Reviewerly/exHarmony huggingface dataset:
Merger &amp; Acquisition and Non-financial performance of Banks in Aguata LGA, Anambra State, Nigeria

The banking sector in Nigeria underwent a drastic restructuring exercise to standardize its operation, and to return trust to stakeholders of deposit money banks in Nigeria, as a result of failing banks that were no longer able to meet up the trends of strategic adjustment exercises by the Central Bank of Nigeria (CBN).
SECURING A CRITICAL MINERALS FRAMEWORK FOR MISSOURI

Missouri is one of the top 10 mineral producing states and has known occurrences of 29 of the 50 mineral commodities identified as critical to the U.S. economy and national security. Recent critical minerals projects in Missouri began in 2014 with USGS/MGS-supported projects related to Precambrian hosted IOA/IOCG deposits that contain known REEs and cobalt. The projects include: a structure contour map of the Proterozoic surface in southeastern Missouri; a compilation map of exposed Precambrian; and a 1:24,000 scale bedrock map of Precambrian surface exposures in an area with exposed IOA/IOCG mineralization in the St. Francois Mountains. USGS Earth MRI (EMRI) funding has supported mapping of the Fredericktown 7.5’ quadrangle, completed fall 2022, and includes petrographic and geochemical analyses. This project area contains known cobalt-nickel mineralization. A second EMRI project was completed this year on the Ordovician Maquoketa Shale as part of a six state group that sampled the formation at outcrops and in cores for REE-enriched sedimentary phosphate. An additional project with the Department of Energy, four surrounding states, and one tribal nation (CORE-CM) began in 2021 with the intent to study Pennsylvanian coal strata for potential REE concentrations. This collaborative effort is collecting new information and correlating existing data across the Cherokee-Forest City Basin. New projects in 2022 include three additional EMRI-funded quadrangles to be mapped near the Fredericktown quadrangle as well as a STATEMAP quadrangle mapping project in the vicinity of the area. Mapping will address basement and bedrock exposures, with accompanying petrographic and geochemical analyses. An EMRI-funded study into Pennsylvanian strata examining black shales for REE potential will have Missouri, along with six other states, investigating and collecting samples for XRF analysis. Investments through existing and new federal programs and increases in state resources are accelerating our understanding of the location and quantities of these minerals in Missouri and surrounding regions. These projects also improve preservation and access to physical and digital collections and are helping our customers in areas such as energy, critical minerals characterization, and infrastructure development.
The Case of A Single Server

We formulated the datacenter capacity provisioning problem (CP) in Chap. 3 . Before solving the complete problem, it would be more intuitive to get the basic ideas from a simplified problem, and then extend the basic ideas to tackle the complete problem in a step-by-step manner. In this chapter, we consider a simplified case. In this case, there is only one server and toggling the server on/off (namely, setting $$x(t) \in \{0,1\}$$ ) is the decision to make at each individual slot. We assume the workload a(t) to take values in [0, 1], such that it can be satisfied by one server. In this chapter, we consider a simplified case. In this case, there is only one server and toggling it on/off is the decision to make at each individual slot.
Manifold-Driven and Feature Replay Lifelong Representation Learning on Person ReID

Lifelong learning, which attempts to alleviate catastrophic forgetting in machine learning models, is gaining increasing attention for deep neural networks. Recent lifelong learning methods which continuously append new classes in the classification layer of deep neural network suffer from the model capacity issue. Representation learning is a feasible solution for this problem. However, representation learning in lifelong learning has not been cautiously evaluated, especially for unseen classes. In this work, we concentrate on evaluating the performance of lifelong representation learning on unseen classes, and propose an effective lifelong representation learning method to match image pairs, without the need of increasing the model capacity. Specifically, we preserve the knowledge of previous tasks in the manifolds learned from multiple network layer outputs. The obtained distributions of these manifolds are further used to generate pseudo feature maps which are replayed in a combination with knowledge distillation strategy to improve the performance. We conduct the experiments on three widely used Person ReID datasets to evaluate the performance of lifelong representation learning on unseen classes. The result shows that our proposed method achieves the state-of-the-art performance compared to other related lifelong learning methods.
Learning Temporal and Spatial Embedding for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs store a large number of temporal facts that simulate the dynamic interactions of entities along the timeline. Since existing temporal knowledge graphs often suffer from incompleteness, it is crucial to build time-aware representation learning models that help to infer the missing temporal facts. However, most of the existing models for temporal knowledge graph reasoning focus on mining temporal associations between entities, and do not fully exploit spatial information contained in entities. To this end, we propose spatial-temporal network(ST-Net), a new representation learning model for temporal knowledge graphs, which has both temporal and spatial awareness capabilities. Specifically, ST-Net enriches the hidden features of entities by simultaneously fusing their temporal and spatial information. At the same time, we introduce the core idea of Copy-Generation Networks, which predicts future facts based on either the historical vocabulary or the whole entity vocabulary. We evaluate our proposed method via link prediction at future times on three benchmark datasets. Through extensive experiments, we demonstrate ST-Net has superior performance on the future link prediction tasks.
Adoption of Blockchain Technology to Secure Electronic Healthcare Record for Efficient Healthcare System

Today, blockchain technology has become one of the most important inventions and unique breakthroughs that have had a significant impact on people and various sectors. Specifically, in the healthcare sector, blockchain shows a promising future in storing medical records on the internet in a secure manner. Through this paper, we are spreading light on the Electronic Health Records (EHR) system, which is based on blockchain technology. As we all know, a blockchain ledger is an immutable, secure, and organized database. Thus, storing medical records of individuals on this ledger will help them to efficiently share documents with their authorized doctors, cloud storage facilities, as well as tamper-proof hash cryptography of data. There are plenty of different architectural approaches to attain this proposed objective. But, the distribution of public keys and private keys is the core system for EHR as it is developed on blockchain. The public and private keys are distributed in EHR to different clients for accessing data, along with that user’s ability to change the rights accordingly from view to edit. Blockchain-based Personal Health Records (PHRs) systems have emerged as the newest thing in patient-driven healthcare.
NATURAL SYNCHRONIZATION OF WIRELESS SENSOR NETWORKS FOR STRUCTURAL HEALTH MONITORING

Time synchronization in communication networks is a common issue: in a sensor network it means that the order of data samples becomes uncertain, which can make it unusable. Dedicated signals and schemes for synchronization of sensor networks has hence been a well-researched topic for decades. Here we bring in an approach to synchronization which uses the sensory data. Drawing inspiration from sensor time synchronization using environmental noise, we consider synchronizing sensory nodes for structural health monitoring–if the physical quantity the sensors measure is correlated, propagating as a wave, or oscillating in regular fashion, it is intuitively clear how to put it to use. We discuss when structural health monitoring signals can aid synchronization; we also connect this synchronization scheme to the idea of using physical human-made structures as reservoirs for reservoir computing, formulating synchronization as a reservoir computing task.
Didactic specialties of teaching economics at the technical university

The implementation of large-scale economic tasks presupposes the intensification of the creative activity of each individual and work collective. Such a situation obliges the teachers of higher education to achieve not only high engineering training of students, but also the education of modern economic thinking in them. The main goal here is to instill in future specialists the skills of analytical comprehension of life situations, on the basis of which they could draw correct conclusions. Teaching students to think actively, developing their ability to independently find knowledge is the duty of a teacher of any discipline. Knowledge is only strong when it is acquired primarily through the efforts of one's thought, and not simply by memory. This is the specification of the educational process. The most effective form of education in modern educational institutions is active forms of education, which is based on an independent search for knowledge and work with information. Such a search is important when working with the humanities, which include economic theory. It is most important here that students receive knowledge not in a finished form, but come to the necessary conclusions on their own, in the process of active creative searches, in the process of independent analysis of life material. In the proposed article, the authors, proceeding from the primary tasks of higher education, share some considerations for the use of active forms and methods of teaching. In the article, the authors touch upon the issue of enhancing the educational process based on taking into account the peculiarities of the thinking of the technical audience.
Aspect-aware Asymmetric Representation Learning Network for Review-based Recommendation

Recently, user-provided reviews have been identified as an essential resource to improve user and item representation in recommender systems. Previous methods focus on the review-based recommender typically leverages symmetric networks to process user and item reviews. However, in reality, these two sets of reviews are markedly different: a user's reviews reflect the experience of buying diverse items and show their heterogeneous interests. In contrast, an item's reviews emphasize the quality of the specific item. Thus an item's reviews are usually homogeneous. This paper seeks to explore the aspect of review difference in the review-based recommendation framework. We propose a novel asymmetric neural network model that accurately learns the user and item representation by identifying this critical difference. We focus on capturing the dynamic change of user interest for the user-aspect reviews via modeling the temporal information into the conventional neural network(CNN). On the other side, we try to identify a specific item's essential yet essential features by utilizing the self-attention neural network. Finally, a factorization machine (FM) is adopted to finish the rating prediction task, where the user and item IDs are encoded as supplementary review embedding. We conduct comprehensive experiments on four Amazon datasets, and the experimental results show that our proposed model consistently outperforms several state-of-the-art methods.
Installing and Configuring Services

Now that we have explored how HTTP and HTTPS work, we can look at how to set up a web server and associated services in a secure way. We will start by looking at service architecture design: how trust boundaries impact on protocol choices. Web frameworks make it easier to write safe code, and we will take a look at some common options.
Designing a System to Mimic Expert Cognition: An Initial Prototype

In this paper, we present a proof-of-concept system to highlight the potential benefits of mimicking higher-order cognitive processes involved in ‘insight seeking’ to create the necessary context for expert sensemaking. We draw upon data from a realistic investigation exercise undertaken by 14 experienced intelligence analysts and use this to develop our prototype to mimic behaviours demonstrated by expert analysts. Our prototype system evaluates different strategies and provides recommendations for an analyst to explore, through a prototype user interface. The recommended strategies, and associated information retrieved, aligns with the actual investigations. We propose that our system presents a novel and promising approach to design AI support systems for tasks that typically require human expert cognitive processes.
E-Authentication System Using QR Code &amp; OTP

With the fast expansion of wireless communication technology, user authentication is becoming increasingly vital in order to assure the system's security. Passwords serve a vital part in the authentication process. The user's password will be submitted with the traffic to the authentication server during the authentication procedure, allowing the server to provide access to the authorized user. The invaders will take advantage of the opportunity to try to sniff out other people's passwords in order to carry out illicit acts under the guise of someone else's identity, keeping them out of danger. Many methods have been offered to improve the security of wireless communication technologies as a result of the challenges. The recommended approach will be utilized to improve the system's security in this study. Onetime passwords, hashing, and two-factor authentication were chosen as the solution. There will also be a new solution that uses the QR code to assist preserve more data. The system outcome's goal is to improve the present login authentication mechanism. It proposes ways to make password cracking more difficult, as well as persuade people to pick and use tough-to-guess passwords.
Investigating Visual Attention-based Traffic Accident Detection Model

Spotting abnormal or anomalous events using street and road cameras relies heavily on human observers which are subject to fatigue, distractions, and simultaneous attention limit. There are several proposed anomalous event detection systems based on complex computer vision algorithms and deep learning architectures. However, these systems are objective-agnostic, resulting in high false-negative cases in task-driven abnormal event detection. A straightforward solution is to use visual attention models. However, these are based on low-level features integrated with object detectors and scene context, rather than on the observers' object of gaze. In this paper, we explore a task-driven visual attention-based traffic accident detection system. We first examine the human fixations in free-viewing and task-driven goals using our proposed, first task-driven, fixation dataset of traffic incidents from different road cameras called TaskFix. We then used TaskFix to fine-tune the visual attention model, in this work called TaskNet. We evaluated the proposed fine-tuned model with quantitative and qualitative tests and compared it with other visual attention prediction architectures. The results indicate the potential of the visual attention models in abnormal event detection. The dataset is available here: https://bit.ly/TaskFixDataset
Trust, but Verify: Using Self-supervised Probing to Improve Trustworthiness

Trustworthy machine learning is of primary importance to the practical deployment of deep learning models. While state-of-the-art models achieve astonishingly good performance in terms of accuracy, recent literature reveals that their predictive confidence scores unfortunately cannot be trusted: e.g., they are often overconfident when wrong predictions are made, or so even for obvious outliers. In this paper, we introduce a new approach of self-supervised probing, which enables us to check and mitigate the overconfidence issue for a trained model, thereby improving its trustworthiness. We provide a simple yet effective framework, which can be flexibly applied to existing trustworthiness-related methods in a plug-and-play manner. Extensive experiments on three trustworthiness-related tasks (misclassification detection, calibration and out-of-distribution detection) across various benchmarks verify the effectiveness of our proposed probing framework.
Efficient Post Quantum Random Oblivious Transfer Based on Lattice

The large scale multiparty computation and private set intersection requires a number of oblivious transfer instances as subroutines, but the implementation of oblivious transfer protocols is relatively slow. An feasible way is to use the oblivious transfer variant called random oblivious transfer. In this paper, we propose a 1-out-of-2 random oblivious transfer protocol and extend it to a 1-out-of-k random oblivious transfer protocol based on the LWE assumption, quantum computation and measurement. Then, we analysis the stand-alone security of our 1-out-of-2 random oblivious transfer protocol under various malicious situations and prove its universally composable security in UC framework. As for the security of our 1-out-of-k random oblivious transfer protocol, the similar results can be obtained.
Blockchains and Distributed Databases

In the previous chapter, we start with Bitcoin. From Bitcoin, we draw out the evolution line of blockchains. Ethereum provides client-coded, Turing-complete smart contracts. It unleashes the decentralization potential of blockchains beyond cryptocurrencies. Furthermore, permissioned blockchains utilize the trusted membership setup in the operational setting. It makes it possible for efficient State-machine Replication and more flexible data operations.
A Study on the Academic Trends of Dynamic Capability for Crisis Management: Systematic Literature Review in Korean Academia

This study reports on the Korean academic trends of dynamic capability theory that will help the crisis management of the state and companies and presents future research directions. Based on the systematic literature review, 644 papers on dynamic capability published in Korean journals listed in the Korean citation index were searched and 118 papers were selected for the analysis. As a result of the analysis, the academic research about dynamic capability in Korean academia began in 2006. Most of researches were mainly conducted with applying empirical research methodology in the field of social science. It was found that the main research fields were divided into seven areas: export, organization, information technology, entrepreneurship, technological innovation, environmental management, and case study. The derived major research fields were found to constitute dynamic capability by being classified into functional and managerial dimensions as key elements of dynamic capability. The results of this study can help establish the status of individual studies related to dynamic capability theory and identify research fields related to crisis management in the future.
Research on Drilling Control Method of Ammunition Shell Based on Fuzzy Algorithm

Aiming at the technical requirements of high precision, unmanned and flexible drilling on the curved surface of large-size ammunition shells, a gantry-type curved surface automatic drilling system is designed based on fuzzy algorithm. The system selects the feed displacement of the drilling spindle and the rotation speed of the drilling rotary motor as the drilling characteristic quantities, and establishes the drilling depth fuzzy alarm model through the steps of fuzzy processing, establishment of fuzzy reasoning and calculation of the fuzzy alarm query table. The hardware composition and software design scheme of the drilling system are introduced. It has been proved that this method can realize the precise control of the drilling depth of the surface of the large-size ammunition shell, and the control accuracy is 0.2mm, which can meet the actual production needs.
AI Based Diagnosis &amp; Classification Of Lymph Node Fine Needle Aspiration Cytology (FNAC)

In clinical practice, an essential technique that is used by pathologists to diagnose lymph node malignancies is the visual examination of cytopathological slides. Manual visual inspection of whole slide images is challenging, time-consuming, and subject to substantial inter-observer variability, which might result in sub-optimal detection of said malignancies. These False-positive and False-negatives can lead to further complications. Furthermore, upon understanding the current situation, we find that pathologists must review hundreds of slides on an everyday basis. Most of the reviewed slides are normal in nature while only a fraction being abnormal or malignant. The abnormal slides need to be submitted for further analysis. This makes it a very time- consuming and tedious process. Our research looks at pathology from a computational perspective. We leverage deep learning technology to learn features, patterns, and detect anomalies to aid in the detection of lymph node malignancies in whole slide images of fine needle aspiration cytology. An initial screening to classify reactive, infectious, and neoplastic slides would reduce the workload of a pathologist largely. Our main goal was to develop a dataset containing digitized images of reactive, infectious, and neoplastic FNAC slides of lymph nodes. Furthermore, we used this dataset to identify techniques for pre-processing these images such as clustering, thresholding and morphological operations. We then identified and implemented 3 classification models that were appropriate for our dataset, namely, Convolutional Neural Network (CNN), InceptionNet and Resnet50. On evaluation of the results obtained, we found that in terms of accuracy, Resnet50 performed the best amongst all three models with a training accuracy of 76.19%. However, further future improvements such as obtaining a larger dataset and performing feature extraction are necessary in order to increase the accuracy of the models.