Reviewerly/exHarmony huggingface dataset:
Compression of Large LSTM Networks for Inference on Space Constraint Systems

In this paper, we propose a technique to compress the size of pre-trained LSTM and GRU models, which also improves their inference time. So that they can be deployed on space-constraint devices. We use KP-factorization (Kronecker Product factorization) to reduce the number of parameters that are used to compute the results, while improving inference time with little loss in accuracy. We take a pre-trained LSTM or GRU model and replace the weight matrix of each layer with its Nearest Kronecker Product Factors, which are smaller in size as compared to the original matrices. The difference between the original matrix multiplication and the Kronecker product is then adjusted using two sparse error matrices (we call them delta matrices) to reduce the loss in accuracy due to compression. These delta matrices are themselves reduced to NKP factors so as to save space. Once the compressed models are obtained then we retrain them on less than 40% of the original data and epochs to improve the accuracy of the compressed models. Compression up to a maximum of 185x and a speedup up to 5x was obtained. Experiments were performed on standard datasets like MNIST, PTB, WikiText, etc to demonstrate the effectiveness of our technique.
MuOE: A Multi-task Ordinality Aware Approach Towards Engagement Detection

With the increasing adoption of online learning, decreasing student engagement is becoming rampant. Detecting this is the first step in making online education more viable and effective. We present MuOE, a Multi-task Ordinality-aware Engagement detection model to identify attention levels from students’ webcam videos. MuOE uses a transformer with exceptional sequence-processing capability and a novel selector-based attention mechanism that picks important video frames. Facial cue detection is used as an auxillary task in our multi-task formulation of the problem, so the shared model base has more supervision. We leverage the ordinal nature of engagement levels by introducing a smooth loss function that penalizes predictions based on closeness to the true label. In this paper, we motivate each component of MuOE, and demonstrate its utility through a set of quantative experiments. We achieve a state-of-the-art accuracy of 57.65% (Top-2 accuracy 95.07%) on the DAiSEE dataset.
Explainable Decision Tree-Based Screening of Cognitive Impairment Leveraging Minimal Neuropsychological Tests

Cognitive impairment detection is challenging because it primarily depends on advanced neuroimaging tests, which are not readily available in small cities and villages in India and many developing countries. Artificial Intelligence (AI)-based systems can be used to assist clinical decision-making, but it requires advanced tests and a large amount of data to achieve reasonable accuracy. In this work, we have developed an explainable decision-tree-based detection model, which serves as a powerful first-level screening on a small basic set of cognitive tests. This minimum set of features is obtained through an ablation study. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) archive provided the data for this study. We obtained 93.10% accuracy for three classes: Cognitive Normal (CN), Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD), and 78.74% accuracy for five classes: CN, Significant Memory Complaints (SMC), Early-MCI (EMCI), Late-MCI (LMCI), AD using Extreme Gradient Boosting (XGBoost) which is comparable to the accuracy of state-of-the-art methods which use a more sophisticated and expensive test like imaging. Current research pays little attention to explainability and is primarily concerned with enhancing the performance of deep learning and machine learning models. Consequently, clinicians find it challenging to interpret these intricate models. With the use of Tree Shapley Additive Explanations (TreeSHAP) values and Local Interpretable Model-agnostic Explanations (LIME), this work intends to give both global and local interpretability respectively. Moreover, we highlight the use of top-2 metrics (Accuracy, Precision, and Recall), which significantly improves corner cases and helps the clinician streamline diagnosis.
Inverse Reinforcement Learning with Constraint Recovery

In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems. In standard IRL problems, the inverse learner or agent seeks to recover the reward function of the MDP, given a set of trajectory demonstrations for the optimal policy. In this work, we seek to infer not only the reward functions of the CMDP, but also the constraints. Using the principle of maximum entropy, we show that the IRL with constraint recovery (IRL-CR) problem can be cast as a constrained non-convex optimization problem. We reduce it to an alternating constrained optimization problem whose sub-problems are convex. We use exponentiated gradient descent algorithm to solve it. Finally, we demonstrate the efficacy of our algorithm for the grid world environment.
Spot the Bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans

Nowadays, technology is rapidly advancing: bots are writing comments, articles, and reviews. Due to this fact, it is crucial to know if the text was written by a human or by a bot. This paper focuses on comparing structures of the coarse-grained partitions of semantic paths for human-written and bot-generated texts. We compare the clusterizations of datasets of n-grams from literary texts and texts generated by several bots. The hypothesis is that the structures and clusterizations are different. Our research supports the hypothesis. As the semantic structure may be different for different languages, we investigate Russian, English, German, and Vietnamese languages.
Multi-criteria Decision-Making Based Classifier Ensemble by Using Prioritized Aggregation Operator

This paper proposes a novel approach for classifier ensemble by employing the concepts of multi-criteria decision-making (MCDM) and aggregation operators. In this framework, a heterogeneous ensemble process has been incorporated where we consider varied set of classifiers to train the model. Each considered classifier is trained on the training data and a score correspondent to it is generated by utilizing the MCDM process. Subsequently, during the training phase, the priority is generated among the classifiers. For the testing phase, these prioritized classifiers are combined using prioritized aggregation operator. The priority order determined during the training phase is used to ensemble the classifiers during the testing phase. The proposed method is tested on UCI benchmark datasets and outperforms existing state-of-the-art methods.
Instance Selection Techniques for Large Volumes of Data

Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that involves eliminating irrelevant and superfluous data while maintaining a high level of classification accuracy. IS, as a strategic filtering mechanism, addresses these challenges by retaining essential instances and discarding hindering elements. This refinement process optimizes classification algorithms, enabling them to excel in handling extensive datasets. In this research, IS offers a promising avenue to strengthen the effectiveness of classification in various real-world applications.
User Interest Drift Identification Using Contextual Factors in Implicit Feedback-Based Recommender Systems

The modeling of appropriate recommendations using the session interactions in the implicit feedback-based recommender systems necessitates the identification of user interest drift. But this identification is challenging due to the presence of unintentional interactions (noise) made by the user. Most of the existing literature focused on understanding the correlation between ongoing session interactions but did not explore the contextual factors, such as the time of occurrence of the session and the item’s popularity, that led the user to perform that specific interaction. This has resulted in the wrongful categorization of interactions between user interest drift and noise. To overcome these limitations, this work proposes a deep learning-based approach that uses both ongoing session information and contextual information. Depending on availability, this work also considers the user’s previous interactions to generate personalized recommendations. In comparison with the existing works, this work effectively identifies the user interest drift and generates the appropriate recommendations for the users. The proposed approach demonstrates superior performance over state-of-the-art baselines in terms of Recall and MRR, as evidenced by experimental results on benchmark datasets.
How Blockchain Technology Affects the Performance of Financial Services

This chapter explores how the application of blockchain technology may enhance business network performance in financial services networks. We apply a recent detailed definition of “blockchain technology” that distinguishes between 12 aspects of blockchain technology and 11 aspects of the application of blockchain technology, based on enterprise ontology theory. We combine the ontology-based definition with business network theory and apply it on the literature-based case of the Australian Stock Exchange. We conclude that blockchain technology implementations affect the structure of business networks (since the technology influences which firms (do not) participate), the transactions and network processes, as well as the communication logic, content logic, consensus logic, and contract logic. We summarize our observations in five simple, practical questions to help predicting blockchain technology success.
Enhanced SVM-SMOTE with Cluster Consistency for Imbalanced Data Classification

The issue of imbalanced data in machine learning has gained significant attention in recent years. Imbalanced data, where one class has significantly fewer samples than others, can lead to poor performance for machine learning models, especially in detecting minority class samples. To address this problem, various resampling techniques have been proposed, including the popular SMOTE (Synthetic Minority Over-sampling TEchnique). However, SMOTE suffers from the overlapping problem and may misclassify samples near the separation boundaries. This paper presents a novel framework to optimise border-based-SMOTEs, including Borderline-SMOTE and SVM-SMOTE which were specifically developed to solve the problem of misclassifying border samples. The proposed method ensures that generated samples improve the decision boundaries and are free from overlapping issues. The proposed method is evaluated on synthetic and real-world datasets, and results demonstrate its effectiveness in enhancing the performance of machine learning models, particularly in classifying minority class samples.
Adaptative Fuzzy Measure for Edge Detection

In this work, an analysis of the influence of the fuzzy measure for Choquet integral and its generalizations is presented. The work has been done in the context of feature fusion for edge detection with gray-scale images. The particular case of adaptative fuzzy measure is considered, testing a variety of approaches. We have tested our proposal using the power measure adapting the exponent depending on the local information of each particular image. For comparison purposes and to test the performance of our proposal, we compare our approach to the results obtained with the Canny edge detector.
Investigating 5,140 Digital Transformation Projects

Today’s organizations are a complex knot of people, processes, rules, IT, responsibilities, tasks, and much more. Complexity begets inefficiencies, so to transform our businesses we need a new generation of organizational tools that lead to greater efficiency. Digital transformation is defined as: “Transformation of the enterprise with a major impact on digital resources and capabilities of the enterprise.” Thus, digital transformation means designing/redesigning and constructing/reconstructing an organizational process in the same way that an engineer would build an automobile, airplane, or computer. A digital transformation project (DTP) is one that completely automates a business workflow, including integration of third-party resources. What distinguishes DTPs from traditional IT projects that support business processes is the elimination of most of the manual processes as part of the workflow, thus truly transforming the business. An example would be online voting.
Detecting Image Forgery Using Support Vector Machine and Texture Features

In the past decades, image manipulation software has become more and more accessible. In addition, the software has improved greatly over the years so it has become harder to detect image forgery with the naked eye as a result. Therefore, this paper proposes a method for analyzing digital images and determining whether or not images have been forged, where two types of image forgery were considered: copy move and splicing. There are three crucial parts for successful forgery detection: quality of data, feature selection, and classification method. Images come in various different sizes, leading to an issue with extracting the same number of features from each image. Thus, we propose two forgery detection models: a size dependent, and a size independent model. We detect forgery using several features such as local binary pattern (LBP), kurtosis, and skewness of pixel values. These features are fed into a support vector machine (SVM) that was further tuned for the considered problem. The best model has an accuracy of 97.90% which is comparable or better in performance when compared to other models from the literature.
Walter Murray Wonham, 1934–2023 [Obituary]

Walter Murray Wonham died on Sunday, 14 May 2023 at the age of 88, leaving a series of legacies in control theory (see “Control Theory Legacies”). He died unexpectedly at home in Toronto, Ontario, Canada, and was active in research, graduate teaching and supervision until the day of his death.
An effective process of VM migration with hybrid heuristic-assisted encryption technique for secured data transmission in cloud environment

The virtualization of hardware resources like network, memory and storage are included in the core of cloud computing and are provided with the help of Virtual Machines (VM). The issues based on reliability and security reside in its acceptance in the cloud environment during the migration of VMs. VM migration highly enhanced the manageability, performance, and fault tolerance of cloud systems. Here, a set of tasks submitted by various users are arranged in the virtual cloud computing platform by using a set of VMs. Energy efficiency is effectively attained with the help of a loadbalancing strategy and it is a critical issue in the cloud environment. During the migration of VMs, providing high security is a very important task in the cloud environment. To resolve such challenges, an effective method is proposed using an optimal key-based encryption process. The main objective of this research work is to perform the VM migration and derive the multi-objective constraints with the help of hybrid heuristic improvement. The optimal VM migration is achieved by the hybrid algorithm as Improved Binary Battle Royale with Moth-flame Optimization (IBinBRMO). It can also be used to derive the multi-objective functions by some constraints like resource utilization, active servers, makespan, energy consumption, etc. After VM migration, the data transmission should take place securely between the source and destination. To secure the data, the HybridHomophorphic and Advanced Encryption Standard(HH-AES) Algorithm, where IBinBRMO optimizes the key. After optimizing the keys, the data are securely transformed along with multi-objective functions using parameters includingthe degree of modification, hiding failure rate and information preservation rate. Thus, the effectiveness is guaranteed and analyzed with other classical models. Hence, the results illustrate that the proposed work attains better performance.
Financial sentiment analysis: Classic methods vs. deep learning models

Sentiment Analysis, also known as Opinion Mining, gained prominence in the early 2000s alongside the emergence of internet forums, blogs, and social media platforms. Researchers and businesses recognized the imperative to automate the extraction of valuable insights from the vast pool of textual data generated online. Its utility in the business domain is undeniable, offering actionable insights into customer opinions and attitudes, empowering data-driven decisions that enhance products, services, and customer satisfaction. The expansion of Sentiment Analysis into the financial sector came as a direct consequence, prompting the adaptation of powerful Natural Language Processing models to these contexts. In this study, we rigorously test numerous classical Machine Learning classification algorithms and ensembles against five contemporary Deep Learning Pre-Trained models, like BERT, RoBERTa, and three variants of FinBERT. However, its aim extends beyond evaluating the performance of modern methods, especially those designed for financial tasks, to a comparison of them with classical ones. We also explore how different text representation and data augmentation techniques impact classification outcomes when classical methods are employed. The study yields a wealth of intriguing results, which are thoroughly discussed.
Controlled Remote Implementation of Operations via Graph States

Abstract Protocols are proposed for controlled remote implementation of operations here. Sharing a ‐partite graph state, 2 N participants collaborate to prepare the stator and realize the operation on N unknown states for distributed systems , only with the permission of a controller. The control power analysis shows that without the controller's permission, eight specified operations can be implemented with the success rate of 50%, other operations can be realized with the success rate of 25%, and thus the control power is reliable. All the implementation requirements of this protocol can be satisfied by means of local operations and classical communications, and the experimental feasibility is presented according to current techniques. The entanglement requirement of this protocol is characterized in terms of geometric measure of entanglement. It turns out to be economic to realize the control function from the perspective of entanglement cost.
Fast online classification of network traffic using new feature-embedded hierarchical structure

The fast online classification (FOC) of network traffic plays a critical role in the network resource management and quality of service support. However, traditional network flow features result in poor performance in FOC (with fewer packets). To tackle the issue, this study proposes two new features: (1) The conditional frequency of packet size (PSize), for which the PSize is quantized into several equal bins and the PSize-level conditional frequency of two consecutive packets is calculated; (2) The statistical feature of rate sequence that is obtained by dividing the inter-arrival time into the PSize sequences. Due to the real-time requirement of online classification, we analyze the time complexity of flow feature calculation and attempt to balance the classification speed and the accuracy in feature selection by reducing the feature dimensionality. In addition, a new feature-embedded hierarchical classification structure is developed for the scenario in which the network video traffic accounts for a relatively large proportion. Fewer packets are used in the early stage of binary classification of non-video vs. video, and then, the subsequent data packets are employed for the fine-grained classification of their respective flows. The effectiveness of the proposed method is evaluated on two real-world network datasets, and our method is compared with the state-of-the-art methods in terms of time performance, resource usage, and classification accuracy. The experimental results confirm the superiority of our approach in fast online classification.
Exploiting recollection effects for memory-based video object segmentation

Recent advances in deep learning have led to numerous studies on video object segmentation (VOS). Memory-based models, in particular, have demonstrated superior performance by leveraging the ability to store and recall information from previous frames. While extensive research efforts have been devoted to developing memory networks for effective VOS, only a few studies have investigated the quality of memory in terms of determining which information should be stored. In fact, in most recent memory-based VOS studies, the frame information is regularly stored in the memory without specific consideration. In other words, there is a lack of explicit criteria or guidelines for determining the essential information that should be retained in memory. In this study, we introduce a new method for evaluating the effect of storing the features, which can be used for various memory-based networks to improve performance in a plug-and-play manner. For this purpose, we introduce the concept of recollection effects, which refers to the stability of predictions based on the presence or absence of specific features in memory. By explicitly measuring the recollection effects, we establish a criterion for evaluating the relevance of information and determining whether features from a particular frame should be stored. This approach effectively encourages memory-based networks to construct memory that contains valuable cues. To validate the effectiveness of our method, we conduct comparative experiments. Experimental results demonstrate the effectiveness of our method to enhance the selection and retention of useful cues within the memory, leading to improving segmentation results.
Understanding the Design Values of Baby Books: Materiality, Co-presence, and Remediation

Abstract In this article we analyze the design values of selected baby books published in England, the United States and Italy across 100 years. The examples we focus on are What is This? What is That? (1905) of the Dean’s Rag books series, Pat the Bunny (1940), I PRELIBRI (1980), and Wiggle! March! (2009) of the Indestructibles series. We group these books into two pairs of simple or complex designs, based on either a drive for durability or the aim to provide a multisensory experience: the Dean’s Rag books and the Indestructibles form one set, and Pat the Bunny and I PRELIBRI the second. We approach the books by examining the relationships among the materiality, narrative and formal design elements, the implied co-presence of a young child and adult care, issues of context and gender, and how the later examples remediate or rework the materials and beliefs of the earlier ones in a contemporary manner.