Reviewerly/exHarmony huggingface dataset:
Text sampling strategies for predicting missing bibliographic links

The paper proposes various strategies for sampling text data when performing automatic sentence classification for the purpose of detecting missing bibliographic links. We construct samples based on sentences as semantic units of the text and add their immediate context which consists of several neighboring sentences. We examine a number of sampling strategies that differ in context size and position. The experiment is carried out on the collection of STEM scientific papers. Including the context of sentences into samples improves the result of their classification. We automatically determine the optimal sampling strategy for a given text collection by implementing an ensemble voting when classifying the same data sampled in different ways. Sampling strategy taking into account the sentence context with hard voting procedure leads to the classification accuracy of 98% (F1-score). This method of detecting missing bibliographic links can be used in recommendation engines of applied intelligent information systems.
Episodes Discovery Recommendation with Multi-Source Augmentations

Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However, historical interaction data is highly sparse, and most items are long-tail items, which limits the representation learning for item discovery. This problem is further augmented by the discovery of novel or cold-start items. For example, after a user displays interest in bitcoin financial investment shows in the podcast space, a recommender system may want to suggest, e.g., a newly released blockchain episode from a more technical show. Episode correlations help the discovery, especially when interaction data of episodes is limited. Accordingly, we build upon the classical Two-Tower model and introduce the novel Multi-Source Augmentations using a Contrastive Learning framework (MSACL) to enhance episode embedding learning by incorporating positive episodes from numerous correlated semantics. Extensive experiments on a real-world podcast recommendation dataset from a large audio streaming platform demonstrate the effectiveness of the proposed framework for user podcast exploration and cold-start episode recommendation.
An Accurate PDDL Domain Learning Algorithm from Partial and Noisy Observations

This paper presents a novel approach to learn PDDL domain called AMLSI (Action Model Learning with State machine Interaction) based on grammar induction. AMLSI learns with no prior knowledge from a training dataset made up of action sequences built by random walks and by observing state transitions. The domain learnt is accurate enough to be used without human proofreading in a planner even with very highly partial and noisy observations. Thus AMLSI takles a key issue for domain learning that is the ability to plan with the learned domains. It often happens that small learning errors lead to domains that are unusable for planning. AMLSI contribution is to learn domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems. Compared to other approaches, AMLSI uses smaller training datasets and exploits both feasible and infeasible generated action sequences.
Lightweight Structure-Aware Attention for Visual Understanding

Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy of the ViT layers, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose a novel attention operator, called lightweight structure-aware attention (LiSA), which has a better representation power with log-linear complexity. Our operator learns structural patterns by using a set of relative position embeddings (RPEs). To achieve log-linear complexity, the RPEs are approximated with fast Fourier transforms. Our experiments and ablation studies demonstrate that ViTs based on the proposed operator outperform self-attention and other existing operators, achieving state-of-the-art results on ImageNet, and competitive results on other visual understanding benchmarks such as COCO and Something-Something-V2. The source code of our approach will be released online.
BERT-PG: A two-branch associative feature gated filtering network for aspect sentiment classification

Abstract Aspect sentiment classification is an important branch of sentiment classification that has gained increasing attention recently. Existing aspect sentiment classification methods typically use different network branches to encode context and aspect words separately, and then use an attention mechanism to capture their associations. This attention-based approach cannot completely ignore the contexts unrelated to the current aspect words, which brings noise interference. In this paper, a gated filtering network based on BERT is suggested as a solution to this issue. We employ BERT to encode the text semantics of contexts and sentence pairs consisting of context and aspect words respectively, and to extract lexical features as well as associative features of context and aspect words. Based on this, we designed a gating module that, unlike the attention mechanism, uses association features to precisely filter irrelevant contexts. Additionally, because the BERT network parameters are so big, there is a tendency to over-fitting during training. To effectively combat this problem, we developed a loss function with a threshold. We carried out extensive experiments using three benchmark datasets to verify the performance of our proposed model. According to the experimental results, the model outperforms other reference models on all three datasets, proving that our approach is effective in improving the presentation of aspect sentiment classification in comparison to other cutting-edge sentiment classification methods.
The Future of Cybersecurity Workforce Development

This paper will discuss the future of cybersecurity workforce development. Cybersecurity is a field that is increasingly becoming important in today's workplaces. Considering the rapid growth of technology, it is expected that the field of cybersecurity will change significantly in the future. As such, preparedness is needed to ensure that the future cybersecurity workforce is not hindered by a lack of training, resources, or technical expertise. The personality traits of a cybersecurity professional should be evaluated before the assumption of a given occupation to ensure that this professional is the best fit and possesses all skills, values, and values required for that post. Teamwork should be integral in future workforce development because, according to the current trend in different industries, being a team player is essential. Lastly, cybersecurity professionals should be trained to observe ethics and civic duty by being loyal to their employers. They should also prioritize continued learning because the cyber domain is ever-changing and requires flexibility and adjustment. This paper will first explore the cyber environment and highlight some of the challenges currently facing the area. Next, the most fundamental skills needed for the furtherance of this field will be covered. One area that will be the paper's focus will be the importance of social skills. The article will finally provide an overview of some of the anticipated changes that will take place in the area of cybersecurity workforce development.
An empirical study of learning-to-rank for spare parts consumption in the repair process

The repair process of devices is an important part of the business of many original equipment manufacturers. The consumption of spare parts, during the repair process, is driven by the defects found during inspection of the devices, and these parts are a big part of the costs in the repair process. In previous work we proposed a data-driven method for Supply Chain Control Tower solutions to provide support for the automatic check of spare parts consumption in the repair process. In this paper, we continue our investigation of a multi-label classification problem and explore alternatives in the learning-to-rank approach, where we simulate the passage of time using more data while training and comparing hundreds of Machine Learning models to provide an automatic check in the consumption of spare parts. We investigate the effects of different train set sizes, retraining intervals, models and hyper-parameter search using Bayesian Optimization. The results show that we were able to improve the trained models and achieve a higher mean NDCG@20 score of 86% when ranking the expected parts. Focusing on the most recent data, we achieve a NDCG@20 score of 90%, while obtaining a ratio of marked parts of just 4% of the consumed parts for use in alert generation.
Overcoming Traditional ETL Systems Architectural Problems Using a Service-Oriented Approach

Developing analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data is a determinant factor in the success of analytical and decision-making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today’s users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on ETL systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up-to-date approaches, having real application evidence. In this paper, we present a service-oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service-oriented architecture, for demonstrating the application and benefits that this kind of approach will provide to ETL systems project development.
Characterization of Probabilistic Structure of Network Traffic During COVID-19: A Study Based on MAWI Data

The COVID-19 pandemic has greatly affected all aspects of human life including working of offices, businesses, industries, educational institutions etc. With more work load shifting online, changes in the network traffic are inevitable. The earlier investigations have generally focused on the qualitative aspects of network traffic data during COVID-19. In contrast, the paper presents a study based on MAWI data characterizing network traffic in terms of multimodal and unimodal probability distributions. It is found that a transition of multimodal Gaussian mixture model of byte and packet counts during normal period to that of unimodal Laplace distribution during COVID-19 period has emerged. Further it is observed that the probability distribution depicts the preponderance of small and large packets during normal period which changes to that of small sized packets during Covid-19 period. These findings are likely to be useful to the administrators to manage network during crisis periods.
Network Forensic of Cryptocurency Miners

As reported by BSSN on August, 2022, Indonesia has traffic anomalies of 1.6 billion traffic, with 55.83% of anomaly findings containing malware and the remaining 14.99% information disclosure is another activity. Some of this data, when further analyzed based on the type of attack, falls into the categories of breaches, successes, attempts, and failures. Investigate crimes related to misuse of resources for illegal cryptocurrency mining activities. Therefore, this study should consider acquisitions from the network side as they apply to existing government agencies/institutions. This observation thereby provides information for later evidence, intruder detection, and prosecution of perpetrators who misuse resources for personal gain. As a result, the harvesting processs can obtain valuable data from routers as a digital evidence for investigating information about network attack activitiy and anomalies traffic.
Weak-Strong Self-Adapting Fuzzy Neural Classifier for Dynamic Object Detection in RGBD Videos

This paper presents a fuzzy neural method to model background from videos in order to detect dynamic objects. The method includes a weak fuzzy classifier that performs an initial foreground and background separation based on color and depth differences between the actual frame and background models. The outputs of this fuzzy system are weighted according to the result of the color and depth noise modeling. A degree of uncertainty and the strength of decisions, in combination with the weighting results, are used by the method to define more accurately the dynamic objects through a strong fuzzy classifier. The final stage of foreground detection is implemented with a Discrete-Time Cellular Neural Network to improve the foreground definition. Finally, the color and depth background models are updated based on a fuzzy learning rate strategy. The method was evaluated with the new SBM-RGBD database and compared against several state-of-the-art methods showing a similar or better performance considering the quantitative and qualitative evaluations.
PENINGKATAN EFEKTIVITAS DAN HASIL BELAJAR PENDIDIKAN JASMANI, OLAHRAGA DAN KESEHATAN MELALUI PENERAPAN APLIKASI GOOGLE CLASSROOM SISWA KELAS VIII-C SMP NEGERI 6 MATARAM TAHUN PELAJARAN 2021/2022

The purpose of this study was to determine: the application of the Google Classroom application in increasing the effectiveness of learning PJOK in class VIII-C students of SMP Negeri 6 Mataram in the 2021/2022 academic year.The study used a classroom action research design that included the components of planning, action, observation and reflection, by taking the subjects of class VIIIC students of SMP Negeri 6 Mataram as many as 35 people consisting of 24 boys and 11 girls. The data were analyzed with Learning Management, learning effectiveness and student learning outcomes.The results show that: the application of the Google Classroom application can increase the effectiveness of learning PJOK in class VIII-C students of SMP Negeri 6 Mataram in the 2021/2022 academic year
IMPLEMENTASI SAMBHAṢAṆA DALAM UPAYA MENGEMBANGKAN KEMAMPUAN DASAR BERBAHASA SANSKERTA

The view of sanskrit as a dead language and a language that is difficult to learn is not completely correct and must slowly be eliminated. Awareness to increase the existence of sanskrit has been initiated in several parts of the world by making it a daily communication language. The basic learning of sanskrit which is initiated at this time places more emphasis on communication skills, not on the grammatical structure. Learning is packaged more effectively and efficiently through the sambhaṣaṇa (conversation) method by applying simple grammar or communication patterns. Conversation is a very common method in learning other languages, but it has become a new design in teaching sanskrit. Thus, this study was conducted to analyze and develop the concept of implementing the sambhaṣaṇa method in relation to developing basic sanskrit language skills. This research was conducted through a qualitative approach by presenting the data descriptively. The results showed that the application of the sambhaṣaṇa (conversation) method was very effective and efficient in developing basic sanskrit language skills. Learning sanskrit through the sambhaṣaṇa method begins with the introduction of objects and activities in the surrounding environment. Sanskrit conversation material can be arranged in the form of special themes that are relevant to the daily lives of students. During the learning process, the teacher has the task of correcting errors that arise from conversations practiced by students. The success of the learning is determined by the motivation of the students, the competence of the teacher, the intensity of the exercise, and the vocabulary mastered by the students.
Handwritten Arabic Digit Recognition Using Convolutional Neural Network

In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy. The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising.
MASTERING READING SKILL FASTER

This is a qualitative study in which the author use a literature review paradigm approach. The goal of this research is to discover the quickest technique to increase reading skills. Several periodicals and articles about how to increase one's reading abilities are the focus of this study.The findings of this study reveal that, reading materials and activities should be very interesting for students so that they can easily absorb books, and must be adapted to their level of competence. Teachers should motivate their students to read these materials, be sensitive to their students' comprehension difficulties, and assist their students in changing their attitudes towards reading and developing positive attitudes towards their reading activities so that they can better understand various texts
PROBLEMATIKA KETERAMPILAN MEMBACA DAN MENULIS PADA MAHASISWA PENDIDIKAN BAHASA INDONESIA UM-TAPSEL

This study aims to determine the causes of problems or problems that often arise in the world of education. In this case, the main cause is the lack of curiosity about reading, whether it is the purpose of reading, the benefits of reading, the important role of reading for oneself and the surrounding environment. The cause of problems in writing is not knowing where to start. Too many things that seem to be the same make it difficult for people to be interested. I don't have the right language. To imagine that everything is already known to everyone. The method used in this research is to collect accurate data through interviews, recording, and taking notes. The expected results in this study are to be able to solve the problems of low reading, increase interest in reading, foster creativity in reading, develop knowledge by reading, change consumptive thinking patterns by reading and writing.Keywords: problematic, reading, writing.
A Low Power Architectural Framework for Automated Surveillance System with Low Bit Rate Transmission

Abstract The changed security scenario of the modern time has necessitated increased and sophisticated vigilance of the countries' borders. The technological challenges involved in accomplishing such feat of automated security system are many and require research at the components-and-algorithms as well as the architectural levels. This paper proposes an architectural framework for automated video surveillance comprising a network of sensors and closed circuit television cameras as well as proposing algorithmic/component research of software and hardware for the core functioning of the framework, such as: communication protocols, object detection, data-integration, object identification, object tracking, video compression, threat identification, and alarm generation. In this paper, we are addressing some general topological and routing features that would be adopted in our system. There are two types of data with regard to data communication – video stream and object detection. The network is broken down into several disjoint, almost equal zones. A zone have one or more one cluster. A zone manager is chosen among the cluster heads depending on their relative residual energies. There are several levels of control that could be implemented with this arrangement with localized decision made, to get distributed effect at all levels. A cell tracks each target in its zone. If the target moves out of the range of a cell, the cell manager will send the target description to estimated next cell. The next cell starts tracking the target. If the estimated cell is wrongly chosen, corrections will be made by the cluster heads to get the new target-tracking. We also propose bitrate reduction algorithms to accommodate the limited bandwidth. One of the main feature of this paper is introducing a Low-Power Low-Bit rate video compression algorithm to accommodate the low power requirements at sensor nodes, and the low bit rate requirement for the communication protocol. We proposed two algorithms the ALBR and LPHSME. ALBR is addressing low bit rate required for sensors network with limited bandwidth which achieves a reduction in Average number of bits per Iframe by approximately 60% in case of low motion video sequences and 53% in case of fast motion video sequences . LPHSME addresses low power requirements of multi sensor network that has limited power batteries. The performance of the proposed LPHSME algorithm versus full search and three step search indicates a reduction in motion estimation time by approximately 89% in case of low motion video sequences (e.g., Claire ) and 84% in case of fast motion video sequences. The reduced complexity of LPHSME results in low power requirements.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN MAHASISWA DALAM MEMILIH KULIAH DI UNIVERSITAS TEKNOLOGI MATARAM (UTM)

This study aims to analyze the factors that influence student decisions in choosing lectures at the Mataram University of Technology (UTM). This research was conducted during September to March, namely on first semester students of the odd semester class of 2021/2022 at the Mataram University of Technology. This study is a causal associative research which means that the dependent variable is influenced by the independent variable, data collection is carried out using an instrument/questionnaire for 36 new students using the sampling method using Non-Probability Sampling. Analysis of the data used is multiple linear regression analysis and hypothesis testing using the Statistical Product and Solution (SPSS) for Windows program.The results also show that 1) Study Program variables, Accreditation Status, Tuition Fees, Promotion, Lecturer Quality, Campus Facilities, Campus Locations, Lecturer Quantity, Physical Evidence, Alumni, Image, Family and Reference Groups have a significant influence as factors that influence consumers choose to study at UTM, 2) Study Program Variable Factors have the most dominant influence on students choosing to study at UTM.