Reviewerly/exHarmony huggingface dataset:
Implementing Natural Language Generation through industry-specific chatbots

Natural Language Generation (NLG) is at the forefront of research in the field of Natural Language Processing (NLP) where an algorithm is taught to create text in any given language. The purpose of this research paper is to implement NLG algorithms into the design of a conventional chatbot to improve its design and make it more usable. The paper explores the details and flaws of widely used chatbot templates and aims at providing a way to resolve these issues by implementing a text generation algorithm. The task is performed by assessing an industry-specific chatbot- in this case, a medical query bot- which runs on the proposed principle. The paper also showcases an array of conversational AI’s crafted using a publicly available version of GPT 2, to further explore the scope of Natural Language Generation.
Role of Cognitive Technology for improving Human Resource Management experience: A Multi-dimensional Perspective

Human cognition has been considered unique as it can recognize and can attach significance and interpret images, emotion, text, A wide variety of tools like ML, Natural language processing, neural networks and deep learning are strong facilitating tools for classifying, interpreting, and generating outcomes and even can analyze images. There are several applications based on the mass images when interpreted whether used for psychological testing for hiring or for technical assessment, aspects may include – thinking like human application, sensing language and interpreting. The present conceptual paper based on previous studies aimed at understanding conceptual underpinnings of cognitive technology and its application for effective employee experience with respect to different HR functions. Today’s technological advancement in cognitive skill-based tech tools have automated and empowered the organization working by reinventing system, policies, and process. It explains the essential aspects while implementing the new system with a Cognitive and psychosocial perspective and emerging tools used across organization to suffice multi stakeholders’ perspective. The role of smart machines and cognitive architecture in ensuring effective employee performance.
Finding the Right Tools for Your Classroom's Tech Needs

In Educational Duct Tape, Jake Miller compares, contrasts, and selects a plethora of technology tools for educators in a creative and entertaining way. By offering snapshots from his personal and professional life, the author created a pleasant and easy-to-read book. He emphasizes that technology should not be employed as the end objective, but rather as a means to an end. Throughout the book, he argues that educational technology tools should be utilized to solve classroom problems, address learning objectives, and accomplish specific goals exactly as we use duct tape to mend minor problems in our daily lives. The book is complemented with educational technology podcasts and online resource hubs for each chapter, which include supplementary tools, videos, and discussion sections.
Network Adaptive Federated Learning: Congestion and Lossy Compression

In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are exposed to, or indeed the cause of, congestion across a wide set of network resources. Lossy compression can be used to reduce the size of exchanged files and associated delays, at the cost of adding noise to model updates. By judiciously adapting clients' compression to varying network congestion, an FL application can reduce wall clock training time. To that end, we propose a Network Adaptive Compression (NAC-FL) policy, which dynamically varies the client's lossy compression choices to network congestion variations. We prove, under appropriate assumptions, that NAC-FL is asymptotically optimal in terms of directly minimizing the expected wall clock training time. Further, we show via simulation that NAC-FL achieves robust performance improvements with higher gains in settings with positively correlated delays across time.
Partial Conditioning for Inferential Models

Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments for practically challenging problems. This paper considers the many-normal-means problem with the means constrained to be in the neighborhood of each other. A new method, called partial conditioning, is proposed to generate valid and efficient marginal inference about the individual means. It is shown that the method outperforms both a fiducial-counterpart in terms of validity and a conservative-counterpart in terms of efficiency.
Deteksi Depresi dan Kecemasan Pengguna Twitter Menggunakan Bidirectional LSTM

The most common mental disorders experienced by a person in daily life are depression and anxiety. Social stigma makes people with depression and anxiety neglected by their surroundings. Therefore, they turn to social media like Twitter for support. Detecting users with potential depression and anxiety disorders through textual data is not easy because they do not explicitly discuss their mental state. It takes a model that can identify potential users who experience depression and anxiety on textual data to get treatment earlier. Text classification techniques can achieve this. One approach that can be used is LSTM as an RNN architecture development in dealing with vanishing gradient problems. Standard LSTM does not capture enough information because it can only read sentences from one direction. Meanwhile, Bidirectional LSTM (BiLSTM) is a two-way LSTM that can capture information without ignoring the context and meaning of a sentence. The proposed BiLSTM model is higher than all traditional machine learning models and standard LSTMs. Based on the test results, the highest accuracy obtained by BiLSTM reached 94.12%. This study has succeeded in developing a model for the detection of depression and anxiety in Twitter users.
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis

This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image. Establishing such a connection facilitates a more convenient local control of GAN generation, where users can alter the image content only within a spatial area simply by partially resampling the latent code. Experimental results confirm four appealing properties of our regularizer, which we call LinkGAN. (1) The latent-pixel linkage is applicable to either a fixed region (\textit{i.e.}, same for all instances) or a particular semantic category (i.e., varying across instances), like the sky. (2) Two or multiple regions can be independently linked to different latent axes, which further supports joint control. (3) Our regularizer can improve the spatial controllability of both 2D and 3D-aware GAN models, barely sacrificing the synthesis performance. (4) The models trained with our regularizer are compatible with GAN inversion techniques and maintain editability on real images.
Head-Free Lightweight Semantic Segmentation with Linear Transformer

Existing semantic segmentation works have been mainly focused on designing effective decoders; however, the computational load introduced by the overall structure has long been ignored, which hinders their applications on resource-constrained hardwares. In this paper, we propose a head-free lightweight architecture specifically for semantic segmentation, named Adaptive Frequency Transformer. It adopts a parallel architecture to leverage prototype representations as specific learnable local descriptions which replaces the decoder and preserves the rich image semantics on high-resolution features. Although removing the decoder compresses most of the computation, the accuracy of the parallel structure is still limited by low computational resources. Therefore, we employ heterogeneous operators (CNN and Vision Transformer) for pixel embedding and prototype representations to further save computational costs. Moreover, it is very difficult to linearize the complexity of the vision Transformer from the perspective of spatial domain. Due to the fact that semantic segmentation is very sensitive to frequency information, we construct a lightweight prototype learning block with adaptive frequency filter of complexity $O(n)$ to replace standard self attention with $O(n^{2})$. Extensive experiments on widely adopted datasets demonstrate that our model achieves superior accuracy while retaining only 3M parameters. On the ADE20K dataset, our model achieves 41.8 mIoU and 4.6 GFLOPs, which is 4.4 mIoU higher than Segformer, with 45% less GFLOPs. On the Cityscapes dataset, our model achieves 78.7 mIoU and 34.4 GFLOPs, which is 2.5 mIoU higher than Segformer with 72.5% less GFLOPs. Code is available at https://github.com/dongbo811/AFFormer.
Heterogeneous Tri-stream Clustering Network

Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid progress, previous works mostly require both positive and negative sample pairs for contrastive clustering, which rely on a relative large batch-size. Moreover, they typically adopt a two-stream architecture with two augmented views, which overlook the possibility and potential benefits of multi-stream architectures (especially with heterogeneous or hybrid networks). In light of this, this paper presents a new end-to-end deep clustering approach termed Heterogeneous Tri-stream Clustering Network (HTCN). The tri-stream architecture in HTCN consists of three main components, including two weight-sharing online networks and a target network, where the parameters of the target network are the exponential moving average of that of the online networks. Notably, the two online networks are trained by simultaneously (i) predicting the instance representations of the target network and (ii) enforcing the consistency between the cluster representations of the target network and that of the two online networks. Experimental results on four challenging image datasets demonstrate the superiority of HTCN over the state-of-the-art deep clustering approaches. The code is available at https://github.com/dengxiaozhi/HTCN.
EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata

We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.
Attack Graphs for Standalone Non-Public 5G Networks

&lt;p&gt;Private Networks (also known as Non-Public Networks) bring significant benefits to Industry 4.0. These networks are typically deployed on-premises of the enterprises, and their isolation from the public (consumer) networks improves the crucial aspects of security and reliability. Despite the isolation, insider attacks can be mounted on these networks. This paper analyses such attacks using attack patterns from Common Attack Pattern Enumerations and Classifications (CAPEC) database. The analysis uses attack graphs, to combine individual domains, in the context of human, device, and network vulnerabilities. The attack graphs help identify paths, the cumulative impact on the system, and possible defense techniques, including security controls to mitigate the impact. Using three sample attack graphs in the context of standalone private 5G networks, this paper analyses possible security mechanisms and captures the difference among legacy enterprise networks (including Wi-Fi for limited mobility), public networks, and private networks.&lt;/p&gt;
INFLUENCE OF EXPLICIT INSTRUCTION METHODS ON THE ABILITY TO READING COMPREHENSION GRADE V

This study aims to determine the ability to read comprehension before and after by applying the Explicit Instruction Method. This type of research is a Pre-Experiment type of Design One Group Pretest-Posttest Design, which is an experiment carried out on only one group, without any comparison. This research was conducted at SD Negeri 006 Baturijal Barat class V which totaled 24 students. Based on the results of the study, it was obtained that the average test score of learning outcomes before treatment (pretest) was 49.58 while the average test score of learning outcomes after treatment (posttest) was 73.33 and the average gain value was 0.48 which is a moderate interpretation, as well as hypothesis test results using a t-test with t-test with tcount = 15.117 and ttable = 1.714 then it was concluded tcount &gt; ttable, then reject Ho means significant. This means that there is an influence on the ability to read comprehension of grade V students of SD Negeri 006 Baturijal Barat between before (pretest) and after (posttest) by applying the Explicit Instruction Method. This shows that the Explicit Instruction Method affects the reading comprehension ability of grade V students of SD Negeri 006 Baturijal Barat.
MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

&lt;p&gt;Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. A deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-Edge Connected Components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithm. Processing a big graph in a parallel and distributed fashion will save considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. In this research, we propose a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ”BiECCA”) and present its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework. The proposed algorithm uses an existing algorithm to find Connected Components (CCs) in a graph as a sub- step. Finally, we suggest a few novel ideas and approaches as extensions to our work. &lt;/p&gt;
Peningkatan Konsentrasi Belajar Matematika Menggunakan Model Pembelajaran Picture And Picture Di SDN Wanasari 01

This research is determine the increase in students' concentration in study mathematics at SDN Wanasari 01. The subjects in this study were 36 elementary school students. This research method is classroom action research which consists of four components, namely planning, implementing, observing, and reflecting. Data collection techniques in this study used observation, interviews, and documentation. Based on the results of the study, that in cycle 1 the process of learning mathematics was said to be not good enough, because most students still lacked concentration on learning. In addition, the teacher's use of the picture and picture model has not been fully implemented at every meeting. Meanwhile, in cycle 2, students' concentration in mathematics has increased.&#x0D;
Eliminating use of non-traceable identity cards to reduce cyber security incidents: focus on Malawi

&lt;p&gt;The paper is about reducing cyber security incidents or cybercrime where perpetrators take advantage of weak SIM card regsitration and launch attacks on the cyber space. The weak SIM card registration has been sampled from Malawi, but this can happen elsewhere in the world where they have a similar weakness.&lt;/p&gt;
TRANSLATION ENGLISH-INDONESIA: LEARNING DEVELOPMENT BASED HYBRID LEARNING IN POST-PANDEMIC ERA FOR EFL STUDENTS

&lt;p&gt;This study aims to describe the development of a Hybrid learning model in Translation: English-Indonesian learning for UINSU English Tadris students. researchers use the Borg and Gall model through ten steps. 30 research subjects were taken from the UINSU English Tadris study program population. Data were collected by observation techniques, questionnaires, and tests. Data analysis techniques use percentages, mean, and t-tests. The results of the validation research from experts I and II on the design of the hybrid learning model were an average of 4.34 in the very good category. Field test results I; average paratest 6.17 and posttest 6.60. Field test II average parates 6.63 and posttest 7.65. Field test III average paratest 6.72 and posttest 7. 69. Different test results through t-test on learning motivation t-count 9.254, student creativity 9.796, and learning outcomes 9.840 and it turns out that t-count&amp;gt; t-table 2.136 is proven there is a significant difference before using the model and after using the hybrid learning model. The conclusion of the study is that the hybrid learning model developed in this study is effectively used in Translation: English-Indonesian learning for UINSU English Tadris Program students.&lt;/p&gt;
HaPPY-Mine: Designing a Mining Reward Function

In cryptocurrencies, the block reward is meant to serve as the incentive mechanism for miners to commit resources to create blocks and in effect secure the system. Existing systems primarily divide the reward in proportion to expended resources and follow one of two static models for total block reward: (i) a fixed reward for each block (e.g., Ethereum), or (ii) one where the block reward halves every set number of blocks (e.g., the Bitcoin model of halving roughly every 4 years) but otherwise remains fixed between halvings. In recent work, a game-theoretic analysis of the static model under asymmetric miner costs showed that an equilibrium always exists and is unique. Their analysis also reveals how asymmetric costs can lead to large-scale centralization in blockchain mining, a phenomenon that has been observed in Bitcoin and Ethereum and highlighted by other studies. In this work we introduce a novel family of mining reward functions, HaPPY-Mine (HAsh-Pegged Proportional Yield), which peg the value of the reward to the hashrate of the system, decreasing the reward as the hashrate increases. HaPPY-Mine distributes rewards in proportion to expended hashrate and inherits the safety properties of the generalized proportional reward function. We study HaPPY-Mine under a heterogeneous miner cost model and show that an equilibrium always exists with a unique set of miner participants and a unique total hashrate. Significantly, we prove that a HaPPY-Mine equilibrium is more decentralized than the static model equilibrium under a set of metrics including number of mining participants and hashrate distribution. Finally, we show that any HaPPY-Mine equilibrium is also safe against collusion and sybil attacks, and explore how the market value of the currency affects the equilibrium.
Evolution of Multimedia Data Types

The evolution in technology during the last few years, such as Internet and Web browser has changed the world. Especially in the area of multimedia computing, where we are living in fast growing era of information and technology, multimedia data becoming more and more common. In a general, the term multimedia (MM) identifies a community that helps us to assemble, develop various records and intensify visual datas into relevant particulars. MM deals with data that essentially speaks to people, places, and/or things. It incorporates film and different sorts of computerized still photography, movement photography, sound, analog along side computerized video recordings, computer-based items, and illustrations.
Comparative Analysis of Machine Learning Algorithms using GANs through Credit Card Fraud Detection

In more recent years, credit card fraudulent transactions became a major problem. These fraudulent transactions not only incur huge monetary losses to commercial banks and financial institutions, but also stress and trouble to the lives of customers. Furthermore, with the passage of time this issue is increasing and the monetary loss is expected to increase significantly. However, efficient fraud detecting and prevention measures can trim down the monetary loss due to financial fraud activities. Credit card fraud detection has gained much interest from academia. Generative Adversarial Networks (GANs) are an effective class of generative approaches that has been able to generate synthetic data to assist with the classification of credit card fraudulent activities. In this research study we’re going to compare architectures of various GAN models which demonstrate the evolution of these models. It was observed that GANs have received much attention from researchers and also attained promising results in the field of credit card fraud detection.