Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatially-Varying Diffuse Reflectance Capture Using Irradiance Map Rendering for Image-Based Modeling Applications

Image-based 3D modelling using Structure-form-Motion (SfM) has matured significantly over the last decade. Standard SfM methods create the object's texture from the appearance of the physical object at the time of acquisition. We propose a method for acquiring the diffuse per-point reflectance of the modelled object, as part of the image acquisition work flow, only adding one extra captured image and an irradiance rendering step, making it easy for anyone to digitize physical objects to create 3D content for AR/VR using only consumer grade hardware. Current state of the art of spatially varying reflectance capture requires either large, expensive, and purpose built setups or are optimization based approaches, whereas the proposed approach is model based. This paper proposes adding a render of irradiance with modelled camera and light source, using off the shelf hardware for image capture. The key element is taking two images at each imaging location: one with just the ambient illumination conditions, and one where the light from an on-camera flash is included. It is demonstrated how to get the ambient illumination to cancel out, and by assuming Lambertian materials, render the irradiance corresponding to the flash-only image, enabling computation of spatially varying diffuse reflectance rather than appearance. Qualitative results demonstrate the added realism of the modelled objects when used as assets in renders under varying illumination conditions, including limited outdoor scenarios. Quantitative tests demonstrate that the reflectance can be estimated correctly to within a few percent even in cases with severe un-even ambient illumination.
Maximization of packet delivery ratio for DADCQ protocol after removal of Eavesdropping and DDoS attacks in VANET

Distribution Adaptive Distance with Channel Quality (DADCQ) uses the distance so to select the forwarding nodes the performance of distance method depends up on the threshold value for the protocol. Node density and spatial distribution also affects this threshold value so these values should be taken into consideration. In this paper we are analyzing the packet delivery ratio (PDR) for the network under Eavesdropping and DDoS attacks and after removal of these attacks the packet delivery ratio (PDR) is increased for the network. The Packet Delivery Ratio i.e. PDR is the number of packets received and the packets generated as recorded in trace file. So we define Packet Delivery Ratio as the total numbers of packets received at the destination to the total number of packets send form the source.
On-Demand Urgent High Performance Computing Utilizing the Google Cloud Platform

In this paper we describe how high performance computing in the Google Cloud Platform can be utilized in an urgent and emergency situation to process large amounts of traffic data efficiently and on demand. Our approach provides a solution to an urgent need for disaster management using massive data processing and high performance computing. The traffic data used in this demonstration is collected from the public camera systems on Interstate highways in the Southeast United States. Our solution launches a parallel processing system that is the size of a Top 5 supercomputer using the Google Cloud Platform. Results show that the parallel processing system can be launched in a few hours, that it is effective at fast processing of high volume data, and can be de-provisioned in a few hours. We processed 211TB of video utilizing 6,227,593 core hours over the span of about eight hours with an average cost of around $0.008 per vCPU hour, which is less than the cost of many on-premise HPC systems.
Characterization of Electroplated Permalloy Film on Microstructure for Bio-MEMS Application

Thick films (approximately 120microns) of microstructure permalloy were electrodeposited by a direct DC electroplating process for 3 hours. The bath contained mixed baths of sulfate and chloride, a buffer and a stress reducing agents. The bath pH was 3.3. The voltage and current value were controlled to get the desired thickness in uniform of permalloy coatings. The permalloy is deposited on microchannel with dimension of 80µm x 4500µm x 1840µm. A negative photoresist of AZ125nXT was used to pattern the microchannel by photolithography process. These electrodeposition of Ni-Fe magnetic films can be utilized to induce live cells manipulation such as separation of blood cells, proteins, etc for Bio-MEMS application. Therefore, the characterization study is important to be understand. Here, we studied the effect of electroplating time to produce thick film of permalloy on the surface morphology, scanning electron microscopy, optical microscope, X-ray diffraction and energy dispersive X-Ray. The thickness film increased due to the long period of plating time with plating rate 0.55 µm/min with ration of permalloy produced 86.08:13.92 wt% of nickel-iron.
A Metamaterial-Based Dual-Band Frequency Selective Surface using Composite Material

In this paper, a single layer frequency selective surface (FSS) covering dual band was designed and simulated. This design composed of a unit cell of T-type resonator inserted inside a rectangular split-ring resonator. A natural composite material, Basalt was used as the substrate. The two-layered Basalt provides a flexible texture with dielectric constant of εr = 2.66, and loss tangent of tanδ = 0.006 respectively. This dual-band FSS was designed to operate at the frequencies of 8 GHz and 15.6 GHz. Significantly, it shows incident angle stability and hence provide screening effect at the satellite communication, including the X band and Ku band frequencies. The proposed metamaterial can be used in radomes, sensors, and reflectors. This FSS is fabricated using copper fabric, Shieldit and simulated using the finite integration technique-based electromagnetic simulator Computer Simulation Technology Microwave Studio Commercial software.
Performance analysis of adaptive tree-based anti-collision protocol using M-ary splitting in RFID

In the large-scale RFID system involving passive tags, tag collisions are preordained, as each tag is unaware of any other tags in the system. Therefore an efficient anti-collision algorithm is a key aspect which greatly affects the identification efficiency. Adaptive Binary Splitting (ABS) has been one of the benchmark protocols in this regard. But this protocol performed binary splitting to resolve collisions, which takes a considerable number of collision cycles to identify a large number of tags. To resolve this, a modified version of ABS has been proposed, where instead of performing binary splitting, an $M$-ary splitting of tags has been done using the tree splitting approach. The value of $M$ has been chosen in the range of 3 to 10 and the results are analyzed. The analysis helps to empirically determine the best value of $M$ among all the variants of ABS at $M\geq 2$. The simulation results show that the variant at $M=3$ gives a better result in terms of the number of collision cycles and transmitted bits overhead while pertaining a similar identification delay as the ABS protocol.
Analysis of Machine Learning Methods in EtherCAT-Based Anomaly Detection

Today, the use of Ethernet-based protocols in industrial control systems (ICS) communications has led to the emergence of attacks based on information technology (IT) on supervisory control and data acquisition systems. In addition, the familiarity of Ethernet and TCP/IP protocols and the diversity and success of attacks on them raises security risks and cyber threats for ICS. This issue is compounded by the absence of encryption, authorization, and authentication mechanisms due to the development of industrial communications protocols only for performance purposes. Recent zero-day attacks, such as Triton, Stuxnet, Havex, Dragonfly, and Blackenergy, as well as the Ukraine cyber-attack, are possible because of the vulnerabilities of the systems; these attacksare carried by the protocols used in communication between PLC and I/O units or HMI and engineering stations. It is evident that there is a need for robust solutions that detect and prevent protocol-based cyber threats. In this paper, machine learning methods are evaluated for anomaly detection, particularly for EtherCAT-based ICS. To the best of the author’s knowledge, there has been no research focusing on machine learning algorithms for anomaly detection of EtherCAT. Before testing anomaly detection, an EtherCAT-based water level control system testbed was developed. Then, a total of 16 events were generated in four categories and applied on the testbed. The dataset created was used for anomaly detection. The results showed that the k-nearest neighbors (k-NN) and support vector machine with genetic algorithm (SVM GA) models perform best among the 18 techniques applied. In addition to detecting anomalies, the methods are able to flag the attack types better than other techniques and are applicable in EtherCAT networks. Also, the dataset and events can be used for further studies since it is difficult to obtain data for ICS due to its critical infrastructure and continuous real-time operation.
The Effect of Illumination Intensity on the Performance of Germanium Based-Thermophotovoltaic Cell

The significance of optimizing power intensity in a thermophotovoltaic system had spurred the efforts on exploiting thermophotovoltaic cells which are typically installed near to the heat source. Germanium with 0.66 eV bandgap is recommended and extensively employed to harvest infrared radiation up to $1.8\ \boldsymbol{\mu} \mathbf{m}$. The integration of this material contributes to a cost-effective thermophotovoltaic system by capturing long wavelength under low blackbody temperature. Contrarily, the atmospheric perturbation between the heat source and the germanium thermophotovoltaic cells affects the intensity of incoming illumination, which alters the electrical performance of the cells. Nonetheless, the investigation of this issue is remained at the infancy stage and more characterization works are required to understand the influence of illumination intensity on the cell conversion efficiency. In this work, Silvaco TCAD software was used to characterize single Ge thermophotovoltaic cell under 2000 K blackbody radiation temperature by manipulating the illumination intensity. A conversion efficiency of 11.8% was achieved under higher illumination intensity of 2000 K due to the increase in the maximum voltage from 0.19 V to 0.42 V when compared to AM 1.5 illumination condition. The success of this work will contribute to the incorporation of understanding the effect of incident illumination intensity on the performance of Ge cell.
SSE: Semantic Sentence Embedding for learning user interactions

Semantics is an integral component in NLP. Semantics provides a meaningful view about the meaning of the language. The meaning of the text is susceptible to the negative words present in them. Thus the impact of semantics in NLP in turn increases the impact of negative words in the sentence. It plays crucial role in shaping the semantics of any sentence. Although all the sub-areas of natural language processing try to find out the impact of negative words on their implementation, the effect of these words are not considered in the word embedding process. Being the foremost step in NLP the omission of these words in embedding can affect the representation of the language. The aim is to propose a model which produces word embeddings by considering the negative words. Although most NLP implementing systems consider all negative words this paper focuses on the word ‘not’ which is followed by an adjective. The antonym for the adjective is identified and is then replaced in the corpus. The model is achieved by replacing the corpus where negative words and this corpus is fed to a three layer neural network to form the embeddings. The proposed technique proves an accuracy of 93.6% in retrieving the relevant answer to the user.
Pharmacovigilance study of opioid drugs on Twitter and PubMed using artificial intelligence

Increased usage and under reporting of adverse drug reactions (ADRs) of opioids instigates us to explore some other data sources like Twitter and PubMed. Our paper aims at discovering illegal trafficking of opioids as well as distinguishing tweets from having ADRs or not using binary classifier. We also evaluated the performance of MetaMap in finding ADRs from Twitter and compared the MedDRA encoding system on ADR terms found from tweets and PubMed. We used Latent Dirichlet Allocation (LDA) to find tweets related to illicit sale and used several neural networks for binary classification. It was reported that out of 98 ADRs found from tweets, 50 could be mapped to Lowest Level Terms (LLTs) and 48 to (Preferred Terms) PTs where only 23 LLTs and 15 PTs were reported from PubMed. Among the binary classifier Convolutional Recurrent Neural Network (CRNN) were found to be more promising with. 71 F1 score though other models are close to the best one with little margin. Effect of skewness was also monitored in our study. Social media is a good choice for mining pharmacovigilance but during extraction a lot more noise data may come which needs to be avoided.
Surface evolution of cylindrical fused silica resonator and its implication on Q factor

Investigating the surface loss mechanism is crucial for further decreasing energy loss in high quality fused silica resonators. Although there are various assumptions on surface loss mechanisms, experimental verification is not enough. In this paper, we present experimental results on the surface evolution and the Q factor change of the cylindrical fused silica resonator. Results on two cylindrical fused silica resonators show that compared with the buffered oxide etch (BOE), the NH4HF2 solution maintained a better surface quality. A series of experiments were performed on the third resonator and a slice manufactured by the same grinding process, both were etched by NH4HF2 solution simultaneously. The surface evolution of the fused silica slice was characterized by a profiler and a scanned electron microscope, and the decay time constant of the cylindrical fused silica resonator was measured by laser Doppler vibrometer. Results show that, as the etching progresses, the surface quality improved, particularly the micro-cracks were completely removed, and the spatial period of the surface increased. The surface roughness first increased, then fluctuated around a certain value. The decay time of the third resonator first increased rapidly, with a peak value of 138.74s at the etching depth of 50. 5μm, then decreased with the increase of etching depth. Interesting facts on surface element distributions were discovered that when the decay time reached the maximum, the content of C reached the minimum and the percentage of Si and O was closest to 1:1. Our results suggested that the decay time constant of the macro-scale cylindrical fused silica resonator is not directly correlated with the surface roughness. More efforts are expected to explain the underlying mechanism behind.
Adversarial Graph Attention Network for Multi-modal Cross-Modal Retrieval

Existing cross-modal retrieval methods are mainly constrained to the bimodal case. When applied to the multi-modal case, we need to train O(K^2) (K: number of modalities) separate models, which is inefficient and unable to exploit common information among multiple modalities. Though some studies focused on learning a common space of multiple modalities for retrieval, they assumed data to be i.i. d. and failed to learn the underlying semantic structure which could be important for retrieval. To tackle this issue, we propose an extensive Adversarial Graph Attention Network for Multi-modal Cross-modal Retrieval (AGAT). AGAT synthesizes a self-attention network (SAT), a graph attention network (GAT) and a multi-modal generative adversarial network (MGAN). The SAT generates high-level embeddings for data items from different modalities, with self-attention capturing feature-level correlations in each modality. The GAT then uses attention to aggregate embeddings of matched items from different modalities to build a common embedding space. The MGAN aims to "cluster" matched embeddings of different modalities in the common space by forcing them to be similar to the aggregation. Finally, we train the common space so that it captures the semantic structure by constraining within-class/between-class distances. Experiments on three datasets show the effectiveness of AGAT.
Distributed Massive MIMO Downlink

The feasibility of adopting distributed massive multiple-input multiple-output (D-MMIMO) for mitigating the achievable rate loss incurred by spatially-correlated fading at space-constrained user antenna arrays when served by co-located massive MIMO (C-MMIMO) counterpart is investigated. In order to explore a practically-viable design, the cumulative effects of spatial correlation at both multi-antenna access points (APs) and multi-antenna users, beamforming uncertainty caused by imperfectly estimated channel state information (CSI), and adverse effects of pilot contamination have been taken into account. The achievable user rates of the proposed D-MMIMO downlink operating in a multi-cell set-up are derived for finite/infinite antenna array regimes at the distributed APs. Thereby, the achievable user rates for C-MMIMO counterpart are deduced and compared with those of the proposed D-MMIMO downlink. Through a rigorous analysis and simulation results, we show that the distributed transmissions rendered by D-MMIMO can provide significant performance gains compared to the C-MMIMO counterpart (i) by minimizing adverse effects of spatial correlation via distinct spatial-directions/signatures rendered by distributed transmissions, (ii) by mitigating shadow fading via leveraging macro-diversity gains, and (iii) by reducing the average path-losses through effectively shortening transmission distances. Moreover, we reveal that the proposed D-MMIMO can be exploited to recover a significant portion of rate loss incurred by the lost of asymptotic channel orthogonality due to doubly-correlated channels when both APs and user nodes are equipped with space-constrained multiple-antenna arrays.
Tangible and Visible 3D Object Reconstruction in Augmented Reality

Many crucial applications in the fields of filmmaking, game design, education, and cultural preservation — among others — involve the modeling, authoring, or editing of 3D objects and scenes. The two major methods of creating 3D models are 1) modeling, using computer software, and 2) reconstruction, generally using high-quality 3D scanners. Scanners of sufficient quality to support the latter method remain unaffordable to the general public. Since the emergence of consumer-grade RGBD cameras, there has been a growing interest in 3D reconstruction systems using depth cameras. However, most such systems are not user-friendly, and require intense efforts and practice if good reconstruction results are to be obtained. In this paper, we propose to increase the accessibility of depth-camera-based 3D reconstruction by assisting its users with augmented reality (AR) technology. Specifically, the proposed approach will allow users to rotate/move a target object freely with their hands and see the object being overlapped with its reconstructing model during the reconstruction process. As well as being more instinctual than conventional reconstruction systems, our proposed system will provide useful hints on complete 3D reconstruction of an object, including the best capturing range; reminder of moving and rotating the object at a steady speed; and which model regions are complex enough to require zooming-in. We evaluated our system via a user study that compared its performance against those of three other stateof- the-art approaches, and found our system outperforms the other approaches. Specifically, the participants rated it highest in usability, understandability, and model satisfaction.
Diagnosis of Chest Diseases in X-Ray images using Deep Convolutional Neural Network

Learning about Chest Diseases and their characterization is one of the most interesting research topics in recent years. With the various uses of medical images in hospitals, pathologies, and diagnostic centers, the size of the medical image datasets is also expanding expeditiously to capture the diseases in hospitals. Though a lot of researches have been done on this particular topic still this field is confusing and challenging. There are lots of techniques exist in literature to classify the medical images. The main drawback of traditional methods is the semantic gap that exists between the low-level visual information captured by imaging devices and high-level semantic information perceived by a human being. The difficulty of querying and managing the large datasets leads to a new mechanism called deep convolutional neural network. Deep learning techniques have recently achieved an impressive result in the field of computer vision along with Medical Engineering. In this paper, we proposed and evaluated a deep convolutional neural network, designed for classifying the Chest Diseases. The proposed model consists of Convolutional layers, ReLU Activations, Pooling layer, and Fully connected layer. Last full connected layer which is consists of fifteen output units. Each output unit will predict the probability of one of the fifteen diseases. A publicly available dataset called Chest X-Ray 14 which consists of fifteen classes named Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, Hernia and No Finding images used to train this model. Surprisingly this model gives a good result in multiclass classification. The average accuracy of 89.77% is achieved for the classification of different diseases. The comparative analysis shows the effectiveness of the proposed model. The proposed technique is best suited for classifying the multiclass medical images for different thorax diseases.
Graphical Mapping of a Data Ontology for Descriptive Patient Data

The decreasing cost of multi-omic health data has enabled the personalization of medical care. Still, capturing a patient’s personal health data in medical forms is challenging. The symptoms of an illness can often be traced to an activity or a body part and communicating this to an untrained patient is often unclear. To address this problem, we mapped an ontology of human disease phenotypes to a graphical depiction of the human body. We hypothesized that describing the experience of illness in a somatic representation would give patients a more accurate and descriptive understanding of their illness. The representation can support health care workers to provide more accurate analyses, aid caretakers in managing health risks and empower patients to take action to better their health.