Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)

Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.
MATERIAL CHARACTERIZATION OF ADDITIVELY MANUFACTURED PA12 AND DESIGN OF MULTIFUNCTIONAL SATELLITE STRUCTURES

Increasing cost pressure on satellite builders and their suppliers push the motivation to open up for new designs and processes. This paper investigates the applicability of thermoplastic additive manufacturing for mass production of satellites. First, the potential of the cost-effective 3D-printing material Polyamide 12 for space structures is examined. Tests include mechanical and thermal-vacuum properties. In the second step, a multifunctional technology demonstrator is designed and a first qualification test is performed. This demonstrator integrates electronic and thermal management components and shows considerable volume savings. Additionally, the automatable processes used for manufacturing enable further cost reductions in series production.
Automatic correction of lithography hotspots with a deep generative model

Deep learning has recently been successfully applied to lithography hotspot detection. However, automatic correction of the detected hotspots into non-hotspots has not been explored. This problem is challenging because the standard supervised learning requires a training dataset with pairs of hotspots and non-hotspots, which is impractical to collect because lithography hotspots involve diverse and complicated lithographic pattern properties. In this paper, we propose a new framework for lithography hotspot correction with a deep generative network combined with a learning strategy optimized for lithography patterns. Our key idea is to learn to translate hotspots to non-hotspots and vice versa, simultaneously. In this way, the training dataset does not have to be paired, and hotspot patterns in variety of background can be learned. Our method does not require the understanding of the cause of hotspots and can correct hotspots that are difficult to recognize by conventional approaches. For evaluation, we propose to synthesize a training dataset that reflects a variety of real-world lithography patterns. Experimental results show that our framework can correct hotspot images with comparable quality as a conventional complicated process, while significantly reducing the overall processing time.
Autonomous "Figure-8" Flights of a Quadcopter: Experimental Datasets

This article describes the data acquired from multiple flights of a custom-built quadcopter. The Quadcopter was programmed to fly a pre-defined “Figure-8” flight path, at a constant altitude. The data set includes flights with a varying number of waypoints (10 and 15 waypoints in each lobe of the “Figure-8”) and at two different velocities (1.5 and 2.5 m/s). The data also contains information on the output of the flight controller in terms of the Pulse Width Modulation (PWM) signals to each of the four Electronic Speed Controllers (ESC) driving the motors, the recorded outputs of the Inertial Measurement Unit (linear accelerations ax, ay, az and angular velocities p, q, r), GPS data (Latitude, Longitude, altitude, Horizontal Dilution of Precision (HDOP) and Vertical Dilution of Precision (VDOP). The data are included as Supplemental Material.
Virtual Force Based Sink Relocation Strategy in Wireless Sensor Networks

Sink relocation has been proven as an effective solution to relieve the network hot point problem, and to balance nodes’ energy consumption and thus prolong the network lifetime for wireless sensor networks. In this paper, we propose a Virtual Force based Sink Relocation strategy, called VFSR, which utilizes the resultant force derived from two kinds of virtual force, gravitational one and border repulsive one, to impel the sink node to relocate to the desired position. In VFSR, the gravitational force exists between each sensor and the sink, and is proportional to the node’s residual energy and inversely proportional to the distance from the node to the sink. The border repulsive force is adapted to avoid the sink node wandering nearby the network border. Under the action of the gravitational and the border repulsive force, the sink node approaches the nodes with high residual energy and makes them relay data from other nodes, which reduces data transmission overload of the nodes with low energy. Through simulation, we validate the effectiveness of VFSR.
Modelling the Semantic Change Dynamics using Diachronic Word Embedding

In this contribution, we propose a computational model to predict the semantic evolution of words over time. Though semantic change is very complex and not well suited to analytical manipulation, we believe that computational modelling is a crucial tool to study such phenomenon. Our aim is to capture the systemic change of word"s meanings in an empirical model that can also predict this type of change, making it falsifiable. The model that we propose is based on the long short-term memory units architecture of recurrent neural networks trained on diachronic word embeddings. In order to illustrate the significance of this kind of empirical model, we then conducted an experimental evaluation using the Google Books N-Gram corpus. The results show that the model is effective in capturing the semantic change and can achieve a high degree of accuracy on predicting words" distributional semantics.
Bezos World Or Levelers: Can We Choose Our Scenario?

Author(s): Lempert, Robert | Abstract: Artificial intelligence (AI) augurs changes in society at least as large as those of the industrial revolution. But much of the policy debate seems narrow – extrapolating current trends and asking how we might manage their rough edges. This essay instead explores how AI might be used to enable fundamentally different future worlds and how one such future might be enabled by AI algorithms with different goals and functions than those most common today.
The aesthetics display, program and learning features: A validation toward STEM based e-module for learning integrated science

The purpose of this study was to develop STEM Based e-module that is a learning software for improving secondary school students interest in STEM field. Four questions are addressed to unpack the feasibility of e-module that is used for integrated science teaching. The questions are 1) How is the aesthetic display of STEM Based e-module 2) How is the program of e-module 3) How are the learning features presented in e module 4) How are STEM aspects performed in e-module. Data were obtained by exploring the view of three experts in STEM education fields. Instrument used was a rubric consisting 29 questions and open questions about e-module strength and weakness as well as the experts recommendation. The study reveals that the aesthetics display, program, learning features and STEM aspects of e-module were categorized into excellent, fair, excellent and excellent respectively. STEM aspects presented in e-module are appropriate for secondary school STEM based teaching. This finding showed that feasibility of e-module implementation in secondary school setting is highly recommended. Nevertheless, an experimental study is necessary to validate the e-module quantitatively.
A Heuristic Method for the Multi-skill Project Scheduling Problem with Partial Preemption

In this article we consider a new scheduling problem known as the Multi-Skill Project Scheduling Problem with Partial Preemption. The main characteristic of this problem is the way we handle the resources release during the preemption periods: only a subset of resources are released. Since this problem is NP-hard, we propose a greedy algorithm based on priority rules, modeling the subproblem of technicians allocation as a Minimum-Cost Maximum-Flow problem. In order to improve the performance of the greedy algorithm, we propose a randomized tree-based local search algorithm. Computational tests are carried out and analyzed.
A filter design method based on higher-order modes of fan-shaped half-mode substrate integrated waveguide resonator

In this paper, a novel 22.5◦ fan-shaped halfmode substrate integrated waveguide resonator (FSHMSIWR) is presented firstly. Based on it, a filter design method by suppression or utilization of higher-order modes is proposed. When feeding on magnetic wall, the fundamental mode (TM101) is excited to generate passband response, while the higherorder modes (TM201 and TM301) can be suppressed. A pair of complementary split-ring resonators (CSRRs) are introduced to strengthen the out-band rejection. Then a single-band bandpass filter (BPF) with ultra-wide and deep stopband is realized. When feeding on electric wall, the higher-order modes can be well excited, which provides flexibility to realize multi-band BPFs only by changing the feeding position. As verification, a single-band BPF and a tri-band BPF with source-load coupling are designed and fabricated. The measured results show good agreement with the simulated ones. key words: fan-shaped half-mode substrate integrated waveguide resonator (FSHMSIWR), single-band BPF, tri-band BPF, higher-order mode Classification: Microwave and millimeter wave devices, circuits, and hardware
Fog Computing Enabled Future Mobile Communication Networks: A Convergence of Communication and Computing

The convergence of communication and computing (COM2P) has been taken as a promising solution for the sustainable development of mobile communication systems. The introduction of fog computing in future mobile networks makes COM2P possible. This article provides an overview on fog computing enabled mobile communication networks (FogMNW), including network architecture, system capacity and resource management. First, this article analyzes the heterogeneity of FogMNW with both advanced communication techniques and fog computing. Then a heterogeneous communication and hierarchical fog computing network architecture is proposed. With both communication and computing resources, FogMNW is enabled to achieve much higher capacity than conventional communication networks. This has been well demonstrated by the coded multicast scheme. Furthermore, a systematic management of communication and computing resources is necessary for FogMNW. By exploiting the communication load diversity in N cells, a communication load aware CLA scheme can achieve much higher computing resource efficiency than comparing schemes. The performance gap increases with N, and CLA can improve efficiency by more than 100 percent when there are 14 cells.
A Novel Allocation Algorithm for Evacuation Simulation Based on Cellular Automaton

In this paper we study the evacuation simulation in buildings using the cellular automaton theory. First, we study the rules of path selection in personnel evacuation. Second, we introduce the individual competence to solve the conflict problem caused by multiple people compete for the same cell. Third, we propose a novel evacuation allocation algorithm and improved the simulation model. The proposed model takes the individual competence, the conformist mentality, and the fog influence into consideration. The experimental results show that the improved simulation model is reliable and close to the actual situation, and we hope this work may contribute to the design of evacuation systems in buildings.
A lexical bottleneck in shadowing and translating of narratives

ABSTRACT In simultaneous interpreting, speech comprehension and production processes have to be coordinated in close temporal proximity. To examine the coordination, Dutch-English bilingual participants were presented with narrative fragments recorded in English at speech rates varying from 100 to 200 words per minute and they were asked to translate the fragments into Dutch (interpreting) or repeat them in English (shadowing). Interpreting yielded more errors than shadowing at every speech rate, and increasing speech rate had a stronger negative effect on interpreting than on shadowing. To understand the differential effect of speech rate, a computational model was created of sub-lexical and lexical processes in comprehension and production. Computer simulations revealed that the empirical findings could be captured by assuming a bottleneck preventing simultaneous lexical selection in production and comprehension. To conclude, our empirical and modelling results suggest the existence of a lexical bottleneck that limits the translation of narratives at high speed.
Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.
Domain-Size Aware Markov Logic Networks

Several domains in AI need to represent the relational structure as well as model uncertainty. Markov Logic is a powerful formalism which achieves this by attaching weights to formulas in finite first-order logic. Though Markov Logic Networks (MLNs) have been used for a wide variety of applications, a significant challenge remains that weights do not generalize well when training domain sizes are different from those seen during testing. In particular, it has been observed that marginal probabilities tend to extremes in the limit of increasing domain sizes. As the first contribution of our work, we further characterize the distribution and show that marginal probabilities tend to a constant independent of weights and not always to extremes as was previously observed. As our second contribution, we present a principled solution to this problem by defining Domain-size Aware Markov Logic Networks (DA-MLNs) which can be seen as re-parameterizing the MLNs after taking domain size into consideration. For some simple but representative MLN formulas, we formally prove that probabilities defined by DA-MLNs are well behaved. On a practical side, DA-MLNs allow us to generalize the weights learned over small-sized training data to much larger domains. Experiments on three different benchmark MLNs show that our approach results in significant performance gains compared to existing methods.
Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment

For the purpose of obtaining an answer to a spoofing IP issue and related problems in the cloud computing (CC) environment, a novel path identifier is recommended, in which a new packet approach with a new identifier (ID) is produced to every user or client system. For the purpose of identification of the spoofing attack, any watermark image pattern is introduced. Following this, the hypervisor is employed to ascertain plausible trust relationships among the VMs by considering personal sources that can be trusted along with utilizing empirical Bayesian inference (EBI) for the purpose of combining the VMs. With a restricted budget of resources for the maximized detection of attacks, an optimal detection load distribution strategy by improved cuckoo search (ICS) over VMs can be used, which thus provides the intended solution to the hypervisor. The ICS algorithm directs a hypervisor so as to assert among the VMs the optimal load distribution, taking into consideration real time, which causes maximization of the DDoS attacks and detection of transmission control protocol (TCP) flood attacks. Lastly, a fuzzy extreme learning machine (FELM) classifier is proposed in order to detect the attacks.
Performing Sentiment Analysis on the Product Brands using Tweets

In this recent years the concept of sentiment analysis of twitter data and semantic analysis with the enhancement of machine learning approaches is an important issue. In the past few years so many techniques have been proposed in the area of sentiment analysis to analyze the social media data and to provide a graphical presentation towards a particular brand. Sentiment analysis shows you the sentiment of people while typing a social media message about some product or brand. This is important information if you know that the sentiment of one person can influence other person about a company or their products. Sentiment analysis platforms are like all other online data mining systems, they are based on Support Vector Machine algorithms. In this case this algorithm which recognize certain words as ‘positive’ or ‘negative’, letting you know if your brand is being adored or floored. So here in this paper we first pre-processed the dataset, after that extracted the adjective from the dataset that have some meaning which is called feature vector, then select the feature vector list and thereafter apply machine learning based classification algorithms namely: Naïve Bays, Maximum entropy and SVM along with the Semantic Orientation based WordNet which extracts synonyms and similarity for the content feature. Finally we measured the performance of classifier in terms of recall, precision and
QR Approximation for Massive MIMO Fronthaul Compression

Massive MIMO's immense potential to serve large number of users at fast data rates also comes with the caveat of requiring tremendous processing power. This favours a centralized radio access network (C-RAN) architecture that concentrates the processing power at a common baseband unit (BBU) connected to multiple remote radio heads (RRH) via fronthaul links. The high bandwidths of 5G make the fronthaul data rate a major bottleneck. Since the number of active users in a massive MIMO system is much smaller than the number of antennas, we propose a dimension reduction scheme based on low rank approximation for fronthaul data compression. Link level simulations show that the proposed method achieves more than 17x compression while also improving the error performance of the system through denoising.
Case Study of Peer-to-Peer Network Environment in a Domestic Network

— The P2P network is widely used in the Internet world. Many people share resources through the P2P network. However, domestic network connections are not stable and we need to consider their own network environment from the design. This paper presents a survey of measuring flow level information of a group. We can utilize the analysis of the information to design a customized P2P overlay network efficiently in our country in the future.
Automatic Recognition of Sport Events from Spatio-temporal Data: An Application for Virtual Reality-based Training in Basketball

Data analysis in the field of sport is growing rapidly due to the availability of datasets containing spatiotemporal positional data of the players and other sport equipment collected during the game. This paper investigates the use of machine learning for the automatic recognition of small-scale sport events in a basketballrelated dataset. The results of the method discussed in this paper have been exploited to extend the functionality of an existing Virtual Reality (VR)-based tool supporting training in basketball. The tool allows the coaches to draw game tactics on a touchscreen, which can be then visualized and studies in an immersive VR environment by multiple players. Events recognized by the proposed system can be used to let the tool manage also previous matches, which can be automatically recreated by activating different animations for the virtual players and the ball based on the particular game situation, thus increasing the realism of the simulation.