Miracle-dz/UniFAR-FTUs huggingface dataset:
Storage in Power Systems: Frequency Control, Scheduling of Multiple Applications, and Computational Complexity

The ongoing worldwide deployment of wind and solar power plants is leading to increasing interest in storage technologies. Indeed, storage can effectively time-shift production/consumption profiles. Furthermore, most storage technologies also feature higher response speed and ramping rate than those of conventional generators. Finally, storage units can be installed at specific grid locations to alleviate local issues. However, storage investment cost is still often prohibitive, and storage requires re-thinking numerous power system concepts, due to i) a strong inter-temporal coupling, ii) operating costs driven by physical degradation, and iii) regulation issues, as storage units cannot be classified as either transmission, generation, nor consumption assets. The main goal of this thesis is to propose different methods to improve storage profitability and reduce computational burden. The focus is on operational problems with characteristic times ranging from minutes to days. It also highlights how the present regulations can hinder storage penetration. More specifically, this thesis first proposes different methods enabling energy-constrained storage units to participate in Frequency Control (FC). These methods take advantage of the higher ramping rate of storage units, and rely on slower conventional generators to take care of the FC signal bias. The methods presented in the fourth chapter are heuristic-based and consider a single storage unit. They are shown to perform better than other existing heuristic-based methods. The method proposed in the fifth chapter relies on distributed optimization and considers a set of generators and storage units. This method additionally features low computational burden and robustness to loss of communication links.
End-to-End Music Transcription Using Fine-Tuned Variable-Q Filterbanks

The standard time-frequency representations calculated to serve as features for musical audio may have reached the extent of their effectiveness. General-purpose features such as Mel-Frequency Spectral Coefficients or the Constant-Q Transform, while being pyschoacoustically and musically motivated, may not be optimal for all tasks. As large, comprehensive, and well-annotated musical datasets become increasingly available, the viability of learning from the raw waveform of recordings widens. Deep neural networks have been shown to perform feature extraction and classification jointly. With sufficient data, optimal filters which operate in the time-domain may be learned in place of conventional time-frequency calculations. Since the spectrum of problems studied by the Music Information Retrieval community are vastly different, rather than relying on the fixed frequency support of each bandpass filter within standard transforms, learned time-domain filters may prioritize certain harmonic frequencies and model note behavior differently based on a specific music task. In this work, the time-frequency calculation step of a baseline transcription architecture is replaced with a learned equivalent, initialized with the frequency response of a Variable-Q Transform. The learned replacement is fine-tuned jointly with a baseline architecture for the task of piano transcription, and the resulting filterbanks are visualized and evaluated against the standard transform.
A Novel Method for Scanpath Comparison Based on Levenshtein Distance

Modeling the scanpath of human saccade on presentation pictures is a challenging task. To evaluate the consistence of saccadic eye movement sequences, traditional methods, such as scanMatch, transform scanpath data to string sequence with grid-based method and computer the similarity of string sequences using Needleman-Wunsch algorithm. However, scanpath data are different from string data in some aspects, and grid-based method would cause errors in measurement. In the present work, the difference between two symbol sequences was measure by a modified Levenshtein Distance (LD) method. Here, the pair-point distance was computed from position of different fixation points directly, and the distance was further converted to 0 or 1 by a thresholding method. In this way we can adjust the matching threshold value between points and improved the accuracy of LD method by overcoming measurement error of point around grid boundary. Our experimental results demonstrated that our method had better performance than traditional scanMatch.
Nonlinear Vectorial Metasurface for Optical Encryption

Nonlinear Vectorial Metasurface for Optical Encryption Yutao Tang,1,2 Yuttana Intaravanne3, Junhong Deng,1,2 King Fai Li,1,2 Xianzhong Chen,3,* and Guixin Li1,2,† 1Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China. 2Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China. 3SUPA, Institute of Photonics and Quantum Sciences, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK.
Privileged Learning using Unselected Features

This thesis proposes a novel machine learning paradigm called Learning using Unselected Features (LUFe), which front-loads computation to training time in order to improve classifier performance, without additional cost at deployment. This is achieved by repurposing and combining techniques from feature selection and Learning Using Privileged Information (LUPI). Feature selection is a means of reducing model complexity, which enables deployment in devices with limited computational power, but this can waste additional resources which may be available at training time. LUPI is a paradigm that allows extra information about the training data to be harnessed by the learner, but this requires an additional set of highly informative attributes. In the LUFe setting, feature selection is used to partition datasets into primary and secondary subsets, instead of discarding the features which are unselected. Both datasets are then passed to a LUPI algorithm, enabling the secondary feature-set to provide additional guidance at training time only, in place of `privileged' information. Only the selected features are used at train time, maintaining low-cost deployment while exploiting train-time resources. Experimental results on a large number of datasets demonstrate that LUFe facilitates an improvement in classification accuracy over standard feature selection approaches in a majority of cases. This performance boost is consistent across a range of feature selection approaches, and is largest when the SVM+ algorithm is used for implementation. This effect is shown to be partially dependent on the usage of information in the unselected features, as well as resulting from the presence of additional constraints on the function space searched for the model. The enhancement by LUFe is shown to be inversely correlated with the performance of standard feature selection and mediated by a further reduction in model variance, beyond that provided by standard feature selection. Aside from demonstrating the direct practical benefit of LUFe, this work makes the contribution of broadening the scope of applications for the LUPI framework.
AnchorViz

When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e., it has feature blindness). Yet it is unrealistic to ask humans to come up with an exhaustive list of items, especially for rare concepts that are hard to recall. This article presents AnchorViz, an interactive visualization that facilitates the discovery of prediction errors and previously unseen concepts through human-driven semantic data exploration. By creating example-based or dictionary-based anchors representing concepts, users create a topology that (a) spreads data based on their similarity to the concepts and (b) surfaces the prediction and label inconsistencies between data points that are semantically related. Once such inconsistencies and errors are discovered, users can encode the new information as labels or features and interact with the retrained classifier to validate their actions in an iterative loop. We evaluated AnchorViz through two user studies. Our results show that AnchorViz helps users discover more prediction errors than stratified random and uncertainty sampling methods. Furthermore, during the beginning stages of a training task, an iML tool with AnchorViz can help users build classifiers comparable to the ones built with the same tool with uncertainty sampling and keyword search, but with fewer labels and more generalizable features. We discuss exploration strategies observed during the two studies and how AnchorViz supports discovering, labeling, and refining of concepts through a sensemaking loop.
A Novel Hybrid FLANN-PsO Technique for real Time Fingerprint Classification

In this paper we are presenting a Particle swarm optimized functional link neural network for classifying a collection of real time fingerprints in the field of biometric recognition. From the collected fingerprints the feature vectors are extracted as a collection of different angle oriented features using the Gabor filter bank. The classes of the fingerprints are assigned as per the Henry System. For classification a novel FLANN-PSO algorithm is used and tested for accuracy through different parameters and different angular features of the fingerprints. In this work we have obtained an accuracy of 98% for real time collected fingerprint images. It has been compared with other classifiers and the results obtained of this work in terms of accuracy and MSE value has shown appreciable improvement over the other algorithms.
Estimating Sustained Service Rates at Signalized Intersections with Short Left-Turn Pockets

A computational approach for incorporating the effects of short left-turn pockets on sustained service rates in a mesoscopic modeling environment is presented. Mesoscopic models, intended to handle fairly large networks while maintaining individual vehicle identity, provide a detailed yet efficient alternative to estimate sustained service rates. However, mesoscopic models typically ignore midlink perturbations and queuing, thus limiting their reliability in the presence of short left-turn pockets at signalized intersections. The model presented here relies on a gating mechanism situated at the entry point to the left-turn pocket. Through a series of logical triggers, the gating mechanism allows for the formation of a (vertical) queue of vehicles upstream of the pocket when arrivals exceed storage capacity. The method satisfies all assumed requirements for integrating the effects of short left-turn bays, which include pocket spillback, pocket starvation, and sensitivity to signal timing and phase sequence. The approach has been implemented within the mesoscopic modeling platform DYNASMART for both single and multiple left-turn pockets, with the use of varying pocket lengths and demand volumes. The resulting sustained service rates favorably compare to those generated by a representative microsimulation model (VISSIM). Further comparisons of the proposed approach against empirical observations are planned.
Choosing features for classifying multiword expressions

A major requirement for a classification of multiword expressions (MWEs) is that it be explicitly based on features of the MWEs. Designing a satisfactory classification involves choosing such features. Accordingly, the resulting classification may be more or less fruitful for computational use. We show the benefits of intensive description of the lexicon. It is costly, but not unfeasible: lexicon-grammars of MWEs with representation of individual features have been published as early as 1974 for support-verb constructions in French, and 1985 for verbal idioms in English, and some are used in parsing now.
Chat Ref Analysis—Exploring and Analyzing Chat Reference Transcripts Specifically Relating to Acquisitions and Discovery

Abstract The Chat Reference Analysis project is an effort to understand and analyze the topics of concern for users with particular focus on issues that can be addressed by the Acquisitions and Discovery (A&D) Department. Conclusions drawn from a sampling of chat transcripts data using topic model analysis revealed a variety of human readable subtopic themes intended to identify improvements in certain domain areas. Additionally the study provided recommendations and opportunities for potential small-group/spin-off projects. The chat reference transcripts referred to here are the conversations between library patrons and staff supporting the chat service featured on several areas on the NC State University Libraries’ website.
High-dimensional statistics, with applications to genome-wide association studies

We present a selective review on high-dimensional statistics where the dimensionality of the unknown parameter in a model can be much larger than the sample size in a dataset (e.g. the number of people in a study). Particular attention is given to recent developments for quantifying uncertainty in high-dimensional scenarios. Assessing statistical uncertainties enables to describe some degree of replicability of scientific findings, an ingredient of key importance for many applications. We also show here how modern high-dimensional statistics offers new perspectives in an important area in genetics: novel ways of analyzing genome-wide association studies, towards inferring more causal-oriented conclusions.
Theorising effective uses of digital technology with activity theory

ABSTRACT Effective uses of digital technologies are vital to full inclusion in a network society. Digital-abilities researchers have produced several major frameworks, but these have generally not incorporated socio-contextual perspectives. To explore this lacuna, and engage in a reflective act of theorisation, activity theory is used to conceptualise four sub-systems of digitally mediated action. Eschewing technological determinism, humans are positioned as active agents capable of identifying, taking up, modifying and even subverting established technology uses in pursuit of meaningful objectives. At the same time, attention is given to contextual conditions shaping diverse activity systems supported by assemblages of humans and machines. Having theorised effective digital-technology uses through the lens of activity theory, the author reflects on this conceptual apparatus itself. In so doing, activity theory is characterised as a fertile, if complex and contested, future-oriented tradition that challenges individual-social dichotomies and addresses both humans and machines as mediators of activity, development and learning.
The power of neural oscillations to inform sentence comprehension: A linguistic perspective

The field of psycholinguistics is currently experiencing an explosion of interest in the analysis of neural oscillations - rhythmic brain activity synchronized at different temporal and spatial levels. Given that language comprehension relies on a myriad of processes, which are carried out in parallel in distributed brain networks, there is hope that this methodology might bring the field closer to understanding some of the more basic (spatially and temporally distributed, yet at the same time often overlapping) neural computations that support language function. In this review we discuss existing proposals linking oscillatory dynamics in different frequency bands to basic neural computations, and review relevant theories suggesting associations between band-specific oscillations and higher-level cognitive processes. More or less consistent patterns of oscillatory activity related to certain types of linguistic processing can already be derived from the evidence that has accumulated over the past few decades. The centerpiece of the current review is a synthesis of such patterns grouped by linguistic phenomenon. We restrict our review to evidence linking measures of oscillatory power to the comprehension of sentences, as well as linguistically (and/or pragmatically) more complex structures. For each grouping, we provide a brief summary and a table of associated oscillatory signatures that a psycholinguist might expect to find when employing a particular linguistic task. Summarizing across different paradigms, we conclude that a handful of basic neural oscillatory mechanisms are likely recruited in different ways and at different times for carrying out a variety of linguistic computations.
PROJECT BASED LEARNING: PEMANFAATAN VLOG DALAM PEMBELAJARAN SEJARAH UNTUK GENERASI PRO GADGET

Generasi pro gadget merupakan generasi yang tidak asing dengan teknologi. Masalah yang seringkali terjadi pada saat pembelajaran ialah siswa cenderung malas memperhatikan guru dan lebih suka menggunakan gadgetnya sehingga motivasi dalam belajar rendah. Dalam menghadapi siswa yang pro gadget maka guru harus dapat memilih media yang sesuai dengan karakteristik siswa. Vlog merupakan media yang dapat dimanfaatkan untuk pembelajaran berbasis Project Based Learning. Melalui tugas proyek siswa secara langsung akan memahami materi yang sedang dipelajari. Tugas proyek berupa pembuatan Vlog cocok digunakan dalam Matapelajaran sejarah. Hal tersebut dikarenakan Vlog cenderung dengan kegiatan untuk bercerita yang hasilnya berupa video (audio visual). Melalui pemanfaatan Vlog siswa dapat lebih kreatif dan termotivasi dalam belajar sejarah karena siswa dalam proses pembuatannya diberikan permasalahan-permasalahan untuk memicu rasa keingintahuan siswa. The pro generation of gadgets is a generation that is no stranger to technology. The problem that often occurs when learning is that students tend to be lazy to pay attention to the teacher and prefer to use the gadget so that motivation in learning is low. In dealing with students who are pro gadgets, the teacher must be able to choose the media that fits the characteristics of students. Vlog is a media that can be used for learning based on Project Based Learning. Through project assignments students will directly understand the material being studied. The project task in the form of making Vlogs is suitable for use in historical subjects. This is because Vlog tends to be an activity to tell stories, the results of which are videos (audio visual). Through the use of Vlog students can be more creative and motivated in learning history because students in the manufacturing process are given problems to trigger students' curiosity. DOI: http://dx.doi. org/10.17977/um020v13i12019p84
Centimeter-Level Recording for All: Field Experimentation with New, Affordable Geolocation Technology

Abstract The methodical recording and representation of spatial data are central to archaeological fieldwork and research. Until recently, centimeter-level precise geolocation equipment was the exclusive domain of researchers who could afford setups costing tens of thousands of dollars. However, high-quality measurements are being made more accessible by rapidly evolving technologies. These new tools, when used together with mobile technology for efficiently recording field data, open up the possibility of capturing the precise location of every find during an archaeological surface survey. An important step in reaching the desired outcome—centimeter-level recording for all—is experimentation with a variety of emerging low-cost setups. Accordingly, we tested the Reach and Reach RS, differential global navigation satellite systems (dGNSS) equipment produced by the company Emlid, during a surface survey in Armenia in June 2018. Our field application demonstrates that the use of dGNSS is already possible and that the described advances in precision enable improved recording and representation of spatial data. El registro y la representación sistemático de datos espaciales son fundamentales para el trabajo de campo y la investigación arqueológicos. Hasta hace poco, el equipo de geolocalización de precisión en centímetros era de dominio exclusivo para los investigadores que podían costearse herramientas de decenas de miles de dólares. Sin embargo, el área se está acercando rápidamente a un momento en que las mediciones de alta calidad se harán más accesibles mediante tecnologías de rápida evolución. Cuando se usan junto con la tecnología móvil para registrar datos de campo de manera eficiente, esto abre la posibilidad de capturar la ubicación precisa de cada hallazgo durante un examen de la superficie arqueológica. Un paso importante para alcanzar el resultado deseado—el registro a nivel de centímetros para todos—es la experimentación realizada por investigadores con una variedad de equipos emergentes de bajo costo. Por consiguiente, probamos el Reach y Reach RS, el equipo diferencial GNSS producido por la empresa Emlid, durante un reciente estudio de superficie en Armenia en junio de 2018, e informamos de nuestros resultados aquí. Concluimos que, incluso con algunos desafíos, nuestra aplicación de campo demuestra que el dGNSS asequible ya es posible, y que los avances descritos en la precisión permitirán un mejor registro y representación de los datos espaciales.
Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 2: Numerical Results for the Overhead and Underground Medium-Voltage Power Grids

With reference to the initial statistical hybrid model (iSHM) and modified statistical hybrid model (mSHM), the theory of the definition procedure of new virtual indicative distribution BPL topologies by appropriately adjusting the channel attenuation statistical distributions (CASDs) parameters of iSHM and mSHM has been presented in [1]. In this paper, the results of the definition procedure for the OV and UN MV BPL networks are first presented through the prism of the proposed class maps of iSHM and mSHM. Citation: Lazaropoulos, A. G. (2019). Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 2: Numerical Results for the Overhead and Underground Medium-Voltage Power Grids. Trends in Renewable Energy, 5, 258-281. DOI: 10.17737/tre.2019. 5.3. 00100
Model Segmentation and Simplification for Electric Power Distribution Systems

Quasi-static time-series (QSTS) simulation is used to simulate the behavior of electric power distribution systems over long periods of time (typically hours to years). The technique involves repeatedly solving the load-flow problem for a distribution system model. This is useful in accounting for solar power variations in distributed energy resource (DER) planning. When a QSTS simulation has a small enough time step and a long enough duration, the computational burden of the simulation can be prohibitive. One way to relieve the computational burden is to simplify the model of the distribution system. This dissertation includes an overview of existing methods of distribution system simplification and also introduces a new method, segment substitution, which addresses many of the limitations of the existing methods. The segment substitution method offers dramatic (i.e. more than 98%) model order reduction with a simplification error that is expected to be acceptable for many applications. In contrast to existing methods of distribution system model simplification, which rely on topological inspection and linearization, the segment substitution method can be used to produce a simplified model using black-box segment data and an assumed simplified topology. It also produces a more realistic simplified approximation of constant-power load models than existing methods. Simplification using segment substitution is demonstrated using two full-scale distribution system models to achieve simulation performance gains of over 90% while introducing a state error less than 0.2%.