Reviewerly/exHarmony huggingface dataset:
Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare

Machine learning (ML) models have the potential to generate biased outcomes, which may exacerbate existing health disparities. With privacy regulations leading to data silos in health data, federated learning (FL) has emerged as a promising solution for this issue by enabling collaborative ML without patient data sharing. Personalization within FL aims to handle performance degradation that arises due to heterogeneous data distributions across organizations. However, the relationship between personalized FL and fairness remains unclear. This paper aims to investigate and analyze the potential impact of personalized FL on fairness for healthcare. Our analysis is expected to have significant implications for fairness in federated learning and healthcare.
Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes

Electronic health records (EHR) have been widely used in building machine learning models for health outcomes prediction. However, many EHR-based models are inherently biased due to lack of risk factors on social determinants of health (SDoH), which are responsible for up to 40% preventive deaths. As SDoH information is often captured in clinical notes, recent efforts have been made to extract such information from notes with natural language processing and append it to other structured data. In this work, we benchmark 7 pre-trained transformer-based models, including BERT, ALBERT, BioBERT, BioClinicalBERT, RoBERTa, ELECTRA, and RoBERTa-MIMIC-Trial, for recognizing SDoH terms using a previously annotated corpus of MIMIC-III clinical notes. Our study shows that BioClinicalBERT model performs best on F-1 scores (0.911, 0.923) under both strict and relaxed criteria. This work shows the promise of using transformer-based models for recognizing SDoH information from clinical notes.
A Study on the Development of Digital Characters for Learning Contents

Since the COVID-19 pandemic, educational content using digital-based media has received significant public attention, and various methods to enhance educational effects, such as educational content using digital illustration characters, are emerging. Characters have long been an essential element of media content based on OSMU (One Source Multi Use), and their utilization is outstanding. In this paper, we propose a study on the production process of digital characters, especially the production of educational content through the characteristics of characters. For a case study, a character considering the elements of 20 chemical elements was created and applied to an elementary school student’s after-school class. Digital illustrated characters can be used in various online and offline learning content. As a result, students’ interest has increased, and they focused on the more.
VIBRATION PREDICTION BASED ON PRESENTING IT PREPARATION PERIOD AS A BLUE-UP PROCESS

The method of forecasting vibrations is considered. The technique is based on the assumption that the vibration preparation process can be attributed to the processes developing in the blowup mode. The properties of the technique are demonstrated on the example of a retro forecast of vibrations that took place in Turkey on February 6 and 20, 2023. The results of testing the methodology confirmed the assumptions underlying it. Approbation of the methodology made it possible to predict the time of exacerbation of the seismic situation in the region of the globe, which is very remote from the control point of the initial seismic information for forecasting.
Advancing Knowledge Discoveries in Criminal Investigations with Semantic Textual Similarity

Criminal investigators, faced with the complexity of knowledge discovery in major cases, can benefit from integrating information retrieval and natural language processing in their workflow. We explore the application of pre-trained sentence embeddings for clustering and semantic search, presenting promising results. Evaluating over 3,700 Norwegian police reports from high-profile cases; we find that sentence embeddings enhance information retrieval speed and reveal previously unnoticed data, offering valuable insights for future applications in the field of criminal investigations.
Universal Quantum Illumination Advantage for Stand-off Target Detection

We provide a proof that Gaussian entanglement can provide up to 6dB of advantage in the error probability exponent compared to using a classical light source of the same signal brightness for the task of discriminating between the presence of two arbitrary objects (whose shapes are known). The proof is based on a perturbative expansion in the high loss and low signal brightness regime of the joint state of the reflected signal and the stored idler mode with which the signal is entangled.
PRIORITISATION OF FACTORS FOR ARTIFICIAL INTELLIGENCE-BASED TECHNOLOGY ADOPTION BY BANKING CUSTOMERS IN INDIA: EVIDENCE USING THE DEMATEL APPROACH

Artificial Intelligence (AI) is a concept of recent origin and is accepted for banking activities such as customer service, detection of fraudulent activities, and suspicious transactions. For the successful implementation of AI in the Indian context, a deep understanding is required in terms of its need and importance compared to the traditional banking system. To date, this outlook of AI has been less focused by industry practitioners and experts for the smooth flow of operational procedures in banks for developing countries, for example, India. This study aims to unearth factors and establish a relationship among the identified factors through the decision-making trial and evaluation laboratory (DEMATEL) approach to categorize the factors and frame the cause-and-effect relationships. Fifteen factors are identified through a literature review of existing studies, and ten experts were solicited to express their outlook on this subject. The result indicated that 'Transparency of information,' 'Perceived security of AI-based technology,' 'Social influence on customer,' 'Government regulation of AI in banks,' 'Awareness level of AI,' 'Efficiency of AI system,' 'Technical requirement,' and 'Cost of AI-based technology' were causative factors that support customer acceptance and penetration of AI in banks. The study presents a unique approach to customer acceptability towards AI in banks in developing countries using the DEMATEL technique. This study also discusses the possible area for the adaption of AI in Indian banks. The findings will support policymakers and practitioners in executing AI-based technologies in the banking sector in emerging nations.
libcdict: fast dictionaries in C

A common requirement in science is to store and share large sets of simulation data in an efficient, nested, flexible and human-readable way. Such datasets contain number counts and distributions, i.e. histograms and maps, of arbitrary dimension and variable type, e.g. floating-point number, integer or character string. Modern high-level programming languages like Perl and Python have associated arrays, knowns as dictionaries or hashes, respectively, to fulfil this storage need. Low-level languages used more commonly for fast computational simulations, such as C and Fortran, lack this functionality. We present libcdict, a C dictionary library, to solve this problem. Libcdict provides C and Fortran application programming interfaces (APIs) to native dictionaries, called cdicts, and functions for cdicts to load and save these as JSON and hence for easy interpretation in other software and languages like Perl, Python and R.
PACMMOD Volume 1 Issue 4: Editorial

Welcome to this issue of the Proceedings of the ACM on Management of Data (Volume 1, Issue 4 (SIGMOD)). While this issue has papers from the SIGMOD track, PACMMOD will soon also have issues with papers from the newly created PODS track. Out of 189 submissions to the round of reviewing for the PACMMOD SIGMOD track whose submission deadline was April 15, 2023, a total of 49 articles were accepted, and are presented in this issue.
A Quantitative Experiment: Inter-Team Retrospectives in Scaled Agile Programs

Despite the current popularity agile project management and scaling such frameworks, there is little academic research inquiring how to scale agile frameworks to be successful at the program level. Retrospectives, are understood to play an important role in attempts to successfully adjust scaled-up project frameworks. This research studies the particular impact of such meetings as a tool to scale a known success factor from agile projects and address current gaps highlighted in recent program management literature. To better understand the impact of retrospectives, a quantitative experiment, to inquire how program value is accomplished with inter-team retrospectives, is conducted. With the proposed research design, we intend to test if there is a measurable impact on program success and efficiency by introducing inter-team retrospectives based on current approaches from the literature. This will allow to better understand and further develop the retrospective meeting design on an inter-team level within scale agile programs.
Language Learning Strategies Instruction via English for Community-based Tourism Blended Lessons to Enhance Speaking Ability of Thai EFL Youth Guides

This study aims to investigate the English-speaking ability of the high and low-proficiency youth guides after studying English for Community-based Tourism at Khokkrachai blended lessons and examine their use of total and individual learning strategies in these blended lessons. The sample group consists of 32 youth guides who were secondary students from Nakhon Ratchasima, Thailand. They were selected by using a cluster random sampling technique. The instruments included an English for Community-based Tourism Youth Guides blended lesson, a speaking test and the rating scale, a language learning strategies questionnaire and a coding scheme. Means, standard deviations, paired-samples t-test and content analysis were used in data analysis. Results from the study showed that the performance of the high- and low-proficiency youth guides’ groups had a significant difference between pre- and post-tests in both the total and six speaking aspect scores at the .00 level. The scores of both groups significantly improved. The results also indicated that the youth guides always used their learning strategies to learn during the three blended lessons. The high-proficiency youth guides utilised each language learning strategy in more meaningful, systematic, sophisticated and holistic ways than the low-proficiency youth guides. These results yielded insightful and substantive data about the instruction of language learning strategies in English within a specific field, English for community-based tourism with blended learning in Thailand. Keywords: Language Learning Strategies; Language for Specific Purposes; Blended learning; Speaking Ability; English for Community-based Tourism
DEVELOPMENT OF NOVEL COMPARISON BASED STEGANOGRAPHY ALGORITHMS ON MULTIMEDIA TO HIDE PRIVATE DATA

Throughout history, humanity has had secrets to safeguard, information that needed to be conveyed to allies in a way that enemies couldn't decipher. To achieve this goal of safeguarding important and valuable information, cryptography and steganography have been frequently used methods both in the past and in today's world. This article introduces a steganographic algorithm designed for hiding data in the color images, along with two different algorithm designs derived from this method. The bits of the hidden message are embedded sequentially into each pixel using the bit comparison. The comparison method works by matching the bits of the image and the message. The least significant bits (LSB) of the carrier (cover) image bytes change depending on the number of matching bits between the carrier image and the hidden message. The proposed method has the potential to hide 1 byte of data within 5 bytes under optimal conditions. The designed algorithms have been tested on a series of color images, and satisfactory results have been achieved in terms of embedding a sufficient amount of data into the images without compromising image quality. The results have been compared with the results of the LSB technique and similar methods based on various performance criteria.
The Distribution of That – Clauses from a Structural Point of View

The paper discusses the distribution of subordinate clauses introduced by the conjunction THAT within complex sentences, focusing on the functions they can perform, and, at the same time, concentrating on a number of transformations that can take place in the surface structure of clauses, namely extraposition, particle movement, passivization, THAT-deletion, analysing the specific structures for each transformation.
Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach uses a variety of clustering techniques to aggregate S-WoT data’s strongly related ontologies into comparable categories. The principle is to match concepts of similar groups rather than full concepts of two ontologies, which necessitates splitting examples of each ontology into similar groups. We design two clustering algorithms for ontology matching using conventional methods, as well as sophisticated clustering techniques. Moreover, we develop an intelligent matching algorithm that uses evolutionary computation to quickly converge to (or ideally identify) optimal matches. Numerous simulations have been conducted using various ontology databases to demonstrate the application and precision of ECOM. Our findings clearly show that ECOM has better results when compared to cutting-edge ontology matching methods. The F-measure of ECOM exceeds 95% whereas it does not reach 90% for all baseline methods. The results also confirm that ECOM scales with big data in S-WoT environments.
Readliness Level Of Mobile-Based Mini English-Indonesia-Korean Dictionary Application Implementation In Improving The Tourism Sector

The tourism sector has the advantage that it is currently undergoing continuous expansion and diversification compared to the manufacturing sector. Current tourism development in various regions offers innovative services through the application of information and communication technology. Currently, the application of mini dictionary applications is more widely applied in city tourism areas that have complete basic infrastructure, adequate availability of information and communication technology and a comprehensive service system. In the tourism sector in Indonesia has considerable potential and attractiveness in the natural and cultural tourism sector, in the development of tourism the central government through The Ministry of Tourism and Creative Economy/Tourism and Creative Economy Agency in each province has the aim of increasing the number of visits and services. This research will examine each tourist attraction, which in its development has developed as an urban area with the availability of supporting tourism facilities / infrastructure that has been fulfilled. The purpose of this study is to identify the level of readiness of tourist attractions in implementing mini dictionary applications in terms of the availability and quality of basic infrastructure services and ICT and tourist support facilities. This research uses the waterfall process method used to describe the research approach to the analysis process. The results of the analysis show that the level of readiness of the application of mini dictionaries at tourist attractions is stated to be against ready. The readiness of the application of the mini dictionary in terms of the availability and quality of basic infrastructure services and ICT and tourist support, based on the results of a review of all components in its application that can be reviewed from infrastructure, facilities and service systems shows that almost all tourist attractions have shown readiness in basic infrastructure and for ICT infrastructure and tourist support facilities are still in a somewhat ready position. This is due to the inadequate quantity and also ICT that has not been applied in tourism management.
Impact of Social Capital on Supply Chain Performance: The Role of Information Flow

The purpose of this paper is to understand how social capital from buyer and suppliers’ perspective effect the performance of the supply chain when information (inflow &amp; outflow) plays a critical role. How social capital enhances the perception of the buyer supplier relationship and their understanding while sharing both ways information inflow &amp; outflow. This change in the perception and how it effects the performance of the supply chain. Also, how social capital plays its role in order to resolve this perceptual difference. With the help of literature review, the buildup of theoretical model and hypothesis was developed. Using the data from 150 respondents collected from many different Pakistani industry manufacturers especially in supply chains and people relate with service industry. For analysis we used structural equation modelling. All of the social capital facets are significant with relation to information inflow, however, with relation to information outflow, structural capital, and performance of the supply chain are not significant. It clearly shows that buyers are more efficient in collection of information, but more reluctant in sharing back (information outflow) with the suppliers. With our results, it shows that buyers should maintain strong social capital and should rely on sharing more information within organization and outside with suppliers, this will enhance, trust, efficacy and will promote performance of the supply chain. Paper develops a framework to test the effect of social capital dimensions on overall supply chain performance where information flow is used as a mechanism facilitating the supply chain performance. This framework has never been tested before. Contribution of the studies and future research has been discussed.
A Framework with Improved Heuristics to Optimize Low-Latency Implementations of Linear Layers

In recent years, lightweight cryptography has been a hot field in symmetric cryptography. One of the most crucial problems is to find low-latency implementations of linear layers. The current main heuristic search methods include the Boyar-Peralta (BP) algorithm with depth limit and the backward search. In this paper we firstly propose two improved BP algorithms with depth limit mainly by minimizing the Euclidean norm of the new distance vector instead of maximizing it in the tie-breaking process of the BP algorithm. They can significantly increase the potential for finding better results. Furthermore, we give a new framework that combines forward search with backward search to expand the search space of implementations, where the forward search is one of the two improved BP algorithms. In the new framework, we make a minor adjustment of the priority of rules in the backward search process to enable the exploration of a significantly larger search space. As results, we find better results for the most of matrices studied in previous works. For example, we find an implementation of AES MixColumns of depth 3 with 99 XOR gates, which represents a substantial reduction of 3 XOR gates compared to the existing record of 102 XOR gates.
A privacy-preserving federated learning algorithm for intelligent inspection in pumped storage power station

As a distributed machine learning architecture, Federated Learning (FL) can train a global model by exchanging users' model parameters without their local data. However, with the evolution of eavesdropping techniques, attackers can infer information related to users' local data with the intercepted model parameters, resulting in privacy leakage and hindering the application of FL in smart factories. To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations, in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption (FHE), called MFHE-PPFL. Specifically, to reduce communication costs caused by deploying the FHE algorithm, we propose a self-adaptive threshold-based model parameter compression (SATMPC) method. It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server. Moreover, to protect model parameter privacy during transmission, we develop a secret sharing-based multi-key RNS-CKKS (SSMR) method that encrypts the device's uploaded parameter increments and supports decryption in device dropout scenarios. Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.
Robust Self-Supervised Extrinsic Self-Calibration

Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization. The project page: https://sites.google.com/tri.global/tri-sesc
Calibration-Free BEV Representation for Infrastructure Perception

Effective BEV object detection on infrastructure can greatly improve traffic scene understanding and vehicle-to-infrastructure (V2I) cooperative perception. However, cameras installed on infrastructure have various postures, and previous BEV detection methods rely on accurate calibration, which is difficult for practical applications due to inevitable natural factors (e.g., wind and snow). In this paper, we propose a Calibration-free BEV Representation (CBR) network, which achieves 3D detection based on BEV representation without calibration parameters and additional depth supervision. Specifically, we utilize two multi-layer perceptrons for decoupling the features from perspective view to front view and bird-eye view under boxes-induced foreground supervision. Then, a cross-view feature fusion module matches features from orthogonal views according to similarity and conducts BEV feature enhancement with front-view features. Experimental results on DAIR-V2X demonstrate that CBR achieves acceptable performance without any camera parameters and is naturally not affected by calibration noises. We hope CBR can serve as a baseline for future research addressing practical challenges of infrastructure perception.