Reviewerly/exHarmony huggingface dataset:
Managing the Strategic Transformation of Higher Education through Artificial Intelligence

Considering the rapid advancements in artificial intelligence (AI) and their potential implications for the higher education sector, this article seeks to critically evaluate the strategic adoption of AI in the framework of “smart universities”. We envisage these innovative institutions as the imminent evolution in higher education, harnessing AI and quantum technologies to reshape academic and administrative processes. The core presumption is that through such integration, universities can achieve personalized learning trajectories, enhanced accessibility, economic efficiency, and a boost in overall operational performance. However, venturing into this new educational paradigm necessitates a thorough exploration of potential pitfalls, including questions surrounding educational quality, potential job losses, risks of bias, privacy breaches, and safety concerns. Our primary objective is to offer a balanced assessment to aid stakeholders in making informed strategic decisions about endorsing and advancing the smart university model. A pivotal factor in this discourse is the acceptance of qualifications from AI-enriched institutions by employers, a variable that may drastically redefine the education sector’s trajectory. Within the context of a comprehensive analysis of its broader societal impact, this article also delves into the ramifications of AI-driven innovations for historically Black colleges and universities (HBCUs).
Distance Encoded Product Quantization for Approximate K-Nearest Neighbor Search in High-Dimensional Space

Approximate K-nearest neighbor search is a fundamental problem in computer science. The problem is especially important for high-dimensional and large-scale data. Recently, many techniques encoding high-dimensional data to compact codes have been proposed. The product quantization and its variations that encode the cluster index in each subspace have been shown to provide impressive accuracy. In this paper, we explore a simple question: is it best to use all the bit-budget for encoding a cluster index? We have found that as data points are located farther away from the cluster centers, the error of estimated distance becomes larger. To address this issue, we propose a novel compact code representation that encodes both the cluster index and quantized distance between a point and its cluster center in each subspace by distributing the bit-budget. We also propose two distance estimators tailored to our representation. We further extend our method to encode global residual distances in the original space. We have evaluated our proposed methods on benchmarks consisting of GIST, VLAD, and CNN features. Our extensive experiments show that the proposed methods significantly and consistently improve the search accuracy over other tested techniques. This result is achieved mainly because our methods accurately estimate distances.
Fast and exact analysis for LRU caches

For applications in worst-case execution time analysis and in security, it is desirable to statically classify memory accesses into those that result in cache hits, and those that result in cache misses. Among cache replacement policies, the least recently used (LRU) policy has been studied the most and is considered to be the most predictable. The state-of-the-art in LRU cache analysis presents a tradeoff between precision and analysis efficiency: The classical approach to analyzing programs running on LRU caches, an abstract interpretation based on a range abstraction, is very fast but can be imprecise. An exact analysis was recently presented, but, as a last resort, it calls a model checker, which is expensive. In this paper, we develop an analysis based on abstract interpretation that comes close to the efficiency of the classical approach, while achieving exact classification of all memory accesses as the model-checking approach. Compared with the model-checking approach we observe speedups of several orders of magnitude. As a secondary contribution we show that LRU cache analysis problems are in general NP-complete.
Assessing staff awareness and effectiveness of educational training on IT security and privacy in a large healthcare organization

Assessing staff awareness and effectiveness of educational training on IT security and privacy in a large healthcare organization Mubashir Aslam Arain, Rima Tarraf, Armghan Ahmad Health Systems Evaluation and Evidence, Alberta Health Services, Calgary, AB, Canada Background: The increased use of health information systems and information technology (IT) in healthcare heightens the risk of security and privacy breaches. Necessary measures such as effective IT training and education are required to meet the challenges of protecting patient information. Purpose: The objective of the study was to determine the effectiveness of existing educational and awareness modules in delivering the key messages around IT security and privacy. Methods: The study was conducted in a large healthcare organization in Western Canada from September 2016 to March 2017. Using proportionate stratified random sampling, an online survey was distributed to all professional groups including clinical and non-clinical staff. In total, 586 participants responded to questions pertaining to whether or not they were aware of the IT education material, common potential breaches, and knowledge in preventing IT security and privacy breaches. Data were analyzed in SPSS version 19. Results: The study found that most of the participants (80.9%) completed the online IT training. Staff perceived the online training as effective (57.5%). There was a significant positive correlation between staff perception about the effectiveness of IT security educational material and satisfaction with IT security in the organization (r=0.34, P&lt;0.01). Those who completed the training were 4.2-times (CI=2.0&ndash;8.8) more likely to correctly report the action upon receiving spam emails than those who had not completed the training. The most common type of breach stated was not knowing how to encrypt emails when sending emails outside the organization. Only a small proportion of clinical (25.5%) and non-clinical staff (30.4%) reported knowing how to encrypt emails. Also, participants identified various strategies for improving the module content and compliance. Conclusion: Online training provides a basic understanding of IT security and privacy concepts to prevent potential breaches. The training should be an integral part of healthcare staff continuing education to protect patient information. Keywords: IT security modules, privacy in healthcare, healthcare staff and IT breaches, information storage &amp; retrieval, IT security &amp; privacy, computerized medical records system
Adaptive online extreme learning machine by regulating forgetting factor by concept drift map

In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.
A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification

At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes.The fuzzy min-max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework.FMM is considered one of the most useful neural networks for pattern classification.This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement.The review is conducted based on a methodical protocol.Through a rigorous searching and filtering process, the relevant studies are extracted and comprehensively analyzed to adequately address the defined research questions.The findings indicate that FMM plays a critical role in providing solutions to pattern classification issues.The FMM model and a number of FMM-based variants are identified and systematically analyzed with respect to their aims, improvements introduced and results achieved.In addition, FMM and its variants are critically analyzed with respect to their benefits and limitations.This paper shows that the existing FMM-based variants still encounter issues in terms of the learning process (expansion, overlap test, and contraction), which influence the classification performance.Based on the review findings, research opportunities are suggested to propose a new model to enhance the number of existing FMM models, particularly in terms of their learning process by minimizing hyperbox overlap pertaining to different classes as well as avoiding membership ambiguity of the overlapped region.In short, this review provides a comprehensive and critical reference for researchers and practitioners to leverage FMM and its variants for undertaking pattern classification tasks.
CutESC: Cutting edge spatial clustering technique based on proximity graphs

In this paper, we propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a cut-edge value for the edge’s endpoints is below a threshold. The cut-edge value is calculated by using statistical features and spatial distribution of data based on its neighborhood. Also, the algorithm works without any prior information and preliminary parameter settings while automatically discovering clusters with non-uniform densities, arbitrary shapes, and outliers. However, there is an option which allows users to set two parameters to better adapt clustering solutions for particular problems. To assess advantages of CutESC algorithm, experiments have been conducted using various two-dimensional synthetic, high-dimensional real-world, and image segmentation datasets.
Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse

Abstract Objective Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks. Materials and Methods Obtaining large datasets to take advantage of highly expressive deep learning methods is difficult in clinical natural language processing (NLP). We address this difficulty by pretraining a clinical text encoder on billing code data, which is typically available in abundance. We explore several neural encoder architectures and deploy the text representations obtained from these encoders in the context of clinical text classification tasks. While our ultimate goal is learning a universal clinical text encoder, we also experiment with training a phenotype-specific encoder. A universal encoder would be more practical, but a phenotype-specific encoder could perform better for a specific task. Results We successfully train several clinical text encoders, establish a new state-of-the-art on comorbidity data, and observe good performance gains on substance misuse data. Discussion We find that pretraining using billing codes is a promising research direction. The representations generated by this type of pretraining have universal properties, as they are highly beneficial for many phenotyping tasks. Phenotype-specific pretraining is a viable route for trading the generality of the pretrained encoder for better performance on a specific phenotyping task. Conclusions We successfully applied our approach to many phenotyping tasks. We conclude by discussing potential limitations of our approach.
Lifelogging in the Wild: Participant Experiences of Using Lifelogging as a Research Tool

Research in the wild has emerged in HCI as a way of studying participant experiences in natural environments. Also, lifelogging tools such as physiological sensors have become more feasible for gathering data continuously in the wild. This could complement traditional in-waves approaches such as observations and interviews. Given the emerging nature of sensors, few studies have employed these in the wild. We extend previous work by exploring the use of a physiological sensor and camera to examine how participants appropriate and experience wearing these. Participants were engaged in viewing the photos taken during the day and used the sensor and camera data to recall details about their daily experiences and reflect on these. However, participants also went through some efforts in making the camera blend into the environment in order not to break social norms.
Tumor Malignancy Detection Using Histopathology Imaging

Image segmentation and classification in the biomedical imaging field has high worth in cancer diagnosis and grading. The proposed method classifies the images based on a combination of handcrafted features and shape features using bag of visual words (BoW). The multistage segmentation technique to localize the nuclei in histopathology images includes the stain decomposition and histogram equalization to highlight the nucleus region, followed by the nuclei key point extraction using fast radial symmetry transform, normalized graph cut based on the nuclei region estimation, and nuclei boundary estimation using modified gradient. Subsequently, features from localized regions termed as handcrafted features and the shape features using BoW are extracted for classification. The experiments are performed using both the handcrafted features and BoW to take the advantages of both local nuclei features and globally spatial features. The simulation is performed on the Bisque and BreakHis data sets (with corresponding average accuracies of 93.87% and 96.96%, respectively) and confirms better diagnosis performance using the proposed method.
Loong: A Family of Involutional Lightweight Block Cipher Based on SPN Structure

In past few years, as security ciphers in the Internet of Things (IoT), the research of lightweight block cipher has attracted tremendous attention in cryptography.The SPN structure has been widely used in the design of block cipher.However, the encryption and decryption processes of ciphers based on the SPN structure are different.We design a new SPN structure, which is perfect for lightweight block cipher.The new SPN structure makes that the encryption process is the same as decryption.Moreover, input and output data directions are the same for encryption and decryption processes.Thus, the same process can absolutely be shared in decryption and encryption both for software and hardware implementation.Further, we propose a family of involutional lightweight block cipher, called Loong, based on the proposed SPN structure and components.Rigorous analysis indicates that Loong is of high security against cryptanalysis, especially the differential attack and linear attack.As shown by our experiments and comparisons, Loong is compact in hardware environment and is suitable for the IoT.
Hierarchical multi-view aggregation network for sensor-based human activity recognition

Sensor-based human activity recognition aims at detecting various physical activities performed by people with ubiquitous sensors. Different from existing deep learning-based method which mainly extracting black-box features from the raw sensor data, we propose a hierarchical multi-view aggregation network based on multi-view feature spaces. Specifically, we first construct various views of feature spaces for each individual sensor in terms of white-box features and black-box features. Then our model learns a unified representation for multi-view features by aggregating views in a hierarchical context from the aspect of feature level, position level and modality level. We design three aggregation modules corresponding to each level aggregation respectively. Based on the idea of non-local operation and attention, our fusion method is able to capture the correlation between features and leverage the relationship across different sensor position and modality. We comprehensively evaluate our method on 12 human activity benchmark datasets and the resulting accuracy outperforms the state-of-the-art approaches.
Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Husøy were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.
Avatars and Embodied Agents in Experimental Information Systems Research: A Systematic Review and Conceptual Framework

Computerised graphical representations of human users and computer agents, known as avatars and embodied agents, have been extensively explored and investigated in Information Systems (IS) research and practice. Such digital representations can be employed in either 2D or 3D. In order to facilitate research on user and agent representations and their applications in IS, we conduct a systematic literature review and establish the current state of research on humans’ perceptions and behaviours when interacting with avatars and embodied agents. Our findings are based on an analysis of 90 articles published in top outlets in the IS field. This review identifies 1) different types of avatar and embodied agent-mediated interactions with users, 2) current application domains of such representations, 3) their dimensionality, 4) affected psychological constructs, and 5) practical considerations for the design of such digital representations. Finally, we discuss limitations of current research and, based on these, directions for future work.
Underwater Image Enhancement Using Scene Depth-Based Adaptive Background Light Estimation and Dark Channel Prior Algorithms

Due to the complexity of the underwater environment, underwater images captured by optical cameras usually suffer from haze and color distortion.Based on the similarity between the underwater imaging model and the atmosphere model, the dehazing algorithm is widely adopted for underwater image enhancement.As a key factor of the dehazing model, background light directly affects the quality of image enhancement.This paper proposes a novel background light estimation method which can enhance the underwater image.And it can be applied in 30-60m depth with artificial light.The method combines deep learning to obtain red channel information of the background light in the dark channel of the underwater image.Then, the background light is obtained by adaptive color deviation correction.Finally, the experiments of underwater images enhancement are carried out, using the dark channel prior algorithm based on the proposed background light estimation method.The results show that the proposed method effectively improves underwater image blur and color deviation, and is superior to other methods in multiple non-reference image evaluation indicators.
Lipschitz constant estimation for Neural Networks via sparse polynomial optimization

We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bound on the Lipschitz constant of neural networks. The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming. We show how to use structural properties of the network, such as sparsity, to significantly reduce the complexity of computation. This is specially useful for convolutional as well as pruned neural networks. We conduct experiments on networks with random weights as well as networks trained on MNIST, showing that in the particular case of the $\ell_\infty$-Lipschitz constant, our approach yields superior estimates as compared to other baselines available in the literature.
The Role of Multiplicative Complexity in Compiling Low $T$-count Oracle Circuits

multiplicative complexity in compiling Low T-count Oracle circuits.
SSAH: Semi-Supervised Adversarial Deep Hashing with Self-Paced Hard Sample Generation

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting sufficient labeled data, which is usually a crucial limitation in practical cases. The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi-supervised learning. However, existing GAN-based methods treat image generations and hashing learning as two isolated processes, leading to generation ineffectiveness. Besides, most works fail to exploit the semantic information in unlabeled data. In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework. The SSAH method consists of an adversarial network (A-Net) and a hashing network (H-Net). To improve the quality of generative images, first, the A-Net learns hard samples with multi-scale occlusions and multi-angle rotated deformations which compete against the learning of accurate hashing codes. Second, we design a novel self-paced hard generation policy to gradually increase the hashing difficulty of generated samples. To make use of the semantic information in unlabeled ones, we propose a semi-supervised consistent loss. The experimental results show that our method can significantly improve state-of-the-art models on both the widely-used hashing datasets and fine-grained datasets.