Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Ensure Security and Privacy While Using QR Code Scanning in Business Applications

One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged device-based barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality.
aSDF: Reconstructing Real-Time Signed Distance Fields with Enhanced Attention

We present aSDF, a continual learning system based on attention for real-time signed distance field reconstruction. Given a stream of posed monocular RGB images from a moving camera, it trains a randomly initialized network to map input 3D coordinates to an approximated signed distance. The model is based on a transformer encoder enhanced with a hash table which acts as a cache, significantly improving the learning time, a crucial component in a continual learning system. In contrast to prior works, our model further improves the compact representation, while maintaining a decent accuracy in terms of the reconstructed signed distances. By employing self-supervised losses with a bigger penalization in the case of wrong prediction, the network is able to improve the accuracy of unobserved regions by filling out with a more plausible signed value. In evaluation to other methods on indoor synthetic and real datasets of indoor rooms, aSDF produces competitive reconstruction with previous state-of-the-art depth based methods and better approximation of gradients, while converging much faster due to the hashing component.
Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application.
Automatic Speaker Verification System Substantiating Children’s Dialects in School Settings

The usage of Automatic Speaker Verification (ASV) in educational contexts has grown as a result of measurements of the speech signal in the claimed identity's native tongue. Even though resource-rich languages have received a lot of attention, low-resource languages, like Punjabi, still require work to be done on the development of ASV systems. In this study, an effort has been put forth to create an ASV system based on an i-vector for children while authenticating the student's dialects in Punjab region. Additionally, it has been determined how changing the Universal Background Model (UBM) size and i-vector dimensionality for the proposed system would affect the performance measure i.e. Equal Error Rate (EER). Finally, the EER values for the Malwa and Majha regions were lower than expected, at 8.47% and 9.26%, respectively. However, the output shows a large EER of 14.25% in Doaba region in comparison to that of Malwa and Majha regions.
Smart Travel Planner using Hybrid Model

Everybody goes on a vacation to take a break from their busy life but planning for these vacations consumes a lot of time. One of the main reasons for this is the lack of platforms that provide personalized information for vacation planning. Users must individually search for good-reviewed restaurants and hotels and plan an appropriate path to visit top tourist places according to their budget. In this project, a user's distinct preferences will be considered to guide them in recommending the route according to their interests. This study has used a hybrid model as the features planned to include are quite complex. The model built is trained on the basis of features that are derived from the collected data. As a result, the model emerged and can successfully be used to create numerous suggestions for consumers. For this Hybrid model, URLs of different tourist places are gathered from websites like TripAdvisor, and Holidify to gather information about the Point of interest using Web scraping. Here, Gaussian Mixture Model (GMM) algorithm and K-Means algorithm are applied to group the nearby attractions and hotels to understand these algorithms better.
Creation and Application of Inter-language CorporaBased on Language Information Processing

The establishment of corpus marks the scientific and modernization of linguistic research tools and promotes the continuous development of corpus-based research on the acquisition of Chinese as a second language. The establishment of inter-language corpus based on language information processing not only serves classroom teaching, but also draws new corpus in classes. At the same time, the expansion of the number and size of the corpus helps linguistic researchers understand the patterns of learners’ second language acquisition and conduct further research in a dynamically evolving corpus, which promotes the two-way development of teaching and learning.
Psychology based Recommender System for Visual E-Learning

The Visual E-learning Recommender System (VERS) recommends the learning materials based on the personality and knowledge of the learners. This paper focuses on recommendation system which has the ability to find out personality of the learners and the knowledge level. The relevant video content can be provided by the system based on the personality and knowledge. This task has been achieved through two mining techniques namely clustering and decision tree. In clustering technique, associated data are categorized based on the personality and knowledge of the learner. The rules are derived based on knowledge level of the learner using Decision Tree and are used for recommendation.
A Hybrid Threat Assessment Model for Security of Cyber Physical Systems

The scope of Cyber Physical Systems (CPS) is extending from being utilized in ordinary applications to critical infrastructures like smart grids, smart transportation, etc. These systems are vulnerable to security breaches that could result in both financial and human losses. Therefore, it is crucial to make sure these systems are secure. The threat modeling approach is a significant way to ensure security prior to the designing phase of a system. In this paper, we explore the use of threat modeling for the security of CPS. Specifically, we discuss some important threat modeling and assessment techniques including STRIDE, DREAD, and Attack Tree. We also discuss the different threat modeling approaches like attacker-centric, asset-centric, vulnerability-centric, and some hybrid approaches. Based on the diversified mechanisms and capabilities of these techniques, we propose a hybrid threat modeling approach for CPS which is based on STRIDE, DREAD, Attack Tree, and Attack Defense/Countermeasure Tree. Our approach highlights the basic loopholes that require significant attention at the designing phase of the system to the evaluation of risks and their countermeasures. We utilize STRIDE to identify and analyse system threats, DREAD to evaluate the level of risk associated with each threat, Attack Tree to provide an overview of goal achievement strategy through the attacker’s perspective, and Attack Defense or Countermeasure Tree to utilize methods to prevent exploitations.
Machine Translation System for Indian Language: Survey

A method known as Machine Translation (MT) is computer software to transform texts from the source language into the target language, with the source language serving as the input and the target language as the output. MT plays an important role in the advancement of communication technologies. After the discovery of the MT, communication technology has been improved. Communication became easy across the world. Early on in the development of the MT, research progressed slowly. Initially, Rule-based and Corpus-based MT technology was employed; however, as time went on, technology transfer took place, and Neural MT's popularity grew. There has been a lot of research in this area, however, most of the models created are for top-level languages, those spoken by many people, whereas research for low-level languages, those spoken by fewer people, is scarce. In this paper, different approaches to MT are discussed. Below is a discussion of various MT models, along with their techniques and limitations, for the future of MT.
A Real-Time Crowd Detection and Monitoring System using Machine Learning

The COVID-19 pandemic has unquestionably warned all of us that, the outbreak of an infection can lead to a pandemic-like situation all over the world. In order to prevent outbreaks and provide better healthcare, appropriate crowd detection and monitoring systems must be deployed in public areas. By effectively implementing social distancing measures, the number of new infections can be greatly decreased. This idea served as the inspiration for the creation of a real-time Crowd Detection and Monitoring System (CDMS) for social distancing. This paper proposes a fully autonomous system for Real-Time Crowd Detection and Monitoring to help the educational institutions to monitor the students inside the premises more effectively. This system is developed using an OpenCV based Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) detector to detect and count the number of people gathered at an instance. The system raises an alarm to alert the people and adhere to the rules if the gathering is more than the threshold/permitted number of people in the cluster.
Non-Linear behavior of CNN model interpretation using Saliency Map

The complex behavior of the network limits human perception of the network’s success, which is among the major challenges of the convolutional neural network. The saliency map is an useful approach for analyzing and visualizing the non -linear behavior of a feed forward network. Since the DNN model is nonlinear, the interpretation is not straightforward. We intro-duce an interpretation method that observes the saliency map and visual input, which is used to calculate the component-wise product to remove the noise. Using gradient-based visualization, multimodal convolutional networks perform for visual inputs. The component-wise product is used to visualize the input by analyzing the learned weights of the layers. The saliency map is generated by the guided back-propagation of the ReLU activation function. In this paper, we aim at a popular branch of description techniques, often referred to as saliency methods. Our main objective is to find input features such as image pixels.
Pathfinding in a 3D Grid for UAV Navigation

In this paper, we propose an approach for computing the 3D trajectory of UAVs between two locations when obstacles are present. The result is an obstruction free path, close to optimal. First, the precomputed 3D environment map is converted into a discrete voxel space. Next, the $\mathrm{A}^{*}$ algorithm is applied on the discretized space. The $\mathrm{A}^{*}$ method provides the optimal path with respect to the graph equivalent representation of the voxel space, however in the continuous 3D space the resulting path will cause unnecessary steering maneuvers for the UAV. The solution is to smooth the resulting path using an iterative linear approximation approach. A new representation of the 3D path is obtained, consisting of line segments and control points. Therefore, we manage to transform the $\mathrm{A}^{*}$ path from the graph equivalent representation of the voxel space to the continuous representation of the 3D environment. The resulting control points can be used as intermediate destinations during autonomous UAV navigation. Experiments are performed on multiple scenarios, demonstrating that the proposed method shortens the standard $\mathrm{A}^{*}$ path for UAV navigation in the 3D environment.
Building Effective Features based on Automatic Learning for Smart Search

Product title quality greatly affects natural language processing problems performed on their data domain. This research work proposes some suitable features for Vietnamese product titles. The provided product title will be considered and in turn features will be extracted through three versions: original, processed, and unmarked. Features are divided into four groups: Binary Features; Feature group based on frequency of occurrence of words and repeating words; Feature groups based on dictionaries; Feature groups based on word2vec. The effectiveness of each feature group as well as the combination between them are evaluated through the title classification problem. The experimental results confirmed the importance of the proposed features.
Ensemble-based Knowledge Distillation for Semantic Segmentation in Autonomous Driving

We investigate the potential of U-Net in performing knowledge distillation, both in the posture of Teacher and Student network. The experiments evaluate multiple configurations of the distillation procedure, as well as various Teacher-Student pairs, in order to exploit the capabilities of the architecture, as it is rarely used in this situation. The study was performed by applying the standard distillation process (standard KD), a pixel-wise approach of transferring Teacher predictions to the Student, as well as an Ensemble Teacher method, whose mean prediction is utilized in the training process of the student. These methods improved the results of the Student models to an extent worthy of being taken into consideration.
Big Data Based Secure Access System of Power Mobile Terminal

A big data based secure access system of power mobile terminal is put forward to ensure the security of power system. The access system architecture of power mobile terminal is analyzed comprehensively, and the system is designed according to the principle of network dedicated and security partition. In addition, after designing the overall structure of the secure access system, the targeted encryption technology is used to encrypt data in the secure communication module. As the key of secure access, the designed terminal system needs to meet its reliability and security in design. Meanwhile, the network security protection, terminal security protection and application security are designed. As can be seen through the implementation process of the system, the system can meet the actual demand and enhance the reliability and security of power transfer access.
Generating and solving the LIGHTS OUT! game in first order logic

We compare here declarative approaches to model the LIGHTS OUT! game and its friends in First Order Logic (FOL). First, we solve the game using: (i) planning in FOL and (ii) a model finder for finite domains, for which we rely on Prover9 and Mace4. Second, we show how LIGHTS OUT! puzzles can be automatically generated by reasoning on finite models of FOL theories. We designed three solutions: (i) using a LIGHTS OUT! game solver in FOL, (ii) using linear algebra and a model generator, and (iii) improving the linear algebra-based method by decreasing the domain size. Third, we show how declarative knowledge can be reused to solve and generate different extensions of the LIGHTS OUT! game. We experimentally compare the proposed declarative methods and we discuss some extensions of theLIGHTS OUT! game.
Understanding Image Classification Tasks Through Layerwise Relevance Propogation

Deep learning techniques for machine learning usually lead to increasingly complex models while enhancing performance, which convert these systems into “black box” approaches and create confusion about how they function and, ultimately, how they make decisions. Layerwise Relevance Propagation, an Explainable AI (XAI) technique, excels at explaining any neural network’s output in the context of its input by calculating relevance iteratively from the output class neuron to the original input neurons. This paper outlines recent advancements in the area and advocates for more interpretability in AI. In this paper, we study in-depth Layerwise Relevance Propagation (LRP) and about its four different LRP approaches, which we have experimented on different dataset. With the aim of explaining predictions of deep learning models in the field of image classification in this paper we have tried to basically understand how this different LRP approach helps in describing which pixels in an image are important for deciding on a classification decision by computing a relevance score on its given input image. Furthermore, in this paper, we did human eye evaluation by conducting a user questionnaire survey on XAI in order to compare the quality of heatmaps generated by different LRP methods.
Implementing LDA Topic Modelling Technique to Study User Reviews in Tourism

Extracting valuable information from unstructured text is a significant difficulty in today's culture due to the ever-increasing proliferation of texts, most of which are generated via online social networks in the form of consumer-generated media. Despite this difficulty, natural language processing algorithms are being developed rapidly, including topic modelling approaches for discovering latent subjects in text documents like online reviews. As a result, topic modelling methodologies have not grown in favour of the tourist industry yet. So Authors decided to work on a tourism dataset. The results show us the importance of topic keywords concerning the tourism dataset the best perplexity score we get us with the D3 Dataset was -7.9947.
Efficient HD Map encoding via disentangled style-structure representation using graph neural networks

Efficiently encoding high definition maps is of great importance for autonomous navigation, thus they are widely used for tasks such as predicting the future behaviour of traffic participants and planning a safe trajectory. Previous methods tackled this problem either by rasterizing the road into a multichannel image, or by sampling the vectorial representation in fixed sized sub-segments (often called lanelets). The latter has become the go-to method due to its efficiency and expressiveness. However, its main limitation is the fact that the points creating a geometrical shape have to be sampled at a fixed spatial dimension, hence not taking full advantage of this representation’s potential. In this work, we address this problem by making 2 additions to the classical architectures used for encoding such a heterogeneous structure. Rather than using a single network to encode a map element, we propose the decomposition of the map attributes such as road lines, traffic signs, road edges, etc. into structure and style features, an approach inspired by the recent progresses in the photo-realistic style transfer domain. The structural features will be encoded by a shared message passing network that treats the most essential positional data without the need of resampling at a fixed resolution, being able to adapt during inference the spatial dimension of the representation based on the initial length and complexity. The style attributes will be encoded separately and will allow for an easier addition of a new type of map element, without the retraining of the whole system. Evaluating the method on various edge prediction and node classification tasks proves that our method has better results than the previously mentioned approaches in both tasks, while having a 53% smaller memory footprint on average when representing a scenario.
Parallel Chaos Hash based LSB Steganography Technique using Logistic Map

This paper deals with hash based secure chaotic steganography technique for hiding secret information, into the cover image. Hash function has been used in the proposed work for computing the Non LSB positions for hiding the secret data bits. Secret is encoded with chaotic sequences and randomness of the sequences has been validated with NIST test suite. Shared memory implementation for faster execution of the proposed security technique has been done in OpenMP platform. Sequential and the parallel versions of the techniques have been implemented in C++, OpenMP and simulated in the Intel Haswell processor based multi-core environment. With the advantages offered by multicore processors the proposed technique ensures low time complexity. Significant speedup and linear scalability have been reported with increase in the number of threads. Standard statistical validation test results viz. PSNR, Euclidean distance, histogram analysis, SSIM index applied to validate the quality of stego image show satisfactory results.
Circumvolution of Centre Pixel Algorithm in Pixel Value Differencing Steganography Model in the Spatial Domain

Sending and receiving sensitive information through any data channel is always a challenging task because of the enormous proliferation of the computer industry. Since steganography algorithms are routinely used to share data, cryptography algorithms have been around for the same objective for a long time, but these cryptographic algorithms are easily brute-forced. Furthermore, more research works have been focused on Steganography models. Initially, steganography models start with a simple LSB data insertion model, which accepts only a limited payload and is easily vulnerable. The PVD algorithms encapsulate more data than conventional LSB approaches whereas still affording a reasonable level of protection to private data. In order to find the set of pixels that can safely store more data bits using circumvolution of the centre pixel algorithm in the PVD domain. This model can embed more data bits when compared with the conventional LSB models. The Confidential data is protected with excellent security with double- layered information hiding. The suggested approach achieves good PSNR and MSE scores and keeps up great quality when more data is added to the stego-image.