Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Analysis and Performance Testing of LoRa Technology

With the rapid growth of the Internet of things (IoT) market, low power wide area network(LPWAN) technology is becoming popular and LoRa (Long Range) is one of newly proposed LPWAN technologies. LoRa provides users with a simple system ensuring long-distance, low battery cost and high capacity applied in lots of fields. This paper first introduces LoRa network architecture, focuses on its key technologies such as LoRaWAN, Adaptive Data Rate and Selective Working Modes. Then this paper further analyzes the packet format and characteristics of LoRa, mainly referring to three basic parameters: Code Rate (CR), Spreading Factor (SF) and Bandwidth (BW). After theoretical analysis of these factors, the influences of these factors on the transmission rate and packet duration are simulated. Finally, tests are taken to verify performance of LoRa such as delay and received signal intensity under three circumstances of open areas, residential quarters and parks.
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain

Neural networks have now long been used for solving complex problems of image domain, yet designing the same needs manual expertise. Furthermore, techniques for automatically generating a suitable deep learning architecture for a given dataset have frequently made use of reinforcement learning and evolutionary methods which take extensive computational resources and time. We propose a new framework for neural architecture search based on a hill-climbing procedure using morphism operators that makes use of a novel gradient update scheme. The update is based on the aging of neural network layers and results in the reduction in the overall training time. This technique can search in a broader search space which subsequently yields competitive results. We achieve a 4.96% error rate on the CIFAR-10 dataset in 19.4 hours of a single GPU training.
Modeling and Analyzing Per-Flow Saturation Throughput for Wireless Ad Hoc Networks with Directional Antennas

Throughput is an important factor to estimate the performance of wireless ad hoc networks. However, what remains unaddressed is how to analyze the per-flow saturation throughput for networks with directional antennas. In this paper, a three-dimensional Markov chain model is used to analyze the per-flow saturation throughput in the typical directional random access protocol (D-MAC protocol). Simulation results show that different flows have different saturation throughputs due to directional hidden terminals. Therefore, this paper focuses on analyzing per-flow saturation throughput in single-hop directional ad hoc networks. It first derives the transmission probability when taking the threedimensional Markov chain model into account, then analyzes the collision probability of each flow considering collisions caused by directional hidden terminals. The paper further studies the per-flow saturation throughput in the network. Extensive simulations demonstrate the accuracy of the proposed model.
LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoT

Internet of Things (IoT) becomes a very important area for providing various services on connected smart devices. For the isolation of different services in IoT, software-defined networking (SDN)-based virtual networks will be a scalable and flexible solution. However, in a large-scale IoT, as smart devices will move long distance between different positions, virtual network management becomes very difficult in providing network services. In this paper, we propose the LS-SDV, an efficient virtual network management framework in large-scale software-defined IoT (SDIoT). In this framework, we design a two-layer distributed control plane to manage devices and virtual networks in a large-scale environment. To the best of our knowledge, the LS-SDV is the first work to apply distributed control plane for virtual network management in softwarized networks. Moreover, based on the novel structure of the LS-SDV, we also provide a solution for network flow scheduling through network analysis. We evaluate the performance of our framework and virtual network management by extensive simulation and experiment in an open SDN framework.
Supporting the Collaborative Research through Semantic Data Integration

With advances in technologies, such as the Internet of Things (IoT), scientific research has become data-driven, based mainly on the intensive collection of raw data and analyses of heterogeneous data sets. Computational resources has allowed a somehow agile data production and analytics, which can be collaboratively assisted by teams of researchers. However, in order to achieve a full collaboration, with members altogether engaged, it is necessary to ensure a continuous data flow between the parts and to validate the compatibility of their data. To accomplish such aim, it is required to stress some aspects of the collaboration model, composed by processes of communication, cooperation, and coordination. This paper presents a collaborative research environment built in a scientific software ecosystem platform that implements an ontology-based data integration strategy to semantically provide comprehensive data models of disparate data sources, with the aim of enhancing the scientific collaboration.
Financial Forecasting using Deep Learning with an Optimized Trading Strategy

Financial forecasting using computational intelligence nowadays remains a hot topic. Recent improvements in deep neural networks allow us to predict financial market behavior. In our work we first implement a novel approach of [1], which converts financial time-series data to 2-D images and then feeds the generated images to a convolutional neural network as an input. We then hypothesize that the performance of the model can be improved using different techniques. Specifically, in our work, we improve the computational and financial performance of the previous approach by 1) fine-tuning the neural network hyperparameters, 2) creating images with 5 channels corresponding to indicator clusters, 3) improving financial evaluation using take profit and stop loss techniques, 4) evolutionary optimized parameters for trading strategy. The results of this study show that the above-mentioned strategies improve the model considerably. We conclude with future work that can be done in order to further improve the computational and financial performance of the model.
Agent-based Modeling of Migration Dynamics in the Mekong Delta, Vietnam: Automated Calibration Using a Genetic Algorithm

Migration is one of the many responses humans and societies make to ongoing demographic, economic, societal and environmental changes. In this work, we use agent-based modeling (ABM) to study the dynamics of migration flows across provinces and cities in the Mekong Delta, Vietnam. The strength of ABM is that it allows a bottom-up approach that focuses on how individuals make decisions in a complex system comprising various factors. Outputs of our agent-based model are automatically calibrated with actual data using a genetic algorithm. This automated calibration yields some significant improvement in the results, with all observed net- and out-migration data captured within the 95% confidence interval. Sensitivity analysis carried out helps to further understand the impact of critical factors on the final migration decision.
References or Preferences – Rethinking Many-objective Evolutionary Optimization

Past decades have witnessed a rapid development in research on multi- and many-objective evolutionary optimization. Reference-based and preference-based strategies are both widely used in dealing with the multi- and many-objective optimization problems. However, little effort has been devoted to a critical analysis of similarities and differences between the two approaches.This paper revisits the methodologies, compares the similarities and differences, and discusses the limitations of reference-based and preference-based many-objective evolutionary algorithms. Our analyses reveal that preference information may be embedded into reference-based methods in dealing with irregular problems so that the objective space can be better explored and a solution set of interest to the user will be obtained. Meanwhile, it is far from trivial for a decision-maker to provide informed preferences without sufficient a priori knowledge of the problem in the preference-based optimization. Therefore, this paper suggests a new approach to many-objective optimization problems that integrates preference-based and reference-based methodologies, where the solutions of natural interest such as the knee regions are identified at first and then the acquired knowledge of the knee regions can be used in reference-based methods. This way, accurate, diverse and preferred solutions can be obtained, and a deeper insight into the problem can be gained.
Energy Efficient Bike-Share Tracking System with BLE Beacons and LoRa Technology

Around the world, vast improvements in public transportation methods in urban environments have been made. However, in densely populated areas, the bicycle remains a very useful means of transportation. Its small size and minimal environmental impact are the critical factors that maintain its relevance. Moreover, the advancement of connected devices and sharing-based services have allowed private vendors to develop bike-sharing programs, giving millions access to bike transportation around the globe. These bike-sharing programs rely on the user to check out and return the bike to a designated bike-holding station. With the growth of Internet of Things (IoT) services and wirelessly connected devices, there is a major benefit in enabling vendors to track their bicycle assets. Satellite navigation has come a long way, however, it requires a large power overhead. This paper proposes an energy-efficient bicycle tracking system that utilizes bicycle powered Bluetooth Low Energy (BLE) beacons and Long Range (LoRa) type base-stations in order to track and maintain a real-time location-based inventory of all assets. The BLE beacons are used to track individual bicycle assets based on Received Signal Strength Indicator (RSSI) proximity and the LoRa base stations exploit longer range communication capabilities to transmit asset location information between each other, for added management capabilities. Preliminary proximity estimations using BLE beacons in an urban outdoor environment show promising results with proximity accuracy consistently under 2 meters.
Query-by-Example Spoken Term Detection System Based on Phoneme Posterior Features

Spoken term detection (STD) provides an efficient means for keyword indexing of speech. However, achieving high detection performance, fast speed, detecting out-ofvocabulary (OOV) words and performing STD on low resource languages are some of the major research challenges. This paper, thus, proposed a new method which combines the advantage of DTW-based keyword spotting and test-based keyword spotting. As to the evaluation metrics recall rate and precision rate, the performance of DTW-based method is achieved, which performs faster than traditional DTW-based methods.
Event Information Organization Algorithm about Enterprises Based on Timeline

To organize process of a number of events containing common topics, the paper proposed an event information organization algorithm by two text clustering, topic clustering which combines Latent dirichlet allocation model (LDA model) and TF-IDF model and event Hierarchical Clustering based on timeline. Topic clustering is a topic clustering through dynamic K-means in which the document vector distance is calculated. It can be known that events sets have a common topic by this clustering method. And another clustering method event hierarchical clustering is a clustering events phase based on timeline. In a word, this paper organizes event information by combining the above two clustering methods.
Simulation Research of Fuzzy Auto-Tuning PID Controller Based on Matlab

in this paper, the fuzzy control and the PID control are combined, and the fuzzy inference method is utilized to realize the on-line auto-tuning of PID parameters. By applying the fuzzy control toolbox and the Simulink inMatlab, it takes the electric steering gear as the object to simulate. By setting the fuzzy auto-tuning controller, the paper draws a conclusion that this method surpasses the conventionalPIDwith higher control accuracy, smaller overshoot, and better dynamic performance.
Cost-Effective Reliable EEPROM Cell Based on Single-poly Structure

A novel single-poly EEPROM cell composed of two PMOS transistors is presented in this paper. A PMOS tunneling transistor is utilized to enhance the reliability of the memory cell during the erase operation, and the other one is used as the coupling transistor. A minimum-sized PMOS is used to improve the capacitance coupling coefficient and saves area consumption. An additional PMOS transistor is added to each array cell to ensure high efficiency and safety. The memory cell is fabricated in a 0. 5μm CMOS process. The test results show that the proposed structure can be cycled for more than 20k times under the write and erase potential of 15V with 10ms operation pulse.
Towards Reproducible Research of Event Detection Techniques for Twitter

A major challenge in many research areas is reproducibility of implementations, experiments, or evaluations. New data sources and research directions complicate the reproducibility even more. For example, Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although some of these works provide their implementation or conduct an evaluation of the proposed technique, it is almost impossible to reproduce their experiments. The main drawback is that Twitter prohibits the release of crawled datasets that are used by researchers in their experiments. In this work, we present a survey of the vast landscape of implementations, experiments, and evaluations presented by the different research works. Furthermore, we propose a reproducibility toolkit including Twistor (Twitter Stream Simulator), which can be used to simulate an artificial Twitter data stream (including events) as input for the experiments or evaluations of event detection techniques. We further present the experimental application of the reproducibility toolkit to state-of-the-art event detection techniques.
Coral Framework - A Big Social Data Approach to Boost Startup Ecosystem

Startup ecosystem is a creative environment that aims to promote entrepreneurship and innovative business. In this complex social network, the activities derive from the ties of interdependence and co-evolution between actors with different purposes, who share resources to achieve common goals. A smarter startup ecosystem demands an understanding of the interests and capabilities of each participant as a way to identify convergences and possible partnerships that can help in the development of new business. This paper presents the Coral framework, an approach based on big social data to assist in the analysis of existing relationships in startup ecosystems. We show the partial results of the research and future works.
Novel photodetectors and image sensors based on silicon-on-insulator substrate

In this paper, we review our recent work on several novel photodetectors and image sensors based on silicon-on-insulator substrate (SOI). Interface coupled photodetector (ICPD) is demonstrated to largely improve the responsivity of the SOI photodetector by interface coupling effect. In order to tune the response spectrum, a MoS2/SOI heterogenous ICPD is developed, where the MoS2 gate is used as sensitizer to tune the response spectrum of the device from UV-band to visible/near-infrared band. Besides, a novel one-transistor active pixel sensor (1T-APS) is presented, which uses deep depletion effect in SOI substrate to collect photoelectrons and the MOSFET on top Si layer to sense the accumulated photoelectron. The 1T-APS in-situ integrates photosensing, charge integration, buffer amplification and random access in only one transistor. This is very attractive for image sensing application thanks to it’s much more compact pixel structure compared to conventional CMOS APS.
Brain Storm Optimization Algorithm for Thermal Image Fusion using DCT Coefficients

Digital images are part of numerous applications and they are used for various tasks. One of the important research topics in digital image processing is image fusion that aims to create more informative image by fusing two or more images from different sources or different points of view. The fused image is obtained as a linear combination of source images. Finding the optimal image fusion parameters, i.e. scaling factors, represents a hard optimization problem and in this paper we proposed brain storm optimization algorithm for solving it. Image fusion is done in frequency domain and the scaling factors for DCT coefficients of the source images are searched for. The proposed brain storm optimization method was tested with thermal and visual images. Standard benchmark dataset from TNO thermal image fusion was used for determining the quality of the proposed method. By comparing the obtained results it can be concluded that the proposed brain storm optimization method outperformed particle swarm optimization method, as well as several standard image fusion methods such as gradient pyramid, Laplacian pyramid, ratio of Laplacian pyramid and shift invariant discrete wavelet transform, in terms of four quality metrics including entropy, QABF, LABF and NABF.
Hyper-heuristics Reversed: Learning to Combine Solvers by Evolving Instances

It is common to find that training of selection hyper-heuristics is done perturbatively. The process usually starts with a random selection module and iterates over a set of instances until finding appropriate values for such module. In this work, however, we present a model for creating selection hyper-heuristics constructively. To achieve so, we use a set of instances evolved for such a task. For each low-level heuristic, we evolved a set of problem instances that are more easily solvable by that particular heuristic (compared to the other ones). Each group contains instances easily solvable with the corresponding heuristic but not with the remaining ones. Thus, our model creates its selector by calculating the centroid of each group. For doing so, the model defines a rule that maps said centroid to one corresponding action (in this case, a low-level heuristic). To test our approach, we select the one-dimensional Bin Packing Problem and set four target performance levels (which we refer to as deltas) for instance generation. Then, we analyze all the possible combinations of deltas. We study how performance of the generated hyper-heuristics shift when they are created using a different number of instances. Our data shows the feasibility of creating a hyper-heuristic under the stated conditions. Effectiveness of the model depends on the deltas used though we observed that higher deltas are useful while lower deltas are not. For example, when considering a delta level of 2.0, our method produced hyper-heuristics with an accumulated average waste 12% lower than that of the best heuristic. But, for a delta level of 0.5, it became impossible to outperform the heuristics.
A Privacy Preserving Public-Key Searchable Encryption Scheme with Fast Keyword Search

Searchable encryption has emerged in order to maintain the balance between data privacy and convenience of keyword search. In 2017, Huang et al. introduced the notion of inside keyword guessing attack (IKGA), which means the inside adversary could try out the keyword specified by a given trapdoor by guessing offline and proposed a scheme called Public-key Authenticated Encryption with Keyword Search (HQ-PAEKS) to solve this problem. However, the search complexity of HQ-PAEKS is linear to the total number of the encrypted indexes and may be not in a position to meet the demand of practical application. This paper combines the idea of Public-key Authenticated Encryption with hidden structure and proposes a practical scheme called Public-key Authenticated Encryption with Fast Keyword Search (PAEFKS), in which the search complexity for the cloud server presents linear relation with the number of the encrypted indexes matching the trapdoor sent from the data user. In addition, it gives security analysis to show that the scheme could protect index and keyword privacy well and experiments on real-world data sets show that the scheme indeed reduces the overhead on keyword search computation in the cloud.
Hypothesis Testing Based Side-Channel Collision Analysis

Side-channel collision analysis has become a research hotspot since its first publication in 2003. Compared with differential power analysis (DPA) and correlation power analysis (CPA), collision analysis does not need to know the intermediate value and does not depend on a specific power model. Collision analysis includes collision detection phase and key recovery phase. After collisions have been detected, the cryptographic key can be recovered from these collisions using such cryptanalytic techniques as linear collision attack and algebraic collision attacks. Therefore, the success rate of the entire attack depends largely on the success rate of collision detection. In this paper, we propose an efficient collision detection method, which combines side channel information with hypothesis testing in statistics. With the traces gathering by repeatedly encrypting a certain plaintext that is unnecessary to be known, we are able to evaluate the cumulative distance between two collision S-boxes, then, define a null hypothesis $H_{0}$ and calculate the rejection region accordingly. Given some traces collected from repeatedly encrypting any identical plaintext, collisions can be detected by determining whether the cumulative distance between the corresponding two S-boxes falls into the rejection region or not. We compare our work with those related to side channel collision attack in recent years from perspective of four hot issues. Comparing with current methods, our proposed collision detection method is simpler and more feasible. Furthermore, the experiments show that the more accurate the constant power is, the higher the success rate of collision detection is.