Reviewerly/exHarmony huggingface dataset:
The Relationship between Strategic Planning, Strategic Flexibility and Firm Performance in SMES of Saudi Arabia: Mediating Role of Strategic Flexibility

The small and medium enterprises (SMEs) sector plays a significant role in any economy, particularly in a competitive global business environment.However, the SMEs sector could achieve its targets only if it embraced proven strategic management practices.Therefore, this study investigates the mediating role of strategic flexibility on the relationship between strategic planning and firm performance within the context of SMEs in Saudi Arabia.The resource-based view and contingency theories support the study's framework and conclusions.A cross-sectional survey was used to collect the primary data from a sample of 400 owners and managers of medium-sized enterprises in Saudi Arabia.Hypotheses were tested using the Structural Equation Modeling (SEM) method.The study tested the correlation and mediation analysis using PLS-SEM.The study found a significant relationship between strategic planning and firm performance.Also, there is a relationship between strategic planning and strategic flexibility.The association between strategic flexibility and firm performance is also significant for medium enterprises in Saudi Arabia.The results also revealed that strategic flexibility mediates the relationship between strategic planning and firm performance.The study's finding contributes to the planning-performance literature.The study provides helpful recommendations for SMEs that they can use to improve the overall systems of their organizations.
Modeling and analytics of multi-factor disease evolutionary process by fusing petri nets and machine learning methods

Recent years, informatization methods have been gradually applied to medical treatment, in which machine learning and evolutionary computation play an important role. However, the effective methods for the study of multi-factor disease evolutionary process are still largely open. There are some issues in the field of disease analysis, such as the lack of visual multi-factor disease evolution model and effective analysis methods. For a universal method of data analysis and medical diagnosis, the machine learning algorithms should be combined with the formal modeling methods to fully realize the complementary advantages, make model has the advantages of visualization and efficient data analysis. This work proposes a novel research idea for the modeling analysis of current multi-factor diseases and reveal its feasibility, so as to explore potential pharmaceutical targets and enable doctors and patients to better understand the evolution process of multi-factor diseases. It is worth mentioning that, in order to verify the feasibility of the proposed idea, we applied it to the analysis of the role of monoamine hormones in depression. The model incorporates the machine learning algorithms, and it finally outputs the pathogenic probability under different hormone levels, reflecting the importance of different factors on depression. The application case proved that we provide a clear process model and a novel research method for multi-factor disease evolutionary process analysis.
Medicine Pillbox Reminder Using IoT Sensors

In recent times IoT (Internet of Things) plays a major role in the daily routine of human life. Majorly this technology is showing its impact on medical applications. As per a report, Health issues become very common for people above the age of 40 and it becomes severe for the elderly population. Sometimes old age people forget to take medicine and also get confused about when to use what type of medicine, which makes them rely on the assistance of others. With the help of IoT, a smart device with RFID (Radio-frequency Identifier), Arduino UNO and NodeMCU is proposed. This not only helps in suggesting the kind of medicine to be taken but also the information is sent to the nearby hospital in case of emergencies. The proposed model achieves an accuracy of 92%, which is very much essential in present days.
Artificial Intelligence for Art Creation with Image Style

Artificial intelligence (AI) has become a great success in the past decade. Powered by efficient hardware and neural network models, AI has made a wide change to almost all disciplines. While still in an early stage, AI has been used in the art field much more frequently than before, with various methods, e.g., generative adversarial networks. However, whether AI can replace human beings in art creation is still an unsolved question. Therefore, how to use AI technology to achieve artistic creation has become very meaningful, and has also been favored by many research groups. In this paper, we demonstrate the ability of AI for art creation with a case study of image style transfer. The result shows that AI is still not satisfactory and consumes too much computation. However, a new creative idea is proposed, and we will conduct lightweight research on this basis in the future. So as to realize the artistic creation of real artificial intelligence.
The meaning of the pianopart in the cycle of instrumental trios INO by V. A. Rotaru

The article analyzes the chamber instrumental piano trios created by V. Rotaru at the beginning of the century, which make up the cycle under the general name INO. The theoretical and interpretative aspects of the music, the history of the creation are considered in detail, some practical recommendations are given. The study aims to fill in the gaps in the field of research into the composer’s chamber instrumental music and draw attention to the important role of the piano in his work in general and in the presented Cycle in particular.
Best Practices for Web Design and Development

Web design and development is a constantly evolving field, with new trends and technologies arising every time.To stay ahead of the wind, it's important to follow stylish practices for creating effective and engaging websites.This journal explores the crucial principles of web design and development, including usability, availability, and stoner experience( UX).We will bandy the significance of creating responsive and mobile-friendly websites, and the part of effective typography, colour, and imagery in web design.We will also explore the stylish practices for rendering and testing web operations, including the use of interpretation control, robotization tools, and testing fabrics.Eventually, we'll look at the significance of ongoing conservation and updates, and how to produce a sustainable web development process.Whether you are a freshman or an educated web inventor, this journal will give precious perceptivity and practical tips for creating effective and engaging web gests.
Special section on IST for ICSOB2021

Mashup is an application with specific functions by combining Web APIs that can provide services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. Recommending suitable Web APIs in the vast number of Web APIs on the Internet for Mashup developers has become a challenging problem. Previous studies often fail to fully exploit and effectively synthesize various types of information between Web APIs and Mashups.This work proposes a Web API recommendation approach - PAREI by combining both explicit and implicit information to progressively optimize the recommendation results.First, PAREI uses the explicit structural information between Mashups and Web APIs to construct the Call Relationship Network (CRN). Second, PAREI calculates explicit semantic similarities between developer’s requirement and Mashups to obtain candidate Mashup nodes in CRN. Then PAREI further mines the implicit structural information between Mashups. A combined similarity score for each Mashup node is calculated. Finally, PAREI uses CRN to obtain candidate Web APIs related to candidate Mashup nodes, and integrates implicit semantic information of Web APIs with combined scores of corresponding Mashups, so as to obtain Top-K Web APIs.Comparison experiments show that PAREI has significantly improved the Recall, Precision, and MAP metrics compared with other approaches. Ablation experiments show that different types of information play various roles in Web API recommendation, and different combination modes have different effects on the recommendation results.This work constructs the PAREI model, which combines explicit and implicit information to obtain Web API recommendation results through a progressive strategy. According to the experiment results, we believe that the PAREI approach can help Mashup developers to find demanded Web APIs rapidly and accurately.
A Scheme of Anti Gradient Leakage of Federated Learning Based on Blockchain

Federated learning provides a new solution for data security and privacy protection in the process of machine learning. In distributed learning, interactive gradient rather than direct exchange of data avoids the direct acquisition of data by malicious participants. The latest research shows that the gradient will also leak the data privacy during user training, current solutions to gradient leakage problems are mainly divided into two categories: 1. Encrypt the parameters of the model updating process by using the cryptography scheme; 2. Increase the interference noise for the gradient through thinning, gradient compression, adding noise and other schemes. However, the cryptographic scheme cannot be targeted at the aggregator, and the noise scheme has too much impact on the performance of the model. This paper uses the trust mechanism of blockchain to design an anti gradient leakage scheme for this problem: when the participants upload the gradient, the noise is added through the blockchain smart contract, and the noise value is randomly divided into multiple copies and stored in the blockchain. The central server obtains the noise sum of all participants through the smart contract, and aggregates the gradient to remove the noise. In the training process, all participants can only obtain the aggregation gradient and noise gradient, and cannot recover user data. At the same time, due to the automatic execution and tamper proof characteristics of blockchain smart contracts, malicious interference in the data exchange process is avoided.
Multi-feature fusion network for person reidentification

Most existing studies on person reidentification (Re-ID) utilize deep feature representation learning. However, images often contain occlusion situations and nondiscriminative personal information. To extract more representative features, some researchers extract implicit deep semantic information by designing complex modules, such as mask maps and human pose landmarks. However, this can introduce complex human annotation and computational work. To overcome these issues, we propose a Re-ID model called multifeature fusion network (MFFNet). Our network does not require any additional auxiliary information and incorporates two new designs: the feature refinement pooling block (FRPB) and the feed-forward conduction structure (FCS). Based on the “split-learn-merge” principle, the FRPB decomposes a person’s features into coarse-grained to fine-grained representations. The FRPB learns the corresponding local detail information and merges the multigranular features into the partial person representation. To address the issue of most current methods heavily relying on accurate bounding boxes, the FCS enables character matching at different resolution scales by learning multiple semantic levels of representation. Through a series of ablation experiments, we demonstrate that the proposed strategy is effective for person Re-ID tasks. The results indicate that MFFNet achieves more competitive experimental results than the existing state-of-the-art methods.
Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation

The repeated user-item interaction now is becoming a common phenomenon in the e-commerce scenario. Due to its potential economic profit, various models are emerging to predict which item will be re-interacted based on the user-item interactions. In this specific scenario, item relevance is a critical factor that needs to be concerned, which tends to have different effects on the succeeding re-interacted one (i.e., stimulating or delaying its emergence). It is necessary to make a detailed discernment of item relevance for a better repetition-aware recommendation. Unfortunately, existing works usually mixed all these types, which may disturb the learning process and result in poor performance.
Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.
Sustainable Blockchain?

Blockchains require much energy. We talk here about options for less.
Out-of-Distribution Detection for Adaptive Computer Vision

It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of a YOLOv4 object detector. As a secondary result, this paper also shows that it is possible to train a normalizing flow model for out-of-distribution detection on the COCO dataset, which is larger and more diverse than most benchmarks for out-of-distibution detectors.
Improving Students' Writing Through Team Game Tournament (TGT) at SMK Negeri 1 Ambon

This research aimed at this class action research: (1) for the description of stages; steps of applying of method TGT in course of study job application letter (2) for description of the effectiveness of applying of method TGT to write job application letter of student of class of XIIE1PROGRAM TOURISM SMK NEGERI 1 AMBON. Research procedure follow the stages; steps of research of class action which relate of proposal of Research of Class Action (PTK) released by Dikti covering four step. Fourth step of according to the Dikti (1) planning, (2) execution, (3) monitoring, and (4) reflection. The outcome of the research was as follows: (1) the effective method of TGT is enough utilized in the study of job application writing; (2) effective TGT method enough to increase the ability to write job application letters; (3) effective to the TGT method, enough to improve the student participation in the study of writing job application letters; (4) effective to the TGT method is enough to improve the motivation and student enthusiasm in the study of writing job application letters.
Library of Congress Subject Headings: A Post-Coordinated Future

This paper is the result of a request from Library of Congress leadership to assess pre-coordinated versus post-coordinated subject cataloging. It argues that the disadvantages of pre-coordinated subject strings are perennial and continue to hinder progress, while the advantages of post-coordinated subject cataloging have expanded, resulting in new opportunities to serve the needs of catalogers and end users alike. The consequences of retaining pre-coordinated headings will have long-term impacts that heavily out-weigh the short-term challenges of transitioning to new cataloging practices. By implementing post-coordinated, faceted vocabularies, the Library of Congress will be investing in the future of libraries.
Design choices in Max and Pure Data

Max and Pure Data are widely used patch languages for real-time media computation, especially live electronic music. Each program was developed with a particular musical production in mind: Philippe Manoury's Pluton (1998) for Max, and Sorensen/Steiger/Puckette's Lemma 1 in 1997 for Pure Data. At the same time, care was taken to make the features of both programs as aesthetically neutral as possible, in order to be useful in as wide a variety of applications as possible. One design principle has been to avoid encoding musical structures such as time or key signatures in either program. Noticeably absent is any built-in facility for machine learning or artificial intelligence. On the other hand, heuristics from programming language design such as modularity, encapsulation, and abstraction have been fundamentally influential in the designs of both Max and Pure Data.
Musical instruments as platforms for open sound design

The combination of open-source audio software and the miniaturization of computing hardware has led to new customizable musical instruments. Artists and sound designers can alter the musical behavior and sound character of such instruments themselves. This opens new opportunities for users to explore sound and fosters community collaboration. We will discuss the design of such instruments and introduce the Organelle, an instrument designed to run software that users can create using popular open-source music environments like Pure Data and Faust.
ADINet: Attention-guided dual-branch integration network for atypical visual saliency prediction

Compared with typically developed people, patients with autism spectrum disorder (ASD) have many differences, including visual attention. Recently, researchers have employed saliency prediction to conduct early diagnosis for patients with ASD by observing whether the fixation maps of the patients are consistent with prediction results. Though numerous saliency models have been designed, which promotes the performance of saliency prediction significantly, the efforts major in atypical attention prediction are fewer. Therefore, we propose a novel atypical saliency model, namely attention-guided dual-branch integration network, to perform atypical visual saliency prediction. Generally, our model is a U-shaped architecture including an encoder and a decoder. In the encoder, we first deploy the dual-branch fusion module to extract multiscale deep features, where the dual branches consist of a coarse branch and a fine branch. Particularly, the multiscale features of each branch are integrated in a bilateral way. Then, we employ the multiattention module consisting of dilated convolution, channel attention, and spatial attention to further elevate the high-level semantic cues. Particularly, the attention units are constructed in a cascaded way. Besides, to give an effective representation for the differences between atypical saliency estimation and the typical saliency estimation, we design a new discriminative mask to compute the loss function. Extensive experiments are performed on challenging datasets, and the results show the superiority and effectiveness of our atypical saliency model when compared with the state-of-the-art saliency prediction models.
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective

Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterating on EBR in production, we still identify some fundamental issues in existing methods. First, when dealing with large corpus of candidate items, EBR models often have difficulties in balancing the performance on distinguishing highly relevant items (positives) from both irrelevant ones (easy negatives) and from somewhat related yet not competitive ones (hard negatives). Also, we have little control in the diversity and fairness of the retrieval results because of the “greedy” nature of nearest vector search. These issues compromise the performance of EBR methods in large-scale industrial scenarios.
Location Tracking using Smartphones

In this paper, we present an android application that facilitates seamless location tracking for an individual. Our application is based on GPS location services that provide accurate and approximately location information of an individual and updates the same on a cloud server. It fetches location every few minutes to provide to save battery of the device the application is running on as fetching location repeatedly might have poor impact on the battery health of the device. It allows authorized people to access this location from the server so that they can know the location an individual or a group of individuals.
Project Management in Digital Forensic Investigations

Digital forensic investigations often occur in crisis mode or with little advanced notice. Digital forensic examiner background and expertise can vary widely, and all too often forensic training is specific to a particular tool set. These factors can limit the ability to manage digital forensic projects. Project management has been used in the Information Technology field for decades, and many of the benefits of this type of structured approach can lead to stronger and more consistent investigation outcomes. This article proposes utilizing project management principles such as developing a project plan with time estimates and dependencies, risk management, and a post case review to improve digital forensic case management. The combination of project management and utilizing best practices in an investigation execution strategy for digital forensic investigations can lead to improved outcomes.
A New Method for Mobile Robots to Learn an Optimal Policy from an Expert Using Deep Imitation Learning

Mobile robots must be able to learn the navigation policy from an expert like human beings. This aspect remains a challenge for mobile robots primarily when relying only on sensory inputs like ultrasound or mobile robots have limited resources. This aspect becomes complex, especially in mapless navigation. In this paper, we present a new technique based on deep imitation learning that serves to develop an efficient and robust policy and transmit it to the mobile robot. The proposed method relies on the collection of a set of data sets from several training environments. Then this dataset is used to train an intelligent model using a Convolution neural network (CNN). Extensive experiments on simulation environments of different complexity for the validation of the trained model show the effectiveness of the proposed approach in terms of success rate.