Reviewerly/exHarmony huggingface dataset:
Design and Fabrication of Imprint Machine

Abstract: This Research Paper aimed at automating the process of marking serial numbers or identification codes on metal, wood, plastic components. This Machine accurately punch numbers onto metal, wood, plastic parts with varying sizes and thicknesses. The machine's design includes a robust frame, a pneumatic system, and a control system that allows for precise control of the number punching process. The punching mechanism consists of a series of hardened steel punches that can be easily replaced and adjusted to accommodate different numbers and character sizes. The control system allows operators to input the desired serial number or identification code, control the punching force, and regulate the individual spacing. The machine also includes safety to prevent injury to operators and damage to the metal parts. Overall, the design and fabrication of a imprint machine for metal parts will provide an innovative solution for manufacturers looking to streamline their product identification process.
Analysis of the Standardization of Administrative Management in Colleges and Universities Under the Background of Information Technology

In the development of the information age, universities should recognize the significant impact of information technology on their own development. In practice, it is possible to effectively improve the efficiency and quality of work by dealing with and constructing information-based methods, innovating administrative management models, and adopting more efficient and scientific methods to address various problems and shortcomings encountered in work. As an important part of university management, administrative management is a complex task. Establishing a standardized management model through information technology can further improve the comprehensive efficiency of administrative management. In this regard, the administrative standardization in universities under the background of information technology is analyzed and studied in this paper.
Semiconductor Architectures Enable Compute in Memory

Compute in memory (CIM) promises faster and lower power processing of data. Recently presented papers at the 2023 IEEE ISSCC gave some examples of how various semiconductor architectures can enable CIM devices for various computing applications.
Understanding auditory intuitive physics

Sound is often caused by physical interactions between objects. Humans have some ability to discern these interactions by listening. This talk will describe our lab’s efforts to understand these perceptual inferences. Our research involves three inter-related approaches. First, we make physical and acoustic measurements of everyday objects and surfacesto characterize the sound of real-world objects. Second, we develop methods to synthesize sounds from physical interactions (impacts, scrapes, and rolls). Third, we use these sounds to investigate the perceptual processes and representations underlying auditory intuitive physics. The results reveal regularities in object sounds that have been internalized by humans and that are used to infer object properties and physical events from sound.
Hand Gesture Signature Recognition with Machine Learning Algorithms

Hand gesture recognition is regarded as an imperative Human–Computer Interaction technology for non-verbal communication and interaction between human and computers. Inspired by hand gesture recognition systems, hand gesture signature recognition is proposed to replace the existing online handwritten signature recognition which requires a stylus pen and tablet as middleware between users and the systems. The possibility of germs contamination on these shared devices alerts public concerns, especially during this COVID-19 pandemic. Hence, an in-air signature recognition, termed as iSign, that utilizes hand gesture signing movement is proposed in this paper. In iSign, a Kinect Sensor is adopted to detect and capture the hand movement of the users while signing. This mode of data acquisition does not require users for any direct touch on any devices. Next, the captured video data is transformed into feature template. Various machine learning algorithms, i.e. Bayesian network, Naïve Bayes, Decision Tree, Random Forest etc., are examined to classify the hand gesture signatures. Empirical results demonstrate that Random Forest performs the best among the machine learning algorithms with accuracy of 93%.
Facial Micro-expressions Analysis: Its Databases, Feature Extraction, and Classification Methods

Facial micro-expressions (ME) are a subtle expression that appears within a shorter duration compared to facial macro-expressions. This expression is a type of universal non-verbal communication that appears on someone’s face unconsciously when he or she is attempting to hide their true emotions. It reveals a person’s true emotional state, which is vital for both mental health diagnosis and security purposes. Because micro-expressions are typically low in intensity and short in duration, humans and/or machine learning models can be specifically trained to recognize some expressions, but the obtained performance is usually consistently poor. The research on facial micro-expression recognition is relatively new in the field of computer vision. This paper presents an overview of facial micro-expressions and their taxonomy. Each posed and spontaneous database, as well as feature representation and classification techniques, are examined and expanded upon. In addition, we conclude the overview by pointing out a few flaws in micro-expression recognition methods and making some recommendations for future research directions that could aid researchers in making progress in this field.
Copy-Move Forgery Detection Using Canny Edge Detector and SIFT-Based Blob Analysis

This work presents a copy-move forgery detection method based on blobs detection from canny edge-response images, followed by SIFT (Scale Invariant Feature Transform) key points analysis. The SIFT key points are extracted from grayscale images, which are then clustered according to their distance from within generated blobs of that image. Different key points in different blobs are ignored (unique non-duplicated key points) while the same key points in different blobs are matched. The assumption made here is that these duplicate regions are highly indicative of forgery. The proposed method is tested on the MICC-F220, MICC-8Multi, and CoMoFoD public benchmark datasets. The results demonstrate that copy-move forgery detection can be reliably performed on geometrically transformed images, i.e., rotated, scaled, combined and post-processed images such as JPEG compression, blurred, noise added, and contrast adjusted. Our method is also robust on multiple copy-move forgeries.
Secret Key Design Using an Algebraic Procedure (KAP) for Encrypted Energy Internet-of-Things (EIoT) Contents

This study designs a secret Key using an Algebraic Procedure (KAP). KAP has the capability to act in a different manner dependent on the plaintext input. Two different secret key length approaches are presented in this work. Increase the unpredictability and privacy of plaintext by using such a scheme is the main objective of this design presented in this paper. A sequence of algebraic procedures has been utilized in order to construct a particular coefficient by which the link between plaintext size and key length is customizable. This design of secret key enhances the privacy of sensitive data notably utilized with smart Internet-of-Things (IoT) based applications. Calculations on how long it takes to produce and encrypt a single secret key have shown that the suggested architecture is efficient. Additionally, the findings have demonstrated that when a directly proportional connection between user entries to size-in-bits, the relationship between user entries and secret key algebraic functions is reversely proportional thus higher unpredictability of plaintext and secret key may be attained.
Modeling coupled oscillators: Applications for musical sequencing and synthesis

There are many naturally occurring systems of coupled oscillators, e.g., the synchronization of firefly flashes, frog calls, and electrical impulses in the brain. The behavior of a large system of coupled oscillators can be approximated using the Kuramoto-Daido model. The model shows that phase calculations for each oscillator can be simplified by comparing each oscillator’s phase to the average phase of the collection, eliminating the need to sum over the whole group. In this session, we will explore this algorithm in Max/MSP. We will look at simple systems with a small number of low frequency oscillators, both free-running and rhythmically quantized/Euclidean, as a means of generating rhythmic patterns and sequences. Then we will look at larger collections of oscillators within the audible frequency range and explore how the algorithm can be used to modulate audio signals for timbral effects. This example will delve into how the coupling algorithm affects oscillators that have frequency relationships drawing from the overtone series. We will also see how delaying the averaged signal simulates a proximity effect that more closely resembles the localized synchronization that occurs in natural systems.
Impact of Intellectual Capital on Developing Organizational Performance in Light of Business Intelligence as a mediating Variable in the Jordanian Telecommunication Companies

This study aimed at revealing impact of intellectual capital on developing organizational performance in light of business intelligence as a mediating variable in the Jordanian telecommunication companies, the descriptive analytical method was used, study population consisted of all employees working in the general departments in the Jordanian telecommunication companies which are (Orange, Omnia and Zain).The number of the working employees in these companies was (1139) male and female employees, study sample for this study has been selected by the stratified random method and using a sample (310) from the employees.The questionnaire was used as the instrument for collecting the study's data, after conducting the statistical analysis process the study reached the absence of significance impact of the human capital on the organizational performance, and the presence of impact of the customer capital on the organizational performance, and the impact with statistical significance at significance level (α<0.05) of the business intelligence on enhancing impact of the intellectual capital with its dimensions (human capital, structural capital, customer capital) on the organizational performance with its collective dimensions (learning and growth, internal processes & stakeholders satisfaction) in the Jordanian telecommunication companies, the study recommended the necessity for directing the Jordanian telecommunication companies towards employing human resources, especially managerial and supervisory leaders having attitudes and contentment to follow services development practices, creating new jobs help in and support for achieving the different skills aspects.
A class of constacyclic BCH codes with length $ \frac{q^{m}+1}{2} $

As a generalization of BCH codes, constacyclic BCH codes have become a good source for constructing optimal codes.However, the parameters of these codes are not easy to determine in general.In this paper, we investigate the parameters of a class of q-ary constacyclic BCH codes with length q m +1 2 and compute their exact dimensions in some ranges of designed distance.In particular, we study the parameters of constacyclic BCH codes with the designed distance in a larger range than known results.
Two classes of two-weight linear codes over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ p\equiv 1\pmod 4 $ be a prime, $ m $ a positive integer, $ \frac{\phi(p^m)}2 $ the multiplicative order of $ 2 $ modulo $ p^m $, and let $ q = 2^{ \frac{\phi(p^m)}2} $, where $ \phi(\cdot) $ is the Euler's function. In this paper, we construct two classes of linear codes over $ \Bbb F_q $ and investigate their weight distributions. By calculating two classes of special exponential sums, the desired results are obtained.&lt;/p&gt;&lt;/abstract&gt;
Detection of Video Image Modification Using a Classifier Based on Adaptive Resonance Theory

The paper proposes an approach to determine the intentional change of video stream data. It includes the following steps: data preparation using an error level analysis algorithm and the formation of compression noise histograms, the construction of concatenated noise vectors, and data classification. As a noise histogram classifier, it is proposed to use artificial neural networks of adaptive resonance theory with a hierarchical memory structure. This allows you to classify data with different similarity thresholds and obtain additional information when analyzing video data. The proposed approach allows you to perform both deferred and real-time analysis. When working in real time, the network allows you to unambiguously classify the received images, but in some cases there is uncertainty. In this case, it is required to carry out an additional analysis of the classifier memory structure, performed in manual mode. In the work, experiments were carried out in determining distortions in video data based on the dataset for detecting video inter-frame forgeries (VIFFD). Experiments have shown that the highest quality results were obtained when detecting copying as a way of distorting video data.
Adaptive Inference for FPGA-Based 5G Automatic Modulation Classification

Automatic Modulation Classification (AMC) is key to the efficient use of the radio frequency spectrum in modern applications, like 5G-based IoT. Optimizing AMC is crucial to achieving the latency, throughput, and energy levels expected by the final user. State-of-the-art solutions to the AMC problem are based on Deep Learning methods (e.g., Deep Neural Networks - DNNs). However, these methods require heavy processing and high energy consumption up to the point that accelerators (e.g., FPGA) are used to carry out such computations. Based on the observation that the classification becomes computationally harder or easier depending on the amount of noise the signal is subjected (i.e., Signal-to-Noise Ratio - SNR), this work proposes a fully adaptive FPGA-based inference system that selects the most appropriate DNN according to the current signal quality (SNR level). Compared to the state-of-the-art static approach, the framework reduces energy consumption by up to 43% while delivering 8.9 $$\times $$ more inferences per second.
Enhancing E-Learning Material Creation: Exploring the Practices, Challenges, and Opportunities of the School Learning Resource Quality Assurance Team (SLRQAT) towards TIMER's Information Manuel of Educational Resources Project

&#x0D; &#x0D; &#x0D; This study assessed the relationship between the practices, challenges and opportunities among School Resource Quality Assurance Team (SLRQAT) members of City Schools Division of Cabuyao when crafting e-learning materials during the school year 2021-2022. Three hundred ninety-eight (398) public elementary and secondary teachers were the respondents of the study. The researcher utilized the descriptive-correlational research design and self- made questionnaire to assess and measure the variables under study. The statistical tools used to analyze the data gathered were Mean, Standard Deviation, Pearson r and Multiple Linear Regression Analysis. Findings revealed that the profile of the respondents moderate the practices, opportunities and challenges of the SLRQAT members. While, the extent of practices and challenges among SLRQAT were moderated by such profile of the respondents. To ensure a more efficient output, recommendations were suggested based from the findings of the study regarding crafting e-learning materials.&#x0D; &#x0D; &#x0D;
Hidden Quantum Memory: Is Memory There When Somebody Looks?

In classical physics, memoryless dynamics and Markovian statistics are one and the same. This is not true for quantum dynamics, first and foremost because quantum measurements are invasive. Going beyond measurement invasiveness, here we derive a novel distinction between classical and quantum processes, namely the possibility of hidden quantum memory. While Markovian statistics of classical processes can always be reproduced by a memoryless dynamical model, our main result shows that this is not true in quantum mechanics: We first provide an example of quantum non-Markovianity whose manifestation depends on whether or not a previous measurement is performed -- an impossible phenomenon for memoryless dynamics; we then strengthen this result by demonstrating statistics that are Markovian independent of how they are probed, but are nonetheless still incompatible with memoryless quantum dynamics. Thus, we establish the existence of Markovian statistics gathered by probing a quantum process that nevertheless fundamentally require memory for their creation.
Wireless interference classification with low complexity multi-branch networks

In non-cooperative communication systems, wireless interference classification (WIC) is one of the most essential technologies. Recently, deep learning (DL) based WIC methods have been proposed. However, conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy, especially when the interference-to-noise ratio (INR) is low. To this end, we propose three effective approaches. Firstly, we introduce multi-branch convolutional neural networks (CNNs) for interference recognition. The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology, and it notably improves the recognition ability for WIC. Our design avoids the carefully crafted selection of each transformation. Unfortunately, multi-branch CNNs are computationally expensive and memory-inefficient. To this end, we further propose Low complexity multi-branch networks (LCMN), which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference. Thirdly, we present novel loss function, which encourages networks to have consistent prediction probabilities for samples with high visual similarities, resulting in increasing recognition accuracy of LCMN. Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.
Novel E2E-QoE metric for PHY optimization: A cross-layered framework

Existing systems use key performance indicators (KPIs) as metrics for physical layer (PHY) optimization, which suffers from the problem of over-optimization, because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste. Therefore, it is necessary to utilize directly the quality of experience (QoE) of user as a metric of optimization, which can achieve the global optimum of QoE under cost and energy constraints. However, QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY. To address this problem, we in this paper propose a novel end-to-end QoE (E2E-QoE) based optimization architecture at the user-side for the first time. Specifically, a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE. Based on this, an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies. Finally, we investigate to optimize the PHY algorithm directly based on the E2E-QoE. The proposed frameworks and algorithms are all validated using the data from real fifth-generation (5G) mobile system, which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.
Improving ellipse feature for wire detection

Ellipse feature appears in the cross-section of a wire, and it can complement the line feature to improve wire detection. The previously proposed ellipse feature extraction method requires a wire to be placed parallel or perpendicular to the cross-track. It is, however, difficult to guarantee in practice. This work advances ellipse feature extraction so that we will be able to obtain the feature regardless of the orientation of a wire. The method first applies the Hough Transform to the surface projection of an object image from the ground penetrating radar, and then rotates the 3-D data image according to the orientation angle from the Hough Transform to align with the cross-track, before the extraction of ellipse feature. The proposed method is quite effective and provides high quality ellipse feature to aid the detection of wire in any orientation.
Cross-Modal Knowledge Discovery, Inference, and Challenges

In recent years, multimodal knowledge has become a popular research topic in many fields, such as knowledge graphs and natural language processing. Multimodal knowledge involves multimodal knowledge graphs, multimodal pre-trained language models, multimodal knowledge inference, etc.; from online shopping to medical care, whether it is theoretical research or engineering application, the knowledge representation, discovery, and inference of multimodal knowledge have become the core technologies of the academic and industrial concern. This tutorial focuses on the state of the art of cross-modal knowledge discovery and inference and presents future research opportunities and challenges.
Identity and Artificial Intelligence in The Adventures of Pinocchio

Pinocchio is, above all, what he is not. His identity is often played to the limit, imagined by himself and everyone he meets along the way. Pinocchio is the name of life that is simultaneously inorganic, human and animal. For this reason, it is the possible name of a radical desertion: to identify at the same time with oneself and with someone other than oneself. One question that can be deduced from The Adventures of Pinocchio is whether such an intelligent machine would like to become "human"? In fact, before Pinocchio becomes a real boy, he does everything that real boys do, including the disobedience to their parents.
INNOVATION UNDER AN UMBRELLA: HOW CAN BLOCKCHAIN CONTRIBUTE TO CORPORATE INNOVATION IN THE AGE OF GLOBALIZATION

In the age of globalization, enterprises have faced new challenges and opportunities for innovative behavior. Increasing cultural exchanges provide enterprises with a wealth of information and knowledge, but also exacerbate the risk of information loss. Therefore, information security has become an increasingly critical issue. Blockchain technology is an effective guarantee for the success of enterprise innovation in such circumstances. Enterprises can access information across national boundaries and avoid the risk of a cultural clash or a leak of information due to the security and traceability of blockchain technology. In the era of globalization, blockchain technology has become an umbrella for enterprises to exploit cultural diversity. This study presents a DDD model that illustrates how blockchain firms can benefit from cultural diversity to produce innovative products. Specifically, firms that utilize blockchain increase their innovation output by 36.17% compared to those that do not. Additionally, it explores how firms can benefit from the spillover of external knowledge and how traditional models of R&amp;D through equipment purchases give way to new models of bringing in talent for knowledge exchange using blockchain. The model presented in this study provides a novel theoretical perspective on how emerging technologies affect corporate innovation, emphasizing the importance of blockchain technology to corporate innovation in an era of globalization with increasing cultural exchanges. It also provides a new perspective on the application of emerging technologies.