Reviewerly/exHarmony huggingface dataset:
Evaluating Parrots and Sociopathic Liars (keynote)

This talk builds on my SWAN (Schematised Weighted Average Nugget) paper published in May 2023, which discusses a generic framework for auditing a given textual conversational system. The framework assumes that conversation sessions have already been sampled through either human-in-the-loop experiments or user simulation, and is designed to handle task-oriented and non-task oriented conversations seamlessly. The arxiv paper also discussed a schema containing twenty (+1) criteria for scoring nuggets (i.e., factual statements and dialogue acts within each turn of the conversations) either manually or (semi)automatically.
DNAttest: Digital-twin-based Non-intrusive Attestation under Transient Uncertainty

Programmable logic controllers (PLCs) are vulnerable to malware, which is a key security risk for Industrial Control Systems (ICSs). Existing attestation solutions are invasive because they require hardware security modules and software upgrades in legacy devices. We propose DNAttest, a Digital-twin-based Noninvasive Attestation solution to attest PLC behaviors in near-real time. DNAttest requires minimal ICS infrastructure changes and does not interfere with normal ICS operations. DNAttest detects PLC deviations by replicating all input messages for a PLC to its digital twin and comparing their output messages. Due to transient uncertainty in the PLC's internal processing state, DNAttest may output an incorrect comparison. To generate all plausible output values for comparison, we instantiate multiple emulated PLCs by replicating input messages with different timing profiles. We demonstrate on a close-to-real-world power grid testbed that DNAttest can provide a timely detection of a wide range of attacks non-invasively and accurately. DNAttest solution is lightweight and scalable. A typical desktop PC can attest more than 20 actual PLCs even if we use 10 emulators to monitor every actual PLC.
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems

Federated online learning to rank (FOLTR) aims to preserve user privacy by not sharing their searchable data and search interactions, while guaranteeing high search effectiveness, especially in contexts where individual users have scarce training data and interactions. For this, FOLTR trains learning to rank models in an online manner -- i.e. by exploiting users' interactions with the search systems (queries, clicks), rather than labels -- and federatively -- i.e. by not aggregating interaction data in a central server for training purposes, but by training instances of a model on each user device on their own private data, and then sharing the model updates, not the data, across a set of users that have formed the federation. Existing FOLTR methods build upon advances in federated learning.
Modified Gauss-Newton Algorithms under Noise

Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when compared to gradient descent in large-scale statistical settings. We explore the contrasting performance of these two classes of algorithms in theory on a stylized statistical example, and experimentally on learning problems including structured prediction. In theory, we delineate the regime where the quadratic convergence of the modified Gauss-Newton method is active under statistical noise. In the experiments, we underline the versatility of stochastic (sub)-gradient descent to minimize nonsmooth composite objectives.
A is for Adele: An Offline Evaluation Metric for Instant Search

Instant search has emerged as the dominant search paradigm in entity-focused search applications, including search on Apple Music, Kayak, LinkedIn, and Spotify. Unlike the traditional search paradigm, in which users fully issue their query and then the system performs a retrieval round, instant search delivers a new result page with every keystroke. Despite the increasing prevalence of instant search, evaluation methodologies for instant search have not been fully developed and validated. As a result, we have no established evaluation metrics to measure improvements to instant search, and instant search systems still share offline evaluation metrics with traditional search systems. In this work, we first highlight critical differences between traditional search and instant search from an evaluation perspective. We then consider the difficulties of employing offline evaluation metrics designed for the traditional search paradigm to assess the effectiveness of instant search. Finally, we propose a new offline evaluation metric based on the unique characteristics of instant search. To demonstrate the utility of our metric, we conduct experiments across two very different platforms employing instant search: A commercial audio streaming platform and Wikipedia.
Health-Care Monitoring of Patient using CNN based Model in Internet of Things

There is rising public concern about exposure to radiofrequency (RF) electromagnetic fields (EMF) as more and more wireless communications become concentrated in everyday living surroundings. There are a number of obstacles, primarily originating from infrastructure expenses, but recent technology breakthroughs are shifting attention to Internet of Things (IoT) devices that enable automatic and continuous real-time EMF monitoring. The Internet of Things (IoT) has made the world a better place by expanding the capabilities of telemedicine and allowing for more precise remote monitoring of patients. Improved healthcare facilities for both patients and doctors/hospitals are only one way that IoT is making a significant impact in the medical field. Five main parts make up the proposed system: patient data gathering, primary report creation, hospital care, pharmacy care, and diagnostics. This study presents an Internet of Things (IoT)-based healthcare scheme accomplished of real-time nursing of energetic signs and environmental factors for discrete patients. Five sensors, including a heart-rate monitor, a body temperature monitor, a room temperature monitor, a carbon monoxide monitor, and a carbon dioxide monitor, are utilised to collect data from the hospital setting. Medical personnel receive patient updates through a portal and use this data for further diagnosis and care. The success of the system demonstrates that the built prototype is ideal for healthcare monitoring. This study has investigated unique approaches to the automatic construction of convolutional neural network (CNN) topologies, and it has also developed a new method for diagnosis. The results of the trials prove that the recommended algorithm achieves improved than the state-of-the-art techniques.
Communication Through Smart Glasses-An IOT Based Eye Blink Detection

Eye blinking is the rapid closure and opening of the eyelids. Research on human eyesight health has many uses, including eye blink detection in human computer interface. To evaluate the, this research provides an adaptive sensor-based eye blink detection system. Many medical conditions, including Locked in Syndrome, Cerebral Palsy, and Amyotrophic Lateral Sclerosis, cause motor speech disorders or paralysis that impair speech or voice production. The sufferer can become unable to communicate with those around them as a result. People in these situations are aware of what they want to say but are unable to express it. The quick closure and opening of the eyelids is known as eye blinking. Eye blink detection in human computer interfaces is one of the many applications for research on human vision health. This research offers an adaptive sensor-based eye blink detection method to analyse the codewords. Many illnesses, such as Locked-in Syndrome, Cerebral Palsy, and Amyotrophic Lateral Sclerosis, result in motor speech disorders or paralysis that affect the ability to speak or express one’s voice. As a result, the affected person may lose their ability to communicate with those around them. In these circumstances, people know what they want to say but are unable to say it.
Construction of an Information System for English Learning Platform Based on Genetic Algorithm

Currently, the process of transforming learning platforms from traditional to modern is at a crucial juncture, marking a significant period of transition in the field of informatization. The swift advancements in information technology, encompassing digitization, informatization, and intelligence, are instigating consequential shifts in English learning, driving the pursuit of modernization. The advent of information technology has engendered the epoch of personalized learning. The focal point of this study resides in the contemporary state and developmental trajectory of English pedagogical reforms within university settings. By centering on universities as the primary research subjects, this article delineates the disharmonious occurrences encountered during the amalgamation of conventional language instruction and modern educational technology. Furthermore, it delves into the construction of a novel English learning information system, employing methodologies while scrutinizing the causes of disharmony through the lens of educational ecology. Moreover, it ventures forth with audacity to explore methods aimed at mitigating and eradicating the discordance. The objective is for the research findings of this article to be effectively implemented in English instruction, providing valuable insights and augmenting the current pedagogical landscape.
Enhanced Visual Cryptosystem Using BLAKE2 Hash Algorithm

Many physical devices in everyday life have been connected to the global web since its inception. The network’s security improves as the number of items connected to it grows. However, current security measures make progress difficult. As a result, based on the BLAKE 2 hash algorithm, we propose a basic security mechanism. Our proposed method aims to improve the transfer of sensitive image data between nodes. The key issue is transmitting data across multiple nodes invisibly without being hacked. The proposed system’s primary objective is to maintain the picture secure and safe from third parties. It is accomplished by combining encryption and decryption into a lightweight image transport technique. It describes a technique for generating secret cryptographic keys from image pixels using the BLAKE 2 cryptographic hash that is image content adaptive. This scheme includes three encryption processes: DC coefficient encryption, AC coefficient encryption, and novel orthogonal transformation. The encrypted image is safely sent to another node over the network using an upgraded visual cryptosystem, and the decrypted image is successfully obtained at the receiver node.
A Comprehensive Analysis on Deep Learning based Image Retrieval

Image retrieval is a computer vision task that entails looking for images that resemble a particular query image in a sizable database. By using visual similarity or other factors, image retrieval seeks to empower users to locate images that correspond to their interests or requirements. The image processing stream is recognized as an essential module since a lot of systems use logic to map inputs to outputs. Semantic gaps prevent some conventional text-and content-based picture retrieval techniques from reflecting the human perception of images. The primary aim of this paper is to provide a research survey on traditional Deep Learning based image retrieval techniques along with their advantages, disadvantages, and limitations. This study focuses on the issues in Content Based Image Retrieval (CBIR), such as the semantic gap that develops among image pixels with a low level (collected by machines) and image pixels with a high level (collected by humans). The extensive approach supports researchers to attain greatest solutions for the present issues in CBIR. The accuracy, precision, recall, and f-score are measured key parameters for determining the efficiency of image retrieval techniques.
Survey on Computer Vision Techniques for Internet-of-Things Devices

Deep neural networks (DNNs) are state-of-the-art techniques for solving most computer vision problems. DNNs require billions of parameters and operations to achieve state-of-the-art results. This requirement makes DNNs extremely compute, memory, and energy-hungry, and consequently difficult to deploy on small battery-powered Internet-of-Things (IoT) devices with limited computing resources. Deployment of DNNs on Internet-of-Things devices, such as traffic cameras, can improve public safety by enabling applications such as automatic accident detection and emergency response. Through this paper, we survey the recent advances in low-power and energy-efficient DNN implementations that improve the deployability of DNNs without significantly sacrificing accuracy. In general, these techniques either reduce the memory requirements, the number of arithmetic operations, or both. The techniques can be divided into three major categories: (1) neural network compression, (2) network architecture search and design, and (3) compiler and graph optimizations. In this paper, we survey both low-power techniques for both convolutional and transformer DNNs, and summarize the advantages, disadvantages, and open research problems.
Sentimental Analysis of Lockdown in India During Covid-19

Sentiment analysis has quickly become one of the most prominent fields of natural language processing due to the spectacular increase in Internet usage (NLP). For a variety of situations, sentiment analysis can successfully extract the text’s inferred emotion. During the COVID-19 epidemic, a tremendous amount of information is being received and shared on social media. Mining such content to assess people’s attitudes may substantially improve decision-making in order to maintain control of the situation. In this study, sentiment analysis of tweets has been completed using NLP and machine learning classifiers. The data have been scraped from Twitter, annotated with Text Blob, and pre-processed using the natural language processing tools provided into Python. The study focuses on the sentiment analysis of people during COVID-19 pandemic in India. RNN is used for obtaining a tolerable level of emotion prediction. The proposed method categorized emotions into three categories such as positive, neutral and negative, and obtained an accuracy rate of 90% with a prediction that people support the government’s decision to impose the lockdown during the spread of COVID-19.
A Twenty-Year Trend Analysis of Literary Warrants on Digital Literacy and Digital Competency Through SCOPUS Database

ABSTRACTThis study employs bibliometric analysis to investigate researchers’ contributions to digital literacy and digital competency between 2002 and 2022. Using the Scopus Elsevier database, the research analyses various aspects of publications, such as Coupling clusters, Normalised Impact Scores, Normalised Local Citation Scores, Thematic Evolution, and Factorial Analysis. The findings suggest that digital literacy and technology use in education are critical topics for education and health. In addition, digital literacies, e-learning, and students were identified as crucial topics, with digital literacies being the most common and significant label. The article provides valuable insights into the relationships between different concepts related to education and technology. It highlights the importance of digital literacy and technology in teaching and their connection to health literacy and students.KEYWORDS: Clustering algorithmdigital literacymultiple correspondence analysisnormalised impact scoretrend analysis Disclosure statementNo potential conflict of interest was reported by the author(s).
Hierarchical Transformer-based Query by Multiple Documents

It is often difficult for users to form keywords to express their information needs, especially when they are not familiar with the domain of the articles of interest. Moreover, in some search scenarios, there is no explicit query for the search engine to work with. Query-By-Multiple-Documents (QBMD), in which the information needs are implicitly represented by a set of relevant documents addresses these retrieval scenarios. Unlike the keyword-based retrieval task, the query documents are treated as exemplars of a hidden query topic, but it is often the case that they can be relevant to multiple topics.
Quantum Steering of Two Tavis-Cummings Atoms with Dipole-Dipole Interaction Under Intrinsic Decoherence

Abstract Asymmetric and hybrid steering, evaluated by entropic uncertainty inequalities between two identical atoms, is studied in the Tavis-Cummings model with dipole-dipole interaction (DDI) under intrinsic decoherence. The results show that phase decoherence stabilizes the direction during time decay. DDI coupling in the system enhances bi-directionality and stability by avoiding collapses and violations, from which the asymmetric and hybrid steering suffer, respectively. When intrinsic decoherence is introduced, the asymmetric direction becomes stationary when DDI coupling is weak. However, the hybrid direction tends to have the same characteristics when DDI coupling is strong.
Rate-Distortion-Perception Tradeoff Based on the Conditional Perception Measure

In this paper, we study the rate-distortion-perception tradeoff generalizing the classical rate-distortion theory by adding a perception constraint to generate visually pleasing reconstructions. The perception metric measures the divergence between the distributions of the input and the reconstruction when both distributions are conditioned on the encoder's output. This metric, originally introduced by Mentzer et al. for the video compression setting, is called as conditional perception measure. We characterize the rate-distortion-perception tradeoff for a general source. In the Gaussian setting, we show that jointly Gaussian reconstructions are indeed optimal. Interestingly, to achieve a perceptually perfect reconstruction, comparing to the minimum mean square error (MMSE) reconstruction, we only need extra 0.5 bits/sample for the compression rate.
Decentralized Token Exchanges in Blockchain Enabled Interconnected Smart Microgrids

Blockchain technology (BCT) is emerging as a game-changer for many industries due to its unique benefits, like the ability to conduct peer-to-peer (P2P) transactions in a trustless environment with increased data security and transparency in real-time. It is employed in Blockchain Enabled Interconnected Smart Microgrids (BSMGs) to automate local energy markets, facilitate energy trading, and manage microgrid operations. However, with the increasing adoption of BSMGs, several challenges regarding interoperability between BSMGs may arise. Different BCT platforms may use their indigenous tokens or specially designed energy tokens for financial transactions. Also, cross-border international energy trades can become P2P which may involve real-time transactions using the countries' digital currencies. Thus, in order to overcome the geographical and technological barriers impeding the widespread usage of BSMGs and access to energy for all, decentralized exchanges (DEX) must be incorporated. DEXs are used in decentralized finance to exchange digital tokens and cryptocurrencies on blockchains. In this paper, an Order Book DEX is implemented on Cosmos which exchanges tokens between different chains using the Inter Blockchain Communication protocol (IBC). The working logic of the DEX is discussed and implemented using Ignite CLI. The financial exchanges and recording using buy and sell orders are demonstrated for the first time in the energy domain.
Intelligent Combination of Approaches Towards Improved Bangla Text Summarization

Text summarization is a technique to extract the main concept from a large document. It turns a big document into a smaller one without changing the main context. Text summarization have widely research area nowadays. There are two types of text summarization, one generates an extractive summary and the other generates an abstractive summary. Here in this paper, an intelligent model is proposed which can make an extractive summary from a given document. After completing some preprocessing steps on the document, some useful combinations of methods are applied such as Named Entity-based scoring, keyword based-scoring, parts of speech-based scoring, and word and sentence based-analysis to rank the sentences of the passage. These methods combined together generated the final summary. The proposed model is compared with multiple human-made summaries and the evaluation was performed with respect to precision, recall, and F-measures. The model is also compared with the state-of-the-art approaches and found to show its effectiveness with respect to Precision (0.606) and F-measure (0.6177) evaluation measures.
SGX Switchless Calls Made Configless

Intel's software guard extensions (SGX) provide hardware enclaves to guarantee confidentiality and integrity for sensitive code and data. However, systems leveraging such security mechanisms must often pay high performance overheads. A major source of this overhead is SGX enclave transitions which induce expensive cross-enclave context switches. The Intel SGX SDK mitigates this with a switchless call mechanism for transitionless cross-enclave calls using worker threads. Intel's SGX switchless call implementation improves performance but provides limited flexibility: developers need to statically fix the system configuration at build time, which is error-prone and misconfigurations lead to performance degradations and waste of CPU resources. ZC-Switchless is a configless and efficient technique to drive the execution of SGX switchless calls. Its dynamic approach optimises the total switchless worker threads at runtime to minimise CPU waste. The experimental evaluation shows that ZC-Switchless obviates the performance penalty of misconfigured switchless systems while minimising CPU waste.
Universal Remote Attestation for Cloud and Edge Platforms

With more computing workloads being shifted to the cloud, verifying the integrity of remote software stacks through remote attestation becomes an increasingly important topic. During remote attestation, a prover provides attestation evidence to a verifier, backed by a hardware trust anchor. While generating this information, which is essentially a list of hashes, is easy, examining the trustworthiness of the overall platform based on the provided list of hashes without context is difficult. Furthermore, as different trust anchors use different formats, interaction between devices using different attestation technologies is a complex problem.
A Bring Your Own Device security awareness survey among professionals

The increasing prevalence of Bring Your Own Device (BYOD) practices in the workplace has posed significant challenges to organizations in terms of security and management. This paper presents a survey-based study aimed at exploring the adoption, implications, and security considerations associated with BYOD policies. The study utilized a questionnaire developed based on guidelines provided by the National Institute of Standards and Technology (NIST). The primary objectives of this research are to investigate the cautiousness and awareness of BYOD users, as well as the effectiveness of security measures implemented by organizations, in order to gain insights into the key aspects of BYOD practices in the workplace. The findings of this paper highlight the need for increased caution among BYOD users regarding device security, a lack of knowledge among users about organizational security measures, and the potential for enhancing security policies and implementing additional measures despite organizations having achieved a satisfactory level of security for BYOD.