Reviewerly/exHarmony huggingface dataset:
AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches.
Hybrid Encryption Based on Fernet and Rivest Shamir Adleman (RSA)

RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. This study uses Fernet and RSA which is the combination of symmetric and asymmetric encryption called hybrid encryption. In addition, the cipher text will be hidden inside an image using Stepic. Hybrid Encryption uses asymmetric encryption to encrypt the symmetric encryption secret key, it will secure the symmetric encryption. The result of this study is the lowest error that we got as the MSE is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche (AVA) with 79.00% and 42.34% in order. Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 46.48% for UACI and 99.86% for the NPCR.
Post-Stroke Therapy Design Based on ROM Classification and Angle Accuracy of Stroke Patients

Stroke is a chronic disease that can have a dangerous impact and cause circulatory disorders in the brain so it experiences blockage of arterial blood that comes from blood deposits in blood vessels. A ruptured blood vessel that affects the blood vessel wall results in a reduced supply of nutrients and oxygen to the brain which causes damage to brain tissue. stroke can result in disability and death in the sufferer. so far, stroke treatment still relies on therapists who carry out therapy in a conventional manner. based on the increasing data on stroke patients, the purpose of this study is to facilitate the therapist in conducting post-stroke therapy, so that the therapist does not move the patient's hand manually and the therapy results can be seen in real-time. This Post-Stroke Therapy Tool is controlled using a microcontroller based on the arduino Mega 2560 Microcontroller using a Power Window motor as a flexion-extension arm drive, and an RDS3235 Motor as a finger drive, and uses an Internet of Things (IoT) System to monitor the progress of therapy in post-stroke patients. This tool can perform flexion-extension movements with 3 angles, namely 30, 45, and 90. In this post-stroke therapy tool, there are several constituent components including the use of dc motors (power window motors and RDS3235 motors as arm and finger drivers. And ESP32 is used as an IoT input to send data to the website which later the results of the motor movement at a predetermined angle can be displayed on a cellphone / PC. In conclusion, motor movements in this tool are expected to provide convenience and reduce the risk of disability with the accuracy of motor movements with a standard error value of 0.02%
Low-Light Image Enhancement Network based on Gamma Correction and Multi-scale Attention Mechanism

A low-light image enhancement method based on Gamma correction and a multi-scale attention mechanism is proposed for the problem of detail loss due to low image brightness and contrast under low-light conditions. First, we extract the image luminance and color feature maps through an attention network, construct Gamma values using each pixel of the feature maps, and perform Gamma correction on the input low-light images to enhance the brightness and contrast of the images. A multi-scale attention network is designed to extract the noise distribution features of the low-light image, and a U-Net network is used to achieve image noise suppression. Experiments show that compared with other existing algorithms, the method in this paper has some improvement in the objective indicators of low-light image enhancement, PSNR, SSIM and NIQE.
Human Action Recognition Based on Residual Networks with Improved Attention Mechanisms

For the current deep learning methods in human movement recognition, there are problems of high number of parameters and insufficient feature extraction., this paper proposes a residual network incorporating improved attention mechanism for human behavior, which introduces an attention mechanism into the residual network and the multilayer vector machine for the channel attention module was replaced using a one-dimensional convolutional, which reduces the feature loss and the parameter number at the same time, and replaces the convolution for the spatial attention module with a dilated convolution, which expands the sensory field. The recognition accuracies of this paper are 66.7% and 96.5% on the public datasets HMDB51 and UCF101, respectively, and the recognition accuracies are improved by 7.3% and 8.5%, respectively, compared with the traditional deep behavior recognition method TwoStream.
Depth Image Restoration Algorithm Based on Bilateral Filtering

Depth images are also known as distance images, whose grey scale values characterise the distance of an object, and depth images play a key role in 3D scene reconstruction, virtual viewpoint synthesis, and 3D video technology. Depth image has a variety of acquisition methods, among which, active ranging uses the device itself to emit energy to complete the acquisition of depth information, with fast and cheap characteristics, but the depth image produced by active ranging equipment in the edges of the object and the occlusion region often produces voids, which is not conducive to the subsequent research applications. In this paper, we propose a depth image quality enhancement algorithm that combines the colour image and the depth image to improve the confidence of bilateral filter repair by evaluating the weights of the pixels in the neighbourhood of the pixel to be repaired, and the experimental results show that the method achieves a better repair effect for the voids in the original depth image.
A Segmentation and Stitching Method on Infrared Images of Porcelain Post Insulator

The infrared thermography has currently played an increasingly important role in nondestructive online testing of porcelain post insulators in power substations. The thermal information in infrared images become essential as the abnormal temperature rise can be utilized for fault diagnosis. A segmentation and stitching method is put forward to form a full-view infrared diagram out of multiple infrared images shot on same post insulator from different angles, which contains all the necessary thermal information for fault diagnosis, with advantages of reduced storage resources, enhanced storage efficiency and runtime performance when dealing with massive infrared images of porcelain post insulators.
Based on Improved YOLOV7 Recognition of Distribution Room Instrument Panel Information

With the development of enterprise intelligence and automation, the reading of instrument panel information in distribution rooms is no longer limited to manual work. This paper proposes an object detection method based on an improved YOLOv7-Tiny network model. By inspecting the image information of pointer instruments collected by robots, YOLOV7-Tiny is used to achieve three key steps: instrument panel positioning, instrument panel pointer and scale positioning, and instrument panel number recognition. By introducing the ShuffleAttention attention module into the existing network structure, combining spatial attention mechanism and channel attention mechanism, we focus on the feature information of small targets, suppress unimportant features, and achieve more accurate detection and recognition. The experimental results show that the improved object detection algorithm outperforms the YOLOV7-Tiny original model in terms of mAP@0.5 improved by 0.5%, significantly improving the detection accuracy of small targets, achieving more real-time and accurate dashboard information detection.
Enterprise Intranet Threat Intelligence Processing Framework Based on Open Source Community

Network attack tactics, techniques, procedures, and IOC are being updated more and more rapidly. Defenders need to obtain such threat intelligence in a timely manner, to identify whether the enterprise intranet is under attack, and take corresponding protective measures. Before the official disclosure of the details of the attack, the open source community often already has users in the discussion of the attack, if we can extract threat intelligence that strong related to the enterprise from the open source community in a timely manner. It will be a great help in monitoring the enterprise intranet. However, new posts are published every day in the open source community, and some of the posts may have nothing to do with network security, and the threat intelligence disclosed in the posts may not be useful to the enterprise intranet. To bridge the gap, we propose EITIP, a framework for automated enterprise intranet threat intelligence processing. EITIP automatically collects new posts from open source community, classifies posts related to cyber security, obtains effective threat intelligence, and keep it updated. To address multiple challenges, EITIP provides: (1) effective threat entities, which can help obtain richer threat intelligence. (2) a method for obtaining strongly correlated threat intelligence on an enterprise intranet. (3) a complete threat intelligence processing framework.
Pedestrian detection based on SENet with attention mechanism

Pedestrian detection is a fundamental task in computer vision with wide-ranging applications such as autonomous driving, surveillance, and human-computer interaction. The advancement of deep learning has revolutionized pedestrian detection, enabling more accurate and robust detection systems. In this study, we propose a novel approach that combines the power of SENet with the attention mechanism to improve pedestrian detection performance. We proposed the pedestrian detection technology based on the SENet attention mechanism consists of several key steps. First of all, the use of residual neural network backbone advanced features extracted from the input image; Then introduces SENet based architecture to the attention of the module, characteristic response to realign the channel direction, emphasizes the information related to the pedestrian characteristics, at the same time suppressing irrelevant details, our attention to the existing channel module modified, increase the space information learning in the feature maps, highlight the characteristics related to the target area in the figure, is helpful to keep out the pedestrian space characteristic of effective information.
GM-HRNet: Human Pose Estimation Based on Global Modeling

Convolutional neural networks have been widely used for human pose estimation tasks, but with some issues. It is limited to local receptive fields and it is difficult to capture global information. To address this problem, we propose the GM-HRNet network. The network aggregates the multi-stage feature information of HRNet, and makes full use of the criss-cross attention and channel attention to obtain the context information in the space and channel dimensions, and realizes the modeling of the global relationship, so as to effectively locate the keypoints of the human body. In this paper, HRNet is used as a benchmark to conduct experiments on MPII dataset. Experimental results show that the accuracy of GM-HRNet is 1.3 percent higher than that of HRNet under the same experimental conditions, which validates the effectiveness of the GM-HRNet model.
Openness and Sensing Capacity: The Case of Micro and Small Furniture Industries in Tanzania

Mainstream open innovation (openness) research has directly linked it to innovation performance, leaving intermediary processes such as innovation opportunity recognition (sensing capacity) not fully explained. This study examined the relationship between openness (inbound and coupled openness) and sensing capacity. The study surveyed a quota sample of 213 micro and small furniture industries (MSFIs) in Tanzania’s cities of Arusha, Mbeya, and Dar es-salaam and applied a close-ended questionnaire to collect data from owners (managers) of the micro and small furniture industries. Subsequently, the study used partial least square structural modeling (PLS-SEM) to analyze the data and found that inbound openness positively affects sensing capacity directly and indirectly through coupled openness. Coupled openness partially mediates the effect of inbound openness on sensing capacity. Also, coupled openness positively affects sensing capacity. Openness and dynamic capabilities views are complementary; small business managers embracing inbound and coupled openness enhance their sensing capacity.
Circulation and Narrative in a Virtual Environment

Taking the notion of narrative space as a point of departure, this paper looks into possibilities of ‘spatializing narrative’ in virtual environments based on natural walking. One way to approach the workings of narrative space in virtual environments is through the lens of circulation. In ‘real-world’ architectural environments, the circulation space consists of multiple elements that program movement through space, connect spaces in a sequence, and direct the gaze. These circulation elements carry an expressive potential that can be brought into use in the design of narrative spaces. Circulation principles from ‘real-world’ architecture, however, do not readily translate to virtual environments. In a virtual environment, the notion of circulation itself needs revisiting. This paper examines to what extent, and how circulation principles come into play in an immersive narrative game called Lavrynthos. It examines the tactics that were used to create a continuous circulation space and assesses how circulation is brought into use in the narrative of the game. It identifies several ways in which the circulation space is deployed as a narrative device. The paper, finally, describes a dynamic wherein the circulation space changes as the player moves through the environment, opening up in the direction of movement. This spatial dynamic produces a range of creative possibilities for designing immersive narrative spaces based on natural walking.
Prompt Engineering for Narrative Choice Generation

Large language models (LLMs) have recently revolutionized performance on a variety of natural language generation tasks, but have yet to be studied in terms of their potential for generating reasonable character choices as well as subsequent decisions and consequences given a narrative context. We use recent (not yet available for LLM training) film plot excerpts as an example initial narrative context and explore how different prompt formats might affect narrative choice generation by open-source LLMs. The results provide a first step toward understanding effective prompt engineering for future human-AI collaborative development of interactive narratives.
Traversing Space in The Under Presents (2019), a VR Game

This paper reports upon a preliminary investigation of narrative structures embedded within The Under Presents (2019), a pioneering VR game produced by independent Los Angeles-based studio, Tender Claws. The narrative experience of the game involves a blend of open-world exploration and quest-based experiences that actively encourage the player's bodily engagement in story creation. With reference to Ryan’s notions of emotional and strategic space in digital narratives (2015a), I explore the player’s manipulation of space in the game world as an embodied activity that generates narrative, mediated through the VR headset and hand controllers. The malleability of space emerges not only as a central theme within the project but also profoundly influences how players interact with the story world. Further to identifying overlapping emotional or strategic spaces within the game world, I propose the notion of ‘corporeal space’ to address the constituting role of the player's body in the 360-degree VR environment and in the generation of story. Considering this interplay between space, story and the body, I propose that The Under Presents serves as a valuable resource for exploring the potential of narrative experiences within the realm of VR gaming.
A Human Capital Perspective on Behavioral Factors Affecting Customers’ Acceptance of Crowd Logistics: A Systematic Literature Review

This paper provides a systematic review of the customer’s behavioral deliberations that govern their choice to participate in a crowd-logistics market. Little attention has been given to the crowd logistics platforms and small businesses. This paper presents the analysis of customers' crowd logistics adoption from a behavioral perspective. The study applied a systematic literature review method and examined previous literature on crowd logistics from Science Direct and Taylor and Francis. Systematic literature review involve the principles of transparency, rigor, and replicability, which bring significant benefits over traditional literature reviews through greater objectivity. The findings suggest that customers’ ability to use technology, safety considerations, desire to make social connections, trust, and convenience are the factors that influence people’s decisions to participate in the crowd-logistics market. The findings of this study are valuable for empirical research in a particular setting and strengthen the body of knowledge on the adoption or acceptance of the crowd-logistics concept.
Differential Evolution with a Level-Based Learning Strategy for Multimodal Optimization

Multimodal optimization aims at efficiently finding multiple optimal solutions of a problem. Owing to the population-based search mechanism, evolutionary algorithms (EAs) are becoming increasingly popular in solving multimodal optimization problems (MOPs). Most existing work focuses on designing and incorporating niching techniques into EAs so that multiple subpopulations can be formed and assigned to locate different optima. To further enhance the exploration and exploitation abilities of existing EAs, this paper developed a multimodal level-based learning strategy. The basic idea is that individuals should be treated differently according to their positions in the subpopulation. In the evolutionary process, a subpopulation is formed for each candidate solution by grouping its neighboring solutions. Then, individuals in the subpopulation are sorted according to their fitness. Subsequently, the multimodal level-based learning strategy applies different mutation operators to different individuals according to their rankings. Experiments are conducted on a set of benchmark problems to verify the efficacy of the multimodal level-based learning strategy. The results show that the proposed learning strategy can significantly enhance the performance of the existing algorithm. In addition, the algorithm integrated with the proposed strategy is applied to the task of finding multiple roots of nonlinear equation systems (NESs). The results indicate that with the support of the proposed learning strategy, the integrated algorithm compares favorably with state-of-the-art root finding algorithms.
Awash: Prospective Story Sifting Intervention for Emergent Narrative

Emergent Narrative (EN) affords extensive agency to participants but conflicts with the desire to guarantee compelling narrative. Recent work presents story sifting, an approach that curates simulation output, believing that intervention compromises the aesthetics of EN. However, this type of retrospective story sifting cannot improve narrative in participatory storyworlds, where the story emerges through play. We propose a new form of prospective story sifting intervention, that uses an incremental story sifter to identify possible stories during play, and passes these to a drama manager that intervenes in the simulation to make those stories more likely to complete. Our approach is incorporated into Awash, a pirate-themed EN game, and through qualitative analysis we find that the intervention increases narrative completeness and does not appear to compromise key EN aesthetics. Our work thus demonstrates a new technique that mixes generative and emergent approaches, and shows that intervention can be compatible with the aesthetics of emergent narrative.
Exploring the Union Between Procedural Narrative and Procedural Content Generation

Level Generation and Narrative Generation have often been separated from each other, even though they are both forms of Procedural Content Generation (PCG). The union of these between level generation and narrative generation, however, is under-explored given their history, and by exploring the union between these two topics will lead to new forms of PCG-enabled gameplay and stories. To address this under-exploration, we list both narrative and level generators and a description of a possible combination of these systems through shared knowledge representation structures. We also lay out various evaluation dimensions for these combined systems, which include level generation evaluation, expressive range, procedural narrative evaluation, and believable character evaluation. Finally, we describe the possible new frontiers that this approach enables, such as generating areas based on character personality traits.
Measuring Narrative Engagement in Interactive Cinematic VR Experiences

This research involves the development of a cinematic VR experience that exhibits narrative engagement and the investigation of possible measurement tools to evaluate that engagement. This is accomplished by the implementation and analysis of standardized self-reporting measures and observational data. The efficacy of these measurement tools is discussed as well as their possible modifications and limitations for storytelling in VR.