Reviewerly/exHarmony huggingface dataset:
Perancangan dan Implementasi Video Media Pembelajaran Produk Kreatif Dan Kewirausahaan di SMKN 2 Batam Menggunakan MDLC

The development of the industry that is not in sync with the provision of sufficient human resources, especially for the management and tourism industries, causes the need for human resources in these fields to increase. SMK Negeri 2 Batam is a public school established in 2006 with "A" accreditation in the heart of Batam, Jalan Pemuda 5, Legenda Malacca. This public school has five academic programs: fashion, hospitality, tourism, fashion, and catering. This study uses the MDLC method, one of the software development methodologies according to Luther from Binanto in research. The multimedia development methodology has six stages: concept, design, material, assembly, testing, and distribution. The results of this study are media for learning creative design products in entrepreneurship.
Simple Identification of the Mineralogy and Geochemistry of Sedimentary Environments

Sedimentary environments are an important part of paleogeography and sedimentology, and there is a long history of using geochemical and mineralogical methods to analyze palaeoenvironmental problems using sedimentary rocks as a research vehicle.By summarizing the important results of previous work on tracing the palaeoenvironment using the composition and distribution characteristics of the macronutrients and trace elements as well as the characteristics of mineral composition, we aim to sort out the mineralogical and geochemical characteristics of the palaeoenvironment.Among them, the identification of paleoclimate, paleoproductivity, paleosalinity, paleodepth and palaeo-oxygen phase are important elements of the sedimentary environment, and it is more important to sort out the mineralogical and geochemical characteristics of them.
Privacy-Aware Cloud Architecture for Collaborative Use of Patients’ Health Information

Cloud computing appears to be the dreamed vision of the healthcare industry; it refers to means of storing and accessing data and programs over the internet instead of the computer&amp;rsquo;s hard drive. However, the adoption of cloud computing requires solving several issues and infor-mation privacy is a major one. This work proposes a cloud architecture design for the healthcare information system. The proposed architecture enables for storing and sharing information in a privacy-preserving manner. Patients&amp;rsquo; information in the proposed architecture is divided into four categories identified in the case study data analysis. User identity management protocol (U-IDM) is employed for controlling the access to patients&amp;rsquo; information, and patients have means of control over who can access their information. A scenario-based instantiation validated the proposed architecture's privacy-preserving patient data exchange. The instantiation proved that the proposed architecture allows sharing healthcare information without violating the privacy of patients.
All Physical Information Is Discretely Connected From the Beginning and All Geometrical Appearance Is a Delayed Statistical Consequence

Information is physically measurable as a selection from a set of possibilities, the domain of information. This defines the term &amp;quot;information&amp;quot;. The domain of the information must be known together reproducibly beforehand. As a practical consequence, digital information exchange can be made globally efficient, interoperable and searchable to a large extent through the online definition of application-optimized domains of information. There are even more far-reaching consequences for physics. The purpose of this article is to present prerequisites and possibilities for a physical approach that is consistent with the precise definition of information. This concerns not only the discretization of the sets of possible experimental results, but also the order of their definition along time. The access to or comparison with the domain of information is the more frequent, the earlier it was defined. The geometrical appearance of our space is apparently a delayed statistical consequence of a very frequent connection with the common primary domain of information.
ESarDet: An Efficient SAR Ship Detection Method Based on Context Information and Large Effective Receptive Field

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.
MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning

In this paper, we introduce MFCCGAN as a novel speech synthesizer based on adversarial learning that adopts MFCCs as input and generates raw speech waveforms. Benefiting the GAN model capabilities, it produces speech with higher intelligibility than a rule-based MFCC-based speech synthesizer WORLD. We evaluated the model based on a popular intrusive objective speech intelligibility measure (STOI) and quality (NISQA score). Experimental results show that our proposed system outperforms Librosa MFCC-inversion (by an increase of about 26% up to 53% in STOI and 16% up to 78% in NISQA score) and a rise of about 10% in intelligibility and about 4% in naturalness in comparison with conventional rule-based vocoder WORLD that used in the CycleGAN-VC family. However, WORLD needs additional data like F0. Finally, using perceptual loss in discriminators based on STOI could improve the quality more. WebMUSHRA-based subjective tests also show the quality of the proposed approach.
Lightning Network’s Evolution: Unraveling Its Present State and the Emergence of Disruptive Digital Business Models

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy, and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. The findings of this study will help businesses make more informed decisions about adopting and leveraging the LN to improve their operations and enhance customer experience.
The Expansion of Data Science: Datasets Standardization

With the recent advances in science and technology, more processing capability and data have become available, allowing a more straightforward implementation of data analysis techniques. Fortunately, the available online data storage capacity follows this trend, and vast amounts of data can be stored online freely or at accessible costs. As happens with every evolution (or revolution) in any science field, organizing and sharing this data is essential to contribute to new studies or validate the obtained results quickly. To facilitate this, we must guarantee interoperability between the existing datasets and the developed software, whether commercial or open source. This article explores this issue, analyzing the current initiatives to establish data standards and comparing some of the existing dataset online storage platforms. Through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, it is possible to understand better the strategy that should be taken to improve the efficiency in this field, which directly depends on the data characteristics.
Contributions of global and local processing on medical image perception

The influential holistic processing hypothesis attributes expertise in medical image perception to cognitive processing of global gist information. However, it has remained unclear whether or how experts use rapid global impression of images for their subsequent diagnostic decisions based on the focal sign of cancer. We hypothesized that continuous-global and discrete-local processes jointly attribute to radiological experts' detection of mammogram, with different weights and temporal dynamics.We examined experienced versus inexperienced observers' performance at first (500 ms) versus second (2500 ms) mammogram image presentation in an abnormality detection task. We applied a dual-trace signal detection (DTSD) model of receiver operating characteristic (ROC) to assess the time-varying contributions of global and focal cancer signals on mammogram reading and medical expertise.The hierarchical Bayesian DTSD modeling of empirical ROCs revealed that mammogram expertise (experienced versus inexperienced observers) manifests largely in a continuous-global component for the detection of the gist of abnormality at the early phase of mammogram reading. For the second presentation of the same mammogram images, the experienced participants showed increased task performance that was largely driven by better processing of discrete-local information, whereas the global processing of abnormality remained saturated from the first exposure. Modeling of the mouse trajectory of the confidence rating responses further revealed the temporal dynamics of global and focal processing.These results suggest a joint contribution of continuous-global and discrete-local processes on medical expertise, and these processes could be analytically dissociated.
Education Committee Webinars for the Second Half of 2023 [Education News]

The IEEE Microwave Theory and Technology Society (MTT-S) Education Committee continues to offer high-quality webinars from experts in their respective fields, a series that started in 2016 <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> , <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> . These webinars have continued through the first half of 2023 and provided collaborative learning opportunities to our MTT-S membership as well as the global microwave/RF community. The MTT-S Education Committee is grateful to our webinar speakers for their efforts in preparing and delivering cutting-edge educational content through our popular MTT-S webinar series. This month’s “Education News” column presents the webinars scheduled for the second half of 2023, which can be found in <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table 1</xref> . These webinar speakers represent a diverse group of leading subject matter experts from around the globe, including current or emeritus Distinguished Microwave Lecturers. The past webinars as well as other MTT-related resources can be found at the MTT-S Resource Center, <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://resourcecenter.mtt.ieee.org/</uri> . Viewing previous MTT-S webinars is free for MTT-S members. IEEE Professional Development Hours are also available.
Observational Equivalence of Conditional Belief Bases

In nonmonotonic reasoning, a conditional of the form ‘If A then usually B’ is typically accepted if a situation where both A and B hold is deemed to be more plausible, more probable, or less surprising, etc., than a situation where A holds, but B does not hold. In a propositional setting, this leads to a relation on the propositional interpretations, also called worlds, by comparing worlds according to their plausibility. In this paper, we address the question of which kind of relations on the set of worlds can be obtained by completing a conditional belief base via an inductive inference operator. As a key concept for our investigations, we introduce and employ the notion of observational equivalence of belief bases that takes an inductive inference operator and a set of queries into account. This leads to the notion of the observational normal form (ONF) and, by focussing on so-called base conditionals, to the base conditional normal form (BCNF). The acceptance of base conditionals corresponds to the plausibility ordering on possible worlds induced by an inference method. Both normal forms ONF and BCNF are combined with renamings as an additional dimension. We establish the interrelationships among the normal forms and evaluate them empirically with respect to systematically generated belief bases.
Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task

Nowadays, artificial neural networks can easily solve the N-bit parity problem. However, each time a different level must be learned, the network must be retrained. This, combined with the exponential increase of learning trials required as N grows, make these models too different from how their biological counterpart solves them. This is because humans learn to recognize patterns, count, and determine if numbers are odd or even. Once they have learned these tasks, they can have them interact to solve any level without further training. This behavior is akin to performing multiple associations of different tasks. Therefore, it is proposed that by using bidirectional associative memory neural networks, it would be possible to solve the N-bit parity problem in a similar fashion to humans. To achieve this, two networks interacted; one served as a task Identifier and the other as a memory Extractor, giving the desired behavior influenced by the Identifier. Results showed that the model could solve the 2- to 9-bit in linear time once the associations were learned. Moreover, this was possible with 97% fewer inputs and no retraining. In addition, because of the recurrent nature of the model, it could also solve the tasks even under high noise levels.
Bayesian Network Conflict Detection for Normative Monitoring of Black-Box Systems

&#x0D; &#x0D; &#x0D; Bayesian networks are interpretable probabilistic models that can be constructed from both data and domain knowledge. They are applied in various domains and for different tasks, including that of anomaly detection, for which an easy to compute measure of data conflict exists. In this paper we consider the use of Bayesian networks to monitor input-output pairs of a black-box AI system, to establish whether the output is acceptable in the current context in which the AI system operates. A Bayesian network-based prescriptive, or normative, model is assumed that includes context variables relevant for deciding what is or is not acceptable. We analyse and adjust the conflict measure to make it applicable to our new type of monitoring setting.&#x0D; &#x0D; &#x0D;
Towards binary encoding in Bidirectional Associative Memories

Bidirectional Associative Memories (BAMs) are Artificial Neural Networks frequently utilized in cognitive modeling. While bipolar encoding is commonly used in BAMs for optimal performance, binary encoding presents interesting properties. As such, this study introduces a novel transmission function for binary encoding and compares its performance to the conventional bipolar transmission function. To evaluate, an auto-association learning task and a noisy recall task were implemented. Results revealed that despite longer learning times, binary encoding preserves or enhances the properties observed in binary encoding. Findings are promising from a cognitive perspective, as they open the possibility of building intricate models of human cognition.
Code Generation for Collectible Card Games with Complex APIs

Large pre-trained language models (LMs) such as GPT-3 Codex are able to generate code remarkably well given prompts of natural language text. But if we want to use such LMs to generate code compatible with a specific API or library (e.g., an API which provides the environments in which certain rules, laws, or orders are to be carried out), the amount of computational and data resources required to fine-tune such models can be cost prohibitive to most organizations. Given these practical limitations, is it possible to utilize these massive code-generation LMs to write code compatible with a given API? We develop an algorithm that selects code examples using a smaller LM trained to predict which features of an API are likely to be used in the resulting code, which is a simpler problem than actually generating the code. The selected examples are then used to build a prompt for the larger LM, which in turn generates the final code. We demonstrate our results on a benchmark dataset derived from the collectible card game "Magic: the Gathering," and obtain state-of-the-art results.
Cybersecurity in Blockchain by Secure Identity Management using Long Short Term Memory (LSTM)

One of the major technologies in delivering infrastructure and data service requirements at low cost and with minimal effort is Cloud Computing (CC), which has been implemented in several aspects of the IT industry.Since the rapid growth of CC has been observed, there is still an information security concern that intruders completely attack.With the potential of being practised in various utilizations, blockchain can be implemented in several cloud service providers.Blockchain platform has basically performed a large computation quantity which doesn't accomplish the practical purpose of building Proof of Work (PoW) with context awareness accord from decentralized participants.Long Short-Term Memory (LSTM)has been designed particularly for overcoming the long-term dependency issues faced by Recurrent Neural Networks (RNN).This paper focuses on a novel consensus mechanism by LSTM which consists of feedback connections in making a difference with several conventional feed-forward Neural Networks.The research directed computation spent to consensus towards RNN optimization for better cyber security in blockchain implemented in the cloud platform.However, the enormous amount of data involved in the blockchain has been trained through the LSTM model that supports serving learning proof through cell states which handle the network of current long-term memory.Therefore, Contextual Identity Management (CAIM) mechanism is adopted through the LSTM model in blockchain for various Cloud Service Provider (CSP) in unifying the contextual details with the process of identity management that generate the probable robust solution.This assists in creating better decisions with respect to policy, authentication, routing and authorization for recent interacted data.Moreover, the proposed LSTM model in blockchain has been compared with the existing cyber-secured model of blockchain to determine the efficiency of the cyber-secured model of blockchain in the cloud platform.
Ranking-Based Case Retrieval with Graph Neural Networks in Process-Oriented Case-Based Reasoning

In Process-Oriented Case-Based Reasoning (POCBR), experiential knowledge from previous problem-solving situations is retrieved from a case base to be reused for upcoming problems. The task of retrieval is approached in previous work by using Graph Neural Networks (GNNs) to learn workflow similarities which are, in turn, used to find similar workflows w.r.t. a query workflow. This paper is motivated by the fact that these GNNs are mostly used for predicting the similarity between two workflows (query and case), while the retrieval in CBR is only concerned with the ranking of the most similar workflows from the case base w.r.t. the query. Thus, we propose a novel approach to extend the GNN-based workflow retrieval by a Learning-to-Rank (LTR) component where rankings instead of similarities between cases are predicted. The main contribution of this paper addresses the changes to the GNNs from previous work, such that their model architecture predicts pairwise preferences between cases w.r.t. a query and that they can be trained using labeled preference data. In order to transform these preferences into a case ranking, we also describe rank aggregation methods with different levels of computational complexity. The experimental evaluation compares different models for predicting similarities and rankings in case retrieval scenarios. The results indicate the potential of our ranking-based approach in significantly improving retrieval quality with only small impacts on the performance.
Player Identification for Collectible Card Games with Dynamic Game States

Collectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allow for flexibility in play styles, thus facilitating behavioral analysis at the individual level rather than the aggregate level. When presented with the same options and the same amount of resources, a player's selection of cards and their choice of moves gives insight into their unique play style and decision-making tendencies. As such, we use the virtual collectible card game Legends of Code and Magic to determine whether we can identify a player from their actions and, conversely, predict the future actions of a known player. Our main contributions to this task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, as well as the first use of large transformer-based language models to address this problem.