Reviewerly/exHarmony huggingface dataset:
Error measurements for a quantum annealer using the one-dimensional Ising model with twisted boundaries

Abstract A finite length ferromagnetic chain with opposite spin polarization imposed at its two ends is one of the simplest frustrated spin models. In the clean classical limit the domain wall inserted on account of the boundary conditions resides with equal probability on any one of the bonds, and the degeneracy is precisely equal to the number of bonds. If quantum mechanics is introduced via a transverse field, the domain wall will behave as a particle in a box, and prefer to be nearer the middle of the chain rather than the ends. A simple characteristic of a real quantum annealer is therefore which of these limits obtains in practice. Here we have used the ferromagnetic chain with antiparallel boundary spins to test a real flux qubit quantum annealer and discover that contrary to both expectations, the domain walls found are non-uniformly distributed on account of effective random longitudinal fields present notwithstanding tuning carried out to zero out such fields when the couplings between qubits are nominally zero. We present a simple derivation of the form of the distribution function for the domain walls, and show also how the effect we have discovered can be used to determine the strength of the effective random fields (noise) characterizing the annealer. The noise measured in this fashion is smaller than what is seen during the single-qubit tuning process, but nonetheless qualitatively affects the outcome of the simulation performed by the annealer.
UAT: Universal Attention Transformer for Video Captioning

Video captioning via encoder-decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when the feature extraction model is used in conjunction with freezing, additional learning of the feature extraction model is not possible by exploiting the backpropagation of the loss obtained from the video captioning training. Specifically, this blocks feature extraction models from learning more about spatial information. Second, the complexity of the model is further increased when multiple CNNs are used. Additionally, the author of Vision Transformers (ViTs) pointed out the inductive bias of CNN called the local receptive field. Therefore, we propose the full transformer structure that uses an end-to-end learning method for video captioning to overcome this problem. As a feature extraction model, we use a vision transformer (ViT) and propose feature extraction gates (FEGs) to enrich the input of the captioning model through that extraction model. Additionally, we design a universal encoder attraction (UEA) that uses all encoder layer outputs and performs self-attention on the outputs. The UEA is used to address the lack of information about the video's temporal relationship because our method uses only the appearance feature. We will evaluate our model against several recent models on two benchmark datasets and show its competitive performance on MSRVTT/MSVD datasets. We show that the proposed model performed captioning using only a single feature, but in some cases, it was better than the others, which used several features.
Learned Pseudo-Random Number Generator: WGAN-GP for Generating Statistically Robust Random Numbers

Abstract Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the “democratization” of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.
THE EFFECT OF AUDIO-VISUAL MEDIA ON VOCABULARY RETENTION OF THE 9th GRADE STUDENTS AT AN ISLAMIC BOARDING SCHOOL IN LOMBOK, INDONESIA

This study focuses on investigating the effect of audio-visual media (AVM) on students’ vocabulary retention and determining students’ perception about learning using AVM. This experimental research was conducted at an Islamic Boarding School in West Lombok Indonesia. The population of the study were the 9th grade students. Two classes were selected using cluster random sampling method. Each class consists of twenty-eight students, 56 in total. The classes were divided into two groups; Experimental and Control group. Pre-Test and Post-Test design and Survey method were used in the data collection. The data indicate that there was no student in the experimental group classified into excellent category, but after doing the treatment the students’ scores in the experimental group improved well. The result of paired sample test showed the Sig. value was 0.00 &lt; 0.05 which means there was a significant difference of students’ learning outcome.
Role of Blockchain in Security of 6G Networks

Over the last decade, the demand for faster data rates has fueled the rapid growth of wireless communication systems that introduce the concept of new wireless technologies such as 6G or beyond 6G. The world is going through an ultimate transformation with the arrival of the intelligent information era. It integrates 6G, internet of things (IoT), blockchain, and other techniques to address the issues and challenges faced by 6G networks. Although an IoT-enabled application is crucial in this case, it faces security, privacy, latency, and reliability issues. The 6G networks can accommodate various heterogeneous devices and infrastructure to provide ultra-high data rates, high reliability, low latency, and secure communication processes. Motivated from these facts, this chapter explores the role of blockchain in addressing security and privacy issues in 6G networks and future application prospects and research objectives.
Repertoire Construction for Critical Cross-Cultural Literacy of English Majors: Based on the Research Paradigm of Systemic Functional Linguistics

The ambiguous development trend of cultural globalization brings both opportunities and challenges to China’s cultural development. English major in colleges and universities, a discipline of cross-cultural education, should look at the cultural communication of the target country dialectically based on the national consciousness of the home country. Since the end of the 20th century, administrators and scholars have paid attention to critical thinking, critical cultural awareness, and critical skills in cross-cultural communication, which are important components of the cross-cultural meaning system. Therefore, all these are collectively referred to as critical cross-cultural literacy (CCCL). On the basis of the research paradigm of systemic functional linguistics (SFL), a language is a semiotic system that creates meaning. Thus, to help students construct and improve their individual CCCL repertoire, teachers need to guide them to critically study and analyze the discourse purpose of the textbook author as well as their language methods and strategies to enrich their meaning potential.
Definition Synthesis of Agility in Software Development: Comprehensive Review of Theory to Practice

Software development agility has been regarded as a critical pillar of modern businesses.However, there is still a way to find whether there exists a consistent, complete, precise, agreed and uniformed definition of it.In this regard, this study firstly reviews the existing definitions of agility in the software development domain from the literature.As one of the main results of this phase, we have seen that although agility has a remarkable root in the software development domain, even its definition is still debatable and there are other concepts close to agility in terms of definition but used interchangeably.There is another confusion about how some researchers define agility over other different concepts, although there is no apparent unifying factor in their origins except their historical co-occurrence.In addition, there are particular practices embedded into the agility definitions mostly from the manifesto and Scrum.After uncovering the deficiencies of the existing definitions, we aimed to ratify the definition of the agility concept.Then, we intended to synthesize the underlying facets of the identified definitions and propose a new yet more comprehensive definition revealing the agility characteristics properly by considering the interpretations of the existing definitions.Our study stands out by using a customized synthesis method for analysis, providing inputs to this analysis with a comprehensive literature review, and the comprehensive evaluation of the facets with the support of the literature.We are aware that agreeing on a definition is a valuable exercise and a good starting point for a better understanding of the agility phenomenon that could enable and lead to more realistic implementations, less disappointment and disillusionment, and possibly greater success rates for both practitioners and researchers.
SCDeep: Single-Channel Depth Encoding for 3D-Range Geometry Compression Utilizing Deep-Learning Techniques

Recent advances in optics and computing technologies have encouraged many applications to adopt the use of three-dimensional (3D) data for the measurement and visualization of the world around us. Modern 3D-range scanning systems have become much faster than real-time and are able to capture data with incredible precision. However, increasingly fast acquisition speeds and high fidelity data come with increased storage and transmission costs. In order to enable applications that wish to utilize these technologies, efforts must be made to compress the raw data into more manageable formats. One common approach to compressing 3D-range geometry is to encode its depth information within the three color channels of a traditional 24-bit RGB image. To further reduce file sizes, this paper evaluates two novel approaches to the recovery of floating-point 3D range data from only a single-channel 8-bit image using machine learning techniques. Specifically, the recovery of depth data from a single channel is enabled through the use of both semantic image segmentation and end-to-end depth synthesis. These two distinct approaches show that machine learning techniques can be utilized to enable significant file size reduction while maintaining reconstruction accuracy suitable for many applications. For example, a complex set of depth data encoded using the proposed method, stored in the JPG 20 format, and recovered using semantic segmentation techniques was able to achieve an average RMS reconstruction accuracy of 99.18% while achieving an average compression ratio of 106:1 when compared to the raw floating-point data. When end-to-end synthesis techniques were applied to the same encoded dataset, an average reconstruction accuracy of 99.59% was experimentally demonstrated for the same average compression ratio.
Performance analysis and development of OPD interconnection network using NDLC method in Boven Digoel diskominfo papua province

Computer network analysis is very important to help improve network performance. Many companies, including digoel boven diskominfo designed their network but not according to their business goals, so their current performance is not what they expected. Boven Digoel Diskominfo uses internet interconnection between Local Government Organizations (OPD) to manage the system. Network analysis at the Boven Diego Diskominfo is carried out using the Network Development Life Cycle (NDLC) method relying on the previous infrastructure development process which has several stages, namely analysis, design, prototype simulation, implementation, monitoring, and management. The results of this network analysis are the optimization of the core router and the optimization of the Wireless Access Point
The Relationship Between Metaverse and Social Life

In today’s world, where time and space have relatively lost their previous significance, the effect of the Internet technologies that have characterised particularly the recent years is an undeniable fact. Striking developments in informatics and the Internet technologies have been discussed over a current occurrence, namely “Metaverse.” Parallel to the rapid development of technology, Metaverse has already become a concept that we encounter quite frequently. It is foreseen that Metaverse, which has been a part of our lives, will gradually increase its impact in the future. Being a medium where people can experience the practices pertaining to daily and social life, Metaverse is considered to be the next step through the development of the Internet technologies. The role and the effect of Metaverse over social life, which has provided alternatives in different areas by creating a universe beyond the physical world will be discussed in this study.
PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Early diagnosis of cardiovascular diseases, which have high mortality rates all over the world, can save many lives. Various clinical findings and past histories of patients play an important role in diagnosing these diseases. These days, the prediction of cardiovascular diseases has gained great importance in the medical field. Pathological studies are prone to misinterpretation because too many findings are studied. For this reason, many automatic models that work with machine learning methods on patients' findings have been proposed. In this study, a model that predicts twelve myocardial infarction complications based on clinical findings is proposed. The proposed model is a deep learning model with three hidden layers with dropouts and a skip connection. A binary accuracy metric is used for measuring the performance of the proposed method. Rectified Linear Unit is set to the hidden layers and sigmoid function to the output layer as an activation function. Experiments were performed on a real dataset with 1700 patient records and carried out on two main scenarios; training on original data and training on augmented data with 100 epochs. As a result of the experiments, a total accuracy rate of 92% was achieved which is the best accuracy rate that has been proposed on this dataset.
A Framework for Stakeholders’ Involvement in Digital Productions for Cultural Heritage Tourism

This paper proposes a new framework for the production and development of immersive and playful technologies in cultural heritage in which different stakeholders such as users and local communities are involved early on in the product development chain. We believe that an early stage of co-creation in the design process produces a clear understanding of what users struggle with, facilitates the creation of community ownership and helps in better defining the design challenge at hand. We show that adopting such a framework has several direct and indirect benefits, including a deeper sense of site and product ownership as direct benefits to the individual, and the creation and growth of tangential economies to the community.
Predicting Relations in SG-CIM Model Based on Graph Structure and Semantic Information

State Grid Enterprise Common Data Model (SG-CIM) is a semantically unified data model for smart grid business applications. The model is divided into entity and relation files. Since the relations in SG-CIM model are manually labeled, which consumes a lot of labor costs and is also prone to omission and mislabeling. In order to solve these problems, this paper introduces a method for predicting complex relations in SG-CIM model based on graph structure and semantic information. The relations of graph structure and semantic information are extracted and used by BERT and ERNIE models to predict the relation types between head and tail entities. In order to improve the efficiency of relation prediction, the five relation types are classified into the parallel type and the inheritance type based on the structural relation between entities, and the pairs of entities are separately mined using frequent term mining and K-nearest neighbor methods. Using these methods for predicting complex relations in SG-CIM models can effectively save labor costs.
Social Action Of Student In Achieving Non-Academic Achievements In Interest And Talent-Based School

This article describes the social action of students' non-academic achievement. Student achievement in education is a measure of student success. Achievement has two categories, namely academic achievement and non-academic achievement. SMA Muhammadiyah 10 Surabaya has a gifted title. In accordance with this predicate, this school focuses on developing the potential interests and talents of students. This study uses social action theory by Max Weber in assessing the motivation for non-academic achievement goals. Researchers used qualitative methods through in-depth interviews and observation primary data, as well as secondary data through the development of student championship news on internet pages. The results showed that in achieving students' non-academic achievements, they achieved these goals through instrumental rational actions in the form of regular physical readiness exercises, value-oriented rational actions on aspects of spirituality, traditional actions on aspects of choosing extracurricular activities based on experience and advice from parents. , affective actions choose extracurricular activities because of feelings of pleasure and interest. The researcher hopes that this article can be a new contribution to society in the field of education and social affairs, especially the achievement of non-academic achievements which the community considers not a significant achievement for the future of students compared to academic achievement.
A Short Study on Minima Distribution

Global optimization is an imperative development of local optimization because there are many problems in artificial intelligence and machine learning requires highly acute solutions over entire domain. There are many methods to resolve the global optimization, which can be classified into three groups such as analytic methods (purely mathematical methods), probabilistic methods, and heuristic methods. Especially, heuristic methods like particle swarm optimization and ant bee colony attract researchers because their effective and practical techniques which are easy to be implemented by computer programming languages. However, these heuristic methods are lacking in theoretical mathematical fundamental. Fortunately, minima distribution establishes a strict mathematical relationship between optimized target function and its global minima. In this research, I try to study minima distribution and apply it into explaining convergence and convergence speed of optimization algorithms. Especially, weak conditions of convergence and monotonicity within minima distribution are drawn so as to be appropriate to practical optimization methods.
Self‐admitted technical debt detection by learning its comprehensive semantics via graph neural networks

Abstract The goal of software development is to deliver software products with high quality and free from defects, but resource and time constraints often cause the developers to submit incomplete or temporary patches of codes and further bear the additional burden. Therefore, the investigations on identifying self‐admitted technical debt (SATD) to improve code quality have been conducted in recent years. However, missing syntactic structure information and the imbalance distribution bias shorten the SATD identification performance. Addressing to this issue, we present a graph neural network based SATD identification model (GNNSI) to improve the performance. Specifically, we obtain the structure information of the missing SATD in a compositional way to obtain different feature maps for different comments, and use focal loss to handle the imbalance between SATD and non‐SATD classes in the comments. Then extensive experiments on 10 open source projects are conducted, and the results show that GNNSI outperforms the baselines and can help developers to better predict SATDs.
Security analysis on the interference-based optical image cryptosystem with a designed amplitude modulator

The security strength of an interference-based optical image cryptosystem with a designed amplitude modulator (AM) is evaluated. Compared to previous improved interference-based cryptosystems in which iterative or post-processing algorithms are involved, to remove the silhouette problem, a pre-designed AM is placed at the image plane to modulate the plaintext directly, which eliminates this drawback. Moreover, the AM is used as an additional private key, which enlarges the key space and further enhances the security system. However, we have noticed that one of the designed parameters in the AM has a low key sensitivity, which is fault-tolerant and is not required for precise reconstruction. Additionally, the AM is plaintext-independent, which means that the AM can be recovered first by using a pair of chosen plaintexts and the corresponding ciphertext. Based on these findings, we propose a hybrid algorithm that includes two chosen-plaintext attacks (CPAs) and a ciphertext-only attack (COA) to break the enhanced cryptosystem. Specially, CPAs with an impulse function and an arbitrarily given plaintext are proposed to retrieve two parameters in the AM. With the retrieved AM, information on the plaintext can be recovered from the corresponding ciphertext by two kinds of COAs without any knowledge of the private keys. To the best of our knowledge, this is the first time that the interference-based cryptosystem with a designed AM has been cracked successfully, which may provide potential evidence for further security improvements. Numerical simulations are carried out to validate the feasibility of the proposed attacks.