Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Focusky Materi Interaksi Keruangan Desa Dan Kota Di SMA Negeri 1 Suwawa Kabupaten Bone Bolango

The purpose of this study is to develop video-animated learning media using the Focusky application on the material of Village and City Spatial Interaction. The purpose of this study is to develop video-animation-based learning media using focusky applications on village and city spatial interaction materials. Focusky is an application that has a variety of templates that can be used in making learning media, especially on village and city interaction materials. This study used the ADDIE model development research method as an approach in compiling the learning media. Media validation is carried out by material experts, media experts, and learning experts. This learning media was tested on a limited and general scale on a limited scale of 17 students each and a general scale of 41 students taken from two schools namely SMA N 1 Suwawa and SMA Negeri 1 Kabila by using a questionnaire or questionnaire. Data from development research results were obtained from the results of the validator which showed that this learning media was declared feasible to use and included in the very valid category. Validation by material experts gets a percentage value of 90% included in the very valid category, media experts get a percentage value of 75%, and learning experts give very valid values with percentages of 86.66% and 88%. The average student response to learning media is 78.18% for a limited scale and 81,68% for a general scale
The Sapienza University of Rome network of automated external defibrillators: a prototype webMap developed to speed access to community defibrillators and increase survival from out-of-hospital cardiac arrest.

In Italy, only around 10% of people who experience out-of-hospital cardiac arrest (OHCA) survive. A large portion of OHCA events in public settings are characterized by an initial shockable rhythm, which requires prompt defibrillation. We aimed to create a system to quickly locate nearby public access automated external defibrillators (AEDs) on the campus of Sapienza University of Rome, the largest public university in Europe.We developed the AED webMap through a 6-step process involving the: 1) collection of information and geographical coordinates for each AED from the university management system; 2) development of a new geolocation database; 3) integration of information contained in the new database with data provided by university departments; 4) geolocation of AEDs in the Google MyMaps environment; 5) graphic representation of all AEDs on digital map templates using specific symbols, with pop-ups containing additional information for each AED; and 6) publication of the webMap on the university website.The AED webMap was published on the university website (https://www.uniroma1.it/it/pagina/defibrillatori-sapienza-in-rete) and facilitates prompt identification of nearby AEDs by providing: 1) detailed AED geolocalization with interactive pop-up information for each AED, including whether the AED is located internally or externally; 2) the option to use different base maps (e.g., digital street map); 3) calculation and display of the route to reach the chosen AED; and 4) the possibility to migrate towards multiple platforms.The webMap can help bystanders quickly identify, locate, and reach nearby AEDs present on the campus of the largest public university in Europe, a measure that could help speed defibrillation and maximize the life-saving potential of AEDs in the event of OHCA.
Benchmarking Analysis of Evolutionary Neural Architecture Search

Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating the architecture design of deep neural networks. For any evolutionary computation-based algorithm, the runtime and convergence are the most important aspects concerned by theoretical analysis. However, because of the lacking of benchmarked fitness functions specialized for ENAS, the corresponding theoretical work is rarely available. To address this issue, we propose three different benchmark functions in this paper based on NAS-Bench-101. Specifically, we first propose a correlation-based feature extraction method, to capture the accuracy relationship between neural architectures and their fitness values. Furthermore, we propose a function toolkit, which allows combining different architecture features to specific benchmark functions. In addition, three benchmark functions are derived upon the toolkit by considering the features of neural net topologies, the features of neural operations, and their combinations. Based on these designs, the search space partition and transition probability calculation could be easily established, which in turn greatly promote the runtime and convergence analysis. We perform the experiments of ranking correlation, and the experimental results demonstrate the correctness of the proposed benchmark functions. To the best of our knowledge, this is the first work focusing on ENAS benchmark functions.
Understanding of Deaf Students Using Interactive Media of the BISINDO Sign Speaking Song (Merakit-Yura Yunita) in Online Learning

This research aims to analyze the understanding of deaf students using the interactive media of the BISINDO sign-speaking song (Merakit-Yura Yunita) in online learning. The method used in this research was qualitative method. The subjects of this research were deaf students in SLB Yakut B Purwokerto. The research instrument used observation and questionnaires. Data analysis used interactive analysis, which includes data reduction, data presentation, and drawing conclusions. The results of the research showed that the use of the interactive media of the BISINDO sign language song (Merakit-Yura Yunita) could be applied to online learning for deaf students at the school. Deaf students could gain an understanding of the content contained in this song. In addition, this media is expected to be an innovation in the online learning of deaf students.
A Multi-criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids

This study proposes a multi-criteria decision-making model for technology selection for renewable-based residential microgrids, which is one of the most important decisions in the planning and installation phase of microgrids. All the three sustainability dimensions named environmental, economic, and social criteria are considered for a problem with such different aspects. Under these three dimensions, sub-criteria are determined by evaluating the structure and needs of residential microgrids. While determining the sub-criteria, the future electricity consumers of the microgrid are also considered. Therefore, a decision-making model is proposed, which considers both the power generation and load consumption sides even during the installation stage. Alternatives of the decision-making model are determined by the region of the case study to be applied. Renewable energy technologies that can be reached and installed in this region are considered. By evaluating the outputs of the model, the most appropriate technology combinations for the residential microgrid in the case study are recommended. The integrated application of AHP and TOPSIS methods is proposed for the handled problem. Quantitative data, expert opinions, and surveys are used to obtain sub-criteria values according to the type of the sub-criterion.
Social Listening Practices towards Social CRM and Customer Relationship Performance in the Fast-Food Industry in Sri Lanka

This study is carried out to identify the impact of social listening practices on social CRM and customer relationship performance in the fast-food industry in Sri Lanka. The target population for this study included the internal stakeholders from the organizations in the fast-food industry located in the Colombo metropolitan area which uses social media. The data was obtained via a structured questionnaire using a sample of 150 registered organizations. The time horizon was cross-sectional, and data were analyzed through both descriptive and inferential analysis. Based on the Resource-Based View (RBV) and the Capabilities Based Perspective, especially the dynamic capabilities theories, the conceptual model of this research study was developed by expecting that, organizations in the fast-food industry in Sri Lanka can exploit capabilities which are social listening practices and social CRM capabilities and resources which are customer relationship orientation and social media technology usage to obtain higher customer relationship performance. Further, gain a competitive advantage over their competitors by effectively utilizing such resources. This is one of the few papers to exclusively focus on the impact of social listening practices on social CRM and customer relationship performance in the fast-food industry in Sri Lanka. The findings of this study have important implications for the fast-food industry in Sri Lanka.&#x0D; Keywords: Customer Relationship Management (CRM); Customer Relationship Orientation (CRO); Customer Relationship Performance; Social Customer Relationship Management (Social CRM) capabilities; Social media technology; Fast-food industry
CREATING HATE SPEECH DETECTION MODEL BY USING MACHINE LEARNING METHODS

This research paper describes the using machine learning algorithms to pre-identify profanity in online content due to concerns about the growing use of profanity on social networks such as Twitter. In this article, comparative studies were carried out to develop special software using machine learning methods for effective detection and monitoring of hate speech in order to minimize the negative impact of hate speech on people and communities. The scientific novelty of this research work an algorithm defined that provides the most effective accuracy of automatic detection of obscene words among machine learning algorithms. The result of the experiment, the effectiveness of the above methods for detecting offensive words evaluated on Twitter. The purpose of this article is to compile a data set of hate speech collected from various sources and approved by experts to ensure that the obscene words detection model works effectively. The practical significance of this research work, using machine learning methods, a comprehensive method for detecting profanity on Twitter proposed, a detection model and software for recognizing complex elements of profanity in online comments are also provided.
Digital space monitoring. Part I. The concept and method of monitoring

The article discusses the current problems of monitoring the digital space, the Internet, examines historical approaches to the definition of the concept of «monitoring», analyzes the phenomenon of monitoring as an empirical research method in socio-humanitarian knowledge and the significance of its application to ensure the information security of the educational environment. The necessity of monitoring digital technologies used in the educational process is noted.
Convergence Analysis of Bi-Attempted Based Optimization Algorithm for Numerical Global Optimum

In this study, the search convergence properties of a recently developed Bi-Attempted Based Optimization Algorithm (ABaOA) on a six-hump camel function are demonstrated. The six-hump camel function, with its six local minima and two global minima, is one of the well-known fixed-dimension multimodal benchmark functions used to assess the effectiveness of optimization techniques. The ABaOA is intended to be tested on this benchmark function because real-world numerical optimization problems necessitate quick processing times. Results that are obtained from experiments are promising in terms of speed and viability. The highly effective search algorithm ABaOA ensures a workable solution while also quickly arriving at the global optimal solution.
Evaluating the Influence of Imperfect Information in Geister Using DREAM Trained Agents

Imperfect Information Games (IIGs) are a popular subject in the field of artificial intelligence. In this study we consider them and propose that they can be classified according to the impact and visualizability of the imperfect information. We use Geister, a Board IIG, to create multiple variant games that we use as an abstraction for IIGs. We then train agents to play each variant using DREAM (Deep Regret Minimization with Advantage Baselines and Model-free Learning), a neural-network variation of Counterfactual Regret Minimization. We observe the performance of our agents and use them to qualitatively assess the characteristics of our IIGs with regards to our proposed terminology.
Towards Grouping in Large Scenes with Occlusion-aware Spatio-temporal Transformers

Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GroupTransformer</i> , for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">We will release the code for research purposes</i> .
Evaluation of Geo-SPEBH algorithm based on Bandwidth for Big Data retrieval in Cloud Computing

The fast increase in volume and speed of information created by mobile devices, along with the availability of web-based applications, has considerably contributed to the massive collection of data. Approximate Nearest Neighbor (ANN) is essential in big size databases for comparison search to offer the nearest neighbor of a given query in the field of computer vision and pattern recognition. Many hashing algorithms have been developed to improve data management and retrieval accuracy in huge databases. However, none of these algorithms took bandwidth into consideration, which is a significant aspect in information retrieval and pattern recognition. As a result, our work created a Geo-SPEBH algorithm to solve this basic gap. The paper then assesses the performance of the Geo-SPEBH algorithm in terms of bandwidth in a distributed computing environment. Geo-performance SPEBH's was compared to existing state-of-the-art approaches using a network analyzer called Wire shark. The simulation results reveal that during retrieval, the same kb/sec of data is carried from source to destination and from destination to user. When the coding length is 8bit, the findings show that 0.091kb/sec of data is required to transport data from source to destination. Each algorithm with the same bit code requires the same amount of bandwidth to convey data.
My favourite Quora answers

I used to be on Quora. I’m not anymore. But I used to be. And I wrote some answers. Here are some of my favourites, categorised by field… sort of. &lt;strong&gt; AI&lt;/strong&gt; If I wrote an AI and trained it to score 200 on a standardized IQ test, what could a human of 100 IQ still do better? There’s something called Moravec’s paradox, which I’d sum up as follows: stuff that’s difficult for humans is not the same as what’s difficult for a computer.
Type-directed Program Transformation for Constant-Time Enforcement

Constant-time is a programming discipline which protects security sensitive code against a wide class of timing attacks. This discipline can be formalised as a non-interference property and enforced by an information flow type system which prevents branching and memory accesses over secret data. We propose a relaxed information flow type system which tracks indirect flows but only rejects programs leaking secrets through direct flows. The main result of this paper is that any program that is accepted using this relaxed type system can be transformed automatically into a semantically equivalent constant-time program. Our algorithms are implemented in the jasmin compiler and validated against synthetic programs.
Multimodal Large Language Model for Visual Navigation

Recent efforts to enable visual navigation using large language models have mainly focused on developing complex prompt systems. These systems incorporate instructions, observations, and history into massive text prompts, which are then combined with pre-trained large language models to facilitate visual navigation. In contrast, our approach aims to fine-tune large language models for visual navigation without extensive prompt engineering. Our design involves a simple text prompt, current observations, and a history collector model that gathers information from previous observations as input. For output, our design provides a probability distribution of possible actions that the agent can take during navigation. We train our model using human demonstrations and collision signals from the Habitat-Matterport 3D Dataset (HM3D). Experimental results demonstrate that our method outperforms state-of-the-art behavior cloning methods and effectively reduces collision rates.
ELDEN: Exploration via Local Dependencies

Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement learning. In these tasks, an agent needs to explore the state space efficiently until it finds a reward. To deal with this problem, the community has proposed to augment the reward function with intrinsic reward, a bonus signal that encourages the agent to visit interesting states. In this work, we propose a new way of defining interesting states for environments with factored state spaces and complex chained dependencies, where an agent's actions may change the value of one entity that, in order, may affect the value of another entity. Our insight is that, in these environments, interesting states for exploration are states where the agent is uncertain whether (as opposed to how) entities such as the agent or objects have some influence on each other. We present ELDEN, Exploration via Local DepENdencies, a novel intrinsic reward that encourages the discovery of new interactions between entities. ELDEN utilizes a novel scheme -- the partial derivative of the learned dynamics to model the local dependencies between entities accurately and computationally efficiently. The uncertainty of the predicted dependencies is then used as an intrinsic reward to encourage exploration toward new interactions. We evaluate the performance of ELDEN on four different domains with complex dependencies, ranging from 2D grid worlds to 3D robotic tasks. In all domains, ELDEN correctly identifies local dependencies and learns successful policies, significantly outperforming previous state-of-the-art exploration methods.
Toward Joint Language Modeling for Speech Units and Text

Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of language modeling, very little effort has been made to model them jointly. In light of this, we explore joint language modeling for speech units and text. Specifically, we compare different speech tokenizers to transform continuous speech signals into discrete units and use different methods to construct mixed speech-text data. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. We also fine-tune the LM on downstream spoken language understanding (SLU) tasks with different modalities (speech or text) and test its performance to assess the model's learning of shared representations. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks and shows zero-shot cross-modal transferability.
A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches

Synthetic aperture radar (SAR) images are widely used in remote sensing. Interpreting SAR images can be challenging due to their intrinsic speckle noise and grayscale nature. To address this issue, SAR colorization has emerged as a research direction to colorize gray scale SAR images while preserving the original spatial information and radiometric information. However, this research field is still in its early stages, and many limitations can be highlighted. In this paper, we propose a full research line for supervised learning-based approaches to SAR colorization. Our approach includes a protocol for generating synthetic color SAR images, several baselines, and an effective method based on the conditional generative adversarial network (cGAN) for SAR colorization. We also propose numerical assessment metrics for the problem at hand. To our knowledge, this is the first attempt to propose a research line for SAR colorization that includes a protocol, a benchmark, and a complete performance evaluation. Our extensive tests demonstrate the effectiveness of our proposed cGAN-based network for SAR colorization. The code will be made publicly available.
Polynomial Time Cryptanalytic Extraction of Neural Network Models

Billions of dollars and countless GPU hours are currently spent on training Deep Neural Networks (DNNs) for a variety of tasks. Thus, it is essential to determine the difficulty of extracting all the parameters of such neural networks when given access to their black-box implementations. Many versions of this problem have been studied over the last 30 years, and the best current attack on ReLU-based deep neural networks was presented at Crypto 2020 by Carlini, Jagielski, and Mironov. It resembles a differential chosen plaintext attack on a cryptosystem, which has a secret key embedded in its black-box implementation and requires a polynomial number of queries but an exponential amount of time (as a function of the number of neurons). In this paper, we improve this attack by developing several new techniques that enable us to extract with arbitrarily high precision all the real-valued parameters of a ReLU-based DNN using a polynomial number of queries and a polynomial amount of time. We demonstrate its practical efficiency by applying it to a full-sized neural network for classifying the CIFAR10 dataset, which has 3072 inputs, 8 hidden layers with 256 neurons each, and over million neuronal parameters. An attack following the approach by Carlini et al. requires an exhaustive search over 2 to the power 256 possibilities. Our attack replaces this with our new techniques, which require only 30 minutes on a 256-core computer.
Quantum Communication Countermeasures

Quantum communication, particularly quantum key distribution, is poised to play a pivotal role in our communication system in the near future. Consequently, it is imperative to not only assess the vulnerability of quantum communication to eavesdropping (one aspect of quantum hacking), but also to scrutinise the feasibility of executing a denial-of-service attack, specifically, stopping quantum communication from working. Focusing primarily on the free-space quantum channel, the investigation of possible denial-of-service attacks from a strategic perspective is performed. This encompasses the analysis of various scenarios, numerical modelling, risk estimation and attack classification. The out-of-FOV (field of view) attack emerges as a particularly severe threat across nearly all scenarios. This is accompanied by proposed counter-countermeasures and recommendations.