Reviewerly/exHarmony huggingface dataset:
Memory-Centric Computing

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by fundamentally avoiding data movement and reducing data access latency & energy. Many recent studies show that memory-centric computing can greatly improve system performance and energy efficiency. Major industrial vendors and startup companies have also recently introduced memory chips that have sophisticated computation capabilities. This talk describes promising ongoing research and development efforts in memory-centric computing. We classify such efforts into two major fundamental categories: 1) processing using memory, which exploits analog operational properties of memory structures to perform massively-parallel operations in memory, and 2) processing near memory, which integrates processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high-bandwidth and low-latency memory access to near-memory logic. We show both types of architectures (and their combination) can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, databases, machine learning, video processing, climate modeling, genome analysis. We discuss adoption challenges for the memory-centric computing paradigm and conclude with some research & development opportunities.
Human or Not? A Gamified Approach to the Turing Test

We present "Human or Not?", an online game inspired by the Turing test, that measures the capability of AI chatbots to mimic humans in dialog, and of humans to tell bots from other humans. Over the course of a month, the game was played by over 1.5 million users who engaged in anonymous two-minute chat sessions with either another human or an AI language model which was prompted to behave like humans. The task of the players was to correctly guess whether they spoke to a person or to an AI. This largest scale Turing-style test conducted to date revealed some interesting facts. For example, overall users guessed the identity of their partners correctly in only 68% of the games. In the subset of the games in which users faced an AI bot, users had even lower correct guess rates of 60% (that is, not much higher than chance). This white paper details the development, deployment, and results of this unique experiment. While this experiment calls for many extensions and refinements, these findings already begin to shed light on the inevitable near future which will commingle humans and AI.
Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision

Attribution algorithms are frequently employed to explain the decisions of neural network models. Integrated Gradients (IG) is an influential attribution method due to its strong axiomatic foundation. The algorithm is based on integrating the gradients along a path from a reference image to the input image. Unfortunately, it can be observed that gradients computed from regions where the output logit changes minimally along the path provide poor explanations for the model decision, which is called the saturation effect problem. In this paper, we propose an attribution algorithm called integrated decision gradients (IDG). The algorithm focuses on integrating gradients from the region of the path where the model makes its decision, i.e., the portion of the path where the output logit rapidly transitions from zero to its final value. This is practically realized by scaling each gradient by the derivative of the output logit with respect to the path. The algorithm thereby provides a principled solution to the saturation problem. Additionally, we minimize the errors within the Riemann sum approximation of the path integral by utilizing non-uniform subdivisions determined by adaptive sampling. In the evaluation on ImageNet, it is demonstrated that IDG outperforms IG, Left-IG, Guided IG, and adversarial gradient integration both qualitatively and quantitatively using standard insertion and deletion metrics across three common models.
Latent Exploration for Reinforcement Learning

In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. Due to the curse of dimensionality, learning policies that map high-dimensional sensory input to motor output is particularly challenging. During training, state of the art methods (SAC, PPO, etc.) explore the environment by perturbing the actuation with independent Gaussian noise. While this unstructured exploration has proven successful in numerous tasks, it can be suboptimal for overactuated systems. When multiple actuators, such as motors or muscles, drive behavior, uncorrelated perturbations risk diminishing each other's effect, or modifying the behavior in a task-irrelevant way. While solutions to introduce time correlation across action perturbations exist, introducing correlation across actuators has been largely ignored. Here, we propose LATent TIme-Correlated Exploration (Lattice), a method to inject temporally-correlated noise into the latent state of the policy network, which can be seamlessly integrated with on- and off-policy algorithms. We demonstrate that the noisy actions generated by perturbing the network's activations can be modeled as a multivariate Gaussian distribution with a full covariance matrix. In the PyBullet locomotion tasks, Lattice-SAC achieves state of the art results, and reaches 18% higher reward than unstructured exploration in the Humanoid environment. In the musculoskeletal control environments of MyoSuite, Lattice-PPO achieves higher reward in most reaching and object manipulation tasks, while also finding more energy-efficient policies with reductions of 20-60%. Overall, we demonstrate the effectiveness of structured action noise in time and actuator space for complex motor control tasks. The code is available at: https://github.com/amathislab/lattice.
A survey on the complexity of learning quantum states

We survey various recent results that rigorously study the complexity of learning quantum states. These include progress on quantum tomography, learning physical quantum states, alternate learning models to tomography and learning classical functions encoded as quantum states. We highlight how these results are paving the way for a highly successful theory with a range of exciting open questions. To this end, we distill 25 open questions from these results.
Findings of the VarDial Evaluation Campaign 2023

This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2023. Three separate shared tasks were included this year: Slot and intent detection for low-resource language varieties (SID4LR), Discriminating Between Similar Languages -- True Labels (DSL-TL), and Discriminating Between Similar Languages -- Speech (DSL-S). All three tasks were organized for the first time this year.
Culture-Creating Potential of Audiovisual Technologies in the Mass Art of the Second Half of the Twentieth and Early Twenty-First Centuries

The purpose of the research is to specify and explicate the culture-creating potential of audiovisual technologies, to determine their impact on mass art in the second half of the twentieth and early twenty-first centuries. Research methodology. The article uses the following methods: cultural-historical, which identifies the main evolutionary stages of technical means and audiovisual technologies; methods of description and analysis, which made it possible to reveal the process of creating mass art products with the help of audiovisual technologies; the principle of synergy as a tool for understanding the mutual coordination and mutual influence of culture and technology during the formation of mass art in the second half of the last century. Scientific novelty. The paper first reveals and considers the cultural potential of audiovisual technologies of this period and explicates the related transformational processes in mass art. Conclusions. It has been found that the phenomenon of mass art today functions in a complex technological regime formed in the last century and continues to be developed and function at the global industrial level. It has been proved that thanks to such inventions and technologies as CDs and DVDs, personal computing, magnetic audio recording, magnetic pickup and electric guitar, CGI animation, digitalization, streaming multimedia and sound design, and others, various forms, types and independent genres of mass art (rock culture, Net Art, show programs, 3D video mapping) emerged and developed, the aesthetic and emotional experience of audiovisual product users was enhanced, as well as new professions and specialists (animator, sound engineer, producer, etc.) emerged to operate and improve audiovisual equipment to create a variety of artistic products in various formats in the future.
Director’s Techniques of Recreating Circus Culture on the Screen and in the Arena

The purpose of the research is to expand scientific ideas about the significance of the culture of circus and film directing in the system of humanitarian knowledge. Research methodology. The researcher applied an interdisciplinary approach based on the use of the following general scientific methods: induction, deduction, identification, complex art analysis, and synthesis to define the culture of directing as a universal variety of artistic-aesthetic and creative-production activity. These methods made it possible to work out the factual basis of the reproduction of circus productions on the screen and in the arena. The methods of systematization and generalization were used to argue for the originality of circus directing in the context of audiovisual art. The typological approach was applied to examine common artistic principles in the creative search for circus and cinema art directors. The scientific novelty of the research lies in defining the culture of the directing profession as a universal type of artistic activity; identifying and an in-depth analysis of the factors influencing the artistic and aesthetic potential of adapting a circus performance on the screen; revealing specific canons for building film and television films based on circus creativity; clarifying the mutual influence of circus and screen arts in the development of directing culture; expanding scientific knowledge about the place and role of screen and circus directing in the system of humanitarian expertise at the present stage of social development; defining directing as a universal multi-vector type of artistic and aesthetic, creative and production, moral and ethical, educational activity. Conclusions. The researcher substantiates that the creativity of film and television directors develops and expands knowledge about circus art, and gives impetus to the development of creativity of directors and circus performers in the screen space. The materials of the scientific article enrich the arsenal of knowledge regarding the specifics of the production and distribution of circus performances by means of screen arts in society and can be applied in educational courses, the creation of educational and methodological literature on the history and practice of the circus, the theory and history of cinema and television, directing, producing.
A Practical Toolkit for Multilingual Question and Answer Generation

Generating questions along with associated answers from a text has applications in several domains, such as creating reading comprehension tests for students, or improving document search by providing auxiliary questions and answers based on the query. Training models for question and answer generation (QAG) is not straightforward due to the expected structured output (i.e. a list of question and answer pairs), as it requires more than generating a single sentence. This results in a small number of publicly accessible QAG models. In this paper, we introduce AutoQG, an online service for multilingual QAG, along with lmqg, an all-in-one Python package for model fine-tuning, generation, and evaluation. We also release QAG models in eight languages fine-tuned on a few variants of pre-trained encoder-decoder language models, which can be used online via AutoQG or locally via lmqg. With these resources, practitioners of any level can benefit from a toolkit that includes a web interface for end users, and easy-to-use code for developers who require custom models or fine-grained controls for generation.
SENTIMENT ANALYSIS OF TEXT AND EMOJI DATA FOR TWITTER NETWORK

Twitter is a social media platform where users can post, read, and interact with 'tweets'.Third party like corporate organization can take advantage of this huge information by collecting data about their customers' opinions.The use of emoticons on social media and the emotions expressed through them are the subjects of this research paper.The purpose of this paper is to present a model for analyzing emotional responses to real-life Twitter data.The proposed model is based on supervised machine learning algorithms and data on has been collected through crawler "TWEEPY" for empirical analysis.Collected data is pre-processed, pruned and fed into various supervised models.Each tweet is assigned to sentiment based on the user's emotions, positive, negative, or neutral.
An Empirical Research on Factors Affecting the Acceptance of Bitcoin in the Northern Region of Malaysia

The purpose of this study is to identify the relationship between factors affecting the acceptance of Bitcoin in the Northern Region of Malaysia. Past studies with reference to the factors affecting the acceptance of Bitcoin were being done on a larger scale outside of Malaysia and none of the studies has narrowed it down to make the research more specific. Therefore, this study proposed to study the factors affecting the acceptance of Bitcoin in the Northern Region of Malaysia based on performance expectancy, risk, social influence, and facilitating conditions. Furthermore, the relationship between the factors affecting the acceptance of Bitcoin and the acceptance of Bitcoin in the Northern Region of Malaysia has been discovered in this research. The population selected for this exploration is the respondents in Perlis, Kedah, Pulau Pinang, and Perak. The primary data for this research has been collected from the respondents through an online survey questionnaire. The data collected is then analyzed using various data analysis methods such as descriptive analysis, Pearson Correlation Coefficient Analysis, and Multiple Linear Regression Analysis. Hypotheses testing have been done after the analysis has been analyzed. This study presents supplementary information to potential researchers who wish to study in-depth in this area. Moreover, the results of this study contribute to the users as they get to know what factors to consider when they want to use Bitcoin and it helps Bitcoin Trader Company in their decision-making processes to attract more users. Nevertheless, the factors affecting the acceptance of Bitcoin most efficiently are identified in this research. Therefore, the acceptance of Bitcoin in the Northern Region of Malaysia can be affected by performance expectancy, risk, social influence, and facilitating conditions factors.
Sharing Moments of “This Is It!” with Colleagues to Research and Develop Optical Communication Technology for Revolutionizing Society

Challenge to overcome the capacity crunch-Please tell us about the research you are currently undertaking.NTT has been a world leader in the research and development (R&D) of optical communication technology.We started practical application of time-division multiplexing (TDM) optical-fiber transmission systems in 1981.By triggering three paradigm shifts concerning optical transport systems, namely, the development of optically amplified transmission systems, wavelength-division-multiplexing (WDM) systems, and digital coherent systems, we have increased transmission capacity by about 10 6 times over the past 40 years.The amount of data traffic continues to increase at an annual growth rate of Sharing Moments of "This Is It!" with Colleagues to Research and Develop Optical Communication Technology for Revolutionizing Society
Development of Real Time AI-Based Vehicle Detection System for Traffic Surveillance

This research paper aims at devising efficient ways of performing vehicle detection using Deep learning techniques. Vehicle detection is most commonly used for traffic surveillance, at toll plazas, etc. In this paper, the efficient execution of detecting vehicles will be discussed using various YOLO architectures. This research provides us with detailed information about the models that can efficiently work on the Jetson nano kit while providing maximum accuracy. It also provides the researchers and developers with practical and comprehensive deep-learningbased solutions for detecting vehicles providing information about the process from development to deployment. In this paper, we have considered multiple YOLO algorithms such as v3, v3 tiny, v4, and v4 tiny. Thus, providing an analysis of all algorithms and their efficiencies. After comparing the results we have analyzed that YOLO v4 provides the best efficiency in terms of accuracy which is 95.41%.
Attending to Transforms: A Survey on Transformer-based Image Captioning

Image captioning is a challenging task that lies at the intersection of Computer Vision and Natural Language Processing. There exists a legion of works that generate meaningful and realistic descriptions of images. Recently, with the advent of attention mechanisms and transformers, there has been a drastic shift in modelling both language and vision tasks. However, there are very few extensive studies that review these approaches based on their progression, advantages and disadvantages. This paper presents a detailed summary of transformer-based models employed for tackling image captioning. In addition to this, we provide an overview of various pre-training tasks, datasets and metrics used for image captioning. Finally, the performance of all the reviewed approaches are compared on the COCO Captions dataset.
Experimental Analysis on Dissimilarity Metrics and Sudden Concept Drift Detection

Learning from non-stationary data presents several new challenges. Among them, a significant problem comes from the sudden changes in the incoming data distributions, the so-called concept drift. Several concept drift detection methods exist, generally based on distances between distributions, either arbitrarily selected or context-dependent. This paper presents a straightforward approach for detecting concept drift based on a weighted dissimilarity metric over posterior probabilities. We also evaluate the performance of three well-known dissimilarity metrics when used by the proposed approach. Experimental evaluation has been done over ten datasets with injected sudden drifts in a binary classification context. Our results first suggest choosing the Kullback-Leibler divergence, and second, they show that our drift detection procedure based on dissimilarity measures is pretty efficient.
A 3D reconstruction algorithm based on spatial geometry constraint assisted phase structured light combined with camera parameters

Multi-frequency temporal phase unwrapping methods can unwrap phase wrapping well, but need to capture more additional images. Therefore, the measurement speed of this method is relatively slow, and it is not suitable for scenarios with high speed requirements. This paper proposes a 3D reconstruction algorithm based on spatial geometric constraints assisted phase-based structured light combined with camera parameters. This method uses the spatial geometric constraints of the camera and projector to construct a reference absolute phase to expand the phase wrapping. The method has the advantages of simple hardware, good robustness, short measurement time and high precision. Experiments show that under the same conditions, the method proposed in this paper is similar to the multi-frequency time-phase expansion method in the measurement accuracy of two standard spheres with different diameters, but the measurement time of the method in this paper has decreased. The average measurement time of 6.0085mm and 6.0077mm standard balls decreased by 32.64% and 33.16%, respectively. Therefore, the method proposed in this paper can promote the development of structured light in terms of speed and low cost.
Measuring the Symbiotic Performance of Single Entities within Networks Using an Lca Approach

Although there has been a significant advance in evaluating industrial symbiosis, there is still a lack of indicators quantifying the environmental benefits of materials and energy flow exchanges at the entity level. This paper proposes a novel indicator called the Symbiotic Performance Indicator to fill such a gap. This new indicator is built upon a life cycle assessment and quantifies the benefits of the by-product exchanges by comparing them with a non-symbiotic scenario. It considers the use of resources –using the Cumulative Exergy Extraction from the Natural Environment method– and the contribution to climate change –using the IPCC 2013 Global Warming Potential (100a) method. The new indicator is tested along with three possible allocation methods to quantify the potential environmental savings at the entity level of a real exchange of recycled plastic between three entities in Mendoza, Argentina. The overall results show that the industrial symbiosis network illustrates a reduction in resource use of nearly 19% and a greenhouse gas emission reduction of roughly 15% by utilizing recycled materials. On the other hand, the indicator results show that the exchange of materials seems less attractive for full allocation methods since one entity benefits more from the by-product exchange than the other. However, the by-product exchange could be convenient for both entities when applying a partial allocation method. Indicators such as the Symbiotic Performance Indicator can be valuable tools for promoting industrial symbiosis and attracting more industries to establish innovative networks.
Performance Analysis on Licensure Examination for Teachers among BEEd Graduates in Dorsu-San Isidro Campus

Performance Analysis on Licensure Examination for Teachers among BEEd Graduates in Dorsu-San Isidro Campus Objective: This quantitative research study aimed to explore the Licensure Examination for Teachers results. Methods: This study employed made use of the analytical method of research, and documentary analysis of data taken from the official results of LEPT released by the Professional Regulatory Commission (PRC). In addition, this study made use of frequency counts and percentage to describe the profile of LEPT takers. On the other hand, percentage was used in reporting the average or rating of graduates in their licensure examination. Moreover, T-Test was used to examine the significant difference in the LEPT performances between graduates from AY 2017-2018 and AY 2018-2019. Findings: When compared to national passing rate, results show that DOrSU performed best in September 2021. On the other hand, DOrSU performed low in March 2022. Also, the results from the t-test for equality of means show that there is no significant difference between the AY 2017-2018 and AY 2018-2019 in terms of their ratings in LEPT. Conclusions: It is recommended that schools should create a mechanism in monitoring the performances of their graduates in their licensure examination. Keywords: Teacher education, performance analysis, analytical method of research, documentary analysis. DOI: http://dx.doi.org/ 10.23960/jpp.v13.i2.202325