Reviewerly/exHarmony huggingface dataset:
Strategy for evaluation, characterization and adoption of immersive technologies to the public of the tourism industry in Maule Region

During the last few years, virtual reality technology has been introduced significantly in different economic sectors, even in the most conservative, such as tourism. Tourists have evolved; now there are Tourists 4.0, those who plan their trips using digital resources, seeking uniqueness and to enhance the emotions that a travel experience gives. Thus, there are users who are willing to explore innovative technologies, such as augmented reality. These immersive experiences open a window to bring the tourist closer to a possible destination and finalize the trip. The simplification of the process is the key to adding followers, and also understanding who the final user will be, connecting users to a new tourist destination through the adoption of this technology. In this context, a strategy is proposed that will characterize the user, their reception and evaluation of the technological adoption of local technological products developed with immersive technologies for tourists in the Maule Region. This study will allow the incorporation of orientation, experiences, and expectations, through the application of different instruments, to identify the users who adopted the technology and defined the travel process through an immersive reality experience.
Signature Verification and Forgery Recognition System Using KNN, Backpropagation and CNN

Signature verification is behavioral biometric and is most widely used among other types of biometric recognition system like fingerprint, voice, iris etc. There are many biometric verification systems like finger print scanning, face recognition, iris scanning, and voice recognition etc. But cost of deploying for some systems is extremely high and some of the systems are not portable. In-order to find a middle ground between efficiency, effectiveness in mass quantity and portability, we take a deeper look into signature verification system which can be deployed to produce fruitful results. In this project there is development of various algorithms to verify the signature and to analyze if it is genuine or not. It involves implementation of KNN, CNN and Backpropagation.
Automatically Identifying Bug Reports with Tactical Vulnerabilities by Deep Feature Learning

Identifying and fixing bug reports with tactical vul-nerabilities in a timely and accurate manner is essential to ensure the security of the software architecture. Manually identifying the bug reports with tactical vulnerabilities is labor-intensive and challenging. This paper presents Itactivul, an approach to automatically identify bug reports with tactical vulnerabilities and recommend their tactical categories to guide the fix. Unlike the existing security bug report prediction approach, we are the first attempt to use deep learning to mine discriminative tactical text features only from the vulnerability descriptions of the National Vulnerability Database (NVD) and apply them to identify bug reports with tactical vulnerabilities. We evaluate Itactivul on three bug reports datasets gathered from three large-scale open-source projects, including Chromium, PHP, and Thunderbird. The experimental results show that Itactivul outperforms baselines by an average of 8.88 %, 13.58 %, and 6.61 % in the F1-score of three datasets, respectively. To improve the explainability of the features mined by Itactivul, we manually analyze the high-weight phrases extracted by using attention backtracking. The results show that Itactivul can mine key and potential tactical vulnerabilities text features.
oVRcome - Self Guided Virtual Reality for Specific Phobias. A Randomised Controlled Trial

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Exploitation Prevention on Network Printer with Signature-Based Suricata on PfSense

In late 2018, more than 50,000 network printers were exposed to a cyberattack that forced them to print spam messages. Were the spam message turned into a message for large-scale political activities, it can cause a major problem. Based on this, two problem arises, namely what is the impact of hacked printers. Can a hacked network printer inflict major damage on the surrounding? Secondly, whether a combination of a firewall with Suricata intrusion detection system (IDS) and intrusion prevention system (IPS) can prevent printer hacking. This research only discusses transmission control protocol (TCP) communication packets, access restriction using pfSense firewall along with Suricata, and printer model Canon imageRunner ADV-4025 as the test printer. The offered solution is to separate the network into two-part and put a firewall in between two networks, and configure the firewall along with Suricata so that the firewall can detect malicious packets. The output obtained is a system that can limit access and perform packet filters from computers on a different network to a printer, which can increase service availability, information confidentiality, and data integrity on printers.
LS-SPP: A LSTM-Based Solar Power Prediction Method from Weather Forecast Information

Solar radiation is an unlimited source of clean energy with huge exploitation potential. To effectively exploit this valuable resource, the arrival of the solar forecast has shown an improvement in incorporating renewable energy into the grid system. Having accurate solar prediction would yield useful information to ensure the power grid’s stability, gain the advantage of renewable energy, and minimize mineral resource consumption. In this paper, we introduce a novel deep learning model, namely LSTM-Based Solar Power Prediction (LS-SPP), combining long short-term memory and a recurring neural network (LSTM-RNN). The proposed model is stacked with two LSTM layers to produce a high prediction accuracy based on historical meteorological time series. Our practical experiment on real datasets shows that the LS-SSP model achieves up to 96.78% accuracy in performance, higher than the best of competitors reported about 94.19%.
Experimentation with NMT models on low resource Indic languages

Today&#x2019;s Artificial Intelligence (AI) is data centric. Unlike earlier rule based systems where we used to write many rules to solve any specific problem, these days, we need to train our machine learning models with the help of huge corpus (data set). In this paper, we have discussed one of the important applications of AI and computational linguistic i.e. Machine Translation (MT) which translates one natural language to another automatically.MT industry has passed through different phases since its earlier popular approach such as statistical machine translation (SMT) systems and its other version such as phrase based SMT. Performance wise NMT always outperformed SMT on various aspects. However, this holds true only for the languages having large parallel corpora. For low-resource languages, it still remains suboptimal. In this paper, we have applied NMT to low resources Indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it.
Delegated Device Attestation for IoT

Cybersecurity and trustworthiness of IoT devices are becoming more and more vital. Device attestation is one approach to increase trust through measured evidence of a device&#x2019;s software integrity. However, the lack of integration to different services still limits the adoption of device attestation in the IoT world. This paper describes integration of Device Identity Composition Engine (DICE) -based device attestation with delegated identity and access management. We present device attestation extensions to two existing authorization flows of the widely used OAuth 2.0 framework. These extensions do not alter the original flows, but they do include attestation evidence and result within the original message content, simplifying integration to existing systems. In addition, we describe our prototype implementation of the solution and present early performance results measured using a physical IoT device. Furthermore, we discuss the benefits and challenges of the approach. The goal of this work is to ease the adoption of device attestation by simplifying integration to existing IoT systems.
Provable Data Possession Schemes from Standard Lattices for Cloud Computing

Abstract Provable Data Possession (PDP) is of crucial importance in public cloud storage since it allows users to check the integrity of their outsourced data without downloading it. However, the existing PDP schemes, which are based on classical number-theoretic assumptions, are insecure under quantum attacks. In this paper, we propose the first PDP scheme from standard lattices, using a specific leveled fully homomorphic signature (FHS) scheme. To remove the complex key management of PDP cryptosystem on the public key infrastructure (PKI) setting, we employ a specific leveled identity-based (ID-based) FHS scheme to construct the first ID-based PDP scheme from standard lattices. Our two PDP schemes are secure under the standard small integer solution (SIS) assumption, which is conjectured to withstand quantum attacks. Furthermore, we conduct experimental evaluations to validate the feasibility of the proposed PDP schemes in practice.
A Brief Review on Existing Techniques for Detecting Digital Image Forgery

Law enforcement in the 21st century works on the evidence present in digital images or videos. Digital image processing is, thus, being heavily applied in the field of law enforcement, especially when it comes to detecting whether digital evidence related to legal matters has been tampered with or not. Due to the easy availability of various software, it is effortless for any law offender to have evidence such as digital images or videos transformed for their cause. Hence, two types of tampering detection techniques are used to maintain the integrity of digital evidence, namely Active and Passive. The active methods require that some kind of pre-embedded data be present in the image, using which detection can be performed while the passive techniques are applicable without any such condition. The differences, working, and classifications of these techniques are elaborately discussed here.
Frontiers of Blockchain for Railways

The objective of this chapter is to provide the reader with the application of blockchain technology in railway system. As the focus of this book is Big Data and Service Operations Management, it is pertinent to understand Big data-blockchain relationship first before we can delve into Railway service operations and application. We begin by linking Big Data and Blockchain, followed by studying blockchain applications in various sectors, in order to provide a solid foundational understanding of the same. Thereafter, we set the context of Indian Railways, moving on to a structured understanding of the railway service and blockchain applications. followed by creating a specific provenance framework followed by conclusion, limitation. Big data signifies a novel technology paradigm for data that are generated at high velocity and high volume, and with high variety. The significance of Blockchain technology has drastically enhanced with the advent of Big Data because blockchain technology can help in recording and storing data for long duration. These technologies are envisioned as a game changer capable of revolutionizing the business drastically. Blockchain which is essentially a ‘distributed ledger’ has found applications in the financial and supply chain space. The value derived through this technology is in terms of transparency, veracity, track ability, traceability and auditing. The possibility of transparent information that can be traced to the origin of materials and processes and operations which are shared on blockchain ledger can provide product provenance, chain of custody and authenticity. Transport is an important service and represents the essence of service operations management. Railways is an eco-system. It provides a service that is asset intensive which has its own challenges in terms of maintenance and infrastructure. At the same time there are service concerns in terms of human safety and comfort. This provides a complex eco-system to work with. Blockchain as a digital tool intervention providing transparency, provenance and auditability can enhance the performance of the system This study attempts to create a model to understand the contribution of blockchain in the Indian Railway system.
Predicting A* search algorithm heuristics using neural networks

At the opposite side of the uninformed search algorithms, performing a systematic search, heuristic search algorithms are based on multiple rules leading it to estimate, in a predictive way, the minimal cost of the path from the current state to the goal. In this sense, A&#x002A; algorithm is an example of heuristics-based algorithms that can guarantee to find a least-cost path to a goal state if this algorithm is using an &#x201C;admissible heuristic&#x201D;. A heuristic is said to be &#x201C;admissible&#x201D; if it never overestimates the real path cost from the current state to the goal. Through this article, the main idea consists of developing a Neural Network that can predict those heuristics to further refine the A star algorithm results. During its learning phase, and as inputs, the neural network will have some representative examples in the form of pairs of several problems (graph and destination node) and heuristics <tex>$\boldsymbol{(\{\mathrm{P}1,\mathrm{h}1\};\{\mathrm{P}2_{9}\mathrm{h}2\}\ldots\{\text{Pn},\text{hn}\})}$</tex>, to finally be able to calculate the best heuristic regardless of the inputs.
Comparison of 32-bit ALU for Reversible Logic and Irreversible Logic

Semiconductor devices has been undergoing large improvement in terms of performance, speed of the device because of large scaling of devices, simultaneously the power dissipation has been one of the major concern. As the scaling of device has been reached its limits the break through could be the emerging technology i.e., Reversible computing in VLSI industry. As this technology has zero power dissipation, this is a major advantage in the reversible logic circuits. The ALU is major component in the system and it is used in the applications such as computers, mobiles, and calculators. The 32 Bit arithmetic logic unit is designed using Verilog Hardware Description Language with operations such as AND logic, OR logic, FullAdder using One bit ALU. The reversible ALU can work fast as compared with irreversible ALU. Reversible logic gates decreases power dissipation, delay and area. So reversible gates are used in VLSI design techniques. This paper presents an ALU designed of reversible logic gates using Toffoli, Fredkin, and Peres Gate which replaces the AND and OR gate for each one bit ALU. All the logics are performed using the softwares Modelsim Altera 6.3g and the synthesis using Xilinx ISE 14.7. The designed ALU using reversible logic reduces the area about 34&#x0025; and delay about 48.91&#x0025;.
Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems

Intrusion detection systems are integral to the security of networked systems for detecting malicious or anomalous network traffic. As traditional approaches are becoming less effective, machine learning and deep learning-based intrusion detection systems are vital research areas for improved detection systems. Past research into computer vision using deep learning revealed that the deep learning-based classifiers themselves are vulnerable to adversarial attacks, and these attacks have been investigated extensively. However, adversarial attacks are restricted not only to the domain of image recognition. As indicated by previous research, various domains employing machine learning/deep learning classifiers are vulnerable to attack. Our work evaluates the effectiveness of adversarial robustness training when applied to intrusion detection systems based on deep learning classification models. We propose a novel, simple adversarial retraining method to build models robust to adversarial evasion attacks.
Moving beyond illiteracy: Is wearable and touch-sensitive technology the way to go forward?

This paper reflects upon the first phase of a participatory speculative design approach to ameliorate the consequences of illiteracy through touch-sensitive technology. The study addresses both digital and traditional illiteracy, in a region in remote Morocco, inhabited by North Africa's indigenous population. After developing fictional stories in English, we translated them into Moroccan Arabic and visual storyboards, we are currently aiming at developing activities to creatively engage with modalities of storytelling and prototyping to explore participatory speculative design and touch-sensitive technology to bypass illiteracy.
Identification of Malignant Lymphoblast Cell in Bone Marrow Using Machine Learning

Leukemia and lymphoma are the neoplastic proliferative disorders of white blood cells (WBCs), which increases the malignant lymphoblast cells in the blood. The objective of this work is to perform segmentation of white blood cell image into dissimilar region and then analyze those reasons for particular application in hematology to localize the lymphoblast cells. The population of lymphoblast cells is enormously grows in bone marrow. Acute leukemia often causes erroneous diagnosis due to its imprecise nature. Moreover, due to the rapid growth of the disease, it may damage the normal blood cells and cause death. Early prediction and diagnosis are very significant and conscientious factor for the hematologist. In this work, an unsupervised learning algorithm, namely k-means is used for segmenting the nucleus and cytoplasm. Various color and geometric features are extracted to analyze the malignant and healthy leukocyte cells. These features are then subjected to the feature elimination to reduce the high dimensional feature space into optimal sized features set using principal component analysis. Furthermore, various binary classifiers like SVM, PNN, k-NN, and SSVM are used for classifying the extracted regions into nucleus, cytoplasm, and background cells. The results yielded through the experimentations establish that the proposed system successfully eradicate the extraneous features, effectively classify the malignant lymphoblast cells from the healthy lymphocytes and preserves the maximum accuracy in comparison to that of other methods.
A Novel System for Detecting and Preventing SQL Injection and Cross-Site-Script

SQL Injection and Cross-Site Scripting are the two most common attacks in database-based web applications. In this paper we propose a system to detect different types of SQL injection and XSS attacks associated with a web application, without the existence of any firewall, while significantly reducing the network overhead. We use properly modifications of the Nginx Reverse Proxy protocols and Suricata NIDS/ IPS rules. Pure work has been done from other researchers based on the capabilities of Nginx and Suricata and our approach with the experimental results provided in the paper demonstrate the efficiency of our system.
FarmersChain: A Decentralized Farmer Centric Supply Chain Management System Using Blockchain and IoT

Globalization has made supply chain business management more complicated over time. The existence of intermediary parties in the supply chain causes major issues like product genuineness, as well as transparency in product quality and quantity information management, etc. Traditional supply chain models depend on intermediaries and also are cloud-based systems. It is very much difficult to track the data state changes across the supply chain&#x2019;s larger network. Latest technologies such as blockchain and the Internet of Things (IoT) play a critical role in bringing transparency to supply chain management. In this paper, we have proposed FarmersChain, a novel decentralized data-centric smart supply chain management system based on blockchain and IoT technologies. In our proposed system FarmerChain, smart contracts are used to automate digital agreements. It was examined and analyzed on a local testbed to demonstrate its potential. Based on the system analysis and testing, we discovered that the proposed supply chain management is feasible in a real-time environment without the interference of a third party and middleman. It also ensures the product&#x2019;s quality and quantity information status is accurate, accessible, and transparent.
Study on Real World Applications of SVM

Nowadays, machine learning is the foundational tool for developing and maintaining digital applications. Machine learning tools enabled organizations to identify profitable opportunities and potential risks more quickly. As there are numerous new advancements that aid in improving the accuracy of machine learning technology. Our goal is to identify the domains in which machine learning works well. In this paper, we use a support vector machine model in two real-world scenarios: behavior analysis on social media platforms and object detection. The paper also uses real-time data and does classification and detection with the help of SVM, producing accurate results.
Decentralised Finance's Unregulated Governance: Minority Rule in the Digital Wild West

Decentralised finance (DeFi) is a category of unlicensed, unregulated, and non-custodial financial services that utilise public, distributed ledgers like Ethereum. The Bloomberg Galaxy DeFi Index, launched in August 2021, includes nine Ethereum-based projects – non-custodial exchanges as well as lending and derivatives platforms. Each project is governed, at least in part, by a community of unregistered individuals that hold tradable voting rights tokens (also known as governance tokens). Voting rights tokens allow holders to vote on proposed changes to a DeFi project’s features, parameters, or rules. DeFi’s governance power is thus linked to the distribution and exercise of tokenised voting rights. Since DeFi projects are typically not managed by companies or public institutions, not much is known about DeFi’s governance. Regulators and law-makers from the United States recently asked if DeFi’s governance entails a new class of “shadowy” elites. In response, we conducted an exploratory, multiple-case study that focuses on the voting rights tokens issued by the nine projects from Bloomberg’s Galaxy DeFi index. Our mixed methods approach draws on Ethereum-based data about the distribution, trading, and staking of voting rights tokens, as well as project documentation and archival records. Our findings contribute knowledge about the entitlements of DeFi’s voting rights tokens, the initial distribution strategies, and the actual voting and delegation activity. Our principal finding is that DeFi’s voting rights are highly concentrated, and the exercise of these rights is very low. Our theoretical contribution is descriptive: minority rule is the probable consequence of tradable voting rights plus the lack of applicable anti-concentration or anti-monopoly laws. We interpret DeFi’s minority rule as timocratic and acknowledge its possible transition to oligarchy.