Reviewerly/exHarmony huggingface dataset:
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models

Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is the lack of domain-specific training data. While DR models can learn from large-scale public datasets like MS MARCO through transfer learning, evidence shows that not all DR models and domains can benefit from transfer learning equally. Recently, some researchers have resorted to large language models (LLMs) to improve the zero-shot and few-shot DR models. However, the hard prompts or human-written prompts utilized in these works cannot guarantee the good quality of generated weak queries. To tackle this, we propose soft prompt tuning for augmenting DR (SPTAR): For each task, we leverage soft prompt-tuning to optimize a task-specific soft prompt on limited ground truth data and then prompt the LLMs to tag unlabeled documents with weak queries, yielding enough weak document-query pairs to train task-specific dense retrievers. We design a filter to select high-quality example document-query pairs in the prompt to further improve the quality of weak tagged queries. To the best of our knowledge, there is no prior work utilizing soft prompt tuning to augment DR models. The experiments demonstrate that SPTAR outperforms the unsupervised baselines BM25 and the recently proposed LLMs-based augmentation method for DR.
FACTS: Facial Animation Creation using the Transfer of Styles

The ability to accurately capture and express emotions is a critical aspect of creating believable characters in video games and other forms of entertainment. Traditionally, this animation has been achieved with artistic effort or performance capture, both requiring costs in time and labor. More recently, audio-driven models have seen success, however, these often lack expressiveness in areas not correlated to the audio signal. In this paper, we present a novel approach to facial animation by taking existing animations and allowing for the modification of style characteristics. Specifically, we explore the use of a StarGAN to enable the conversion of 3D facial animations into different emotions and person-specific styles. We are able to maintain the lip-sync of the animations with this method thanks to the use of a novel viseme-preserving loss.
PAID: Perturbed Image Attacks Analysis and Intrusion Detection Mechanism for Autonomous Driving Systems

Modern Autonomous Vehicles (AVs) leverage road context information collected through sensors (e.g., LiDAR, radar, and camera) to support the automated driving experience. Once such information is collected, a neural network model predicts subsequent actions that the AV executes. However, state-of-the-art research findings have shown the possibility that an attacker can compromise the accuracy of the neural network model in predicting tasks. Indeed, mispredicting the subsequent actions can cause harmful consequences to the road user’s safety.
An Improved Approach in Cloud-Assisted IoT Infrastructure with Anonymous Key Issuing

Lightweight cryptography is a technique in which a scheme is proposed to have a minimum computation overhead on the end users. Indeed, this is a required solution for the many real-time applications involving cloud servers, IoT, etc. CP-ABE (Ciphertext Policy Attribute Based Encryption) is a method intended for efficient multicast. AKI (Anonymous Key Issuing) is a feature in which honest-cum-curious authorities cannot learn the credentials of users to whom a secret key is given. Recently, Zhishuo et al. proposed an efficient scheme to resolve the lightweight CP-ABE scheme for the IoT-based environment. However, as it fails to provide the AKI feature, thus this research has the all-in-one feature, i.e. constant length ciphertext, AKI, and single authority.
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation

Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental concerns. Yet, progress on model efficiency has been impeded by practical challenges in model evaluation and comparison. For example, hardware is challenging to control due to disparate levels of accessibility across different institutions. Moreover, improvements in metrics such as FLOPs often fail to translate to progress in real-world applications. In response, we introduce Pentathlon, a benchmark for holistic and realistic evaluation of model efficiency. Pentathlon focuses on inference, which accounts for a majority of the compute in a model's lifecycle. It offers a strictly-controlled hardware platform, and is designed to mirror real-world applications scenarios. It incorporates a suite of metrics that target different aspects of efficiency, including latency, throughput, memory overhead, and energy consumption. Pentathlon also comes with a software library that can be seamlessly integrated into any codebase and enable evaluation. As a standardized and centralized evaluation platform, Pentathlon can drastically reduce the workload to make fair and reproducible efficiency comparisons. While initially focused on natural language processing (NLP) models, Pentathlon is designed to allow flexible extension to other fields. We envision Pentathlon will stimulate algorithmic innovations in building efficient models, and foster an increased awareness of the social and environmental implications in the development of future-generation NLP models.
Hierarchical Semantic Perceptual Listener Head Video Generation: A High-performance Pipeline

In dyadic speaker-listener interactions, the listener's head reactions along with the speaker's head movements, constitute an important non-verbal semantic expression together. The listener Head generation task aims to synthesize responsive listener's head videos based on audios of the speaker and reference images of the listener. Compared to the Talking-head generation, it is more challenging to capture the correlation clues from the speaker's audio and visual information. Following the ViCo baseline scheme, we propose a high-performance solution by enhancing the hierarchical semantic extraction capability of the audio encoder module and improving the decoder part, renderer and post-processing modules. Our solution gets the first place on the official leaderboard for the track of listening head generation. This paper is a technical report of ViCo@2023 Conversational Head Generation Challenge in ACM Multimedia 2023 conference.
Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks

The paradigm of federated learning (FL) to address data privacy concerns by locally training parameters on resource-constrained clients in a distributed manner has garnered significant attention. Nonetheless, FL is not applicable when not all clients within the coverage of the FL server are registered with the FL network. To bridge this gap, this paper proposes joint learner referral aided federated client selection (LRef-FedCS), along with communications and computing resource scheduling, and local model accuracy optimization (LMAO) methods. These methods are designed to minimize the cost incurred by the worst-case participant and ensure the long-term fairness of FL in hierarchical Internet of Things (HieIoT) networks. Utilizing the Lyapunov optimization technique, we reformulate the original problem into a stepwise joint optimization problem (JOP). Subsequently, to tackle the mixed-integer non-convex JOP, we separatively and iteratively address LRef-FedCS and LMAO through the centralized method and self-adaptive global best harmony search (SGHS) algorithm, respectively. To enhance scalability, we further propose a distributed LRef-FedCS approach based on a matching game to replace the centralized method described above. Numerical simulations and experimental results on the MNIST/CIFAR-10 datasets demonstrate that our proposed LRef-FedCS approach could achieve a good balance between pursuing high global accuracy and reducing cost.
Deep learning for classification of noisy QR codes

We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR codes (Quick Response codes) fall into this category of abstract images: one bit corresponding to one encoded character, QR codes were not designed to be decoded manually. To understand the limitations of a deep learning-based model for abstract image classification, we train an image classification model on QR codes generated from information obtained when reading a health pass. We compare a classification model with a classical (deterministic) decoding method in the presence of noise. This study allows us to conclude that a model based on deep learning can be relevant for the understanding of abstract images.
Administering Online Writing Assessments in English Language Teaching: Challenges and Solutions

In the digital age, assessment becomes one of the most important factors to consider when planning English courses virtually. In actuality, however, many English teachers struggle to manage and administer online writing assessments efficiently and successfully. This article will give a general overview of the difficulties associated with online writing assessments and their workable solutions. Qualitative principles of data gathering and analysis were applied to provide answers to the issues and served as valuable references. The finding reveals three main problems with online writing assessments: academic integrity, limited facilities and infrastructure, and a lack of digital literacy. However, despite the drawbacks of online writing assessments, students are gaining more benefits and knowledge in digitalizing education. Thus, practical solutions such as using task performance, particular strategies to overcome technical issues, and increasing teacher awareness of digital literacy must be properly implemented.
Synchronous and asynchronous online teaching of a foreign language

The article evaluates the influence of synchronous and asynchronous types of learning in the online environment on mastering a foreign language. The field of online learning provides a flexible and efficient learning environment that makes it easy for students to form, explore and learn information. The author considers the characteristic features of synchronous and asynchronous types of learning, as well as their joint, so-called hybrid use in the educational process. The hybrid online environment combines synchronous learning with an asynchronous set of electronic activities, as it combines simultaneity and non-simultaneity. The article provides examples of the practical use of these types of training in educational activities. The author concludes that the combination of synchronous and asynchronous foreign language teaching creates an ideal environment for electronic language learning and increases the effectiveness of foreign language learning.
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

Abstract We establish a novel relation between delete-free planning, an important task for the AI planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.
Detection of Poikilocytosis using Segmentation based Approach

Detecting a sickle cell manually is not an impossible job but it is a tedious one where the image processing is applied. It involves the analysis of cells by detecting the cells to identify the disease for proper treatment. We can make accurate detection of sickle cells by conducting a proper segmentation of such cells. Since we are dealing with the structural framework of the cell morphology which plays a crucial part in separating sickle cells from healthy blood cells and they differ from each other by structural integrity. This will substantially speed up the segregation and identification of sickle cells in healthy human blood cells. Standard validation strategies are adopted to improve the performance and yield of various methods. The methodology and techniques used in this paper are investigated and analyzed through this model.
PEMANFAATAN INTERNET SEBAGAI MEDIA BELAJAR KEPADA GURU TAMAN KANAK KANAK DI TANJUNG PIAYU KOTA BATAM

Information and communication technology is growing rapidly. Information technology enters various fields ranging from the world of offices to medicine, business, the arts, culture, and many other fields, including education. In the world of education, there are many ways to take advantage of information technology, from making learning media to the learning process to presenting the material in the learning itself. All can rely on information technology; all that remains is to choose which technology is suitable to use. New problems arise when teachers cannot utilize the media. They continue to rely on traditional and outdated learning media. So it is certain that the material produced is less interesting and seems boring. In addition, today's students focus more on what they see. If you provide a special attraction, it will automatically impress them. In this training, the target is PAUD teachers. They will be given training on how to use the internet as a learning resource for their students. Many internet sites can be used as learning media. The material that will be delivered can be in the form of getting to know letters, knowing numbers, getting to know animals, getting to know plants, and much more. If you have to present material on the internet, of course you can't display it right away because it will take time. This is due to the advertisements that appear and other disturbances that must be transferred to the display media, such as Microsoft PowerPoint. Methods like these must be mastered by a teacher. The target of this training itself is to be able to use the internet as a learning medium and also use it for various other needs. This service activity was successfully carried out, and the results can be seen in the increased skills of the trainees in utilizing the internet as a learning medium. With this skill, it will certainly have an impact on the quality of learning and the material provided. The material provided becomes more varied and not boring.
Resenha do livro Intelligence Sharing, Transnational Organized Crime and Multinational Peacekeeping

Resenha de: Chido, Diane E., 2018. Intelligence Sharing, Transnational Organized Crime and Multinational Peacekeeping. Palgrave MacMillan. 110p. ISBN: 9783319711829. https://doi.org/10.1007/978-3-319-71183-6
Unsupervised Domain Adaptation with Transformer-Based GAN for Semantic Segmentation of High-Resolution Remote Sensing Images

&lt;p&gt;Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) has become a research hotspot in geoscience research. Recently, transformer, with its ingenious and versatile architecture, has been successfully applied in a wide range of RS tasks. Despite some attempts to integrate transformers with convolutional neural networks (CNNs) in UDA, existing works appear ineffective at leveraging transformer structures, which is evidenced by the large performance gap between these existing UDA methods and supervised learning-based methods. In this work, we adopt a radical and subversive stance by completely overhauling the UDA framework using transformer structures to bridge the gap across different domains based on generative adversarial networks (GAN). To this end, we first present a transformer-based generator (TransG) consisting of two components, namely a transformer-based encoder (TransEn) and a transformer-based decoder (TransDe) equipped with global-local transformer blocks (GLTB). The TransEn can extract contextual details from high-resolution RS images more efficiently as compared to CNN-based encoders, whereas the TransDe restores accurate spatial and local details simultaneously during the segmentation process. Furthermore, a novel transformer-based discriminator (TransDx) is proposed to encourage TransG to learn better domain-invariant representations. The TransG and TransDx together constitute the proposed transformer-based GAN, named DA-TransGAN, which provides an easily comprehensible UDA framework that can be extended to general UDA tasks in a straightforward manner. Extensive experiments on two large-scale fine-resolution benchmark datasets, namely ISPRS Potsdam and Vaihingen, highlight the effectiveness and superiority of the proposed DA-TransGAN as compared to the state-of-the-art UDA methods.&lt;/p&gt;
Block Crime: Criminal Incidence Detection Using Facial Recognition Based on Concepts of Blockchain

With advancements in technology, a high criminal rate has been observed which has led to a major concern with respect to issues of security. Every individual tends to possess a specific characteristic and hence can therefore be identified through facial recognition. One of the primary usages of facial recognition falls under the application of video surveillance followed by biometric technology. This technology is responsible to extract features and further identify the human face in places of security to diminish the alarming criminal rates. Hence, the primary aim of the research study is to automate this process using CCTV cameras that are installed in public and private places. The footage captured in the CCTV could further be used to detect the suspects of a crime scene and notify the police through mail. The detection of the criminal would however be done by identifying and matching the face of the suspect with respect to the database repository that would contain information of individuals based on their Aadhar card. Hence, the photographs captured on the camera would be processed and sent to police servers along with individual details. The entire deployment of police servers is proposed to be executed on a website whose transactions are monitored on a blockchain server. The backend of this server utilizes Ganache to store such blocks of ethers. Since the face detection is done in real time; OpenCV is used to automate the entire process of facial recognition system.
Evaluasi Pemanfaatan Koleksi E-book dan E-journal Terhadap Kinerja Studi Mahasiswa UAJY Selama Masa Pandemi Covid (2020-2021)

Since March 24, 2020 Atma Jaya University Yogyakarta has started implementing distance learning so that teaching and learning activities is carried out online. UAJY supports units have adjusted the policy by providing online services. This of course will make it difficult for libraries to serve their collections. Service that was originally carried out face-to-face must be done online, such as book lending services, which can no longer be done in person. In order to continue to be able to assist users in accessing the information provided, library services are emphasized on online services such as electronic books (e-books) and electronic journals (e-journals). The presence of a collection of e-books and e-journals is expected to be able to meet the needs of users in finding reference sources. This research will be prioritized to explore answers to the following questions: How do students perceive online services to support to study performance? To what extent is the level of use of online collections (e-books and e-journals) in supporting to study performance? The method of this research is descriptive quantitative. Quantitative research with descriptive format aims to explain, summarize various conditions, situations, or variables that arise in the community that are the object of research based on what happened.
Characterization and classification of binary linear codes with various hull dimensions from an improved mass formula

The hull of a linear code over finite fileds is the intersection of the code and its dual, which was introduced by Assmus and Key to classify finite projective planes. The main purpose of this paper is to obtain the closed mass formula for binary linear codes with various hull dimensions, which simplifies the mass formula obtained by Sendrier in (SIAM J. Discrete Math., 10(2): 282-293, 1997). We show that almost all binary linear codes with ℓ-dimensional hull are odd-like codes with odd-like duals for fixed ℓ. We also study the largest minimum distance of a binary linear [n, k] code with ℓ-dimensional hull. Most importantly, we give a complete classification of binary linear codes with various hull dimensions for n ≤ 12 using a building-up construction, which is confirmed by double-checking with our mass formula. We also give the classification of optimal binary linear [n, k] codes with various hull dimensions for n ≤ 13. Combining with known results, we obtain the classification of (optimal) binary linear codes with small parameters.
Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract)

This paper proposes a novel method, Enhanced Discrete Multi-modal Hashing (EDMH), which learns binary codes and hash functions simultaneously from the pairwise similarity matrix of data for large-scale cross-view retrieval. EDMH distinguishes itself from existing methods by considering not just the binarization constraint but also the balance and decorrelation constraints. Although those additional discrete constraints make the optimization problem of EDMH look a lot more complicated, we are actually able to develop a fast iterative learning algorithm in the alternating optimization framework for it, as after introducing a couple of auxiliary variables each subproblem of optimization turns out to have closed-form solutions. It has been confirmed by extensive experiments that EDMH can consistently deliver better retrieval performances than state-of-the-art MH methods at lower computational costs.
Sum of Exponential Model for Fitting Data

As an approach to feature estimation, exponential fitting has attracted research interests in mathematical modeling. Semantic networks are used for numerous applications in computers, physics, and biology. However, such applications may have fitting troubles with various mathematical tools. Therefore, we present a novel method of fitting 2n data points of a signal to a sum of n exponential functions. The experiments proved that the proposed method operated well for linear and nonlinear functions, as its algorithm was straightforward, practical, and easy to determine. At the same time, the computational intricacy was considerably low, which has specific worth in use.