Reviewerly/exHarmony huggingface dataset:
Deep Q Network for Selective Device Invoking in a Multi-Device Wakeup Environment

A deep learning model that successfully learns to select a device from a Multi-Device Wakeup environment, takes input from IoT devices where sensors record and transfer data to reinforcement learning models. The deep reinforcement neural network is trained with a different variant of deep Q-learning, whose input is recorded by the sensors in various devices and further converted into required numerical values, and whose output is an action function that will be executed by the user-intended device. The proposed methodology is applied in an imaginary world where there are no devices and sensors to record the inputs; instead, there are some random numerical values, which can be used by the model to learn and process the output. It is found that this methodology outperforms all previous approaches and gives an efficient and accurate result.
FPGA-Based Prototype of a Quantum Annealing Simulator for Sparse Ising Model

Quantum annealing (QA) is a probabilistic approx-imation method to find the global optimum of a combinatorial optimization problem. QA is done on quantum annealers such as D-wave using quantum properties. Since the number of qubits in quantum annealers is limited, it is difficult to use those to solve large-scale real-world problems. Therefore, quantum annealing simulation on digital computers is necessary. In this paper, we discuss an FPGA based quantum annealing simulator for sparse Ising model. Unlike a fully-connected Ising model, the number of connections among spins in sparse model is limited. Highly sparse Ising models require significantly low amount of computations while allowing more parallel operations. One the other hand, sparsity and the connections among spins are not the same for different Ising models, and it is difficult to propose one specific accelerator architecture for all. We propose a method to automatically generate an application specific accelerator archi-tecture for a given sparse Ising model. The proposed accelerator fully exploits the parallelism to increase the processing speed. We design an FPGA prototype of the proposed accelerator and confirmed the correct behavior. In future, we expect to extend the proposed method to execute large quantum annealing simulations using multiple FPGAs.
MIKIDQD: Machine Intelligence and Knowledge Infused Duplicate Question Detection

Quora is a flourishing online question answer forum where millions of users post questions and answer questions in the subjects they have knowledge. A plethora of questions are posted by users and some of them can be similarly worded. This would make it tedious for the users as answering similarly worded questions can be time consuming. Hence, there is a need to detect similarly worded questions or duplicate questions. This research work proposes a MIKIDQD framework for detecting duplicate questions in the Quora forum by using the Quora dataset. The knowledge-centric, ontologybased and semantically-driven strategy incorporated in the proposed model surpasses the other baseline models in terms of the performance measures. The proposed model yields the highest accuracy value of 98.07% and the least False Negative Rate of 0.01 indicating the best performance.
Blockchain-based investigation of the food supply chain using the Distributed Hash Tables algorithm

Blockchain is a developing mathematical science that enables continuous economic transactions with distributed untrusted bodies without the need for brokers, as banks do. This article examines the impact of blockchain electronics on the farming and supply chain, presents existing continuous projects and pushes, and evaluates general relationships, difficulties, and possibilities, with a pessimistic outlook on the future of these endeavors. Our judgments display that blockchain is a promising technology but many obstructions and challenges still exist that hinder allurement to more expansive recognition with labours and systems. These challenges include mechanical facets, instruction, policies, and supervisory foundations. Therefore, there is a need for a trustworthy system that guarantees traceability, trust, and confidentiality in the Agri-Food supply chain. The proposed system has a blockchain at the core of every activity, which finally transfers information to the Interplanetary File Storage System (IPFS). The depository structure proceeds a mess of the dossier, which is stocked on blockchain and guarantees an adept, secure, and trustworthy solution. Furthermore, simulations and judgments of smart contracts, in addition to the protection and vulnerability studies, are still bestowed in this piece of work.
A State-of-the-Art Approach for Securing Online Auction System

Bidding is one of the most recent monetary establishments that have been set up. They have been utilized since relic, to sell a wide assortment of products, and their essential structure has stayed unaltered. This paper investigates the sufficiency of conventional auctions, when values depend on the worth to a specific bidder, could rely on information accessible just to other people and vice versa. Here, noticeable fixed bid bargains do not achieve proficient results generally, because they don't permit the information carried by numerous bidders to be shared. This paper extends the comprehensive report of various existing works that have concentrated on e-auction system and its technological advancements. This extended research found that even though there were many studies carried out on e-auction system, the major challenge in e-auction system is still under progress, which is the security system of e-auction system. Therefore, this paper presents a novel security scheme for the e-auction system with improved blockchain framework named “Blockchain Enabled E-Auction System””. Based on the improved cost and time efficiency of the proposed framework, a website “Bidding Zone” has been developed.
A Decision Support System for Day-to-day Shopping Travel Scheduling

This paper proposes a Decision Support System for daily travel for shopping based on home needs list and recorded locations. This application will notify the user about important travels at the specific times, i.e., if a time is scheduled to travel to a particular place, the user will be notified by the system. This mobile application has a smart chatbot that can talk with the user, and it reminds of everyday traveling places. Also, the app can store the number of times the user visits a certain place. Using this application, these features can be managed using the fingertips. This paper describes the design principles used, the main features, examples for evaluation, and experimental results.
A Cross-Domain Semantic Similarity Measure and Multi-Source Domain Adaptation in Sentiment Analysis

Domain adaptation becomes crucial when there is a lack of labelled data in various domains. The accuracy of traditional machine learning models degrades largely if they are trained on one domain (called the source or training domain) and classify the data of a different domain (called the target domain or test domain, which is different from the source domain). The machine needs to train on a corresponding domain to improve the classification accuracy, but labelling each new domain is a complex and time-consuming task. Hence, the domain adaptation technique is required to solve the issue of data labeling. The similarity measure plays a vital role in selecting important pivot features from the target domain that match source domains. This research article has introduced an enhanced cross-entropy measure for matching the normalized frequency distribution of different domains and found an important domain-specific feature set. In addition, the technique of using enhanced cross entropy measures is proposed in the multi-source domain adaptation model to effectively classify the target domain data. The result shows that there is an improvement of 3.66% to 9.09% using our approach.
Face Mask Detection Using Viola-Jones and Cascade Classifier

Globally, the COVID-19 coronavirus outbreak is causing chaos in human health and therefore, the healthcare sector is in serious disarray. Many precautions have been taken to prevent the spread of this disease, including the usage of masks, which is strongly recommended by the World Health Organization (WHO). This research study has used the Viola-Jones algorithm for detecting face masks, where Histogram Equalization, Unsharp Filter and Gamma Correction are used as the preferred image pre-processing techniques to improve the overall accuracy. Haar Feature Selection is applied for creating integral images and AdaBoost training is performed on these images. Cascade classifier, a machine learning-based approach, is also integrated with the base algorithm where a cascade function assists Viola-Jones in accurately detecting objects in images. A total number of 1670 images is used in this work and our system is compared with four other machine learning algorithms, where Viola-Jones outperforms these ML-based classifiers and the overall accuracy obtained is 96%.
Sound Encryption using Wavelet Transform and Composite Chaotic Function

This paper proposes an encryption algorithm for speech signals based on the discrete wavelet transform and a new chaotic map composed by three chaotic systems: Sine-Cubic-Tent chaotic map. The results of the encryption with the composite chaotic map are compared with the results obtained with each chaotic system. The metrics obtained with this new chaotic map are very good in terms of robustness and speed.
Nuclei Localization in Pap Smear Images for Ovarian Cancer Visualization

According to healthcare data, cervical cancer is currently the second most frequent disease among women globally. Regular pap image analysis might be used to treat ovarian cancer. The test is for examining the pre-cancerous alterations in the epithelial tissue so effective screening may lower the number of fatalities brought on by the condition basis. The examination of a Pap smear sample is a laborious and time-consuming procedure that is done visually by a cytopathologic. This may sometimes make it difficult to notice with one's eyes. In normal cells, the size of the nucleus is proportionately lower than in defective cells, which have larger nuclei. The defective nucleus is larger, and sometimes the size cannot be determined precisely by sight alone when dividing cervical cancer into phases. This is due to the fact that each physician has a unique viewpoint on how to classify the various stages of cancer by looking at the nucleus without precise dimensionality reduction in the classifier's accuracy. However, the majority of nations lack reliable screening methods for this form of cancer. In this work, we used some learning model to classify normal and cancerous cervical cells as well as their types. We then compare how well these models work. A technique to recognize and categorize the smear cell pictures for the detection of cancer was recently put forward by several researchers. The accuracy of the segmented picture for research analysis may also be increased with a considerable cut-off level according to our upgraded method.
Intelligent Optimization Model for Cloud Sharing Edge Application

The smart financial information system under the network and virtual cloud computing environment is playing a pivotal role in modern intelligent enterprises, and some loopholes have been exposed at the same time. Among them, enterprises are most concerned about the security of the financial information systems. Hence, this paper studies the intelligent financial informatization model optimization of the cloud sharing edge algorithm mechanism. We firstly introduce the cloud sharing edge algorithm mechanism, and we focus on the data management technology, the data management of the cloud computing mainly adopts a distributed management mode, and a lot of big data needs to be managed through the relatively complete management technology. Then, to consider the security and efficiency, the Hadoop is combined and the homomorphic encryption scheme is combined. Lastly, the smart system is implemented with the technologies. We tested the designed model considering the different data sizes and the platforms.
Distribution Of Renewable Energy In Light-Rail Traction Grids

Electric powered Light-Rail is considered as a green sustainable alternative for public transport in highly dense populated areas. However, in the electrical-energy-transition, the power source should be renewable. Solar farms, and solar modules on public buildings in the inner city, could be this renewable source. Traction overhead lines, are used for only 20% of their capacity. Therefore it is proposed, to use the traction overhead lines as a DC grid, for the distribution of the renewable energy. Renewable energy from solar farms and city facades, is transported via the traction overhead lines into the inner city center. The concept of controlled distribution through traction overhead lines is discussed and demonstrated.
High-Performance Asynchronous CNN Accelerator with Early Termination

Convolutional Neural Network (CNN), especially very deep networks, are highly computation intensive, resulting in long delays and high-power consumption. The dynamically varying environmental conditions in real world inference can result in highly complex problems, and hence the need for these inefficient deep networks to guarantee satisfactory accuracy all the time. Several studies employ approximation techniques to execute partial computation of the network, in attempt to reduce the amount of computation were unnecessary. However, such approaches are still highly sequential in nature, since they still need to run the whole network. This paper proposes an early termination architecture on an already-trained CNN to allow for testing the partial results midway through the network, reducing computations by terminating the main network when it is sufficient as the inference results. The first proposal is implemented in synchronous circuit, however, due to its nature all memory elements are required to capture even when no new data is generated. The second proposal employs the use of asynchronous circuit to significantly reducing power consumption and further sped up the architecture since an operation need not wait the slowest critical path in the circuit. The proposed circuits were designed on FPGA platform. The results of the asynchronous circuit show a nearly 20% increment in speed with about 12% reduction in power consumption in comparison with a synchronous circuit.
The poly-alphabetic substitution ciphers - a viable solution for IoT applications?

Trying to answer to a simple question, with a rather complicated answer, as “are poly-alphabetic substitution ciphers a viable solution for IoT?”, this work approaches the domain of symmetric cryptography through two poly-alphabetic encryption algorithms - Vigenere's and Baptista's. The former, from classical cryptography, the latter from chaos-based enciphering domain. Text and image have been subject to encryption using both methods in MATLAB simulation and their performances were compared. They proved to be similar and weaker when opposed to a third algorithm from the literature, when applied to the same data from two frequently used databases. A basic analysis of the 802.14.5 technical standard used in the Internet of Things (IoT) private-key cryptography-based security is investigated. Furthermore, the same statistical attack utilizing the coincidence index and the Chi-Squared test proven efficient on Vigenère's cipher is advanced as a possible valid cryptanalysis for all poly-alphabetic substitutions, including Baptista's cryptographic system and IoT's Advanced Encryption Standard (AES).
Privacy Preserving Systems Adoptable in Cloud Image Retrieval

Due to rapid growth in Internet of things (IoT) and cloud computing technology, numerous accumulation of data arrive at the cloud for storage. Stacking of various data, without degrading the cloud performance is the continuous challenge in cloud service premises. Utilization of cloud platform has become more common in all industries, in terms of efficiency, Quality of Service (QoS), where cloud services included with appealing advantages. Privacy preserving of data is more demandable in cloud to ensure data security in dynamic transmissions. Cloud services become reliable and flexible for almost all industries that enable the users to access various data sources, store and retrieve the data easily. The cloud system reduces the usage of physical storage and its cost, maintenance etc. Various mechanisms have been proposed in recent research works towards the security aspect of cloud storage and retrieval of the same at any instant of time. Even though the data in the cloud is stored in an encrypted format, restrictions and authorizations are provided by the cloud maintenance agent to safeguard the data from unauthorized access. The study of various cloud security system, the challenges of the cloud storage and retrieval is focused here. User personal data, high sensitive medical records, confidential documents are the sample form of data available in the cloud storage. These data are collectively gathered through public internet devices, and probably from android devices etc. To design privacy preserving systems using deep learning model various criteria's are considered that need to be clearly studied with the presented system.
A Hybrid Opto-Electrical Floating-point Multiplier

The performance improvement by CMOS circuit technology is reaching its limits. Many researchers have been studying computing technologies that use emerging devices to challenge such critical issues. Nanophotonic technology is a promising candidate due to its ultra-low latency, high bandwidth, and low power natures. The advanced research activity of nanophotonic computing is to design hardware accelerators for AI inference applications. However, few considerations about nanophotonic accelerators for AI training applications have been conducted. The main reason is that state-of-the-art nanophotonic AI accelerators involve integer operations, whereas floating-point (FP) sum-of-products dominate the training process. However, to the best of the authors' knowledge, there are no optical circuits that target floating-point arithmetic units. This study proposes a novel Opto-Electrical Floating-point Multiplier (OEFM) toward ultra-low-latency, a power-efficient nanophotonic accelerator for AI training applications. We design a microarchitecture of OEFM, including a novel optical integer multiplier and other electrical components. Based on our evaluation framework, we analyze the calculation accuracy of the proposed multiplier and OEFM. Experimental results show that OEFM achieves a 56 % reduction in latency and a 41 % reduction in energy consumption compared with a conventional electrical circuit.
A Realization of IO Physical Memory Protection for RISC-V Systems

Physical memories or RAMs are essential components in a computer system to hold temporary information required for both software and hardware to work properly. When a system's security is compromised (e.g., due to a malicious application), sensitive information being held in the memories can be leaked out for example to “the cloud”. The RISC-V privileged architecture standard adopts a method called Physical Memory Protection (PMP) to segregate a system's memory into regions with different policy and permissions to prevent unprivileged software from accessing unauthorized regions. However, PMP does not prevent malicious software from hijacking an Input/Output (IO) device with Direct Memory Access (DMA) capability to indirectly gain unauthorized accesses and hence, a similar method commonly termed as “IOPMP” is being worked on in the RISC-V community. This paper describes an early implementation of IOPMP and how it is used to protect physical memory regions in a RISC-V system. Then, the potential performance impact of IOPMP is briefly elaborated. There are still work to be done and this early IOPMP implementation allows various aspects of the protection method such as its scalability, practicality, and effectiveness etc. to be studied for future enhancement.
A Lightweight End-to-end Network for Wearing Mask Recognition on Low-resolution Images

In realistic scenarios, resolution is still one of the major problems in wearing mask recognition. Due to the large distances between surveillance cameras and human faces, facial images captured by low-power devices usually have low resolution and lead to poor recognition results. To address the above issue, we propose a lightweight end-to-end network to reconstruct Super-resolution (SR) images and achieve wearing mask recognition. Besides, to apply to challenging real applications, we combine hardware devices and software technology to simulate the recognition process of wearing masks in real scenarios. To demonstrate the effectiveness of the method, we comprehensively evaluate our proposed method by comparing it with state-of-the-art methods. The recognition accuracy using super-resolution is 98.44%, outperforming RepVGG-A2 (97.00%) and ResNet34 (93.75%). Moreover, experimental results show that the number of parameters and FLOPs in our recognition model is 9.34 million and 1.85 billion, respectively, both of which outperform traditional CNN methods (20 million+ parameters and 3 billion+ FLOPs). The performance of our recognition system is competitive with state-of-the-art methods in terms of low memory usage and computational complexity, showing that the system can be cost-effectively and widely applied in real-world environments and thus has potential applications in respiratory disease prevention.
Performance Evaluation of Histogram Equalization based Enhancement on Lung CT Scan Images

Lung cancer also referred as lung carcinoma is caused due to smoking. The recent report released during World Cancer Day (2022), states that the incidents of lung cancer is radically increasing year by year. It is estimated that India will record about one lakh victims of lung cancer in the next five years. As per the statistics, the number of women affected by lung cancer has also increased over the last decade. Computed Tomography (CT) is the most effective medical imaging techniques used to visualize the location of tumors in lungs for lung cancer detection. However accurate localization of lung cancer tumors still remains as a challenge due to the excessive brightness of CT Scan image. Image enhancement as a pre-processing step plays a major role in the subsequent steps of lung cancer detection from CT scan lung images. This article, reports performance of four select image enhancement methods namely Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE) and Dualistic Sub-Image Histogram Equalization (DSIHE) based on the inferences of quantitative and qualitative metrics. The suitability of these enhancement methods for lung CT scan image is analyzed. It is apparently confirmed that incorporation of suitable image enhancement techniques is a CAD system of lung cancer detection, shall help in the detection of lung cancer with greater accuracy. It is also concluded that the enhanced lung CT scan image facilitate early detection of lung cancer in CAD systems, which is a boon in saving the lives of the victims.