Reviewerly/exHarmony huggingface dataset:
Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling

The present study aimed to assign a risk score for breast cancer recurrence based on pathological whole slide images (WSIs) using a deep learning model.A total of 233 WSIs from 138 breast cancer patients were assigned either a low-risk or a high-risk score based on a 70-gene signature. These images were processed into patches of 512x512 pixels by the PyHIST tool and underwent color normalization using the Macenko method. Afterward, out of focus and pixelated patches were removed using the Laplacian algorithm. Finally, the remaining patches (n=294,562) were split into 3 parts for model training (50%), validation (7%) and testing (43%). We used 6 pretrained models for transfer learning and evaluated their performance using accuracy, precision, recall, F1 score, confusion matrix, and AUC. Additionally, to demonstrate the robustness of the final model and its generalization capacity, the testing set was used for model evaluation. Finally, the GRAD-CAM algorithm was used for model visualization.Six models, namely VGG16, ResNet50, ResNet101, Inception_ResNet, EfficientB5, and Xception, achieved high performance in the validation set with an overall accuracy of 0.84, 0.85, 0.83, 0.84, 0.87, and 0.91, respectively. We selected Xception for assessment of the testing set, and this model achieved an overall accuracy of 0.87 with a patch-wise approach and 0.90 and 1.00 with a patient-wise approach for high-risk and low-risk groups, respectively.Our study demonstrated the feasibility and high performance of artificial intelligence models trained without region-of-interest labeling for predicting cancer recurrence based on a 70-gene signature risk score.
SimTyper: sound type inference for Ruby using type equality prediction

Many researchers have explored type inference for dynamic languages. However, traditional type inference computes most general types which, for complex type systems—which are often needed to type dynamic languages—can be verbose, complex, and difficult to understand. In this paper, we introduce SimTyper, a Ruby type inference system that aims to infer usable types—specifically, nominal and generic types—that match the types programmers write. SimTyper builds on InferDL, a recent Ruby type inference system that soundly combines standard type inference with heuristics. The key novelty of SimTyper is type equality prediction , a new, machine learning-based technique that predicts when method arguments or returns are likely to have the same type. SimTyper finds pairs of positions that are predicted to have the same type yet one has a verbose, overly general solution and the other has a usable solution. It then guesses the two types are equal, keeping the guess if it is consistent with the rest of the program, and discarding it if not. In this way, types inferred by SimTyper are guaranteed to be sound. To perform type equality prediction, we introduce the deep similarity (DeepSim) neural network. DeepSim is a novel machine learning classifier that follows the Siamese network architecture and uses CodeBERT, a pre-trained model, to embed source tokens into vectors that capture tokens and their contexts. DeepSim is trained on 100,000 pairs labeled with type similarity information extracted from 371 Ruby programs with manually documented, but not checked, types. We evaluated SimTyper on eight Ruby programs and found that, compared to standard type inference, SimTyper finds 69% more types that match programmer-written type information. Moreover, DeepSim can predict rare types that appear neither in the Ruby standard library nor in the training data. Our results show that type equality prediction can help type inference systems effectively produce more usable types.
High-Performance ANFIS-Based Controller for BLDC Motor Drive

The BLDC motors are extensively used in aerospace, electric vehicles, medical equipment, etc., owing to their outstanding speed–torque characteristics. However, BLDC motors require controllers to control the speed, torque and output power based on the application. The PID, fuzzy and ANN-based controllers that are used for the control of BLDC motor have limitations due to their design complexity and implementation. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been developed to control the speed of BLDC motor drive and the simulation results are investigated and compared with existing control techniques under the specified operating conditions.
Learning target-aware background-suppressed correlation filters with dual regression for real-time UAV tracking

Traditional discriminative correlation filters have been rapidly developed in UAV tracking field due to their effectiveness and computational efficiency. Unfortunately, these methods are limited by unwanted boundary effects, which decrease the discriminative power between the target and background. Besides, due to the fast target appearance change and complex scenarios, such as background clutter and similar object, there often exists distractors around the response peak, which may cause tracking drift or even failure. In this paper, we propose a novel correlation filter method with dual regression, which aims at obtaining high-quality and reliable response maps for more robust tracking. Specifically, benefiting from a saliency detection method, the target feature is efficiently acquired from the global feature. Then, employing the dual regression strategy, these features are used to regress dual filters, that is, the target filter and the global filter. Response maps generated by dual filters, i.e., the target and global response maps, are merged in the detection phase to increase the response value of the target. Furthermore, a novel response-level background suppression regularization is proposed to solve boundary effects. Through the mutual restriction between the target and global response maps, the discrimination of dual filters can be promoted. We perform extensive and comprehensive analysis on three challenging UAV tracking benchmarks. Results confirm that the dual regression strategy and the background suppression regularization can facilitate the tracking accuracy improvement. The proposed tracker has comparable performance against other 27 state-of-the-art trackers while running at ∼34 FPS on a single CPU.
Synthetic data generation with probabilistic Bayesian Networks

Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.
Real-life contextualization of exposure therapy using augmented reality: A pilot clinical trial of a novel treatment method

Phobias, including arachnophobia, are common and can be treated with exposure therapy, a method that is limited by a lack of feared objects/situations in clinical settings.In a pilot parallel randomized controlled trial (RCT) to test the feasibility and efficacy of augmented reality exposure therapy (ARET), 25 men and women ages 18 to 45 with arachnophobia were designated (ABAB block allocation) to ARET for arachnophobia (n = 13) or waitlist control (n = 12). Data were collected at baseline, 1-week, and 1-month follow-up, and single-session ARET occurred immediately following baseline collection for the intervention group.All ARET participants were able to touch a live tarantula or the tank containing it after single-session exposure; the control group remained >1 meter away from the tank. Effects persisted or improved at 1-month followup. The Fear of Spiders Questionnaire (FSQ) and Behavioral Approach Test (BAT) showed large, significant beneficial effects of ARET compared with a waitlist control group (BAT: P < .001, partial eta squared = .542; FSQ: P < .001, partial eta squared = .720).We found ARET can feasibly be delivered using a wearable device running novel software with rapid responses and sustained effects. Replication and expansion of this pilot RCT will further support use of ARET for this and other specific phobias.
A New Privacy Protection Approach Based on K-Anonymity for Location-Based Cloud Services

With the rapid development of cloud computing and the popularization of mobile equipment, location-based cloud services (LBCS) are widely used, bringing great convenience to human industrial production and daily life. However, since LBCS is a location-based service, how to protect personal privacy when receiving the service is a matter of widespread concern for cloud users. To solve this problem, this paper introduces a new privacy protection method based on K-anonymity, which not only protects the cloud user’s query privacy as well as location privacy but also resolves the contradiction between privacy protection and service quality. From the simulation experimental results, we verify the effectiveness of our proposed method.
AKMA: Delegated Authentication System of 5G

Authentication and key management for applications (AKMA) is the new cellular-network-based delegated authentication system of 5G. In this article, we explain what a delegated authentication system is and how it relates to concepts like federated identity management. We also explain why a cellular-network-based delegated authentication system is more secure than a password-based delegated authentication system. We present a concise description of how AKMA works and compare its functionalities to other cellular-network-based delegated authentication systems. Finally, we describe privacy issues of AKMA and point to cryptographic techniques that could help in resolving these issues.
Blockchain for IOT-based NANs and HANs in Smart Grid

Smart Grid – an intelligent connected grid consisting of millions of smart devices, used to collect data from the grid to improve the efficiency of its operation. These smart devices communicating wirelessly are susceptible to attacks and hence the security and privacy of the smart devices along with the smart grid is a major challenge. Blockchain-based systems provide improved security and privacy and hence gained a lot of attention in the recent past. This paper proposes a blockchain-based architecture for Neighborhood Area Networks (NANs) and Home Area networks (HANs) in Smart Grid. The paper presents a security analysis in terms of confidentiality, integrity and availability to show that the proposed blockchain-based Smart Grid architecture is secure. Also, the impact of the improved security on packet delays, energy and computational overhead is discussed.
Sapphire Rapids

Next-Gen Intel Xeon Scalable Processor New Standard for Data Center Architecture Designed for Microservices & AI Workloads Pioneering Advanced Memory & IO Transitions
NyaYa: Blockchain-based electronic law record management scheme for judicial investigations

In digitization, judicial investigations have transitioned towards digital storage of forensic shreds of evidence as electronic law records (ELRs). The shift poses varied challenges of ELR preservation, homogeneity of case formats, chronology in recorded statements by suspects, time-stamping and digital signatures on ELRs, and the chain of transfer of cases to different law enforcement agencies (LEAs) over open channels. Thus, privacy and trust among judicial stakeholders-case appellant (CA), case defendant (CD), the police officer (PO), defence lawyer (DL), prosecutor lawyers (PL), LEAs, and court judge is a prime concern. Motivated from the aforementioned discussions, the paper presents a blockchain (BC)-based ELR management scheme, NyaYa, that operates as a four-phased scheme of judicial stakeholder registration in BC, case registration with meta-hash keys in public BC, that reference an external off-chain interplanetary file storage (IPFS), chronology of investigative updates among LEAs, and case hearing and settlement through smart contracts (SCs). In the simulation, NyaYa is compared to traditional ELR storage schemes for parameters like mining cost, query fetching time, block processing time, obtained IPFS throughput, signing latency, the effect of collusion attacks, ELR processing time, and corrupted indexes in IPFS. We also presented formal verification and proposed functionalities of SCs. In simulation, at 6 USD mining cost, NyaYa can append 22456 transactions, compared to 21497 and 3000 transactions respectively in existing schemes. The achieved query fetching time is 0.852 milli-seconds (ms), at 25 blocks, with cache support of 32 kilobyte (KB). The scheme has an average signing latency of 622.95ms, and achieves a high-trust probability of 0.887 %, compared to 0.765 % in consortium BC, and 0.455 % in private BC, at 500, colluding nodes. An improvement of 6.77 % is achieved in ELR uploading latency, and the scheme has only 21 corrupted IPFS indexes for 350 fetched ELRs. The obtained results indicate the efficacy of the proposed scheme against conventional schemes.
Certified Everlasting Zero-Knowledge Proof for QMA

In known constructions of classical zero-knowledge protocols for $${\textbf {NP}}$$ , either zero-knowledge or soundness holds only against computationally bounded adversaries. Indeed, achieving both statistical zero-knowledge and statistical soundness at the same time with classical verifier is impossible for $${\textbf {NP}}$$ unless the polynomial-time hierarchy collapses, and it is also believed to be impossible even with a quantum verifier. In this work, we introduce a novel compromise, which we call the certified everlasting zero-knowledge proof for $${\textbf {QMA}}$$ . It is a computational zero-knowledge proof for $${\textbf {QMA}}$$ , but the verifier issues a classical certificate that shows that the verifier has deleted its quantum information. If the certificate is valid, even an unbounded malicious verifier can no longer learn anything beyond the validity of the statement. We construct a certified everlasting zero-knowledge proof for $${\textbf {QMA}}$$ . For the construction, we introduce a new quantum cryptographic primitive, which we call commitment with statistical binding and certified everlasting hiding, where the hiding property becomes statistical once the receiver has issued a valid certificate that shows that the receiver has deleted the committed information. We construct commitment with statistical binding and certified everlasting hiding from quantum encryption with certified deletion by Broadbent and Islam [TCC 2020] (in a black-box way), and then combine it with the quantum sigma-protocol for $${\textbf {QMA}}$$ by Broadbent and Grilo [FOCS 2020] to construct the certified everlasting zero-knowledge proof for $${\textbf {QMA}}$$ . Our constructions are secure in the quantum random oracle model. Commitment with statistical binding and certified everlasting hiding itself is of independent interest, and there will be many other useful applications beyond zero-knowledge.
RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Financial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.
Latency-aware automatic CNN channel pruning with GPU runtime analysis

The huge storage and computation cost of convolutional neural networks (CNN) make them challenging to meet the real-time inference requirement in many applications. Existing channel pruning methods mainly focus on removing unimportant channels in a CNN model based on rule-of-thumb designs, using reduced floating-point operations (FLOPs) and parameter numbers to measure the pruning quality. The inference latency of pruned models is often overlooked. In this paper, we propose a latency-aware automatic CNN channel pruning method (LACP), which aims to search low latency and accurate pruned network structure automatically. We evaluate the inaccuracy of measuring pruning quality by FLOPs and the number of parameters, and use the model inference latency as the direct optimization metric. To bridge model pruning and inference acceleration, we analyze the inference latency of convolutional layers on GPU. Results show that the inference latency of convolutional layers exhibits a staircase pattern along with channel number due to the GPU tail effect. Based on that observation, we greatly shrink the search space of network structures. Then we apply an evolutionary procedure to search a computationally efficient pruned network structure, which reduces the inference latency and maintains the model accuracy. Experiments and comparisons with state-of-the-art methods on three image classification datasets show that our method can achieve better inference acceleration with less accuracy loss.
Decision Transformer: Reinforcement Learning via Sequence Modeling

We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.
MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Purpose Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling. Design/methodology/approach Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption. Findings The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state. Originality/value This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks

Deep neural networks (DNNs) enable the booming intelligent mobile applications owing to the effectiveness and reliability. With the rapid development of fifth generation (5G) communication networks, edge devices can be deployed with computation capability, which makes the distributed implementation of DNN over a wireless network possible. However, the simplified model splitting method cannot guar-antee the accuracy and latency performance. They call for the iterative design of model splitting and parameter updating. In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to deploy the generated DNN model over mobile edge networks. Considering both computing and communication resource constraints, we re-formulates the multi-split problem to a computational graph search problem and optimizes the objective function to realize the trade-off between model accuracy and completion latency. The experiment results reveal the superiority of the proposed JMSNAS over state-of-the-art split machine learning model designs.