Miracle-dz/UniFAR-FTUs huggingface dataset:
Super-resolution of THz time-domain images based on low-rank representation

Terahertz time-domain spectroscopy (THz-TDS) employs sub-picosecond pulses to probe dielectric properties of materials giving as a result a 3-dimensional hyperspectral data cube. The spatial resolution of THz images is primarily limited by two sources: a non-zero THz beam waist and the acquisition step size. Acquisition with a small step size allows for the visualisation of smaller details in images at the expense of acquisition time, but the frequency-dependent point-spread function remains the biggest bottleneck for THz imaging. This work presents a super-resolution approach to restore THz time-domain images acquired with medium-to-big step sizes. The results show the optimized and robust performance for different frequency bands (from 0.5 to 3.5 THz) obtaining higher resolution and additionally removing effects of blur at lower frequencies and noise at higher frequencies.
A Novel Weakly Supervised Learning Method for Industrial Surface Defect Detection

Surface defect detection plays a decisive role in controlling product quality which is an important part of achieving automated industrial production. To solve the problems of the high cost of pixel-wise labeling effort, this work proposes a novel weakly-supervised method for surface defect detection which only requires image-level labels. Our approach only uses image-level labels and a small number of samples for training, which is mainly divided into three folds, (1) image classification, (2) class activation map (CAM) generation or CAM modification, (3) foreground and background segmentation. Firstly, ResNet-38 with similar classification performance and better segmentation performance is selected as the backbone of a modified CAM network. Then, we introduce the self-supervised equivariant attention mechanism (SEAM) method, which contains equivariant regularization and pixel context correlation modules to solve the problem of CAM over-activation and under-activation. Extensive experimental results demonstrate the effectiveness of our method.
A Research on Analysis Methods of B&B’s Image Perception Based on Online UGC Big Data

In the era of Web3.0, User Generated Content (UGC) has broken the One-to-Many Model of traditional media and become a new driving force and trend of media development. UGC types of text, picture and video have more and more influence on consumers’ products image perception. With the application of UGC in hotel industry research, it is a good attempt to study the consumers’ image perception of B&B (Bed and Breakfast) based on UGC data. In order to help researchers fully perceive the image of B&B, this research intends to talk about how to collect and sort out three types UGC data about B&B through text mining technology. Based on the theory of cognitive-affective model, SnowNLP and LDA model are used to analysis emotional image and cognitive image, which may help researchers to analysis the overall consumers’ image perception of B&B’s. Meanwhile, the paper will also talk about how to apply the received image perception of B&Bs to better provide B&B service.
Multi-level Operation in Ultra-scaled MRAM

Magnetoresistive random access memory (MRAM) prototypes demonstrate fast operation and are suitable for the last level caches. MRAM possesses high endurance, long retention, and requires less masks for fabrication than its competitor flash memory. MRAM is nonvolatile and scalable. Strong perpendicular magnetic anisotropy in most advanced single-digit nanoscale footprint devices is enhanced by elongating the magnetic layers. To facilitate the switching and to increase the interface-induced magnetic anisotropy even further, the free magnetic layers are made of several elongated pieces separated by tunnel barriers with multiple interfaces. To properly model such devices, accurate evaluation of the spin-transfer torques is required. The interfacial and bulk-torques are not independent, and the use of a spin-charge transport approach coupled to the magnetization dynamics allowing to treat the torques on equal footing in magnetic tunnel junctions with elongated layers becomes mandatory. By employing this advanced modeling approach a multi-level memory operation in an ultra-scaled MRAM cell with a composite free layer is demonstrated.
Techniques and Tools for Runtime Security Monitoring and Analysis of Microservices

Microservice architectures are highly scalable, flexible, and manageable applications. However, their distributed nature and high service granularity increase the challenge of monitoring and identifying attacks. Protecting microservice applications and developing security solutions capable of dealing with unique microservice characteristics is essential. This Ph.D. work aims to develop innovative solutions for enhancing the security of microservice applications at runtime. Through extensive research, the project will explore techniques for monitoring and detecting potential attacks in microservice scenarios at application-layer, considering the existing challenges. Thus, when a possible attack is detected, adaptation actions can be triggered to mitigate the threats improving application security.
Metasurface-Inspired UWB Antenna Design for Wireless Power Transfer Systems

In recent years, there has been significant interest and attention given to wireless power transfer and electromagnetic energy harvesting. This study presents an ultra-wideband (UWB) antenna with improved radiation and metamaterial-based structure for future wireless power transfer systems. Its structure contains a modified CPW-fed monopole antenna resonator with a trapezius-shaped ground plane inspired by an $8\times 8$ metasurface which contains compact metamaterial unit cells. The employed unit cell of the metasurface is in the form of a square ring with a protruded circular ring parasitic structure. Both structures have been implemented in FR4 substrate. The results indicate that the design operates at 2.8-12 GHz, supporting the required spectra coverage (from 3.1 to 10.6 GHz). It has been determined that the suggested design is capable of achieving sufficient frequency response, radiation, and gain levels. The system meets the requirements well for UWB and wireless power transfer systems.
Performance Improvement on a Learning Assessment Web Application Using AWS DynamoDB as a Cache Database

When the COVID-19 pandemic began, the world had to step into a new normal world, forcing real-life activities to transform into online activities. Formal teaching, learning, and examination were also affected. The online learning assessment system is a highly complex system because it must be able to handle a mass of students taking exams simultaneously, especially during the midterm and the final exam week. Therefore, the system must have an acceptable response time and high reliability. Our assessment system had been developed utilizing MySQL as a database, but the response time was unsatisfactory for actual usage. This paper proposes methods to improve the system's efficiency during high traffic by utilizing Amazon Web Services (AWS) DynamoDB as a cache of MySQL since it has a higher read-and-write efficiency than MySQL database. The read operation will be conducted in the Viewing exam session operation. At the same time, the write policy used for Managing exam sessions is write-through, while the policy for Saving examinees' answers is write-back. Choosing between write-through and write-back policies should take into account the acceptable response time and required instant consistency validation. The evaluation results with JMeter reveal that the method can double the efficiency of the read operation. On the other hand, the write operation is less than 3 seconds, which is still within an acceptable limit. For improving the DynamoDB efficiency, further suggestions have also been given in this paper.
Seagrass Classification Using Differentiable Architecture Search

Seagrasses are among the most ecologically significant and diverse ecosystems on Earth, playing a crucial role in maintaining the health and productivity of coastal environments. However, these important habitats are threatened by various human activities, including pollution, habitat destruction, and climate change. To address these challenges, it is essential to develop effective conservation and management strategies that protect seagrass ecosystems and the species that depend on them. Accurately identifying various seagrass species is essential to understanding their habitat and overall health. The researchers have developed a seagrass species identification model to address this challenge using a differentiable architecture search with an early stopping strategy. This model achieved an impressive overall accuracy of 93.3% within a relatively short training time of 4 hours and 11 minutes using a commercially-available Apple MacBook device. This model has the potential to greatly improve the efficiency and accuracy of seagrass species identification, providing valuable insights for conservation efforts and supporting the conservation of these vital ecosystems.
Real-Time Ransomware Detection Method Based on TextGCN

Ransomware detection methods are essential for safeguarding cybersecurity and user privacy. Current detection methods based on CNN and RNN prioritize order and local information, which can be easily captured in continuous word sequences, but these methods ignore global word co-occurrence. Global word co-occurrence, which carries discontinuous information and long-distance semantic information, is critical for ransomware identification. To solve this problem, in this paper, we propose a novel ransomware detection method based on the Text Graph Convolution Network (TextGCN). In TextGCN, weights of the word nodes are adjusted based on the sensitive API call functions, which are summarized according to the five critical steps in the execution of ransomware. We also proposed a co-occurrence information retaining pointwise mutual information theory (COIR-PMI) to calculate weights of word nodes in order to retain both order and co-occurrence information. Experimental results show that the efficiency of our optimized novel approach achieves more than 96.6% ransomware detection accuracy.
Some New Observations on SLO-aware Edge Stream Processing

The emergence of edge stream processing has created a new way of processing real-time data from the Internet of Things (IoT), which comprises a plethora of geographically dispersed physical devices equipped with sensors and actuators that exchange data with the Cloud. Nevertheless, edge stream processing systems face new challenges, including dynamic workloads, resource limitations, and multi-tenant application hosting. Adaptive resource management has been proposed to address these issues. However, this technique may lead to Service Level Objective (SLO) violations when the system encounters resource constraints. To mitigate this problem, we investigate the benefits of using priority-based stream data to reduce the SLO violations associated with adaptive resource management. Our findings demonstrate that segregating data according to their priority levels and processing them accordingly can significantly enhance the efficiency and stability of the system. We implemented this technique on the Storm Streaming system and used RIOT as a benchmark, employing VRebalance and other approaches to adjust system resources dynamically.
A Parametrizable Template for Approximate Logic Synthesis

This paper presents XPAT, a novel algorithm for the generation of approximate circuits which employs an SMT solver to shape the final resulting circuit on a given parametrizable template. The solver outlines which products of which input literals must be included in the final circuit in order to undergo a given error constraint. A miter is created containing the exact circuit description, the template, and a measure of the tolerated error, and by carefully tuning some template key parameters, such as limiting the number of literals per product, this algorithm is able to derive circuits that outperform the state of the art in terms of area. XPAT retrieved circuits with area smaller than those found by state of the art methods in 75% of the cases, and on average obtained 9.85% (up to 60.4% in some cases) improvement in area savings.
Improvement of Message-Oriented Middleware (MOM) for the Surveillance Platform

Cloud computing technology has become increasingly popular and has expanded the capabilities of closed-circuit television (CCTV) systems, especially with the emergence of applications that allow easy access to CCTV. Cloud computing and CCTV capabilities has led to the development of cloud-based video processing applications, including video processing for surveillance and security, which utilize artificial intelligence (AI) technology to detect events in surveillance cameras and convert them into user-friendly results. In order to improve the processing speed in surveillance platform and support future surveillance camera functionalities, this paper proposes an optimized cloud video processing pipeline that leverages Apache Kafka and Distributed File System (DFS) technologies. We conducted experiments by applying configuration parameters to the message-oriented middleware (MOM) task and compared our approach to existing research on our test machines. We used the Node.js framework to run data producers and consumers. The results demonstrate that our proposed concept can reduce latency and increase system throughput, with a throughput increase of 88.55% for SD resolution image and 190.75% for HD resolution image compared to existing research.
Reconstruction of reduced-order EHD flow fields using the proper orthogonal decomposition

The proper orthogonal decomposition (POD) algorithm was applied to reconstruct the instantaneous velocity field of the EHD jet in this work. Particle image velocimetry (PIV) measurements were conducted on EHD jets in a bladeplane configuration at different voltages and electrode spacings. The acquired instantaneous flow fields were used as input to yield the orthogonal spatial modes, the temporal coefficients and the eigenvalues that are automatically ranked by kinetic energy magnitudes. It is shown that the dominant spatial modes obtained by POD usually correspond to large-scale coherent structures, while the higher modes characterize finer-scale turbulent vortex structures. Although POD-based flow field reconstruction allows for the elimination of non-coherent noise and spurious vectors in the original PIV data, the number of modes available for reconstructing the instantaneous flow field tends to increase due to the decrease in the energy content of the dominant modes.
Evaluation Study of Pressure-Strain Correlation Models in Compressible Flow

This paper is devoted to the second-order closure for compressible turbulent flows with special attention paid to modeling the pressure-strain correlation appearing in the Reynolds stress equation. This term appears as the main one responsible for the changes of the turbulence structures that arise from structural compressibility effects. The structure of the gradient Mach number is similar to that of turbulence, therefore this parameter may be appropriate to study the changes in turbulence structures that arise from structural compressibility effects. Thus, the incompressible model (LRR) of the pressure-strain correlation and its corrected form by using the turbulent Mach number, fail to correctly evaluate the compressibility effects at high shear flow. An extension of the widely used incompressible model (LRR) on compressible homogeneous shear flow is the major aim of the present work. From this extension the standard coefficients Ci became a function of the compressibility parameters (the turbulent Mach number and the gradient Mach number). Application of the model on compressible homogeneous shear flow by considering various initial conditions shows reasonable agreement with the DNS results of Sarkar. The ability of the models to predict the equilibrium states for the flow in cases A1 and A4 from DNS results of Sarkar is examined, the results appear to be very encouraging. Thus, both parameters Mt and Mg should be used to model significant structural compressibility effects at high-speed shear flow.
A Safety Fallback Controller for Improved Collision Avoidance

We present an implementation of a formally verified safety fallback controller for improved collision avoidance in an autonomous vehicle research platform. Our approach uses a primary trajectory planning system that aims for collision-free navigation in the presence of pedestrians and other vehicles, and a fallback controller that guards its behavior. The safety fallback controller excludes the possibility of collisions by accounting for nondeterministic uncertainty in the dynamics of the vehicle and moving obstacles, and takes over the primary controller as necessary. We demonstrate the system in an experimental set-up that includes simulations and real-world tests with a 1/5-scale vehicle. In stressing simulation scenarios, the safety fallback controller significantly reduces the number of collisions.
Big Data, Transmission Errors, and the Internet

A cursory look at the Internet protocol stack shows error checking capability almost at every layer, and yet, a slowly growing set of results show that a surprising fraction of big data transfers over TCP/IP are failing. As we have dug into this problem, we have come to realize that nobody is paying much attention to the causes of transmission errors in the Internet. Rather, they have typically resorted to file-level retransmissions. Given the exponential growth in data sizes, this approach is not sustainable. Furthermore, while there has been considerable progress in understanding error codes and how to choose or create error codes that offer sturdy error protection, the Internet has not made use of this new science. We propose a set of new ideas that look at paths forward to reduce error rates and better protect big data. We also propose a new file transfer protocol that efficiently handles errors and minimizes retransmissions.
Radar and acoustic observations during VTMX field-campaign

During Oct 2000, the University of Massachusetts participated in the first VTMX field campaign with the deployment of two radars and a sodar. This paper presents a overview of nighttime stable boundary layer observations made during this campaign. Radar-detectable activity consists mainly of sporadic brief episodes of turbulence, typically confined to the lowest 200 m of the atmosphere.
A Trust Mechanism of Internet of Vehicles Based on Hotstuff Consensus Algorithm

In the Internet of Vehicles (IoV), the interaction and sharing of traffic information between vehicle nodes is required, but there are problems in the current IoV system that it is difficult to efficiently synchronize traffic data information between nodes, and malicious nodes will spread false information. In view of the above problems, this paper proposes a trust mechanism of Internet of Vehicles based on Hotstuff consensus algorithm. First, the roadside unit (RSU) aggregates the evaluations submitted by vehicles to form trust values and create blocks. Then, we improve the Hotstuff consensus algorithm by introducing the reputation mechanism, and elects the leader node with probability according to the reputation of the node, so as to improve the system throughput and reduce the delay of reaching consensus between nodes. The experimental results show that the scheme proposed in this paper can effectively improve the consensus efficiency of the IoV system, enhance the system’s ability to resist malicious nodes and enable efficient and secure data sharing between vehicles.
The Effect of Pulsating Dielectrophoresis on Liquid Film Flow Boiling

Electrohydrodynamics (EHD) has been explored for a variety of applications, including thermal management of electronics for both terrestrial and space applications. EHD conduction pumping of dielectric liquids provides a lightweight, low-power, and solid-state method for cooling. Dielectrophoresis (DEP) offers a mechanism for phase-separation of liquid and vapor for enhancement of two-phase heat transfer. EHD conduction pumping of a thin liquid film in conjunction with DEP vapor extraction is being explored as a next generation thermal management technology for satellites and deep space missions. This study is exploring the effect of a pulsating diverging electric field applied to boiling heat transfer of a thin liquid film. The results show that there is an optimal frequency of application at a given heat flux, which can be related to the frequency of bubble departure from the heated surface. In addition, at higher heat fluxes, the void fraction of vapor increases and the measured electric current decreases. The heat transfer enhancement of continuous DC voltage application is greater than the pulsating cases due to the electroconvection and mixing being stronger. However, there are potential efficiency benefits due to the periodic application of voltage.
International Responsibility and Liability for Damage Caused by Environmental Interferences

0378-777X/01/$12.00 © 2001 IOS Press over environmental disputes. Dismantling technical hurdles such as standing to sue has facilitated public interest litigation in the service of sustainable development. 14. Strengthening linkages with journalists, women and young people would give a frame for environmental protection regimes. Journalists in developing countries, particularly, play an important role in identifying environmental causes and drawing national attention to the need to prioritize their redress. 15. The empowerment of women and marginalized groups and their utilization in resource management can be an important factor in the success of environmental governance.
Assessment of Fish Flesh Tainting Substances

Increased use of lakes and rivers for sport and commercial fishing in the vicinity of large municipal and industrial discharges has led to the realization that fish in many bodies of water are not palatable. The dischargeof organic compounds has led to the contamination of fish flesh to such an extent that fishing is curtailed more because of poor eating quality rather than the lack of fish. To evaluate the magnitude of the tainting of fish flesh in a large river, studies were conducted on the flavor of channel catfish (Ictalurus punctatus Rafinesque) flesh from the Ohio River from Pittsburgh, Pa. , to Cairo, Ill. Tasting of fish flesh determined that a panel could differentiate between fish held upstream and downstream from a wastewater discharge. Caged test fish exposed for three days acquired a minimum of 70 percent of the off-flavor of native fish.