Reviewerly/exHarmony huggingface dataset:
BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.
An Ecosystem for the Dairy Logistics Supply Chain with Blockchain Technology

In 2020, the annual consumption of fluid cow milk worldwide was 81 million tons, and the use of dairy products was 222.5 billion pounds [1]. However, in such a big dairy market, the supply system has had a series of problems including diarrheal disease which causes 760,000 childhood deaths annually [2]. Among all the reasons, food fraud, illegal production, and foodborne illness are critical. This paper presents a brand new dairy logistic supply ecosystem with blockchain technology. Taking advantage of many attributes of blockchain such as decentralization, security, and transparency, it solves the problems mentioned above that the centralized dairy supply systems cannot solve. The blockchain approach guarantees to provide customers with fresh and safe dairy products at fair prices. All the data in the system is open to all participants and the Internet of things (IoT) lowers the labor and decrease errors. The most important attribute is its smart contracts with crowdfunding function, with which an eco-society is created. We finally discuss the difference between the e-commence model in the Internet and eco-commence model in the Blockchain. Which system is the future? The answer is waiting for us to dig out.
Covert Learning: How to Learn with an Untrusted Intermediary

We consider the task of learning a function via oracle queries, where the queries and responses are monitored (and perhaps also modified) by an untrusted intermediary. Our goal is twofold: First, we would like to prevent the intermediary from gaining any information about either the function or the learner's intentions (e.g. the particular hypothesis class the learner is considering). Second, we would like to curb the intermediary's ability to meaningfully interfere with the learning process, even when it can modify the oracles' responses.
Expert System for the Machine Learning Pipeline in Manufacturing

Many success stories already exist with regard to the implementation of Machine Learning (ML) and Artificial Intelligence (AI) in manufacturing. However, companies with traditional focus on production technologies face challenges in conducting AI-projects successfully and lack knowledge of which obstacles may occur and how to decide in the implementation phase. In this paper, we develop an approach that focuses on the methodological necessary steps for the successful application of ML and AI in manufacturing. Optimization potentials and decisions to be made are outlined in every step. A main focus is put on optimizing hyperparameters of ML-models as one promising approach for improving overall ML-model performance. An expert system is presented that enables the selection of suitable hyperparameter optimization techniques. The concept is validated based on manufacturing of compressor components of a turbofan engine.
Classification of Waste Objects Using Deep Convolutional Neural Networks

Pollution induced by waste is one of the major problems across the globe for a long time. During the difficult natural calamities, it worsens the situation due to the improper way of disposing of the materials subsequently causes danger to human life. This also creates a risk for sanitation workers during the process of waste collection, sorting, and recycling. So there is a need for adapting the technology in real-time for proper waste segregation and management. In this paper, a model is proposed for the classification of waste objects according to their materials which can help in the automatic recycling process. The proposed approach uses the State-of-art deep learning architecture to construct the classifier. The architecture focusing on minimizing the structural complexity and maintain better classifier accuracy. The classifier uses the features extracted automatically from the Convolutional Neural Networks (CNN) to build a model. This model classifies the waste into plastic, paper, cardboard, trash, glass, and metal with an accuracy of 87%.
Typealike

We propose a style of hand postures to trigger commands on a laptop. The key idea is to perform hand-postures while keeping the hands on, beside, or below the keyboard, to align with natural laptop usage. 36 hand-posture variations are explored considering three resting locations, left or right hand, open or closed hand, and three wrist rotation angles. A 30-participant formative study measures posture preferences and generates a dataset of nearly 350K images under different lighting conditions and backgrounds. A deep learning recognizer achieves over 97% accuracy when classifying all 36 postures with 2 additional non-posture classes for typing and non-typing. A second experiment with 20 participants validates the recognizer under real-time usage and compares posture invocation time with keyboard shortcuts. Results find low error rates and fast formation time, indicating postures are close to current typing and pointing postures. Finally, practical use case demonstrations are presented, and further extensions discussed.
Mapping Road Safety Barriers Across Street View Image Sequences

Road safety barriers (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety barriers are critical components of safety infrastructure management systems at federal or state transportation agencies. In current practice, mapping road safety barriers is largely done manually (e.g., driving on the road or visual interpretation of street view imagery), which is slow, tedious, and expensive. We propose a deep learning approach to automatically map road safety barriers from street view imagery. Our approach considers road barriers as long objects spanning across consecutive street view images in a sequence and use a hybrid object-detection and recurrent-network model. Preliminary results on real-world street view imagery show that the proposed model outperforms several baseline methods.
Research on the Construction of English Teachers’ Classroom Teaching Ability System Based on Artificial Intelligence

In the construction of English teachers’ classroom competence system, traditional evaluation methods are incomplete in data collection, inaccurate in classification and insufficient in decision-making basis. Therefore, this paper introduced the K-means clustering algorithm in artificial intelligence algorithm to evaluate the English teaching ability. Based on the research of the K-means clustering algorithm and the implementation method, a fuzzy clustering algorithm combined with big data and information analysis is established to cluster various indicators in the English teaching ability system, which is the basis for improving teaching plan and evaluating teaching ability. It is proved by the simulation experiment that the evaluation of the English teaching ability is more accurate, and the scientific nature of the construction of the teaching ability system is effectively improved.
A 10pJ/bit 256b AES-SoC Exploiting Memory Access Acceleration

Low-cost high-volume Internet of Things sensors require an end-to-end secure private-content exchange. Restricted energy and computing of system-on-a-chip for low-cost sensor applications drive lightweight hardware acceleration of security primitives. A trade-off between power and time needs to be achieved to develop such primitives. The main challenge is to handle memory transportation in the crypto-accelerator. Here we propose different approaches for AES-128/256 Sbox acceleration schemes. We designed a Sbox unit to be connected as a logic unit. The unit was tested as a RISC-V ISA instruction in the RISC-V processor to reduce software execution times. This unit was also included in an AES hardware core with direct memory access (DMA), which avoids using the main processor to access the data. We fabricated a RISC-V based 32-bit SoC in a standard 180nm CMOS technology that includes the Sbox calculation using pipeline and a pure-combinational approach. Measurement results indicate lower memory access and lower energy requirements than pure software approaches up to 100 times with a custom Sbox instruction. The AES core also provides a minimum energy consumption of 3.58 pJ/cycle. At its best, the combined SoC performs the encryption in active-mode with energy consumption of 9.7 pJ/bit. This solution offers up to three times energy savings compared to previously reported work.
Device-independent oblivious transfer from the bounded-quantum-storage-model and computational assumptions

Abstract We present a device-independent protocol for oblivious transfer (DIOT) and analyse its security under the assumption that the receiver’s quantum storage is bounded during protocol execution and that the device behaves independently and identically in each round. We additionally require that, for each device component, the input corresponding to the choice of measurement basis, and the resulting output, is communicated only with the party holding that component. Our protocol is everlastingly secure and, compared to previous DIOT protocols, it is less strict about the non-communication assumptions that are typical from protocols that use Bell inequality violations; instead, the device-independence comes from a protocol for self-testing of a single (quantum) device which makes use of a post-quantum computational assumption.
Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.
A Blockchain-based Infrastructure for Distributed Internet of Services

The distributed services from different domains, organizations, and regions in the real and virtual world are converged together to form the Internet of Services (IoS). It is required to provide a trusted, orderly and efficient platform environment for service collaboration and delivery. Blockchain is currently recognized as the best practice technique to solve the problem of orderly and trusted execution of system. However, the efficiency issue introduced by it is also a problem that every platform needs to take seriously. Therefore, this paper proposes a blockchain-based IoS architecture, which uses the virtual chain and the embedded EVM(Ethereum Virtual Machine) notary technology to ensure the security and credibility of the service collaboration process in the IoS, and also the efficient operation of the entire system. This architecture has been compared with non-blockchain architecture without credible guarantees and also blockchain-only architecture. This comparative experiment shows that the proposed architecture not only ensures the orderly and trusted execution of the system, but also effectively controls the loss of performance.
Some thoughts on extending digital language learning research

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Repository Mining for Changes in Simulink Models

Model-Based Development (MBD) is widely used for embedded controls development, with MATLAB/Simulink being one of the most used environments in the automotive industry. Simulink models are the primary design artifact and as with all software, must be constantly maintained and evolved over their lifetime. It is necessary to develop models that support likely changes in order to assist with evolution/maintenance processes. In order to do so, the types of frequently performed changes must be understood and appropriate language mechanisms must be available to support these changes. However, Simulink model changes are currently not well understood. We analyze a real industrial software repository of our industrial partner and its version control system to provide insights into the likely changes for Simulink. The intent with this analysis includes providing guidance on how Simulink is used in industrial practice and how particular model changes can impact system evolution.
Word Embedding-based Web Service Representations for Classification and Clustering

Web services classification and clustering rely on discriminative representations of service functional semantics. Most existing topic-based methods for Web service representation simply leverage word co-occurrence relations, which may cause sub-optimal results for some specific-tasks due to the sparse features in the short service descriptions. Despite sparse and discrete word statistical features, other information such as word-topic and word-tag relationships can be utilized to accurately represent Web services. In this paper, we propose a novel word embedding-based approach for representing Web services. First, we use Word2vec to generate word embeddings from service descriptions. Then, the word-topic and word-tag relationships are preserved for Web service representation generation via the topic-weighted and tag-weighted word embedding stages based on Gaussian Mixture Model and Bayesian rule, respectively. Experiments and validations on the real-world dataset demonstrate that our method outperforms the state of the arts by a wide margin on service classification and clustering.
Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.
Analysis of Path Planning Algorithms For Service Robots in Hospital Environment

Medical robotics is an interdisciplinary domain devoted to the development of electromechanical machines for therapeutic purposes. It has the history of 34 years in enabling the new medical treatments by giving physicians additional powers or by assisting them. The pandemic COVID-19 has changed the world wherein humans are hiding in the masks and doctors are highly pressurized to save the lives of humans. On this note, this paper is concerned with the service robots in a hospital ward to offer medication and monitor patients to help the doctors. The purpose of this paper is to compare two well-known grid-based algorithms BFS & DFS and conclude with the optimal path finding algorithm for the medical robots in a virtualized ward of 20x20 grid plan. The results are simulated in MATLAB and it is found that BFS is more worthy in terms of finding an optimal path for service robot in a hospital ward.
Replacing Financial Audits with Blockchain: The Verification Issue

This paper examines the features of blockchain technology from the viewpoint of those responsible for external financial reporting and their auditors. Financial professionals worry about the potential disconnect between digital and real-world transactions that can create potentially misleading illusions-of-truth. The largest objection from financial professionals surrounds the widespread assertion that the consensus verification may replace the financial statements or their required audit. The discussion explains why verified “on chain” data falls considerably short of audited financial statements. In a structured literature review, this work contrasts the treatment of blockchain’s consensus verification feature in IT and accounting literature over a four-year period, showing a dramatic increase in this topic but little convergence between IT and financial communities on the problems with blockchain verification in auditing contexts. Knowing the auditing community’s concerns, as explored in this work, can help direct future developments in blockchain technology and target implementation suited to user needs.
An Application of Fuzzy ANP in Evaluating Stress Factors Among University’s Students During Online Distance Learning

Abstract The outbreak of Coronavirus disease 2019 (COVID-19) has bring so many impacts on various aspects including education. Nearly two years into the pandemic, close to half of the world’s students, including Malaysia, are still affected by partial or full closures of educational institution. In spite of this, to ensure the continuity of learning, one of the initiatives taken by our government is introducing online distance learning (ODL) as the new platforms of learning. However, undeniably, ODL is so stressful and this situation is obviously non-pleasant, especially for the long-term development of the students. Therefore, a research to find out the most influential factors, which contribute to these stresses has been conducted. In this research, a decision-making tool known as the Fuzzy Analytic Network Process (FANP) has been applied. Five main factors which are time management, environment of studies, resources, family and lecturer’s concerns, with additional of twenty-one sub-factors were studied. The percentage value of each factor was calculated using the FANP, which produced the rank for each factor. Results shows that, environment of study dominates the ranks, followed by time management, lecturer’s concern, resources and the last one is family’s concern.
Cryptographic Shallots: A Formal Treatment of Repliable Onion Encryption

Onion routing is a popular, efficient, and scalable method for enabling anonymous communications. To send a message m to Bob via onion routing, Alice picks several intermediaries, wraps m in multiple layers of encryption—a layer per intermediary—and sends the resulting onion to the first intermediary. Each intermediary peels off a layer of encryption and learns the identity of the next entity on the path and what to send along; finally Bob learns that he is the recipient and recovers the message m.
Soft Computing Techniques for Energy Consumption and Resource Aware Allocation on Cloud: A Progress and Systematic Review

Cloud computing plays a vital role in storage and transfer of immense capacity data due to a rapid growth in size and the quantity of organizational tasks. There are many studies in which varied soft computing methods are applied to the cloud environment. In large data centers the cloud services indorse not only the energy consumption price of the substructure resources but also with a considerable growth in environmental costs. These subjects are significant requisites to decrease the energy cost and carbon footprint of cloud computing systems. To minimize energy consumption, the intelligent machines are required to achieve crossways numerous diverse machines, and strategies corresponding across the hardware and software layers to balance performance and energy, as well as to proficiently exploit multiple resources. Energy-efficient Cloud Organization Resource Allocation Framework is getting acceptance as it is paying operative consideration to cloud data management with an interpretation to achieve maximum revenue and minimum cost. The primary objective of the chapter is to conduct the systematic study and mapping of recent soft computing techniques to resolve the resource allocation and energy consumption problems in cloud computing. The chapter discuss the various soft computing techniques which are used in cloud environment for energy-resource allocation, workflow scheduling and performing the migration on cloud computing system. The first section of the chapter comprises of Introduction, motivation, background works which includes Framework for Energy and resource aware allocation using soft computing techniques, various issues, benefits of the work and application areas of soft computing techniques for cloud. The next section of the chapter highlighted the reported work which covers the detailed study of the researchers for energy efficiency and resource allocation using soft commuting techniques. The final section of the chapter discuss the comparative analysis which compares the work of different researchers by using various performance parameters such as execution time, power consumption, energy efficiency, resource utilization, response time and makespan.