Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Heterogeneity Prevents Synchronization of Fridges With Deterministic Frequency Control

Appliances that cycle on and off throughout the day, such as fridges, freezers, and air-conditioners can collectively provide second-by-second electricity supply-demand balancing known as frequency response. Previous studies have shown that deterministic temperature set-point control of a homogeneous population of such appliances can cause herding behavior with detrimental effects on the system. Here, we use computational modeling to establish the minimum population heterogeneity required to prevent herding problems without requiring centralized or stochastic control. We discover a linear relationship between the benefits that fridges can provide and their number. The impact on system benefits and on fridge temperatures of varying fridge frequency sensitivity is also explored, and a viable range for sensitivity (the control parameter) is proposed. Our approach involves simulating a large heterogeneous population of frequency-sensitive fridges using 12 months’ GB system data from National Grid. We compare the historic frequency response from other response providers with their response in our fridge simulations to determine the benefits of the fridge population response. We find that a fridge population can offer a valuable demand-side response service to the electricity system operator, requiring neither the expensive infrastructure of centralized control nor the regular intervention of stochastic control for temperature cycle desynchronization.
The deflection network: a reliable high speed packet network for computer communication

The performance of deflection networks (extension of Manhattan Street network) under bursty traffic has been analyzed assuming the use of a preference routing algorithm. The throughput performance under these conditions has proven to be almost independent from the burstiness of the traffic. Packets of a single burst are often received in the wrong alignment. Analyses simulating the generation of bursts that do not use all the bandwidth of the links are required. The reliability of the network has been studied in conjunction with the use of the minimum distance (MD), minimum weight (MW) and preference algorithms, and the backword learning (BL) technique, and the results are presented. <<ETX>>
MCF-homomorphisms of cost functions for minimum cost flow InSAR phase unwrapping

Branchcut methods represent an important class of phase unwrapping (PU) approaches in SAR interferometry (InSAR). Their main idea is to circumvent the problem of path dependency for the integration of the wrapped gradient of the wrapped interferometric phase by placing suitable barriers (branchcuts) to the possible integration paths in the image. In 1996 M. Costantini proposed to transform the problem of InSAR PU to a minimum cost flow (MCF) problem. The critical point of this new approach is to generate cost functions which represent the a priori knowledge necessary for phase unwrapping. Since many algorithms for generating cost functions from a priori knowledge have been proposed in the last years, it has become important to decide whether two given methods of computing cost functions lead to the same branchcut system on every interferogram. A formal tool to treat this problem mathematically are universal MCF-homomorphisms. Any mapping a from the space of cost functions into itself is called an universal MCF-homomorphism if every cost function c and its image /spl alpha/(c) have the same MCF solution and if /spl alpha/ can be defined separately for each arc. This paper presents the proof of the following mathematical theorem: The only existing universal MCF-homomorphisms are the componentwise multiplications with a fixed positive scalar.
Detector response models for statistical iterative image reconstruction in high resolution PET

One limitation in a practical implementation of an interative image reconstruction is to build a transition matrix accurately modeling the relationship between projection and image spaces. Detector response functions (DRF) in PET are broad and spatially-variant, leading to large transition matrices taking too much space to store. In this work, we investigate the effect of simpler DRF models on image quality in ML-EM reconstruction. We studied 6 cases of increasing complexity: simple tube/pixel geometric overlap; geometric overlap with tubes centered on DRF maximum; two different fixed-width Gaussians centered on DRF maximum; same as previous with a Gaussian of same spectral resolution as DRF; simulated DRF based on linear attenuation of /spl gamma/-rays in detector arrays. Oversimplification of DRFs may affect image quality dramatically. Simulated DRFs generate a transition matrix that can be easily fitted into the memory of current stand-alone computers and yield images of excellent quality for a small animal PET system with long narrow detectors.
A Calibration-Free 13-Bit 10-MS/s Full-Analog SAR ADC With Continuous-Time Feedforward Cascaded Op-Amps

This paper presents a calibration-free 13-bit 10-MS/s full-analog successive-approximation-register analog-to-digital converter (SAR ADC) in 40-nm CMOS, which fully utilizes the timing and power budgets in a bit-conversion cycle by eliminating the digital circuits entirely. Continuous-time feedforward cascaded (CTFC) op-amps are proposed to enhance the residue power without the necessity of high-precision quantizers. As opposed to the residue amplifiers (RAs) in multi-step/pipelined ADCs, the CTFC op-amps in the open-loop configuration are implemented with the relaxing gain–bandwidth product (GBW) and without the limitations of accurate gain, precise settling, and voltage swing. Inverter-based regenerative-amplifier (IRA)-based zero-crossing detectors (ZCDs) with self-triggered amplification-to-regeneration (A-to-R) operation are developed to perform serial full-analog SAR bit conversions. The stability in multiple negative feedback (NFB) loops formed by the CTFC op-amps and ZCDs is analyzed and confirmed. The input-referred noise (IRN) and input-referred offset (IRO) in each bit conversion are suppressed by the CTFC op-amps and tolerated by the digital-to-analog converter (DAC) radix and sub-ADC arrangements. The LSB repeating is adopted to entirely cancel out the suppressed IRO mismatches among the bit conversions in the fine ADC. This work occupies an active area of 0.013 mm2 and achieves the Nyquist-rate signal-to-noise-and-distortion ratio (SNDR), spurious-free dynamic range (SFDR), Walden figure-of-merit (FoMw), and Schreier FoM (FoMS) of 67.6 dB, 77.2 dB, 3.3 fJ/conversion-step, and 176.5 dB, respectively.
Performance aspects of WCDMA systems with high speed downlink packet access (HSDPA)

In this paper, we consider the link and network layer performance aspects of a WCDMA/UTRA system with high speed downlink packet access. The study considers packet scheduling and the tradeoff among user fairness and cell throughput. We show performance numbers for different network setups and study the applicability of proportional scheduling methods. Even with conservative system and traffic settings, the best effort methods produce high user data rates and cell throughput.
Australasian Association for Logic 1999 Annual Conference Co-Sponsored by The Association for Symbolic Logic

A bracket abstraction algorithm is a means of translating lambda terms into combinators, and so, via the Curry-Howard isomorphism, of translating natural deduction style proofs in implicational intuitionistic logic into Hilbert style proofs. Broda and Damas (in [1]) introduce a new, rather natural set of combinators and a new form of abstraction which introduces at most one combinator for each lambda abstraction. This leads to particularly compact combinatory terms (and so proofs). A disadvantage of their abstraction process is that it includes the whole original Schonﬁnkel algorithm plus two mappings which convert the Schonﬁnkel abstract into the new abstract. This paper shows how the new abstraction can be done more directly, in fact, using only 2 n − 1 algorithm steps if there are n occurrences of the variable to be abstracted in the term. Some properties of Broda-Damas combinators are considered and the logic corresponding to the new combinators is shown to be represented most simply by two axioms and three rules (none of them modus ponens ). [
An Integrated Approach for Probabilistic Assessment of Ionized Fields of HVDC Lines

An integrated statistical approach, based on Monte Carlo simulation, is developed in this paper for probabilistic exposure assessment under HVDC transmission lines. The validity and adequacy of traditional, deterministic, models for HVDC ionized fields have been repeatedly questioned in literature. The present work recognizes the probabilistic nature of the exposure variables, electric field intensity and ion current density, and caters for their inherent uncertainties in order to simulate the realistic dc line environment . Randomness in the factors influencing the ionized field quantities is identified and accounted for, namely, the wind speed, the line sag, the corona onset gradient, and the location of objects under the line. Based on an overall probabilistic approach, the integrated influence of these factors on the exposure parameters is evaluated and assessed.
Object Serialization Formats and Techniques a review

Serialization is a process of converting an object into a stream of data so that it can be easily transmittable over the network or can be continued in a persistent storage location. This storage location can be a physical file, database or Network Stream. This paper concludes some the work that is going on in the field of Object Serialization This paper presents Object Serialization Techniques that can be useful for various purposes, including object serialization Minimization which can be used to decrease the size of Serialized data.
Abnormal traffic flow data detection based on wavelet analysis

In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.
KONTROL ROBOT BERBASIS ARDUINO MEGA 2560 DENGAN MENGGUNAKAN SINYAL EEG-SMT GERAK TANGAN

EEG-SMT adalah perangkat untuk mendeteksi sinyal biomedis dengan menggunakan sensor katoda dan anoda yang menggambarkan gelombang bio listrik seseorang. Parameter yang digunakan dalam penelitian ini adalah tangan sebagai kendali gerak maju dan mundur robot yang dikomunikasikan melalui komputer yang dihubungkan ke mikrokontroler arduino sebagai pengatur gerak motor. Sinyal output EEG-SMT ditampilkan dan direkam menggunakan software openvibe untuk keperluan identifikasi sinyal. Dengan bantuan program matlab yang dibangun untuk mengklasifikasi sinyal output EEG SMT dengan mengidentifikasi karakter sinyal dari variasi frekuensi dan amplitudo sinyal yang dihasilkan EEG SMT, pengolahan sinyal digital dan berfungsi dan sebagai GUI yang menghubungkan antara EEG-SMT dengan mikrokontroler arduino mega 2560 dengan mengirimkan inisialisasi karakter sebagai sinyal deklarasi dari EEG-SMT ke arduino mega 2560. Adapun respons gerak motor yang dihasilkan adalah maju, mundur dan berhenti.
Convolutional Neural Networks for Financial Text Regression

Forecasting financial volatility of a publicly-traded company from its annual reports has been previously defined as a text regression problem. Recent studies use a manually labeled lexicon to filter the annual reports by keeping sentiment words only. In order to remove the lexicon dependency without decreasing the performance, we replace bag-of-words model word features by word embedding vectors. Using word vectors increases the number of parameters. Considering the increase in number of parameters and excessive lengths of annual reports, a convolutional neural network model is proposed and transfer learning is applied. Experimental results show that the convolutional neural network model provides more accurate volatility predictions than lexicon based models.
Analysis of Influence of Topic Models and Different External Corpus to Text Classification

With the topic analysis models increasingly used in text categorization, many methods are developed to utilize topic analysis models to deal with the noises and sparseness of the text. It is very necessary to estimate the influence of the topic models to classification. And although exploiting external knowledge to enrich semantic of the text has achieved satisfactory results, choosing an appropriate universal corpus is still a knotty problem. In this study, we use topics extracted from texts by the LDA algorithm in two ways (using topics only and combining the topics and texts) to analyze the effect of topic models. And we also make use of different external corpus to value the importance of the external knowledge. The experimental results show that the topic models and the combination of different external datasets benefit categorization a lot.
DIKW Application on Knowledge Based Framework with Situational Awareness

Currently, there is no formal method of collecting valuable tacit knowledge especially on MILOBS which they gained through experiences and formal training skills. The problem arises when the MILOBS have come to the end of their service in which they will bring along the knowledge. Furthermore, these issues become crucial when the knowledge is not fully transferred to the new person in charge. Therefore, there is a need to extract the tacit knowledge through a systematic approach. The approach in developing a new framework as a guideline combines elements of Data Information Knowledge and Wisdom (DIKW) hierarchy. In short, DIKW hierarchy can help us to understand more about MILOBS’ tacit knowledge. The objective of this paper is to interpret knowledge and wisdom in the DIKW hierarchy into the knowledge based framework with situational awareness analysis. The findings of this study are to propose a new framework and to improve the traditional model of knowledge creation on Nonaka’s model by adding the element of situational awareness and wisdom.
Automatic Sequential to Parallel Code Conversion

The way software programs are being written has been redefined since the introduction of multicore processors. Software developers have started writing parallel programs that are robust and scalable. This would ensure use of processor power being made available in the form of multiple cores. Though this trend is increasing, there are legacy applications that have been developed over the past few decades. Most of these applications are inherently sequential making no use of multithreading or parallel programming. If such applications are ported to execute on the multicore hardware as they are then optimal usage of all cores is not guaranteed. Such applications would ideally utilize only one core and the other cores would remain idle, unless the operating system supports some parallelism while scheduling. Hence there is a need to convert such legacy sequential codes to their parallel versions so that multicore hardware is exploited to the fullest. In this paper we present a tool that we have developed to automatically convert a sequential C code to parallel code. This Sequential to Parallel (S2P) tool is still in the development phase. We also discuss other parallelization tools available today, compare such tools with S2P tool and present our performance analysis results on different kind of multicore hardware.
Active Reading Comprehension: A dataset for learning the Question-Answer Relationship strategy

Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where a high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader’s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.
Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

i TITLE PAGE . iii COPYRIGHT . iv i DEDICATION . vi ACKNOWLEDGEMENTS . vi LIST OF TABLES . xiii LIST OF FIGURES . xiii LIST OF ACRONYMS AND ABBREVIATIONS . xvii
Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers. Our experiments using a real-world dataset of 33,000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood "valence and arousal" with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.
Vehicle-borne laser mapping system (VLMS) for 3-D GIS

We have developed a vehicle-borne laser mapping system (VLMS), which consists of Laser Scanners, Line Cameras, GPS and INS. The system uses range data, scanned by the laser scanners as the main source of 3D data. In this paper, we present about the development of VLMS, which includes the system architecture, calibration and geo-referencing of sensors and positioning devices. Next, we present the extraction of some basic features like building surfaces, road surfaces (man made features) and trees (natural features). Finally, we will show how these features can be used to build 3D urban GIS database, which will assist in various applications as mentioned above. Besides, we will also discuss about the capabilities and difficulties of such a system.
Level-Up: Learning to Improve Proficiency Level of Essays

We introduce a method for generating suggestions on a given sentence for improving the proficiency level. In our approach, the sentence is transformed into a sequence of grammatical elements aimed at providing suggestions of more advanced grammar elements based on originals. The method involves parsing the sentence, identifying grammatical elements, and ranking related elements to recommend a higher level of grammatical element. We present a prototype tutoring system, Level-Up, that applies the method to English learners’ essays in order to assist them in writing and reading. Evaluation on a set of essays shows that our method does assist user in writing.
Near optimal routing protocols for source location privacy in wireless sensor networks: modelling, design and evaluation

Wireless Sensor Networks (WSNs) are collections of small computing devices that are used to monitor valuable assets such as endangered animals. As WSNs communicate wirelessly they leak information to malicious eavesdroppers. When monitoring assets it is important to provide Source Location Privacy (SLP), where the location of the message source must be kept hidden. Many SLP protocols have been developed by designing a protocol using intuition before evaluating its performance. However, this does not provide insight into how to develop optimal approaches. This thesis will present an alternate approach where the SLP problem is modelled using different techniques to give an optimal output. However, as this optimal output is typically for a restricted scenario, algorithms that trade optimality for generality are subsequently designed. Four main contributions are presented. First, an analysis is performed based on entropy and divergence to gain insight into how to reduce the information an attacker gains via the use of competing paths, and ways to compare the information loss of arbitrary routing protocols. Secondly, the SLP problem is modelled using Integer Linear Programming. The model result guides the design of a generic protocol called ILPRouting that groups messages together to reduce the moves an attacker makes. Thirdly, a timing analysis of when events occur is used to dynamically determine fake source parameters for the Dynamic and DynamicSPR algorithms. These fake sources lure the attacker to their location instead of the real source. Finally, the first SLP-aware duty cycle is investigated, and implemented for DynamicSPR to make it more energy efficient. These techniques are evaluated through simulations and deployments on WSN testbeds to demonstrate their effectiveness.
School OASIS: Virtual outreach - facilitating the transition to university study

Too few people are choosing engineering careers, and many engineering faculties are attempting to address this problem by reaching out to schools. This paper describes how a home-grown, web-based software tool, already used successfully in university-level engineering courses, is being modified for high-school use. The software package, OASIS, comprises a large question database and server-side program that delivers individualised tasks, marks student responses, supplies prompt feedback, and logs student activity. OASIS can be used for both skills practice and formal assessment. Because the Web server carries out all processing, students need only a computer with internet access and a standard browser, making OASIS well suited to student-centered and distance learning.