Reviewerly/exHarmony huggingface dataset:
SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning

With the widespread application of deep neural networks (DNNs), the risk of privacy breaches against DNN models is constantly on the rise, resulting in an increasing need for intellectual property (IP) protection for such models. Although neural network watermarking techniques are widely used to safeguard the IP of DNNs, they can only achieve passive protection and cannot actively prevent unauthorized users from illicit use or embezzlement of the trained DNN models. Therefore, the development of proactive protection techniques to prevent IP infringement is imperative. To this end, we propose SecureNet, a key-based access license framework for DNN models. The proposed approach involves injecting license keys into the model through backdoor learning, enabling correct model functionality only when the appropriate license key is included in the input. To ensure the reusability of DNN models, we also propose a license key replacement algorithm. In addition, based on SecureNet, we designed defense mechanisms against adversarial attacks and backdoor attacks, respectively. Furthermore, we introduce a fine-grained authorization method that enables flexible granting of model permissions to different users. We have designed four license-key schemes with different privileges, tailored to various scenarios. We evaluated SecureNet on five benchmark datasets including MNIST, Cifar10, Cifar100, FaceScrub, and CelebA, and assessed its performance on six classic DNN models: LeNet-5, VGG16, ResNet18, ResNet101, NFNet-F5, and MobileNetV3. The results demonstrate that our approach outperforms the state-of-the-art model parameter encryption methods by at least 95% in terms of computational efficiency. Additionally, it provides effective defense against adversarial attacks and backdoor attacks without compromising the model’s overall performance.
Tri-stage offline Telugu character recognition system based on fusion of HOG and ULBP

The domain of pattern recognition has experienced a notable upsurge in the prevalence of handwritten character recognition, which is a research area that has been widely recognized for decades.However, this task presents significant challenges, especially for Indian languages, due to the vast array of character shapes and curved linear structures they possess. Despite some research having been conducted on Indian languages such as Kannada, Hindi, Tamil Devanagari, and Telugu, among others, the field is still in its nascent stages of development. The nascent phase of handwritten Telugu script presents a promising opportunity for further investigation and exploration. We introduce a Tri-stage Telugu handwritten recognition system that utilizes hyper parameters of k- NN, SVM, and Random Forest based on the fusion of HOG and ULBP features. By implementing a Tri-stage classifier in a sequential manner, the system's recognition accuracy is significantly enhanced.The proposed Tri-stage classifier achieves exceptionally high accuracies on Telugu handwritten character HP datasets.
An ultra-lightweight method for individual identification of cow-back pattern images in an open image set

Cow recognition forms the foundation of smart livestock management. Current closed-set cow recognition models can exclusively identify cows within their training set and are ill-equipped to recognize other subjects in the open world. Additionally, there is a dearth of comprehensive research on open-set cow recognition. Existing open-set recognition models not only exhibit suboptimal accuracy but also impose a substantial burden on hardware resources due to their extensive parameterization. These challenges hinder the practical deployment of cow recognition models. This study introduces an innovative open-set metric learning-based recognition framework for cow-back patterns, aiming to rectify a significant limitation of individual cow recognition models, which is their inability to identify cows not included in the training set. The proposed framework combines various loss functions, metric methods, and backbone networks. This integration enables the open-set recognition of cow back pattern images, thereby facilitating the identification of cows previously unrecognized by the model. To mitigate hardware resource consumption, we have devised a novel ultra-lightweight backbone network known as LightCowsNet. This network draws inspiration from existing lightweight backbone networks such as MobileFaceNet, MobileViT, and EfficientNetV2. Its purpose is to extract features from cow back pattern images efficiently. LightCowsNet employs attention mechanisms, inverted residual structures, and depthwise separable convolutions. LightCowsNet first uses attention mechanisms, inverted residual structures, and depthwise separable convolutions to design multiple new feature extraction modules. These modules, which have different structures, are subsequently placed to extract different levels of image information, which enables complete extraction of the textural information of the cows’ back patterns. The present study reorganized the Cows2021 dataset to render it suitable for open-set recognition of cow-back patterns, with the test set comprising image pairs. The results revealed that using A-SoftMax as the loss function and Euclidean distance as the metric method enabled LightCowsNet to achieve the highest accuracy of 94.26 %, with a model weight space occupation of only 4.06 MB. Compared to MobileFaceNet, MobileViT, and EfficientNetV2, LightCowsNet achieved 6.24 %, 8.82 %, and 2.3 % greater accuracy on the test set, respectively. Its weight also decreased by 0.71 MB, 0.24 MB, and 4.3 MB, respectively. These results demonstrate that the proposed model achieved both accuracy improvement and model lightweightness, and these findings could serve as reference solutions for intelligent farming on cow farms.
LPSRGAN: Generative adversarial networks for super-resolution of license plate image

This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognitioin (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.
IDaPS – Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop

The execution of Map-Reduce applications on the Hadoop cluster poses significant challenges due to the non-consideration of data locality, i.e., assigning tasks to compute nodes where input data sets are located. Due to such non-consideration, high data transfer overheads are caused. Further, it increases latency, which may arise if input data needs to be transferred across the network, thereby significantly increasing execution time. To address this issue, an Improved DAta Placement Strategy IDaPS based on the intra-dependency among the data is proposed. IDaPS re-organizes the default data layouts in HDFS to ensure higher degree of parallelism. The efficiency of IDaPS is demonstrated in Hadoop clusters (10 and 15 nodes) by executing Hadoop Benchmark performance tests viz. WordCount, Grep on Project-Gutenberg book dataset (50GB) and Least Square Linear Regression (LSLR) on weather dataset (10.67GB). The results were compared with state-of-the-art algorithms viz. Hadoop Default Data Placement (HDDP), Load-Balancer and literary work RENDA. The results demonstrate that IDaPS significantly reduces execution time by 28.2% and 38.4% in 10-node and 15-node clusters while executing WordCount, and 35% and 38.1% in 10-node and 15-node clusters for Grep. Similarly, for LSLR, it reduces execution time by 32.7%.
Quantum Clustering with k-Means: a Hybrid Approach

Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degrees of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.
Deep Learning-Based Approaches for Abusive Content Detection and Classification for Multi-class Online User-Generated Data

With the rapid growth of social media culture, the use of offensive or hateful language has surged, which necessitates the development of effective abusive language detection models for online platforms. This paper focuses on developing a multi-class classification model to identify different types of offensive language. The input data is taken in the form of labeled tweets and is classified into offensive language detection, offensive language categorization, and offensive language target identification. The data undergoes pre-processing, which removes NaN value and punctuation, as well as performs tokenization followed by the generation of a word cloud to assess data quality. Further, the tf-idf technique is used for the selection of features. In the case of classifiers, multiple deep learning techniques, namely, bidirectional gated recurrent unit, multi-dense long short-term memory, bidirectional long short-term memory, gated recurrent unit, and long short-term memory, are applied where it has been found that all the models, except long short-term memory, achieved a high accuracy of 99.9% for offensive language target identification. Bidirectional LSTM and multi-dense LSTM obtained the lowest loss and RMSE values of 0.01 and 0.1, respectively. This research provides valuable insights and contributes to the development of effective abusive language detection methods to promote a safe and respectful online environment. The insights gained can aid platform administrators in efficiently moderating content and taking appropriate actions against offensive language.
Strictly Frequentist Imprecise Probability

Strict frequentism defines probability as the limiting relative frequency in an infinite sequence. What if the limit does not exist? We present a broader theory, which is applicable also to data that exhibit diverging relative frequencies. In doing so, we develop a close connection with the theory of imprecise probability: the cluster points of relative frequencies yield a coherent upper prevision. We show that a natural frequentist definition of conditional probability recovers the generalized Bayes rule. Finally, we prove constructively that, for a finite set of elementary events, there exists a sequence for which the cluster points of relative frequencies coincide with a prespecified set which demonstrates the naturalness, and arguably completeness, of our theory.
A privacy-preserving key transmission protocol to distribute QRNG keys using zk-SNARKs

High-entropy random numbers are an essential part of cryptography, and Quantum Random Number Generators (QRNG) are an emergent technology that can provide high-quality keys for cryptographic algorithms but unfortunately are currently difficult to access. Existing Entropy-as-a-Service solutions require users to trust the central authority distributing the key material, which is not desirable in a high-privacy environment. In this paper, we present a novel key transmission protocol that allows users to obtain cryptographic material generated by a QRNG in such a way that the server is unable to identify which user is receiving each key. This is achieved with the inclusion of Zero Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARK), a cryptographic primitive that allow users to prove knowledge of some value without needing to reveal it. The security analysis of the protocol proves that it satisfies the properties of Anonymity, Unforgeability and Confidentiality, as defined in this document. We also provide an implementation of the protocol demonstrating its functionality and performance, using NFC as the transmission channel for the QRNG key.
Human Face Reconstruction using Divine Proportions and Gestalt for Occluded Video Face Recovery in Forensic Analysis using Deep Learning

Forensic video analysis has been used in diverse kind of high-profile cases, global discrepancies, and conflict zones. It is a three-phase process of scientific examination, comparison, and evaluation of video in legal matters. Human face reconstruction using deep learning for occluded video face recovery to aid in forensic analysis is the main objective of this paper. Forensic facial reconstruction is a combination of both scientific methods and artistic skill. In this paper, we introduce a method to reconstruct human faces occluded due to short noise innight-time video clips. A skull database is created with unique skull models with varying shapes, forms and proportions. Human body mathematical model biometric using golden ratio algorithm is proposed and used to find the occluded face proportions. Closure principle of gestalt theory of visual perception is used to fill in the missing parts of a face design and to create a whole face image using gan. The proposed model is found to have 50% lesser reduced Median error rate and 20% reduced Stdev than PrNet and 10% lower Mean error rate than 3Dddfav2.
‘What a funny looking video’: Using allegorical representations of technological change to reflect on future digital communication and design challenges

This paper presents a reflective examination of challenges to design and communication from the current digital revolution, using the prism of a 1980s television advertisement for the Yellow Pages. Originating at the same time as the height of the desktop publishing revolution, the advert illuminates a transitional period in the evolution of digital technology and media communications, marked by changing user practices and experiences. The advert’s storyline follows a young man’s quest to convert an old cine film (of his father) to videotape for his mother’s birthday, in the process showcasing the impending shift from analogue to digital technologies and encapsulating the multifaceted implications and transitional challenges of the period. The hybrid setting of technologies, alongside the tensions, confusion and ambiguities of different stakeholders metaphorically symbolises and can be contrasted alongside, the challenges impacting the design, media and communications industry of the period and can inform reflection on contemporary challenges. Engaging with the narrative, the Yellow Pages advert is used as a creative device which functions as a cultural, historical, narrative lens with which to contrast against contemporary (and future) transitional challenges within the digital revolution.
Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis

Abstract Aspect-based sentiment analysis (ABSA) aims to mine the sentiment tendencies expressed by specific aspect terms. The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.
Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.
Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions

In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ensuring drugs are allocated and stored strategically to meet diverse regional and demographic needs. Summarizing our key findings, our research focuses on the promising area of drug demand forecasting using artificial intelligence (AI) and machine learning (ML) techniques to enhance predictions in the pharmaceutical field. Supplied with a rich dataset from Kaggle spanning 600,000 sales records from a singular pharmacy, our study embarks on a thorough exploration of univariate time series analysis. Here, we pair conventional analytical tools such as ARIMA with advanced methodologies like LSTM neural networks, all with a singular vision: refining the precision of our sales. Venturing deeper, our data underwent categorisation and were segmented into eight clusters premised on the ATC Anatomical Therapeutic Chemical (ATC) Classification System framework. This segmentation unravels the evident influence of seasonality on drug sales. The analysis not only highlights the effectiveness of machine learning models but also illuminates the remarkable success of XGBoost. This algorithm outperformed traditional models, achieving the lowest MAPE values: 17.89% for M01AB (anti-inflammatory and antirheumatic products, non-steroids, acetic acid derivatives, and related substances), 16.92% for M01AE (anti-inflammatory and antirheumatic products, non-steroids, and propionic acid derivatives), 17.98% for N02BA (analgesics, antipyretics, and anilides), and 16.05% for N02BE (analgesics, antipyretics, pyrazolones, and anilides). XGBoost further demonstrated exceptional precision with the lowest MSE scores: 28.8 for M01AB, 1518.56 for N02BE, and 350.84 for N05C (hypnotics and sedatives). Additionally, the Seasonal Naïve model recorded an MSE of 49.19 for M01AE, while the Single Exponential Smoothing model showed an MSE of 7.19 for N05B. These findings underscore the strengths derived from employing a diverse range of approaches within the forecasting series. In summary, our research accentuates the significance of leveraging machine learning techniques to derive valuable insights for pharmaceutical companies. By applying the power of these methods, companies can optimize their production, storage, distribution, and marketing practices.
Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system’s efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.