Reviewerly/exHarmony huggingface dataset:
Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
Editing Large Language Models: Problems, Methods, and Opportunities

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
Cloud Storage Technology and Its Applications

In order to provide data storage services,cloud storage employs software to interconnect and facilitate collaboration between different types of storage devices.Compared to traditional storage methods,cloud storage poses new challenges in data security,reliability,and management.This paper introduces four layers of cloud storage architecture:data storage layer connecting multiple storage components,data management layer providing common supporting technology for multiple services,data service layer sustaining multiple storage applications,and user access layer.It then examines a typical cloud storage application—backup cloud (B-Cloud)—and discusses its software architecture,characteristics,and main research questions.
Representing Structure and Structured Representations in Connectionist Networks

Annotation: A. Browne (ed.) Neural Network Perspectives on Cognition and Adaptive Robotics, IOP Press, Bristol, UK.
Modeling Item and Category Learning

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN is a three layer model where learning between the first two layers is unsupervised, while learning between the top two layers is supervised. SUSTAIN clusters inputs in an unsupervised fashion until it groups input patterns inappropriately (as signaled by the supervised portion of the network). When such an error occurs, SUSTAIN alters its architecture, recruiting a new unit that is tuned to correctly classify the exception. Units recruited to capture exceptions can evolve into prototypes/attractors/rules in their own right. SUSTAINS adaptive architecture allows it to master simple classification problems quickly, while still retaining the capacity to learn difficult mappings. SUSTAIN also adjusts its sensitivity to input dimensions during the course of learning, paying more attention to dimensions relevant to the classification task. SUSTAIN successfully fits item and category learning data from Medin, Dewey, and Murphy (1983). SUSTAINS performance on other data sets is discussed. SUSTAIN is compared with other models of category learning.
Hierarchical Afaan Oromoo News Text Classification

The advancement of the present day technology enables the production of huge amount of information.Retrieving useful information out of these huge collections necessitates proper organization and structuring.Automatic text classification is an inevitable solution in this regard.However, the present approach focuses on the flat classification, where each topic is treated as a separate class, which is inadequate in text classification where there are a large number of classes and a huge number of relevant features needed to distinguish between them.This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of Afaan oromoo News Text.The approach utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree.An experiment had been conducted using a categorical data collected from Ethiopian News Agency (ENA) using SVM to see the performances of the hierarchical classifiers on Afaan Oromoo News Text.The findings of the experiment show the accuracy of flat classification decreases as the number of classes and documents (features) increases.Moreover, the accuracy of the flat classifier decreases at an increasing number of top feature set.The peak accuracy of the flat classifier was 68.84 % when the top 3 features were used.The findings of the experiment done using hierarchical classification show an increasing performance of the classifiers as we move down the hierarchy.The maximum accuracy achieved was 90.37% at level-3(last level) of the category tree.Moreover, the accuracy of the hierarchical classifiers increases at an increasing number of top feature set compared to the flat classifier.The peak accuracy was 89.06% using level three classifier when the top 15 features were used.Furthermore, the performance between flat classifier and hierarchical classifiers are compared using the same test data.Thus, it shows that use of the hierarchical structure during classification has resulted in a significant improvement of 29.42 % in exact match precision when compared with a flat classifier.
EVIDENCE-DRIVEN POLICY MAKING USING HETEROGENEOUS SOURCES OF DATA - THE CASE OF A CONTROL PARKING SYSTEM

Policy making is generally and largely based on intuition and not backed up by evidence.Our goal is to transform the existing paradigm of policy making into an evidence-driven approach enabled by heterogenous sources of data.Methods for data collection, efficient storage and data analysis are implemented to measure the economic activity, assess the environmental impact and evaluate the social consequences of certain policy decisions.The extracted pieces of evidence are used to inform, advise, monitor, evaluate and revise the decisions made by policy planners.In this paper, we examine the use case of Thessaloniki's controlled parking system and its optimization balancing between economic growth, environmental impact and citizen's satisfaction, by harmonizing and visualizing heterogenous sources of data.The case study evaluation consists of a multi-step process that involves user recruitment, user training, formulation of platform-usage scenarios, platform evaluation based on questionnaires, and finally in-depth questionnaire analysis.
Facial expression synthesis images using hybrid neural network with particle swarm optimisation techniques

In the advance life trend, the facial expression is the visual facial outer structure of the human affective state, intellectual action, and human interchanges and facial expression go about as the key role in the movement of communication. In this paper, the facial expression synthesis performance is done using different facial expressions such as angry, sad, smile, surprise and cry of various peoples. In the proposed method, two procedures are used namely hybrid neural network (HNN) and particle swarm optimisation (PSO) algorithm. By training particle swarm optimisation and hybrid neural network, we take the desired output. In the result section, various evaluation metrics namely peak signal to noise ratio (PSNR), mean square error (MSE) and a second-derivative-like measure of enhancement (SDME) value is calculated using diverse algorithms. In this evaluation performance, the particle swarm optimisation is given enhanced output while comparing it with other techniques and the existing methods of facial expression.
Numerical Errors and Addition of a Sentence

They also seem to consider the correlational findings of insession processes as strong evidence for how therapies work.Many hundreds of studies have indeed found associations between improvement in patients and characteristics of the therapy.However, because these are only uncontrolled and correlational findings, they cannot be considered as causal evidence. 5If these processes were as well understood as Kazantzis and Hofmann assume, one would wonder why the overall low response rates to treatments have not improved over time and a relatively small number of patients benefit from them.As argued in my Viewpoint, substantial progress has been made in the past decades in the research and development of treatments for depression.However, it is also time to recognize that these treatments have limitations and that focused approaches are needed to further reduce the huge disease burden of depression.
Azure Tooling

In one way or another, the cloud has moved to the center of most software development. Whether you are writing desktop applications, creating websites, or working with mobile devices, there is likely a component that utilizes the cloud, or could utilize the cloud, under the right circumstances.
Hardware design methodology of multilayer feedforward neural network for spectrum sensing in cognitive radio

This paper aims to design a simple hardware architecture of Multilayer Feedforward Neural Network (MFNN) and verify its performance in the detection of vacant/busy state of channels. A single neuron with tansigmoid activation function is proposed utilising the rule of matrix multiplication for simplification in computation. The proposed hardware module of the single neuron, utilising parallel processing, is assembled to obtain the architecture of desired MFNN. The area optimised hardware architecture of MFNN is achieved by reutilising the hardware resources. The hardware module of the single neuron is compared with the allied design methods which exhibits its outperformance in terms of mean square error and accuracy over the existing ones. The proposed optimised MFNN provides almost 62% reduction in hardware resources as compared with standard non-optimised MFNN. Further, the performance analyses of proposed hardware architectures demonstrate almost 90% accuracy in the detection of both vacant and busy states of channels.
Engagement Detection Framework for Hand Gesture and Posture Recognition

Engagement Detection Framework for Hand Gesture and Posture Recognition
Pulling Data from Multiple Tables

As I explained in Chapter 13 , one of the major strengths of a relational database is the ability to link data in different tables by using the primary key from one table as a foreign key in another table. The phpsols database has two tables: images and blog. It’s time to add some more and join them, so that you can assign categories to blog entries and associate images with individual articles.
A hardware based technique to reduce the timing complexity of number factoring problem

Most digital circuits which have been developed to implement algorithms, can benefit from an increase in clock speed, but do not completely map the problem to all available silicon resources. We have introduced a hardware based scheme capable of effectively using technology, specifically the increase in silicon area, to improve the computational time of complicated applications. In this thesis, we applied this scheme to solve the factoring problem, which requires exponential time (with respect to the number of bits in n) in conventional computers and could be only solved in polynomial time with quantum computers. The scheme successfully mapped the problem to most of the silicon area of Altera Stratix FPGA. The results show that the scheme is capable of reducing the time complexity to a polynomial rate with respect to the number of bits of the number n. The results also show an exponential rate of use for silicon with respect to the number of bits of n. Our analysis shows that the new scheme is scalable with technology speed and available space, could be applied to other applications to solve the performance limitations of conventional systems.
AUTONOMOUS POSITIONING AND FULL TRAVERSAL PATH PLANNING TECHNOLOGY FOR INDOOR INTELLIGENT SWEEPING ROBOT

To reduce the complex cleaning workload, people developed indoor intelligent sweeping robot, which can help people clean the indoor environment intelligently and autonomously.Based on previous studies, the current conventional technologies of sweeping robot are introduced, and further studies are carried out.Firstly, low-cost photoelectric encoder and MPU6050 sensor combination positioning are selected for the robot to conduct modeling for the sensor, the system error and random error in the positioning process are analysed, and different correction methods are adopted.Secondly, the system error is corrected by analytical method, and the random error is corrected by extended Kalman filter algorithm.The feasibility of the algorithm is verified by simulation, which provides a guarantee for the research of full traversal path planning.For the area connection path planning of irregular obstacles between adjacent partitions, the sensor is used to clean the irregular obstacles along the side, so that the expansion area can also be covered for cleaning, and the overall cleaning coverage rate can be improved.In order to reduce the repetition rate of the regional connection path between regular obstacles, the improved A* algorithm is used to obtain the rules of starting point, direction and ending point of round-trip traversal in rectangular partition, and the round-trip artificial cleaning mode is used in the rectangular partition without obstacles.The combination of Manhattan distance and diagonal distance is introduced, and the corner cost is introduced into the evaluation function, which effectively reduces the repetition rate of regional connection path.Finally, RobotBASIC is used to carry out the simulation experiment.The results show that the proposed method enables the cleaning robot to find full coverage, low repetition rate and efficiently traverse the cleaning path.The results show that the proposed method enables the cleaning robot to find a traverse clean path with full coverage, low repetition rate and high efficiency.
Implementation of Authenticated Hybrid QKD Protocols for 802.11 LANs

Quantum cryptography (QC) is used to give approved secure correspondence between the sender and beneficiary. In QC, Authentication Hybrid Quantum Key Distribution Protocols (AHQKDPs) use quantum segments to isolate session keys and open talks to check for covert operatives and affirm the rightness of a session key. In any case, open talks require additional correspondence adjusts between sender beneficiaries. The benefit of QC adequately is against replay and inert assaults. An AQKDP with certain customer approval, which ensures that protection, is achievable for valid down customers and the mutual check is practiced carefully when secure correspondence using the session key starts. AQKDP have two phases, for instance, set up the stage and the appropriation stage to give 3, parties QKDP. In this system, there is no basic perception between the sender and beneficiary. Both sender and beneficiary must focus on confided in core interest. In this express, check blend AHQKDP has two phases, for instance, set up the stage and the dispersion stage to give three gatherings affirmations, and secure session key appointment. In this structure, there is basic perception between the sender and beneficiary. Both sender and beneficiary must compare direct with approval of confided in core interest.
Deploying vSmart

The vSmart controller sits between the vEdge devices and the vBond orchestrator; it helps with the authentication of edge devices to the vBond and manages the connectivity between the edge devices.
Using decision trees for inductively driven semantic integration and ontology matching

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.
Indirect Reciprocity Game for Dynamic Channel Access

Cooperation is a promising approach to simultaneously achieving efficient spectrum resource use and improving the quality of service of primary users in dynamic spectrum access networks. However, due to their selfish nature, how to stimulate secondary users to play cooperatively is an important issue. In this chapter, we discuss a reputation-based spectrum access framework where the cooperation stimulation problem is modeled as an indirect reciprocity game. In this game, secondary users choose how to help primary users relay information and gain reputation, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision-making as a Markov decision process, the optimal action rule can be obtained, according to which the secondary user will use maximal power to help the primary user relay data and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, the uniqueness of the stationary reputation distribution is proved, and the conditions under which the optimal action rule is evolutionarily stable are theoretically derived.
The Development of Computer Network and the Opportunity of Integration Automation

Under the background of rapid development of science and technology in China, computer network technology has also made a significant development.At this stage, how to promote the development of computer network towards integrated automation is a problem that scholars in this industry pay special attention to.This paper mainly analyzes the problems related to the development of computer network and the opportunities of integrated automation.The purpose of this paper is to conduct in-depth research on the development of computer network's integrated automation, and accelerate the development of China's integrated automation field.
Integrated Social Media into Mobile-Assisted Foreign Language Learning

In today's college campuses, having a smartphone and being active on social media has become a fashion.Though many mobile apps have been designed for learning languages, there are few studies on applying social media based mobile apps to promote the effective learning of foreign languages.As an emerging social media mobile app, WeChat is the most popular social tool in Chinese community around the world.The WeChat app has many communication functions: live chat, group chat, and multimedia input and document transmission, which are essential for language learning.In this research project, we integrate WeChat into a mobile-assisted Mandarin learning app that we have designed.WeChat and the mobile app allow effective communication among instructors and students, and student groups.It offers a variety of communication methods, including voice, text, graphic (abundant icons) and video, which fits the needs of language learning and practices.This integration between WeChat and the mobile app helps to expand the traditional language teaching and learning outside the classroom.Instructors can provide students instant instructions and feedback, and students can learn and practice the exercises anywhere.We have piloted this new approach and conducted various experiments in two sections of Chinese level I in a US college to assess its effectiveness.
Heart Patient Triage Prediction of Clinical Outcomes Using Machine Learning Models

In the medical field, the analysis of different clinical and pathological data by medical experts is a complicated process.Therefore, it is important to build a framework that can instantly and effectively identify the prevalence of heart disease in thousands of samples.Hence, the paper proposed a new method for diagnosing heart disease.The proposed machine learning method is based on a hybrid optimization algorithm of random search and grid search of a random forest and a parameter optimization algorithm of support vector machine grid search.The model, in predicting the presence or absence of heart disease in patients, has a maximum accuracy of 89%, which not only aided doctors in accurately predicting and diagnosing various diseases, but also helps patients with early diagnosis.