Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Online Social Ties from Offline Geographical Activities

As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed. Each mobile individual can belong to an online social network. Unfortunately, the underlying online social relationships are hidden and only available to service providers. Acquiring the social network of mobile users would enrich lots of mobile applications, such as friend recommendation and energy-saving mobile database management. In this work, we propose to infer online social ties using purely offline geographical activities of users, such as check-in records and spatial meeting events. To tackle the problem, we devise a novel inference framework, O2O-Inf, which consists of two components, Feature Modeling and Link Inference. Feature modeling is to characterize both direct and indirect geographical interactions between nodes from co-location and graph features. Link inference aims to infer the social ties based on a small set of observed social links, and the idea is that pairs of nodes sharing similar geographical behaviors have the same tendency of linkage (i.e., either being friends or non-friends). Experiments conducted on a Gowalla location-based social network and a Meetup event-based social network exhibit a satisfying performance in comparison to state-of-the-art prediction methods under the settings of offline-to-online network inference and geo-link prediction.
Mining Moodle Data to Detect the Inactive and Low-performance Students during the Moodle Course

In web-based learning systems such as massive open online course (MOOC) and modular object-oriented developmental learning environment (Moodle), monitoring the student's activities as well as predict the low-performance students is an important task because it enables the instructors to award the students when their activities level drops from normal activities levels as well as having lower grades. We used several machine learning (ML) classification and clustering techniques to extract the pattern from student data during completing the Moodle course; which enables the instructor to detect the low-performance student in advance before the examination. The experimental result shows that the fuzzy unordered rule induction algorithm (FURIA) classification technique achieves high accuracy in detecting inactive students as well as predicts the different categories of the student during the Moodle course. The K-means clustering is also able to group the inactive and active users and poorly performed users. The result demonstrates that our proposed system will be easily integrated to Moodle system to send alert to inactive and low- performance students while completing the course and build efficient education environment for the students.
Evaluation of IoT gateways for developing communities: smart Maputo

Connectivity is the essential requirement for a smart city, and traditional techniques are too costly to connect a great number of devices which might be served with lower data throughput, but require longer ranges to accommodate the requirements of IoT. Although cellular technologies and Wi-Fi have proved an enormous success in connecting billions of users to the Internet, they are not the best to connect objects with limited throughput requirements, limited processing power and low power consumption requirements. These needs are being addressed by Low Power Wide Area Networks (LPWAN) that use unlicensed frequencies and by newer cellular protocols aimed at lowering the cost of the devices by limiting their bandwidth capabilities in the licensed bands. This paper discusses these technologies and the choice made to provide an IoT infrastructure for the city of Maputo in Mozambique.
Low Precision Constant Parameter CNN on FPGA

We report FPGA implementation results of low precision CNN convolution layers optimized for sparse and constant parameters. We describe techniques that amortizes the cost of common factor multiplication and automatically leverage dense hand tuned LUT structures. We apply this method to corner case residual blocks of Resnet on a sparse Resnet50 model to assess achievable utilization and frequency and demonstrate an effective performance of 131 and 23 TOP/chip for the corner case blocks. The projected performance on a multichip persistent implementation of all Resnet50 convolution layers is 10k im/s/chip at batch size 2. This is 1.37x higher than V100 GPU upper bound at the same batch size after normalizing for sparsity.
PointWise: An Unsupervised Point-wise Feature Learning Network

We present a novel approach to learning a point-wise, meaningful embedding for point-clouds in an unsupervised manner, through the use of neural-networks. The domain of point-cloud processing via neural-networks is rapidly evolving, with novel architectures and applications frequently emerging. Within this field of research, the availability and plethora of unlabeled point-clouds as well as their possible applications make finding ways of characterizing this type of data appealing. Though significant advancement was achieved in the realm of unsupervised learning, its adaptation to the point-cloud representation is not trivial. Previous research focuses on the embedding of entire point-clouds representing an object in a meaningful manner. We present a deep learning framework to learn point-wise description from a set of shapes without supervision. Our approach leverages self-supervision to define a relevant loss function to learn rich per-point features. We train a neural-network with objectives based on context derived directly from the raw data, with no added annotation. We use local structures of point-clouds to incorporate geometric information into each point's latent representation. In addition to using local geometric information, we encourage adjacent points to have similar representations and vice-versa, creating a smoother, more descriptive representation. We demonstrate the ability of our method to capture meaningful point-wise features through three applications. By clustering the learned embedding space, we perform unsupervised part-segmentation on point clouds. By calculating euclidean distance in the latent space we derive semantic point-analogies. Finally, by retrieving nearest-neighbors in our learned latent space we present meaningful point-correspondence within and among point-clouds.
Reducing Age-of-Information for Computation-Intensive Messages via Packet Replacement

Freshness of data is an important performance metric for real-time applications, which can be measured by age-of-information. For computation-intensive messages, the embedded information is not available until being computed. In this paper, we study the age-of-information for computation-intensive messages, which are firstly transmitted to a mobile edge server, and then processed there to extract the embedded information. The packet generation follows zero-wait policy, by which a new packet is generated when the last one is just delivered to the edge server. The queue in front of the edge server adopts one-packet-buffer replacement policy, meaning that only the latest received packet is preserved. We derive the expression of average age-of-information for exponentially distributed transmission time and computing time. Numerical results show that the average age with packet replacement is smaller than that without packet replacement, especially when transmission rate is close to or greater than computing rate. In addition, the region where edge computing outperforms local computing is characterized.
A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones

Fully miniaturized robots (e.g., drones), with artificial intelligence (AI)-based visual navigation capabilities, are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nano-drones with a size of a few cm2. In this paper, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on board resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultralow-power computing platform, and a 27-g commercial, open-source Crazyflie 2.0 nano-quadrotor. As part of our general methodology, we discuss the software mapping techniques that enable the DroNet state-of-the-art deep convolutional neural network to be fully executed aboard within a strict 6 frame-per-second real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner, it achieves 18 frames/s while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-unmanned aerial vehicles (UAVs), we publicly release all our code, datasets, and trained networks.
Fine-grained quantum computational supremacy

(pp1089-1115) Tomoyuki Morimae and Suguru Tamaki doi: https://doi.org/10.26421/QIC19.13-14-2 Abstracts: Output probability distributions of several sub-universal quantum computing models cannot be classically efficiently sampled unless some unlikely consequences occur in classical complexity theory, such as the collapse of the polynomial-time hierarchy. These results, so called quantum supremacy, however, do not rule out possibilities of super-polynomial-time classical simulations. In this paper, we study ``fine-grained" version of quantum supremacy that excludes some exponential-time classical simulations. First, we focus on two sub-universal models, namely, the one-clean-qubit model (or the DQC1 model) and the HC1Q model. Assuming certain conjectures in fine-grained complexity theory, we show that for any a>0 output probability distributions of these models cannot be classically sampled within a constant multiplicative error and in 2^{(1-a)N+o(N)} time, where N is the number of qubits. Next, we consider universal quantum computing. For example, we consider quantum computing over Clifford and T gates, and show that under another fine-grained complexity conjecture, output probability distributions of Clifford-T quantum computing cannot be classically sampled in 2^{o(t)} time within a constant multiplicative error, where t is the number of T gates.
The Smart2 Speaker Blocker: An Open-Source Privacy Filter for Connected Home Speakers

The popularity and projected growth of in-home smart speaker assistants, such as Amazon's Echo, has raised privacy concerns among consumers and privacy advocates. Notable questions regarding the collection and storage of user data by for-profit organizations include: what data is being collected and how is it being used, who has or can obtain access to such data, and how can user privacy be maintained while providing useful services. In addition to concerns regarding what the speaker manufacturer will do with your data, there are also more fundamental concerns about the security of these devices, third-party plugins, and the servers where they store recorded data. To address these privacy and security concerns, we introduce an intermediary device to provide an additional layer of security, which we call the \textit{smart, smart speaker blocker} or Smart\textsuperscript{2} for short. By intelligently filtering sensitive conversations, and completely blocking this information from reaching a smart speaker's microphone(s), the Smart$^2$ Speaker Blocker is an open-source, network-local (offline) smart device that provides users with decisive control over what data leaves their living room.
Realidad Virtual (HMD) e Interacción desde la Perspectiva de la Construcción Narrativa y la Comunicación: Propuesta Taxonómica

Virtual reality is a term popularised by some authors since the eighties and the nineties, in order to define features for immersive environments generated artificially by digital means. Immersive processes in virtual environments have dramatically changed since early experiences in this area, such as Sensorama (1962), flight simulators (in the sixties) or Aspen Movie Map Experiment (1978). Those changes offer new possibilities in communication and storytelling domains. This work does a systematic literature review about those phenomena, approaching also the emergence of interdisciplinary fields such as interaction design or user experience. The study aims also to configure a taxonomic classification of components in the domain of storytelling for the use of virtual reality technologies, trying to generate models which help to complement the current ones. This taxonomy is a qualitative milestone for studying interactive dimensions between technology, person and environment.
Technical Report on Visual Quality Assessment for Frame Interpolation

Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r. t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.
Weighted Label Propagation Algorithm based on Local Edge Betweenness

In complex networks, especially social networks, networks could be divided into disjoint partitions that the ratio between the number of internal edges (the edges between the vertices within same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting these communities helps data scientists to extract meaningful information from graphs and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. However, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. In this paper, we have tried to improve Label Propagation Algorithm by using edge betweenness metric, so that it is able to identify distinct communities in both real world and artificial networks in near linear time complexity with acceptable accuracy. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable.
Regular Expression Search on Compressed Text

We present an algorithm for searching regular expression matches in compressed text. The algorithm reports the number of matching lines in the uncompressed text in time linear in the size of its compressed version. We define efficient data structures that yield nearly optimal complexity bounds and provide a sequential implementation –zearch– that requires up to 25% less time than the state of the art.
Mathematical models of cardiovascular control by the autonomic nervous system

This PhD thesis develops an integrated mathematical model for autonomic nervous system control on cardiovascular activity. The model extensively covers cardiovascular neural pathways including a wide range of afferent sensory neurons, central processing by autonomic premotor neurons, efferent outputs via preganglionic and postganglionic autonomic neurons and dynamics of neurotransmitters at cardiovascular effectors organs. We performed over 500 cardiovascular experiments using clinical autonomic tests on 72 subjects ranging from 11 to 82 years old and collected typical cardiovascular signals such as electrocardiogram, arterial pulse, arterial blood pressure, respiration pattern, galvanic skin response and skin temperature. After statistical evaluation in the time and frequency domains, the data were especially used to resolving a constrained optimization task. Results bring evidences supporting the hypothesis that Mayer waves result from a rhythmic sympathetic discharge of pacemaker-like sympathetic premotor neurons. Simulation also shows that vagally-mediated tachycardia, observed during vagal maneuvers on some subjects could be related to the secretion of vasoactive neurotransmitters by the vagal nerve. We additionally identified model parameters for estimating the resting sympathetic and parasympathetic tone which are believed to be linked to some pathological states. Results show higher vagal tone on young subjects with a decreasing trend with aging, what agrees with the data from heart rate variability studies. Tonic sympathetic activity was found to possibly emerge from pacemaker premotor neurons, but also from activation of chemoreceptors to a lesser extent. The thesis opens perspectives for future work including validating the markers of autonomic tone provided by our model against data from experiments with pharmacological blockers and invasive neural activity recordings.
Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using Machine Learning Algorithms

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.
A Predictive Model for Forecasting Spare Parts Demand in Military Logistics

The proportion of the stock range that is devoted to spare parts is often considerable in industrial context. Accordingly, even small improvements in forecasting spare parts demand might lead to substantial cost savings. Time series analysis has been the most popularly applied method in the prior spare part demand forecasting models. However, these approaches need to be improved in terms of prediction accuracy. In this study, we gathered component consumption data including structured and unstructured data from a spare part management information system in military logistics. We proposed demand forecasting models based on data mining and text mining techniques. The results show that our approach can improve the prediction performance compared to that of existing approaches.
Multi-hypothesis prediction for disparity compensated light field compression

In this paper, we describe a multi-hypothesis prediction technique for disparity-compensated light field compression. Multi-hypothesis prediction has been used extensively in video compression. In this work, we apply multi-hypothesis prediction to the problem of light field compression. Most current techniques for light field compression utilize some form of disparity compensation with one hypothesis. We demonstrate that a multi-hypothesis approach, where two hypotheses are used, improves the overall efficiency of a light field coder. Our experimental results show an image quality gain of up to 1 dB in PSNR on our test data sets.
Weightless Neural Network WiSARD Applied to Online Recommender Systems

Recommender systems generally are made to predict user preferences' for items. However, in high dimensional datasets this task demands high computational costs. Taking into account that data distribution changes through time, it is important that online recommender systems have a fast retraining process in order to keep the model updated, delivering accurate predictions. Therefore, we propose a new approach for recommender systems using a weightless neural network, denominated WiSARD. We show that our proposal increases training and prediction processing speed, without decreasing the quality of predictions. First results show that our proposal is 306% faster than the improved regularized singular value decomposition (IRSVD), a well-known state-of-the-art algorithm. Moreover, our proposal still had an improvement of 3.7% regarding the mean absolute error (MAE). We show how to apply the WiSARD algorithm for online recommender systems, its drawbacks, and insights for further research.
The Role of Participation in the Factory Planning Process

The continuous improvement of the current construction and planning processes is a relevant factor for the company’s success. New and existing factories must be economical, flexible and attractive at the same time. On the basis of constantly changing or even new requirements, continuous planning adjustments are required. An increasingly important need in factory planning, in addition to the participation of internal planning stakeholders, is the participation of external actors. More and more often, inadequate project-specific involvement of these actors leads to delays or cost increases in factory planning projects. This can be caused, for example, by increasing regulatory requirements, declining technology acceptance of the population in the direct environment and growing legal capacity to appeal to residents ("Not-In-My-Backyard"). Therefore, the participation of external actors in factory planning projects has not been sufficiently considered in the relevant literature. In order to enable stakeholder acceptance, appropriate project-specific integration of relevant stakeholders into the construction project is crucial. The basis for efficient participation is the choice of the right participation features. This article addresses the current state of research in the intersecting point of factory planning and participation research and shows the need for further research.
Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem

It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.