Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized quadratically constrained quadratic programming for signal processing

In this paper, we introduce and solve a particular generalization of the quadratically constrained quadratic programming (QCQP) problem which is frequently encountered in different fields of signal processing and communications. Specifically, we consider such generalization of the QCQP problem that comprises compositions of one-dimensional convex and quadratic functions in the constraint and the objective functions. We show that this class of problems can be precisely or approximately recast as the difference-of-convex functions (DC) programming problem. Although the DC programming problem can be solved through the branch-and-bound methods, these methods do not have any worst-case polynomial-time complexity guarantees. Therefore, we develop a new approach with worst-case polynomial-time complexity that can solve the corresponding DC problem of a generalized QCQP problem. It is analytically guaranteed that the point obtained by this method satisfies the Karsuh-Kuhn-Tucker (KKT) optimality conditions. Furthermore, the global optimality can be proved analytically under certain conditions. The new proposed method can be interpreted in terms of the Newton's method as applied to a non-constrained optimization problem.
Antidiabetic activity of stem bark of Bauhinia variegata in alloxan-induced hyperglycemic rats

1. Debnath S, KannadasanM, Soumik G, Nansri Saha G, Chakraborty R,Sen S. Anti epilepticactivity of thehydroalcoholicextract of Erythrina fusca Leer. , barkagainstthe animal modelsMES, PTX and PTZ inducedEpilepticseizure. Int JChem Res 2010;1:610. 2. Gamble JS. Flora of madras, 1, University of California, Adlardand son Ltd. W.C. ; 1935. p.353-62. 3. Kokate CK, Purohit AP, Gokhale SB. Practical Pharmacognosy. 2nd ed. Pune:Nirali Publication; 1995. p.123. 4. Balamurugan G, Muralidharan, Selvarajan S.Anti-epileptic Activity of Poly Herbal Extract from Indian Medicinal Plants.J Sci Res 2009;1:153-7. 5. Vyawahare NS, Pujari RR, Rajendran R, Khsirsagar AD, Ingawale DK, Patil MN. CNS effect of Phoenix Dactylifera in mice. J Young Pharm 2009;1: 225-8. 6. Yadav AV, Kawale LA, Nade VS.Anti-anxiety effect in Mulberry leaves. Indian JPharmacol2008;40:32-6. 7. Ganong WF. Synaptic and junctional transmission. In: Review of medical physiology, 2008. 22nd ed. Boston: McGraw Hill;2005. p.85. 8. Nishino T, Takeuchi T, Takechi K, Kamei C. Anxiolytic Effects by Hypnotics. JPharmacolSci 2008;107:349-54. 9. Rauniar GP, Deo S, Bhattacharya SK. Evaluation of anxiolytic activity of tensarin in mice.Kathmandu Univ Med J (KUMJ) 2007;2:188-94. 10. Hisashi K, Yoichiro H, Sakutaro T, Effect of central depressants on rota-rod and traction performances in mice. Jpn JPharmacol1997;27:117-26.
SQLUnitGen: Test Case Generation for SQL Injection Detection

More than half of all of the vulnerabilities reported can be classified as input manipulation, such as SQL injection, cross site scripting, and buffer overflows. Increasingly, automated static analysis tools are being used to identify input manipulation vulnerabilities. However, these tools cannot detect the presence or the effectiveness of black or white list input filters and, therefore, may have a high false positive rate. Our research objective is to facilitate the identification of true input manipulation vulnerabilities via the combination of static analysis, runtime detection, and automatic testing. We propose an approach for SQL injection vulnerability detection, automated by a prototype tool SQLUnitGen. We performed case studies on two small web applications for the evaluation of our approach compared to static analysis for identifying true SQL injection vulnerabilities. In our case study, SQLUnitGen had no false positives, but had a small number of false negatives while the static analysis tool had a false positive for every vulnerability that was actually protected by a white or black list. Future work will focus on removing false negatives from SQLUnitGen and at generalizing the approach for other types of input manipulation vulnerabilities.
Brain Inspired Episodic Memory Deep Q-Networks for Sparse Reward

Although deep reinforcement learning has achieved great success in recent years, it still suffers from slow convergence, low sample efficiency, and large computational resources due to the existence of reward sparsity in many decision problems. Therefore, exploring more effective algorithms that can cope with sparse rewards is of great importance. Episodic memory reinforcement learning has received a lot of attention for its ability to collect past dominant strategies, which can serve as successful experiences to efficiently guide agents in sparse reward environments. In this paper, the episodic memory deep Q-networks, which incorporates the fast convergence property of episodic memory into neural networks, is employed to solve decision making problem with sparse rewards. Atari games are the test beds. Experiments show that the episodic memory deep Q-networks outperforms the deep Q-networks and the prioritized experience replay, which demonstrates the sample efficiency and the effectiveness of episodic memory deep Q-networks for the sparse reward problem.
Gradient Phase Profiled Reflecting Surface Design for Sectoral Sensing Application

With the advent of next generation communication technologies catering to high frequency systems for 5G and beyond applications, efficient non-LoS connectivity requirements have become the focal point of research nowadays. In this paper, Metasurface-based gradient phase reflecting structures are shown to act in coordination with the primary source Access Points (APs) like Reconfigurable Intelligent Surface (RIS) ‘Wings’ (in folded/unfolded combinations) to ensure effective distribution/redirection of radiated power for sectoral sensing applications. Using the concept of phase gradient metasurface design with Minkowski unit cells, two novel phase-profiled configurations of reflecting surfaces are presented, namely the Concentric and Symmetric configurations operating at 26.5 GHz. These designs offer distinctly different reflection performances, with anomalous and sectoral reflection properties. Here the incoming conical beam from a primary source radiator is converted to a fan-beam pattern for specific sectoral use, suitable for precision sensing applications. Detailed simulation-based results are reported to highlight the potential of such phase profiled structures as suitable reflecting surfaces with sectoral, directive, and 360-degree manoeuvrable RIS Wing like operations.
AUDIO VISUAL MEDIA DESIGN FOR PRACTICAL MICROBIOLOGY LEARNING

This study aims to describe the process of making audio-visual media and to determine the feasibility of it based on the assessment of material experts, media experts and student responses. This research is a descriptive study that adopts the ADDIE development step which has five stages. However, this research is limited to only three stages, the analysis, design, and development stages. The population used in this study were all majoring students of Biology in the 2018 at Unimed and the sample of this study were 40 majoring students of Biology in the 2018 who had taken microbiology courses at Unimed. Theinstrument used in this study was a questionnaire using a Likert scale. The results showed that videos of sterilization and media making, videos of observing bacterial colonies and counting the number of bacteria using the pour plate dilution method, isolation videos of pure bacteria cultures, videos of bacterial staining, videos of bacterial biochemical activity, and video of testing of antibacterial activity on sponge symbionts against bacteria MDR (Multi Drug Resistance) is categorized as very feasible based on the assessment of material experts, media experts and student responses.
On Rejecting Low Quality Images to Improve Deep Smartphone Wound Assessment

Automatic assessment of wounds using smartphones can facilitate the tracking of healing progress outside the clinic. Prior work proposed SS-PMG-EfficientNets, a state-of-the-art, multi-scale, convolutional neural network architecture for accurate wound assessment. However, the model was previously only trained and evaluated on high-quality wound images captured in controlled environments. This study systematically evaluated the effects of low-quality images on SS-PMG-EfficientNets, finding that the model's performance and consistency are reduced when input wound images are blurry or captured in adverse lighting (too bright or too dark), two factors previously identified as the most prevalent issues in crowdsourced images. Specifically, SS-PMG-EfficientNet was first trained on three high-quality wound datasets containing images labeled with Photographic Wound Assessment Tool (PWAT) wound healing scores, stages of pressure injury wounds, and wound types, respectively. The model was then evaluated on a test wound dataset that was augmented with real-world image luminance, blur, and image compression effects and found that wound assessment accuracy decreased by up to 27%. To improve model performance, we propose a method for instant image quality assessment on the smartphone, enabling low-quality wound images to be rejected immediately after image capture so that the nurse or patient can be prompted to recapture the image. Our evaluation of the proposed image quality assessment and rejection method showed that it increases average accuracy from 73 % to 87%.
University of Birmingham Observation of local temporal correlations in trapped quantum gases

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Autoencoders in Graphical Document Retrieval: Cost Function Influence and Rotation Impact

This study explores autoencoder application in graphical document retrieval, focusing on rotation’s impact. Specific to this paper are scanned floor plan images. We investigate the pivotal role of cost functions—specifically, Mean Squared Error (MSE) and Consistency Loss—in shaping autoencoder behavior and retrieval accuracy. By subjecting autoencoders to the challenges posed by rotated graphical documents, we decipher the extent to which rotation impacts retrieval outcomes. Concurrently, we dissect the nuanced influence of distinct cost functions on retrieval accuracy, shedding light on their diver-gent contributions. Through rigorous experimentation, we show autoencoder adaptability to rotation and cost function effects on retrieval precision. Our findings underline the significance of cost function selection in enhancing retrieval accuracy. Our insights hold implications for optimizing, refining autoencoder-based methods, empowering digital document analysis practitioners.
Dense-YOLO: Improving Object detection Accuracy for Small Targets in UAV Perspective

Unmanned aerial vehicle (UAV) images are characterized by high spatial resolution, multiple temporal phases, and a wide range of target elements. These images are extensively utilized in the development of intelligent transportation systems. However, the significant challenges for existing object detection models arise from the drastic change in object size and the prevalence of small targets from the UAV viewpoint. To address the aforementioned issues, this paper introduces Dense-YOLO, a densely connected object detection model. By adding a small object detection layer, the problem caused by the drastic change in image scale during detection is alleviated. Additionally, a dense connection module is incorporated into the convolutional block of the backbone network to reduce the loss of shallow position information. Furthermore, a multi-scale feature fusion module is introduced to perform cross-scale connections and weighting operations on contextual information. This enhances the feature extraction of the target to be detected and reduces the leakage of small object detection. A new decoupling head has been designed to achieve the isolation and decoupling of the location regression and classification tasks. This design aims to enhance the accuracy of the model for both localization and classification. The proposed algorithm underwent ablation experiments on the VisDrone2019 dataset, resulting in a 6.5% improvement in mean average precision (mAP) compared to the original YOLOv5L structure. This improvement is particularly significant in enhancing the accuracy of small object detection from a UAV's perspective. Additionally, on the DOTA dataset, the mAP increased by 1.6% to reach 76.3%. These results confirm the effectiveness of the proposed algorithm in various application scenarios.
Lymphoscintigraphy for sentinel node mapping using a hybrid SPECT/CT system.

UNLABELLED Lymphoscintigraphy is performed before sentinel node (SN) biopsy for SN mapping. It is of clinical importance mainly if the tumor is located in body parts with ambiguous lymph node drainage. The purpose of this study was to assess the clinical benefit of fused SPECT/CT images to planar images for SN mapping. METHODS Thirty-four consecutive patients with cutaneous malignant melanoma (n = 28) and squamous cell carcinoma (n = 6) and scheduled for SN biopsy were enrolled. Primary tumors were located in the trunk (n = 12), in the extremities (n = 12), in the head and neck (n = 9), and in the penis (n = 1). Scintigraphy was performed using a hybrid gamma-camera/low-dose CT system. Planar images and fused SPECT/CT images were interpreted separately. RESULTS SPECT/CT identified multiple draining basins in 6 of 12 patients (50%) with trunk melanoma and in 3 of 9 patients (33%) with head and neck melanoma or mucosal tumor. In 9 of 21 patients (43%) with a primary tumor located in the head and neck or trunk region, SPECT/CT-fused images identified SNs that were missed on planar images, 2 of which were involved with tumor. Three of the 9 nodes were located close to the injection site and were hidden by its scattered radiation, and 2 were in-transit nodes. Another 4 nodes, identified on fused images only, were located in an additional basin to those identified on planar images. Fused images were of no added value either in patients with limb melanoma or in a patient with a penile melanoma. CONCLUSION SPECT/CT SN mapping provides additional data that are of clinical relevance to SN biopsy in patients with trunk or head and neck melanoma and in patients with mucosal head and neck tumor.
Comparative Morphology of the Wing Base Structure Illuminates Higher-Level Phylogeny of Holometabola

Simple Summary Insects that undergo complete metamorphosis, collectively known as Holometabola, are the most successful lineage of living organism, including about 830,000 species. Understanding the intricate relationships among the major groups of holometabolous insects is a critical task in systematic biology, particularly given their immense diversity. This research analyzed the wing base structure of Holometabola using comparative morphology to further clarify several crucial relationship nodes within Holometabola. Morphological data were selected from both the forewing base and hindwing base, comprising fifty-three discrete characters. Many phylogenetic relationship nodes among Holometabola were recovered exclusively using the wing base structure. Our research further highlights the efficacy of wing base morphology data in understanding insect phylogeny and evolution. Abstract Phylogenetic relationships among Holometabola have been the subject of controversy. The value of the wing base structure in phylogenetic analysis has been demonstrated but remains largely underexplored and scarce in studies of Holometabola. We studied the phylogenetic relationships among Holometabola (excluding Siphonaptera), focusing exclusively on wing base structure. Cladistic assessments were conducted using 53 morphological data points derived from the bases of both the forewing and hindwing. The results of wing base data revealed a sister relationship between Hymenoptera and remaining orders. The sister-group relationships between Strepsiptera and Coleoptera, Mecoptera and Diptera, Trichoptera and Lepidoptera, and Neuropterida and Coleopterida were corroborated. In Neuropterida, our results recovered the sister relationship between Megaloptera and Neuroptera, as well as the monophyly of Megaloptera.
Benefits of Using Multiple Post-Hoc Explanations for Machine Learning

EXplainable AI (XAI) offers a wide range of algorithmic solutions to the problem of AI's opacity, but ensuring of their usefulness remains a challenge. In this study, we propose an multi-explanation XAI system using surrogate rules, LIME and nearest neighbor on a random forest. Through an experiment in an e-sports prediction task, we demonstrate the feasibility and measure the usefulness of working with multiple forms of explanation. Considering users' preferences, we offer new perspectives for XAI design and evaluation, highlighting the concept of data difficulty and of the idea of prior agreement between users and AI.
Power Passive Filter Tuning Based on Transient Response Specification Mapping for Inverter in Residential-Scale Renewable Energy Systems

This paper proposes a new method to tune LC filter parameters that will be utilized to attenuate the undesirable harmonic output voltage of a single-phase inverter to generate output waveform approaching pure sinusoidal. This method transforms two-order transient time-domain equation specification through LC filter transfer function parameter equations, thus forming simultaneous non-linear equations. These non-linear simultaneous equations are solved by applying the Jacobian iteration method. The iteration is run until it converges to any points that represent the required filter parameters. This method offers better results than the existing related methods through Spice simulation in voltage and current THD. The simulation results show the identical voltage and current THD value, which is 0.016%. The impact of the utilization of damper resistor is also analyzed. By using Spice simulation, it is found that the filter circuit without damper has a power efficiency approaching 99%. Meanwhile, by using a damper, the power efficiency is reduced to 89,039% due to the dissipated power in the damper element. (Abstract)
Enterprise Architecture Reference Models: A Shared Vision for Service-Oriented Architectures

The business- and mission-driven objectives of service-oriented architectures depend on the dynamic interaction of collaborative integrated operations in a federated operational environment. The theme of this paper is that integrated operations can and should be considered service-oriented nodes in a dynamic information environment. In the Department of Defense, this dynamic information environment has often been referred to as "the global information grid" or simply "the grid. " We describe enterprise architecture reference models as a dynamic abstract reference environment for net-centric collaboration between the service-oriented "nodes on the grid. " The complex, dynamic interaction of service-oriented nodes in the net- centric environment creates an opportunity to view enterprise architecture as a taxonomy of well-defined systems architectures governed by a set of enterprise architecture reference models.
Proposal of LEO Based Population Estimation System Using Smartphone Emitted WLAN signals

Japan has repeatedly suffered from damages caused by earthquakes, notably the Great East Japan Earthquake in 2011. Furthermore, it is said that there is a 90% probability of a disaster occurring within the next 40 years that will surpass the scale of the Great East Japan Earthquake, known as the Nankai Trough. Establishing defense measures against such imminent large-scale disasters is an urgent task. Rapid rescue efforts are crucial when a disaster strikes. However, both material and human resources for rescue are limited. Therefore, if it is possible to estimate the population within the affected area, it would be beneficial for allocating appropriate resources based on the number of victims seeking aid. This can potentially facilitate and accelerate rescue operations, leading to a reduction in casualties. In this research, we examined the feasibility of a population estimation method using Low Earth Orbit (LEO) and WLAN signals emitters from smartphone users located inside the disaster area. Numerical investigation are conducted to show the potential feasibility of our proposed system.
Empowering Smart Cities: A Comprehensive Edge Computing Framework for Enhanced IoT Situation Awareness

The Internet of Things (IoT) / Web of Things (WoT) presents numerous advantages for the development of intelligent cities. With the help of a vast array of diverse IoT devices, these cities can collect a tremendous amount of data, opening up opportunities for advanced analysis and insights. Given the diversity of information sources in smart cities, processing these data and extracting meaningful insights for decision-makers is a significant hurdle. While the conventional cloud computing paradigm offers substantial computing and storage capabilities for this purpose, it necessitates transferring all data from user endpoint edge equipment to the cloud, thereby causing considerable latency. Our study aims to mitigate the latency issue in data processing by utilizing the edge computing technique. Since a significant amount of data originates from user endpoint devices, handling the data at the edge can enhance overall performance. Our findings reveal that conducting raw IoT data processing at the edge devices yields a seamless situational awareness for smart city decision-makers while minimizing latency.
Enabling Smart Banking AI and IoT: Challenges and Opportunities

This paper examines Smart Banking, where AI and IoT are transforming the financial system. We examine Smart Banking's difficulties and potential and use mathematical models to assess AI and IoT usage in banking.Our results show that AI-driven consumer personalization positively correlates with customer happiness, demonstrating its transformational potential. IoT technology may boost operational efficiency, saving financial institutions money and giving them a competitive edge. AI-powered security methods successfully reduce data breaches, protect consumer data, and build confidence.We stress regulatory compliance and ethical responsibility to demonstrate the industry's commitment to responsible AI and IoT. Financial institutions can transform the sector and benefit customers by investing in AI and IoT technologies and aligning them with regulatory and ethical issues to deliver highly customized, efficient, and secure financial services.For financial institutions, politicians, and technology suppliers, this study provides practical advice. It shows Smart Banking's disruptive potential, practical methods to achieve its advantages, and the industry's commitment to responsible innovation. A more safe, efficient, and customized financial environment is possible by harnessing Smart Banking potential and tackling its difficulties.