Reviewerly/exHarmony huggingface dataset:
Comparative analysis of White Blood Cells Classification using Deep Learning Architectures

TheWhite Blood Cells (WBCs) are essential for the body's protective components of our immune system. To monitor a person's health status, WBCs are used. The variation in WBCs count in blood cells leads to hematological disorders. The exact diagnosis of variation in number of WBCs can prevent different types of disease. The illness includes immune system infections, including anaemia and leukaemia. A doctor must identify the number of WBCs in man's blood and classify the WBCs to avoid the disease by a more experienced clinician. The architectures of the Convolutional Neural Network (CNN) in deep learning are AlexNet, LeNet, and Visual Geometry Group Network (VGG-16) are embedded with softmax classifier. The architecture of CNN in existing work is modelled using python which provides minimum accuracy. Features are extracted from images using the proposed CNN framework with the help of two convolution layers. Features are fed into a completely linked blood cell classification layer using CNN, VGGNet and GoogleLeNet, to classify the types such as lymphocytes, monocytes, eosinophils, basophils and Neutrophils. Experiments on standard benchmark data sets are performed to estimate the classification accuracy of blood cells by the suggested system. As a result it was observed that the GoogLeNet with the accuracy of 93.43 and 91.72 using Relu and LRelu activation function overlooks comparisons between CNN, and VGGNet
Science vs. Sophistry—A historical debate on bipolar fuzzy sets and equilibrium-based mathematics for AI&amp;QI

The road from bipolar fuzzy sets to equilibrium-based mathematical abstraction is surveyed. A continuing historical debate on bipolarity and isomorphism is outlined. Related literatures are critically reviewed to counter plagiarism, distortion, renaming, and sophistry. Based on the debate, the term “isomorphistry” is coined. It is concluded that if isomorphism is used correctly it can be helpful in mathematics. If abused it may become isomorphistry—a kind of historical, socially constructed, entrenched, and “noble” hypocrisy hindering major scientific advances. It is shown that isomorphistry can be motivated by “denying” the originality of bipolar fuzzy sets and aimed at “justifying” plagiarism and distortion. Thus, isomorphistry is sophistry on isomorphism. Some (-,+)-bipolar isomorphistry behaviors are critiqued. YinYang vs. YangYin are distinguished. The geometrical and logical basis of equilibrium-based AI&amp;QI computing machinery is introduced as a new computing paradigm with logically definable causality for mind-body unity. A philosophical joke on sophistry is appended.
Tiled Squeeze-and-Excite: Channel Attention With Local Spatial Context

In this paper we investigate the amount of spatial context required for channel attention. To this end we study the popular squeeze-and-excite (SE) block which is a simple and lightweight channel attention mechanism. SE blocks and its numerous variants commonly use global average pooling (GAP) to create a single descriptor for each channel. Here, we empirically analyze the amount of spatial context needed for effective channel attention and find that limited local-context on the order of seven rows or columns of the original image is sufficient to match the performance of global context. We propose tiled squeeze-and-excite (TSE), which is a framework for building SE-like blocks that employ several descriptors per channel, with each descriptor based on local context only. We further show that TSE is a drop-in replacement for the SE block and can be used in existing SE networks without re-training. This implies that local context descriptors are similar both to each other and to the global context descriptor. Finally, we show that TSE has important practical implications for deployment of SE-networks to dataflow AI accelerators due to their reduced pipeline buffering requirements. For example, using TSE reduces the amount of activation pipeline buffering in EfficientDet-D2 by 90% compared to SE (from 50M to 4.77M) without loss of accuracy. Our code and pre-trained models will be publicly available.
Software Defect Prediction Using Process Metrics Systematic Literature Review: Dataset and Granularity Level

Software defect prediction is an important part to ensure software quality and increase cost-efficiency. The core aspect prior to software defect prediction is software metrics. More research of process metrics existed and rapidly developed as the fact that process metrics can outperform code metrics. The development of process metrics is diverse aligning with various process metrics generated in the different publication using different dataset and granularity level, thus the development state of process metrics need to be further tracked. Recent research on software defect prediction using process metrics has been carried which complements or argues another studied related to the implementation of process metrics, dataset, and granularity level. This literature review has collected 17 literature studies that aim to depict the current research used of software defect prediction applying software process metrics, dataset, and granularity. Seven commonly used process metrics are identified such as NDC, NML, ADEV, EXP, LA, LD, and COMM. Process metrics development has been implemented in the previous research such as the application of aggregated process metrics and commits detailed information metrics. The selected research shows that 87&#x0025; of research are using a public dataset while the rest are using a combined dataset. This result depicts the limited accessibility of private and industrial dataset for defect prediction. Following analysis of software granularity presents 4 levels of granularity used in selected research such as a functional, class, file, and features level. File-level defect predictions are mostly used for defect prediction in selected research and prove good defect prediction performance. The results of this literature review also identify the proposed studies which is software defect prediction using developed process metrics in the different datasets and features-level granularity to strengthen the previous research.
Graph Convolution Networks for Seismic Events Classification Using Raw Waveform Data From Multiple Stations

This letter proposes a multiple station-based seismic event classification model using a deep convolution neural network (CNN) and graph convolution network (GCN). To classify various seismic events, such as natural earthquakes, artificial earthquakes, and noise, the proposed model consists of weight-shared convolution layers, graph convolution layers, and fully connected layers. We employed graph convolution layers in order to aggregate features from multiple stations. Representative experimental results with the Korean peninsula earthquake datasets from 2016 to 2019 showed that the proposed model is superior to the single-station based state-of the-art methods. Moreover, the proposed model significantly reduced false alarms when using continuous waveforms of long duration. The code is available at. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref>
Covert Communication using Address Resolution Protocol Broadcast Request Messages

A covert channel is a hidden communication channel that is used to transfer information in a way that breaks the security policy of a system. A Network covert channel uses network protocols to send the secret information. In this paper, a technique for Network covert communication is proposed that uses Address Resolution Protocol (ARP) Broadcast Request messages to communicate covertly over a Local Area Network. The proposed technique uses a common seed value already known to both the sender and receiver to encode the secret data and thereby strengthening the imperceptibility of the secret message. The proposed technique is robust against traffic normalization and is also resilient to frequency analysis based decoding techniques. Further, it offers a covert capacity of 7 bits per ARP Broadcast Request packet.
Trace It Like You Believe It: Time-Continuous Believability Prediction

Assessing the believability of agents, characters and simulated actors is a core challenge for human computer interaction. While numerous approaches are suggested in the literature, they are all limited to discrete and low-granularity representations of believable behavior. In this paper we view believability, for the first time, as a time-continuous phenomenon and we explore the suitability of two different affect annotation schemes for its assessment. In particular, we study the degree to which we can predict character believability in a continuous fashion through a two-player game study. The game features various opponent behaviors that are assessed for their believability by 89 participants that played the game and then annotated their recorded playthrough. Random forest models are then trained to predict believability based on ad-hoc designed in-game features. Results suggest that a discrete annotation method leads to a more robust assessment of the ground truth and subsequently better modelling performance. Our best models are able to predict a change in perceived believability with a 72.5% accuracy on average (up to 90% in the best cases) in a time-continuous manner.
Toward Reliable and Confidential Release for Smart Contract via ID-Based TRE

The concept of time release provides a new mode of sending information to the future, where the message will be available after a certainly specified period. Time-release encryption (TRE), as a promising approach, has a widespread releasing application and ensures data confidentiality. TRE relies on two main frameworks, one based on the time-lock puzzle and the other based on a trusted third party, while this primitive is impeded by frameworks’ unreliability limitations and time disclosure. We present ReleaseSC, a decentralized and privacy-aware system that combines smart contract with TRE. ReleaseSC leverages a novel three-part architecture that harmonizes time release and contracts, enabling efficient time-hidden smart contract. Our prototype is with reasonable performance through EVM evaluations on both private chain and official test network. ReleaseSC is built on the top of a new cryptographic notion named ID-based TRE that supports a flexible time policy and preserves the to-be-released time. We propose an ID-based TRE instantiation and show rigorous security analysis in formal security models. The insight from ReleaseSC will open more possibilities to address security issues in hybridized cryptography-blockchain systems.
Data integrity algorithm based on additive generators and hash function

Ensuring the integrity of data is one of the staple objectives in information security. For this purpose, one can use different algorithms and approaches based on cryptographic hash functions, block ciphers in message authentication code mode of operation, etc. In the case of dynamic integrity checking, such known approaches require significant computing resources. In this paper, we propose a class of algorithms that represents a rational compromise between the resource usage, the performance and the security level. The proposed algorithms are based on a combination of additive generators, s-boxes and a hash function that meets modern cryptographic strength requirements. By methods of the matrix-graph approach, we study the mixing properties of the input data code generation algorithm, which is essential for integrity checking. By means of computational experiments the performance and a number of cryptographic properties of the algorithm under research were compared with those of well-known algorithms. The comparison results show the performance advantage of the proposed algorithm with parity of other cryptographic properties.
Adaptive Bias-Aware Feature Generation for Generalized Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to recognize unseen classes that never appear during training. Recently, generative adversarial networks (GANs) have been introduced to convert ZSL into a supervised learning problem by synthesizing unseen visual features. However, since unseen classes are never experienced for the generator during training, the synthesized unseen visual features often become heavily biased towards seen classes, or sometimes there is even no meaningful class that can be assigned to them. This is known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bias problem</i> . In this paper, we propose a novel method, namely Adaptive Bias-Aware GAN (ABA-GAN), to alleviate generating biased visual features. For this purpose, we build a semantic adversarial network to regularize the feature generator. Specifically, an adaptive adversarial loss is proposed to constrain the feature distributions, which avoids the generation of meaningless visual features. Meanwhile, a domain divider is presented to explicitly distinguish synthesized visual features between seen and unseen domains, such that the bias towards seen classes can be alleviated. Moreover, we propose a novel metric named bias score (BS) to explicitly quantify the degree of the strong bias. Extensive experiments on four widely used benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art approaches under both ZSL and GZSL protocols.
Improving the robustness and accuracy of biomedical language models through adversarial training

Deep transformer neural network models have improved the predictive accuracy of intelligent text processing systems in the biomedical domain. They have obtained state-of-the-art performance scores on a wide variety of biomedical and clinical Natural Language Processing (NLP) benchmarks. However, the robustness and reliability of these models has been less explored so far. Neural NLP models can be easily fooled by adversarial samples, i.e. minor changes to input that preserve the meaning and understandability of the text but force the NLP system to make erroneous decisions. This raises serious concerns about the security and trust-worthiness of biomedical NLP systems, especially when they are intended to be deployed in real-world use cases. We investigated the robustness of several transformer neural language models, i.e. BioBERT, SciBERT, BioMed-RoBERTa, and Bio-ClinicalBERT, on a wide range of biomedical and clinical text processing tasks. We implemented various adversarial attack methods to test the NLP systems in different attack scenarios. Experimental results showed that the biomedical NLP models are sensitive to adversarial samples; their performance dropped in average by 21 and 18.9 absolute percent on character-level and word-level adversarial noise, respectively, on Micro-F1, Pearson Correlation, and Accuracy measures. Conducting extensive adversarial training experiments, we fine-tuned the NLP models on a mixture of clean samples and adversarial inputs. Results showed that adversarial training is an effective defense mechanism against adversarial noise; the models' robustness improved in average by 11.3 absolute percent. In addition, the models' performance on clean data increased in average by 2.4 absolute percent, demonstrating that adversarial training can boost generalization abilities of biomedical NLP systems. This study takes an important step towards revealing vulnerabilities of deep neural language models in biomedical NLP applications. It also provides practical and effective strategies to develop secure, trust-worthy, and accurate intelligent text processing systems in the biomedical domain.
The Application of 3D Dense Face Alignment in Esthetic Rehabilitation

This article describes a technique for making an esthetic treatment plan by using a virtual face scan reconstructed from digital photographs through three-dimensional dense face alignment (3DDFA) and intraoral scans. Portrait photographs were captured with a digital camera and were reconstructed to a virtual 3D face scan by 3DDFA. After scaling the virtual face scan to the correct size, intraoral scans and virtual face scans were aligned to create a 3D view of the patient. A virtual diagnostic waxing was created accordingly. In select cases, this technique can provide 3D esthetic predictions when 3D face imaging devices are not available.
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN

Image super-resolution is important in many fields, such as surveillance and remote sensing. However, infrared (IR) images normally have low resolution since the optical equipment is relatively expensive. Recently, deep learning methods have dominated image super-resolution and achieved remarkable performance on visible images; however, IR images have received less attention. IR images have fewer patterns, and hence, it is difficult for deep neural networks (DNNs) to learn diverse features from IR images. In this paper, we present a framework that employs heterogeneous convolution and adversarial training, namely, heterogeneous kernel-based super-resolution Wasserstein GAN (HetSRWGAN), for IR image super-resolution. The HetSRWGAN algorithm is a lightweight GAN architecture that applies a plug-and-play heterogeneous kernel-based residual block. Moreover, a novel loss function that employs image gradients is adopted, which can be applied to an arbitrary model. The proposed HetSRWGAN achieves consistently better performance in both qualitative and quantitative evaluations. According to the experimental results, the whole training process is more stable.
Review of Data and Data Sources for the Assessment of the Potential of Utility-Scale Hybrid Wind–Solar PV Power Plants Deployment, under a Microgrid Scope

Utility-scale hybrid wind–solar PV power plants (which might include some storage as well) are an attractive option for the transition of conventional grids to incorporate high renewable energy (RE) shares. Along with lower generation costs, they offer increased dispatch capabilities and flexible operation. However, when assessing their potential, they present higher needs in terms of input data, as they are forced to consider both spatial and temporal variations to evaluate their techno-economic viability, as well as other common inputs such as economic, social or environmental data. The availability of the different data influences the type of analysis to be implemented. The microgrid approach of segmenting the information into layers will be adopted for the classification of data. Three different levels of analysis are sought: long-term energy scenarios, geo-spatial planning, and production cost estimation. The analysis of necessary data for each planning stage, and the available data sources for the assessment of utility-scale hybrid power plants, under this microgrid approach, is the main focus of this review.
Integrating Cardinality Constraints into Constraint Logic Programming with Sets

Abstract Formal reasoning about finite sets and cardinality is important for many applications, including software verification, where very often one needs to reason about the size of a given data structure. The Constraint Logic Programming tool $$\{ log\} $$ provides a decision procedure for deciding the satisfiability of formulas involving very general forms of finite sets, although it does not provide cardinality constraints. In this paper we adapt and integrate a decision procedure for a theory of finite sets with cardinality into $$\{ log\} $$ . The proposed solver is proved to be a decision procedure for its formulas. Besides, the new CLP instance is implemented as part of the $$\{ log\} $$ tool. In turn, the implementation uses Howe and King’s Prolog SAT solver and Prolog’s CLP(Q) library, as an integer linear programming solver. The empirical evaluation of this implementation based on +250 real verification conditions shows that it can be useful in practice. Under consideration in Theory and Practice of Logic Programming (TPLP)
Theory-Specific Proof Steps Witnessing Correctness of SMT Executions

Ensuring hardware and software correctness increasingly relies on the use of symbolic logic solvers, in particular for satisfiability modulo theories (SMT). However, building efficient and correct SMT solvers is difficult: even state-of-the-art solvers disagree on instance satisfiability. This work presents a system for witnessing unsatisfiability of instances of NP problems, commonly appearing in verification, in a way that is natural to SMT solving. Our implementation of the system seems to often result in significantly smaller witnesses, lower solving overhead, and faster checking time in comparison to existing proof formats that can serve a similar purpose.
An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods

The planning of individualized pilot training programs is an intensive process. Over 120 maneuvers are introduced into the training program over time while ensuring maneuver competencies. This work introduces a novel, deep-learning based approach for automatically generating training plans for pilot trainees to significantly reduce instructor pilot planning requirements.
Collaborative Explainable AI: A Non-algorithmic Approach to Generating Explanations of AI

An important subdomain in research on Human-Artificial Intelligence interaction is Explainable AI (XAI). XAI attempts to improve human understanding and trust in machine intelligence and automation by providing users with visualizations and other information that explain decisions, actions, and plans. XAI approaches have primarily used algorithmic approaches designed to generate explanations automatically, but an alternate route that may augment these systems is to take advantage of the fact that user understanding of AI systems often develops through self-explanation []. Users engage in this to piece together different sources of information and develop a clearer understanding, but these self-explanations are often lost if not shared with others. We demonstrate how this 'Self-Explanation' can be shared collaboratively via a system we call collaborative XAI (CXAI), akin to a Social Q&A platform [] such as StackExchange. We will describe the system and evaluate how it supports various kinds of explanations.
COVID-19 One Year on – Security and Privacy Review of Contact Tracing Mobile Apps

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic has caused 3.8 million deaths since December 2019. At the current vaccination pace, this global pandemic could persist for several years. Throughout the world, contact tracing (CT) apps were developed, which play a significant role in mitigating the spread of COVID-19. This work examines the current state of security and privacy landscape of mobile CT apps. Our work is the first attempt, to our knowledge, which provides a comprehensive analysis of 70 CT apps used worldwide as of the year Q1 2021. Among other findings, we observed that 80% of them may have handled sensitive data without adequate protection, 70% of them used weak cryptographic algorithms, and 35% of them embedded data trackers. We also observed key developments in app privacy protection and security assurance initiatives. Our findings provide useful insights into the design and deployment of more secure and privacy-preserving CT apps moving forward.