the metric in a suitable neighbourhood of the origin.