Reviewerly/exHarmony huggingface dataset:
Reliable Federated Learning for Age Sensitive Mobile Edge Computing Systems

The conventional approach for Federated Learning (FL) is to train a global model by averaging local models trained on local data sets. However, given the limited computing resources at the mobile-edge nodes, unreliable models may be received from the Edge Nodes (ENs), which can lead to a significant performance degradation in the FL. Thus, this paper proposes a reliable and age sensitive FL framework that captures the dynamic nature of the local data and computing resources at each participating EN. Specifically, we formulate two optimization problems to select the optimal subset of ENs that can upload their local models in each round of the global model training, given a limited learning cost budget. The first problem aims at selecting the most reliable ENs that should cooperate to complete the FL process, while considering stationary data distributions at different ENs. The second problem aims at minimizing the average age of information experienced by each EN while selecting the most reliable ENs, given fast changing data distributions. Efficient solutions are proposed for the two problems with a worst-case linear complexity. Our Results, leveraging a real-world dataset, depict the efficiency of our solutions in obtaining a better performance compared to conventional FL approach.
Successive Interference Cancellation Based Defense for Trigger Backdoor in Federated Learning

Federated Learning (FL) provides a decentralized training mechanism that ensures users' data privacy. However, FL is vulnerable to backdoor attacks, a type of data poisoning attack. The adversaries tampered with the local models by injecting a trigger into a subset of training data. After the aggregation process, the global model would be poisoned and mispredict the input images that injected a trigger designed by an adversary. Unlike the existing defense methods attempting to identify and remove the abnormal model updates on the aggregation step, this paper proposes a Successive Interference Cancellation-based Defense Framework (SICDF) to detect and eliminate the trigger during model inference. SICDF first employs Explainable AI to infer where the trigger is and then uses image processing skills to eliminate potential trigger effects. Experiment results show that SICDF can effectively recover the poisoned data while only slightly reducing the accuracy of the clean model and benign data.
Rancang Bangun Fingerprint Absensi Guru Disekolah Balitaku Khatam Qur’an Berbasis Arduino

the attendance list also includes data collection which must be recapitulated every day against the admin of my toddler khatam quran school. The attendance system can read every finger identity that is registered for all teachers at my toddler Khatam Koran school easily and does not read for fingerprints that have not been registered before.The display for the LCD is the name of each teacher and written attendance is stored if the fingerprint is registered, if the fingerprint is not registered then the display on the LCD displays an unknown fingerprint
Desain Sistem Informasi Perpustakaan Universitas Antakusuma Menggunakan Platform SLiMS (Senayan Library Management System)

Library is one of the important elements in higher education institutions because library provides various forms of references and literature for lecturers and students according to their needs and scientific fields. The same thing happened at Antakusuma University which is one of the higher education institutions located in Pangkalan Bun, Central Kalimantan. At Antakusuma University, the library management is divided into 1 (one) Central Library Unit which is managed at the university level and 4 (four) faculty library sub-units which include: 1) Faculty of Economics Library; 2) Law Faculty Library; 3) Library of the Faculty of Agriculture; and 4) Engineering Faculty Library. However, the existence of the library units at Antakusuma University are not managed properly, because all activities are carried out manually so that they are considered less effective and efficient. In addition, there is no interconnection between the central library and the faculty library units which makes the management of the library units and sub-units is still independent. Based on the background mentioned above, this research aims to design and build a library information system using the open source Senayan Library Management System (SliMS) platform which can facilitate the computerized library management process which is more effective and efficient than before. In addition, this research also aims to integrate the central library and the faculty library which were previously managed independently. The output of this study is in the form of a library information system application that can be used by the Antakusuma University library unit.
NDTAEP: Design of a novel deadline-aware task scheduling model using augmented ensemble pattern analysis

A wide variety of scheduling models have been proposed by researchers over the years, and each of them has varying performance in terms of deadline hit ratio, scheduling effort, efficiency of task mapping, etc. However, these models are highly context-sensitive and cannot be scaled to heterogeneous task types due to their internal mapping characteristics. To improve task scalability, this work proposes a design of a novel deadline-aware task scheduling model that uses augmented ensemble pattern analysis for task clustering. The pattern analysis module uses a combination of K-means, hierarchical, and Fuzzy C Means (FCM) clustering to effectively segregate tasks depending on their completion and deadline parameters. These tasks are given to a modified deadline-aware League Championship Algorithm (LCA) optimizer, which assists in mapping the clustered tasks with worker threads. The modified LCA model uses a combination of task priority, task deadline, and worker capacity for scheduling. The model maps tasks that require higher execution effort with moderately performing worker nodes, while tasks with nearer deadlines are allotted to higher-performance workers. Due to the use of an ensemble augmented pattern analyzer with a modified LCA optimizer, the proposed model can improve execution speed by 8%, deadline hit ratio by 1.5%, and scheduling efficiency by 6.5% when compared with various state-of-the-art scheduling approaches. The proposed model was evaluated and showcased a deadline hit ratio of 99.95%, computational efficiency of 96.26%, and average task-scheduling delay of less than 0.1 ms, which makes it highly useful for a wide variety of task scheduling application scenarios.
The Digital Bill Ecosystem

The Digital Bill Ecosystem presents a cutting-edge financial paradigm that merges the stability of precious metals with the revolutionary capabilities of blockchain technology. This ecosystem introduces the Digital Bill (DB), a digital currency backed by tangible assets such as gold, silver, and platinum, providing a secure and transparent medium of exchange. Employing tamper-proof consensus mechanisms and smart contracts, the DB ecosystem offers augmented security, transparency, and efficiency across various domains, including voting systems, financial transactions, and international trade. The redesigned barcode, QR code, and wallet within the DB ecosystem not only enhance the security and transparency of financial transactions but also offer a unique advantage to global citizens – the ability to avoid double taxation. Moreover, it empowers users, fosters financial inclusion, and unlocks opportunities for seamless innovation. This article delves into the intricacies of the Digital Bill Ecosystem, exploring its mechanisms, potential benefits, and the significance of leveraging blockchain and precious metals to redefine the global financial landscape.
How Cryptography Can Augment Zero Trust

The scope of this work presents the security architecture of the Cloud provider/consumer and showcases how “Cryptography” augments the Zero Trust Architecture security strategy. In addition, this work will delve into enforcing the minimum expected set of security controls that assures secure isolation, data protection, access control and monitoring/logging at all tiers of the hosted applications. Cryptographic solutions play an important role in the cloud environment in which customers hand over data to a cloud provider for storage, and processing. One of the biggest hurdles for the adoption of cloud computing by enterprises is security and confidentiality. The premise of the paper will be that the cloud provider acts as a distrusted black box from the client's perspective, and whose duty and interest is the protection of their information and privacy. The techniques presented are shared jointly between the service provider and client, and should protect the interests of both, in establishing a successful and trustworthy service.
Semi-supervised graph regularized concept factorization with the class-driven constraint for image representation

&lt;abstract&gt;&lt;p&gt;As a popular dimensionality reduction technique, concept factorization (CF) has been widely applied in image clustering. However, CF fails to extract the intrinsic structure of data space and does not utilize the label information. In this paper, a new semi-supervised graph regularized CF (SGCF) method is proposed, which makes full use of the limited label information and the graph regularization to improve the algorithm of clustering performance. Particularly, SGCF associates the class label information of data points with their new representations by using the class-driven constraint, and this constraint forces the new representations of data points to be more similar within the same class while different between classes. Furthermore, SGCF extracts the geometric structure of the data space by incorporating graph regularization. SGCF not only reveals the geometrical structure of the data space, but also takes into the limited label information account. We drive an efficient multiplicative update algorithm for SGCF to solve the optimization, and analyze the proposed SGCF method in terms of the convergence and computational complexity. Clustering experiments show the effectiveness of the SGCF method in comparison to other state-of-the-art methods.&lt;/p&gt;&lt;/abstract&gt;
Modulation classification analysis of CNN model for wireless communication systems

&lt;abstract&gt;&lt;p&gt;Modulation classification (MC) is a critical task in wireless communication systems, enabling the identification of the modulation class in the received signals. In this paper, we analyzed a novel multi-layer convolutional neural network (CNN) to extract hierarchical features directly from the raw baseband samples. Moreover, we compared the training and testing accuracy of the CNN model for various decimation rates, input sample size and the number of convolutional layers. The results showed that the three-layer CNN model provided better classification accuracy with less computation cost. Furthermore, we observed that the MC performance of the proposed CNN model was better than the other deep learning (DL) and cumulant-based models.&lt;/p&gt;&lt;/abstract&gt;
Effects of pre-editing operations on audiovisual translation using TRADOS: an experimental analysis of Saudi students’ translations

Abstract Neural Machine Translation (NMT) is a revolutionary innovation that has had a significant impact not only on the translation industry but also on translation studies. This technology is creating new opportunities for shaping identities and generating new knowledge and perspectives. This led researchers to undeniably and increasingly conduct studies to improve NMT performance. These studies involve assessing the quality of the output, determining the extent to which humans can be involved in the machine-based process, and proposing solutions to redress the deficiencies. In this regard, the paper focuses on the impact of pre-editing (PE) operations on the translation of audiovisual children’s literary texts (subtitles) from English to Arabic using TRADOS. The work seeks to accomplish a number of pre-editing-related research goals in the context of Arabic NMT systems. Initially, it aims to define and clarify the idea of PE and its function in enhancing Arabic NMT systems’ performance. Second, it looks at PE mechanisms and methods and how they might improve the appropriateness and accuracy of Arabic machine translation. Additionally, the study attempts to investigate the value of PE in enhancing the Arabic translation of audiovisual children’s literature. The study also seeks to list the difficulties and restrictions related to PE and suggest viable strategies to get over them. Finally, the paper offers suggestions for further investigation into NMT systems using Arabic and PE. To achieve these goals, the researchers conducted a human evaluation of NMT for two animated movie subtitles and determined the necessary PE operations. The findings revealed that PE has the potential to improve the quality of literary translations and enhance their comprehensibility and acceptability.
Metadata as a tool of the semantic analysis of the complex contents of the big data. The images

The purpose of the research is to specify effective approaches for improving the semantic analysis of graphic contents of big data. This article considers images or video scenes as examples of such complex contents. Proposed approach takes into account the special features of these contents and create a hybrid annotation model that extends the text annotation model with more specific elements. For the visual data, these are characteristics of visualization. Determining the similarity of information contents is a critical problem for solving big data tasks. It is the basis for the big data categorization and enables the composition of the documents, conversion of an unstructured contents to relevant knowledge structures and the visualization of the information. Semantic analysis of information contents is usually based on their metadata, which form the basis of semantic annotations. Also, they are elements of a structured semantic description of the content and the basis for its automated processing. The approach is based on using ontologies to define semantic annotations. Ontologies provide various sources of knowledge to measure semantic similarity, contain a lot of information about the interpretation of concepts and other semantic relationships with a hierarchical structure based on hyponymy relations. But, in recent years, there is the rapid growth of the number of images and video resources. And, at this time, we can note a significant enrichment of available visual information. From a visual point of view, it is easier to understand whether two concepts are similar. Therefore, the integration of semantic and visual information of the image ensures the optimization of the ontological methods for similarity estimation and allows to obtain similarity metrics that are more consistent with human perception. De facto, such assessments of the complex semantic similarity of concepts are defined by the composition of two functions, the first of which, in fact, is an ontological measure of similarity, and the second is built on the basis of a complex facilities vector. It is a concatenation of semantic and visual characteristics with an established weight balance between these two types of features. The combination of visualization features with semantic and ontological characteristics of the contents in the similarity metrics is the central idea of this study.
Mathematical and Graph Models of Specialized Multi-Agent Systems with Distributed Solution of Applied Problems

The concept of a specialized multi-agent system (SMAS) as a system designed for a distributed solution of either one applied problem or a highly limited range of applied problems is considered. Such problems frequently require a large number of homogeneous operations, which can lead to a significant increase in the size of the system and, as a result, to inefficient control and a drop in performance. Therefore, it is advisable to divide a large-sized open SMAS into classes of agents and find relationships between them. This corresponds to dividing the applied task T into subtasks and determining relations between these subtasks. Such a path entails a two-level distributed control over SMAS agents. At the first and second levels, respectively, the relations of agents within classes and between classes of agents are studied. This paper proposes a multi-model approach to constructing, modeling, and analyzing SMAS. The properties of the agent and its mathematical model, described by a 5-digit set, as well as the mathematical model of the class of agents, described by a 12-digit set, which includes such components as the collective knowledge base of agents, the collective communication environment, mental, social, and other components, are discussed. Relations between classes of agents are reflected in a mathematical model of open SMAS. If a partition of a problem T is represented by an undirected acyclic graph GR′ whose terminal vertices have degree one and whose interior vertices have degree two, then GR′ is defined as a simple tree. In the case when any class of agents is a complete graph К then the Cartesian product CP′ = GR′ × Kn, according to the proved theorem, is a graph model of an open SMAS, consisting of the components К and connectives between them. According to the corollary of this theorem, it is possible to pass from CP′ to the multigraph model MYL′ = (MV′, ME′) of the system, if the components К are associated with the vertices of this model mv′ com  MV′, and the connectives are associated with its edges me′ con1  ME′. Moreover, the number of edges between any two vertices MYL′ is equal to n. The analysis of multigraph models of open SMAS of large sizes will significantly reduce the complexity of their study at the structural level.
Adaptive Particle Swarm Optimization for Energy Minimization in Cloud: A Success History Based Approach

Large-scale virtualized data centers have been built to meet the growing processing needs of modern service applications and the shift to Cloud computing. Cloud data centers have high energy consumption, high emissions, and high electricity expenses. Cloud providers could enhance resource use and reduce energy consumption through live migration and dynamic VM consolidation. Downsizing resources extensively may affect performance; therefore, we must handle the energy-performance trade-off to meet our goal to provide exceptional service to our clients. VM placement must be optimized in real time as application workloads become more sophisticated and dynamic. We undertake market research and compare online deterministic algorithms for VM migration and adaptive VM consolidation to understand the problem. By analyzing past VM resource usage, we provide adaptive algorithms for dynamic consolidation. The suggested algorithm decreased energy utilization and met SLA. Extensive simulations using real-world workload traces from over a thousand Planet Lab VMs prove the technique's effectiveness. This paper offers SHA-PSO, a PSO-based meta-heuristic technique that schedules workloads among Virtual Machines (VM) to minimize energy. Our algorithm stores each generation's best dynamic PSO solutions. After gathering enough achieved data, PSO parameters from the archive are used to arrange the incoming workload among VMs. The suggested technique outperformed existing scheduling approaches on Cloudsim, a Java-based cloud simulator.
Modal Logics with Non-rigid Propositional Designators

In most modal logics, atomic propositional symbols are directly representing the meaning of sentences (such as sets of possible worlds). In other words, they use only rigid propositional designators. This means they are not able to handle uncertainty in meaning directly at the sentential level. In this paper, we offer a modal language involving non-rigid propositional designators which can also carefully distinguish de re and de dicto use of these designators. Then, we axiomatize the logics in this language with respect to all Kripke models with multiple modalities and with respect to S5 Kripke models with a single modality.
Unknown Truths and Unknowable Truths

Notions of unknown truths and unknowable truths are important in formal epistemology, which are related to each other in e.g. Fitch’s paradox of knowability. Although there have been some logical research on the notion of unknown truths and some philosophical discussion on the two notions, there seems to be no logical research on unknowable truths. In this paper, we propose a logic of unknowable truths, investigate the logical properties of unknown truths and unknowable truths, which includes the similarities of the two notions and the relationship between the two notions, and axiomatize this logic.
Indicative Conditionals in Awareness Framework

We propose a novel approach to capturing the acceptability conditions of indicative conditionals using awareness logic. Specifically, we posit that the relevance between the antecedent and consequent of a conditional is determined by the concomitant occurrence of their awareness. We provide a sound and complete axiomatization of the logic. We compare the properties of the indicative conditionals in our framework with other existing theories.
Reasons in Weighted Argumentation Graphs

The philosophical literature that tackles foundational questions about normativity often appeals to normative reasons—or considerations that count in favor of or against actions—and their interaction. The interaction between normative reasons is usually made sense of by appealing to the metaphor of (normative) weight scales. This paper substitutes an argumentation-theoretic model for this metaphor. The upshot is a general and precise model that is faithful to the philosophical ideas.
Epistemic Monadic Boolean Algebras

Epistemic monadic Boolean algebras are obtained by enriching monadic Boolean algebras with a knowledge operator. Epistemic monadic logic as the monadic fragment of first-order epistemic logic is introduced for talking about knowing things. A Halmos-style representation of epistemic monadic Boolean algebras is established. Relativizations of epistemic monadic algebras are given for modelling updates. These logics are semantically complete.
Of Temporary Coalitions in Terms of Concurrent Game Models, Announcements, and Temporal Projection

We use Concurrent Game Models (CGM) in which simple conditional promises are assigned the role of negotiation steps aiming to represent the formation of temporary coalitions and their agendas. By transforming these extended CGMs into equivalent CGMs with incomplete information, established methods for rational synthesis become enabled. The interpretation of promises is compatible with that of announcements as in dynamic epistemic logics. To accommodate requirements on plays that are written wrt the runs of the original model, we use temporal projection that hides negotiation steps.
Knowing the Value of a Predicate

In 1989, Plaza introduced the “knowing value” operator $$\textsf{Kv}_id$$ to characterize the “Mr. Sum and Mr. Product” puzzle in propositional modal logic. Previous research had primarily focused on the $$\textsf{Kv}d$$ operator, which captures the idea of knowing the value of a designator d. This paper expands the scope of application for the $$\textsf{Kv}$$ operator beyond designators to include predicates, interpreting the $$\textsf{Kv}P$$ operator as denoting knowledge of the value of a predicate P. Additionally, we present two distinct semantics - MS (Mention-Some) semantics and MA (Mention-All) semantics - for the $$\textsf{Kv}P$$ operator, and prove the strong completeness theorem for two axiom systems containing only the $$\textsf{Kv}P$$ operator, as well as two axiom systems containing both the $$\textsf{Kv}P$$ and $$\textsf{Kv}d$$ operators.