Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Data Processing Infrastructure by Using Apache Spark Big Data Platform: Twitter Data Analysis

Social media provide continuous data streams that contain information with different level of sensitivity, validity and accuracy. Therefore, this type of information has to be properly filtered, extracted and processed to avoid noisy and inaccurate results. The main goal of this work is to propose architecture and workflow able to process Twitter social network data in near real-time. The primary design of the introduced modern architecture covers all processing aspects from data ingestion and storing to data processing and analysing. This paper presents Apache Spark and Hadoop implementation. The secondary objective is to analyse tweets with the defined topic --- floods. The word frequency method (Word Clouds) is shown as a major tool to analyse the content of the input dataset. The experimental architecture confirmed the usefulness of many well-known functions of Spark and Hadoop in the social data domain. The platforms which were used provided effective tools for optimal data ingesting, storing as well as processing and analysing. Based on the analytical part, it was observed that the word frequency method (n-grams) can effectively reveal the tweets content. According to the results of this study, the tweets proved their high informative potential regarding data quality and quantity.
The Impact of Group Size on the Discovery of Hidden Profiles in Online Discussion Groups

Online discussions help individuals to gather knowledge and make important decisions in diverse areas from health and finance to computing and data science. Online discussion groups exhibit unique group dynamics not found in traditional small groups, such as staggered participation and asynchronous communication, and the effects of these features on knowledge sharing is not well understood. In this article, we focus on one such aspect: wide variation in group size. Using a controlled experiment with a hidden profile task, we evaluate online discussion groups’ capacity to share distributed knowledge when group size ranges from 4 to 32 participants. We found that individuals in medium-sized discussions performed the best, and we suggest that this represents a tradeoff in which larger groups tend to share more facts, but have more difficulty than smaller groups at resolving misunderstandings.
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis

The application of unsupervised learning approaches, and in particular of clustering techniques, represents a powerful exploration means for the analysis of network measurements. Discovering underlying data characteristics, grouping similar measurements together, and identifying eventual patterns of interest are some of the applications which can be tackled through clustering. Being unsupervised, clustering does not always provide precise and clear insight into the produced output, especially when the input data structure and distribution are complex and difficult to grasp. In this paper we introduce EXPLAIN-IT, a methodology which deals with unlabeled data, creates meaningful clusters, and suggests an explanation to the clustering results for the end-user. EXPLAIN-IT relies on a novel explainable Artificial Intelligence (AI) approach, which allows to understand the reasons leading to a particular decision of a supervised learning-based model, additionally extending its application to the unsupervised learning domain. We apply EXPLAIN-IT to the problem of YouTube video quality classification under encrypted traffic scenarios, showing promising results.
Mining Tourist's Perception toward Indonesia Tourism Destination Using Sentiment Analysis and Topic Modelling

Indonesia's Tourism industries is the second-best contributor to country's foreign exchange income for years. The growth it produced were in range of 7 to 10 percent per year since 2009. In reality, Bali is always at the top of the mind among majority of international tourists inspite of many hidden gems with spectacular qualities to rival Bali's popularity. Therefore, Indonesia's government has set new tourist destinations to increase their presence thus increasing visitation numbers. Researches on utilizing big data to support industry 4.0 model in tourism businesses are encouraged as part of national research priorities. Data Analytics models such as Sentiment Analysis and Topic Modelling can be used to reveal hidden patterns from abundant user-generated content data available in social media sites, one of which, TripAdvisor. This study aims to mine the visitors' perceptions of 10 most visited sites in Indonesia. Emotions and topics discussed in comments are two features to be extracted. Using data mining framework, five types of emotion and topics related to tourism were discovered. Data collection was done using Parsehub to acquire 3494 comments generated in 4 years. Both Sentiment Analysis and Topic Modelling were processed in Orange 3. Results shows that Joy is the most prominent emotion accompanying visitors' experiences. Topic modeling shows several important keywords toward preferences. Results of this study can be used to improve Indonesia's tourism stakeholder's decision quality especially in terms of marketing and operations.
Chunking for Dynamic Linear Pipelines

Dynamic scheduling and dynamic creation of the pipeline structure are crucial for efficient execution of pipelined programs. Nevertheless, dynamic systems imply higher overhead than static systems. Therefore, chunking is the key to decrease the synchronization and scheduling overhead by grouping activities. We present a chunking algorithm for dynamic systems that handles dynamic linear pipelines, which allow the number and duration of stages to be determined at run-time. The evaluation on 44 cores shows that chunking brings the overhead of dynamic scheduling down to that of a static scheduler, and it enables efficient and scalable execution of fine-grained dynamic linear pipelines.
Optimizations in C++ Compilers

There’s a tradeoff to be made in giving the compiler more information: it can make compilation slower. Technologies such as link time optimization can give you the best of both worlds. Optimizations in compilers continue to improve, and upcoming improvements in indirect calls and virtual function dispatch might soon lead to even faster polymorphism.
Near Optimal Parallel Algorithms for Dynamic DFS in Undirected Graphs

Depth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm [54] for building a DFS tree requires O(m+n) time for a given undirected graph G having n vertices and m edges. Recently, Baswana et al. [5] presented a simple algorithm for updating the DFS tree of an undirected graph after an edge/vertex update in Õ(n)1 time. However, their algorithm is strictly sequential. We present an algorithm achieving similar bounds that can be easily adopted to the parallel environment. In the parallel environment, a DFS tree can be computed from scratch in expected Õ(1) time [2] on an EREW PRAM, whereas the best deterministic algorithm takes Õ(√ n) time [2, 27] on a CRCW PRAM. Our algorithm can be used to develop optimal time (to poly log n factors) deterministic parallel algorithms for maintaining fully dynamic DFS and fault tolerant DFS of an undirected graph. (1) Parallel Fully Dynamic DFS: Given an arbitrary online sequence of vertex or edge updates, we can maintain a DFS tree of an undirected graph in Õ(1) time per update using m processors on an EREW PRAM. (2) Parallel Fault tolerant DFS: An undirected graph can be preprocessed to build a data structure of size O(m), such that for any set of k updates (where k is constant) in the graph, a DFS tree of the updated graph can be computed in Õ(1) time using n processors on an EREW PRAM. For constant k, this is also work optimal (to poly log n factors). Moreover, our fully dynamic DFS algorithm provides, in a seamless manner, nearly optimal (to poly log n factors) algorithms for maintaining a DFS tree in the semi-streaming environment and a restricted distributed model. These are the first parallel, semi-streaming, and distributed algorithms for maintaining a DFS tree in the dynamic setting.
Analysis of AMI, Smart Metering Deployment and Big Data Management Challenges

Sustainability, rise of consumer power, IOT, Cloud Computing and digitalization era are major factors driving the fourth industrial revolution. Electric energy stakeholders are automating technological and business processes to the new requirements of the energy sector. Deploying the right combination of AI, Advanced Metering Infrastructure and Big Data Analysis, will increase efficiency and self-optimize operations. The emerging markets including the Middle East will deploy nearly 250 million meters, representing an investment of almost $35bn. The GCC increasing appetite power generating capacity is 157 GW; which is almost half the total Middle East and North Africa power generating capacity Smart Grid market in Gulf Council Countries will top US$ 1.68 billion by 2026. In addition, GCC is projected to spend US$137 Billion to increase its power generating capacity, Transmission and Distribution by 69 MW within five years. This research focus on the analysis of AMI and smart metering deployment and challenges in GCC region.
A Hybrid Model for Ride-hailing Service Demand Forecasting

The accurate prediction of the ride-hailing service demand is important for the improvement of transport capability. Due to the nonlinearity and instability of the time series, the prediction accuracy is not ideal. In this paper, we proposes a hybrid model that combines Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), and Radial Basis Function Neural Network (RBFNN) together, which takes advantage of the good fitting ability of the ARIMA model and the strong nonlinear relation mapping ability of the RBFNN model, and use DWT to decompose the time series in order to isolate the influence of each resolution coefficient to a certain extent. The experimental results show that the proposed model has higher prediction accuracy than the single model such as ARIMA, RBFNN and other hybrid models such as ARIMA-DWT, RBFNN-DWT, RBFNN-ARIMA, SVR-ARIMA-DWT, LSTM-ARIMA-DWT.
Response to the editor decision and the reviews, concerning the discussion paper “A multi-proxy analysis of late Quaternary Indian monsoon dynamics for the Maldives, Inner Sea”

As a natural sediment trap, marine sediments of the sheltered central part of the Maldives Inner Sea represent an exceptional archive for paleoenvironmental and climate changes of the equatorial Indian Ocean. To evaluate the complex 10 interplay between high-latitude and monsoonal climate variability, related dust fluxes, and regional oceanographic responses, we focused on Fe/Al, Ti/Al and Si/Ca ratios as proxies for terrigenous sediment delivery, and total organic carbon (TOC) and Br XRF counts as proxies for marine productivity. Benthic foraminiferal fauna distributions, grain size, and stable δ 18 O and δ 13 C data were used for evaluating changes in the benthic ecosystem, as well as changes in the intermediate water circulation, bottom water current velocity and oxygenation. 15 Our multi-proxy data record reveals an enhanced dust supply during the glacial intervals, causing elevated Fe/Al and Si/Ca ratios, an overall coarsening of the sediment and an increasing amount of agglutinated benthic foraminifera. The enhanced dust fluxes can be attributed to higher dust availability in the Asian desert and loess areas and its transport by intensified winter monsoon winds during glacial conditions. These combined effects of wind-induced mixing of surface waters and dust fertilisation during the cold phases resulted in an increased surface-water productivity and related organic 20 carbon fluxes. Thus, the development of highly diverse benthic foraminiferal faunas with certain detritus and suspension feeders were fostered. The difference in the δ 13 C signal between epifaunal and deep infaunal benthic foraminifera reveals intermediate water oxygen concentrations between approximately 40 and 100 μmol kg -1 during this time. The precessional fluctuation pattern of oxygen changes resembles that from the deep Arabian Sea, suggesting an expansion of the Oxygen Minimum Zone (OMZ) from the Arabian Sea into the tropical Indian Ocean with a probable regional signal of strengthened 25 winter-monsoon-induced organic matter fluxes and oxygen consumption, and further controlled by the varying inflow intensity of the Antarctic Intermediate Water (AAIW). In addition, the bottom water oxygenation pattern of the Maldives Inner Sea reveals a long phase of reduced ventilation during the last glacial period. This process is likely linked to the combined effects of generally enhanced oxygen consumption rates during high-productivity phases, reduced AAIW production, and the restriction of upper bathyal environments of the Inner Sea during sea-level lowstands. Thus, our multi30 proxy record reflects a close linkage between the Indian monsoon oscillation, intermediate water circulation, productivity and sea-level changes on orbital time-scale.
Energy Minimization and Preconditioning in the Simulation of Athermal Granular Materials in Two Dimensions

Granular materials are heterogenous grains in contact, which are ubiquitous in many scientific and engineering applications such as chemical engineering, fluid mechanics, geomechanics, pharmaceutics, and so on. Granular materials pose a great challenge to predictability, due to the presence of critical phenomena and large fluctuation of local forces. In this paper, we consider the quasi-static simulation of the dense granular media, and investigate the performances of typical minimization algorithms such as conjugate gradient methods and quasi-Newton methods. Furthermore, we develop preconditioning techniques to enhance the performance. Those methods are validated with numerical experiments for typical physically interested scenarios such as the jamming transition, the scaling law behavior close to the jamming state, and shear deformation of over jammed states.
Efficient Hardware Implementation of Incremental Learning and Inference on Chip

In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. The proposed design is able to accommodate for both new examples and new classes directly on the chip. We detail the hardware implementation of the method (implemented on FPGA target) and show it requires limited resources while providing a significant acceleration compared to using a CPU.
Graph Learning for Spatiotemporal Signal with Long Short-Term Characterization

Mining natural associations from high-dimensional spatiotemporal signals have received significant attention in various fields including biology, climatology and financial analysis, etcetera. Due to the widespread correlation in diverse applications, ideas that taking full advantage of correlated property to find meaningful insights of spatiotemporal signals have begun to emerge. In this paper, we study the problem of uncovering graphs that better reveal the relations behind data, with the help of long and short term correlated property in spatiotemporal signals. A spatiotemporal signal model considering both spatial and temporal relationship is firstly presented. Particularly, a low-rank representation together with a Gaussian Markov process is adopted to describe the signals' time-correlated behavior. Next, we cast the graph learning problem as a joint low-rank component estimation and graph Laplacian inference problem. A Low-Rank and Spatiotemporal Smoothness-based graph learning method (GL-LRSS) is proposed, which novelly introduces spatiotemporal smooth prior to the field of time-vertex signal analysis. Through jointly exploiting the low-rank property of long-time observations and the smoothness of short-time observations, the overall performance is effectively improved. Experiments on both synthetic and real-world datasets demonstrate the significant improvement on learning accuracy of the proposed GL-LRSS over current state-of-the-art low-rank estimation and graph learning methods.
Nonlinear Model Predictive Control with Actuator Constraints for Multi-Rotor Aerial Vehicles

In this paper we propose, test, and validate an online Nonlinear Model Predictive Control (NMPC) method applied to multi-rotor aerial systems with arbitrarily positioned and oriented rotors. This work brings into question some common modeling and control design choices that are typically adopted in order to guarantee robustness and reliability but which may severely limit the attainable performance. In particular the proposed method \emph{does not} resort to common simplifications such as: 1) linear model approximation, 2) cascaded control paradigm used to decouple the translational and the rotational dynamics of the rigid body, and 3) use of low level reactive trackers for stabilization, 4) unconstrained system or use of fictitious constraints. The method addresses simultaneously the problem of local reference trajectory planning and that of stabilizing the vehicle dynamics. Furthermore, by considering as control inputs the derivatives of the forces generated by the multi-rotor vehicle and by means of a novel actuator modeling approach, the method avoids conservative -- and often fictitious -- input/state saturations which are present, e.g., in cascaded approaches. The control algorithm is implemented using a state-of-the-art Real Time Iteration (RTI) scheme with partial sensitivity update method. The performances of the control system are finally validated by means of real-time simulations and in real experiments, with a large spectrum of multi-rotor systems: an \emph{under-actuated} quadrotor, a \emph{fully actuated} hexarotor, a multi-rotor with \emph{orientable} propellers, and a multi-rotor with an unexpected \emph{rotor failure}.
Computation Offloading in the Untrusted MEC-aided Mobile Blockchain IoT System

Deploying mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of MEC server. To address this problem, we first propose an untrusted MEC proof of work scheme in mobile blockchain network where plenty of nonce hash computing demands can be offloaded to MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user's nonce selection strategy as a non-cooperative game, where the utilities of individual user are maximized in the untrusted MEC-aided mobile blockchain network. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for the blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain's difficulty adjustment mechanism to ensure stable block times during a long period of time.
Deep Unsupervised Clustering with Clustered Generator Model

This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both continuous and discrete latent variables. Discrete latent variables model the cluster label while the continuous ones model variations within each cluster. The learning of the model proceeds in a unified probabilistic framework and incorporates the unsupervised clustering as an inner step without the need for an extra inference model as in existing variational-based models. The latent variables learned serve as both observed data embedding or latent representation for data distribution. Our experiments show that the proposed model can achieve competitive unsupervised clustering accuracy and can learn disentangled latent representations to generate realistic samples. In addition, the model can be naturally extended to per-pixel unsupervised clustering which remains largely unexplored.
Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Cultural and social dynamics are important concepts that must be understood in order to grasp what a community cares about. To that end, an excellent source of information on what occurs in a community is the news, especially in recent years, when mass media giants use social networks to communicate and interact with their audience. In this work, we use a method to discover latent topics in tweets from Colombian Twitter news accounts in order to identify the most prominent events in the country. We pay particular attention to security, violence and crime-related tweets because of the violent environment that surrounds Colombian society. The latent topic discovery method that we use builds vector representations of the tweets by using FastText and finds clusters of tweets through the K-means clustering algorithm. The number of clusters is found by measuring the $C_V$ coherence for a range of number of topics of the Latent Dirichlet Allocation (LDA) model. We finally use Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction to visualise the tweets vectors. Once the clusters related to security, violence and crime are identified, we proceed to apply the same method within each cluster to perform a fine-grained analysis in which specific events mentioned in the news are grouped together. Our method is able to discover event-specific sets of news, which is the baseline to perform an extensive analysis of how people engage in Twitter threads on the different types of news, with an emphasis on security, violence and crime-related tweets.
Optimal Single-Choice Prophet Inequalities from Samples

We study the single-choice Prophet Inequality problem when the gambler is given access to samples. We show that the optimal competitive ratio of $1/2$ can be achieved with a single sample from each distribution. When the distributions are identical, we show that for any constant $\varepsilon > 0$, $O(n)$ samples from the distribution suffice to achieve the optimal competitive ratio ($\approx 0.745$) within $(1+\varepsilon)$, resolving an open problem of Correa, Dutting, Fischer, and Schewior.
Phonon-induced Floquet topological phases protected by space-time symmetries

The co-existence of spatial and non-spatial symmetries together with appropriate commutation/anticommutation relations between them can give rise to static higher-order topological phases, which host gapless boundary modes of co-dimension higher than one. Alternatively, space-time symmetries in a Floquet system can also lead to anomalous Floquet boundary modes of higher co-dimensions, presumably with alterations in the commutation/anticommutation relations with respect to non-spatial symmetries. We show how a coherently excited phonon mode can be used to promote a spatial symmetry with which the static system is always trivial, to a space-time symmetry which supports non-trivial Floquet higher-order topological phase. We present two examples -- one in class D and another in class AIII where a coherently excited phonon mode promotes the reflection symmetry to a time-glide symmetry such that the commutation/anticommutation relations between spatial and non-spatial symmetries are modified. These altered relations allow the previously trivial system to host gapless modes of co-dimension two at reflection-symmetric boundaries.