Reviewerly/exHarmony huggingface dataset:
AutoSurveyGPT: GPT-Enhanced Automated Literature Discovery

In this work, we introduce AutoSurveyGPT, a novel framework for literature discovery. Designed to accommodate brief user-provided descriptions of academic papers, ideas, or proposals, this system is capable of autonomously extracting keywords for subsequent exploration within scholarly search engines. By leveraging large language model like GPT-4, the system further evaluates the relevance of the retrieved papers to the user-provided idea. This process, based on examining the introduction and related work sections, drives a repeating cycle of creating new keywords and finding more papers. The system generates a list of related papers, effectively aiding researchers in their search for relevant work. The open-source code for this tool is available on GitHub https://github.com/a554b554/AutoSurveyGPT.
Virtual Buddy: Redefining Conversational AI Interactions for Individuals with Hand Motor Disabilities

Advances in artificial intelligence have transformed the paradigm of human-computer interaction, with the development of conversational AI systems playing a pivotal role. These systems employ technologies such as natural language processing and machine learning to simulate intelligent and human-like conversations. Driven by the personal experience of an individual with a neuromuscular disease who faces challenges with leaving home and contends with limited hand-motor control when operating digital systems, including conversational AI platforms, we propose a method aimed at enriching their interaction with conversational AI. Our prototype allows the creation of multiple agent personas based on hobbies and interests, to support topic-based conversations. In contrast with existing systems, such as Replika, that offer a 1:1 relation with a virtual agent, our design enables one-to-many relationships, easing the process of interaction for this individual by reducing the need for constant data input. We can imagine our prototype potentially helping others who are in a similar situation with reduced typing/input ability.
The Ultimate Interface

This talk will explore the possibilities of what emerging AI architectures could mean for interaction. My perspective and examples are based on personal opinions, informed by observations and experience gained from building advanced interactions over the last few decades. While I aim to provide insights, speculations, and suggest some essential conditions for creating such an ultimate interface, my insights may not be exhaustive. I invite open discussion and debate on this subject, recognizing that the conditions that I propose might be necessary but perhaps not sufficient in themselves.
Sacriface: A Simple and Versatile Support Structure for 3D Printing

Digital fabrication serves as a potent instrument for facilitating interaction between the real and digital realms. However, the process is becoming increasingly complex amidst its development. We present Sacriface, a simple and versatile support structure for 3D printing which allows us to obtain further efficiency and flexibility in 3D printing. We simplified the strategy of expanding support structures taking advantage of a stable, growing overhang that gradually recovers its original shape as printing thickness increases. Sacriface is effortlessly designed and printable using an unmodified 3D printer, which enhances user customization.
Data Processing Unit's Entry into Confidential Computing

One of the biggest transformations in the datacenter in the past decade has been the rapid growth of heterogenous computing involving HW accelerators to allow application specific offloads for power and performance efficiencies. There is a major shift from the monolithic servers to dynamically composable systems consisting of disaggregated memory, storage and compute resources to meet the needs of compute intensive applications such as AI/ML and HPC. At the same time, with the increased adoption of confidential computing in the public cloud, the industry has realized the need to extend the security assurance of trusted execution environment (TEE) from the CPU to devices. While this has resulted in the emergence of new security standards such as TDISP (PCI-SIG's TEE Device Interface Security Protocol), the interface security is only useful if the device also implements a TEE. Data Processing Unit (DPU), the next generation of SmartNICs, have a key role in the new datacenter architecture and must provide high-speed networking, efficient data transfer protocols, and new services while meeting the confidential computing workload needs. In this position paper, we explain how the problem of making a TEE capable DPU differs from those of compute accelerators like GPUs and FPGAs. We describe DPU's threat model which include DPU specific threats, discuss the challenges and suggest design considerations that can help in exploring DPU TEE architecture options.
MODELING PROCESSES OF MANAGING THE EFFICIENCY AND ACCESSIBILITY OF HIGHER EDUCATION

The article is devoted to the study of issues related to the importance of modeling and its place in the processes of managing the effectiveness and accessibility of higher education. The article examines the main problems that impede the accessibility of higher education, in addition, it reveals characteristics that make it possible to assess the accessibility of education in practice. It is concluded that prioritizing and striving for effective and accessible management creates an equal environment for all students. Higher education institutions must recognize that this is an ongoing process that needs continuous improvement.
Development of Problem-Based Learning (PBL)-Based Digital Books (E-Books): Public Administration at SMK NU Lamongan

This paper discusses the e-book development process based on Problem-based Learning in public administration learning material, produces and tests product feasibility, and finds out students’ responses to e-book development. The type of research is RnD (Research and Development) with IDI procedural type method (Instructional, Development, Institute), which consists of three stages, namely define, develop, and evaluation stages. Research data are e-book development and e-book quality acquisition through validation sheets and students’ questionnaire responses. The product validation stage is carried out by design experts, material experts, and linguists by giving a score of 0-100 while the students’ questionnaire responses are interviews. The average of assessment results from validation sheets by design experts is 73,25%, which means valid and feasible. The average score of material experts is 83,12%, which means valid and feasible. The average score from linguists is 92,25%, which means very feasible. Thus, the final score of the three validations is 84,3%. It can be concluded that the use of e-books is valid and feasible. It is reinforced by the results of the students’ questionnaire response questionnaire results indicated an average score of 93.5%, which means that the digital book is valid and feasible to use.
Barriers to English Listening Comprehension for Indonesian University Students: Implications for Listening Curriculum Development

English has been considered an important means of international communication not only for education but also for career advancement. In general, students find it difficult to understand expressions or sentences in English. The same thing occurs when learning listening comprehension for EFL students in Indonesia. Listening is one of the most difficult skills experienced by students in learning English. The research in this paper aims to investigate the barriers to English listening comprehension of university students in Indonesia and their implications for listening curriculum development. Data were garnered through semi-structured interviews with eight Indonesian higher-education students. The results depict that the barriers experienced by students in listening comprehension include a lack of pronunciation understanding and different accents, a lack of English vocabulary, a lack of voice clarity quality, and difficulty in understanding the speed of pronunciation. To improve listening comprehension, students need to practice listening to audio from native speakers repeatedly, English podcasts, speeches, and inspiring videos via YouTube, English songs, and watching movies. The implications for listening curriculum development were discussed in this paper.
Robust Watermarking Using FFT and Cordic QR Techniques

Digital media sharing and access in today’s world of the internet is very frequent for every user. The management of digital rights may come into threat easily as the accessibility of data through the internet become wide. Sharing digital information under security procedures can be easily compromised due to the various vulnerabilities floating over the internet. Existing research has been tied to protecting internet channels to ensure the safety of digital data. Researchers have investigated various encryption techniques to prevent digital rights management but certain challenges including external potential attacks cannot be avoided that may give unauthorized access to digital media. The proposed model endorsed the concept of watermarking in digital data to uplift media security and ensure digital rights management. The system provides an efficient procedure to conduct over-watermarking in digital audio signals and confirm the avoidance of ownership of the host data. The proposed technique uses a watermark picture as a signature that has been initially encrypted with Arnold's cat map and cyclic encoding before being embedded. The upper triangular R-matrix component of the energy band was then created by using the Fast Fourier transform and Cordic QR procedures to the host audio stream. Using PN random sequences, the encrypted watermarking image has been embedded in the host audio component of the R-matrix. The same procedure has been applied to extract the watermark image from the watermarked audio. The proposed model evaluates the quality of the watermarked audio and extracted watermark image. The average PSNR of the watermarked audio is found to be 37.01 dB. It has also been seen that the average PSNR, Normal cross-correlation, BER, SSMI (structure similarity index matric) value for the extracted watermark image is found to be 96.30 dB, 0.9042 units, 0.1033 units, and 0.9836 units respectively. Further, the model has been tested using various attacks to check its robustness. After applying attacks such as noising, filtering, cropping, and resampling on the watermarked audio, the watermark image has been extricated and its quality has been checked under the standard parameters. It has been found that the quality of the recovered watermark image satisfying enough to justify the digital ownership of the host audio. Hence, the proposed watermarking model attains a perfect balance between imperceptibility, payload, and robustness.
A Systematic and Comparative Analysis of Semantic Search Algorithms

Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best.
Multi-object Tracking in Remote Sensing Video Based on Motion and Multi-scale Local Cost Volume

Multi-object tracking (MOT) in remote sensing videos is significant in many application scenarios. That task in ordinary scenarios has been widely used in vehicle tracking and monitoring. However, due to the peculiarities of remote sensing video, many new challenges will be brought to the task. So many frameworks for ordinary scenarios are inappropriate. In this paper, we propose a novel network called a multi-scale local cost volume network (MLCVNet) that can extract multi-scale features and inter-frame motion information. We use a multi-scale local cost volume module to obtain the object’s displacement information between current and historical frames, and the historic features will be mapped into the current features to obtain enhanced features through which objects can be detected and tracked. Some experiments have been conducted on remote sensing videos, which are collected from the Jilin-1 satellite, and the results have demonstrated the effectiveness and robustness of the proposed method. Experimental results show that our method achieves state-of-art performance.
Forensic Science Laboratories in India

Forensic science is an integral part of investigating and solving criminal cases, providing scientific analysis and expert testimony to aid the legal system. In this chapter, we will explore the essential functions and services of forensic science laboratories in the criminal justice system. Our focus will be on the current state of forensic science laboratories in India, including their organisational structure, challenges, and significant contributions to the pursuit of justice. We will also examine the establishment and evolution of forensic laboratories in India. It is noteworthy that these laboratories exist to ensure that the legal system has access to reliable scientific evidence that can help in the administration of justice. The role of forensic science laboratories in India is vital, and their contributions are invaluable to the pursuit of justice. The advancements made in technology, infrastructure, and expertise should be celebrated and utilised to ensure that the legal system has access to the most credible scientific evidence possible.
A few-shot image classification method based on feature cross-attention

In neural networks, obtaining complete position information from image feature extraction is a difficult task. In order to overcome this issue, an embedded feature cross-attention network (CAN) is proposed in this paper to extract useful key information from a small sample. Firstly, channel and spatial features are generated using global pooling of the channel and spatial dimensions, respectively. Secondly, an attention map is generated by intersecting the channel and spatial features with the original features. Next, the channel and space-crossing attention maps are combined to produce fused feature information. Finally, the fusion features are embedded into the neural network architecture for end-to-end training. To evaluate its effectiveness, the proposed feature cross-attention module was embedded in a prototype network of classical and relational networks for few-shot learning during the experiment. The mean squared error loss method trained the relational attention model to transform the image classification problem into a classification problem with a label space of {0,1}. The experimental results demonstrate and validate that the network embedded with the proposed feature attention module outperforms the original network.
Facial and Body Gesture Recognition for Determining Student Concentration Level

Online learning has gained immense popularity, especially since the COVID-19 pandemic. However, it has also brought its own set of challenges. One of the critical challenges in online learning is the ability to evaluate students' concentration levels during virtual classes. Unlike traditional brick-and-mortar classrooms, teachers do not have the advantage of observing students' body language and facial expressions to determine whether they are paying attention. To address this challenge, this study proposes utilizing facial and body gestures to evaluate students' concentration levels. Common gestures such as yawning, playing with fingers or objects, and looking away from the screen indicate a lack of focus. A dataset containing images of students performing various actions and gestures representing different concentration levels is collected. We propose an enhanced model based on a vision transformer (RViT) to classify the concentration levels. This model incorporates a majority voting feature to maintain real-time prediction accuracy. This feature classifies multiple frames, and the final prediction is based on the majority class. The proposed method yields a promising 92% accuracy while maintaining efficient computational performance. The system provides an unbiased measure for assessing students' concentration levels, which can be useful in educational settings to improve learning outcomes. It enables educators to foster a more engaging and productive virtual classroom environment.
Optimizing LQ45 Stock Portfolio To Maximize Sharpe Ratio Value Using LSTM

Investment is an investment activity within a certain period with the hope of getting a profit. Things that need to be considered by investors when investing are not just yields (return), but investors need to consider the purpose of the investment and the investment period. This study optimizes the formation of portfolios by utilizing the predicted value of stock prices using LSTM. The test used five daily stock indices from LQ45, namely BBCA, BBRI, TLKM, UNVR, and BMIR, from April 2010 – April 2020. The portfolio was built using the Genetic Algorithm and Equal-Weight (EW) method. Portfolio of Genetic Algorithm and Equal-Weight (EW) without predictions used as a benchmark. The experimental results show that using the LSTM prediction and Genetic Algorithm can produce an optimal portfolio with the highest Sharpe ratio value at 1.3950.
Influence of Corporate Cost Leadership Strategies on Organizational Performance among Mobile Service Providers: A Comparative Analysis of Safaricom Plc and Airtel (K) Ltd in Kenya

The study objective was to comparatively determine the influence of corporate cost leadership strategies on the organizational performance of Airtel and Safaricom. The study applied quantitative approach, used positivism paradigm, and adopted correlational research design. The target population was 235 managerial staff of Safaricom and Airtel and used a census approach. The data was collected using questionnaires and secondary data collection sheets for the period 2013 to 2022. Descriptive statistics and multiple regression analysis were used to analyze the data. Tables and figures were used in the presentation of the data. From the correlation analysis, corporate cost leadership strategies (r=0.283, p&lt;0.05) were established to be positively correlated to performance of mobile service providers in Kenya. The study concluded that cost leadership strategies aid in optimizing costs, offering competitive prices based on customer numbers, and diligently curtailing operating expenses and thus affecting performance of mobile service providers. The study recommends that mobile service providers continually refine operational efficiency. Regular evaluations of processes can uncover inefficiencies that, when streamlined, lead to significant cost reductions.
Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes

Clinical prediction models tend only to incorporate structured healthcare data, ignoring information recorded in other data modalities, including free-text clinical notes. Here, we demonstrate how multimodal models that effectively leverage both structured and unstructured data can be developed for predicting COVID-19 outcomes. The models are trained end-to-end using a technique we refer to as multimodal fine-tuning, whereby a pre-trained language model is updated based on both structured and unstructured data. The multimodal models are trained and evaluated using a multicenter cohort of COVID-19 patients encompassing all encounters at the emergency department of six hospitals. Experimental results show that multimodal models, leveraging the notion of multimodal fine-tuning and trained to predict (i) 30-day mortality, (ii) safe discharge and (iii) readmission, outperform unimodal models trained using only structured or unstructured healthcare data on all three outcomes. Sensitivity analyses are performed to better understand how well the multimodal models perform on different patient groups, while an ablation study is conducted to investigate the impact of different types of clinical notes on model performance. We argue that multimodal models that make effective use of routinely collected healthcare data to predict COVID-19 outcomes may facilitate patient management and contribute to the effective use of limited healthcare resources.
Annotate and retrieve in vivo images using hybrid self-organizing map

Abstract Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making.
P1.21-20 Tertiary Lymphoid Structures Assessed by an AI-powered Analysis of H&amp;E Images is Correlated with Favorable Outcomes of Immunotherapy in NSCLC

Recent data indicate that immune cells present in the tertiary lymphoid structures (TLS) surrounding the tumor site could potentially serve as a favorable predictor for immunotherapy. To examine this further, an AI model was created to provide an assessment of TLS in H&E whole-slide images, and to determine its correlation with immune phenotype and immunologic signatures.
Trends of Future Development

The rapid development of Information and Communication Technology (ICT) has and will continue to shape and transform the development of education. Blended learning, as an important educational innovation, has brought about profound changes in teaching and learning.