Reviewerly/exHarmony huggingface dataset:
Temporally Language Grounding With Multi-Modal Multi-Prompt Tuning

The task of temporally language grounding (TLG), aiming to locate a video moment within an untrimmed video that matches a given textual query, has attracted considerable research attention in recent years. Typical retrieval-based TLG methods are inefficient due to their reliance on a large number of pre-segmented candidate moments, while localization-based TLG solutions adopt reinforcement learning, resulting in unstable convergence. Meanwhile, the cutting-edge capabilities of multi-modal architecture, especially pre-training paradigm, have not been fully exploited. Therefore, how to perform TLG task efficiently and stably is a non-trivial task. In this work, we propose a novel TLG solution named Multi-modal Multi-Prompt Tuning (MMPT), which formulates the TLG task as a prompt-based multi-modal problem and integrates multiple sub-tasks to tune the performance. In this way, off-the-shelf pre-trained models can be directly leveraged to achieve more stable performance. Specifically, a flexible multi-prompt strategy is contributed to rewrite the query firstly, which contains the query, the start and end timestamps. Among them, various prompt templates are integrated to enhance robustness. Thereafter, a multi-modal Transformer is adopted to fully learn the multi-modal context. Moreover, we design various sub-tasks to optimize this novel framework including the matching task, localization task and joint learning task. Extensive experiments on two real-world datasets validate the effectiveness and rationality of our proposed solution.
Interfacing On-Demand Quantum Dot Single Photons with a Resonant Atomic Quantum Memory

We demonstrate coherent interactions between quantum dot single photons and a resonant 87 Rb ensemble in the experiment and show an open quantum system analysis. These results could help build fast hybrid quantum networks.
Sistem Informasi Perusahaan Pada E - Bussines

This research was written with the aim of knowing how company information systems work in e-business. This research was written using the library research method or library research using several books and journals as the source of this research. Library research is research that does not use research data such as observations and interviews. The data source used is secondary data from journals and books related to the material for writing this paper. The background of this research is how management information systems are used in online businesses. The results of this study can be seen that the information system is a combination of software, hardware, and telecommunication networks to collect useful data, especially within an organization. Few companies today are built entirely on information technology, such as eBay, Amazon, Alibaba and Google. There are 4 types of known information systems namely operations support systems, management information systems, decision support systems andexecutive information systems
Threats to validity in software engineering research: A critical reflection

In the contemporary body of software engineering literature, some recurrent shortcomings characterize how threats to validity (TTV) are considered in studies. With this position paper, we aim to open a discourse on the current use of TTV sections. The goal of our position is to jointly reflect and systematically improve how we, as a research community, consider TTV in our studies. Based on our personal experience as researchers, authors, reviewers, and editors, we critically reflect on the treatment of TTV in current empirical software engineering literature. We discuss the key shortcomings of TTV consideration, including the failure to acknowledge different types of validity categorizations and the tendency to treat threats just as an afterthought. For each identified problem, we propose a vision for an improved state, intending to catalyze thoughtful engagement and improvements the way our community addresses TTV. We posit there is an urgent need to reconsider how we approach, document, and evaluate TTV in software engineering research.
Enhancing Power Grid Resilience With Blockchain-Enabled Vehicle-to-Vehicle Energy Trading in Renewable Energy Integration

There has been a growing penetration of renewable energy sources (RES) into power grids in recent years. As extreme weather events and cyber-attacks frequently occur, grid resilience has been an important issue. Vehicle-to-Vehicle (V2V) energy trading has great potential to improve the stability and reliability of resilient power grids integrated with high-penetrated RES. V2V energy trading is a distributed peer-to-peer (P2P) application. As a decentralized distributed ledger technology, blockchain is an ideal platform for V2V energy trading. The consensus mechanism of blockchain determines whether the V2V energy trading blockchain (ETB) can improve grid resilience. However, most studies in the ETB currently utilize conventional consensus mechanisms. Due to their substantial computational requirements and communication overhead, these consensus algorithms are not well-suited for real-time service applications like energy trading. We propose a novel BAC-SDS consensus specifically for V2V ETB, thus enabling resilient grids to maximise the use of renewable energy. We propose an Electric Vehicle (EV) leader election based on cryptography and adopt the sharding technique to enhance the system's scalability. Furthermore, our approach ensures the secure transfer of energy and value, contingent upon the condition that all EVs exhibit reasonable behavior and retain the proofs they possess. We implement the V2V ETB on Hyperledger Fabric. The experiments demonstrate (1) that V2V ETB significantly enhances the resilience of the power grid compared to traditional centralized trading models and (2) the consensus mechanism proposed in this paper is better suited for V2V energy trading than existing mechanisms, exhibiting superior performance in terms of security, throughput, and scalability, thus further enhancing the resilience of the power grid.
Using Large Language Models to Shape Social Robots’ Speech

Social robots are making their way into our lives in different scenarios in which humans and robots need to communicate.In these scenarios, verbal communication is an essential element of human-robot interaction.However, in most cases, social robots' utterances are based on predefined texts, which can cause users to perceive the robots as repetitive and boring.Achieving natural and friendly communication is important for avoiding this scenario.To this end, we propose to apply state-of-the-art natural language generation models to provide our social robots with more diverse speech.In particular, we have implemented and evaluated two mechanisms: a paraphrasing module that transforms the robot's utterances while keeping their original meaning, and a module to generate speech about a certain topic that adapts the content of this speech to the robot's conversation partner.The results show that these models have great potential when applied to our social robots, but several limitations must be considered.These include the computational cost of the solutions presented, the latency that some of these models can introduce in the interaction, the use of proprietary models, or the lack of a subjective evaluation that complements the results of the tests conducted.
Investigating Day-to-day Experiences with Conversational Agents by Users with Traumatic Brain Injury

Traumatic brain injury (TBI) can cause cognitive, communication, and psychological challenges that profoundly limit independence in everyday life. Conversational Agents (CAs) can provide individuals with TBI with cognitive and communication support, although little is known about how they make use of CAs to address injury-related needs. In this study, we gave nine adults with TBI an at-home CA for four weeks to investigate use patterns, challenges, and design requirements, focusing particularly on injury-related use. The findings revealed significant gaps between the current capabilities of CAs and accessibility challenges faced by TBI users. We also identified 14 TBI-related activities that participants engaged in with CAs. We categorized those activities into four groups: mental health, cognitive activities, healthcare and rehabilitation, and routine activities. Design implications focus on accessibility improvements and functional designs of CAs that can better support the day-to-day needs of people with TBI.
Multi-wavelength diffractive neural network with weighting method

Recently, the diffractive deep neural network (D 2 NN) has demonstrated the advantages to achieve large-scale computational tasks in terms of high speed, low power consumption, parallelism, and scalability. A typical D 2 NN with cascaded diffractive elements is designed for monochromatic illumination. Here, we propose a framework to achieve the multi-wavelength D 2 NN (MW-D 2 NN) based on the method of weight coefficients. In training, each wavelength is assigned a specific weighting and their output planes construct the wavelength weighting loss function. The trained MW-D 2 NN can implement the classification of images of handwritten digits at multi-wavelength incident beams. The designed 3-layers MW-D 2 NN achieves a simulation classification accuracy of 83.3%. We designed a 1-layer MW-D 2 NN. The simulation and experiment classification accuracy are 71.4% and 67.5% at RGB wavelengths. Furthermore, the proposed MW-D 2 NN can be extended to intelligent machine vision systems for multi-wavelength and incoherent illumination.
Applications of Generative Adversarial Networks in Single Image Datasets

One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible.Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization.ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.
Hybrid AES-Modified ECC Algorithm for Improved Data Security over Cloud Storage

As cloud computing has been increasingly used by businesses to address their data storage and processing needs. The private data can be stored and retrieved by the end user through remote storage using an affordable Internet connection in the cloud computing environment. The user may access the information whenever they want, from wherever. However, the data transmitted through the cloud is not always secure and data integrity and authentication may be compromised as the end user is only able to access the data through a third party. Cloud computing also enables numerous people to access information simultaneously over separate Internet connections, which might increase the risk of information loss or leakage. Several cryptographic methods like DES, 3DES, Blowfish, RSA, Diffie-Hellman have been developed to guarantee the safety and privacy of stored data. Since the business clients hesitate to adopt the cloud due to its less security. This paper suggests a method for exchanging cloud data that is both secure and efficient. The proposed method is mainly focused on higher security for the cloud computing platform by applying hybrid Modified Elliptic Curve Cryptography (MECC) method with the Advanced Encryption Standard (AES). In comparison to other methods, the hybrid ECC-AES strategy takes less time to encrypt and decode data owing to its reduced key size.
Technologies of Power in Digital Inclusion

This study moves beyond the conventional focus on technology access, adoption, and participation in communities, and instead presents a perspective on digital inclusion as a complex process of both empowerment and subjugation. Drawing on Foucault’s theorization on subjectivity and power, our case study of community health workers in rural India reveals that the adoption of an mHealth application simultaneously subordinated and strengthened the subjectivity of the community health workers. The study explicates the process through which the health workers oscillated between the enhancement of their individual efficacy, and their submission to institutionalized power as facilitated by the mHealth application during their everyday work practices. Thus, by shifting the focus from “who” is digitally included to “how” they become digitally included, our research provides a fresh perspective that enriches and deepens the discourse of digital inclusion. It generates both theoretical and practical implications for anyone interested in understanding digital inclusion from a more granulated and practice-based perspective.
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.
Findings from the Bambara - French Machine Translation Competition (BFMT 2023)

Ninoh Agostinho Da Silva, Tunde Oluwaseyi Ajayi, Alexander Antonov, Panga Azazia Kamate, Moussa Coulibaly, Mason Del Rio, Yacouba Diarra, Sebastian Diarra, Chris Emezue, Joel Hamilcaro, Christopher M. Homan, Alexander Most, Joseph Mwatukange, Peter Ohue, Michael Pham, Abdoulaye Sako, Sokhar Samb, Yaya Sy, Tharindu Cyril Weerasooriya, Yacine Zahidi, Sarah Luger. Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023). 2023.
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective

Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs). Among various KD approaches, Intermediate Layer Distillation (ILD) has been a de facto standard KD method with its performance efficacy in the NLP field. In this paper, we find that existing ILD methods are prone to overfitting to training datasets, although these methods transfer more information than the original KD. Next, we present the simple observations to mitigate the overfitting of ILD: distilling only the last Transformer layer and conducting ILD on supplementary tasks. Based on our two findings, we propose a simple yet effective consistency-regularized ILD (CR-ILD), which prevents the student model from overfitting the training dataset. Substantial experiments on distilling BERT on the GLUE benchmark and several synthetic datasets demonstrate that our proposed ILD method outperforms other KD techniques. Our code is available at https://github.com/jongwooko/CR-ILD.
Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning

Due to their similarity-based learning objectives, pretrained sentence encoders often internalize stereotypical assumptions that reflect the social biases that exist within their training corpora. In this paper, we describe several kinds of stereotypes concerning different communities that are present in popular sentence representation models, including pretrained next sentence prediction and contrastive sentence representation models. We compare such models to textual entailment models that learn language logic for a variety of downstream language understanding tasks. By comparing strong pretrained models based on text similarity with textual entailment learning, we conclude that the explicit logic learning with textual entailment can significantly reduce bias and improve the recognition of social communities, without an explicit de-biasing process.
Can Pretrained Language Models (Yet) Reason Deductively?

Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks.Their good performance has led the community to believe that the models do possess a modicum of reasoning competence rather than merely memorising the knowledge.In this paper, we conduct a comprehensive evaluation of the learnable deductive (also known as explicit) reasoning capability of PLMs.Through a series of controlled experiments, we posit two main findings.(i) PLMs inadequately generalise learned logic rules and perform inconsistently against simple adversarial surface form edits. (ii) While the deductive reasoning fine-tuning of PLMs does improve their performance on reasoning over unseen knowledge facts, it results in catastrophically forgetting the previously learnt knowledge.Our main results suggest that PLMs cannot yet perform reliable deductive reasoning, demonstrating the importance of controlled examinations and probing of PLMs' deductive reasoning abilities; we reach beyond (misleading) task performance, revealing that PLMs are still far from robust reasoning capabilities, even for simple deductive tasks.
Assistive Recipe Editing through Critiquing

There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied on small paired recipe data (e.g., a recipe paired with a similar one that satisfies a dietary constraint). However, pre-trained language models generate inconsistent or incoherent recipes, and paired datasets are not available at scale. We address these deficiencies with RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques. The model is trained for recipe completion to learn semantic relationships within recipes. Our work’s main innovation is our unsupervised critiquing module that allows users to edit recipes by interacting with the predicted ingredients; the system iteratively rewrites recipes to satisfy users’ feedback. Experiments onthe Recipe1M recipe dataset show that our model can more effectively edit recipes compared to strong language-modeling baselines, creating recipes that satisfy user constraints and are more correct, serendipitous, coherent, and relevant as measured by human judges.
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning

Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.However, the joint training scheme of multitask learning suffers from inter-task interference, and diagnosis prediction among the multiple tasks has the generalizability issue due to rare diseases or unseen diagnoses.To solve these challenges, we propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning.We also incorporate semantic task information to improve the generalizability of our task-conditioned multitask model.Experiments on early and discharge notes extracted from the real-world MIMIC database show our method can achieve better performance on multitask patient outcome prediction than strong baselines in most cases.Besides, our method can effectively handle the scenario with limited information and improve zero-shot prediction on unseen diagnosis categories.
A dual-stage transformer and MLP-based network for breast ultrasound image segmentation

Automatic segmentation of breast lesions from ultrasound images plays an important role in computer-aided breast cancer diagnosis. Many deep learning methods based on convolutional neural networks (CNNs) have been proposed for breast ultrasound image segmentation. However, breast ultrasound image segmentation is still challenging due to ambiguous lesion boundaries. We propose a novel dual-stage framework based on Transformer and Multi-layer perceptron (MLP) for the segmentation of breast lesions. We combine the Swin Transformer block with an efficient pyramid squeezed attention block in a parallel design and introduce bi-directional interactions across branches, which can efficiently extract multi-scale long-range dependencies to improve the segmentation performance and robustness of the model. Furthermore, we introduce tokenized MLP block in the MLP stage to extract global contextual information while retaining fine-grained information to segment more complex breast lesions. We have conducted extensive experiments with state-of-the-art methods on three breast ultrasound datasets, including BUSI, BUL, and MT_BUS datasets. The dice coefficient reached 0.8127 ± 0.2178, and the intersection over union reached 0.7269 ± 0.2370 on benign lesions when the Hausdorff distance was maintained at 3.75 ± 1.83. The dice coefficient of malignant lesions is improved by 3.09% for BUSI dataset. The segmentation results on the BUL and MT_BUS datasets also show that our proposed model achieves better segmentation results than other methods. Moreover, the external experiments indicate that the proposed model provides better generalization capability for breast lesion segmentation. The dual-stage scheme and the proposed Transformer module achieve the fine-grained local information and long-range dependencies to relieve the burden of radiologists.
Politeness Strategies of the Main Characters of Pride and Prejudice Movie

In conversations, people tend to speak politely to keep their interlocutors’ feelings so the conversation can be maintained in harmony. They follow several rules and principles that already exist in society. They apply politeness strategies so that the communication can run effectively. This study aims to discover the politeness strategies performed by the main characters of Pride and Prejudice movie and explain the pattern of different types of strategies used in the movie. The data were taken from the movie’s transcript as the primary source. A descriptive qualitative method was used to analyze politeness strategies proposed by Brown and Levinson. Based on the analysis, the politeness strategies were used by the main characters of Pride and Prejudice movie. The types of politeness strategies employed are bald-on record, positive politeness, negative politeness, and off-record strategy. The most frequent strategy was positive politeness which occurred 98 times from 149 data. The patterns of politeness strategies applied are in declarative, interrogative, imperative, and exclamatory sentences. All types of sentences contain bald-on record, positive politeness, negative politeness, and off-record strategies. By using politeness strategies, people appreciate their interlocutors so that the conversation will be well-maintained. Besides, this study contributes to English language teaching in comprehending cross-cultural understanding (CCU) through the movie, especially in western culture.