Miracle-dz/UniFAR-FTUs huggingface dataset:
GROUND AND AERIAL DIGITAL DOCUMENTATION OF CULTURAL HERITAGE: PROVIDING TOOLS FOR 3D EXPLOITATION OF ARCHAEOLOGICAL DATA

Abstract. Archaeology is by its nature strictly connected with the physical landscape and as such it explores the inter-relations of individuals with places in which they leave and the nature that surrounds them. Since its earliest stages, archaeology demonstrated its permeability to scientific methods and innovative techniques or technologies. Archaeologists were indeed between the first to adopt GIS platforms (since already almost three decades) on large scale and are now between the most demanding customers for emerging technologies such as digital photogrammetry and drone-aided aerial photography. This paper aims at presenting case studies where the “3D approach” can be critically analysed and compared with more traditional means of documentation. Spot-light is directed towards the benefits of a specifically designed platform for user to access the 3D point-clouds and explore their characteristics. Beside simple measuring and editing tools, models are presented in their actual context and location, with historical and archaeological information provided on the side. As final step of a parallel project on geo-referencing and making available a large archive of aerial photographs, 3D models derived from photogrammetric processing of images have been uploaded and linked to photo-footprints polygons. Of great importance in such context is the possibility to interchange the point-cloud colours with satellite imagery from OpenLayers. This approach makes it possible to explore different landscape configurations due to time-changes with simple clicks. In these cases, photogrammetry or 3D laser scanning replaced, sided or integrated legacy documentation, creating at once a new set of information for forthcoming research and ideally new discoveries.
Detection of Uric Acid Using Kretschmann-based SPR Biosensor with MoS2-Graphene

The paper investigates a Kretschmann-based surface plasmon resonance (SPR) biosensor based on graphene with high detection accuracy for uric acid sensing. Kretschmann arrangement is the most effective method for plasmon excitation. In this research, we examined the outcome of MoS2 with a single layer of graphene deposited on top of gold thin film. Finite-difference-time-domain simulation technique is employed for investigation. The sensitivity and full-width-at-half-maximum (FWHM) of the SPR spectrum was analyzed to evaluate the performance of the SPR biosensor. The measurements are observed at visible and near-infrared wavelengths for uric acid detection. The sensitivity of the designed SPR sensor with a monolayer of MoS2 and graphene layer is achieved at 85.25°/RIU and 74.61°/RIU at 670 nm and 785 nm respectively. It is found that the proposed sensor has better detection accuracy at near-infrared wavelength with smaller FWHM compared to visible wavelength for detection of uric acid.
Crail-KV: A High-Performance Distributed Key-Value Store Leveraging Native KV-SSDs over NVMe-oF

A Key-Value SSD (KV-SSD) is a new type of storage device that natively exposes a key-value interface. In this paper, we leverage KV-SSDs to develop new techniques to remove unnecessary layers of indirection traditionally imposed by block devices on distributed storage systems. Specifically, we extend the Crail distributed system [1] to leverage the KV-SSD's native key-value interface exposing it directly to clients through the NVMe-oF protocol. This architectural change simplifies key-value metadata management, as the metadata manager need only track key-value tuples rather than files comprised of blocks. These changes enable fewer RPCs and require less memory for metadata management, resulting in a performance improvement of up to 5x.
Distributed Sequential Coalition Formation Algorithm for Spectrum Allocation in Underlay Cognitive Radio Networks

In this paper, we study the resource allocation problem, including the joint consideration of user grouping and power allocation, of an, underlay cognitive radio (CR) network employing non-orthogonal multiple access (NOMA), which is referred to as the NOMA-CR network. Based on the non-transferable utility coalition formation game theory, the so-called distributed sequential coalition formation (DSCF) algorithm is leveraged to solve the user grouping problem, where multiple cognitive users within a group can access the primary user’s spectrum band while satisfying a minimum rate requirement of the primary user. The simulation results show that a near-optimal performance can be achieved by implementing the proposed DSCF as well as the optimal power allocation algorithms. Furthermore, the results validate that NOMA-CR can significantly outperform the traditional orthogonal multiple access CR, in terms of throughput. Finally, it is shown that by applying a novel adaptive trellis turbo coded modulation technique to the underlay NOMA-CR network, a significant throughput gain can be achieved.
Cellular uptake of Nigella sativa oil-PLGA microparticle by PC-12 cell line

Abstract The aim of this study is to investigate the cell uptake of Nigella sativa oil (NSO)-PLGA microparticle by neuron-like PC-12 cells in comparison to surfactants; hydrophilic (Tween 80 & Triton X100) and hydrophobic (Span 80). Solvent evaporation was used to precisely control the size, zeta potential and morphology of the particle. The results revealed varying efficiencies of the cell uptake by PC-12 cells, which may be partially attributed to the surface hydrophobicity of the microparticles. Interestingly, the uptake efficiency of PC-12 cells was higher with the more hydrophilic microparticle. NSO microparticle showed evidence of being preferably internalised by mitotic cells. Tween 80 microparticle showed the highest cell uptake efficiency with a concentration-dependent pattern suggesting its use as uptake enhancer for non-scavenging cells. In conclusion, PC-12 cells can take up NSO-PLGA microparticle which may have potential in the treatment of neurodegenerative disease.
Cryptocurrency Price Analysis with Artificial Intelligence

Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and mechant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that infiuence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyse the price dynamics of Bitcoin, Etherum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilise useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This study provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model.
The magnetic field impact on the 1/f noise and the charge-pumping measurements in MOSFETs

An investigation of the Low Frequency Noise and Charge-pumping measurements in n-MOSFETs was conducted at room temperature under the influence of perpendicular-to-the-surface magnetic fields. The anomalous response of the transistor under these conditions was analyzed in detail to understand the gate oxide trapping mechanisms and its correlation to the oxide-semiconductor interface. The analysis revealed a magneto-modulation of the noise characteristic and the pumped current, possibly, as a result of the magnetic field altering the trapping dynamics.
Distributed Finite-Time Secondary Control for DC Microgrids With Virtual Impedance Arrangement

In direct current (DC) microgrids, the accurate current sharing could be achieved by regulating the virtual impedance (droop coefficient) and the output voltage average value of each distributed generator (DG) could be restored by regulating the voltage reference value with a secondary controller. However, there is a coupling relationship between the current sharing and the voltage restoration process. In this paper, a distributed finite-time secondary control strategy is proposed aiming at overcoming the coupling effect. The proposed controller consists of four control loops, which are the current sharing controller (CSC), the virtual impedance average value restoration controller (AIRC), the reference voltage consensus controller (RVCC), and the voltage average value restoration controller (AVRC). With the proposed controller, the coupling effect is eliminated and the virtual impedance of each DG is only determined by the system load distribution. Besides, a simplified control strategy is also proposed which only contains the CSC and AIRC, and the reference voltage value can be calculated using the local information. The stability of the proposed controller is analyzed using the Lyapunov method. At last, the proposed controller is simulated in MATLAB/Simulink to verify its effectiveness.
Classifying User Activities in the Encrypted WeChat Traffic

The security and privacy of encrypted mobile applications have attracted the attention of researchers. However, most of the existing researches focus on analysis of SSL/TLS traffic, while few studies focus on proprietary encrypted traffic, which is also important and challenging. In this paper, we make a deep study of WeChat, which is one of the most popular social applications in the world with over one billion active users. The application uses a proprietary encryption protocol called as MMTLS for most of its communications. It is designed based on Transport Layer Security (TLS) 1.3 drafts for both performance and security. We explore the fine-grained classification of typical user activities inside the MMTLS encrypted channels and compare the MMTLS with the HTTPS (e.g. flow duration and packet size), which are jointly used in WeChat. It is found that MMTLS is suitable for scenarios of low latency and lightweight messaging. With the WeChat traffic collected from different platforms (Android, iOS) and devices (Huawei, Samsung, iPhone, iPad, etc.) by different users, we classify seven typical activities, encrypted by MMTLS protocol such as payment, advertisement click, browsing moments and so on. The experimental results show that both of the average precision and recall can reach over 92%. Our work is the first to perform classification on this proprietary encrypted protocol and understanding the difference between MMTLS and TLS. It is believed that the work will benefit the security and privacy of WeChat and other proprietary encryption applications.
Preliminary Analysis of Short-term Solar Irradiance Forecasting by using Total-sky Imager and Convolutional Neural Network

The installation of photovoltaic system (PV) is increasing rapidly across the world. However, the fluctuation of PV output causes serious challenges in the power grid operation. Among the fluctuation, a quick part of fluctuation is mainly caused by the change of cloud coverage. A total-sky imager (TSI), measuring device to take sky and cloud, could be useful for short-term solar irradiance forecasting. In this paper, a convolutional neural network (CNN) is applied to forecasting model (called CNN model) to forecast 5-20 min ahead of global horizontal irradiance (GHI) using total-sky images and lagged GHI. To verify the effectiveness of CNN, three forecasting models are compared. They are the persistence model, the CNN model using only total-sky images, and the CNN model using both total-sky images and lagged GHI. From the computation, the proposed CNN model using both total-sky images and lagged GHI performs root-mean-square error (RMSE) of 49-177W/m2, 93-146W/m2, 71-118W/m2 in sunny day, partly cloudy day and overcast day, respectively. From these results, the proposed method is shown to be suitable for short-term solar irradiance forecasting.
GAA CNT TFETs Structural Engineering: A Higher ON Current, Lower Ambipolarity

Based on the nonequilibrium Green’s function (NEGF) ballistic transport simulation, an optimized homojunction carbon nanotube (CNT) gate-all-around tunnel field effect transistor (GAA TFET) is proposed in this study. This TFET enhances the ON-state current and suppresses the ambipolarity extensively. By changing the channel diameter, gate length, and insulator thickness along with different values of doping levels and dielectrics of the source/channel/drain regions, a subthreshold swing <inline-formula> <tex-math notation="LaTeX">$({SS})$ </tex-math></inline-formula> equal to <inline-formula> <tex-math notation="LaTeX">${11} ~\text {mV}/\text {dec}$ </tex-math></inline-formula> with an ON/OFF ratio of 10<sup>8</sup> is obtained at <inline-formula> <tex-math notation="LaTeX">${V}_{G}={V}_{D}={0.4}$ </tex-math></inline-formula> V. Source (drain) underlap/overlap with the gate is also considered in reaching an optimum result. Such advantages can be followed on other 1-D TFET devices and are explained in detail on the energy band diagram of the device.
Inhibition of Hepatitis C Virus (HCV) Replication by Specific RNA Aptamers against HCV NS5B RNA Replicase

ABSTRACT This study identified specific and avid RNA aptamers consisting of 2′-hydroxyl- or 2′-fluoropyrimidines against hepatitis C virus (HCV) NS5B replicase, an enzyme that is essential for HCV replication. These aptamers acted as potent decoys to competitively impede replicase-catalyzed RNA synthesis activity. Cytoplasmic expression of the 2′-hydroxyl aptamer efficiently inhibited HCV replicon replication in human liver cells through specific interaction with, and sequestration of, the target protein without either off-target effects or escape mutant generation. A selected 2′-fluoro aptamer could be truncated to a chemically manufacturable length of 29 nucleotides (nt), with increase in the affinity to HCV NS5B. Noticeably, transfection of the truncated aptamer efficiently suppressed HCV replication in cells without escape mutant appearance. The aptamer was further modified through conjugation of a cholesterol or galactose-polyethylene glycol ligand for in vivo availability and liver-specific delivery. The conjugated aptamer efficiently entered cells and inhibited genotype 1b subgenomic and genotype 2a full-length HCV JFH-1 RNA replication without toxicity and innate immunity induction. Importantly, a therapeutically feasible amount of the conjugated aptamer was delivered in vivo to liver tissue in mice. Therefore, cytoplasmic expression of 2′-hydroxyl aptamer or direct administration of chemically synthesized and ligand-conjugated 2′-fluoro aptamer against HCV NS5B could be a potent anti-HCV approach.
Effect of Device Variation on Mapping Binary Neural Network to Memristor Crossbar Array

In memristor crossbar array (MCA)-based neural network hardware, it is generally assumed that entire word-lines (WLs) are simultaneously enabled for parallel matrix-vector multiplication (MxV) operation. However, the error probability of MxV in a memristor crossbar array (MCA) increases as the resistance ratio (R-ratio) of a memristor decreases and the resistance variation and the number of simultaneously activated WLs increase. In this paper, we analyze the effect of R-ratio and variation of memristor devices on read sense margin and inference accuracy of MCA-based Binary Neural Network (BNN) hardware. We first show that only a limited number of WLs should be enabled to ensure correct MxV output when the R-ratio is small. On the other hand, we also show that, if the resistance variation becomes higher than a certain level, simultaneous activation of large number of WLs produces the higher accuracy even when R-ratio is small. Based on the analysis, we propose the Accuracy Estimation (AE) factor to find the optimal number of word lines that are simultaneously activated.
Leaf-Compatible Autonomous RFID-Based Wireless Temperature Sensors for Precision Agriculture

This work presents an RFID-based autonomous leaf-compatible temperature sensing system for precision agriculture. The wireless transponders are based on the EM4325 chip by EM Microelectronic, which features an integrated temperature sensor. The chip is made to operate in semi-active mode and the required energy is supplied by a flexible commercial solar cell. The antenna is manufactured on a PLA substrate by adopting the adhesive copper tape technology and the layout is optimized to obtain a compact transponder, suitable for being applied to leaves. A field test demonstrates that the sensors can be successfully deployed to monitor leaf temperature and, through the analysis of the leaf-to-air temperature differential, they can be used to check the water stress status of crops.
Autism Screening Using a Video Game Based on Emotions

Autism spectrum disorders (ASDs) is referred to as a range of mental and physical. Usually these children deal with disorders such as social problems, repeated patterns of behaviors and inability to understand abstract concepts. If at the very childhood the occupational therapy exercises are done on a person with autism, he can return to normal life. Given the importance of early diagnosis of autism, this study presents the structure of a serious smart game for screening and rehabilitation of autism. This computer game is based on training of three important skills in understanding emotions i.e. the recognition of emotions, the emergence of emotions, and emotion regulation. The game was performed on children with autism and normal children for designing pattern-recognition algorithms for screening children with autism. Then the screening precision reached 93% using a support vector machine with Gaussian kernel.
Hardware Trojans in Emerging Non-Volatile Memories

Emerging Non-Volatile Memories (NVMs) possess unique characteristics that make them a top target for deploying Hardware Trojan. In this paper, we investigate such knobs that can be targeted by the Trojans to cause read/write failure. For example, NVM read operation depends on clamp voltage which the adversary can manipulate. Adversary can also use ground bounce generated in NVM write operation to hamper another parallel read/write operation. We have designed a Trojan that can be activated and deactivated by writing a specific data pattern to a particular address. Once activated, the Trojan can couple two predetermined addresses and data written to one address (victim’s address space) will get copied to another address (adversary’s address space). This will leak sensitive information e.g., encryption keys. Adversary can also create read/write failure to predetermined locations (fault injection). Simulation results indicate that the Trojan can be activated by writing a specific data pattern to a specific address for 1956 times. Once activated, the attack duration can be as low as 52. 4μs and as high as 1.1ms (with reset-enable trigger). We also show that the proposed Trojan can scale down the clamp voltage by 400mV from optimum value which is sufficient to inject specific data-polarity read error. We also propose techniques to inject noise in the ground/power rail to cause read/write failure.
Sign Language Recognition Information System Development Using Wireless Technologies for People with Hearing Impairments

The article presents the development of a sign language recognition information system for people with hearing and / or voice impairments, allowing to improve the quality of life and interaction in society with other people. The device, software, functional blocks and information system subsystems are described. Examples of possible application and placement of the system in various spheres of public life are given. One of the types of implementation of the information system of gesture recognition is described in the local network. The system requirements are listed based on the characteristics of the life of the target audience of people and the life cycle of gesture recognition. The decomposition of information system subsystems was made on the basis of belonging to each of the program blocks: the client and the server.
Detection of Hardware Trojans in SystemC HLS Designs via Coverage-guided Fuzzing

High-level Synthesis (HLS) is being increasingly adopted as a mean to raise design productivity. HLS designs, which can be automatically translated into RTL, are typically written in SystemC at a more abstract level. Hardware Trojan attacks and countermeasures, while well-known and well-researched for RTL and below, have been only recently considered for HLS. The paper makes a contribution to this emerging research area by proposing a novel detection approach for Hardware Trojans in SystemC HLS designs. The proposed approach is based on coverage-guided fuzzing, a new promising idea from software (security) testing research. The efficiency of the approach in identifying stealthy behavior is demonstrated on a set of open-source benchmarks.
Blood lead levels and growth status of children

Several studies have shown that elevated blood lead (Pb) levels in children are associated with decreased growth. Among 139 children aged 1 to 10 years (66 male, 73 female) who attended a Pb clinic in Dallas, Texas, growth was decreased by 1.6 cm in height, 1.4 kg in weight, and 0.6 cm in head circumference for each 10 μg/dl increase in blood Pb. Consistent with previously reported effects of Pb, several clinical symptoms (hearing deficit, dental problems, hyperactivity) were increased in frequency among children with high blood Pb levels (X̄Pb = 34.6 μg/dl ± 5.6) compared to the children in the low Pb level group (X̄Pb = 11.9 μg/dl ± 3.3). Interestingly, pica was significantly increased in frequency among children in the high Pb group, indicating oral consumption of non‐food matter was a major source of Pb intoxication. These results suggest that increased Pb level is an important risk factor for growth deficits in children.
Towards Optimal Management and Control of Virtual Storage Plants for Flexible Operation in Future Power Networks

The recently introduced concept of virtual storage plant (VSP) focuses on optimal coordination of a large number of distributed storage devices (of the same or different technology) to provide multiple network services, e.g., primary frequency response (PFR), voltage regulation. In this paper, the potential benefits that VSPs can bring about are discussed from different perspectives, including the potential to reduce network investment needs, improve reliability at the distribution and transmission network levels, and provide service to the grid. VSPs are expected to provide the grid with flexible operation to facilitate high penetration of renewable generation. In order to achieve this thought, mathematical tools are to be developed, which optimally coordinate the storage units comprised in a VSP. This aspect is particularly explored in this paper. An illustrative case study is discussed, which shows how VSPs comprising battery storage systems can effectively support frequency regulation when the storage units are optimally managed.