Reviewerly/exHarmony huggingface dataset:
Smart Consulting Approach for Sustainable Business and Technology Innovation: A Case Study Construction and IT Industry in Korean

Since a business model is described as a key element for generating a company"s value and profit, companies try to utilize their innovative business model to secure a competitive advantage in the market. Therefore, companies spend tremendous amounts of time and expenses on business consulting. In reality, however, it is a dire circumstance for small and medium-sized enterprises (SMEs) with insufficient finances to receive such business consultations. The purpose of this study is to suggest a new, smart consulting method that will allow SMEs to be able to self-diagnose their business models. Prior to the experiment, 542 IT companies and 506 construction manufacturing business model canvases were collected. A fine-tuned BERT model was used to predict scores for each of the nine factors that constitute the business model canvas of IT companies and that of construction manufacturing industries. As a result, in the case of 5 labels evaluated from A to E, the average accuracy of the manufacturing industry and that of the IT companies were 0.606 and 0.508, respectively. In addition, the results from the proximity label prediction indicated that the average accuracy of the manufacturing industry and the IT companies were 0.933 and 0.933, respectively, which are in close approximation to the ones of expertise. Therefore, it is a definite prospect for SMEs to identify the strengths and improvements of their business models at low cost using the suggested model. We believe that this study will contribute to SMEs to further develop and thus become sustainable in the future.
The Expressiveness of Cinema and the Expressiveness of Myth: On the Affinity of Cinema Language and Mythological Thought

The article is focused on discovering similar principles and mechanisms of communication in such information transmission systems as myth and cinema. The author considers this text as a prolegomena to the model of analysis of the mythological content in a film. A myth is a way to construct a model of reality; it projects the contents of this model onto the facts and phenomena surrounding a person, giving them new meanings and organizing new connections between them. These facts and phenomena can be perceived from two points of view: profane and sacred. The sacred mode of perception is possible in the case of “synchronous reading” of these facts and phenomena — a certain object expressing a part of the mythological model of the world is perceived as such only in the case of presentation of this model in general and other objects representing its other segments. The elements of a film text, like those of a myth, also can be perceived in two dimensions: linear, that is consecutive in time, and “spectacular-programmable”, that is as self-contained phenomena, not tied to the film’s linear plot. In the second case, these elements can express the connotations preceding the film, determined not by its plot, but by the conditions and concepts of the social group of authors and spectators of the movie. The meanings that images represent on the screen also depend on the organization of these images within each particular system — that is, the principle of “synchronous” perception also works for them. The author proposes principles of identification of mythological signifiers in cinema, and describes in general terms the mechanism of their analysis. The main question to ask in such an analysis is: how do the signifiers of mythological ideas and representations, revealed in a film at its “synchronous”, “nonlinear” level, interact with the linear plot of this film?.
Bayesian Network-based Active Learning for Ontology-based Video Retrieval using Hidden Markov Logic Model with Edge Histogram Descriptor

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM_EHD)" in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%.
A Hybrid Cryptographic Approach for Secure Cloud-Based File Storage

The tremendous increase of sensitive data on the cloud has rendered it more vulnerable. Thus, undoubtedly, the risk stems from an expanding number of individuals with harmful motives. Because the cloud is handled by a third party, guaranteeing cloud security services is critical. The security of the data may be strengthened by combining different symmetric key techniques to store the information on the server so that even if someone gains access, they are unable to access the original data. It must be decrypted, thus. A hybrid model that combines Advanced Encryption Standard (AES) algorithm and Elliptical Curve Cryptography (ECC) is put forth. ECC is employed to complete the user verification process and maintain the security of private data. Data is encrypted on the client side and then decrypted after being downloaded from the cloud, the AES technique is used to securely store and retrieve users' data in the cloud. Data is protected by encryption on the client's side and then decoded after it has been downloaded from the cloud. Enabling a suitable access mechanism to stop unlicensed access to information in the system and safe storage to allow access to information over a cloud-based network would be advantageous for the complete prototype of the suggested solution. The proposed method is efficient for encrypting small to moderate amounts of data as 1.02 milliseconds is a relatively fast encryption time.
Video Based Exercise Recognition Using GCN

Researchers frequently employ artificial intelligence models to estimate human pose. Pose recognition can be used to monitor and supervise personal workout sessions. Exercises for self-rehabilitation without supervision and improper physical training can result in significant injury if the procedure is not followed correctly. The majority of the work that has formerly been done on exercise classification depends on sensors that are either worn or externally attached. However, these sensors frequently are unable to discern between similar workouts. Additionally, they are rarely ideal due to a variety of other issues, including connectivity, defective sensors, excessive sweating, expense, and improper alignment. This study classifies each person’s workouts before predicting whether or not the posture that corresponds with each exercise is correct. The person’s pose sequence and ten other novel features are used to train a Graph Convolutional Network (GCN) architecture to capture the relationship between the 2D coordinates of the body joints, their angles with key limbs, and a specific workout. We introduce a dataset with 2 different physical exercises (squats and lunges) to assess our methodology. Our method produces recognition accuracy for the proper workout postures for lunges and squats of 94.44% and 98.65% respectively.
A Computer Vision based Vehicle Counting and Speed Detection System

The number of vehicles on the roads are increasing with every passing year. Appropriate measures are required to gain some information about the traffic density for traffic management. Moreover, higher the number of vehicles on roads, higher are the chances of rash driving and Overspeeding. This paper addresses the issue by proposing a vision-based approach to estimate vehicle speed and set up an overall vehicle counter as well a counter of vehicles belonging to different classes. This paper provides practical significance for traffic management on roads. The implementation requires three steps: video acquisition, object detection and multiple object tracking. After video acquisition, the task of vehicle detection is done using YOLOv5 which also classifies the vehicle. To track multiple vehicles in every passing frame of the video, we have used the StrongSORT algorithm which is an improvement of DeepSORT algorithm. The research experiment provided an accuracy of 85.27% for vehicle detection. The accuracy for speed of the vehicles was 87.9% with marginal room for errors from their ground truth values. Moreover, the model performs well in terms of counting the number of vehicles.
Wheel Chair Navigation Based on Head Movements for Quadriplegia Patients

The development of technology for wheelchair mobility for someone with disabilities is growing very rapidly. Many researchers have developed wheelchairs to help people with disabilities. However, wheelchair navigation is hard for quadriplegia patients, which have a state of paralysis in four parts of the hands and feet caused by an injury to the spinal cord or brain. Implementing a navigation system using head movement is an alternative solution to this problem. We propose a navigation system that utilizes the accelerometer where the signal is processed with a Kalman filter so that noise on all axis signals can be removed and the navigation system will be more optimal. Navigation is classified into 4 classes, namely forward, backward, turn right and turn left. The signal source is acquired using an accelerometer located above the head. It is hoped that the results of this study will be able to control wheelchairs that are applied to people with quadriplegia disabilities.
Image Edge Detection Using Fuzzy Logic Controller

Edge detection finds a greater significance in image processing and computer vision, as many machine learning models require images as input data. Edge detection can be used to extract important features to simplify the visual data. With the increased use of AI, latency can be reduced by processing the data locally which enhances the performance capabilities of the model. This paper reviews the effectiveness of the Fuzzy Inference System over traditional gradient-based approaches such as the Canny edge detection technique and presents a fuzzy logic-based approach for image edge detection. The fuzzy-based approach uses an open-loop fuzzy logic controller which comprises a series of steps instead of a simple thresholding techniques whose values are emperically determined. The performance is analysed for implementation in Python and MATLAB Platforms, with some variations in logic for algorithms in each software. The proposed model is applied to MRI images inorder to detect abnormalities such as tumours.
Identification of high-risk groups of falciparum malaria in western region of Ghana: The predictive value of ABO Blood group typology

Background: Several studies have linked malaria to ABO blood groups with still others reporting insignificant association between ABO blood group system and malaria. Blood group ‘O’ has been shown to confer protection against severe malaria by studies in various populations but indecisive reports have been given about non-O blood groups in relation to their protection or vulnerability to severe malaria.&#x0D; Objective: The present study sought to investigate the ABO blood group typology and the risk of developing severe malaria.&#x0D; Materials and Methods: A total of 280 participants (140) with P. Falciparum malaria patients and 140 healthy controls) screened for ABO blood groups by Tile method were enrolled into the study. Thick and thin blood films stained with 10 % Giemsa were prepared for falciparum – infected individuals and their full blood counts obtained from the haematology analyzer (Cell Dyn 1800, Abbot Diagnostic Division, USA). Parasite counts were categorized into severe and uncomplicated malaria and further grouped into varying degrees of parasitaemia. The effects of parasite densities on some haematological parameters were then studied. Severe malaria was defined as hyperparasitaemia, malarial anaemia and thrombocytopenia.&#x0D; Results: The frequency of blood group ‘A’ was significantly higher in patients with severe malaria compared to other blood groups (p = 0.042). Blood groups ‘A’ and ‘B’ showed higher parasite densities while ‘A’ and ‘AB’ blood groups revealed low platelet counts. Anaemia was severe in blood groups ‘A’ and ‘O’ (p ≤ 0.05). Previous studies including this current one highlighted the protected nature of blood group ‘O’ to severe malaria (p ≤ 0.05).&#x0D; Conclusion: The present study provides the evidences that, individuals of blood group ‘A’ are highly susceptible to malaria infection and inferably with an increased risk of developing severe malaria than the other blood groups.
Instructional Competencies of Laboratory School Teachers in Remote Blended Learning

The teaching-learning process depends heavily on teachers.As a result, they are expected to be knowledgeable in a wide range of competencies and to continue to develop their knowledge, abilities, and experiences, especially in terms of mastery of the subject matter, teaching, classroom management, and evaluation now that institutions have adopted a remote blended learning approach.The participants were eighteen teachers from Bukidnon State University, specifically the Elementary Laboratory School, and they were purposively chosen.A descriptive method of research using quantitative data was employed.The data the researcher wanted to collect needed to come directly from people, in this case, the teachers.The mean and standard deviation were used to explore the teachers' instructional competencies, while frequency and percentage were used to determine the factors affecting the acquisition of teachers' instructional competencies.Evaluation skills emerged as the topmost instructional competence practiced by teachers.From the instructional competencies of teachers, the following were the topmost indicators: teach the subject matter in a clear and simple manner; organize and present the subject matter clearly and coherently utilizing various learning materials such as PPT, learning kits, modules, worksheets, video clips, and others; share the link for the virtual class in advance and start the learning activities on time; and evaluate the performance of the learners on the basis of the learning objectives through quizzes and major examinations done in a self-paced manner.
SGAT: Scene Graph Attention Network for Video Recommendation

As a widely studied topic in recommender systems, collaborative filtering (CF) methods help users discover potential items of interest by assuming that behavioral similar users would have similar preferences on items. A recent trend is to develop models based on graph neural networks (GNNs). One limitation of existing methods is that they predict user preferences only by modeling user-item interactions, failing to identify user-item relations at the finer-grained level of semantics. For more accurate and explainable recommendation, it is compulsory to take side information into account and model user-item relations at the granularity of semantics. In this paper, we investigate the utility of semantic scene graphs in video recommendation scenario, which provide detailed, graph-based annotations of social situations depicted in movie clips. We propose a new method named Scene Graph Attention Network (SGAT), which explicitly models user-item relations at the granularity of semantics by capturing certain aspects of particular interest to the user from semantic scene graphs of historical items. Empirical results on public datasets show that SGAT significantly outperforms the state-of-the-art methods like LightGCN [2] and DGCF [3]. Further studies verify the effectiveness of SGAT and the explainability for predictions.
David Kraehenbuehl Prize

Announcement| April 01 2023 David Kraehenbuehl Prize Journal of Music Theory (2023) 67 (1): 207. https://doi.org/10.1215/00222909-10635922 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation David Kraehenbuehl Prize. Journal of Music Theory 1 April 2023; 67 (1): 207. doi: https://doi.org/10.1215/00222909-10635922 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsJournal of Music Theory Search Advanced Search The David Kraehenbuehl Prize, named for the visionary founding editor of the Journal of Music Theory, was established in 2008 and is given biennially for the best article published in JMT by a scholar untenured at the time of submission. It carries a cash award of $2,000 and is determined by a selection committee of scholars unaffiliated with Yale University or with any of the eligible authors. The two-year cycle on which the current award is based was 2020–21, or volumes 64 and 65 of the journal. The selection committee for the award consisted of consisted of Victoria Malawey (chair), Nathan Martin, and Joti Rockwell.The Journal of Music Theory is pleased to announce that the 2022 David Kraehenbuehl Prize is presented to Rebecca Simpson-Litke for her article “Flipped, Broken, and Paused Clave: Dancing through Metric Ambiguities in Salsa Music,” which appeared in volume 65, number 1 of JMT... You do not currently have access to this content.
Two-wheeled Robot Motion Planning Based on A* Algorithm with Anti-collision Mechanism and Immune Algorithm with Cloning Mechanism

The two-wheeled robot motion control and route planning are studied from two perspectives. Firstly, based on the simplified model and basic data assumptions of the two-wheeled robot, a set of dynamics equations based on the basic mechanics of the robot is derived. Then a three-dimensional motion model based on the Newton Euler method is further established to derive a set of differential equations. After that, a two-wheeled robot motion planning model based on the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm with anti-collision mechanism and the immune algorithm with cloning mechanism is proposed by analyzing the ergonomics, safety, rationality and stability. In order to further consider the rationality, stability comprehensively and combine the characteristics that the shortest motion trajectory consists of a series of unit displacement points, a multi-objective motion planning evaluation function is constructed, and the clone-immune algorithm is used for the optimal solution, so as to derive the optimal velocity, acceleration, vehicle inclination angle, and vehicle inclination velocity on each displacement point. Finally, the transformed relations are imported into the simulation model constructed by Simulink to obtain the relational curves that yield the variation of the torque on the wheels with time, which leads to the robot motion control strategy for the robot operating environment map.
A Low-cost Embedded System Integrated with Computer Vision for an Electro-hydraulic Implement Guidance System

A guidance system is an electro-hydraulic device that solves the problem of complicated control system by simplifying it. It works with computer-controlled hydraulic power technology and controls the steering, speed, and attachments (e.g., a loader). It auto-enhances safety for the operator and is easy to operate. Minimalistic design solutions are visible in all mobile hydraulic implement guidance technologies today: no bulky hydraulic machinery needed to move large amounts of heavy metals and moving parts to assemble a guidance system geared up not for transportation but utility. In this paper, a low-cost embedded system is developed which is integrated with computer vision for electro-hydraulic implement guidance system. The BPPD method proved to be effective in various lighting conditions and crop conditions.
Improved Golden Jackal Optimization-based 3D Path Planning of UAV in Distribution Network Inspection

This paper proposes an improved Golden Jackal optimization (IGJO) to solve the three-dimensional path planning of unmanned aerial vehicle (UAV) in the complex inspection environment of distribution network. The proposed Golden Jackal observation strategy improves the location update formula by considering the boundary information of the search space of the solution. At the same time, a balanced renewal mechanism is proposed to enhance the ability of the population to jump out of the local extremum. According to the inspection task and environment characteristics of distribution network of UAV, the fitness function model of route planning is constructed, and a three-dimension (3D) simulation map model is built. The effect of IGJO is verified by two groups of simulation experiments. The experimental results show that IGJO has better solution quality, good algorithm robustness and fast convergence characteristics, which verify the feasibility and superiority of IGJO in solving complex 3D path planning problems.
A Method for Robot to Detect the Facial Landmarks with Masks

As the epidemic intensifies aroun d the world, more and more people are wearing masks, which also makes it more difficult for robots to recognize key points of faces. Because of the distribution of face data with masks is completely different from the distribution of face data without masks, the accuracy of existing face points detection algorithms in academia and business is greatly reduced. Inthis paper, we present a facial keypoints detection algorithm with masks, which is as accuracy as the keypoints detection algorithm without masks. We propose 1) a low-cost facial landmark detection method with masks. 2) a realtime network that enhances the cognitive ability of each layer’s receptive field. This solution can be implemented at low-cost and has high accuracy, contributing to the epidemic prevention and control work.
Website Fingerprinting Defensive Algorithms Against Various Attacks

A local listener can identify the websites a user is accessing via an encrypted connection thanks to website fingerprinting. Modern web fingerprinting techniques have been demonstrated to be successful even against Tor. RBP and Walkie-Talkie, two recent lightweight website fingerprinting protections for Tor that significantly weaken current attacks, have been suggested. We attempted to compare the current Website fingerprinting attack strategies and defense algorithms in this paper. RBP and Walkie-Talkie algorithms have been used as protection mechanisms in some of our studies. Our research is based on determining the efficacy of defense algorithms against different attacks and contrasting the various attack algorithms that are now accessible. To do this, we used homology analysis.