Miracle-dz/UniFAR-FTUs huggingface dataset:
An IoT-Based PDCS System

Pulse DC sintering (PDCS) systems are an effective way of producing sintered materials. Such systems are commonly used for research by universities and for series manufacturing by factories. It is important to measure and record currents, voltages, container resistance, power, and consumed energy of a PDCS system during sample production period. Industry 4.0 is the new industry revolution and is happening now. Internet of things (IoT) is also enveloped by Industry 4.0. In the future, more devices in factories or laboratories are to be connected to ethernet or internet. A sintering system can also be connected to such networks using IoT. This allows remote control and data acquisition of sintering process. The data acquired by IoT can be used to make a sintering process database. In this paper, it is shown how to make such an IoT-based sintering system using off-the shelves integrated circuits. Also, the algorithm needed to control such system and manage the material database is given.
Distributed Q-Learning Aided Heterogeneous Network Association for Energy-Efficient IIoT

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed $Q$-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed $Q$-learning based power allocation algorithm.
A read/write front-end for an antifuse One-Time-Programmable memory in High Voltage Silicon-On-Insulator technology

A front-end for read/write operations on a One-Time-Programmable (OTP) memory, designed and fabricated in High-Voltage Silicon-On-Insulator (HVSOI) technology, is presented. In the considered integrated HV technology, the memory element consists of an antifuse type structure and it is implemented using a 5V NMOS with $\mathrm{L}=1 {\mu} \mathrm{m}$ and $\mathrm{W}=1.2 {\mu} \mathrm{m}$. The design of the front-end circuit that enables the bit's read/write operations within the memory is described and experimental results validate the successful implementation.
Short-term Bus Passenger Flow Forecast Based On Deep Learning

The public transportation system is an essential part of the life of the citizens and it’s the basis of intelligent transportation system(ITS). This paper tries to predict shortterm bus passenger flow by using deep learning approach that called SAE model and DBN model. The model training and evaluation were carried out using the credit card records of the Suzhou bus IC card. The experimental results show that the SAE and DBN models can reduce the prediction error by 9.51% and 10.48%, respectively, compared with the traditional method. The methods of deep learning show a good application prospect in the short-term bus passenger flow forecasting.
Achieving Energy Efficiency for Near-Threshold Circuits Through Postfabrication Calibration and Adaptation

Scaling supply voltage to the near-threshold voltage (NTV) region is an effective approach for energy-constrained circuit design at the cost of acceptable performance reduction. However, by operating in the NTV region, the sensitivity of circuits to process and runtime variations significantly aggravates. Therefore, the performance and power consumption of a circuit is largely impacted by the variabilities, which affects the operating voltage for the most efficient computation, i.e., the minimum energy point (MEP). Accordingly, finding an optimum operating voltage for near-threshold computing (NTC) to account for variabilities is very challenging. In this article, we propose an MEP calibration and adaptation approach based on machine learning to tune for minimal energy operation on a per-chip basis by considering process and runtime variations. In the proposed approach, the optimal supply voltage of each chip is determined during manufacturing tests by characterizing dynamic and leakage power and at runtime by considering the impact of temperature variation. The presented method does not require costly power measurement circuitry on chip. The simulation results show that the proposed method has high MEP prediction accuracy and achieves near-optimal operation by only 1.2% higher energy consumption compared with the optimal operation.
Fine-Tuning Deep Hybrid Long Short-Term Memory and Restricted Boltzmann Machine Network for Urban Traffic Speed Prediction

Accurate urban traffic speed prediction plays a crucial role in intelligent transportation systems (ITSs). However, the potential spatial-temporal information of big urban traffic data captured from the complex behaviors of urban systems cannot be efficiently mined based on the state-of-art intelligent models. Therefore, we propose a hybrid Long Short-Term Memory (LSTM) and Restricted Boltzmann Machine (RBM) neural network with a fine-tuning strategy for urban traffic speed prediction. Firstly, the LSTM-RBM model dynamically combines the LSTM and RBM, such that the LSTM extracts the time-varying characteristics of the speed sequences to dynamically adjust the RBM, and then the RBM can capture the deep and detailed features of the speed sequences in the optimal way. Secondly, a transfer learning fine-tuning strategy is proposed to effectively pre-train the LSTM-RBM to achieve higher accuracy. Experimental results based on traffic speed data of the second ring road in Xi'an indicate that the proposed hybrid LSTM-RBM model with fine-tuning can outperform the existing deep models.
Investigating Tangible User Interaction in Mixed-Reality Robotic Games

Among the emerging trends in Human-Robot Interaction, some of the most frequently used paradigms of interaction involve the use of Tangible User Interfaces. This is especially true also in the field of robotic gaming and, more specifically, in application domains in which commercial off-the-shelf robots and projected Mixed Reality (MR) technology are combined together. The popularity of such interfaces, also in other domains of Human-Machine Interaction, has led to an abundance in the number of gestures that can be used to perform tangible action using these interfaces. However, there are not sufficient pieces of evidence on how these different modalities can impact the user experience, in particular when interacting with a robot in a "phygital play" environment. By moving from this consideration, this paper reports on the efforts that are ongoing with the aim to investigate the impact of diverse gesture sets (which can be performed with the same physical prop) on the perception of interaction with the robotic system. It also presents preliminary insights obtained, which could be exploited to orient further research about the use of such interfaces for interaction in MRbased robotic gaming and related scenarios.
A Memristor-CMOS Braun Multiplier Array for Arithmetic Pipelining

A multiplier is a core building block in most digital signal and image processing systems and increasingly so in neural processing units. The latter must parallelize very large scale multiply-and-accumulate operations in deep learning with reconfigurable weights to be multiplied with varying input. In light of this, we propose a memristor-CMOS hybrid multiplier which uses a Braun structure to enable segmentation of the multiplier array to process various multiplication operations simultaneously with reconfigurable bit-widths by varying a control signal, all on the same multiplier. This enables an increase in computational throughput. Memristors are used as basic logic elements and improve area utilization by fabricating them in the back end of the line before the pad level. The CMOS chip is fabricated in the SK Hynix 180-nm process with TiO2 memristors separately deposited on top. A performance evaluation is undertaken under a 4-point Fast Fourier Transform, demonstrating a decrease in area by 17% and power consumption by 35%-49% with a 100MHz system clock when compared to similarly behaving CMOS multipliers.
GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.
Construction and Application of Knowledge Graph System in Computer Science

This paper proposes a method for constructing a knowledge graph system for computer science. According to the domain knowledge collected and summarized, the data mapping model is established by analyzing the structure of the data, and the data is parsed by the stream processing method. Then, the property graph model is established according to the structure of the data to obtain the entity, the attributes of the entity and the relationship between the entities. The Neo4j graph database is also used as a storage carrier to realize the construction of the knowledge graph system in the field of computer science.
FPGA-Based Tactile Sensory Feedback System with Optical Fiber Data Communication Link for Prosthetic Applications

Tactile sensory feedback systems could enable the prosthetic system to convey touch information to the amputee. Employing sensor arrays with a high number of sensors gives high-resolution tactile information but imposes challenges on the embedded electronics in processing and transmitting a large number of tactile data to the prosthetic user. This work proposes the design of a FPGA-based approach for tactile sensory feedback systems that employs an optical fiber data communication link for prosthetic applications. The system consists of an interface electronics to manage the data acquisition from the tactile sensor array, a digital coding unit, an optical fiber-based communication link, a digital decoding unit and a further interface electronics to communicate with external apparatus. The data acquisition process followed by a UWB-based optical modulation allows for the transmission of pulsed coded tactile data through the optical fiber to a final user by means of, for example, an electrotactile stimulator combined with flexible electrodes. The transmitter and receiver systems have been implemented on two different FPGA boards with the optical communication channel connecting the two boards. The designed system functionality was demonstrated by employing an experimental setup where sequence of sensor data, emulating an array of 32 sensors sampled at 2kHz, were employed to verify the correctness of the data transmission at 100Mbps data rate through the optical fiber. Experimental results validated the functionality of the proposed design and demonstrated that the optical communication link highly improves the robustness to electromagnetic disturbances, the transmission data rate as well as the power consumption.
Robust Semipassivity and Practical Stability for Uncertain Switched Nonlinear Systems

This paper addresses several issues including robust semipassivity and practical stability analysis for a switched nonlinear system with structural uncertainties. First, robust semipassivity is firstly defined to describe the overall semipassivity property of switched nonlinear systems without requiring the conventional semipassivity property of each active subsystem. Then, the robust semipassive system is shown to be practically stable. Second, a state-dependent switching law is designed to render the switched system robust semipassive. This switching law is a generalization of KYP lemma. Third, robust semipassification for a switched nonlinear system is achieved by the design of a state-dependent switching law and a set of feedback controllers. Finally, a composite state-dependent switching law is designed to render the feedback interconnection of switched nonlinear systems robust semipassive. The effectiveness of the obtained resultsis verified by two numerical examples.
KOGNITIVNO-BIHEVIORALNI TRETMAN ZDRAVSTVENE ANKSIOZNOSTI

Zdravstvena anksioznost je pretjerana zabrinutost za zdravlje koja izaziva značajnu emocionalnu uznemirenost i utječe na svakodnevno funkcioniranje osobe. Javlja se na kontinuumu i cijelim spektrom psihijatrijskih poremećaja, kao što su somatoformni poremećaji, anksiozni poremećaji, poremećaji raspoloženja i psihotični poremećaji (1). Česti okidači straha i brige za zdravlje su tjelesni simptomi. Ponekad su ti simptomi uvjetovani dijagnosticiranom tjelesnom bolešću, ponekad je riječ o somatskim smetnjama bez jasne organske etiologije, a zabrinutost nerijetko izazivaju i tjelesne senzacije koje su po svojoj prirodi posve normalne (2). Soc. psihijat. , 43 (2015) 227 – 234 Prikaz bolesnika
The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections

This work presents two methods that facilitate a 3D reconstruction of microscopic blood vessels in the volume slightly larger than 1 mm3. The source of the data are histological serial sections, i.e., microscopic images of probes, stained with immunohistochemistry. Odd and even sections have different stainings in our primary data set. Thus, firstly, an approach to register an alternately-stained series is presented. With image filtering and a feature-detection-based registration we obtain a registered stack of 148 serial sections. The series has missing sections, locally damaged sections, artifacts from acquisition. All these hinder correct connectivity of blood vessels. With our second approach we interpolate the missing information while maintaining the connectivity. We achieve this with deformations based on dense optical flow. The presented methodology is applicable to further histological series. A combination of both approaches allows us to reconstruct more than 76% larger volumes. An important detail was the composition mode of images. Summarizing, we use methods from image processing and computer vision to create large-scale 3D models from immunostained histological serial sections.
A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods.
Migrating from monolithic architecture to microservices: A Rapid Review

Microservices architecture has become enormously popular because traditional monolithic architectures no longer meet the needs of scalability and rapid development cycle, and the success of some large companies in building and deploying services is a strong motivation for others to consider making the change. However, performing the migration process is not trivial. Most systems acquire too many dependencies between their modules, and thus can't be sensibly broken apart. It is for this reason that studies that provide information associated with the migration process to practitioners are necessary. This study gathers, organizes and analyzes 20 migration techniques proposed in the literature. Results show that most proposals use approaches based on design elements as input; 90% of the proposals were applied to object-oriented software (Java being the predominant programming language); And that the main challenge is to perform the database migration.
Cooperative Visual-Inertial Odometry: Analysis of Singularities, Degeneracies and Minimal Cases

This letter provides an exhaustive analysis of all the singularities and minimal cases in cooperative visual-inertial odometry. Specifically, the case of two agents is analysed. As in the case of a single agent and in the case of other computer vision problems, the key of the analysis is the establishment of an equivalence between the cooperative visual-inertial odometry problem and a Polynomial Equation System (PES). In the case of a single agent, the PES consists of linear equations and a single polynomial of second degree. In the case of two agents, the number of second degree equations becomes three and, also in this case, a complete analytic solution can be obtained. The power of the analytic solution is twofold. From one side, it allows us to determine the state without the need of an initialization. From another side, it provides fundamental insights into all the structural properties of the problem. This letter focuses on this latter issue. Specifically, we obtain all the minimal cases and singularities depending on the number of camera images and the relative trajectory between the agents. The problem, when non singular, can have up to eight distinct solutions. The usefulness of this analysis is illustrated with simulations. In particular, we show quantitatively how the performance of the state estimation worsens near a singularity.
Social Influences in Recommendation Systems

Social networking sites such as Flickr and Facebook allow users to share content with family, friends, and interest groups. Also, tags can often assign to resources. In the previous research [4] using few association rules FAR, we have seen that high-quality and efficient association-based tag recommendation is possible, but the set-up that we considered was very generic and did not take social information into account. The proposed method in the paper [4], FAR, in particular, exhibited a favorable trade-off between recommendation quality and runtime. Unfortunately, recommendation quality is unlikely to be optimal because the algorithms are not aware of any social information that may be available. Two proposed approaches take a more social view on tag recommendation regarding the issue: social contact variants and social groups of interest. The user data is varied and used as a source of associations. The adoption of social contact variants has two approaches. The first social variant is User-centered Knowledge, to contrast Collective Knowledge. It improves tag recommendation by grouping historic tag data according to friend relationships and interests. The second variant is dubbed ‘social batched personomy’ and attempts to address both quality and scalability issues by processing queries in batches instead of individually, such as done in a conventional personomy approach. For the social group of interest, ‘community batched personomy’ is proposed to provide better accuracy groups of recommendation systems in contrast also to Collective Knowledge. By taking social information into account can enhance the performance of recommendation systems.
Why is Short-Time PM2.5 Forecast Difficult? The Effects of Sudden Events

The existing forecast models for PM2.5 concentration can be classified into long term and short term models depending on whether the forecast is performed for the next few hours or days. However, short term forecast models feature narrow forecast time and are thus vulnerable in their sensitivity to soaring variations in air quality, defined as sudden events. The purpose of this work is to investigate the causes behind these sudden events. The PM2.5 data were obtained from monitoring devices deployed in Taichung as a part of the Airbox project. The data were fed into the current short-term forecast model to forecast air quality for the next hour. Event timing was detected by feeding the forecast result as an input to the sudden event detection model. We then combined the filtered timing with factors in environment and human activities. With the application of Hierarchy Clustering, the clustering result was analyzed to find the causes of sudden events. In the springtime and summertime, unexpected changes in rainfall and temperature were critical for forecast models. Moreover, unanticipated changes in the intensity of rainfall and wind are important in the autumn and winter. For human activities, crowds of commuters, tourists, and pilgrims also have influence on unusual air quality. By carefully considering the effects of sudden events, we believe that the response ability of short time forecast can improve significantly in the near future.