Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Apple Variety Classification Based on the Combination of Hyperspectral and Deep Learning

At present, the classification of apple varieties is mainly manual sorting. Due to the small differences between some apple types, manual discrimination has the problems of strong subjectivity, low efficiency and high cost. Therefore, in order to realize the real-time detection of apple varieties sold by merchants, the spectral data of different apple varieties were collected by the hyperspectral image acquisition system, and an automatic apple recognition model based on double-branch structure was proposed. One of the branches is the basic TCN network to extract the morphological information of the signal. The other branch is an enhancement module composed of two long and short-term memory networks (LSTM) to capture the timing characteristics of the signal. Then, the apple spectrum data under the feature band is input into the two branches at the same time, and the features from the two branches are merged using vector splicing, and finally the soft-max classifier is applied to output the network classification results. The experimental results show that the overall classification accuracy of seven kinds of apples reached 99.74%.
Deep Learning Applied to SFRA Results: A Preliminary Study

The SFRA (Sweep Frequency Response Analysis) is a measurement technique that allows to evaluate the integrity of power transformers. The goal of SFRA is to determine the transfer function (TF) of each winding of the transformer in a certain frequency range. The technique has recently been successfully used for the characterization of rotating machines, such as induction motors, widely found in industry. Promising results have also recently been demonstrated in the use of the technique for the identification and characterization of household appliances. This article illustrates the possibility of using TFs, obtained by applying the SFRA technique, as an input to Deep Learning algorithms, in order to create systems capable of making decisions based on these results. Furthermore, the work reports the results obtained by a Deep Learning system applied to the recognition of household appliances through the use of this analysis.
Quantified Us: A group-in-the-loop approach to team network reconstruction

Understanding team collaboration processes is key for the development of new technologies towards increasing groups effectiveness. In particular, the need and challenges of coordinating potentially large-scale, self-organized, collaborative initiatives have been made even more salient by the COVID-19 pandemic. Key to this challenge is the difficulty for participants to situate themselves within the larger social context. At the individual level, the Quantified-Self movement has showcased how insights from one’s own data can trigger changes in behavior, sometimes leading to fundamental insights through self-research. Building on these premises, here we present CoSo (Collaborative Sonar), a digital platform for participatory collective sensing and social research. CoSo is a web and mobile data collection platform for team network reconstruction and visualization. It leverages a “group-in-the-loop” intrinsic motivator to collect data on collaborative activities performed by a team through a mobile app, with summary statistics and visualizations of the collected data made available on a web dashboard. We showcase its use and discuss the perspectives offered by promoting group-level metacognition and collective introspection. By highlighting the invisible relationships in a group, CoSo facilitates in-the-wild studies, remote/hybrid data collection, and multi-modal sensing approaches of group interactions within professional and other social contexts.
Understanding Structural Hole Spanners in Location-Based Social Networks: A Data-Driven Study

Location-based social networks (LBSNs) have been popular around the world. Some recent studies have focused on using online/offline social interactions among individuals to explain social phenomena, strongly demonstrating that data collected by LBSNs can be utilized to analyze the behavior of users. However, how structural hole spanners (SHS) behave in a LBSN requires more investigation. In this paper, we crawl the entire social network and all published tips of Foursquare, a leading LBSN app with more than 60 million users, using a distributed approach. Based on the crawled massive user data, we discuss the behavior characteristics of SHS in demographic, spatiotemporal and linguistic aspects. We further develop a classification model to accurately identify SHS and ordinary users based on their behavioral data. Our model achieved a high classification performance, with an F1-score of 0.821 and an AUC value of 0.879.
Lessons Learned: Analysis of PUF-based Authentication Protocols for IoT

The service of authentication constitutes the spine of all security properties. It is the phase where entities prove their identities to each other and generally establish and derive cryptographic keys to provide confidentiality, data integrity, non-repudiation, and availability. Due to the heterogeneity and the particular security requirements of IoT (Internet of Things), developing secure, low-cost, and lightweight authentication protocols has become a serious challenge. This has excited the research community to design and develop new authentication protocols that meet IoT requirements. An interesting hardware technology, called PUFs (Physical Unclonable Functions), has been the subject of many subsequent publications on lightweight, low-cost, and secure-by-design authentication protocols. This has turned our attention to investigate the most recent PUF-based authentication protocols for IoT. In this paper, we review the security of these protocols. We first provide the necessary background on PUFs, their types, and related attacks. Also, we discuss how PUFs are used for authentication. Then, we analyze the security of PUF-based authentication protocols to identify and report common security issues and design flaws, as well as to provide recommendations for future authentication protocol designers.
TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification

Accurately and continuously measuring and collecting data on human gait is critical for human activity recognition and individual identification, enabling various applications in smart homes/buildings, including security authentication, personal healthcare, and intelligent automation. Many sensing technologies have been investigated by researchers recently, such as camera-based, laser-based, and mobile approaches, which have limitations in particular sensing situations, such as environments with fewer privacy concerns, line-of-sight, and the use of wearables, etc. On the other hand, the floor with the embedded sensor is stable and robust to different circumstances, enabling non-intrusive gait recognition and human identification. Therefore, a triboelectric nanogenerator (TENG)-based gait sensor system installed on the floor is proposed in this paper. Our approach has many advantages in comparison to the existing gait recognition systems, including low cost, simple fabrication, lightweight, and high durability. The TENG-based sensors can be simply embedded into a smart carpet to discern mechanical motions through electrical signals. Furthermore, a deep learning model, deep residual bidirectional long short-term memory network with dense layers (Residual Dense-BiLSTM), is proposed for multichannel floor-based gait recognition. By utilizing this model to analyze the electrical outputs, our system can accurately detect various human activities and distinguish different individuals’ walking patterns, with a recognition rate over 98% and 97%, respectively. We conclude that the proposed deep learning enabled triboelectric gait sensor system has broad applications in security and healthcare.
Synthesis and characterisation of catalytically active metal-organic frameworks based on porphyrins

Metal-organic frameworks (MOFs) are proving to be an exciting class of materials with unprecedented properties and high potential applications such as gas storage and separation, selective adsorption and separation of organic molecules, ion exchange and catalysis. The main aim of this work is to incorporate multifunctional organic ligands, which ideally contain two types of coordinating sites (primary and secondary), as building blocks for constructing catalytically active MOFs. The ligand chosen for this study was porphyrin with carboxylic group which has two types of coordinating sites; porphyrin core and carboxylic moiety. In this thesis, a three-step synthesis will be conducted to yield 4,4',4'',4'''-(porphyrin-5,10,15,20-tetrayl) tetrabenzoic acid, which will react with metal complexes such as Zn(II) or Cu(II) to form the MOF. The final target is to convert these MOFs into active catalyst by incorporating molybdenum via post-synthetic metalation. X-ray diffraction analysis of the crystals of the synthesized porphyrin ester revealed that the porphyrin core was coordinated to zinc atom. This result indicates that a metalloligand approach could also be used to get the MOF, by first incorporating the molybdenum to the porphyrin core (metalloligand) followed by the reaction of carboxylic groups with other metals to form the catalytically active MOF. Which either way is chosen, a solid ground work has been done for the continued research.
Finite-Time Projective Synchronization of Neural Networks with Uncertainties via Adaptive Fault-Tolerant Control

In this study, problems of finite-time projective synchronization of neural networks with uncertainties are investigated using adaptive fault-tolerant control methods. When the system parameters are known or unknown, different adaptive controllers are designed. The fault-tolerant controller is designed to ensure that the drive response system can achieve synchronization in finite time. Finally, the validity of the method and the correctness of the theoretical results are verified by two kinds of numerical simulation.
Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests

In recent years, portable laser scanning devices and their applications in the context of forest mensuration have undergone rapid methodological and technological developments. Devices have become smaller, lighter, and more affordable, whereas new data-driven methods and software packages have facilitated the derivation of information from point clouds. Thus, terrestrial laser scanning (TLS) is now well established, and laser–object interactions have been studied using theoretical, modeling, and experimental approaches. The representation of scanned objects in terms of accuracy and completeness is a key factor for successful feature extraction. Still, little is known about the influence of TLS and survey properties on point clouds in complex scattering environments, such as forests. In this study, we investigate the influence of laser beam diameter and signal triggering on the quality of point clouds in forested environments. Based on the Swiss National Forest Inventory data, we simulate the TLS measurements in 684 virtual forest stands using a 3-D content creation suite. We show that small objects lack sufficient representation in the point cloud and they are further negatively influenced by large laser beam diameters, dense stands, and large distances from the scanning device. We provide simulations that make it possible to derive a rationale for decisions regarding the appropriate choice of TLS device and survey configuration for forest inventories.
Simplified Model of Distributed PV Generation and Influence Analysis on Load Characteristics

With a large number of distributed PV generation (DPVG) integrated into the distribution network, the equipment composition in the load region changes significantly as well as the overall dynamic characteristics of the load region. Therefore, the DPVG must be considered in load modeling. The detailed model of the PV generation system is complex and has many parameters, which is not easy to be combined into the existing composite load model. Therefore, a simplified DPVG model is proposed which uses the controlled current source to represent the DPVG system and only retains the active power control loop. The number of the model parameters is reduced to three including the two coefficients of a PI controller and an equivalent impedance of distribution lines. The simulation shows that with high DPVG penetration, the active power fluctuation of the entire load region increases significantly under voltage sag comparing to the pure load of the same power, while the reactive power fluctuation decreases after the fault is cleared. Therefore, it is not appropriate to directly subtract the DPVG power from the load power when modeling the load region with a high proportion of DPVG. A separate DPVG model must be combined into the model of the load region with a high DPVG proportion.
Target Detection and Recognition of Ground Penetrating Radar using Morphological Image Analysis and Graph Laplacian Regularisation

Ground Penetrating Radar (GPR) is often used for detecting non-intrusively buried targets, in road engineering, manufacturing, and in military fields. Based on transmitting high frequency electromagnetic waves, GPR generates 3D data of the underground structure enabling accurate and fast target detection. However, after inverse Fourier Transform, the 3D GPR images are often out-of-focus and contain high measurement noise. This calls for advanced signal and image processing methods to improve signal-to-noise ratio, isolate the most discriminative features, and perform target detection and localisation. Using a vehicle-mounted GPR array data provided in the 2020 UDRC GPR data challenge, we show that morphological image analysis and semi-supervised learning via graph Laplacian regularisation can detect different types of targets buried at various depths with very low false alarm rate.
Fault Identification in Power Distribution Systems based on Domain Adaptation Probabilistic Learning

To improve the fault identification accuracy in power distribution systems, a diagnosis method for incipient faults is proposed based on Domain Adaption Probabilistic Learning. The proposed method finds the best linear mapping by waveform decomposition and feature similarity, which maps simulation data and field data into the same feature space. In this space, the distribution difference between two kinds of features reaches its minimum, allowing the model to use simulation data for training and field data for test. The whole process solves the essential problem of few samples in fault identification. Experiments show that this method is much better than other commonly-used classification models, such as Convolutional Neural Network, Support Vector Machine and K-nearest Neighbors, in the aspects of reliability and accuracy. This research provides a new idea for fault identification in power distribution systems under the condition of domain adaptation.
A Frictionless and Secure User Authentication in Web-Based Premium Applications

By and large, authentication systems employed for web-based applications primarily utilize conventional username and password-based schemes, which can be compromised easily. Currently, there is an evolution of various complex user authentication schemes based on the sophisticated encryption methodology. However, many of these schemes suffer from either low impact full consequences or offer security at higher resource dependence. Furthermore, most of these schemes don’t consider dynamic threat and attack strategies when the clients are exposed to unidentified attack environments. Hence, this paper proposes a secure user authentication mechanism for web applications with a frictionless experience. An automated authentication scheme is designed based on user behavior login events. The uniqueness of user identity is validated in the proposed system at the login interface, followed by implying an appropriate user authentication process. The authentication process is executed under four different login mechanisms, which depend on the profiler and the authenticator function. The profiler uses user behavioral data, including login session time, device location, browser, and details of accessed web services. The system processes these data and generates a user profile via a profiler using the authenticator function. The authenticator provides a login mechanism to the user to perform the authentication process. After successful login attempts, the proposed system updates database for future evaluation in the authentication process. The study outcome shows that the proposed system excels to other authentication schemes for an existing web-based application. The proposed method, when comparatively examined, is found to offer approximately a 10% reduction in delay, 7% faster response time, and 11% minimized memory usage compared with existing authentication schemes for premium web-based applications.
Application of Students' Cognitive Concept based on EEG Data Mining in Chinese English Translation Guiding

The application of corpus in translation studies began in the mid-1980s, when it was mainly used as a tool for the study of linguistic contrast and translation criticism. Based on electronic texts and by means of computer statistics, corpus translation studies describe various translation phenomena in a wide range or in a specific range. Tourism translation refers to the translation practice of tourism activities and tourism. At the same time, it is a kind of cross - psychological, cross - cultural, cross - language, cross - regional, cross - time communication behavior. Therefore, tourism translation is no longer a simple process of language conversion, but the dissemination, exchange and acceptance of national cultural aesthetics and values. Tourism translation has been widely concerned by translators and tourists, and the research on tourism translation has also made qualitative progress. This paper uses the EEG data mining to construct the efficient system considering the cognitive data.
Enhanced Image Compression Using Fractal and Tree Seed-Bio Inspired Algorithm

In the field of Image compression and particularly for the process of image encoding, Fractal image compression (FIC) technique plays a vital role. This technique is based on fractals present in an image and also it is capable of generating the copying blocks dependent on numerical changes. The only drawback in the Fractal image compression is that the time taken during the encoding process when the data is large. Thus, it is necessary to optimize the encoding process for efficient resource utilization as optimization algorithms are generally known for converging behavior. This research work attempts to apply the Tree Seed Algorithm as a characteristic enlivened high-level algorithm to improve FIC. Output shows the improvement of such calculation under various types (encoding time, pressure proportion, top sign to clamor proportion, and mean square error. Moreover, an examination with a portion of the current techniques underlines this addition.
Tumor Inhibition Achieved by Targeting and Regulating Multiple Key Elements in EGFR Signaling Pathway Using a Self‐Assembled Nanoprodrug

Comprehensive understanding of signaling pathways regulating cancer progression has led to tremendous advances of molecularly targeted therapies. The epidermal growth factor receptor (EGFR) pathway is an attractive target for cancer therapy, and targeting multiple key elements in the pathway may further facilitate therapeutic efficacy. Here, an EGFR‐targeted nanoprodrug is demonstrated for in vivo imaging and tumor inhibition, which is assembled by a disulfide‐bridged quercetin (QSSQ) and an EGFR inhibitor erlotinib. QSSQ is synthesized via chemical manipulation of multiple phenolic hydroxyl groups on quercetin; and the nanoprodrug is then fabricated through the disulfide‐facilitated assembly of QSSQ and erlotinib. The nanoprodrug is of high drug loading (87.3%) since its only inert component is the disulfide linker. The nanoprodrug is stable in physiological environment, whereas overexpressed glutathione in tumor tissue breaks the disulfide bridge, thereby disrupting the nanostructure and releasing active drugs quercetin and erlotinib. Upon release, erlotinib serves as an active drug blocking the EGFR tyrosine kinase, and quercetin generates strong aggregation‐induced emission of fluorescence for imaging drug release and acts as another drug inhibiting the downstream EFGR signaling, as evidenced by Western blotting analyses. The combined action thereof results in remarkable antitumor efficacy toward xenograft tumor‐bearing mice.
Clickbait Detection in Telugu: Overcoming NLP Challenges in Resource-Poor Languages using Benchmarked Techniques

Clickbait headlines have become a nudge in social media and news websites. The methods to identify clickbaits are largely being developed for English. There is a need for the same in other languages as well with the increase in the usage of social media platforms in different languages. In this work, we present an annotated clickbait dataset of 112,657 headlines that can be used for building an automated clickbait detection system for Telugu, a resource-poor language. Our contribution in this paper includes (i) generation of the latest pre-trained language models, including RoBERTa, ALBERT, and ELECTRA trained on a large Telugu corpora of 8,015,588 sentences that we had collected, (ii) data analysis and benchmarking the performance of different approaches ranging from hand-crafted features to state-of-the-art models. We show that the pre-trained language models trained on Telugu outperform the existing pre-trained models viz. BERT-Mulingual-Case [1], XLM-MLM [2], and XLM-R [3] on clickbait task. On a large Telugu clickbait dataset of 112,657 samples, the Light Gradient Boosted Machines (LGBM) model achieves an F1-score of 0.94 for clickbait headlines. For Non-Clickbait headlines, F1-score of 0.93 is obtained which is similar to that of Clickbait class. We open-source our dataset, pre-trained models, and code ‘ We show that the pre-trained language models trained on Telugu outperform the existing pre-trained models viz. BERT-Mulingual-Case [1], XLM-MLM [2], and XLM-R [3] on clickbait task. On a large Telugu clickbait dataset of 112,657 samples, the Light Gradient Boosted Machines (LGBM) model achieves an F1-score of 0.94 for clickbait headlines. For Non-Clickbait headlines, F1-score of 0.93 is obtained which is similar to that of Clickbait class. We open-source our dataset, pre-trained models, and code11https://github.com/subbareddy248/Clickbait-Resources
Classification of Foot Thermograms using Texture Features and Support Vector Machine

Foot ulcers in Diabetic Mellitus patients are a serious concern as they could lead to amputation of the foot if not detected at early stages. Routine monitoring of the foot using reliable diagnostic tools is essential to assess the risk of developing the ulcer. As ulcers exhibit high vascularity in ulcer-affected areas in the foot, this temperature can be captured and analyzed using image processing techniques. In this paper, 50-foot thermograms are considered for the study. The grey level texture features are calculated in the form of a matrix from the intensity values of normal and diabetic foot thermogram, and they are given as input vectors to the support vector machine classifier. The study performed well in classification and resulted in 96.42 % of accuracy.