Reviewerly/exHarmony huggingface dataset:
Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.
Security and Autonomic Management in System of Systems

A system of systems integrates systems that function independently but are networked together for a period of time to achieve a higher goal. These systems evolve over time and have emergent properties. Therefore, even with security controls in place, it is difficult to maintain a required level of security for the system of systems as a whole because uncertainties may arise at runtime. Uncertainties can occur from internal factors, such as malfunctions of a system, or from external factors, such as malicious attacks. Self-adaptation is an approach that allows a system to adapt in the face of such uncertainties without human intervention. This work outlines the progress made towards security mitigation in system of systems using a generic autonomic management system to assist engineers in developing self-adaptive systems. The manuscript describes the proposed system design, its implementation as part of the Eclipse Arrowhead framework, and its functionality in a smart agriculture use case. The system is designed and implemented in such a way that it can be reused and extended for a variety of use cases without requiring major changes.
A web-based Voice Interaction framework proposal for enhancing Information Systems user experience

Nowadays, numerous organisations of different dimensions and business sectors operate in highly challenging and dynamic environments, wherein the supporting information systems (IS) are becoming increasingly complex. In this context, assistive tools capable of tackling such complexity have the potential to aid users improving their performance and effectiveness, as well as to streamline businesses’ processes and promote entrepreneurial-level competitivity. Following this line of research, a web-based speech-to-term recognition approach is presented as a solution to endow IS with advanced capabilities for providing an easier (more natural) and straightforward interaction with baseline functionalities, by combining relevant techniques such as Voice Activity Detection (VAD), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and an Ontological Database (OD), mapping the IS’ functionalities and characteristics, is proposed. The developed interoperable system allows the conversion of speech to text - deriving into IS instructions - that is, in turn, submitted to on an ontological database wherein a term-based query is performed to elicit a set of available commands to be executed in the web context. These commands, fully mapped in the ontological database, are divided into three categories: a) navigation by menus/links, b) buttons interaction (e.g., submit forms) and c) completion of form fields. The proposed framework was experimentally tested in close to real conditions, resorting to an Enterprise Resource Planning (ERP) tool supplied by ERP Company.
Graph Convolutional Network Based on Multi-Head Pooling for Short Text Classification

The short text, sparse features, and the lack of training data, etc. are still the key bottlenecks that restrict the successful application of traditional text classification methods. To address these problems, we propose a Multi-head-Pooling-based Graph Convolutional Network (MP-GCN) for semi-supervised short text classification, and introduce its three architectures, which focus on the node representation learning of 1-order, 1&2-order of isomorphic graphs, and 1-order of heterogeneous graphs, respectively. It only focuses on the structural information of the text graph and does not need pre-training word embedding as the initial node feature. A graph pooling based on self-attention is introduced to evaluate and select important nodes, and the multi-head method is used to provide multiple representation subspaces for pooling without adding trainable parameters. Experimental results demonstrated that, without using pre-training embedding, MP-GCN outperforms state-of-the-art models across five benchmark datasets.
A Provably Secure Three-Factor Authentication Protocol Based on Chebyshev Chaotic Mapping for Wireless Sensor Network

Wireless sensor network has been widely used and plays a vital role in the Internet of Things, smart cities, military, and other fields, and its security has also attracted the attention of many researchers. In view of the security defects in Shin and Kwon’s scheme such as failure to provide three-factor security, lack of anonymity and untraceability, user impersonation attack, desynchronization attack and privileged insider attack, we suggest an improved provably secure three-factor user authentication scheme based on Chebyshev chaotic mapping for wireless sensor network, which employs fuzzy verifier technique to prevent attacker from offline guessing attack on user identity and password when the stolen/lost smartcard is acquired by the attacker. During the authentication phase, a dynamic identity mechanism is used to ensure the anonymity of the user and sensor to prevent desynchronization attack, and the Chebyshev chaotic mapping is introduced to improve security and reduce computation overhead. The rigorous security proof under the random oracle model and the formal verification via ProVerif show that our protocol overcomes the weaknesses in Shin and Kwon’s scheme. In addition, by comparing the performance of our proposed scheme with that of others, we demonstrate that our proposal not only solves the security risks of Shin and Kwon.’s protocol, but also achieves a better tradeoff between security and efficiency, therefore, it is more suitable for user authentication in wireless sensor network environments.
Region-aware network: Model human’s Top-Down visual perception mechanism for crowd counting

Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number of human and where they are through attention the crowd regions and the congestion degree of crowd regions with a global receptive field. Hence, in this paper, we propose a novel feedback network with Region-Aware block called RANet by modeling human's Top-Down visual perception mechanism. Firstly, we introduce a feedback architecture to generate priority maps that provide prior about candidate crowd regions in input images. The prior enables the RANet pay more attention to crowd regions. Then we design Region-Aware block that could adaptively encode the contextual information into input images through global receptive field. More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity. The relevance matrix obtained would be utilized to build global relationships between pixels. Our method outperforms state-of-the-art crowd counting methods on several public datasets.
Universal fault-tolerant quantum computing with stabilizer codes

The quantum logic gates used in the design of a quantum computer should be both universal, meaning arbitrary quantum computations can be performed, and fault-tolerant, meaning the gates keep errors from cascading out of control. A number of no-go theorems constrain the ways in which a set of fault-tolerant logic gates can be universal. These theorems are very restrictive, and conventional wisdom holds that a universal fault-tolerant logic gate set cannot be implemented natively, requiring us to use costly distillation procedures for quantum computation. Here, we present a general framework for universal fault-tolerant logic with stabilizer codes, together with a no-go theorem that reveals the very broad conditions constraining such gate sets. Our theorem applies to a wide range of stabilizer code families, including concatenated codes and conventional topological stabilizer codes such as the surface code. The broad applicability of our no-go theorem provides a new perspective on how the constraints on universal fault-tolerant gate sets can be overcome. In particular, we show how nonunitary implementations of logic gates provide a general approach to circumvent the no-go theorem, and we present a rich landscape of constructions for logic gate sets that are both universal and fault-tolerant. That is, rather than restricting what is possible, our no-go theorem provides a signpost to guide us to new, efficient architectures for fault-tolerant quantum computing.
Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.
Implementation of Blockchain Technology in Learning Management System (LMS)

It is well known by the public that the development of science and technology has progressed very rapidly and even continues to increase from year to year, especially in the education system. But in reality, there are still many learning systems that use conventional methods. This method makes the learning process passive and inactive. But now by implementing blockchain into the online learning process and there is no conventional learning process that makes learning activities boring and technology-saturated in the educational learning system at Raharja University, making the learning process more effective and efficient. The objectives of using blockchain technology in education include developing educational curricula, improving the use of educational applications, processing data about education. The research method is carried out by collecting data and using the literature study. So that the presence of blockchain technology makes the education system at the forefront. This application is done so that the learning system can be done with easy access, without being limited by space and time so that it is more efficient. It can even motivate to increase exploration in the learning process which in turn will increase student productivity, of course.
Masking Modalities for Cross-modal Video Retrieval

Pre-training on large scale unlabelled datasets has shown impressive performance improvements in the fields of computer vision and natural language processing. Given the advent of large-scale instructional video datasets, a common strategy for pre-training video encoders is to use the accompanying speech as weak supervision. However, as speech is used to supervise the pre-training, it is never seen by the video encoder, which does not learn to process that modality. We address this drawback of current pre-training methods, which fail to exploit the rich cues in spoken language. Our proposal is to pre-train a video encoder using all the available video modalities as supervision, namely, appearance, sound, and transcribed speech. We mask an entire modality in the input and predict it using the other two modalities. This encourages each modality to collaborate with the others, and our video encoder learns to process appearance and audio as well as speech. We show the superior performance of our "modality masking" pre-training approach for video retrieval on the How2R, YouCook2 and Condensed Movies datasets.
Distance-based clustering using QUBO formulations

In computer science, clustering is a technique for grouping data. Ising machines can solve distance-based clustering problems described by quadratic unconstrained binary optimization (QUBO) formulations. A typical simple method using an Ising machine makes each cluster size equal and is not suitable for clustering unevenly distributed data. We propose a new clustering method that provides better performance than the simple method, especially for unevenly distributed data. The proposed method is a hybrid algorithm including an iterative process that comprises solving a discrete optimization problem with an Ising machine and calculating parameters with a general-purpose computer. To minimize the communication overhead between the Ising machine and the general-purpose computer, we employed a low-latency Ising machine implementing the simulated bifurcation algorithm with a field-programmable gate array attached to a local server. The proposed method results in clustering 200 unevenly distributed data points with a clustering score 18% higher than that of the simple method. The discrete optimization with 2000 variables is performed 100 times per iteration, and the overhead time is reduced to approximately 20% of the total execution time. These results suggest that hybrid algorithms using Ising machines can efficiently solve practical optimization problems.
Identity-based remote data checking with a designated verifier

Abstract Traditional remote data possession auditing mechanisms are usually divided into two types: private auditing and public auditing. Informally, private auditing only allows the original data owner to check the integrity of its outsourced data, whereas anyone is allowed to perform the checking task in public auditing. However, in many practical applications, the data owner expects some designated verifier (instead of others) to audit its data file, which cannot be covered by the existing private or public auditing protocols. Thus, in a recent work, Yan et al. proposed a new auditing technique with a designated verifier [ IEEE Systems Journal, 12(4): 1788-1797, 2020 ]. Nevertheless, we note that this protocol suffers from complicated management of certificates and hence heavily relies on public key infrastructure. To overcome this shortcoming, in this paper, we propose an identity-based auditing protocol with a designated verifier, which not only avoids the introduction of certificates, but also has the desired property of only allowing specific verifier to audit. Its security is based on the classical computational Diffie-Hellman and Weil Diffie-Hellman assumptions. Finally, performance analysis shows that our proposed protocol is very efficient and suitable for some real-life applications.
Rules of Smart IoT Networks within Smart Cities towards Blockchain Standardization

Motivation. Standardization in smart city applications is restricted by the competitive pressures of proprietary innovation and technological compartmentalization. Interoperability across networks, databases, and APIs is essential to achieving the smart objectives of technology-supported urban environments. Methodology. The issues that smart cities face, as well as the usage of blockchain in Internet of Things (IoT) applications, are discussed in this research paper. Problem Statement. The study shows the obstacles to the establishment of an IoT-driven smart city agenda, including system security, dispersed node interoperability, data resource management, and scalability of a diverse IoT network. Results. To resolve these challenges, this research proposes a working infinite loop model for establishing a standardized, intermediary cloud-based blockchain for IoT networking within smart cities. The blockchain intermediary function will resolve critical gaps in the existing, distributed IoT-based smart cities’ standards, drawing connections between nodes, users, and service providers that are enabled through autonomous, immutable, and nonrepudiated transactions.
Task Switching Network for Multi-task Learning

We introduce Task Switching Networks (TSNs), a task-conditioned architecture with a single unified encoder/decoder for efficient multi-task learning. Multiple tasks are performed by switching between them, performing one task at a time. TSNs have a constant number of parameters irrespective of the number of tasks. This scalable yet conceptually simple approach circumvents the overhead and intricacy of task-specific network components in existing works. In fact, we demonstrate for the first time that multi-tasking can be performed with a single task-conditioned decoder. We achieve this by learning task-specific conditioning parameters through a jointly trained task embedding network, encouraging constructive interaction between tasks. Experiments validate the effectiveness of our approach, achieving state-of-the-art results on two challenging multi-task benchmarks, PASCAL-Context and NYUD. Our analysis of the learned task embeddings further indicates a connection to task relationships studied in the recent literature.
Swallowing the Black Pill: Involuntary Celibates’ (Incels) Anti Feminism within Digital Society

Involuntary celibates (incels) are part of the online ‘manosphere’ and have been widely discussed in contemporary media in recent years due to their involvement in several offline mass murders. This article presents empirical data that specifically map aspects of the incel worldview: the ‘black pill’. Analysis of online discussion forums demonstrates how incels believe society is ordered through a hetero-patriarchal racial hierarchy and justify their sexlessness through beliefs rooted in biological determinism and victimisation by women and feminism. It is argued that the black pill is a disciplinary device that aids in building a digital counter-public that engenders a collective incel identity. Further, the article argues that the black pill produces a form of ‘stochastic terrorism’ in which users interpret its spectrum of beliefs to enact harms from online gender-based hate speech through terrorist violence in the offline world. As a point of departure, the article argues that incel counter-publics transcend the false distinctions between online and offline; both ‘worlds’ contribute to the (re)production of incel anti-feminism and misogyny.
A Survey of Automatic Source Code Summarization

Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code summarization is mismatched with the source code, missing, or out of date. Manual source code summarization is inefficient and requires a lot of human efforts. To overcome such situations, many studies have been conducted on Automatic Source Code Summarization (ASCS). Given a set of source code, the ASCS techniques can automatically generate a summary described with natural language. In this paper, we give a review of the development of ASCS technology. Almost all ASCS technology involves the following stages: source code modeling, code summarization generation, and quality evaluation. We further categorize the existing ASCS techniques based on the above stages and analyze their advantages and shortcomings. We also draw a clear map on the development of the existing algorithms.
Emerging Technologies: Factors Influencing Knowledge Sharing

As advances in emerging technologies continue to transform the business landscape, knowledge sharing will become increasingly important to leveraging the unique core competencies of organizations so as to gain a competitive advantage. Despite emerging technologies being so popular in news and media publications, knowledge sharing remains an area of research that is under-researched in the emerging technologies context. This research aims to identify the barriers and factors to knowledge sharing within an emerging technology context. A comprehensive in-depth interview was conducted with 38 professionals working in 5 organizations engaged in the field of emerging technologies in order to arrive at the findings discussed in this paper. Based on the analysis of the surveys, we found that there are six main factors driving the need for sharing knowledge. The six factors are: regular cadence, integrating expertise from different teams, diversity-inclusive social environment, Interconnected platforms that are accessible to all, a regular update schedule that needs to be followed, to create points of contact within different departments of an organization in order to facilitate sharing. It is important to note that the distribution and use of knowledge in organizations is dependent on the interactions between individuals.
SeisTutor: A Custom-Tailored Intelligent Tutoring System and Sustainable Education

Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.