Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Image Dehazing and Super-Resolution: Closed Shared Source Residual Attention Fusion Network

In real world, due to the existence of floating particles such as smoke and dust in the atmosphere, images taken by camera are susceptible to different levels of blurring, low contrast, color distortion and visual degradation, which would be amplified when enlarging the image resolution. Therefore, it is a new trend to join the image dehazing and image super-resolution tasks. To generate sharp high-resolution images from low-resolution images with severe haze, a common way is to connect the dehazing network and the super-resolution network in series. However, two-stage joint approach easily introduces blurring artifacts and is time-consuming. In addition, although there are a few existing one-stage methods, their training modes are relatively complicated and the restoration effects on some texture details are still blurry. In this paper, we focus on exploring one-stage joint model and propose a back-projection network based on shared source attention fusion (BPSAF), which forms a closed frame through back-projection mechanism. BPSAF can remove non-uniform haze and extend the resolution simultaneously. Specifically, a shared source attention fusion (SAF) module is presented to fuse high-frequency information of different level features using shared source skip connections more effectively, which filters out abundant thin haze and low-frequency information from merged images. To enhance the definition of restored images, a feedback error correction module based on error attention mechanism (FEC-EA) is designed to further correct distorted texture details by eliminating the feedback error from initial super-resolution result to the input hazy image. Experimental results demonstrate that our back-projection framework is superior to other existing methods in terms of quantitative indicators and visual quality.
Effective Method for Electrodynamic Analysis of a Three Dimensional Lattice Consisting of Perfectly Conducting Hollow Cylinders

A strict effective electrodynamic method for analyzing photonic crystal made of hollow PEC cylinders with walls of finite thickness has been developed. The software developed has high accuracy and short computing time. It is used to find the dependences of the delays of eigenwaves on dimensions of the lattices and cylinders (length, diameter, wall thickness) for different directions of propagation and polarization of the wave. Analysis of rectangular and hexagonal lattices is performed.
A Flexible Gimli Hardware Implementation in FPGA and Its Application to RFID Authentication Protocols

Radio Frequency Identification (RFID) systems have bestowed numerous conveniences in a multitude of applications, but the underlying wireless communications architecture makes it vulnerable to several security threats. To mitigate these issues, various authentication protocols have been proposed. The literature accommodates comprehensive proposals and analysis of authentication protocols, but not many of them provide hardware implementations. In addition, there is diverse demand for hardware area and throughput (TP) requirements from RFID system components (tags, readers, database servers), which demand a flexible implementation strategy. This paper proposes a flexible implementation strategy for the lightweight authenticated encryption (AE) and hash function called Gimli, and applies it to a state-of-the-art authentication protocol. This allows the authentication protocol to be implemented efficiently, wherein the area and TP can be adjusted flexibly according to the RFID system requirements. This implementation strategy is generic; it can be used to implement any other AE and hash functions. This strategy can also be applied to other authentication protocols that heavily use AE and hash functions. To provide a detailed analysis, the hardware optimization techniques in each component of the RFID system for a state-of-the-art authentication protocol are analyzed. When implemented with the most area-optimized versions, we achieve TP of 740 Mbps and 420 Mbps for Gimli hash and Gimli AE, respectively, and for throughput-oriented implementation, the results are 3.08 Gbps and 1.43 Gbps, respectively. This shows that the proposed implementation strategies allow us to implement authentication protocols in a flexible manner to meet the differing requirements in TP and area for RFID applications.
Meandered and Dispersion-Enhanced Planar Leaky-Wave Antenna With Fast Beam Scanning

Controlling the scan rate of frequency-scanned antennas can be useful for any bandwidth-limited application. Two techniques to control the scan rate of planar periodic leaky-wave antennas are discussed: optimizing unit cell dispersion and meandering unit cells with a desired periodicity. Each technique presents a tradeoff of limited matching bandwidth or reduced antenna gain, respectively. These techniques are used together to design a broadside-scanning <italic>Ka</italic>-band antenna. Measurements demonstrate a very high scan rate of <inline-formula><tex-math notation="LaTeX">$10.5^{\circ }/\%\text{BW}$</tex-math></inline-formula>. The 8<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> available bandwidth and <inline-formula><tex-math notation="LaTeX">$80^{\circ }$</tex-math></inline-formula> scan range through broadside is very favorable for frequency-scanned applications.
Identification of Timber Defects Using Convolution Neural Network

Timber is important in the construction industry. Random checking in the manufacturing line, randomized product selection, and condition monitoring are some of the most common quality control methods for wood panels. Manufacturing can continue if the checked products' expectations are met; however, if flaws or anomalies are discovered, the supply chain is shut down, a remedy sought, and the manufacturer loses productivity. Skilled human operators continue to perform the bulk of visual quality inspections in the timber products industrial processes. Visual inspection is a time-consuming and labor-intensive process that is prone to human error. New automation processes based on high-resolution images and inspection systems are currently being developed, but these are yet quick or not accurate enough for real-time industrial processes. Using an automated visual inspection device, the whole project proposes a method for recognizing and classifying defects on the surface of wood. Here, a convolution neural network has been used to detect the defects present on wooden surface layers.
Sparse Bayesian 3-D Imaging for Low-RCS Objects via Dyadic Green's Function

Sparse Bayesian (SB) 3-D imaging resolution transcends classical radar cross section (RCS) imaging, providing a novel solution for low-RCS objects. To better resolve low-RCS objects, we propose an improved SB 3-D imaging algorithm via dyadic Green's function, i.e., 1) in the building of the radar observation model, a novel dyadic Green's function-form dictionary (DGFD) is designed to replace traditional scalar form, contributing to resolving low-RCS objects; 2) in the implementation of 3-D imaging, an improved SB algorithm using DGFD is proposed, further ensuring high precision. Electromagnetic simulation results show the precision of the proposed algorithm is less than 1 dB in mean absolute deviation and less than 1.5 dB in standard deviation, better than existing algorithms.
Multiband Printed Planar Inverted-F Antenna(PIFA)For LTE Technology

The demands of a multiband antenna in the era of modern communication for longer connectivity, high data rate, low latency, and much more features the LTE band is developing. The designer can also consider the miniaturization of the antenna so that a large integrated circuit will be developed for various applications in smartphones. In this paper, a multiband printed Planar Inverted-F Antenna (PIFA) is designed on the FR4 (∊r=4.4) substrate. Our proposed antenna covers the multiple bands of LTE which are 1700MHz, 2300MHz, and 3500MHz with a reflection coefficient of −31.50dB, −30dB, and −32.50dB respectively. By putting multiple slots in the ground element and by applying parasitic element with PIFA design. The performance of the antenna can be easily improved and the observed gain is 3dBi at 1700MHz, 3dBi at 2300MHz, and 2.5dBi at 3500MHz. The proposed research work has designed and simulated the antenna on CAD-FEKO software.
An Enhanced Machine learning data aggregation model for Efficient data processing in wireless sensor networks

Wireless Sensor Networks has more resource constraint on executing efficient data transmission, and it needs a robust routing protocol. Data processing efficiency in WSN depends on scalability, energy consumption, and QoS optimization with overhead. Achieve improved WSN proposing Machine Learning Data Aggregation Model (EML-DA). The proposed EML-DA architecture will focus on hybrid CH selection and robust data aggregation. Hybrid Cluster formation and CH selection work based on Artificial Neural Networks and robust data aggregation work based on Independent Component Analysis (ICA) ML technique. Considering the parameters as bandwidth allocated, the distance between Sensor nodes, and Base Station (BS), residual energy ANN architecture solves the problem by choosing the best CH of each cluster. Independent Component Analysis (ICA) ML technique performs efficient data aggregation at the CH node of each cluster to reduce energy consumption and minimal overhead; ICA is computationally efficient and uses differential entropy to minimize redundant data. EML-DA outperforms current ML-based clustering and data aggregation algorithms, affording to the outcomes of the experiments.
Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.
Multifunctional Leaky-Wave Antenna With Tailored Radiation and Filtering Characteristics Based on Flexible Mode-Control Principle

In this paper, a class of single-layered multifunctional leaky-wave antennas (LWAs) with flexibly engineered radiation and filtering characteristics are proposed and demonstrated for microwave and millimeter-wave applications. Radiating discontinuities (RDs) exhibiting multiple resonances while particularly possessing flexible model-control capability are exploited to accomplish such design freedoms and multifunctionalities of LWAs. By properly engineering the resonance characteristics of RDs under the mode-control principle, the attenuation constant of relevant LWAs can not only be freely tailored for diverse beamwidth/directivity requirements, but also simultaneously maintain a flat frequency response for radiation stability. Meanwhile, controllable filtering behaviors can be obtained as well by the LWAs thanks to the transmission zeros introduced by resonances. Consequently, both the radiation and filtering performances of LWAs can be adequately tailored by taking advantage of the mode-control capability of RDs. Under this design concept, two types of LWAs based on substrate-integrated waveguide and microstrip techniques are respectively developed for different system integration platforms. The substrate-integrated waveguide LWA whose unit cells consist of different longitudinal slots is firstly examined. Additionally, the microstrip LWA, which depends on stub-loaded resonators, is further investigated. The proposed two LWAs are all with flexible engineered electrical behaviors, single-layer, low-cost, and easy integration; they may be a potential candidate for various system applications such as 5G communication and Internet of Vehicles.
Application of Computer Science and Engineering Technology in College Computer Online Education

In today's society, the popularization rate of computers in the whole country and even all over the world is increasing rapidly year by year. This makes People's Daily work closely connected with computers, information technology and the Internet. As an auxiliary tool, the computer provides great convenience for the process of teaching. Intelligent tools make the learning process more convenient, so the significance of computer science and technology to computer education is huge. In the teaching process, teachers should try their best to use the advantages of computers to assist teaching, and provide students with better network learning environment and learning conditions. Different from the traditional teaching methods, the application of computer technology to education has gradually formed a new type of computer education. This has caused a great change to the current situation of China's education development, and at the same time, China's education model is also faced with various problems.
An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network

Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
ViBERT: Visual Behavior Regression Testing

Regression testing is a type of software testing that aims at identifying faults caused by code changes. Regression testing is important especially during software evolution and maintenance. As developers integrate programs or make updates to a software system, they need to make sure the changes do not adversely affect other parts of the system. Using dynamic analysis, behavioral regression testing (BERT) is one of the techniques proposed to solve the problem by re-executing test cases that target the affected area. It compares the behavior of a program before and after the changes upon certain test cases. This paper proposes Visual BEhavioral Regression Testing (ViBERT) , a visualization approach to comparing the behavioral differences between the new and old versions of a program in regression testing. We build a prototype called SoftLink , a visual environment that shows correlation/difference between two versions of a program behavior. SoftLink displays call graphs of two executions on angled parallel planes in a 3D space
Parsing Indian English News Headlines

Parsing news headlines is one of the difﬁcult tasks of Natural Language Processing (NLP). It is mostly because news headlines (NHs) are not complete grammatical sentences. News editors use all sorts of tricks to grab readers’ attention. For instance, unusual capitalization as in headline ‘Ear SHOT ashok rajagopalan’; some demand world knowledge like ‘Church reformation celebrated’ where ‘Church refor-mation’ refers to a historical event and not a piece of news about an ordinary church. The lack of transparency in NHs can be linguistic, cultural, social, or contextual. The lack of space provided for a news headline has led to creative liberty. Though many works like news value extraction, summary generation, emotion classiﬁcation of NHs have been going on, parsing them had been a tough challenge. Linguists have also been interested in NHs for creativity in the language used by bending traditional grammar rules. Researchers have conducted studies on news reportage, discourse analysis of NHs, and many more. While the creativity seen in NHs is fascinating for language researchers, it poses a computational challenge for NLP researchers. This paper presents an outline of the ongoing doctoral research on the parsing of Indian English NHs. The ultimate aim of this research is to provide a module that will generate correctly parsed NHs. The intention is to enhance the broad applicability of news-paper corpus for future NLP applications.
Claim extraction from text using transfer learning

Building an end to end fake news detection system consists of detecting claims in text and later verifying them for their authenticity. Although most of the recent works have focused on political claims, fake news can also be propagated in the form of religious intolerance, conspiracy theories etc. Since there is a lack of training data speciﬁc to all these scenarios, we compiled a homogeneous and balanced dataset by combining some of the currently available data. Moreover, it is shown in the paper that how recent advancements in transfer learning can be leveraged to detect claims, in general. The obtained result shows that the recently developed transformers can transfer the tendency of research from claim detection to the problem of check worthiness of claims in domains of interest.
Exploration of Cross-lingual Summarization for Kannada-EnglishLanguage Pair

Cross-lingual summarization(CLS) is a process in which given a document in source language aims at generating summary in a different, destination language. Low resource languages like Kannada greatly beneﬁt from such systems because they help in delivering a concise representation of the same information in a different popular language. We propose a novel dataset generation pipeline and a ﬁrst of its kind dataset that will aid in CLS for both English-Kannada and Kannada-English pair. This work is also an attempt to in-spect the existing systems and extend them to the Kannada-English language pair using our dataset.
The WEAVE Corpus: Annotating Synthetic Chemical Procedures in Patents with Chemical Named Entities

The modern pharmaceutical industry depends on the iterative design of novel synthetic routes for drugs while not infringing on existing intellectual property rights. Such a design process calls for analyzing many existing synthetic chemical reactions and planning the synthesis of novel chemicals. These procedures have been historically available in unstructured raw text form in publications and patents. To facilitate automated synthetic chemical reactions analysis and design the novel synthetic reactions using Natural Language Processing (NLP) methods, we introduce a Named Entity Recognition (NER) dataset of the Examples section in 180 full-text patent documents with 5188 synthetic procedures annotated by domain experts. All the chemical entities which are part of the synthetic discourse were annotated with suitable class labels. We present the second-largest chemical NER corpus with 100,129 annotations and the highest IAA value of 98.73% (F-measure) on a 45 document sub-set. We discuss this new resource in detail and highlight some speciﬁc challenges in annotating synthetic chemical procedures with chemical named entities. We make the corpus available to the community to promote further research and development of downstream NLP systems applications. We also provide base-line results for the NER model to the community to improve on.
A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.