Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Performance LDO Regulator Enabling Low-Power SoC With Voltage Scaling Approaches

Low-power system-on-a-chip (SoC) with multiple voltage domains often adopts voltage scaling approaches to optimize power usage while maintaining enough performance. Voltage regulators having flexible output configurability, fast transient response, and high-power noise rejection ability are indispensable for this application scenario. A low-dropout (LDO) regulator was proposed in this article to convert an input of 1.9–1.1 V to an output of 1.1–0.2 V with a 10-mV tuning resolution by raising the concept of programmable recursively divide-by-two resistor array (PRDTRA). A high gain-bandwidth main regulation loop of the proposed LDO regulator was accompanied by a transient acceleration (TA) path and a unity power noise gain generator to achieve a 28-mV output variation during 0–100-mA load transient test while keeping a 60-dB power supply rejection ratio (PSRR) over a frequency band of 0–1 MHz. Performance evaluations show the performance superiority of the proposed LDO regulator.
Unsupervised Dependency Parsing

Unsupervised dependency parsing is an alternative approach to identifying relations between words in a sentence. It does not require any annotated treebank, it is independent of language theory and universal across languages. However, so far quite low parsing quality is its main disadvantage. This thesis discusses some previous works and introduces a novel approach to unsupervised parsing. Our dependency model consists of four submodels: (i) edge model, which controls the distribution of governor-dependent pairs, (ii) fertility model, which controls the number of node’s dependents, (iii) distance model, which controls the length of the dependency edges, and (iv) reducibility model. The reducibility model is based on a hypothesis that words that can be removed from a sentence without violating its grammaticality are leaves in the dependency tree. Induction of the dependency structures is done using Gibbs sampling method. We introduce a sampling algorithm that keeps the dependency trees projective, which is a very valuable constraint. In our experiments across 30 languages, we discuss the results of various settings of our models. Our method outperforms the previously reported results on a majority of the test languages.
A Classification of Memory-Centric Computing

Technological and architectural improvements have been constantly required to sustain the demand of faster and cheaper computers. However, CMOS down-scaling is suffering from three technology walls: leakage wall, reliability wall, and cost wall. On top of that, a performance increase due to architectural improvements is also gradually saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of research is focusing on proposing and developing new technologies and architectures. In this article, we present a comprehensive classification of memory-centric computing architectures; it is based on three metrics: computation location, level of parallelism, and used memory technology. The classification not only provides an overview of existing architectures with their pros and cons but also unifies the terminology that uniquely identifies these architectures and highlights the potential future architectures that can be further explored. Hence, it sets up a direction for future research in the field.
Exploring Social Media Group Use Among Breastfeeding Mothers: Qualitative Analysis

Background Breastfeeding is well known as the optimal source of nutrition for the first year of life. However, suboptimal exclusively breastfeeding rates in the United States are still prevalent. Given the extent of social media use and the accessibility of this type of peer-to-peer support, the role of social networking sites in enabling and supporting breastfeeding mothers needs to be further explored. Objective This study aimed to leverage mothers’ attitudes and behaviors of social media usage to understand effects on breastfeeding outcomes. Methods Participants were recruited from 1 probreastfeeding social media group with over 6300 members throughout the United States. Online focus group discussions were conducted with 21 women; interviews were conducted with 12 mothers. Qualitative data were aggregated for thematic analysis. Results Participants indicated that the social media group formed a community of support for breastfeeding, with normalizing breastfeeding, empowerment for breastfeeding, resource for breastfeeding, and shared experiences in breastfeeding as additional themes. Conclusions According to participants, social media groups can positively influence breastfeeding-related attitudes, knowledge, and behaviors as well as lead to longer duration of breastfeeding. The results of this study should be taken into account when designing interventions for breastfeeding mothers.
Expanding Taxonomies with Implicit Edge Semantics

Curated taxonomies enhance the performance of machine-learning systems via high-quality structured knowledge. However, manually curating a large and rapidly-evolving taxonomy is infeasible. In this work, we propose Arborist, an approach to automatically expand textual taxonomies by predicting the parents of new taxonomy nodes. Unlike previous work, Arborist handles the more challenging scenario of taxonomies with heterogeneous edge semantics that are unobserved. Arborist learns latent representations of the edge semantics along with embeddings of the taxonomy nodes to measure taxonomic relatedness between node pairs. Arborist is then trained by optimizing a large-margin ranking loss with a dynamic margin function. We propose a principled formulation of the margin function, which theoretically guarantees that Arborist minimizes an upper-bound on the shortest-path distance between the predicted parents and actual parents in the taxonomy. Via extensive evaluation on a curated taxonomy at Pinterest and several public datasets, we demonstrate that Arborist outperforms the state-of-the-art, achieving up to 59% in mean reciprocal rank and 83% in recall at 15. We also explore the ability of Arborist to infer nodes’ taxonomic-roles, without explicit supervision on this task.
A Novel Approach to Secure Cloud Storage Using Network Coding

Exchanging data streams to an advantage rich cloud server for in ward thing appraisal, an essential building block in various surely understood stream applications (e.g., accurate checking), is addressing various associations and individuals. On the other hand, checking the eventual outcome of the remote count accept a basic part in watching out for the issue of trust. Since the outsourced data assembling likely starts from various data sources, it is hungered for the system to have the ability to pinpoint the originator of bumbles by assigning each data source an uncommon secret key, which requires the inward thing affirmation to be performed under any two get-togethers' assorted keys. Regardless, the present plans either depend on upon a single key assumption or extraordinary yet for all intents and purposes wasteful totally homomorphic cryptosystems. In this paper, we focus on the all the more troublesome multi-key circumstance where data streams are exchanged by various data sources with unmistakable keys. We initially exhibit a novel homomorphic unquestionable name system to transparently check the outsourced inward thing figuring on the dynamic data streams, and after that extend it to reinforce the affirmation of cross section thing computation. We exhibit the security of our arrangement in the self-assertive prophet show. What's more, the exploratory outcome moreover shows the practicability of our design.
Research contributions published on betweenness centrality algorithm: modelling to analysis in the context of social networking

Social network analysis has become an inevitable tool for the prosperity of modern civilisation. The process of accumulating relational information from structured/unstructured sources, modelling networks, and extracting actionable information requires expertising in several knowledge domains. This paper presents an approach for the analysis of documents in the context of social networking. The approach is illustrated by using a case study related to research contributions published on betweenness centrality algorithm. Distinct networks in terms of article, article-author, and author are modelled and analysed to understand the insights. Consequently, it is possible to identify crucial articles, active authors, groups along with their expertise, research directions, the correlation among documents, and many more. Thus the paper conferred techniques for document collection, pre-processing, network modelling, and network analysis methods for the directed, undirected, weighted, unweighted, connected, disconnected, and bipartite networks.
An Improved Particle Swarm Optimization Algorithm Based on Restart Strategy

In order to solving the problems of premature convergence and local extremum caused by traditional particle swarm optimization algorithm (PSO algorithm), this paper proposes an improved particle swarm optimization algorithm based on restart strategy (BRSPSO algorithm). In the process of optimization, BRSPSO algorithm can update the speed and location of individual particles, increase the influence of excellent particles according to the mutual influence between particles. It introduces a restart strategy, at the same time, to enlarge the search scope of the algorithm and enhance the searching ability for the sake of preventing the search from falling into the local extremum. In this paper, BRSPSO algorithm is used to optimize the parameters of the support vector machine (SVM). Experiments show that compared with SVM optimized by traditional PSO algorithm and SVM without PSO, SVM optimized by BRSPSO algorithm can expand the search space and effectively avoid the local convergence problem of the traditional PSO algorithm.
Research Commentary: Data as Facts of the World

Information Systems research so far has not come up with a clear conceptualization of the central term of ‘information’. A similar claim can be made about the term ‘data’, which is used inconsistently, but often taken to mean raw information. The field would benefit from clear definitions that distinguish these terms from each other. This paper argues for seeing data as facts of the world and information as data stored and processed in information systems. It aligns this with the ontology of critical realism and outlines how this view can guide future research. Information systems are seen as efforts to capture the facts of the world from the domain of the actual and store them in the domain of the empirical in order to make them accessible for analysis. This view is especially useful in the context of big data research, but also helps to justify why explanatory research in the social science tradition is still essential.
The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical models can ever outperform the random walk in forecasting out-of-sample exchange rates has received scholarly attention. Recently, a combination of fundamental models with machine learning methodologies was found to outcompete the predictability of random walk (Amat et al. 2018). This paper focuses on combining modern machine learning methodologies with traditional economic models and examines whether such combinations can outperform the prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the monetary model, and the Taylor rule models). We performed a thorough robustness check using six government bonds with different maturities and four price indexes, which demonstrated the superior performance of fundamental models combined with modern machine learning in predicting future exchange rates in comparison with the results of random walk. These results were examined using a root mean squared error (RMSE) and a Diebold–Mariano (DM) test. The main findings are as follows. First, when comparing the performance of fundamental models combined with machine learning with the performance of random walk, the RMSE results show that the fundamental models with machine learning outperform the random walk. In the DM test, the results are mixed as most of the results show significantly different predictive accuracies compared with the random walk. Second, when comparing the performance of fundamental models combined with machine learning, the models using the producer price index (PPI) consistently show good predictability. Meanwhile, the consumer price index (CPI) appears to be comparatively poor in predicting exchange rate, based on its poor results in the RMSE test and the DM test.
Research on the clustering algorithm of ocean big data based on self‐organizing neural network

In the construction of a smart marine, marine big data mining has a significant impact on the growing maritime industry in the Beibu Gulf. Clustering is the key technology of marine big data mining, but the conventional clustering algorithm cannot achieve the efficient clustering of marine data. According to the characteristics of marine big data, a marine big data clustering scheme based on self‐organizing neural network (SOM) algorithm is proposed. First, the working principle of SOM algorithm is analyzed, and the algorithm's two‐dimensional network model, similarity model and competitive learning model are focused. Secondly, combining with the working principle of algorithm, the marine big data clustering process and algorithm achievement based on SOM algorithm are developed; finally, experiments show that all vectors in marine big data clustering are stable, and the neurons in the output layer of clustering result have obvious consistency with the data itself, which shows the effectiveness of SOM algorithm in marine big data clustering.
Deep optical character recognition: a case of Pashto language

Abstract. Over the past decades, text recognition technologies have focused immensely on noncursive isolated scripts. A text recognition system for the cursive Pashto script will serve as a great contribution, allowing the traditional, cultural, and educational Pashto literature to be converted into machine-readable form. We propose the use of deep learning architectures based on the transfer learning for the recognition of Pashto ligatures. For recognition analysis and evaluation, the ligature images in the dataset are preprocessed by data augmentation techniques, i.e., negatives, contours, and rotated to increase the variation of each sample and size of the original dataset. Rich feature representations are automatically extracted from the Pashto ligature images using deep convolution layers of the convolution neural network (CNN) architectures using fine-tuned approach. Pretrained CNN architectures: AlexNet, GoogleNet, and VGG (VGG-16 and VGG-19) are used for classification by feeding the extracted features to a fully connected layer and a softmax layer. The proposed deep transfer-based learning has achieved phenomenal recognition rates for Pashto ligatures on benchmark FAST-NU Pashto dataset. An accuracy of 97.24%, 97.46%, and 99.03% is achieved using AlexNext, GoogleNet, and VGGNet architectures, respectively.
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color

3D Instance segmentation is a fundamental task in computer vision. Effective segmentation plays an important role in robotic tasks, augmented reality, autonomous driving, etc. With the ascendancy of convolutional neural networks in 2D image processing, the use of deep learning methods to segment 3D point clouds receives much attention. A great convergence of training loss often requires a large amount of human-annotated data, while making such a 3D dataset is time-consuming. This paper proposes a method for training convolutional neural networks to predict instance segmentation results using synthetic data. The proposed method is based on the SGPN framework. We replaced the original feature extractor with “dynamic graph convolutional neural networks” that learned how to extract local geometric features and proposed a simple and effective loss function, making the network more focused on hard examples. We experimentally proved that the proposed method significantly outperforms the state-of-the-art method in both Stanford 3D Indoor Semantics Dataset and our datasets.
Fault Detection for Low-Voltage Residential Distribution Systems With Low-Frequency Measured Data

Increasing the number of active consumers in distribution networks necessitates transforming the current control, monitoring, and protection schemes. However, on one hand, installing high-frequency measurement devices and fast communication platforms in low-voltage (LV) distribution networks is not cost effective and scalable. On the other hand, the fault detection approaches, which can provide acceptable accuracy by relying only on low-frequency measured data (with 1–30-min sampling rates), are not developed yet. Currently, the overcurrent fault detectors work mainly based on fixed current thresholds, which makes them inefficient in a system with high-distributed-energy resources. This is due to high volatility and uncertainty in the measured profile of the current. In this article, a data-driven fault detection framework with dynamic fault current thresholds is proposed. The motivation here is to develop a framework that can locally detect and isolate faults within the LV distribution networks without requiring high-frequency sampling meters. The proposed model is based on quantile regression as a statistical method to generate the quantiles of distributions of the current measurements. Two different fault current thresholds are formulated for instantaneous and definite time fault detection schemes. The thresholds are dynamically predicted for each next time step. The proposed framework is evaluated using data from a real distribution network with 169 houses. The results suggest that the proposed model is very promising for LV residential distribution networks.
Dataspaces in L3

L3 is a new multitasking and multi user operting system running on 386 based computers. One basic idea of the system is the concept of Dataspace, which is strongly influenced by early multics ideas. Dataspace is the abstract data type modelling storage. The details of memory, disk etc. are hidden. Higher levels of L3 only use Dataspaces for holgding data, no matter whether program code, program data, messages, files and databases. Special advantages of dataspace are cheap (lazy) copyng and the checkpointing facility.
Edge based RGB-D SLAM and SLAM based navigation

Visual Simultaneous Localisation and Mapping (SLAM) is a vital technology for the advancement of autonomous robotics, providing a means of both mapping and pose estimation from visual data. Many advances in visual and RGB-D SLAM however have built upon established point feature and dense reconstruction methodologies, leaving other approaches relatively uncharted in comparison. This thesis instead explores semi-dense edge based approaches for RGB-D SLAM, in addition to path planning approaches for SLAM based indoor aerial robotics. We first present an edge based RGB-D SLAM approach utilizing edges from both depth and RGB channels, along with a method of depth edge detection exploiting the temporal similarity between RGB-D video frames. Detected edge pixels are back-projected to form semi-dense point clouds and a fast edge based Iterative Closest Point (ICP) algorithm is utilized for sensor pose estimation. Evaluation of the proposed SLAM system demonstrates compelling results, achieving similar accuracy to a number of alternative systems while having significantly lower computational cost, thus making it fit for real-time application on small robotic platforms. An extension of this SLAM system to use 3D line features is then presented, along with methods of extracting and registering such line features from semi-dense edge point clouds. Secondly we present a belief space planning approach for autonomous indoor MAVs relying on bearing measurements to environmental landmarks for localisation in cluttered environments. This approach can produce trajectories which minimize uncertainty in the vehicle’s state from such bearing measurements, while reaching an end goal. The approach allows for a tunable trade-off between minimizing trajectory length and state uncertainty, and examples trajectories are given comparing these two extrema in various 3D environments and landmark layouts. Lastly a SLAM-aware path planning approach is presented for indoor MAVs, utilizing the proposed edge based SLAM system for localisation. In addition to ensuring collision avoidance this approach ensures that SLAM localisation is maintained by producing trajectories along which the vehicle’s RGB-D sensor always observes sufficient information from the existing SLAM map. These trajectories are ”keyframe centric” consisting of many connected ”segment” trajectories, each defined within the reference frame of a specific keyframe, together forming a complete trajectory across the map. This scheme allows the MAV to navigate across the SLAM map, even if it has become globally inconsistent due to issues such as sensor drift accumulation.
New insights on laser-based structured light for underwater 3D reconstruction

Three-dimensional visual maps of the seafloor provide objective information to reliably characterise these scenes. These maps are of great study value for archaeological sites or geologically interesting areas. Often, spatial scales over 8 orders of magnitude are needed, from the millimetre resolution to the > 100 km2, to recognise key features of interest to areas over which they are distributed. Delivering precise resolution at scale has been an issue for the last decade. Nowadays, new vision based sensors such as lasers and structured light provide a higher resolution than known acoustic sensors. Using these innovative sensors poses new challenges in underwater localization as most of the mapping error comes now from the self-localization rather than from the measurement sensor. This thesis presents two different solutions to the visual mapping problem. The first part of the PhD presents a novel laser-based structured light system to increase 3D perception resolution, accuracy and frame rate when compared to acoustic counterparts and cameras. The system consists of a laser Diffractive Optical Element (DOE) that diffracts the beam in 25 parallel planes and a camera to recover the intersected lines to the seafloor. We propose a calibration procedure and solve the correspondence problem using a Maximum Spanning Tree algorithm. The experimental results show that the system draws a better representation of the objects in front and outperforms plain stereoscopy in featureless scenarios. The second part of the thesis uses a standard laser stripe instead, to cast a single line on the seafloor, in a bathymetric SLAM solution to correct both the navigation and overall shape of the sensed environment. Two different algorithms are presented, one is sub-map bathymetric SLAM, which saves small map portions to be registered at a later stage using ICP and BPSLAM, a 2.5D grid that treats every scan as a particle in a Particle Filter to find the position that better suits the known map. The developed bathymetric SLAM algorithms are tested in a close-to-shore small rocky area in Valldemossa using the AUV SparusII and in a large survey in the Hydrate Ridge using the AUV AE2000f.