Reviewerly/exHarmony huggingface dataset:
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.
Audio-driven Talking Face Generation by Overcoming Unintended Information Flow

Audio-driven talking face generation is the task of creating a lip-synchronized, realistic face video from given audio and reference frames. This involves two major challenges: overall visual quality of generated images on the one hand, and audio-visual synchronization of the mouth part on the other hand. In this paper, we start by identifying several problematic aspects of synchronization methods in recent audio-driven talking face generation approaches. Specifically, this involves unintended flow of lip, pose and other information from the reference to the generated image, as well as instabilities during model training. Subsequently, we propose various techniques for obviating these issues: First, a silent-lip reference image generator prevents leaking of lips from the reference to the generated image. Second, an adaptive triplet loss handles the pose leaking problem. Finally, we propose a stabilized formulation of synchronization loss, circumventing aforementioned training instabilities while additionally further alleviating the lip leaking issue. Combining the individual improvements, we present state-of-the-art visual quality and synchronization performance on LRS2 in five out of seven and LRW in six out of seven metrics, and competitive results on the remaining ones. We further validate our design in various ablation experiments, confirming the individual contributions as well as their complementary effects.
OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction. Current state-of-the-art methods fall into an offline pattern, in which each clip independently interacts with text embedding for cross-modal understanding. They usually present that the offline pattern is necessary for RVOS, yet model limited temporal association within each clip. In this work, we break up the previous offline belief and propose a simple yet effective online model using explicit query propagation, named OnlineRefer. Specifically, our approach leverages target cues that gather semantic information and position prior to improve the accuracy and ease of referring predictions for the current frame. Furthermore, we generalize our online model into a semi-online framework to be compatible with video-based backbones. To show the effectiveness of our method, we evaluate it on four benchmarks, \ie, Refer-Youtube-VOS, Refer-DAVIS17, A2D-Sentences, and JHMDB-Sentences. Without bells and whistles, our OnlineRefer with a Swin-L backbone achieves 63.5 J&F and 64.8 J&F on Refer-Youtube-VOS and Refer-DAVIS17, outperforming all other offline methods.
IMPLEMENTASI ALGORITMA TF-IDF DAN SUPPORT VECTOR MACHINE TERHADAP ANALISIS PENDETEKSI KOMENTAR CYBERBULLYING DI MEDIA SOSIAL TIKTOK

Cyberbullying is the act of sending text, images, or videos using the internet, mobile phones, or other devices with the aim of hurting and shaming other people. Cyberbullying is often done through several social media platforms, one of which is through comments on the TikTok application. According to a report by We Are Social, TikTok has 1.4 billion monthly active users aged 18 and above globally. Indonesia currently ranks second in the world in terms of active TikTok users. As a result, the potential for cyberbullying instances will grow as the number of users grows. By using data mining, the public can create a detection system, which can perform analysis on comments in the TikTok application. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The stages passed are to collect comments that are labelled manually. Then, text preprocessing, tokenizing, and weighting were carried out with TF-IDF. Then, implement the Support Vector Machine algorithm to detect cyberbullying comments. This study uses 80% training data and 20% testing data. From the performance results of the Support Vector Machine algorithm, 88% overall accuracy, 88% precision, 96% recall, and 92% f1-score were obtained in detecting cyberbullying comments on social media TikTok.
Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer

Video-based human pose transfer is a video-to-video generation task that animates a plain source human image based on a series of target human poses. Considering the difficulties in transferring highly structural patterns on the garments and discontinuous poses, existing methods often generate unsatisfactory results such as distorted textures and flickering artifacts. To address these issues, we propose a novel Deformable Motion Modulation (DMM) that utilizes geometric kernel offset with adaptive weight modulation to simultaneously perform feature alignment and style transfer. Different from normal style modulation used in style transfer, the proposed modulation mechanism adaptively reconstructs smoothed frames from style codes according to the object shape through an irregular receptive field of view. To enhance the spatio-temporal consistency, we leverage bidirectional propagation to extract the hidden motion information from a warped image sequence generated by noisy poses. The proposed feature propagation significantly enhances the motion prediction ability by forward and backward propagation. Both quantitative and qualitative experimental results demonstrate superiority over the state-of-the-arts in terms of image fidelity and visual continuity. The source code is publicly available at github.com/rocketappslab/bdmm.
On the Robustness of Split Learning against Adversarial Attacks

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy protection, with little investigation into model security. Specifically, by exploring full models, attackers can launch adversarial attacks, and split learning can mitigate this severe threat by only disclosing part of models to untrusted servers.This paper aims to evaluate the robustness of split learning against adversarial attacks, particularly in the most challenging setting where untrusted servers only have access to the intermediate layers of the model.Existing adversarial attacks mostly focus on the centralized setting instead of the collaborative setting, thus, to better evaluate the robustness of split learning, we develop a tailored attack called SPADV, which comprises two stages: 1) shadow model training that addresses the issue of lacking part of the model and 2) local adversarial attack that produces adversarial examples to evaluate.The first stage only requires a few unlabeled non-IID data, and, in the second stage, SPADV perturbs the intermediate output of natural samples to craft the adversarial ones. The overall cost of the proposed attack process is relatively low, yet the empirical attack effectiveness is significantly high, demonstrating the surprising vulnerability of split learning to adversarial attacks.
Fast Algorithms for Energy Games in Special Cases

In this paper, we study algorithms for special cases of energy games, a class of turn-based games on graphs that show up in the quantitative analysis of reactive systems. In an energy game, the vertices of a weighted directed graph belong either to Alice or to Bob. A token is moved to a next vertex by the player controlling its current location, and its energy is changed by the weight of the edge. Given a fixed starting vertex and initial energy, Alice wins the game if the energy of the token remains nonnegative at every moment. If the energy goes below zero at some point, then Bob wins. The problem of determining the winner in an energy game lies in $\mathsf{NP} \cap \mathsf{coNP}$. It is a long standing open problem whether a polynomial time algorithm for this problem exists. We devise new algorithms for three special cases of the problem. The first two results focus on the single-player version, where either Alice or Bob controls the whole game graph. We develop an $\tilde{O}(n^\omega W^\omega)$ time algorithm for a game graph controlled by Alice, by providing a reduction to the All-Pairs Nonnegative Prefix Paths problem (APNP). Thus we study the APNP problem separately, for which we develop an $\tilde{O}(n^\omega W^\omega)$ time algorithm. For both problems, we improve over the state of the art of $\tilde O(mn)$ for small $W$. For the APNP problem, we also provide a conditional lower bound which states that there is no $O(n^{3-\epsilon})$ time algorithm for any $\epsilon > 0$, unless the APSP Hypothesis fails. For a game graph controlled by Bob, we obtain a near-linear time algorithm. Regarding our third result, we present a variant of the value iteration algorithm, and we prove that it gives an $O(mn)$ time algorithm for game graphs without negative cycles, which improves a previous upper bound.
Analysis of Factors Affecting Numeracy Literacy of Students in Class VIII of SMP Darus Salam

This study aims to determine the ability of numeracy value as well as factors that influence the numeracy value of students in class VIII of Darus Salam Junior High School. Quantitative method is the method used in this research. The number of samples in this study were 54 students from a population of 62 students in class VIII of SMP Darus Salam. Questionnaires and tests given to 54 respondents were used as data collection techniques. The results of the data were analyzed by analysis. The results of this study are (1) The numeracy skills of students in grade VIII of SMP Darus Salam fall into the low category with a total percentage of 61.1%; (2) Factors that can affect the numeracy of students in grade VIII of SMP Darus Salam are (a) intelligence; (b) learning motivation; (c) fatigue; (d) family environment; (e) facilities and infrastructure; and (f) teacher competence; and (3) The factor that is more influential on the numeracy of students in grade VIII of SMP Darus Salam is teacher competence.
An ASIC Accelerator for QNN with Variable Precision and Tunable Energy-Efficiency

&lt;p&gt;This paper presents TULIP, a new architecture for a variable precision Quantized Neural Network (QNN) inference. It is designed with the goal of maximizing energy efficiency per classification. TULIP is constructed by arranging a collection of unique processing elements (TULIP-PEs) in a single instruction multiple data (SIMD) fashion. Each TULIP-PE contains binary neurons that are interconnected using multiplexers. Each neuron also has a small dedicated local register connected to it. The binary neurons are implemented as standard cells and used for implementing threshold functions, i.e., an inner-product and thresholding operation on its binary inputs. The neurons can be reconfigured with a single change in the control signals to implement all the standard operations used in a QNN. &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;This paper presents novel algorithms for implementing the operations of a QNN on the TULIP-PEs in the form of a schedule of threshold functions. TULIP was implemented as an ASIC in TSMC 40nm- LP technology. A QNN accelerator that employs a conventional MAC-based arithmetic processor was also implemented in the same technology to provide a fair comparison. The results show that TULIP is 30-50X more energy-efficient than an equivalent design, without any penalty in performance, area, or accuracy. Furthermore, TULIP achieves these improvements without using traditional techniques such as voltage scaling or approximate computing. Finally, the paper also demonstrates how the run- time trade-off between accuracy and energy efficiency is done on the TULIP architecture.&lt;/p&gt;
Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages

We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. Our approach relies on both knowledge distillation and consistency training. The modeling framework leverages knowledge from a large language model (XLMRoBERTa) pre-trained on the source language, with a student-teacher relationship (knowledge distillation). The student model incorporates unsupervised consistency training (with KL divergence loss) on the low-resource target language. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information, and focus on exhibiting the transfer of knowledge from English to Arabic. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic. We show that our modeling approach, while efficient, performs best overall when compared to state-of-the-art approaches like DistilBERT pre-trained on the target language or a supervised model directly trained on labeled data in the target language. Our experiments show that it is enough to learn to recognize entities in English to reach reasonable performance in a low-resource language in the presence of a few labeled samples of semi-structured data. The proposed framework has implications for developing multi-lingual applications, especially in geographies where digital endeavors rely on both English and one or more low-resource language(s), sometimes mixed with English or employed singly.
Inisiasi dan pengembangan produk handsanitizer pada center of excellence Fakultas Farmasi Universitas Hasanuddin sebagai upaya pengembangan usaha intelektual kampus

PPUPIK is a community service program for 3 years. The purpose of this activity is to produce the intellectual products from the Faculty of Pharmacy UNHAS, especially Hand sanitizer and Disinfectant products, so that the Center of Excellence of the Faculty of Pharmacy UNHAS (CoE-FFUH) would become an independent business unit that able to support UNHAS' role as PTNBH. The specific target for this first year is the initiation of the development of a production unit of hand sanitizer and disinfectant products at CoE-FFUH. The initiation consists of the determination of documents for the production process, determination of product packaging material and designs, and also pilot-scale production for limited sales. As for the following year, hopefully, this product will be able to obtain a distribution permit for household health supplies (PKRT) so that the commercialization of licensed products can be carried out legally and able to produce more types of PKRT products.
Hilirisasi produk Smart ENT Unit dinilai sangat bermanfaat bagi mitra Fakultas Kedokteran Universitas Mataram

Medical technology continues to grows, both for services and education, thus, innovation is needed. One of these innovations is through the development of the Smart Ear Nose and Throat (ENT) Unit, especially for ear, nose, throat, head and neck surgery (ENT-HNS). In the field of education, this can support problem-based learning (PBL) systems. The development of hardware, software and electronic systems is required in the actualization of this Smart ENT Unit. The method for this community service is by transferring the science and technology that has been produced by the authors to partners. This is to support the use of technology that has been developed by academician to partners, namely the Mataram University Primary Clinic. The result of this community service is in the form of handing over a smart ENT Unit to partners. When the equipment is handed over, assistance is also provided to partners regarding the operation and maintenance of the unit. Eventually, the unit has been used to delivering the health service and education. The conclusion of this community service is that the transfer of the Science and Technology Smart ENT Unit has been successfully completed by the authors. Based on the partner testimonials, the benefits have been felt in regards to this program.
Analyzing Dataset Annotation Quality Management in the Wild

Data quality is crucial for training accurate, unbiased, and trustworthy machine learning models as well as for their correct evaluation. Recent works, however, have shown that even popular datasets used to train and evaluate state-of-the-art models contain a non-negligible amount of erroneous annotations, biases, or artifacts. While practices and guidelines regarding dataset creation projects exist, to our knowledge, large-scale analysis has yet to be performed on how quality management is conducted when creating natural language datasets and whether these recommendations are followed. Therefore, we first survey and summarize recommended quality management practices for dataset creation as described in the literature and provide suggestions for applying them. Then, we compile a corpus of 591 scientific publications introducing text datasets and annotate it for quality-related aspects, such as annotator management, agreement, adjudication, or data validation. Using these annotations, we then analyze how quality management is conducted in practice. A majority of the annotated publications apply good or excellent quality management. However, we deem the effort of 30\% of the works as only subpar. Our analysis also shows common errors, especially when using inter-annotator agreement and computing annotation error rates.
Perancangan Sistem Informasi Imunisasi di Poliklinik Anak RS Bhayangkara TK I Pusdokkes POLRI

According to the World Health Organization (WHO), in 2018 there were around 20 million children in the world who didn’t get complete immunization, some even didn’t get immunized. In fact, obtaining community immunity (herd immunity) requires high immunization coverage (at least 95%) and is evenly distributed. Therefore, at this time to support the government's program in implementing child immunization, the concentration is needed by all health service facilities, as well as at Bhayangkara TK I Pusdokkes Polri Hospital as a military and police hospital which is assigned the task of supporting the program. For this reason, Bhayangkara TK I Pusdokkes Polri Hospital built an information system, particularly in the children's polyclinic to support and facilitate the implementation of child immunization. to develop this software using SDLC (Software Development Life Cycle) modeling. With this information system, it is hoped that it will assist in the availability of valid and transparent information for the community, especially parents, and officers to facilitate data management regarding immunization such as determining doctor's schedules, reporting, and printing children's immunization certificates.
The Development of Digital Book Media Materials for Asean Countries Learning in Elementary School

This research is motivated by optimizing ASEAN material in sixth grade Elementary School in social studies learning through digital book media, the use of digital book media is used because of the lack of use of learning media during teaching and learning activities, then the use of media can provide interest and motivation to learn in students with a good display. interesting. The purpose of this study is to develop digital book media for ASEAN countries, and to find out the results of the feasibility of the media through experts, teacher users and sixth grade elementary school students. The method used in this research is the D&amp;D method with the ADDIE research procedure. Based on the research results, there are validation assessments from various experts, including social studies material experts, media experts, and linguists. Based on the results of the assessment of material experts, media experts, and linguists, it can be concluded that the Digital Book Media of ASEAN Countries is in the "Very Feasible" interpretation based on the indicators contained in the validation of material experts, media experts, and linguists, namely the suitability of the material, accuracy material, material clarity, presentation support, learning presentation, digital book size, cover design, content design, readability, usability, straightforwardness, communicativeness, language suitability, and the use of terms to be used as media in social studies learning for sixth grade. Meanwhile, the results of the research on teacher and student users received a "Very Feasible" interpretation based on indicators for users including material suitability, material clarity, material usability, design, readability, language suitability, usability, ease of use, materials, media, and language. It can be concluded that this digital book media is very suitable to be used in social studies learning, especially on the material of ASEAN countries in sixth grade elementary school. Keywords: D&amp;D Research, ADDIE Procedure, Digital Book Media, ASEAN Countries
Effectiveness of Early Childhood Online Learning

This research was conducted to examine the effectiveness of the implementation of learning in early childhood networks at Umbuik Nagari Kindergarten. The purpose of this research is to effectively implement learning in early childhood networks at Umbuik Nagari Kindergarten. This study uses a qualitative research design with interview techniques to obtain an overview of the learning system at Umbuik Nagari Kindergarten. In this study, the data collection techniques required used the interview, documentation, and observation methods. Then the data were analyzed descriptively. From the research findings that there is no online learning curriculum, limited supporting infrastructure, the ability of educators who are technologically stuttering, lack of communication between educators and parents, lack of support from parents to carry out the online learning process, many parents who do not have outdated communication tools. information, children's interest in learning decreases.