Miracle-dz/UniFAR-FTUs huggingface dataset:
i2Graph: An Incremental Iterative Computation Model for Large Scale Dynamic Graphs

Due to the rapid changes in graph data sets, the mined information will quickly become obsolete, thus the entire data set needs to be re-computed from the beginning, which will result in the waste of computing time and resources. To reduce the cost of such computations, this paper proposes a model called i2Graph to support incremental iterative computation for dynamic graphs. Different from the way of traditional iteration, i2Graph executes the graph algorithm by reusing the results of the previous graph and performs computation on parts of the graph that has changed. i2Graph contains two components: (1) an incremental iterative computation model to improve the execution efficiency of the iterative graph algorithm; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. It is implemented based on Spark GraphX, a popular parallel and distributed computing framework for large-scale graph processing. Experiment results verify the performance advantages of i2Graph model when performing some iterative graph algorithms on the dynamic graph, compared with the traditional iteration.
Benchmarking of FinFET, Nanosheet, and Nanowire FET Architectures for Future Technology Nodes

Nanosheet (NS) and nanowire (NW) FET architectures scaled to a gate length (<inline-formula> <tex-math notation="LaTeX">$L_{\textbf {G}}$ </tex-math></inline-formula>) of 16 nm and below are benchmarked against equivalent FinFETs. The device performance is predicted using a 3D finite element drift-diffusion/Monte Carlo simulation toolbox with integrated 2D Schrödinger equation based quantum corrections. The NS FET is a viable replacement for the FinFET in high performance (HP) applications when scaled down to <inline-formula> <tex-math notation="LaTeX">$L_{\textbf {G}}$ </tex-math></inline-formula> of 16 nm offering a larger on-current (<inline-formula> <tex-math notation="LaTeX">$I_{\textbf {ON}}$ </tex-math></inline-formula>) and slightly better sub-threshold characteristics. Below <inline-formula> <tex-math notation="LaTeX">$\text{L}_{\textbf {G}}$ </tex-math></inline-formula> of 16 nm, the NW FET becomes the most promising architecture offering an almost ideal sub-threshold swing, the smallest off-current (<inline-formula> <tex-math notation="LaTeX">$I_{\textbf {OFF}}$ </tex-math></inline-formula>), and the largest <inline-formula> <tex-math notation="LaTeX">$I_{\textbf {ON}}/I_{\textbf {OFF}}$ </tex-math></inline-formula> ratio out of the three architectures. However, the NW FET suffers from early <inline-formula> <tex-math notation="LaTeX">$I_{\textrm {ON}}$ </tex-math></inline-formula> saturation with the increasing gate bias that can be tackled by minimizing interface roughness and/or by optimisation of a doping profile in the device body.
A Simple Proactive Routing Protocol with Multiple Base Stations Deployment for Unmanned Aerial Vehicular Networks

Unmanned Aerial Vehicles (UAVs) or aerial drones have a very encouraging future owing to the flexibility of their use in plenty of applications and the provision of less costly solutions in comparison to fixed infrastructure networks. It is a very challenging design problem to realize a collaborating end-to-end routing solution for a UAV network, which is very prone to link failures due to its highly dynamic nature. In this paper, a simple Proactive Routing scheme with Multiple ground Base Stations deployment (PRMBS) is presented. In PRMBS, periodic messages called DNA for Drones and Network Association from the ground base stations side are broadcast and relayed into the whole network. There are no separate hello messages in PRMBS and the role of a hello message is played by DNA messages. The path for data traffic is formed in a reverse manner to reach the respective ground base station from which the DNA message was received. The proposed proactive scheme is simulated in Network-Simulator-2 and its hows very promising results for dynamic UAV networks.
A Cooperative Lane Change Model for Connected and Automated Vehicles

The emerging technology of vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication makes it possible for vehicles to sense the environment information, which can be exploited to assist the vehicle in cooperative motion planning. In this paper, we focus on the cooperative trajectory planning of lane changes for connected and automated vehicles (CAVs). The proposed model considers the traffic scene with multiple mandatory lane change demands and completes the trajectory planning for vehicles by taking the safety and efficiency into consideration. The model solves two critical issues: the vehicle grouping and the motion planning. In the first issue, CAVs in the cooperative zone are divided into different groups. Then the problem is simplified and divided into several subproblems. In the second issue, the trajectory planning is conducted in each group. Trajectories are generated for vehicles with and without lane change demands. Besides, these two steps are iterated and updated in the fixed time interval, which makes full use of the dynamic cooperation ability of vehicles. Extensive simulation tests are conducted to validate the performance of the model. Results show that the cooperation of vehicles realizes safe and effective lane changes.
Linear Time Invariant State Space System Identification Using Adam Optimization

In this paper, a new state space system identification method is proposed. Adam optimizer is used as to optimize the state and input matrices of linear time invariant systems. A method of solving state space model numerically is presented and is used to build the TensorFlow graph of the identification method to get the gradients from a loss function for both matrices and to run the optimizer. The simulation results show that proposed method can succeed even when data is disturbed with random noise.
SYNERGISM OR COMPETITION BETWEEN ZINC AND CHROMIUM DIETARY LEVELS ON INSULIN ACTION MECHANISM. A METHOD TO INVESTIGATE

VINCENZO MONDA1,§, ERSILIA NIGRO2,§, MARIA RUBERTO3,§, GIUSEPPE MONDA1, ANNA VALENZANO4, ANTONIO I. TRIGGIANI4, FIORENZO MOSCATELLI4, EMANUELE MONDA1, INES VILLANO1, MICHELE ROCCELLA5, LUCIA PARISI5, ANTONIETTA MESSINA1,* 1Department of Experimental Medicine, Università degli Studi della Campania-Luigi Vanvitelli, Italy 2CEINGE-Biotecnologie Avanzate Scarl, Napoli, Italy 3Department of Medical-Surgical and Dental Specialties; Università degli Studi della Campania-Luigi Vanvitelli, Italy, Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy 4Department of Psychological, Pedagogical and Educational Sciences, University of Palermo, Italy
Unsupervised learning of wildlife behaviour for activity-driven opportunistic beacon networks

Monitoring wild animals in their natural habitat and in real time constitutes an essential aspect of biological and environmental studies. Monitoring is mainly conducted through wireless wildlife monitoring systems (WMS) due to their energy-efficiency and scalability properties. However, using WMS often involves the deployment of energy demanding wireless radio technologies and protocols that significantly increase energy consumption while tracking mobile animals. Thanks to the raise of IoT devices capable of sensing, computing, and wireless networking, WMS can become more efficient and overcome the initial drawbacks. This paper, describes an activity driven beaconing mechanism based on unsupervised activity classification scheme. The algorithm is evaluated for different parameters involving the sampling rate, processing window as well as different cluster sizes. The evaluation shows that use of lightweight algorithms and low sampling rates provides the possibility to reliably monitor the activity of the animal. The evaluation results showed that the proposed mechanism could reduce energy consumption by increasing communication sleep-time while the objects were stationary.
Computation of the Corona Onset Voltage for DC at Two Different Altitudes

In this paper, a methodology based on the finite element method is used to analyze the corona onset voltage on different structures of HVDC lines, the methodology includes atmospheric conditions such as pressure and temperature. The results obtained in simulations were verified through two experimental set-ups (rod-to-plane and corona cage) at sea level and 2200 m above the sea level. According to the results, the predicted values are in good agreement with the measured values in most of the tested configurations. Once the accuracy of the methodology was verified, it was used to modify the conductor bundle geometry of an HVDC line to maximize the corona onset voltage. The importance of this work is due to the prediction of the corona onset voltage since corona discharge in elements of a high voltage transmission line is an undesirable phenomenon because it causes power losses and electromagnetic interference.
Detecting Interest-Factor Influenced Abnormal Evaluation of Teaching via Multimodal Embedding and Priori Knowledge Based Neural Network

Student evaluations of teaching (SET) helps to quantitatively measure the teaching performance of classes. However, concerns arouse when the result of SET and students' final grades are highly positively correlated, which means the students may make the evaluation based on the final grades they take. For the purpose of avoiding retaliation and mutual reward rating, some methods based on statistics or rules are used to detect the presence of anomaly evaluation. Whereas, the problem is still challenging since the characteristics of this abnormal evaluation are implicit and multimodal. Besides, additional information such as the domain of subject, instructors' teaching style, and the reputation spread by students also requires corresponding prior knowledge to supplement the abnormal evaluation detection model. Therefore, in this paper, we proposed a multimodal embedding and prior knowledge based neural network to detect potential so called Interest Factor Influenced Abnormal Evaluation (IFAE). The method proposed in this paper uses SDNE and PV-DM to embed the evaluation network between students and teachers and the comment text of students. Taking into account the continuity of mutual evaluation between students and teachers during the teaching cycles and the different emphasis of students' comment texts on teachers, the features of students and teachers are comprehensively constructed. Then, the attention mechanism is used in the comment text to perform final prediction jointly with above features. The experiment result shows that our proposed model outperforms other state-of-art models which based on single type of features on F1 score by 9.25%.
Anorectal malformations and Hirschsprung disease: Functional and psychosocial challenges

The title of this thesis “Anorectal malformations and Hirschsprung disease: functional and psychosocial challenges” reflects on the patients, their parents and their doctors. Within this manuscript we have addressed all these different concepts. We exposed some not often investigated aspects, such as QoL and anxiety of parents of patients with an anorectal malformation or Hirschsprung disease. Anxiety among mothers of newborn children with an anorectal malformation or Hirschsprung disease is an important aspect that we need not to forget. Also the ongoing parental stress during the first years of parenting a child, with one of these disorders, is something we need to keep in mind. And if necessary we need to offer psychological support to parents. And also we proved that a multidisciplinary approach for these patients is the only way to address all complaints that can influence the (health-related) QoL of the patients and their parents. These patients can develop for example sexual and urological problems during their life. Also psychological problems can manifest. Our patients need (transitional) outpatient care that can provide in all these aspects. With this thesis we have addressed that, both for parents and patients, functional and psychosocial problems can occur. In the future we want to focus even more on interventions that can lead to a better (health-related) QoL of both parents and patients.
Experimental research on vacuum breakdown characteristics in a long gap under microsecond pulses

By conducting vacuum breakdown experiments under 30 μs pulses with a uniform field for gap separations d ranging from 2.5 to 5.5 cm, it is found that the dependence of the breakdown voltage Ub on d obeys the total voltage effect (TVE) relation Ub=Cda, where a ranges from 0.76 to 0.84. By fixing d as 3.5 mm, Ub for stainless steel (304C), titanium (TC4), and aluminum (LY14) electrode materials are obtained, and it is found that Ub(304C)b(TC4)b(LY14). Microscopic images of the electrode surfaces show the presence of melting traces coming from the opposite electrodes. By extending Farrall's model of vacuum breakdown under microsecond pulses, an expression for the power exponent of a in the TVE is derived as a=(5 − k)/6, where k is a parameter related to the uniformity of the field. In the experiments performed here, electrodes with the Bruce configuration are used, and k is close to 0. Therefore, a is close to 0.833, which agrees with the values of the power exponents found in the experiments.
Traffic Data Classification to Detect Man-in-the-Middle Attacks in Industrial Control System

Industrial Control Systems (ICS) are widely used in critical infrastructure in industries such as power, rail transit, and water conservancy. As the connection between the corporate network and the Internet continues to increase, the industrial control system has gradually become the target of hackers, which constantly threaten the personal safety of citizens. The Man-inthe-Middle (MITM) attack is one of the most famous attacks in the field of computer security. Once being used in the factory control network, it will not only cause data leakage, but also control the core industrial component PLC and cause serious security accidents. This paper proposes a method for classifying network traffic data in industrial control system to detect MITM attacks. In the simulation experiment, the method can identify normal and abnonnal data packets that have been tampered by the MITM, and the classification accuracy is up to 99.74%.
A 28GHz CMOS Differential Bi-Directional Amplifier for 5G NR

A 28GHz differential bi-directional amplifier in a standard 65nm CMOS process is presented. This work is realized based on the neutralized bi-directional core together with the fully shared inter-stage matching networks. The core chip area is only 0.11mm2. At 28GHz, a 15.1-dBm saturation output power and a 4.2-dB noise Figure are realized for PA mode and LNA mode, respectively. The DC power consumptions for PA mode and LNA mode are 149mW and 31mW, respectively, under 1-V DC supply.
Outage Probability-based Adaptive Guard Band Allocation for Mixed Numerology

In this paper, we propose the adaptive guard band allocation based on outage probability for the sub-bands with different numerologies on Nakagami-m fading channels in uplink cellular networks. First, we analyze inter-numerology interference and derive the outage probability of each sub-band in closed form. Then, the optimization problem is formulated to obtain the guard band assigned to each sub-band. The validity of the analysis is verified by comparing it to the Monte-Carlo simulation results. It is also shown that applying the guard band determined through the proposed algorithm can allocate smaller guard band than symmetric guard band allocation algorithm.
Timber Transportation Vehicle Detection Based on SSD-GIoU

Timber transportation surveillance is the key and important means of forest resource management. In order to surveille timber transportation in the forest road, we carry out timber transportation vehicle detection from the video of bayonet, with which we propose an improvement of SSD (Single Shot Multibox Detector) based on MXNet. We establish a timber transportation vehicle dataset for training and testing the ResNet50 and Darknet53 with three thresholds. As mAP (mean Average Precision) is closely related to IoU (Intersection over Union), the original calculation of which does not consider overlap and disjoint between prediction box and the ground truth, we find it contributes the major part of error-detection with SSD. Inspired by this, we combine a novel metric loss calculation called GIoU (Generalized Intersection over the Union) with SSD to improve mAP, which is proved to be greatly promoted in timber transportation vehicle detection.
Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

In recent years, computation offloading has become an effective way to overcome the constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive mobile application tasks to remote cloud-based data centers. Smart cities can benefit from offloading to edge points in the framework of the so-called cyber–physical–social systems (CPSS), as for example in traffic violation tracking cameras. We assume that there are mobile edge computing networks (MECNs) in more than one region, and they consist of multiple access points, multi-edge servers, and $N$ MDs, where each MD has $M$ independent real-time massive tasks. The MDs can connect to a MECN through the access points or the mobile network. Each task be can processed locally by the MD itself or remotely. There are three offloading options: nearest edge server, adjacent edge server, and remote cloud. We propose a reinforcement-learning-based state-action-reward-state-action (RL-SARSA) algorithm to resolve the resource management problem in the edge server, and make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay. We call this method OD-SARSA (offloading decision-based SARSA). We compared our proposed method with reinforcement learning based Q learning (RL-QL), and it is concluded that the performance of the former is superior to that of the latter.
Mitigating the Passing Word Line Induced Soft Errors in Saddle Fin DRAM

In this article, a novel technique using the work-function (WF) engineering of passing word line (PWL) has been proposed to mitigate zero-failure in Saddle-Fin-Recessed-Channel-Access-Transistor (S-RCAT). The zero-failure caused by the hammered access [i.e., row hammer (RH)] of PWL is shown to be due to the interference between the PWL and the adjacent storage node (SN). We propose a localized introduction of a high WF PWL to minimize the interference between the PWL and the SN significantly. Using TCAD 3-D process simulations, we analyze the mechanism of PWL hammer-induced zero-failure. We show that a high WF PWL reduces the electron current density by 92% near the shallow-trench-isolation (STI) during the accessing of PWL. The lesser electron density near STI isolates the SN potential to rise due to the hammered access of PWL, which results in the improvement of 77% in zero-failure mitigation. The introduction of high WF PWL does not have any substantial effect on active access transistor characteristics (<inline-formula> <tex-math notation="LaTeX">${I}_{{\mathrm {ON}}}$ </tex-math></inline-formula>, SS, and <inline-formula> <tex-math notation="LaTeX">${V}_{T}$ </tex-math></inline-formula>). The results obtained in this article demonstrate the potential of using localized high WF PWL in S-RCAT to mitigate the zero-failure in the future dynamic-random-access-memory (DRAM) technology.
Privacy and Cryptocurrencies—A Systematic Literature Review

Our transaction history in the current centralized banking system has the ability to reveal a lot of private information for each spender, both to the banking system itself, but also to those entities that surround it (e.g., governments, industry etc). Examples of leaking information constitute the amounts spent, the goods on which the amounts were spent, the spending locations and the users we exchange money with. This knowledge is powerful in the hands of those who have it, and can be used in multiple ways, not always to our benefit. Cryptocurrencies, such as the famous Bitcoin, were proposed as a means to address the limitations of centralized banking systems and to offer its users privacy with regards to their transactional data. In this work, we perform a systematic literature review on the realm of privacy for electronic currencies. We present the development of digital money from electronic cash to cryptocurrencies and focus on the techniques that are employed to enhance user-privacy. Furthermore, we present flaws of the current cryptocurrency systems, which reduce the privacy of the cryptocurrency users. Finally, we describe three research directions to enhance privacy for cryptocurrencies: transaction propagation mechanisms, succinct ZK proof systems without a trusted setup, and specialised trustless zero-knowledge proofs.
Concerning Quantum Identification Without Entanglement

Identification schemes are interactive protocols typically involving two parties, a prover, who wants to provide evidence of his or her identity and a verifier, who checks the provided evidence and decide whether it comes or not from the intended prover. In this paper, we comment on a recent proposal for quantum identity authentication from Zawadzki, and give a concrete attack upholding theoretical impossibility results from Lo and Buhrman et al. More precisely, we show that using a simple strategyan adversary may indeed obtain non-negligible information on the shared identification secret. While the security of a quantum identity authentication scheme is not formally defined in [1], it is clear that such a definition should somehow imply that an external entity may gain no information on the shared identification scheme (even if he actively participates injecting messages in a protocol execution, which is not assumed in our attack strategy).