Reviewerly/exHarmony huggingface dataset:
Genetic Approach based Optimized Load Balancing in Cloud Computing: A Performance Perspective

Cloud computing is a technology that facilitates information interchange and provides a large number of resources to cloud users. It is fast growing; a large number of customers are being drawn to cloud administrations in search of greater delight. In this field, balancing the load has turned into a really intriguing examination subject. For powerfully scattering load among various cloud nodes in the framework, a superior load balancing algorithm will boost performance and resource usage. For cloud workload, successful VM migration using effective genetic algorithm approach brings about ideal load management. The work in this paper puts forward an advancement in existing genetic algorithm i.e., IGA (Improved Genetic Algorithm) for effective VM migration and progressive load handling in cloud domain. The achieved results shows that proposed IGA method notably reduces the time taken for the load balancing procedure as compared against the results of the existing used genetic algorithm (GA). Furthermore, the proposed approach minimizes virtual machine (VM) migrations delay while simultaneously reducing energy usage and the number of migrations. As a result, the proposed solution will eliminate VM downtime, improve Quality of Service, and save costs for cloud clients.
How Secure Are The Adversarial Examples Themselves?

Existing adversarial example generation algorithms mainly consider the success rate of spoofing target model, but pay little attention to its own security. In this paper, we propose the concept of adversarial example security as how unlikely themselves can be detected. A two-step test is proposed to deal with the adversarial attacks of different strengths. Game theory is introduced to model the interplay between the attacker and the investigator. By solving Nash equilibrium, the optimal strategies of both parties are obtained, and the security of the attacks is evaluated. Five typical attacks are compared on the ImageNet. The results show that a rational attacker tends to use a relatively weak strength. By comparing the ROC curves under Nash equilibrium, it is observed that the constrained perturbation attacks are more secure than the optimized perturbation attacks in face of the two-step test. The proposed framework can be used to evaluate the security of various potential attacks and further the research of adversarial example generation/detection.
A personalized agent-based chatbot for nutritional coaching

Intelligent systems increasingly support users’ behavior change, including exercise adherence, smoking cessation, and healthy diet adoption. Their effectiveness is affected by the personalization degree of advice/coaching and HMI mechanisms. This paper proposes a personalized agent-based chatbot platform assisting the user in healthy nutrition via pervasive technologies leveraging dynamical, multi-modal, and personalized interactions. The system provides diet recommendations and tracks the user’s food intake and nutritional behaviors to promote a healthy lifestyle. The study concludes with a user study and performance evaluation.
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms

In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices. Double compression is accompanied by various types of video manipulation, and its traces can be exploited to determine whether a video is a forgery. To this end, we present a neural network-based approach with discriminant features for capturing peculiar artifacts in the discrete cosine transform (DCT) domain caused by double MPEG-4 compression. By analyzing the intracoding process of MPEG-4, which performs block-DCT-based quantization, we exploit multiple DCT histograms as features to focus on the statistical properties of DCT coefficients on multiresolution blocks. Furthermore, we improve detection performance using a vectorized feature of the quantization table on dense layers as auxiliary information. Compared with neural network-based approaches suitable for exploring subtle manipulations, the experimental results reveal that this work achieves high performance.
Weakly supervised setting for learning concept prerequisite relations using multi-head attention variational graph auto-encoders

An increasing number of learners can benefit from educational resources in Massive Open Online Courses (MOOCs) through self-regulated learning. However, it is difficult for learners to organize a suitable learning path from educational resources in MOOCs if they don't know the prerequisite relation between concepts. Manually labeling the prerequisite relation between concepts is time-consuming and requires significant domain knowledge. In addition, it isn't notably easy for large concept datasets to label. How should learners start learning when they face massive knowledge concepts in MOOCs? To address these problems, we propose an end-to-end graph network-based model called Multi-Head Attention Variational Graph Auto-Encoders (MHAVGAE) to automatically label the prerequisite relation between concepts via a resource-concept graph. Firstly, we model a resource-concept graph according to learning resources, concept, and their relations, then introduce the multi-head attention mechanism to operate and compute the hidden representations of each vertex over the resource-concept graph. The purpose is to reduce the cognitive difference of manually labeling and consider the mutual influence between vertices in the resource concept graph. Secondly, we design a gated fusion mechanism to fuse the feature of the resource and concept graphs to enrich concept features. Thirdly, we propose a metric named Resource Prerequisite Reference Distance (RPRD), which generates inaccurate concept prerequisite relations to help us reduce manual labeling and then extend MHAVGAE with the weakly supervised setting for learning concept prerequisite relations. Finally, we conduct numerous experiments to demonstrate the effectiveness of MHAVGAE across multiple widely used metrics compared with the state-of-the-art methods. The experimental results show that the performance of MHAVGAE almost outperforms all the baseline methods and the weakly supervised setting of MHAVGAE is beneficial.
An Industry-4.0-Compliant Sustainable Bitcoin Model Through Optimized Transaction Selection and Sustainable Block Integration

Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.
Compensating for Local Ambiguity With Encoder-Decoder in Urban Scene Segmentation

Semantic segmentation plays a critical role in scene understanding for self-driving vehicles. A line of efforts has proven that global context matters in urban scene segmentation due to massive scale changes. However, we find that existing methods suffer from local ambiguities when dissipating continuous local context, i.e. scrambling to a huge receptive field of global cues by coarse pooling. To this end, this paper proposes a new Context Aggregation Module (CAM) that consists of two primary components: context encoding using no coarse pooling but encoder-decoders with appropriate sampling scales and gated fusion that extends gate attention mechanism to balance different-scale context during feature fusion. Weeding out coarse pooling and applying the encoder-decoder inherits the merits of exploring global context while avoiding the drawback of losing local contextual continuity. We then construct a Context Aggregation Network (CANet) and conduct extensive evaluations on challenging autonomous driving benchmarks of Cityscapes, CamVid and BDD100K. Consistently improved results evidence the effectiveness. Notably, we attain competitive mIoU 82.7% on Cityscapes and optimal mIoU 80.5% on CamVid.
The Application of Deep Learning in Micro-Expression Recognition

With the advent of the era of big data, the application of artificial intelligence and big data technology has led to widespread interest in facial expression recognition. In addition to the common macro-expressions in daily life, facial expressions also have an imperceptible subtle expression called micro-expressions. Micro-expression is a very fast expression, lasting only 1/25 seconds to 1/5 seconds, expressing the real emotions that people try to suppress and hide. Micro-expressions have important applications in public safety, judicial criminal investigation, clinical medicine, etc. Therefore, the research on micro-expression recognition has been increasing in recent years. In the year of 2006, Hinton's proposal of deep learning in an article in «Science» made it appear in front of the world as a new field of machine learning, which set off a wave of deep learning. Deep learning is currently one of the hottest research directions in artificial intelligence and machine learning, and it is widely used in speech recognition, natural language processing, image recognition and other fields. Because of the characteristics of micro-expressions such as short time and subtle changes, traditional machine learning algorithms have poor robustness. Therefore, this paper summarizes the research on micro-expression recognition based on deep learning methods, mainly including some mainstream algorithms such as DBN, CNN, and discusses the development problems and trends of deep learning on micro-expression recognition, hoping to provide reference for the subsequent research in this field.
Structure and Motion for Intelligent Vehicles Using an Uncalibrated Two-Camera System

In this article, the structure and motion of a moving object are asymptotically estimated by an uncalibrated vehicle-mounted two-camera system under uneven road condition. Concurrent learning techniques are utilized to compensate for the cameras’ extrinsic parameters, and a time-varying gain is introduced to accelerate the estimation. Meanwhile, geometric relationships are used along with the visual information to obtain depth and 3-D coordinates of a feature point located on the moving object. Homography techniques are utilized to construct a measurable auxiliary state whose dynamics is related to the moving point’s velocity, which is further obtained by a nonlinear observer. Lyapunov-based analysis is conducted to prove the asymptotic convergence of the cameras’ extrinsic parameters as well as the moving point’s depth and velocity. The proposed method is applicable for different sensor configurations and road conditions as long as proper linear parameterization is done for the vision-based dynamics. Both simulations and experiments are conducted to verify the effectiveness and performance of the proposed method.
LAME: Layout-Aware Metadata Extraction Approach for Research Articles

The volume of academic literature, such as academic conference papers and journals, has increased rapidly worldwide, and research on metadata extraction is ongoing.However, high-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers.To accommodate the diversity of the layouts of academic journals, we propose a novel LAyout-aware Metadata Extraction (LAME) framework equipped with the three characteristics (e.g., design of automatic layout analysis, construction of a large meta-data training set, and implementation of metadata extractor).In the framework, we designed an automatic layout analysis using PDFMiner.Based on the layout analysis, a large volume of metadata-separated training data, including the title, abstract, author name, author affiliated organization, and keywords, were automatically extracted.Moreover, we constructed a pre-trained model, Layout-MetaBERT, to extract the metadata from academic journals with varying layout formats.The experimental results with our metadata extractor exhibited robust performance (Macro-F1, 93.27%) in metadata extraction for unseen journals with different layout formats.
The optimal use of computer aided detection to find low prevalence cancers

People miss a high proportion of targets that only appear rarely. This low prevalence (LP) effect has implications for applied search tasks such as the clinical reading of mammograms. Computer aided detection (CAD) has been used to help radiologists search mammograms by highlighting areas likely to contain a cancer. Previous research has found a benefit in search when CAD cues were correct but a cost to search when CAD cues were incorrect. The current research investigated whether there is an optimal way to present CAD to ensure low error rates when CAD is both correct and incorrect. Experiment 1 compared an automatic condition, where CAD appeared simultaneously with the display to an interactive condition, where participants could choose to use CAD. Experiment 2 compared the automatic condition to a confirm condition, where participants searched the display first before being shown the CAD cues. The results showed that miss errors were reduced overall in the confirm condition, with no cost to false alarms. Furthermore, having CAD be interactive, resulted in a low uptake where it was only used in 34% of trials. The results showed that the presentation mode of CAD can affect decision-making in LP search.
Particle Filter Based Track Before Detect Method for Space Surveillance Radars

Space surveillance radars deal with targets posing high range migration and low signal to noise ratio, making them difficult to detect and track. This paper illustrates a particle filter based track before detect approach using coherently integrated target reflections. The method not only tracks the target but also gives the probability of the presence of a target in a given frame. The paper also presents a feedback-based weight update approach to particle filtering, discusses its performance and the potential advancements of this research.
Enhanced System for Securing Password Manager Using Honey Encryption

Brute-force is a threat for the password manager since they include all user's passwords in one consolidated place. To overcome this drawback, we present an enhanced system that implements honey encryption (HE) in the password manager to provide resilience against brute-force attacks. The proposed system detects and prevents the intruder's attempt to grant access to any account. The detection recognizes the abnormal tries of invalid passwords by matching the intruder's entered passwords with auto-generated honeywords that mimic the actual passwords. Additionally, prevention works by redirecting the intruder to a fake account with dummy passwords to mislead the intruder. The proposed system provides high security and integrity to the password manager with 0.36 seconds of execution time. In addition, we perform a comparative analysis between honey with the OTP algorithm and our three scenarios of the proposed solution Experiments show that our system that applying the honey encryption with other security techniques would require more time, but it would result in achieving better security features than applying honey encryption alone.
Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method

With Gaia EDR3 data, velocity dispersion of Milky Way field stars around satellite galaxies have been investigated. We have fitted velocity dispersion against distance to satellite galaxy and found the gradient of velocity dispersion is related to the mass of satellite galaxy. With order-of-magnitude approximations, a linear correlation has been fitted between the mass of satellite galaxy and gradient of velocity dispersion caused by its gravitational drag. Though our result is an observational qualitative result, it shows better relation could be obtained with more observations in the future.
Evolving neural networks through bio-inspired parent selection in dynamic environments

Environmental variability often degrades the performance of algorithms designed to capture the global convergence of a given search space. Several approaches have been developed to challenge environmental uncertainty by incorporating biologically inspired notions, focusing on crossover, mutation, and selection. This study proposes a bio-inspired approach called NEAT-HD, which focuses on parent selection based on genetic similarity. The originality of the proposed approach rests on its use of a sigmoid function to accelerate species formation and contribute to population diversity. Experiments on two classic control tasks were performed to demonstrate the performance of the proposed method. The results show that NEAT-HD can dynamically adapt to its environment by forming hybrid individuals originating from genetically distinct parents. Additionally, an increase in diversity within the population was observed due to the formation of hybrids and novel individuals, which has never been observed before. Comparing two tasks, the characteristics of NEAT-HD were improved by appropriately setting the algorithm to include the distribution of genetic distance within the population. Our key finding is the inherent potential of newly formed individuals for robustness against dynamic environments.
Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches

Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the "gold standard" of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels-for example, a summary of the work and the reviewer's appraisal-but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)-which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.
Using Model Theory to Find Decidable and Tractable Description Logics with Concrete Domains

Abstract Concrete domains have been introduced in the area of Description Logic to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. Unfortunately, in the presence of general concept inclusions (GCIs), which are supported by all modern DL systems, adding concrete domains may easily lead to undecidability. To regain decidability of the DL $$\mathcal {ALC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ALC</mml:mi></mml:math> in the presence of GCIs, quite strong restrictions, in sum called $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissibility, were imposed on the concrete domain. On the one hand, we generalize the notion of $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissibility from concrete domains with only binary predicates to concrete domains with predicates of arbitrary arity. On the other hand, we relate $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissibility to well-known notions from model theory. In particular, we show that finitely bounded homogeneous structures yield $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissible concrete domains. This allows us to show $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissibility of concrete domains using existing results from model theory. When integrating concrete domains into lightweight DLs of the $$\mathcal {EL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>EL</mml:mi></mml:math> family, achieving decidability is not enough. One wants reasoning in the resulting DL to be tractable. This can be achieved by using so-called p-admissible concrete domains and restricting the interaction between the DL and the concrete domain. We investigate p-admissibility from an algebraic point of view. Again, this yields strong algebraic tools for demonstrating p-admissibility. In particular, we obtain an expressive numerical p-admissible concrete domain based on the rational numbers. Although $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ω</mml:mi></mml:math> -admissibility and p-admissibility are orthogonal conditions that are almost exclusive, our algebraic characterizations of these two properties allow us to locate an infinite class of p-admissible concrete domains whose integration into $$\mathcal {ALC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ALC</mml:mi></mml:math> yields decidable DLs.
Roman Urdu Sentiment Analysis Using Pre-trained DistilBERT and XLNet

Roman Urdu is a resource-poor language, therefore training a deep learning-based model from scratch is not that fruitful due to the lack of a large dataset. This is where the magic of transfer learning comes to the rescue. Using Huggingface's transformer models DistilBERT and XLNet we see a huge improvement in the results compared to the popular machine learning models Logistic Regression and Naïve Bayes. DistilBERT achieved 100% accuracy on our Roman Urdu dataset with only 2 epochs of fine-tuning while XLNet, though initially expected to perform better than the BERT model, achieved only about 86% accuracy with 4 epochs of fine-tuning.