Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Embedding Matrix Sharing With Differential Privacy

Graph embedding maps a graph into low-dimensional vectors, i.e., embedding matrix, while preserving the graph structure, solving the high computation and space cost for graph analysis. Matrix factorization (MF) is an effective means to achieve graph embedding since maintaining the utility of the graph structure. The personalized graph structure features implied in the embedding matrix can identify the individual, which potentially breaches individual sensitive information in the original graph. Currently, protecting individual privacy without compromising the utility is the key to sharing the embedding matrix. Differential privacy is a gold standard for publishing sensitive information while protecting privacy. The existing methods on differentially private MF, however, cannot be directly incorporated onto MF-based graph embedding as they undergo either high global sensitivity or iterative noise error accumulation, potentially rendering poor utility of MF-based graph embedding. To address the deficiency, this study proposes PPGD, a differentially private perturbed gradient descent method for MF-based graph embedding matrix sharing. Specifically, a Lipschitz condition on the objective function of the MF and a gradient clipping strategy are devised for bounding global sensitivity. Along the way, a scalable solution to global sensitivity that is independent on the original dataset is proposed. Further, a composite noise added means in the gradient descent is designed to guarantee privacy while enhancing the utility. The theoretical analysis shows that PPGD can generate processed embedding matrix with the utility maximization while achieving $(\varepsilon, \delta)$ -differential privacy. The experimental evaluations confirm the effectiveness and efficiency of PPGD.
Sensor-enabled Functional-Mobility Assessment: An Exploratory Investigation

The population of adults aged 65 years and older is expected to double by 2050. Healthcare systems must adapt to in order to manage the care of this increasing population. Older adults with complex care needs require a significant amount of additional support from caregivers. To maintain, and possibly improve, their quality of life, it is ideal that they receive this support while continuing to live in their own homes. Recent advances in sensing technologies offer the ability to recognize and collect multiple different types of data around a person’s movement and physical ability. This data can subsequently be analyzed in order to inform a person’s functional-mobility assessment. In this paper, we present an exploratory feasibility study around the use of Microsoft Kinect™ and KINVENT’s K-FORCE plates for the purpose of assessing balance skills. Our results indicate that the analysis of data streams from these two sensors can effectively lead us towards a portable and adaptable gesture-evaluation system.
RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery life-time, since there are additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this paper is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.
Installation of the METU Defocusing Beamline to Perform Space Radiation Tests

METU Defocusing Beamline (DBL)is being installed at TAEA SANAEM Proton Accelerator Facility [1] for radiation tests of electronic devices to be used in satellites and spacecrafts which are exposed to a high radiation dose in space or at the Hi-Lumi LHC [2]. 15 − 30 MeV protons from the accelerator are spread out over an area of 15.40 × 21.55 cm to provide large irradiation in accordance to ESA/ESCC No. 25100 standard with METU-DBL. A wide selectable flux menu ranging from 105 − 1010p/cm2/s will be available to users starting in summer 2019. The final design has been updated with the experience gained from the pre-test setup installation [3] and the ensuing irradiation campaign with different users [4]. In this paper, magnetic measurement results of a custom design quadrupole magnet, as well as updates to the mechanical, robotic and control subsystems are presented.
Genetic Determinants of Musculoskeletal Adaptations to Unloading and Reloading

Lack of weight bearing is one of the most critical limitations for long term health of bone tissue in space missions. In this study, we performed a series of Quantitative Trait Locus (QTL) analysis of musculoskeletal traits to define genomic modulators adaptations to mechanical unloading and subsequent reloading using a genetically heterogeneous (F2 BALBxC3H) female mouse population. The identified regions on genome contain genes that regulate musculoskeletal adaptations to weightlessness and further studies may help to categorize individuals that are at risk for greater tissue loss during weightlessness and/or low tissue recovery during reambulation.
Physical-Layer-Security-Oriented Frequency Allocation in Ultra-Dense-Networks Based on Location Informations

In this paper, we investigate location-based frequency allocation schemes in a two-layer ultra-dense network (UDN) with a wideband eavesdropper to efficiently enhance the macro layer security in the whole working bandwidth. The cross-tier interference, treated as evil by traditional wisdom, is employed to confuse the malicious node as well as to tackle the conflict between the secrecy and traditional performances through the prudent spectrum allocation among the small cells. Three games are designed to progressively give insight into the frequency assignment problems under increasingly strict scenes. With the help of game theoretic stochastic learning approaches and location information, small cell base stations (SBSs) are endowed with the ability to distributedly select the subchannel with no requirement on the eavesdropper’s CSI. First, more specifically, we focus on the security problem, aiming to promote the safety transmission of macro users by leveraging a state field, and thus formulate the spectrum selection of SBSs as a state-based potential game, which guarantees a budget-balanced utility design. The existence of a recurrent state equilibrium point is proved, and that it is able to maximize the total safety transmission probability of all subchannels. A step forward, we take the service delay of SBSs into consideration, nowadays, as numerous new services are delay-sensitive. To this end, an exact potential game, in which the equilibrium always exists, is built to help SBSs strike a balance between altruistically helping the macro users and selfishly keeping their own performances. Furthermore, a fully distributed non-cooperative game that requires no exchange among SBSs is put forward. The proposed scheme may work well even when the backhaul is limited or even unavailable since all the SBSs only depend on the observation of their own instantaneous performances. Finally, the numerical results validate the effectiveness of the proposed games on improving the safety transmission probability while guaranteeing a better service within small cells.
Evolution-Based Deployment Scheme for Green Internet of Things

In this generation of advance technologies, where communication between physical devices is vital, Internet of Things(IoT) play an important part in connecting the cyber and physical world. IoT is used in various applications such as agriculture, smart home and smart cities, through the deployment of Wireless Sensor Networks(WSNs). Although, in hostile environments, where human intervention is impossible, the life cycle of a deployed WSN becomes critical. Conserving the energy consumed by a wireless sensor network, is imperative, in prolonging the life cycle of the network. This paper addresses the challenging issue of minimizing the energy consumption of WSNs on a large scale. The contributions made by this paper are using the optimization model proposed in [25] to compare Genetic Algorithm(GA) and the Mixed Integer Linear Programming(MILP) algorithm, to solve the minimum energy consumption problem for an IoT. The MILP and GA approach in solving the minimum energy consumption problem, is flexible and efficient, and helps us to achieve our goal, i.e. minimum energy consumption and maximum network lifetime of a deployed WSN.
Distributed Detection in Massive MIMO Wireless Sensor Networks Under Perfect and Imperfect CSI

This paper considers the problem of distributed detection for massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). Neyman–Pearson criterion based fusion rules are developed at the fusion center (FC) that also incorporate the local probabilities of detection and false alarm of the constituent sensor nodes. Closed-form expressions are obtained for the probabilities of detection and false alarm at the FC for various signaling schemes employed by the sensors. The fusion rules and analysis are extended to the scenario with imperfect channel state information (CSI). Furthermore, signaling matrices are determined for the massive MIMO WSN to enhance detection performance. The asymptotic detection performance of the WSN is analyzed for the large antenna regime, which yields pertinent power scaling laws with respect to the number of antennas at the FC. Simulation results demonstrate the improved performance of the proposed schemes and also validate the theoretical findings.
A Fog Computing Architecture for Energy Demand Scheduling in Smart Grid

The demand-side management is considered as an interesting functionality offered by the recent smart grid. This functionality allows the control and scheduling of consumer energy demand, which helps avoiding the problems of offer-demand gap and consumption peaks. The cloud is considered as a powerful tool that ensures scheduling appliances’ energy demand in centralized manner. However, the distance between the end users and the cloud might be a problem for latency. In addition, the more and more increasing number of connected objects, generating a huge amount of data that must be transmitted over the communication network, worsens the situation. Fortunately, the novel paradigm of fog computing came to mitigate these issues. In this paper, we propose a cloud-fog computing architecture for the energy demand scheduling. We propose to use this architecture to improve the consumption distribution over the day to reduce the total energy cost for smart buildings. Our work includes two parts: a distributed game-based approach for demand scheduling, and a model for the selection of fog nodes that will perform this distributed approach. The simulation results of the two proposals show that the integration of the fog architecture helps to considerably reduce the energy scheduling delay while determining the optimal demand schedule.
Botnet’s Obfuscated C&C Infrastructure Take-down Approaches Based on Monitoring Centralized Zeus Bot Variant’s Propagation Model

While botnets still pose a big threat, they have also developed to be the most dangerous dark applications over the web. They are able to compromise a multitude of computers under the Command and Control (C&C) infrastructure, that is mainly controlled by a botherder/botmaster. Normally, a botnet uses malicious code to achieve its objectives and usually the motivation is based on either financial gain or Denial of Service (DoS) attack. The problem that is being addressed in this paper is structured to explore how a botnet’s C&C infrastructure can be taken down based on how the botnet propagates itself within a network. The authors have used Zeus Botnet (ZBot) propagation model as a basis for this study. The main objective is to identify ZBot propagation patterns in order to be able to propose the take down approaches of the C&C infrastructure which acts as botnet control point. It is imperative to note that, even though ZBot was mainly resilient to attacks because of its Peer-to-Peer (P2P) nature, still other Zeus variants were controlled or acted as centralized bots. The study is more inclined to exploring the centralized Zeus variants like GameOver Zeus (GOZ) and ICE-IX for purposes of identifying the approaches. Based on the ZBot attack study, the C&C infrastructure can effectively be infiltrated hence averting unwarranted botnet attacks.
Emergent LBS: If GNSS Fails, How Can 5G-enabled Vehicles Get Locations Using Fogs?

With the advent of 5G mobile communication systems, the evolution of the Internet of Vehicles (IoV) will be greatly promoted, and it will be able to meet people’s demands extremely in terms of performance and functional requirements. Moreover, fog computing has been proposed at the edge of the 5G-enabled IoV architecture, to provide data storage, calculation, and interaction with vehicles. Furthermore, the Global Navigation Satellite Systems (GNSS) play a significant role in future 5G-enabled IoV environment. However, navigation systems usually lose effectiveness because of various factors and disturbances in the 5G-enabled IoV, such as severe weather, GNSS device failure, and skyscrapers that block GNSS signals. Meanwhile, since some measurements come from the NLOS path, this can result in very large positioning errors. To address these challenges, this paper proposes a GNSS-free emergency location-based service (LBS) using fog computing in 5G-enabled IoV. Firstly, we propose the topology structure of the 5G-enabled IoV to address the problem of GNSS failure. Then, the operation procedures of emergency location service and the hierarchical logical architecture of the fog based service networks are designed. Finally, a vehicle positioning algorithm is proposed based on semi-definite programming (SDP) for positioning errors in the 5G-enabled IoV environment. Simulation results verify that the method can achieve high-precision location estimation for the 5G-enabled IoV.
A green paradigm for Internet of Things: Ambient backscatter communications

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.
Feedback Control of Robot Manipulators by Using Gravity and Inertial Effects Compensation

The paper describes an approach to the robot manipulator control based on a compensation of non-linear gravity terms and inertial terms in the robot motion equations and applying the PD or PID-type control law. While benefits of the compensation of the gravity force effects are well known, the inertial effects compensation, which seems to be more difficult to implement, brings additional enhancement of the control quality. An approximate compensation can be carried out in a relatively simple and efficient way, if the robot dynamics is represented by a mass-point model. The remaining influences can be treated as disturbances. Unlike the inverse dynamics approach, complete and precise mathematical model of the robot is not needed, which is an important advantage, since models of robots containing more than three links are usually very complex and difficult to obtain.
Evaluation of Open-Source Linear Algebra Libraries in Embedded Applications

Many emerging applications based on technologies like machine learning or cryptography mandate increased numerical computations being carried out close to the sensors in respective embedded processing units. Rather than reinventing the wheel, adoption of existing mathematical software libraries from standard desktop computing seems viable. To that end, this paper evaluates existing implementations of the Basic Linear Algebra Subprograms (BLAS) interface for their use on embedded computing devices. In particular, various implementations are benchmarked with regard to their performance and memory consumption on a comparatively small RISC CPU. To facilitate classfication on a broader range we use a diverse selection of test applications from small synthetic benchmarks like simple vector and matrix operations to real-world applications using Artificial Neural Networks (ANNs).
Autonomous Free-Form Trenching Using a Walking Excavator

This letter shows accurate and autonomous creation of free-form trenches using a walking excavator. We present hardware extensions and modifications for full automation, a mapping approach specifically tailored to excavation, environment collision-free trajectory planning on these maps, an arm controller aware of various limits and an improved state machine that enables the execution on real hardware. Furthermore, previous work about excavation planning and the design of a single soil-independent dig cycle is extended and transferred from simulation to hardware. The entire system is tested on a four-segment, piecewise-planar trench, and a free-form curved trench. Both shapes were successfully excavated with unprecedented accuracy.
Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition

Speech is the most effective way for people to exchange complex information. Recognition of emotional information contained in speech is one of the important challenges in the field of artificial intelligence. To better acquire emotional features in speech signals, a parallelized convolutional recurrent neural network (PCRN) with spectral features is proposed for speech emotion recognition. First, frame-level features are extracted from each utterance and, a long short-term memory is employed to learn these features frame by frame. At the same time, the deltas and delta–deltas of the log Mel-spectrogram are calculated and reconstructed into three channels (static, delta, and delta–delta); these 3-D features are learned by a convolutional neural network (CNN). Then, the two learned high-level features are fused and batch normalized. Finally, a SoftMax classifier is used to classify emotions. Our PCRN model simultaneously processes two different types of features in parallel to better learn the subtle changes in emotion. The experimental results on four public datasets show the superiority of our proposed method, which is better than the previous works.
An interoperable platform for the digital transformation of the agricultural sector

One of the objectives of the agricultural sector is to increase the competitiveness, quantity, and quality of the products obtained, optimizing the use of natural and energy resources. The fact that sensors, actuators, and other agricultural devices are connected through the Internet gives us the possibility of developing interoperable solutions aligned with European standards to meet the requirements of any monitoring and control scenario. This work presents a platform that allows for the integration of sensor networks, as well as different systems responsible for the management of agricultural production. Generating, therefore, solution that provides the farmer with tools to improve decision making. The document details the 3-layer architecture, the integration with the water management model promoted by the TRAGSA Group through NGSI-LD, the integration with GIS and the deployments carried out together with the benefits they have generated.
Instability of Gypsum Mining Goaf Under the Influence of Typical Faults

Mine collapse is a common geological hazard associated with mining areas. In this paper, the goaf of a gypsum mine under the influence of typical faults is chosen as an example, and the stress on the center of the goaf roof is examined through theoretical analysis. The safety factors of the pillar at −127 m, −140 m, and −160 m are evaluated to be 1.471, 1.469, and 1.467, respectively, based on the Wilson theory. It is concluded that the structural parameters of the mining stope are reasonable without considering the influence of the fault. Furthermore, the numerical simulation by FLAC3D is used to verify the analytical results and to study the influence of typical faults on the stress and displacement of the goaf. The results showed that the load of the roof and the pillars at a distance from the fault was larger than the load near the fault. First, the roof at this location is pulled. Next, cracks appear and develop. Then, the middle of the stope roof is pulled and falls, and the overburden strata move. Finally, the unstable strata are transmitted to the surface, and the ground surface of the mining area subsides.
Towards improving the Privacy in the MQTT Protocol

The Internet of Things (IoT) is growing rapidly as more and more household appliances, sensors and actuators are connected to the internet and communicate with each other. Because these IoT devices usually have only limited processing and communication capabilities, an efficient communication protocol is required to reduce the protocol overhead. The Message Queue Telemetry Transport (MQTT) protocol provides such properties. MQTT is a publish/subscribe protocol, where each client can subscribe to a message topic in order to receive all messages published under that topic. The payload of a message (i.e., the content) can be encrypted to hide private information. However, to forward messages, the topic of a message needs to be read by the broker and thus cannot be encrypted and might reveal private information. This paper presents methods to avoid this problem. It features and evaluates a one-time password approach to provide a fully obfuscated communication method for MQTT topics. Thereby, the user tracking and the generation of profiles is prevented.
IoT Driven Business Model Innovation and Sustainability: a literature review and a case Study in Brazil

The Internet of Things (IoT) promises a revolution in terms of new services, products and possibilities for innovation in business models. Those possibilities, however, are linked to a competitive and social context in which concerns with data privacy and sustainability must be balanced at the risk of getting negative results and damage to firms’ image and reputation. This paper presents a review of the literature that aimed to bring together these concepts (IoT, BMI and privacy/sustainability) and identify how academic research is studying those issues and to identify the main topics and research questions in those studies. We will then present a Brazilian case of Iot Implementation in the context of smart cities and discuss this case considering the topics and questions found in the literature.