Reviewerly/exHarmony huggingface dataset:
Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health

Modern smartphones possess hardware for audio acquisition and to perform speech processing tasks such as speaker recognition and health assessment. However, energy consumption remains a concern, especially for resource-intensive DNNs. Prior work has improved the DNN energy efficiency by utilizing a compact model or reducing the dimensions of speech features. Both approaches reduced energy consumption during DNN inference but not during speech acquisition. This paper proposes using a masking kernel integrated into gradient descent during DNN training to learn the most energy-efficient speech length and sampling rate for windowing, a common step for sample construction. To determine the most energy-optimal parameters, a masking function with non-zero derivatives was combined with a low-pass filter. The proposed approach minimizes the energy consumption of both data collection and inference by 57%, and is competitive with speaker recognition and traumatic brain injury detection baselines.
Prompting for Multimodal Hateful Meme Classification

Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification. We conduct extensive experiments on two publicly available hateful and offensive meme datasets. Our experimental results show that PromptHate is able to achieve a high AUC of 90.96, outperforming state-of-the-art baselines on the hateful meme classification task. We also perform fine-grained analyses and case studies on various prompt settings and demonstrate the effectiveness of the prompts on hateful meme classification.
Hierarchical Event Grounding

Event grounding aims at linking mention references in text corpora to events from a knowledge base (KB). Previous work on this task focused primarily on linking to a single KB event, thereby overlooking the hierarchical aspects of events. Events in documents are typically described at various levels of spatio-temporal granularity (Glavas et al. 2014). These hierarchical relations are utilized in downstream tasks of narrative understanding and schema construction. In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB. Our proposed task involves linking a mention reference to a set of event labels from a subevent hierarchy in the KB. We propose a retrieval methodology that leverages event hierarchy through an auxiliary hierarchical loss (Murty et al. 2018). On an automatically created multilingual dataset from Wikipedia and Wikidata, our experiments demonstrate the effectiveness of the hierarchical loss against retrieve and re-rank baselines (Wu et al. 2020; Pratapa, Gupta, and Mitamura 2022). Furthermore, we demonstrate the systems' ability to aid hierarchical discovery among unseen events.
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech

Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the coverage of these corpora. We believe the ability to handle such diversity is crucial for AI systems to achieve human-level communication. Our work explores the use of more abundant real-world data for building speech synthesizers. We train TTS systems using real-world speech from YouTube and podcasts. We observe the mismatch between training and inference alignments in mel-spectrogram based autoregressive models, leading to unintelligible synthesis, and demonstrate that learned discrete codes within multiple code groups effectively resolves this issue. We introduce our MQTTS system whose architecture is designed for multiple code generation and monotonic alignment, along with the use of a clean silence prompt to improve synthesis quality. We conduct ablation analyses to identify the efficacy of our methods. We show that MQTTS outperforms existing TTS systems in several objective and subjective measures.
Multiobjective variational quantum optimization for constrained problems: an application to Cash Management

Combinatorial optimization problems are ubiquitous in industry. In addition to finding a solution with minimum cost, problems of high relevance involve a number of constraints that the solution must satisfy. Variational quantum algorithms have emerged as promising candidates for solving these problems in the noisy intermediate-scale quantum stage. However, the constraints are often complex enough to make their efficient mapping to quantum hardware difficult or even infeasible. An alternative standard approach is to transform the optimization problem to include these constraints as penalty terms, but this method involves additional hyperparameters and does not ensure that the constraints are satisfied due to the existence of local minima. In this paper, we introduce a new method for solving combinatorial optimization problems with challenging constraints using variational quantum algorithms. We propose the Multi-Objective Variational Constrained Optimizer (MOVCO) to classically update the variational parameters by a multiobjective optimization performed by a genetic algorithm. This optimization allows the algorithm to progressively sample only states within the in-constraints space, while optimizing the energy of these states. We test our proposal on a real-world problem with great relevance in finance: the Cash Management problem. We introduce a novel mathematical formulation for this problem, and compare the performance of MOVCO versus a penalty based optimization. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima that do not satisfy any of the mandatory constraints.
Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events

Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs). We propose an efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly learn the tasks of named entity recognition and relation extraction for documents of variable length. The architecture uses positional encoding and unitary batch sizes to process variable length documents and uses a weight-shared Fourier network layer for low-complexity token mixing. Finally, we reach the theoretical computational complexity lower bound for relation extraction using a selective pooling strategy and distance-aware attention weights with trainable polynomial distance functions. We evaluated the JNRF architecture using the 2018 N2C2 ADE benchmark to jointly extract medication-related entities and relations in variable-length EHR summaries. JNRF outperforms rolling window BERT with selective pooling by 0.42%, while being twice as fast to train. Compared to state-of-the-art BiLSTM-CRF architectures on the N2C2 ADE benchmark, results show that the proposed approach trains 22 times faster and reduces GPU memory consumption by 1.75 folds, with a reasonable performance tradeoff of 90%, without the use of external tools, hand-crafted rules or post-processing. Given the significant carbon footprint of deep learning models and the current energy crises, these methods could support efficient and cleaner information extraction in EHRs and other types of large-scale document databases.
The Test of Tests: A Framework For Differentially Private Hypothesis Testing

We present a generic framework for creating differentially private versions of any hypothesis test in a black-box way. We analyze the resulting tests analytically and experimentally. Most crucially, we show good practical performance for small data sets, showing that at epsilon = 1 we only need 5-6 times as much data as in the fully public setting. We compare our work to the one existing framework of this type, as well as to several individually-designed private hypothesis tests. Our framework is higher power than other generic solutions and at least competitive with (and often better than) individually-designed tests.
Error-tolerant oblivious transfer in the noisy-storage model

The noisy-storage model of quantum cryptography allows for information-theoretically secure two-party computation based on the assumption that a cheating user has at most access to an imperfect, noisy quantum memory, whereas the honest users do not need a quantum memory at all. In general, the more noisy the quantum memory of the cheating user, the more secure the implementation of oblivious transfer, which is a primitive that allows universal secure two-party and multi-party computation. For experimental implementations of oblivious transfer, one has to consider that also the devices held by the honest users are lossy and noisy, and error correction needs to be applied to correct these trusted errors. The latter are expected to reduce the security of the protocol, since a cheating user may hide themselves in the trusted noise. Here we leverage entropic uncertainty relations to derive tight bounds on the security of oblivious transfer with a trusted and untrusted noise. In particular, we discuss noisy storage and bounded storage, with independent and correlated noise.
Meningkatkan Hasil Belajar Matematika Materi Penjumlahan dengan Mengunakan Media Pohon Pintar Pada Siswa Kelas 1 SDK Wegoknatar

This study aimend to improve the learning Outcomes of addition material using smart tree media for 1students at SDK wegoknatar. This research employed clasroom action research ( CAR) which was designed to help teacher ( recearchers) in dinding and solving learning problems that occur in the classroom.This clasroom action research was conducted in cycles, between cycle 1 and 11. The pacticipants were 12 students. The results of the study indicated that after giving action for two cycles the average score of the test result of improving learning Outcomes in addition material using smart tree media in Mathematics subject had increased. The first meeting of the cycle 1 resulted in a score of 45%, and the second meeting 55% meaning that not yet reached the KKM. In the first meeting of cycle ll, there was an increase in value of 65%, and at the second meeting 95%. Thus, the learning Outcomes from cycle ll increased and were includded in the very good category or fulfilled the KKM. This indicated that improving learning Outcomes in Mathematic addition material by using smart tree media could be further applied to improve student learning achievement, especially in the mathematics subject.
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration

To generalize across tasks, an agent should acquire knowledge from past tasks that facilitate adaptation and exploration in future tasks. We focus on the problem of in-context adaptation and exploration, where an agent only relies on context, i.e., history of states, actions and/or rewards, rather than gradient-based updates. Posterior sampling (extension of Thompson sampling) is a promising approach, but it requires Bayesian inference and dynamic programming, which often involve unknowns (e.g., a prior) and costly computations. To address these difficulties, we use a transformer to learn an inference process from training tasks and consider a hypothesis space of partial models, represented as small Markov decision processes that are cheap for dynamic programming. In our version of the Symbolic Alchemy benchmark, our method's adaptation speed and exploration-exploitation balance approach those of an exact posterior sampling oracle. We also show that even though partial models exclude relevant information from the environment, they can nevertheless lead to good policies.
A review of the development and future challenges of case-based reasoning

Abstract Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.
Optimization of YOLOV7's hyper parameters for simultaneous object detection in satellite imagery

Object detection is crucial for computer vision applications that use satellite imagery, such as precision agriculture, urban planning, and military applications.Recognizing objects in satellite images is challenging due to numerous factors, including the sheer number of objects, the variety in their positions, the range in their sizes, the quality of the lighting, and the existence of a dense background.Background complexity, differences in data capture geometry, geography, and illumination, and an abundance of different types of objects all contribute to making automatic detection in satellite images particularly difficult.There have been many advancements in object detection methods over time, including YOLO and its variations, CNN and its offshoots, DETR and its offshoots, and so on; nonetheless, it is still required to test these methods on the requisite data set to determine their true efficacy.Researchers have investigated the idea of autonomously detecting structures, automobiles, and other things to reduce the risk of human error and speed up the procedure.Improvements in deep learning algorithms and hardware systems have allowed us to accurately identify a broader range of objects in ultra-high-resolution satellite imagery.Through parameter adjustment and analysis of results on the Xview dataset, we determine the most effective technique for multiple item detection and compare it to other models.
Digital Identity and Promotion of Research Works - Case Study of Social Science Researchers at Taiwanese Institutions

This study aims to address the topic of digital identity and its building mechanisms in the digital space, with a focus on explaining the importance of this as a means of promoting scientific content at a time when it is necessary for researchers to have a digital fingerprint that reflects their scientific activities in a variety of digital spaces restricted by search engines. In the end, the nature and features of the Taiwanese social sciences scholars' identity are judged from one side and their effectiveness and activity from another side. This study is divided into a theoretical aspect which revolves around the concept of digital identity and an applied aspect on the social science researchers at Taiwanese universities and their activities on 4 platforms; Research Gate, Google Scholar, ORCID, and Twitter.
The Effects of Middle School Students' Academic Enthusiasm and Lethargy on Academic Achievement and Private Education Expenses

The purpose of this study is to study the effect of middle school students' academic enthusiasm and academic lethargy on private education expenses and academic achievement. To this end, the potential growth model of middle school 1st to 3rd grade is analyzed using 2018 child and youth panel data provided by the Korea Youth Policy Institute, assuming that academic enthusiasm and academic lethargy could be formed to a certain point and changed according to students' environment and conditions rather than staying consistent.&#x0D; As a result of the analysis of this paper, if academic enthusiasm is high, academic achievement is high, and if academic enthusiasm is low, then achievement is low. On the other hand, if academic lethargy is high, academic achievement is low, and if it is low, then achievement is high. The effect of academic enthusiasm and academic lethargy on private education expenditure is not statistically significant. It is judged that the amount of private education expenditure is not a factor that actively changes children's academic enthusiasm or academic lethargy.
An Energy-Aware Agent-Based Resource Allocation Using Targeted Load Balancer for Improving Quality of Service in Cloud Environment

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity.
Rancang Bangun Sistem Pakar Diagnosa Penyakit Tanaman Pisang Canvendish Dengan Metode Forward Chaining

Bananas are part of the horticultural commodity group of fruits which are widely consumed because of their delicious taste and high nutritional content. One of the problems that exist when cultivating canvendish banana plants is the existence of disease disorders that occur in these plants. To prevent this from happening, knowledge about the diseases experienced by these plants is based on the symptoms experienced. Sources of knowledge about the disease and its symptoms can be obtained from an expert. However, the difficulty of consulting with experts in the field of canvendish bananas can be overcome with the help of an expert system to diagnose diseases of canvendish bananas using the Forward Chaining method. The purpose of this research is to help banana farmers or ordinary people to be able to carry out prevention and treatment when bananas are attacked by diseases or pests and overcome existing problems regarding canvendish bananas.
Sustainable and Unsustainable Reading Styles: Typology Attempt

By using the tools of in-depth anthropological research, this work explores the so-called reading styles, i.e. complex and connected reading practices which have a “common denominator” and result from the socially and biographically determined attitude of the individual towards reading and text. Four reading styles are distin-guished and analyzed, and the main distinguishing mark between them runs along the “persistency–non-persistency” axis.
Rancang Bangun Mobile Secure Chat dengan Mengimplementasikan Metodologi SSDLC-Agile dan Kriptografi

Along with the times, the exchange of information is becoming faster. People now use chat applications as a form of communication. With this application, users can easily exchange messages, whether in the form of text, images, audio, or video, without being limited by differences in distance and time. The information circulating varies, ranging from unclassified, private, to maybe confidential. Therefore, an encryption-decryption mechanism is needed so that users can exchange information safely through the mobile chat application. This study aims to implement the Rabin cryptosystem, symmetric encryption, and the use of session keys in a secure chat infrastructure for mobile applications. The test results showed that the combination of cryptographic applications and the Secure SDLC can increase security in application development, in this case for mobile secure chat.
Correlation of Students’ Vocabulary Range to their Listening and Speaking Performance

This study aimed to identify the correlation of vocabulary range to performance in listening and speaking. The study used the descriptive correlation research design to establish the relationship of the students’ vocabulary size to their Listening and Speaking Skills. Pearson's correlation coefficient (r) was used to measure the strength of relationship between the two variables- vocabulary size and Listening and Speaking Skills. Vocabulary Level Test, adapted from Nation (1990) [1], was used to measure vocabulary range, and the listening and speaking skills final mark was used for students’ performance in listening and speaking. Based on the findings, vocabulary range has no significant relationship to listening and speaking skills of students. The result shows that students who have average vocabulary range can still achieve high performance grade in speaking and listening courses.
Design and Development of Management Information Systems at the University of Lampung Library Repository Using the Laravel Framework.

The University of Lampung has a library that is used to support the university's academic activities. One of the services at the Unila library is a digital library service that is used to upload scientific works into the repository, but there are still manual processes that are carried out repeatedly such as collecting requirements files, giving accounts and coordinating with students who require to come to the library continuously. so that the process carried out is not efficient. This study aims to build a management information system on the repository using the Laravel framework which can be used as a solution for the Unila library to make it easier for users to upload scientific works into the digital library repository. The development of this application uses the kanban method, which includes the process of analyzing system requirements, designing, developing to testing. The results of this study succeeded in providing a file upload service for account application requirements and uploading a repository link that was validated by the admin. The service is then tested using the blackbox method which produces the expected output so that it can be concluded that the application passes the test.
IDO: USER EVALUATION OF ONLINE FUNDRAISING MOBILE APPLICATION

Many aid organizations have historically relied on conventional methods of fundraising. The technology evaluation has enabled online funding which recently has gained popularity especially during the COVID-19 pandemic. This article's major discussion will focus on the evaluation of iDo, a mobile app for online fundraising. The evaluation processes were thoroughly covered, and the findings demonstrated that this application is usable and effective. This finding indicates that donations made via the internet are accepted, and the features used in this mobile application can serve as a model for other similar applications.