Reviewerly/exHarmony huggingface dataset:
A Note on Optimal Retraining Strategy for ML Systems

In this paper, we discuss the maintenance problem of ML (Machine Learning) systems. In general, the ML system is fine-tuned with the data observed from the operational environment before deployment. However, it is known that the performance of the ML system degrades with time. This is called model drift. There are a variety of causes of model drift, such as the degradation of the hardware machine itself and the climate condition. To deal with the model drift, ML system should be maintained with retraining. The retraining is to perform fine tuning of ML systems with the data collected from the current operating environment. On the other hand, the retraining of ML system causes the maintenance cost, so it is important to make a balance between the retraining cost and the penalty of degraded system. This paper presents probabilistic models to represent the retraining strategy for a simple ML system with discriminator. Specifically, we consider the periodic retraining strategy and the detector-based retraining strategy in our probabilistic models. Through the numerical experiment, these strategies are compared in terms of maintenance cost.
[People In Control]

In this issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Control Systems</i> , we speak with Marc Bodson, who is a professor of electrical and computer engineering at the University of Utah, Salt Lake City, UT, USA; Shun-ichi Azuma, who is a professor at Kyoto University, Kyoto, Japan; and Serdar Yüksel, who is a professor in the Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada.
Learning Self-Correlation in Space and Time as Motion Representation for Action Recognition

Human skeleton, as a compact representation of human action, has received increasing attention in recent years due to the easy accessibility of the human skeleton data. Many previous works capture local physical dependencies among joints, which may miss implicit joint correlations. In this light, we propose a novel motion representation scheme to infer action categories, which includes self-correlation transformation, feature extraction and feature fusion modules. Our network takes a sequence of 2D skeletal heat map as the input, and captures the correlation across frames and adjacent skeleton key points to reason key motion features. Extensive experiments have been conducted on 3 mainstream skeleton-based action recognition datasets, in which our network exhibits superior performances than other state-of-the-art methods
Motion Compression using Structurally Connected Neural Network

Motion compression technologies can significantly reduce the redundant information of motion data and increase the efficiency of storage and transmission. Current methods mainly utilize some ready-made universal algorithms, such as signal processing and dimensionality reduction, to model the statistical characteristics of motion data, while the individual structure of motion data is ignored. In this paper, we propose to use a deep neural network with specially designed architecture to represent motion data considering the similarity between the articulated structure of a human skeleton and the architecture of neural networks. The network parameters are then taken as the compressed data. We design a structurally connected network which just looks like a human skeleton. Within the network, only the neurons corresponding to the joints connected to each other in a human skeleton are connected. It effectively exploits the correlations between connected joints to cut down the unnecessary connections between the neurons, which leads to the significant improvement of compression efficiency. Additionally, we extract the two inherent DOFs instead of the original three DOFs of each joint by representing its movement on a sphere according to the rigidity of the articulated human skeleton. This actually achieves the theoretically lossless pre-compression with the ratio of 3:2. Extensive experiment results demonstrate the superior performances of the proposed model at the high compression ratios over other state-of-the-art methods.
External Dynamic List and Middle Relay Tor for Internet Access Control in an Enterprise Network

The use of Tor Browser in an organization allows network users to bypass Internet access controls without being detected, in addition, onion services may contain malware or be used to distribute it, which could negatively affect the organization’s reputation. This research aims to evaluate the effectiveness of configuring the External Dynamic List (EDL) mechanism and a Middle Relay Tor (MRT) combined for Internet access control in an enterprise network versus using both techniques independently. Three scenarios or configurations were deployed, Tor’s EDLs directories were also used, and a VPS as a middle relay that captures the IP addresses of the bridges that establish circuits with the same VPS in order to add them to the firewall’s blacklist. The operation of the scenarios was automated with scripts in Python. As part of the results, it was found that 67% of the total connection attempts to the Tor network were blocked by the firewall with EDL&MRT combined configuration, compared to 1% average if each configuration is used independently. The impact of the configurations made in the firewall on Internet access with a traditional browser (Google Chrome) was also contrasted under the assumption of normal user behavior, and it was obtained as a result that only 3.0% of the connection attempts were unsuccessful.
Simulation and Analysis of Optical Neural Network Accelerators Based on Multimode Interferometers and Phase Shifters

Optical unitary converters, which are implemented by multiport directional couplers or multimode interferometers, offer compactness and lower-power operation when performing unitary matrix operations. Multimode interferometers are particularly advantageous due to their good tolerance to fabrication errors and insensitivity to wavelength and polarization. Moreover, cascading optical unitary converters with Mach-Zehnder Interferometer arrays can achieve arbitrary matrices, which are frequently used in neural network computation. In this paper, we simulate and analyze an optical neural network accelerator based on multimode interferometers and phase shifters to perform arbitrary matrices. We compare its performance under different phase deviation conditions and evaluate its feasibility in neural network computations through convolutional operations. The simulation results demonstrate that the mean square error of the proposed accelerator is approximately -20dB and it exhibits excellent performance in realizing convolutional operations.
Design and implementation of an ecological big data sharing incentive system based on contract theory and game theory

The Three Rivers Source region is located in the high-altitude hinterland of the Qinghai-Tibet Plateau, and its ecological complexity and biodiversity render its ecological data invaluable. To address the issue of improving people's willingness to share data, an incentive system for ecological big data sharing based on contract theory and game theory is proposed in this paper. The combination of these two incentive mechanisms comprehensively considers various scenarios in actual situations and has a positive incentive effect on sharing ecological big data. The incentive mechanism based on contract theory formulates detailed contractual data sharing guidelines based on the quality, scarcity, and demand of shared data. This mechanism clarifies the accurate benefits and costs of data sharers and data demanders and effectively promotes the sharing of non-competitive data sets. Additionally, the incentive mechanism based on game theory designs the data sharing process as a game model. This approach maximizes the benefits between data sharers and data demanders, thus promoting the sharing of competitive data sets. Finally, the paper provides a specific implementation of the incentive system, and the results align with expectations.
Exploring the Interplay between Perceived Organizational Support, Employee Performance, and Innovation: A Study of the Moderating Role of Organizational Learning

The study purpose is to examine the relationship between perceived organizational help (POS), worker performance, and innovation, and to investigate the moderating function of organizational studying in this relationship. The observe employed a quantitative studies layout and a correlational survey approach to accumulate statistics from 500 employees from extraordinary agencies. The consequences of the look at discovered that there is a fantastic and huge courting among POS, worker performance, and innovation. Furthermore, the study determined that organizational studying played a extensive moderating role in the dating between POS, worker performance, and innovation. Specifically, the findings confirmed that the effective effect of POS on worker overall performance and innovation became more potent in companies with a excessive stage of organizational gaining knowledge of. The study's findings contribute to the literature on organizational help, worker performance, innovation, and organizational mastering, and provide realistic implications for businesses searching for to enhance worker overall performance and innovation via effective POS.
Development of Research Progress Tracking System Based on Shneiderman’s Eight Golden Rules

Monitoring the progress of research is crucial to ensuring the timely completion of work for postgraduate students. However, the current practice of relying on a text editor tool to document research work sessions has its drawbacks, such as the risk of data loss and difficulties in accurately calculating the total duration of meetings. In order to tackle these concerns, the development of the E-Logbook System for Postgraduate Research Students (ESPRES) was undertaken with the aim of optimising and augmenting the procedure of documenting and reporting meetings held between postgraduate research students and their supervisors. ESPRES offers an efficient method of recording, reporting, and monitoring meetings by providing postgraduate research students with access to a centralised platform that records and stores meeting details, reducing the risk of record loss and improving the overall monitoring and evaluation process conducted by postgraduate studies management. ESPRES was developed using the Adapted Waterfall Model, which includes six stages: planning, requirement analysis, design, development, testing, and documentation. Five features from Shneiderman's Eight Golden Rules were adopted to design the user interface. To ensure quality, the system underwent expert evaluations, with feedback provided by two experts. In the future, various enhancements can be made to further improve ESPRES, such as adding a grading system with colour coding and integrating a chat box to facilitate communication within the system.
The Acceptance of Customer Towards Service Robots in Restaurants

The purpose of this thesis is to examine the readiness of customers towards service robots in restaurants. Problems faced by customers who are not satisfied with service robots in restaurants such as the issue of not being familiar with service robots and feeling uncomfortable engaging with them. The other issue is that service robot interactions' emotional and functional components may affect consumers and significantly impact their experiences and performances. However, the use of robot service in restaurant help to reduce physical contact between staff and customers due to the pandemic Covid-19 cases right now. Through the use of the quantitative method, this research focuses on Technology readiness, Perceived ease of use, Perceived Usefulness, and Readiness of Customers for service quality of robot service restaurants. A literature review was conducted to identify information on independent and dependent variables. Furthermore, this study's main objective is to identify customer readiness towards service robots in restaurants. The response of this study was a total of 119 respondents. The results show that Technology readiness, Perceived ease of use, Perceived usefulness, and Readiness of customer affect customer satisfaction. The three independent variables of this research had a positive relationship. In conclusion, it is important to maintain the quality of services to customers to make them continue to enjoy the experiences and give them a sense of satisfaction while using the service robots in restaurants.
AOL's got mail ... from an unhappy investor

Herein is a decision case-study based on the 2012 proxy contest between AOL and one of its institutional investors, Starboard value (Starboard). At the heart of the dispute is AOL's digital media strategy championed by its new CEO and Board Chair, Tim Armstrong. Starboard asserts AOL's expensive and unimpressive digital media operations more than offset its relatively profitable internet service. Furthermore, Starboard suggests AOL's misguided strategy was spawned and nurtured by CEO duality, interlocking board directors, conflicts of interests, and misaligned executive compensation. The case requires students to evaluate strategic, behavioural, social, and political considerations in formulating AOL's response to Starboard's claims.
Unsupervised learning of local features for person re-identification with loss function

Many methods for person re-identification focus on making full use of local features, which typically requires either comprehensive manual labelling or complex pretreatment. This paper proposes a novel loss function, termed feature channels dropout and de-similarity loss that drives the autonomous learning of discriminative local features in convolutional neural networks. The proposed loss function consists of two components. The first is a feature channels dropout component designed to compel each feature channel to be discriminative. A novel channel-dropout function and a cross-channel-element-max function are applied in this component. The second component is a de-similarity component that uses the Pearson correlation coefficient to constrain feature channels and ensure they differ from each other. This component is conducive to diverse local features in mining. Extensive experiments on three large-scale re-identification datasets demonstrate that feature channels dropout and de-similarity loss achieve superior performance compared with state-of-the-art methods.
Natural language and mathematical reasoning

Every natural language presents certain regularities that have been studied for years. From statistical approaches to machine learning, many authors have found that automatic processing is far more complex than any other brain production. Despite the current status in the field, many applications like chatter-bots, speech recognition, and sentiment analysis, show that there is still an interesting gap between analysis and production of sentences. Despite the solutions used nowadays, the deep essence of linguistic reasoning dynamics keeps mostly not revealed, and many of the current approaches involve the conception of restricted patterns for human linguistic reactions, usage, and interpretation. This chapter presents a perspective of this problem centered in the idea followed by many authors that considers the brain as a complex device working under some kind of fractal rules, and deeply related to entropy. As part of the scope, there is a short review of some of the most paradigmatic proposals of language mathematical descriptions related to entropy and fractals, the relationship between them in this context and a summary of previous publications of the author that aim to reveal the deep nature of natural language, modeling linguistics-related brain activity.
Quantum Computing for Data Scientists and Quantum Machine Learning

Quantum computation and quantum information have sparked tremendous interest across a wide range of scientific disciplines, from physics to chemistry and engineering, as well as computer science, mathematics, and statistics. Data science is the application of statistical methodologies, computer algorithms, and domain scientific data to extract knowledge and insights from large amounts of data and solve complicated real-world issues. As a result, quantum machine learning has carved itself a distinct niche in the world of computers. When the potential of quantum computing characteristics is employed for machine learning, quantum technology advances to an advanced degree. When quantum computing capabilities are incorporated into standard methods, they give extraordinary parallel computing power for addressing complicated problems. The core of this work is a comparison of the fundamental principles of quantum computing and their superiority over traditional computing. This paper discusses application-based algorithms including QSVM, QPCA, and Q-KNN, as well as Grover's algorithm, the most common and foundational quantum machine learning technique.
Conclusion

This book presents state-of-the-art research in various fields of multimedia processing and computer vision along with the applications of artificial intelligence, machine learning, and deep learning to perform various processing tasks in numerous applications, including medical imaging, robotics, remote sensing, autonomous driving, law enforcement, biometrics, multimedia enhancement and reconstruction, agriculture, and security. The book also provides a detailed discussion of the latest trends in processing tools required for computer vision applications. This is an attempt to provide a practical and adequate platform for researchers and practitioners from all over the world working in the fields of image processing, biometrics, computer vision, machine learning, and deep learning.This book covered cutting-edge research from reputed research and academic organizations with a particular emphasis on interdisciplinary approaches, novel techniques, and solutions to provide intelligent multimedia for potential applications. We first cover recent trends, new concepts, and state-of-the-art approaches in the field of multimedia information processing for various emerging applications. We end the book with a chapter on future perspectives and research directions. A brief discussion of the major topics covered in this book is given below. The chapter-wise conclusion of this book is as follows.
AI- and ML-based multimedia processing for surveillance

Nowadays, surveillance systems are yielding the most critical and large volumes of data in the world from various sources. These data require proper management and analysis to produce relevant security information for modern security operations. However, it is still a challenge for humans to vigilantly monitor these large volumes of surveillance data for security assurance. Considering the upsurge in smart technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and much more, the present security systems can be equipped with these technologies to radically increase the efficacy of the surveillance systems. The self-learning capabilities of AI and ML technologies make great impact on surveillance systems. This chapter comprehensively discusses the possible amalgamation of AI and ML technologies with the modern surveillance systems that will give them a technological edge. It also discusses all new findings based on object detection, visual sentiment analysis, video analytics, vehicle analytics, tracking people for potential crimes to ensure security for the society. Moreover, the specific types of AI and ML surveillance infrastructure being deployed are also discussed.
Action recognition techniques

Due to the common availability of surveillance as well as mobile camera, data generation in the form of images and videos has increased a lot. This in turn increased the requirement of data storage and, so manual analysis and interpretation is almost impracticable. Researchers have been working on the development of efficient as well as high-speed technologies capable of intelligent analysis and interpretation of visual data.Video surveillance is in practice since long, but accuracy and high speed in detection and recognition still remain to be a challenge. This includes a variety of areas of research such as:Pattern recognition: This aims at classifying the objects or data in the input images based on their inherent characteristic features.Object tracking: It aims at tracking the object&apos;s location and movements in video sequences to monitor its activities and behavior.Reconstruction: It typically aims at building two-dimensional or three-dimensional models of objects for advanced feature detailing.Feature extraction: It is the process of reducing the dimensionality of the input image by extracting and selecting a subset of salient features representing the input data.Segmentation: This provides for the extraction of the region of interest (ROI) from the gallery image based on different features. It divides the probe image into nonoverlapping areas, namely ROI areas and non-ROI areas.Action recognition is a basic task in computer vision that focuses on identifying and understanding human activities from video stream. It is important for various applications, including video surveillance (static or dynamic), human-computer interaction (HCI), sports video analysis, and autonomous vehicles. Human action recognition covers an extremely large number of research topics in computer vision and has a wide range of applications in visual surveillance. Action recognition in visual surveillance refers to the process of automatically analyzing and understanding human actions and activities captured by surveillance cameras or video feeds. This plays a crucial role in various applications, such as security monitoring, anomaly detection, and behavior understanding.The following sections present a review of action recognition in visual surveillance, including its challenges, techniques, and recent advancements.
Improved Detection and Tracking of Objects Based on a Modified Deep Learning Model (YOLOv5)

Recent years have seen advances in deep learning, including in the field of traffic management. Detecting distant objects that occupy a small number of pixels in the input image is one of the major challenges in computer vision for several reasons, including limited resolution. The challenges of detecting the rotation of objects may be attributed to the deflection of the camera when taking photographs. We recommend enhancing the features of the YOLOv5 network. The proposed method is to train a model on a traffic dataset, which achieves the best inference results through training, testing, and detection on a 1280 × 1280 image for 300 epochs. Moreover, modifications were made to some structural elements of the YOLOv5. In addition to detecting round objects by increasing degrees from 0 to 270, it also increases the probability of flipping in all directions: up, down, left, and right. In addition, the degree of rotation of the image was increased to 90 degrees. The results showed optimized accuracy in detecting distant and small objects, as 73 objects were detected compared to the original YOLOv5 23 objects. It achieved the best number of objects detected in the video (people, cars, and others), and detecting rotating objects increases the number of detected objects (32 objects). The inference time was (23 Ms.) this dataset can make excellent traffic monitoring applications. This model can be deployed on an Android mobile device to provide accurate data about current traffic at a specific location. This is because a mobile device can be used at any time and place. Therefore, in the future, we are working on designing models for object detection that can be operated on mobile devices.