Reviewerly/exHarmony huggingface dataset:
XDoc: Unified Pre-training for Cross-Format Document Understanding

The surge of pre-training has witnessed the rapid development of document understanding recently.Pre-training and fine-tuning framework has been effectively used to tackle texts in various formats, including plain texts, document texts, and web texts.Despite achieving promising performance, existing pre-trained models usually target one specific document format at one time, making it difficult to combine knowledge from multiple document formats.To address this, we propose XDoc, a unified pre-trained model which deals with different document formats in a single model.For parameter efficiency, we share backbone parameters for different formats such as the word embedding layer and the Transformer layers.Meanwhile, we introduce adaptive layers with lightweight parameters to enhance the distinction across different formats.Experimental results have demonstrated that with only 36.7% parameters, XDoc achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models, which is cost effective for real-world deployment.The code and pre-trained models are publicly available at https://aka.ms/xdoc.
Controlling Bias Exposure for Fair Interpretable Predictions

Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race).However, when sensitive information is semantically entangled with the task information of the input, e.g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.Existing approaches perform this trade-off by eliminating bias information from the latent space, lacking control over how much bias is necessarily required to be removed.We argue that a favorable debiasing method should use sensitive information 'fairly', rather than blindly eliminating it (Caliskan et al., 2017;Sun et al., 2019;Bogen et al., 2020) .In this work, we provide a novel debiasing algorithm by adjusting the predictive model's belief to (1) ignore the sensitive information if it is not useful for the task;(2) use sensitive information minimally as necessary for the prediction (while also incurring a penalty).Experimental results on two text classification tasks (influenced by gender) and an open-ended generation task (influenced by race) indicate that our model achieves a desirable trade-off between debiasing and task performance along with producing debiased rationales as evidence.
Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task

Actions' play a vital role in how humans interact with the world.Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning about Actions & Change' (RAC).This has been an important research direction in Artificial Intelligence (AI) in general, but the study of RAC with visual and linguistic inputs is relatively recent.The CLEVR_HYP (Sampat et al., 2021) is one such testbed for hypothetical visionlanguage reasoning with actions as the key focus.In this work, we propose a novel learning strategy that can improve reasoning about the effects of actions.We implement an encoderdecoder architecture to learn the representation of actions as vectors.We combine the aforementioned encoder-decoder architecture with existing modality parsers and a scene graph question answering model to evaluate our proposed system on the CLEVR_HYP dataset.We conduct thorough experiments to demonstrate the effectiveness of our proposed approach and discuss its advantages over previous baselines in terms of performance, data efficiency, and generalization capability 1 .
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation

For years the model performance in machine learning obeyed a power-law relationship with the model size.For the consideration of parameter efficiency, recent studies focus on increasing model depth rather than width to achieve better performance.In this paper, we study how model width affects the Transformer model through a parameter-efficient multi-path structure.To better fuse features extracted from different paths, we add three additional operations to each sublayer: a normalization at the end of each path, a cheap operation to produce more features, and a learnable weighted mechanism to fuse all features flexibly.Extensive experiments on 12 WMT machine translation tasks show that, with the same number of parameters, the shallower multi-path model can achieve similar or even better performance than the deeper model.It reveals that we should pay more attention to the multi-path structure, and there should be a balance between the model depth and width to train a better large-scale Transformer.
On Distributed Training of Foundation Models: Challenges and Observations

We are currently at an inflection point in the ongoing AI revolution. The emergence of highly parallelizable transformer-based neural architectures, along with self-supervised learning, has made it possible to use widely available unlabeled datasets to train large foundation models. These models have shown remarkable performance across various benchmarks and continue to exhibit new and improved emergent properties as we scale across parameter and data sizes. Over a year ago, our team at IBM Research embarked on a mission to perform distributed training of these large-scale foundation models. We aimed to do so on cost-effective commodity hardware that is cloud native. In this presentation, we will discuss the challenges we faced - from selecting model architectures and hyper-parameters to parallelization choices for distributed training at scale - and the valuable lessons we learned.
Internet of Things Assisted Road Traffic and Safety Monitoring Scheme using Sensitive Alcohol Detectors with Speed Analyzing Protocol

The term "Internet of Things" refers to a network of interconnected, distinctive gadgets. In the IoT, there is a vast variety of functions that a single device may serve. This study proposes a paradigm based on the Internet of Things to prevent intoxicated and sleepy driving, particularly at night. In this study, we use IoT technology to create an alcohol screening device for safer road mobility in a smart city. Limits for the amount of alcohol in one's blood are programmed and tracked with the help of a microcontroller. When the predetermined limit is achieved, the system sends the driver's blood alcohol concentration and the GPS coordinates of the car to a central monitoring unit. These models will aid in reducing drunk driving and keeping people safe on the road. Safer roads and fewer accidents are possible thanks to increased surveillance. The testing outcomes demonstrate the system's low cost, usefulness, precision, and dependability.
COMPSYS 2023 Invited Speaker Towards ML-Driven Resource Orchestration in Disaggregated Memory Systems: Challenges and Opportunities

The fixed resource capacity of modern Cloud servers poses several challenges regarding the efficient orchestration of computing resources. To alleviate this resource wall challenge, hardware (memory) disaggregation has been proposed as a new design paradigm, where the underlying infrastructure is organized as a pool of heterogeneous resources that can be composed on demand into compute units tailored to workload-specific requirements. However, even though hardware disaggregation offers more fine-grained organization of computing resources, it also introduces new optimization knobs, which have to be properly managed to truly exploit its benefits. ML-driven resource orchestration is a key player in this direction, however, questions like “where”, “when” and “how” to use ML for resource orchestration of disaggregated systems are still vague. This talk highlights some of the key research questions and open problems in this area, gives examples of recent research that demonstrate the potential of ML-driven resource orchestration, and provides insights into the exciting new opportunities that ML presents for the future of disaggregated memory systems.
STUDY ON THE TYPES OF ASSESSMENT IN THE LIGHT OF THE THEORY OF LEARNING STYLES

This article aimed to verify whether students actually achieve the best performance in the evaluation methodology that matches their learning style.Therefore, this style of each one was identified after condensing the information obtained through the interview and the test with the students of the 3rd year of high school at Colégio Estadual Edivaldo Boaventura, located in Brejões-BA, 276 km away from Salvador.Thus, this work proposed to discuss about evaluation and learning styles, punctuating and relating the interferences of one area in the other.This investigation brings both the conception and suggestions of several evaluative methodologies, such as Online test, oral, written, games, among others.In addition to different conceptions of learning styles, such as Gregorc (1979), Kolb (1984), Felder-Silverman (1988) and Neil Fleming (1992).The discussions on evaluation presented here were based on Jussara Holffmann's theory of Mediating Evaluation and Professor Cipriano Luckesi's Learning Evaluation.We also sought to understand how, based on the understanding that students and teachers have of learning styles, as well as the mechanisms to use their innate abilities in favor of acquiring knowledge, it would be possible to use them in assessments, achieving with it performs better.To follow this path, a didactic sequence with the theme Acid Rain was applied.Even though it was carried out in the area of chemistry, the results obtained in this study can help educators in any discipline, both in Elementary and High School.
Kehidupan di Tepi Sungai Batang Arau dalam Karya Seni Grafis

The purpose of the creation of this work is as social criticism and conveying messages about river pollution behavior that occurs in the Batang Arau river in Padang city by visualizing the impact of river pollution on humans. which is poured into graphic artworks. The creation of the work uses the art consortium method with five stages, namely, the preparation stage (observation and exploration), the elaboration stage (searching and collecting references), the synthesis stage (application of main ideas), the realization of concepts (making works), and the completion stage (artist report and final work exhibition).So that the realization of ten works that the author made with the theme of the impact of river pollution, the resulting works are 10 works with a size of 55 x 40 cm with titles including: Hal Baik yang Terganggu, Blind, Seiring Sejalan, Tanpa Sadar, Ingin Tapi Tak Bisa, Malam yang Sulit, Dimatanya, Meniru, Luapan, Dari dan untuk diri sendiri.
Correlation between Increased Leukocyte Counts in Patients with Acute Phase Hemorrhagic Stroke with Early Neurological Deterioration Events

Stroke is the second most common cause of death and the leading cause of disability worldwide. Hemorrhagic stroke occurs in 15% of stroke cases. Increased leukocyte counts in the acute phase of hemorrhagic stroke are believed to be related to the incidence of early neurological deterioration. This study aims to determine the relationship between increased leukocyte count and early neurological deterioration in hemorrhagic stroke patients while being treated at UKI General Hospital. This research is analytic research with a cross-sectional design. There were 38 research subjects. The sample was selected using total sampling with specified inclusion and exclusion criteria. The study subjects were grouped into leukocytosis with leukocyte values &gt; 11,000/µL blood and normoleukocytes with leukocyte ranges ranging from 4000-11000/µL blood. The data used is secondary data. The results of the study were processed using the SPSS computer program. It was found that early neurological deterioration in patients with leukocytosis was significantly higher than in patients with normoleucosis, with a Relative Risk of early neurological deterioration in patients with leukocytosis of 3.003 (CI 95%); p=0.031). A significant relationship exists between increased leukocyte count (leukocytosis) upon admission and early neurological deterioration while being treated for hemorrhagic stroke patients at the UKI General Hospital.
An Enhanced Load Balancing Algorithm for Cloud Enterprise Resource Planning (ERP) Data in a Multi-Cloud Environment

Businesses and individuals have seen the need to adopt the cloud and mulit-cloud environment for their businesses and storage of data. The load balancing concerns especially in the multi-cloud environment was investigated and a new algorithm proposed. In this research, a proposed new load balancing algorithm is presented and compared with the Round Robin (RR) and Weighted Round Robin (WRR) algorithms. The proposed scheduling algorithm considered several Cloud ERP Data chunks to analyse the data transmission rate or throughput, the transmission delay, data loss and the Cloud ERP Data drop ratio. The proposed algorithm performed better compared to the Round Robin (RR) and Weighted Round Robin (WRR) in a multi cloud environment with data chunks above 150 in terms of throughput. The proposed algorithm again outperformed the RR and WRR with a recorded lower transmission delays and lower data loss.
Virtual Mouse and Keyboard for Computer Interaction by Hand Gestures Using Machine Learning

Human-computer interaction has changed since the advent of computer technology. Gestures are a useful way to communicate, and the Covid-19 era had an impact on us. Both the keyboard and the mouse are tools used to communicate with computers. Here, we’ve attempted to use hand gestures to interact with the mouse and keyboard. Eventually, get rid of the electronics. Consequently, use a virtual keyboard and your finger to move the mouse cursor. Using different hand gestures, actions like clicking, dragging, and typing data will be carried out. A webcam is the IOT device required to accomplish this. The output from the camera will be displayed on the system’s screen so that the user can fine-tune it. We employ tools like Python, Media- Pipe, and Open-CV. The Media-Pipe library offers features that improve the model’s effectiveness and is particularly helpful in AI projects. The user will be able to move the computer cursor with various hand motions, type on the virtual keyboard while holding coloured caps or tapes, and left-click and drag objects. In this research, we suggest a hand gesture detection system for a natural human computer interface that can control a virtual mouse and keyboard.
Text Linguistics of “Textuality”: Making a Sequence of Sentences Communicative English Written Texts

Textuality makes a sequence of sentences a communicative English written text. It, nonetheless, hardly becomes a major theme of foreign language learning in the Indonesian English Writing Classes/context nowadays. This study, therefore, aimed at diagnosing the “incorrect/missing uses of the seven elements of the textuality in the students’ cause and effect essay. Qualitatively, Politeknik Negeri Padang was the locus of the study. The 2nd-year ED students (n=32) were the research participants selected through the convenience sampling techniques. The documentation and observation were the techniques of data collection while the 1967 Corder’s Clinical elicitation was exercised as ways of analysing the data. The findings disclosed that the students missed using nominal substitution and lexical cohesion of repetition and synonym along with incorrectly used verbal and clausal substitution, nominal, verbal and clausal ellipsis, anaphoric and cataphoric references, and lexical cohesion of grammatical and lexical collocations. They, however, succeeded in correctly using three different types of conjunctions and coherence though they flopped to utilise the acceptable phrases of drawing a conclusion. Hyponymy, metonymy and antonymy were ignored. In conclusion, the deficiencies made the students’ sequences of sentences flawed and characterless. It, therefore, encourages EFL teacher to place emphasis on teaching textuality in the English Writing Classes.
A Hybrid Model for Anomaly Detection in Cloud Using Deep Learning

The exponential growth of cloud infrastructure and services has resulted in a sharp increase in the demand for effective anomaly detection methods. Traditional rule-based and statistical methodologies have a tough time keeping up with cloud systems because of their dynamic and complex nature. As a result, there is a growing need to apply deep learning techniques to address the issue. Here we provide a unique method for utilizing deep learning to identify anomalies in cloud environments. One of the main contributions of this research is the design and implementation of deep learning-based anomaly detection system that is specially tailored to the cloud’s data properties. Convolution Neural Networks and auto encoders are used to extract the patterns and spatial representations from structured cloud data. Thorough experiments are conducted on a large scale cloud dataset with a range of usage scenarios and anomaly types to evaluate the effectiveness of the recommended technique. The deep learning model has benefits over more conventional anomaly detection methods when compared in terms of accuracy, sensitivity and false- positive rates, according to analyses. The findings of my analysis suggest that deep learning based LSTM and RSU algorithms significantly increase the security and reliability of network data by identifying abnormal behaviors. The RSU algorithm forecasts an accuracy of 99 comparable to the LSTM algorithm. Key Words: Convolution Neural Networks, LSTM algorithm, RSU algorithm, Deep Learning
Intelligent Billboards Targeted Advertising Systems

Technological developments in recent decades and the inclusion of Internet of Things (IoT) and Internet of Behavior (IoB) technologies are leading to a revolution across all areas of advertising, and Out Of Home Advertising (OOH) is not excluded.One of OOH's main problems is that it still lacks the ability to target audiences accurately according to their preferences and behaviors.The aim of the research is studying how IoT/IoB can develop OOH targeting abilities: then proposing a case scenario for future opportunities.The research assumes that IoT/IoB implementation can add accurate targeting functions to OOH.Firstly, the research uses the descriptive methodology to present a literature review of OOH targeted advertising systems, as well as real world examples.Then, the research follows with an analytical comparative study of the most important OOH targeted advertising systems regarding the technologies used, the targeting functions, the targeted advertising process, and the most relevant advertisements selection.Finally, the research uses the Scenario Methodology in proposing a future case scenario of a targeted OOH advertising system at one of the largest and most important interchange railways stations in Egypt and the Middle East: Adly Mansour.The proposed scenario includes an overview of system architecture, targeting process, ad selection and display; including target audience interests and preferences identification by DOOH advertising can elevate OOH advertising capabilities to the level of online advertising and advertising via the Internet of Things.
Chapter 17. Approximate Answering of Graph Queries

Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we would still want to be able to answer queries as if the graph were complete. In this chapter, we will give an overview of several methods which have been proposed to answer queries in such a setting. We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations. Then, we give an overview of the different approaches and describe them in terms of expressiveness, supported graph types, and inference capabilities.
T-RecX

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research focuses on model compression techniques that trade accuracy (and model capacity) for compact models to fit into the KB-sized tiny-edge devices. In this paper, we show how such models can be enhanced by the addition of an early exit intermediate classifier. If the intermediate classifier exhibits sufficient confidence in its prediction, the network exits early thereby, resulting in considerable savings in time. Although early exit classifiers have been proposed in previous work, these previous proposals focus on large networks, making their techniques suboptimal/impractical for tinyML applications. Our technique is optimized specifically for tiny-CNN sized models. In addition, we present a method to alleviate the effect of network overthinking by leveraging the representations learned by the early exit. We evaluate T-RecX on three CNNs from the MLPerf tiny benchmark suite for image classification, keyword spotting and visual wake word detection tasks. Our results show that T-RecX 1) improves the accuracy of baseline network, 2) achieves 31.58% average reduction in FLOPS in exchange for one percent accuracy across all evaluated models. Furthermore, we show that our methods consistently outperform popular prior works on the tiny-CNNs we evaluate.
Scaling up Deep Learning: Efficiency in AI

In recent years, the usage of deep learning has surged. We are still in the early stages of AI proliferation, but the energy consumption for even a single training run of today’s large language models is already significant. Moreover, inference is expected to be the largest energy consumer, accounting for over 70% of the total energy for a model. Growth in energy consumption for AI models is unsustainable, and unless we can find a way to drastically increase the world’s energy production, we must make computing more efficient. This talk will explore the challenges of scaling deep learning in an energy-efficient way and discuss potential research directions.
Kindergarten Teacher Competency Development Program to Increase Pupils Reading Skill with Jolly Phonics Method

Lately, problems related to pupils’ reading skill at school is increasing. So, teachers have to give more attention to the addressed problems. This study aims to examine the effectivity of the training for kindergarten teacher to uplift pupils’ reading skill through the jolly phonics method. This study involved 29 participants who work as kindergarten teacher in Jember. The training consisted of the presentation from experts, group discussion, role play, pre-test, and post-test. Data was analyzed using Wilcoxon Signed Ranks Test to examine the score difference before and after the training was implemented. Result shows that there is significant increasement (p=0,000) of the participants’ score after the training was implemented. Result of this study could be used as additional reference for implementing jolly phonics method to increase reading skill of the pupils at school.