Reviewerly/exHarmony huggingface dataset:
Protecting Voice-Controllable Devices Against Self-Issued Voice Commands

Self-issued voice commands leverage the voice-controllable device’s internal speaker to issue malicious voice commands to the device itself. These attacks are a class of voice spoofing attacks particularly challenging to protect from, as it is very hard for a countermeasure solution to infer whether the command comes from an external entity or from the device itself. In this paper, we propose a countermeasure against self-issued voice commands by training a Twin Neural Network to recognise the differences between what is being played and what is being recorded by the voice-controllable device. In fact, these audios are very similar in case of voice command self-issue attacks and different in case of legitimate commands. We start with a security and usability trade-off analysis of countermeasures against voice spoofing attacks, by describing different classes of synthesised voice commands that need to be blocked or allowed, depending on the necessities of the user. Then, we present our solution to protect voice-controllable devices from self-issued commands and show that it correctly classifies commands in the benign (real-user) and malign (self-issued) categories 97% of the times on average. We compare this result with state-of-the-art anomaly detection techniques as a baseline and show that our solution outperforms them. Furthermore, we instantiate our countermeasure on three different classes of devices to measure its performance, and we find that the additional overhead is negligible. Finally, we measure the usability impact of our solution when users interact with the tested device under different conditions, showing that our solution is resistant to environmental changes and regardless of the identity of the user issuing the commands.
Privformer: Privacy-preserving Transformer with MPC

The Transformer is a deep learning architecture that processes sequence data. The Transformer attains the state-of-the-art in several tasks of sequence data analysis, and its variants, such as BERT and GPT-3, are used as a defacto-standard for solving general tasks in natural language processing (NLP). This work presents a 3-party multi-party computation (MPC) protocol for secure inference of the Transfomer in the honest majority setting. The attention layer is the most time-consuming part when implementing an MPC protocol for the Transformer with existing building blocks. The attention mechanism is a core component of the Transformer that captures and exploits complex dependencies among elements in the input sequences. The attention mechanism invokes the exponentiation function O(S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) times, which becomes a major bottleneck when implementing the Transformer with existing MPC primitives. To deal with this, we employ the Performer [11], a variant of the Transformer where the sigmoid function that invokes the exponentiation function is replaced with the ReLU function, a more MPC-friendly nonlinear function. Also, by introducing a kernel-based approximation of the attention matrix with random orthogonal matrices, we show that the attention layer can be processed with O(S) times calls of the ReLU function. We investigate the efficiency of the proposed method by an end-to-end implementation of the Transformer with 3-party MPC. Experimental evaluation shows that, for translating a sequence where the output sequence length is 64, the entire computation time takes about 19 minutes in the LAN environment.
CCA-FPN: Channel and content adaptive object detection

Feature pyramid network (FPN) is a typical detector commonly for solving the issue of object detection at different scales. However, the lateral connections in FPN lead to the loss of feature information due to the reduction of feature channels. Moreover, the top-down feature fusion will weaken the feature representation in the process of feature delivery because of features with different semantic information. In this paper, we propose a feature pyramid network with channel and content adaptive feature enhancement module (CCA-FPN), which uses a channel adaptive guided mechanism module (CAGM) and multi-scale content adaptive feature enhancement module (MCAFEM) to alleviate these problems. We conduct comprehensive experiments on the MS COCO dataset. By replacing FPN with CCA-FPN in ATSS, our models achieve 1.3 percentage points higher Average Precision (AP) when using ResNet50 as backbone. Furthermore, our CCA-FPN achieves 0.3 percentage points higher than the AugFPN which is the state-of-the-art FPN-based detector.
Video Anomaly Detection via Visual Cloze Tests

Although great progress has been sparked in video anomaly detection (VAD) by deep neural networks (DNNs), existing solutions still fall short in two aspects: (1) The extraction of video events cannot be both precise and comprehensive. (2) The semantics and temporal context are under-explored. To tackle above issues, we are inspired by cloze tests in language education and propose a novel approach named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual Cloze Completion</i> (VCC), which conducts VAD by completing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual cloze tests</i> (VCTs). Specifically, VCC first localizes each video event and encloses it into a spatio-temporal cube (STC). To realize both precise and comprehensive event extraction, appearance and motion are used as complementary cues to mark the object region associated with each event. For each marked region, a normalized patch sequence is extracted from several neighboring frames and stacked into a STC. With each patch and the patch sequence of a STC regarded as a visual “word” and “sentence” respectively, we deliberately erase a certain “word” (patch) to yield a VCT. Then, the VCT is completed by training DNNs to infer the erased patch and its optical flow via video semantics. Meanwhile, VCC fully exploits temporal context by alternatively erasing each patch in temporal context and creating multiple VCTs. Furthermore, we propose localization-level, event-level, model-level and decision-level solutions to enhance VCC, which can further exploit VCC’s potential and produce significant VAD performance improvement. Extensive experiments demonstrate that VCC achieves highly competitive VAD performance.
Computationally Efficient Stochastic Algorithm Supported by Deterministic Technique: A Futuristic Approach

The challenge of creating a map of the territory solely based on information collected from one or more sensors without any prior knowledge is addressed by simultaneous localization and mapping.Most of the time, a human operator controls the robot, but certain systems can navigate autonomously while mapping; this process is known as active simultaneous localization and mapping.The locomotion mechanism is frequently the primary design consideration for Exploration Robots because of the difficult conditions in which they are typically deployed.Strategies for locomotion that are based on biological systems are frequently advantageous.A common focus is on overall platform design and system integration to build robots that can endure harsh settings long enough to complete their tasks.The aim of the paper is to present the integration of the deterministic method (MAE) with the biologically inspired method for robotic space exploration purposes.The method is called the Multi-Agent Exploration Adaptive Aquila Optimizer (MAE-AAO).The occupancy grid is used as a map for exploration.The algorithms run by first calculating the cost & utility values of all the adjacent surrounding cells of the agent.To increase the rate of exploration, adaptive aquila is used.Upon comparing with other contemporary algorithms, the proposed method outshines in terms of rate of exploration, execution time, and number of aborted simulation runs.The proposed algorithm offers an average of 98% exploration rate with a mean time of only 29 seconds.The method has another distinct feature: zero failed simulation runs, which is the added advantage in the exploration rate.
A Secure Two-Party Euclidean Distance Computation Scheme Through a Covert Adversarial Model Based on Paillier Encryption

Existing secure two-party Euclidean distance computation schemes are mostly performed based on a semi-honest model, which faces bottlenecks in computation efficiency and security. In view of this, this study proposed a secure two-party euclidean distance computation (SEDC) scheme under a covert adversarial model based on Paillier encryption. In the scheme, the secure two-party Euclidean distance was computed by using technologies, such as Paillier encryption, and the computation results were verified with each other to ensure that both parties sent real data and had no cheating in the process of parameter interaction. Through verification, the scheme is correct, confidential and unforgeable under chosen-plaintext attack. Scheme comparison and efficiency analysis show that the scheme achieves a balance in the security model, which not only has relatively high security, but also is more in line with the value of Euclidean distance computation in practice. In the meanwhile, this ensures the slight communication complexity and time complexity in the computation of the scheme and enables the scheme to be of high application prospect.
Continuous-Variable Quantum Secret Sharing Based on Multi-Ring Discrete Modulation

We propose a continuous-variable quantum secret sharing scheme based on multi-ring discrete modulation, which we called MR-CVQSS. In this scheme, phase shift keying (PSK)-modulated coherent states are allowed to be further discretely modulated with different amplitudes, so that these modulated coherent states can be scattered on different amplitude rings in phase space. The advantage for this multi-ring structure is that the error probability of quantum detector for discriminating discretely-modulated coherent states (DMCSs) is more lower than that of original single-ring discretely modulated CVQSS, which is beneficial for improving the performance of whole CVQSS system. We derive the security bound for MR-CVQSS against both eavesdroppers and dishonest users, and the numerical simulation shows that the performance of MR-CVQSS in terms of both maximal transmission distance and maximal available number of users can be largely improved. Moreover, security analysis shows that the modulation variance of MR-CVQSS does not need to be very small, thereby improving the signal-to-noise ratio (SNR) of whole CVQSS system.
Integrated Quantum Health Care with Predictive Intelligence Approach

According to recent studies, nondeterministic computers have an edge over standard computer devices. The use of nondeterministic computers accelerates illness detection and therapy. It delivers an exponential improvement in computer speeds, reducing processing time from years to minutes. In health care, these computers have the potential to provide a broad range of applications for healthcare service providers, including healthcare planning, diagnostic speed, medicine customisation and pricing optimisation. Moreover, as connectivity to mental wellbeing data sources increases, so does the use of quantum computing and traditional modelling techniques that can save civilian lives. Since quantum technologies are believed to be incapable of forming unusual patterns that classical technologies are supposed to be not capable of producing, it is plausible to expect quantum computers to surpass classical computers on tasks involving machine learning. The mapping from quantum optics to quantum circuits will be explained further below.
Implementation of Machine Learning Techniques in Breast Cancer Detection

Breast cancer is still the most common disease among females worldwide. When breast cancer is detected early, the survival rate improves because better treatment can be provided to patients at the earliest. Sometimes, doctors may make incorrect decisions to identify non-cancerous tumors (benign) as cancerous tumors (malignant). Computer-aided detection systems (CAD) with the help of machine learning algorithms are able to deliver precise breast cancer diagnoses. In this paper, the WISCONSIN dataset was taken from Kaggle.com for the research. Overall, the collection contains 569 records, in which 357 are innocuous and 212 are cancerous. This dataset consists of 32 attributes. A comparative study has been done on the dataset using ML techniques like SVM, KNN, RF, and logistic regression on the basis of N-fold cross-validation techniques accuracy, f1-score, and recall. As a result, SVM outperforms with maximum accuracy of 0.95.
GUIs, Robots, and Language: Toward a Neo-Davidsonian Procedural Semantics

This chapter will continue to explore the interconnections between human language, computer languages, and intermediate forms of communication that may be appropriate for (e.g.) human–robot interaction (HRI). We argue that much of the cognitive processing intrinsic to understanding language is actually extra linguistic, and in particular that the detailed propositional content conveyed by linguistic expressions is, in many contexts, not structurally present in expressions themselves (or at least, not in its entirety). We consider semantic models that accordingly construe language as providing an “interface” to cognitive “procedures” rather than a logical encoding of predicate meaning. To the degree that these approaches have merit in the domain of human language, they offer potential insights in the realm of programming languages as well because here again the surface form of expressions (in computer code) should be analyzed as an entry point to procedural capabilities—these are the actual means through which applications are able to represent or simulate external objects. From that theoretical perspective, we consider how linguistic models may be adapted to human–robot communications and outline plausible conventions governing the interface through which humans will examine, guide, or visualize robots’ movements and surroundings. We consider GUI programming elements and human–computer interaction (HCI) conventions in traditional computing environments as analogs to HRI, suggesting that configurations of GUI controls, on-screen in application windows, serve as a proxy structure for developing technologies to model robots’ orientation—insofar as they are guided and monitored by people, by analogy to how users examine GUI windows to obtain facts about application state and the data on which applications operate.
Recommendation Mechanism to Forge Connections Between Users with Similar Interests

This paper addresses the problem individuals face when searching for people with similar interests. It is common for us to look for people who have similar interests when traveling to an unfamiliar city. A person's interests can range from films or music to hobbies, certain personality traits, lifestyle, and more. These traits and interests will be input by the user during the time of registration and will be a part of their profile. The aim of this research is to develop a matchmaking system based on user profiles to recommend like-minded individuals. Users' profiles contain the text that serves as their fingerprints which can be embedded in a vector on a dimensional space using deep learning. With this project, several widely used and newly developed clustering algorithms, such as K-means, hierarchical agglomerative clustering, and DBSCAN, are scrutinized in order to categorize large-scale behavioral data into groups of individuals with comparable interests. The effectiveness of these algorithms is measured and assessed using various performance evaluation metrics on the basis of simplicity, efficiency, and accuracy.
One-Time Pad

Abstract The one-time pad is a simple cipher. It ensures a perfect form of confidentiality known as perfect secrecy by combining a plaintext and a key of the same length with the exclusive-or (XOR) operator to produce a ciphertext. It is interesting from a theoretical point of view, but it could be more complex and questionable to use in practice. One-time pad is very appealing because of its perfect secrecy property. However, it lacks basic security properties shared by standard ciphers, namely authentication and integrity. In practice, its usage is rare and limited to circumstances where perfect secrecy is of utmost importance and integrity and authenticity can be guaranteed by other means.
Secure Data Hiding in Binary Images Using Run-Length Pairs

The popularity of digital media is on the rise today. Besides multilevel images, video, and audio, binary document images are also digitized in many applications. These include legal documents, digital books, maps, pictures, and architectural drawings. Security and confidentiality of information transmitted through digital media are the primary concerns. Employing cryptography is an imperfect solution because doing so can expose sensitive information to intruders. Information hiding is more promising as it focuses on imperceptibility. However, hiding information in binary media is more difficult than in color and greyscale media, as it causes more visual distortion due to two-shade limitations. Apart from this, the hiding technique one uses needs to take into account the requisite hiding capacity and the security of hidden information. This chapter presents an approach for hiding information in binary images which tries to address these issues. The message to be hidden is initially encrypted by a key known to both transmitter and receiver. This encrypted message is then represented as a two-dimensional sparse matrix and then transformed to a one-dimensional matrix where only information about locations of black pixels ‘1’ in the sparse matrix is preserved. An arithmetic compression algorithm is used to compress the sparse matrix. Black and white pixel run length (RL) pairs are utilized for hiding information which satisfy the threshold set for RL pair length. On the receiver side, a reverse process is applied to extract secret messages. The encryption, sparse matrix representation and arithmetic data compression secures the information and enhances hiding capacity. Utilization of run length pairs minimizes visual distortion. The results show that-high capacity information is hidden with less distortion. As digital multimedia sources become more specialized, the information hidden within them is used for steganography and watermarking.
Classification of Lung and Colon Cancer Using Deep Learning Method

Cancer seems to have a significantly high mortality rate as a result of its aggressiveness, significant propensity for metastasis, and heterogeneity. One of the most common types of cancer that can affect both sexes and occur worldwide is lung and colon cancer. It is early and precise detection of these cancers which can not only improves the rate of survival but also increase the appropriate treatment characteristics. As an alternative to the current cancer detection techniques, a highly accurate and computationally efficient model for the rapid and precise identification of cancers in the lung and colon region is provided. For the training, validation and testing phases of this work, the LC25000 dataset is used. Cyclic learning rate is employed to increase the accuracy and maintain the computational efficiency of the proposed methods. This is both straightforward and effective which facilitates the model to converge faster. Several transfer learning models that have already been trained are also used, and they are compared to the proposed CNN from scratch. It is found that the proposed model provides better accuracy, reducing the impact of inter-class variations between Lung Adenocarcinoma and another class Lung Squamous Cell Carcinoma. Implementing the proposed method increased total accuracy to 97% and demonstrate computing efficiency in compare to other method.
Efficient Multi-Receptive Pooling for Object Detection on Drone

Object detection is the most fundamental and important research in computer vision to discriminate the location and class of the object in the image. This technology has been continuously researched for the past few years. Recently, with the development of hardware such as GPU computing power and cameras, object detection technology is gradually improving. However, there are many difficulties in utilizing GPUs on low-cost devices such as drones. Therefore, efficient deep learning technology that can operate on low-cost devices is needed. In this paper, we propose a deep learning model to enable real-time object detection on a low-cost device. We experiment to reduce the amount of computation and improve speed by modifying the CSP Bottleneck and SPPF parts corresponding to the backbone of YOLOv5. The model has been trained on MS COCO and VisDrone datasets, and the mAP values are measured at 0.364 mAP and 0.19 mAP, which are about 0.07 and 0.04 higher than Refinedetlite and Refinedet, respectively. The speed is 23.010 frames per second on the CPU configuration, which is enough for real-time object detection.
Secure Multi-Party Computation

Abstract Secure multi-party computation enables a group of parties to compute a function while jointly keeping their private inputs secret. The term “secure” indicates the latter property where the private inputs used for computation are kept secret from all other parties. A significant benefit of using secure multi-party computation is that many constructed protocols are information-theoretically secure, avoiding many problems using cryptographic hardness assumptions. Some notable use cases are secure auctions, privacy-preserving network security monitoring, spam filtering on encrypted emails, and secure machine learning. Secure multi-party computation can be used to secure and enable privacy-preserving applications from privacy-preserving network security to secure machine learning.
Digital Signature

Abstract A digital signature uses cryptographic hashing functions and asymmetric cryptography to sign data. It also provides origin authentication, data integrity, and signer non-repudiation. The main idea is to generate a hash of the data using a cryptographic hashing function and then encrypt it by the signing party using its private key. As part of the verification process, the verifying party generates the hash of the data using the same cryptographic hashing function. With the increasing digitalization of business processes and other processes, the ability to apply and verify digital signatures will become increasingly important.
The Datafication Of Teaching And Learning In UK Higher Education: Towards Postdigital Pedagogies?

This chapter considers teaching and learning in the context of datafication. After some initial explanations of key terms that will be referred to in setting the context in which the datafication of university students has taken place, it explains the relationships between datafication and the ways in which learners experience the curriculum. The apparent neoliberal turn in UK Higher Education (HE) does indeed suggest that students' experiences are in a process of transformation and that student identities are potentially subject to increased surveillance and manipulation. However, the chapter also identifies a range of pedagogical approaches where an emphasis on student agency implies that datafication may be resisted or subverted. It also considers learning realms where data processes have a more limited ability to reach or intrude. It concludes by presenting these radical or alternative pedagogies as a site of hope that may enable learners to adopt progressive dispositions and to act as agents of change in relation to Human Data Interaction (HDI), within and beyond the academy.