Reviewerly/exHarmony huggingface dataset:
An Entropic Approach to Technology Enable Learning and Social Computing

Understanding one’s own behavior is challenging in itself; understanding a group of different individuals and the many relationships between these individuals is even more complex. Imagine the amazing complexity of a large system made up of thousands of individuals and hundreds of groups, with countless relationships between those individuals and groups. However, despite this difficulty, organizations must be managed. Indeed, ultimately the organization’s work is done by people, individually or collectively, alone or in combination with technology. Therefore, organizational behavior management is the central task of management work – it involves understanding the behavior patterns of individuals, groups, and organizations, predicting what behavioral reactions will be elicited by various managerial actions and finally applying this understanding. Undeniably, society’s work is often done by organizations, and the role of management is to make organizations do that work. Without it, our entire society would quickly stop operating. Not only would the products you have come to know and love swiftly to evaporate from store shelves; food itself would suddenly become scarce, having drastic effects on huge numbers of people. To this end, the term Technology- Enhanced Learning is used to support workers’ learning about technology; the gap between what is understood to be satisfactory and the current level of knowledge of the workforce is addressed by a Logic-programming-based Social Computing Framework entitled An Entropic Approach to Knowledge Representation and Reasoning, which relies on computational structures built on Artificial Neural Networks and Cases -based Thinking, as well as predictions and/or assessments, to empower the level of knowledge of the employees, here in technology, later in other areas.
Advanced Topics in Machine Learning

This chapter reveals the infancy of the striking experience near the “Internet of Things (IoT)”. Machine learning technology is a part of Artificial Intelligence that grew from the training of computational learning approaches and pattern recognition of artificial intelligence. Over the last few years, Machine Learning approaches have been advanced inquisitively for various sectors such as smart city, finance, banking, education, etc. Today machine learning is not similar to the previous machine learning because of various new advanced computing techniques. Machine learning technique is defined as data analysis that automates the building of analytical models. The iterative factor of learning algorithms is significant as models are uncovered to new datasets; they are skilled in autonomously adjusting. The study from earlier computations generates reliable, efficient, repeatable decisions and experiment results. Therefore, Machine Learning measures have been used to protect various smart applications from any illegal activities, threats, and various attacks. Furthermore, Machine Learning provided suitable solutions for preserving the security of various advanced applications. The patent growth rate is 34% in the machine learning field from the year 2013 to 2017, according to Patent Service IFI Claims. Also, in the world, 60% of companies are using various learning algorithms for numerous purposes. In this chapter, we have discussed efficient, advanced, and revolutionary machine learning algorithms in detail.
Neural Topic Modeling via Discrete Variational Inference

Topic models extract commonly occurring latent topics from textual data. Statistical models such as Latent Dirichlet Allocation do not produce dense topic embeddings readily integratable into neural architectures, whereas earlier neural topic models are yet to fully take advantage of the discrete nature of the topic space. To bridge this gap, we propose a novel neural topic model, Discrete-Variational-Inference-based Topic Model (DVITM), which learns dense topic embeddings homomorphic to word embeddings via discrete variational inference. The model also views words as mixtures of topics and digests embedded input text. Quantitative and qualitative evaluations empirically demonstrate the superior performance of DVITM compared to important baseline models. In the end, case studies on text generation from a discrete space and aspect-aware item recommendation are presented to further illustrate the power of our model in downstream tasks.
K-means clustering algorithm based on improved flower pollination algorithm

The K-means clustering algorithm is affected by the initial cluster center, resulting in a low accuracy of the clustering results. The standard flower pollination algorithm (FPA) has slow convergence and low optimization accuracy in the later stage. Therefore, the FPA is improved, and k-means is optimized accordingly. First, a random reverse learning strategy is used to uniformly distribute the population; second, the dynamic transition probability is used to balance the search mode to improve the overall performance of the algorithm; third, the nonlinear inertia weight parameter is introduced into the global search process to improve the global exploration ability; fourth, the optimal individual improves the diversity of the population while decreasing the probability of the algorithm failing. Six standard test functions are used to test the performance of improved flower pollination algorithm (IFPA), and the results show that IFPA is better than FPA in convergence speed and search optimization accuracy. The experimental comparative analysis of k-means cluster optimization based on improved flower pollination algorithm (IFPA-KM) on the University of California Irvine dataset shows that compared with k-means and FPA-KM, IFPA-KM improves the accuracy of clustering and has better stability.
Adaptive multilabel image segmentation model with prior constraints based on geometric structure characteristics

Image segmentation is an essential digital image processing problem closely related to computer vision. The limitations of the total variational regular term for the continuous Potts model make the transition of segmentation results over-smooth. In this paper, we propose an improved model with prior conditions to realize the improvement of image segmentation results quality. To extract the structural features of the images, we utilize the simple and practical K-mean clustering (KM) algorithm to set the corresponding volume structure as a prior condition, which is also crucial for the initial label selection of the proposed model. Then, we choose two convex relaxation methods to solve the original nonconvex variational problem. Using these methods, we verify that increasing the constraint of volume structure can maintain slender structures in the original image and achieve a good balance between image segmentation quality and computation. Consequently, we use the unified, convergent primal-dual (PD) algorithm to solve the minimization problem in the proposed model. Extensive experimental comparisons between our method and the pure KM method, the graph-cut method, and the corresponding Potts model without volumetric structure are provided. The segmentation results illustrate that our model performs better in terms of both visualization and evaluation metrics.
Hierarchical multiple proxy loss for deep metric learning

Recent studies on deep metric learning have taken efforts to learn a common feature representation for each category, which is conflict to the intrinsic intra-class variance caused by the fact that latent attributes of the subcategories enlarge the variability of samples from the same class, leading to difficulties of inferring more fine-gained similarity retrieval. This paper proposes a hierarchical multi-proxy representation to establish both the common feature representation of the main-proxy and the complementary representations of their sub-proxies. Specifically, the main-proxy represents the intrinsic characteristics of a certain category for inter-class distinction. Meanwhile, sub-proxies belonging to each main-proxy provide additional capacity for describing intra-class discrepancy. Therefore, a hierarchical multi-proxy representation naturally captures both inter-class and intra-class diversity, yielding a more complete and distinguishable data representation. Significantly, the instances from distinct categories with similar main-proxy attributes can be further separated by the multiple proxy representation due to distinguishable difference among sub-proxy attributes. Correspondingly, we propose Hierarchical Multi-proxy loss as a reliable guidance for deep metric learning. Performance improvement of around 0.5% in precision on the benchmarks as well as the theoretical analysis demonstrate the effectiveness of the proposed method, especially on datasets with high intra-class variance.
Moving on from the pandemic in school- a roadmap to flexible modalities

<ns4:p>Teaching during the remote episodes of the Covid pandemic demonstrated that the majority of classroom teachers replicated classroom practices rather than adapting to a new system for learning. During the rapid shift online, professional development of teachers focused on upskilling rather than changing and challenging teaching practices. As a result, students were unmotivated and disengaged. The problem now exists that teachers are stuck with a lack of understanding of how to teach online with the likelihood of further external uncontrollable occurrences. Additionally, there is no roadmap forward on how to harness the benefits of learning online through the emerging flexible modalities of blended or hybrid teaching in schools. The current risk is that schools return to classroom based pedagogies and miss the opportunities that learning online brings. This paper responds to this crucial stage in schooling offering a way forward. To do this, there is a need to pause in time, to reflect on what is known and what is important to consider to be able to move forward effectively. As such this paper firstly examines the rush to get online and the frenzied up-skilling of teachers so that we have a better understanding of what skills were developed. This paper then synthesises the literature to identify what pedagogical skills are needed to effectively teach online in a schooling context. Drawing extensively from theories and empirical studies over the last 30 years this paper then presents a roadmap forward that offers professional development of a different kind identifying how to change teachers’ practices from replicating the classroom to teaching effectively online. Directives and implications are provided for school leaders, teacher educators and researchers in the field of digital pedagogical transformation. It is a critical time in education to shift from classroom based pedagogies to flexible modalities.</ns4:p>
Host-graph-sensitive RETE nets for incremental graph pattern matching with nested graph conditions

Efficient querying of large graph structures is a problem at the heart of several application domains such as social networks and model driven engineering. In particular in the context of model driven engineering, where the same query is executed frequently over an evolving graph structure, incremental techniques based on RETE nets are a popular solution. However, the construction of adequate RETE nets for a specific problem instance is a challenge in and of itself. In this article, we propose an approach to RETE net construction for queries in the form of graph patterns equipped with nested graph conditions that considers not only the structure of the query, but also the structure of the host graph to improve the net's performance regarding execution time and memory consumption. Furthermore, we suggest a technique for adapting the net structure to changing host graph characteristics. We evaluate the presented concepts empirically based on queries and data from two independent benchmarks, using a mature tool for incremental graph pattern matching as a reference. Our evaluation results indicate that considering host graph structure during RETE net construction can improve performance of the resulting net. The experiments also demonstrate that adapting the net structure to changing host graph characteristics can be performed with an acceptable execution time overhead for our examples and may yield more adequate RETE nets. Overall, our approach never performed worse than the reference by more than factor 1.2 regarding execution time and achieved an improvement by up to factor 20.
Recommender system expedited quantum control optimization

Quantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient quantum state transfers. The computational resource required for the optimization is an essential consideration in order to scale toward quantum control of larger registers. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the challenge of enhancing computational efficiency. Given a sparse database of a set of products and their customer ratings, RS is used to efficiently predict unknown ratings. In the quantum control problem, each iteration of a numerical optimization algorithm typically involves evaluating a large number of parameters, such as gradients or fidelities, which can be tabulated as a rating matrix. We establish that RS can rapidly and accurately predict elements of such a sparse rating matrix. Using this approach, we expedite a gradient ascent based quantum control optimization, namely GRAPE, and demonstrate the faster construction of two-qubit CNOT gate in registers with up to 8 qubits. We also describe and implement the enhancement of the computational speed of a hybrid algorithm involving simulated annealing as well as gradient ascent. Moreover, the faster construction of three-qubit Toffoli gates further confirmed the applicability of RS in larger registers.
EFFECTIVENESS OF THE LANGUAGE OF FAST FOOD ADVERTISEMENTS THROUGH J. BAUDRILLARD'S POSTMODERNISM

One of the dominant fields in the scope of advertising is Fast Food promotion. Junk food advertisements are viral on the Internet, television, billboards and among other types of advertisements. This has led to many people preferring it to other types of food. Several studies have also investigated the effects of junk food advertisements on people’s attitudes and preferences. Firstly, it creates some expectations which raise the desire to buy. Secondly, it associates purchase of these foods with some positive feelings such as satisfaction and happiness. Thirdly, it uses a certain entertaining dimension, thus, implying that purchase will boost a pleasant mood. Finally, people are not always conscious about the persuasive nature of advertising. In this connection, the present article aims to study how some linguistic techniques and devices in Fast Food advertisements call people for certain actions (purchase) and make these advertisements effective. The linguistic analysis of all advertisements carried out in the research mainly focused on defining and characterizing the linguostylistic devices used in fast food advertising, taking into consideration specific language levels such as lexicon, phonology, stylistics and grammar. The analysis demonstrates that nearly every advertisement uses rhetorical figures not depending on the thematic domain they come from.
Knowledge Distillation for Automatic Receipt Identification in Jakarta Super App Platform

Computer vision research has been used in daily applications, such as art, social media app filter, and face recognition. This emergence is because of the usage of the deep learning method in the computer vision domain. Deep learning research has improved many qualities of services for various applications. Starting from recommended until detection systems are now relying on deep learning models. However, currently many models require high computational processing and storage space. Implementing such an extensive network with limited resources on an embedded device or smartphone becomes more challenging. In this study, we focus on developing a model with small computational resources with high accuracy using the knowledge distillation method. We evaluate our model on the public and private datasets of receipt and non-receipt images that we gathered from Badan Pendapatan Daerah, CORD, and Kaggle dataset. After that, we compare it with the regular convolutional neural network (CNN) and pre-trained model. We discovered that knowledge distillation only uses 12% and 5% of the total weight of the CNN and the pre-trained model, respectively. As a result, we see a possibility that knowledge distillation illustrates potential outcomes as a method that could implement for automatic receipt identification in the Jakarta Super App.
Development of CNN Pruning Method for Earthquake Signal Imagery Classification

The real-time detection of earthquake occurrences in a seismic wave, drawn by seismograph, is crucial for disaster mitigation. The earlier the earthquake warning, the more lives can be saved. One approach that can monitor and detect earthquake occurrence is binary classification between earthquake and noise signals. The use of deep learning models such as CNN (Convolutional Neural Network), is considered quite accurate to perform an image classification of seismograph signals. Nevertheless, the tendency to use the large CNN model is rated to have better accuracy than smaller models. In fact, the disadvantage of utilizing large model is the inference time and the deployment of a large model to obtain real-time inference is more costly than the smaller model. This paper aims to reduce the size of a CNN model (Resnet50) by pruning the unnecessary filters and neuron on the model architecture without sacrificing the accuracy. The task of the model was to classify two classes (earthquake and noise) of spectrogram images, the dataset is STEAD (Stanford Earthquake Dataset). To prioritize which filter or neuron to be eliminated, L2-norm was calculated on each filter or neuron weights. We assumed that a filter or neuron with the lowest L2-norm had the least significant role in the model. By pruning 90% of the filter and neuron of the model and retraining the pruned model, the inference time was improved from 22. 45ms to 3. 6ms (on NVIDIA GTX 1050) per image with the accuracy of 99.405%.
An Automatic Track Initialization Based On Combining The Hough Transform Method And A Multi-level Filter

Modern surveillance systems require high accuracy in track initialization. However, data processing in dense clutter environments is always a challenging issue as the majority of the observations from radars are false. To solve this problem, we propose to use a method that combines the Hough transform technique with a multi-level filter system. This strategy took advantage of the advantages of the Hough Transform algorithm while overcoming its limits by utilizing the strength of the intuitive method via orbital information filters. This method has proven effective in the data initialization procedure, particularly in densely cluttered environments, it can perform track initialization fast while eliminating the appearance of false targets. We have conducted different experiments on the same data set with the same configuration parameters within 10 radar scans. We got 10 trajectories without any false targets using the multi-level filter system, Without it, we obtained 145 trajectories, of which 135 trajectories are false ones.
Face Recognition Using Deep Learning as User Login on Healthcare Kiosk

This paper proposes the development of a login system to a Healthcare Kiosk using facial images. The use of the face as an example of a unique biometric system other than fingerprint and iris is considered better than conventional systems using RFID cards that are prone to being lost or left behind, or passwords that are often forgotten. In this paper, we propose the use of faces as input for the login system to a healthcare kiosk by utilizing deep learning technology. We tested four types of Convolutional Neural Network (CNN) architectures such as VGG16, ResNet50, Xception and MobileNet. In the accuracy testing process, VGG16 got a total accuracy of 100% but still showed the wrong class during realtime detection testing, ResNet50 got a total accuracy of 99.531% and was able to show the correct class during realtime detection testing, Xception got a total accuracy of 80.018% but still shows the wrong class when testing realtime detection, and MobileNet gets a total accuracy of 92.934%.
Binarized and Full-Precision 3D-CNN in Action Recognition

As a part of the video classification task, action recognition is also known as a task with heavy computational load, with models mostly trained on devices with multiple GPUs. The pre-trained models suffer from large size and take a long time to infer the test data, especially on devices with low specifications and mobile devices. The recent development of neural networks introduces the Binarized Neural Network (BNN), which offers a solution to these problems. BNNs are trained with binary activations and weights, which reduces the computation from 32-bits to 1-bit. Theoretically, this feature can perform using 32x less memory and hardware resource compared to the conventional, full-precision neural networks. Theoretically, the conversion from full-precision CNN to BNN should result in a smaller model size and faster inference time. In this research, a binarized 3D CNN model is built using the principles of BNN and tested against the full-precision CNN. The BNN model is able to reach 78.7% train accuracy, 76.3% validation accuracy, and 79.6% inference accuracy, which means that the model is working according to the standards defined in this research.
Transformer Approaches in Image Captioning: A Literature Review

Image captioning is one of the challenging tasks that cross the computer vision and the Natural Language Processing (NLP) domain. Its main task is to interpret images in a descriptive text similar to humans. Image captioning is useful to help humans understand visual content. The main challenge is to get a coherent caption that could be understood by a human. With the trend of Transformer in computer vision that has proven successful to reach new results in state-of-the-art, the interest to implement it in Image Captioning is also increased. This paper presents a literature review of image captioning using transformer methods. The literature is reviewed from reputable journals and conferences. Our review focus on transformer approaches in order to improve the model performance in image captioning. We also explore the existing public datasets that are used in image captioning. The limitations and future research on image captioning are also discussed with additional potential subsidiary research.
Automatic Vehicle Classification and Counting System Using Inception Model

Transportation is very important in life. The width of road is unable to accommodate the total number of vehicles because every year there is a rapid increase in the number of vehicles, causing congestion. In Indonesia, in 2019 the number of motorized vehicles has reached more than 133 million. The process of calculating vehicle volume data which is still done manually has several drawbacks, such as it takes a long time and errors can occur due to human error. In this study, the design of the system used to classify and calculate the number of vehicles automatically utilizes the Deep Learning Convolutional Neural Network with a pre-trained Inception model. The results of this study on the minimum score threshold scenario of 0.4, the highest True Positive (TP) value was 70.75% and the model get 5 FPS during inferencing process.