Miracle-dz/UniFAR-FTUs huggingface dataset:
Updatable Searchable Symmetric Encryption with Fine-Grained Delete Functionality

With the wide spread of Cloud service, users are outsourcing their data to the third party servers. When we delegate our data to the cloud servers, there is such a potential risk that the data stored in the servers is stolen. If the data are stored in the encrypted form, the damage caused by data theft is low because the data will not leak to other parties. However, a new problem arises, that is, users are not able to operate encrypted data, which loses the merit of cloud computing. To solve such a problem, searchable symmetric encryption(SSE) is proposed. SSE is a cryptographic protocol that can execute keyword search over encrypted documents without decrypting them. Secure Indexes is one of realizations of SSE that provides constant time keyword search per document with the help of pseudo-random function and Bloom filters. However, Secure Indexes dose not support addition and deletion of documents. Tekin and Sahin proposed an improved Secure Indexes scheme that supports addition and deletion by employing Bloom filter and counting Bloom filter. The drawback of their scheme is that these operations are implemented in user side, which is not desired in the cloud computing since the aim of cloud computing is to delegate as many operations as possible. This paper proposes new SSE scheme. The proposed scheme also employ counting Bloom filter, but provide more fine-grained operations compared to the scheme by Tekin and Sahin in the sense that the proposed scheme provides not only search documents but add and delete the elements of document. Moreover, these operations are executed in the server side. The performance of the proposed SSE is comparable to the existing scheme which provides constant time for search, add and delete per document. We also implemented this SSE scheme, and verified that the scheme performed well not only theoretically but also in practice.
Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation

The objective of this paper was to compare the incidence of a rehabilitation game in motor ability with dynamic difficulty adjustment (ADD) in comparison to a manual configuration. To achieve that, a virtual tool called “Bug catcher” was developed, which is focused in upper limb rehabilitation. This tool uses a dynamic difficulty adjustment based in fuzzy logic. The population involved for the present study were made by 2 users, a 18-year-old patient with a hemiparesis that limits her motor ability in her left upper limb, and a 37-year-old patient with motor monoparesis in his right upper limb. This tool was used in both users, each one with a different configuration (automatic or manual), and the motor ability from both participants was objectively measured using Box and Blocks Test, applied before, during and after each session; additionally, a performance index (percentage of success) was defined in order to determine the progress of the participants in the virtual tool. As a result, it was obtained that user number one using the game with ADD, managed to obtain not only a better performance in the sessions but also an important advance in her motor skill in comparison to the user 2 with the manual configuration.
A Case Study on the Correction of Atmospheric Phases for SAR Tomography in Mountainous Regions

Synthetic aperture radar (SAR) tomography with repeat-pass acquisitions generally requires a priori phase calibration of the interferometric data stack by compensating for the atmosphere-induced phase delay variations. These variations act as a disturbance in tomographic focusing. In mountainous regions, the mitigation of these disturbances is particularly challenging due to strong spatial variations of the local atmospheric conditions and propagation paths through the troposphere. In this paper, we assess a data-driven approach to estimate these phase variations under a regression-kriging framework. The vertical stratification of the troposphere is modeled functionally, while the impact of the spatial turbulence is considered in a stochastic sense. The methodology entails an initial persistent scatterer interferometry (PSI) analysis. The atmospheric phases isolated for the persistent scatterers (PS) within the PSI processing are considered as samples of the 3-D distribution of the phase delay variations over the scene. These atmospheric phases are regressed against the spatial coordinates in map geometry at PS locations. In turn, kriging predictions are obtained at each location along the elevation profile, where tomographic focusing is intended. A key point of this approach is that the requisite atmospheric corrections are incorporated within the tomographic focusing model. A case study has been performed on a data stack comprising 32 COSMO-SkyMed stripmap images acquired over the Matter Valley in the Swiss Alps, in the summers of 2008–2013. The results show locally improved deformation sampling with tomographic methods compared to the initial PSI solution, primarily due to the improved phase calibration. In general, this paper underscores the indispensability of height-dependent correction of atmospheric phases for SAR tomography.
Image Denoising with Deep Convolutional and Multi-directional LSTM Networks under Poisson Noise Environments

Image denoising especially in low-light conditions is a very challenging task because the noise characteristics of different noise sources contributed in varying proportions at different signal levels are difficult to capture. Inaccurate modeling of the noise often results in producing undesirable artifacts in the restored images. This paper presents a new image denoising method in Poisson noise called Deep Convolutional and Multidirectional Long-short Term Memory Networks (DCSLNet). Deep Convolutional Neural Network (CNN) is firstly used to extract features and estimate noise components of the images. Multi-directional Long-short Term Memory (LSTM) network is then introduced in the second stage to effectively capture the long-range correlations of the noise in the deeper CNN layers. The proposed DCSLNet is trainable end-to-end to restore the clean image from the input noisy image. Experimental results in both subjective and objective qualities show that the proposed DCSLNet is very competitive in denoising low-light images under heavy-noise conditions compared with the other state-of-the-art Poisson image denoising methods.
Approach to Indoor Location of Mobile Robot Using Wi-Fi Fingerprint and a Variant of the K-NN Method

Indoor location based services have attracted a lot of attention in recent years because of their social and commercial potential, with an estimated market value of $ 10 billion by 2020 [3]. As satellite and cellular signals fail to penetrate well into the internal environment, with external walls and internal obstacles, the use of the Wi-Fi signal for localization has attracted increasing attention in both the academic and industrial area due to the large penetration of wireless LANs (WLANs) and mobile devices with Wi-Fi enabled. In this sense, we investigated the combined use of Wi-Fi fingerprint with interpolation of the nearest points through the K-NN algorithm, due to its ease of implementation, efficient identification of nearby neighbors, and rational use of processing and memory, which are limited resources in mobile robots.
Fuzzy Artefacts: Formality of Communication in Agile Teams

Agile Software Development (ASD) is often thought of as to predominantly use face-to-face communication. Research over the last years already led to adjustment of this conception. ASD also uses formal communication where it blends both traditional and agile artefacts. A next step should then be to explore the entire spectrum from informal to formal communication. This raised our research question: Which artefacts do agile teams use in between formal communication with artefacts and informal face-to-face communication? In an exploratory multiple single-case study we found 38 agile teams in as many organizations willing to participate in answering our question. The findings from our case study confirm the existence of what we called fuzzy artefacts, concepts which are not formally documented but are of such a pervasive nature that an agile team is well aware of their existence. We found in total 73 individual fuzzy artefacts in 38 agile teams. Most of them are related to requirements. Populating a product or sprint backlog with user stories equipped with priorities and efforts is another area where they were found. In this way we extended the typology of formal communication (artefacts) and informal communication (face-to-face) in ASD with an intermediate concept: fuzzy artefacts.
Security and Design Analysis of Certificateless Signature Schemes as Teaching Cases of Cryptography and Security Course Education

More and more security and privacy issues have been exposed which lead to the urgent requirement of solving the security and privacy. Many universities and colleges offer the course education of cryptography and security and make it be an important compulsory course. However, cryptography as the key technology of solving security and privacy is very theoretical and very hard to be mastered by the students. In this paper, we use the hot cryptosystem - certificateless signature scheme as the teaching case. By adopting two current certificateless signature schemes, we analyze the existing design and security problems of these two certificateless signature schemes in cryptography course education. We give the details attacking process of forging a valid certificateless signature without known the signer’s private key. In the same time, we analyze these problems and present some simple improvement methods. By using case teaching, it can make the student easier and better master the cryptosystem in cryptography course education.
Application of Signal Flow Network on Calibration Capacitive Rotary Encoder

The paper proposes an offline self-calibration scheme about establishing a signal flow network(SNF) to calibrate capacitive rotary encoder. This scheme proposes to simulate the flow of signals and store model parameter information in each node of the network. Unlike traditional optimization algorithms, the intermediate variables in the proposed solution are considered in the optimization pipeline, with the ability to converge fast and accurately. The proposed scheme no longer uses the traditional model linearization method. Instead, the method uses a nonlinear model to establish the network structure, ensures the independence of parameters, and uses an in-depth learning algorithm for improving the convergence speed as well as ability to a global optimal solution. According to the simulation results, the method proposed here is able to get good accuracy of identification, with a relative error of identification below 0.01‰. The validity of the method have also been verified in experiments and the error after the compensation is reduced to 13.02%. The reasons for the inconsistency between simulation and experiment were analyzed. Although the compensation effect is limited, it provides a new method to calibrate capacitive rotary encoder.
Development of an Intelligent Dialogue Agent with Smart Devices for Older Adults: A Preliminary Study

This study aimed to examine the application of an Intelligent Dialogue Agent (IDA) in preventive care frameworks for healthy older adults. Introducing the agent increases familiarity with the frameworks, encourages performance of preventive care exercises, and helps older adults turn using the frameworks into a habit. We used a questionnaire to collect data on older adults' impressions of Information Technology (IT) devices, smart speakers in particular (main components of the IDA), and interviewed the participants after they actually used the smart speaker in order to discuss required functions and expected roles in developing the IDA. Results from the questionnaire and interview revealed promising characteristics of smart speakers and problems concerning Japanese speech recognition.
Localization and Mapping in Semi-Urban Environments with Unmanned Vehicles for Agriculture-Oriented Labours

The overcrowding access to unmanned aerial vehicles (UAVs) by companies and farmers has motivated their use in tasks such as monitoring and image capture. In order to increase the safety and autonomy of these vehicles inside semi-urban environments during monitoring or data collection tasks, this paper aims to address a strategy of Simultaneous Location and Mapping (SLAM) within the robotic operating system (ROS) for a terrestrial and aerial robotic platform to navigate in a three-dimensional space, with the ability to auto-locate and make real time decisions avoiding collisions with static or dynamic obstacles. Thus giving farmers a capable, modular and low-cost alternative for obstacle avoidance during the task in an a-priori unknown area. As a result, a SLAM algorithm is integrated into a ground wheeled robot of Mobile Robots, Pioneer-3DX, and within an aerial multirotor robot, DJI Matrice Pro 600, obtaining high functionality during repeated field tests, capturing environmental maps and recording possible obstacles to be avoided during execution of an autonomous trajectory.
Android Malware Detection and Crypto-Mining Recognition Methodology with Machine Learning

The paper proposes a Machine Learning methodology for Android malware detection and recognition, including crypto-mining applications using the blockchain. The design is based on a hierarchical classification method, with several decision stages. A combination of functional and statistical features is proposed to be applied for data classification in order to provide a high-performance malware recognition process. The specific contribution of this design methodology is the hierarchical classifier with detection and discrimination stages, respectively. Further works should be done for various features sets in order to achieve an optimized and high-accuracy modeling process supporting an innovative Machine Learning-based solution for Android malware detection.
Comparison between Control Methods of Active Clamp Flyback for Adaptor Application

High efficiency and high switching frequency are the key points for the miniaturization of the power adapter. The ACF (Active Clamp Flyback) can achieve ZVS (zero voltage switching) and recycle the leakage energy at all load and line conditions. This paper presents the comparison between the ACF with the complementary control mode and the ACF with the non-complementary control mode. Key differences between the two control modes at all load and line conditions are analyzed. Performances of the ACF with the two control modes are verified on a sample of prototype adaptor with 65W (19.5V, 3.33A) output.
Short-Term Traffic Flow Forecasting Using a Distributed Spatial-Temporal k Nearest Neighbors Model

Accurate and timely information on the current and predicted traffic flows is important for the successful deployment of intelligent transport systems. These data plays an essential role in traffic management and control. Using traffic flow information, travelers could plan the routes to avoid traffic congestion, reduce travel time, environmental pollution, and improve traffic operation efficiency in general. In this paper, we propose a distributed model for short-term traffic flow prediction based on the k nearest neighbors method, that takes into account spatial and temporal traffic flow distribution. The proposed model is implemented as a MapReduce based algorithm on an Apache Spark framework. An experimental study of the proposed model is carried out on a traffic flow data in transportation network of Samara, Russia.
Studying and Developing a Resource Allocation Algorithm in Fog Computing

Internet of Things (IoT) is becoming popular. Many IoT devices will bring huge data moving within a network infrastructure. Cloud has become so widespread that almost many business or end users are using private and/or public cloud. Cloud servers will be responsible for processing incoming data and sending them back after processing. As a result, the amount of data moving will be extremely large and processing of Big Data will gradually appear. Many enterprise companies included Cisco, Dell, Microsoft, Intel has pioneered Fog computing since 2015. In Fog computing, data will be scattered instead of concentrating data like the traditional Cloud computing. The main goal of this study is to propose a resource allocation algorithm to distribute modules of an IoT application to the entire network in Fog computing environment. This proposed algorithm must run successfully on a simulator. With different environments, the algorithm will allocate different modules depending on the configuration of each device. To evaluate the algorithm, this study uses the iFogSim toolkit, which extends from the CloudSim toolkit, for modeling and simulation of Fog computing environment. Current version of iFogSim is sufficient to be able to implement some criteria necessary for this study.
μfluidic Sensor for Optical Monitoring of Bacteria Growth with Improved Limit of Detection

We present an approach and device to monitor on-chip bacteria growth based on the absorption measurement with a low limit of detection. Because of the small height of microfluidic channels, a standard optical density method is not applicable. In our approach, the optical path is 20 times longer by performing an in-plane optical density measurement compared to an out-of-plane approach so that the sensitivity is improved. An LED (580 nm center wavelength) is used to propagate light through a sample in the measurement channel. The passing light intensity is measured at the outlet by a photodiode. The relation between the absorbed light and the bacteria concentration agrees well with the theory. A particular focus is laid on reproducible setup based on 3D-printed holders where external disturbances such as ambient light are minimized. In combination with the increased sensitivity, the limit of detection is only 1.5. 106bac/mL. By applying the method of this contribution, additional standard laboratory operations can be integrated into chips.
Development of Local Mapping Generating Technique Using Planar Regions Applied for an Active Orthosis

This paper presents the development of a computer vision algorithm that uses planar regions obtained images captures through a stereo camera in order to provide essential information about the environment allied with low computational cost. The proposed algorithm is used to detect obstacles in the field of view of a stereo camera embedded in an active orthosis for lower limbs, developed for assistive walking applications. The main objective is to collect data from the surroundings of the orthosis in order to construct a local map, based on dense planar SLAM, that will be used to detect obstacles and other ground irregularities, providing the orthosis with adequate information for planning autonomous movements during walking. The paper presents the mechanical exoskeleton developed to implement the orthosis, the proposed strategy adopted for creating local maps from planar regions, and some preliminary results.
A proposal for a SoC FPGA-based image processing in RGB-D sensors for robotics applications

The current robots follow clear, repetitive and logical instructions, but generally, they have problems in managing unstructured environments and reacting dynamically to these. Thus, modern robots require improved vision systems capable of obtaining information about such environments at a high acquisition rate and with high processing speeds. The growing demand for robotic platforms, both industrial and mobile, has greatly boosted the development of advanced vision systems. A weak point of traditional computer vision is that it depends on algorithms executed on a computer or server connected to the robot, often involving the need for high computing resources. Therefore, much of the efforts of the last decades have been focused on the improvement of those algorithms. Nevertheless, when the limit of traditional software processing systems (PCs, microcontrollers and microprocessors) is reached, it is necessary to migrate to a more versatile platform -which generally leads to hardware solutions-. The HW/SW design is possible because of high-frequency bridges between the Hard-Processor System (HPS) and the FPGA. Commonly, the most demanding tasks of the image processing are made in the FPGA, whereas the HPS handles the processed data and performs the high-level control function. This work presents a proposal for HW/SW integration using a SoC FPGA, for the images processing provided by the Intel Realsense®3D camera (an RGB-D sensor). This approach seeks to enhance the streamlining and filtering stages to obtain faster results compared to a traditional system.
Research on Interaction Between AC side and DC side in VSC-HVDC

For a single converter, AC and DC are coupled to each other; for a converter cascade system, the converters interact with each other in some cases, which in turn affects the stability of the system. In this paper, the HVDC system is taken as the research object, and the harmonic linearization method is adopted to consider the interaction between the DC side and the AC side to analyze the stability of the cascade system. Experimental tests are performed to verify the effectiveness of the proposed method. According to the results, relevant measures to improve the stability of the cascade system are proposed.
Diode Junction Temperature Measurement from Power Converter Output Voltage

For power electronics devices, the junction temperature that can be measured by the Temperature Sensitive Electrical Parameter(TSEP) indirectly is a key condition variable. In this paper, the dv/dt of converter output voltage is measured as the TSEP for diode junction temperature, which mainly depends on the reverse recovery characteristic affected by temperature and current. To clarify the method, the measurement system, procedure and principle are presented. Simulation and experiments were implemented to verify the effectiveness and feasibility of the method. Research results demonstrate that the diode junction temperature can be measured accurately during PWM operation from the terminal of power converter with the advantages of simple and cost-effective.
Towards a Continuous Feedback Loop for Service-Oriented Environments

Agile software engineering practices aim at unifying software development (Dev) and software operation (Ops) in order to quickly release new software and gather feedback on new features in the latest version. The feedback loop gets closed mostly after new releases are deployed to production. During development, engineers do not receive feedback whether their changes are still aligned with the requirements and the formalized concept. This increases the risk of removing implemented code and decreases productivity. In this paper, we propose a tool for closing the feedback loop after each continuous deployment stage i.e. development, test and production. The continuous feedback is provided via a dependency model that represents the current software architecture on early stages. Hereby, each deployment phase and final release are compared against each other in order to uncover inconsistencies in regard to the predefined requirements.