Reviewerly/exHarmony huggingface dataset:
Pendampingan Pengolahan Sistem Informasi Pendataan dokumen Sekolah Berbasis Komputerisasi di SMK Negeri 3 Lhokseumawe

The document data collection process is one of the administrative management processes that are still using manually for now. this is for such administrative management takes a long time so it is not effective and efficient. To overcome these problems, efforts are needed to provide knowledge about computer applications. With the existence of an information system for computerized document administration will facilitate the work properly and effectively. The problem raised in this service is how to make computer applications in management information systems administratively and computerized document data collection and can be neatly arranged for all documents. The second problem is how to implement a management information system in a directed manner so that the documents you are looking for are arranged properly. The problem so far is that the management information system data processing mostly uses a manual processing system, still uses a ledger and is stored in a cupboard. The system, of course, has many weaknesses and in terms of administration, poor presentation, sub-optimal service, and low data quality. The purpose of this service is to help manage data and finances to be more effective and efficient. So that the information from the data entered can be conveyed properly. The result of this service is an explanation of how the Computerized Document Data Collection Administration Management Information System with various features can be used to manage student, teacher, staff data in managing all files.
UPAYA PENGUATAN LITERASI INFORMASI DAN KOMUNIKASI INTERNASIONAL UNTUK MENINGKATKAN EFEKTIVITAS PENGAJARAN

INFORMATION LITERATION AND INTERPERSONAL COMMUNICATION STRENGTHENNING EFFORTS TO IMPROVE TEACHING EFFECTIVENESSThis study was intended to find strategic and method to increase teaching effectiveness through information literation and interpersonal variables. The study utilyzed a correlational approach and SITOREM analysis which consist of two independent variables, they were information literacy and interpersonal communication, and one dependent variable that was teaching effectiveness. This research was conducted at a private vocational school in Rancabungur District in 2021. The population in the research was 155 teachers and 112 samples were selected using proportional random sampling. The method used in this study was a survey and data analysis techniques, with statistical tests of correlation and simple liniear regression and multiple linear. The results were as follow; firstly, there was a significant positive relationship between literation information (X1) and Teaching Effectiveness (Y) variables where correlation coefficient ry1 = 0,494 and determination coefficient r2 = 0,244. Secondly, There was a significant positive relationship between Interpersonal communication variable (X2) with Learning Activity (Y), where the correlation coefficient ry2 = 0.788 and determination coefficient r2 = 0.620. Thirdly, there was a significant positive relation between Literation Information (X1) and Interpersonal Communication variable altogether compare with Learning Effectiveness (Y) which was pointed as correlation Coefficient ry12 = 0.825 and also Determination Coefficient r2y12 = 0.681. Based on those findings, it could be concluded that Learning Effectiveness could be risen up by strengthening Information Literation and Interpersonal Communication.
Students’ Difficulties in Acquiring English Writing

Writing skill is an essential skill which includes all knowledge and abilities to express one’s ideas through the written word. However, English writing has always been a challenge to master by the students, especially students in junior high schools. The main objective of this research is focused on finding out student’s difficulties in acquiring writing English. This study used case study with descriptive qualitative research. About 26 students are as the participants of the research. The data gained through document analysis, questioners and interview. The result indicates students had a significant problem in writing English. The findings revealed that the most significant problems are because of their lack of vocabulary mastery and language used. They assumed that they had limited knowledge in constructing sentence grammatically and also their lack of vocabulary mastery made them felt confused in choosing the word. Besides, they also were not interested in English.&#x0D;
The Implementation on Genre Approach on Students' Skill in Writing Anecdote Text at Senior High School

This study aims is to determine the results of the development of interactive multimedia-based anecdotal text learning modules by students State of Vocational High School 1 of Batang Toru. The module developed is in the form of a digital module. This type of research is development research based on the 4-D development model. With the subject of the trial consisting of design experts, material experts, Indonesian language teachers, and class X students of State of Vocational High School 1 Batang Toru. The results showed that (1) the validation of the material experts included the content eligibility is 88.0% in the good category, the feasibility of presenting is 93.7% learning module Very good category, Interactive Mutimedia aspects is 95.8% very good category, language assessment aspects 92, 5% category is very good, and aspects of project based learning is 83.3% Good category (2) validation of design experts includes initial design is 89.2% with good category, content design is 83.9% good category, digital module typography is 83.3 category good, and 87.5% illustration in good category. (3) limited trials obtained a score is 82.79% with good criteria. (4)Expanded trials obtained a score is 80.04% in the good category. The difference obtained was 13 from before and after using a digital module. This research has implications, namely the module developed will contribute to the practice, especially in the implementation of the learning process for teachers as additional teaching materials to provide convenience to the material being taught can be independent or classical.&#x0D; &#x0D; Keywords : Digital Modules; Anecdotal Texts.&#x0D;
EXPERIMENT ON PRODUCING DISPARITY MAPS FROM AERIAL STEREO IMAGES USING UNSUPERVISED AND SUPERVISED METHODS

Abstract. Recent advancement in hardware and software provides the possibility of realizing full automation in stereo-image tasks. This paper investigated disparity map generation from aerial images with different methods: unsupervised method and supervised methods. The datasets were from aerial stereo dense matching benchmark dataset for deep learning in ISPRS 2021: Vaihingen dataset and the WHU MVS/Stereo Dataset released in the CVPR 2020. Two neural networks: GC-net and PSMnet have been trained with the Vaihingen dataset and the WHU MVS/Stereo Dataset. With unsupervised methods, stereo block matching(StereoBM) and Stereo Semi-Global Matching (StereoSGM) methods from the OpenCV were studied. We selected seven image pairs from the Vaihingen dataset and six image pairs from the WHU dataset for testing and evaluation. Difficulty scenes such as textureless areas, reflective surfaces, and repetitive patterns were also included in our study. The performance from different methods was compared by both visualization and quantitative means. The advantages and disadvantages are presented.
Automatic dense annotation of large-vocabulary sign language videos

Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training data. One key challenge in the usability of such data is the lack of sign annotations. Previous work exploiting such weakly-aligned data only found sparse correspondences between keywords in the subtitle and individual signs. In this work, we propose a simple, scalable framework to vastly increase the density of automatic annotations. Our contributions are the following: (1) we significantly improve previous annotation methods by making use of synonyms and subtitle-signing alignment; (2) we show the value of pseudo-labelling from a sign recognition model as a way of sign spotting; (3) we propose a novel approach for increasing our annotations of known and unknown classes based on in-domain exemplars; (4) on the BOBSL BSL sign language corpus, we increase the number of confident automatic annotations from 670K to 5M. We make these annotations publicly available to support the sign language research community.
Leveraging the HW/SW Optimizations and Ecosystems that Drive the AI Revolution

This paper presents a state-of-the-art overview on how to architect, design, and optimize Deep Neural Networks (DNNs) such that performance is improved and accuracy is preserved. The paper covers a set of optimizations that span the entire Machine Learning processing pipeline. We introduce two types of optimizations. The first alters the DNN model and requires NN re-training, while the second does not. We focus on GPU optimizations, but we believe the presented techniques can be used with other AI inference platforms. To demonstrate the DNN model optimizations, we improve one of the most advanced deep network architectures for optical flow, RAFT arXiv:2003.12039, on a popular edge AI inference platform (Nvidia Jetson AGX Xavier).
Decentralized Risk-Aware Tracking of Multiple Targets

We consider the setting where a team of robots is tasked with tracking multiple targets with the following property: approaching the targets enables more accurate target position estimation, but also increases the risk of sensor failures. Therefore, it is essential to address the trade-off between tracking quality maximization and risk minimization. In our previous work, a centralized controller is developed to plan motions for all the robots -- however, this is not a scalable approach. Here, we present a decentralized and risk-aware multi-target tracking framework, in which each robot plans its motion trading off tracking accuracy maximization and aversion to risk, while only relying on its own information and information exchanged with its neighbors. We use the control barrier function to guarantee network connectivity throughout the tracking process. Extensive numerical experiments demonstrate that our system can achieve similar tracking accuracy and risk-awareness to its centralized counterpart.
Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics

Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation~(MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation~(DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices even under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight~(LoS) propagation paths.
Equivalence between Time Series Predictability and Bayes Error Rate

Predictability is an emerging metric that quantifies the highest possible prediction accuracy for a given time series, being widely utilized in assessing known prediction algorithms and characterizing intrinsic regularities in human behaviors. Lately, increasing criticisms aim at the inaccuracy of the estimated predictability, caused by the original entropy-based method. In this brief report, we strictly prove that the time series predictability is equivalent to a seemingly unrelated metric called Bayes error rate that explores the lowest error rate unavoidable in classification. This proof bridges two independently developed fields, and thus each can immediately benefit from the other. For example, based on three theoretical models with known and controllable upper bounds of prediction accuracy, we show that the estimation based on Bayes error rate can largely solve the inaccuracy problem of predictability.
Fundamental Limitation on the Detectability of Entanglement

Entanglement detection is essential in quantum information science and quantum many-body physics. It has been proved that entanglement exists almost surely for a random quantum state, while the realizations of effective entanglement criteria usually consume exponential resources, and efficient criteria often perform poorly without prior knowledge. This fact implies a fundamental limitation might exist in the detectability of entanglement. In this work, we formalize this limitation as a fundamental trade-off between the efficiency and effectiveness of entanglement criteria via a systematic method to theoretically evaluate the detection capability of entanglement criteria. For a system coupled to an environment, we prove that any entanglement criterion needs exponentially many observables to detect the entanglement effectively when restricted to single-copy operations. Otherwise, the detection capability of the criterion will decay double-exponentially. Furthermore, if multi-copy joint measurements are allowed, the effectiveness of entanglement detection can be exponentially improved, which implies a quantum advantage in entanglement detection problems.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting.
InitialGAN: A Language GAN with Completely Random Initialization

Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle this problem. Existing language GANs adopt estimators like REINFORCE or continuous relaxations to model word probabilities. The inherent limitations of such estimators lead current models to rely on pre-training techniques (MLE pre-training or pre-trained embeddings). Representation modeling methods which are free from those limitations, however, are seldomly explored because of their poor performance in previous attempts. Our analyses reveal that invalid sampling methods and unhealthy gradients are the main contributors to such unsatisfactory performance. In this work, we present two techniques to tackle these problems: dropout sampling and fully normalized LSTM. Based on these two techniques, we propose InitialGAN whose parameters are randomly initialized in full. Besides, we introduce a new evaluation metric, Least Coverage Rate, to better evaluate the quality of generated samples. The experimental results demonstrate that InitialGAN outperforms both MLE and other compared models. To the best of our knowledge, it is the first time a language GAN can outperform MLE without using any pre-training techniques.
Timelike correlations and quantum tensor product structure

The state space structure for a composite quantum system is postulated among several mathematically consistent possibilities that are compatible with local quantum description. For instance, unentangled Gleason's theorem allows a state space that includes density operators as a proper subset among all possible composite states. However, bipartite correlations obtained in Bell type experiments from this broader state space are in-fact quantum simulable, and hence such spacelike correlations are no good to make distinction among different compositions. In this work we analyze communication utilities of these different composite models and show that they can lead to distinct utilities in a simple communication game involving two players. Our analysis, thus, establishes that beyond quantum composite structure can lead to beyond quantum correlations in timelike scenario and hence welcomes new principles to isolate the quantum correlations from the beyond quantum ones. We also prove a no-go that the classical information carrying capacity of different such compositions cannot be more than the corresponding quantum composite systems.
Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Models

Although masked language models are highly performant and widely adopted by NLP practitioners, they can not be easily used for autoregressive language modelling (next word prediction and sequence probability estimation). We present an LSTM-based autoregressive language model which uses prefix embeddings (from a pretrained masked language model) via fusion (e.g. concatenation) to obtain a richer context representation for language modelling. We find that fusion helps reliably in lowering the perplexity (16.74 $\rightarrow$ 15.80), which is even preserved after a transfer to a dataset from a different domain than the training data. We also evaluate the best-performing fusion model by correlating its next word surprisal estimates with human reading times. Contradicting our expectation, and despite the improvement in perplexity overall, the correlation remains the same as for the baseline model. Lastly, while we focus on language models pre-trained on text as the sources for the fusion, our approach can be possibly extended to fuse any information represented as a fixed-size vector into an auto-regressive language model. These include e.g. sentence external information retrieved for a knowledge base or representations of multi-modal encoders.
Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing

This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. Finally, candidate images are ranked by computing the feature similarity with a given input query. A robust experimental protocol evaluates the proposed approach considering each representation scheme (real-valued and binary code) on the DocExplore image database. The experimental results show that the proposed deep models compare favorably to the state-of-the-art image retrieval approaches for images of historical documents, outperforming other deep models by 2.56 percentage points using the same techniques for pattern spotting. Besides, the proposed approach also reduces the search time by up to 200x and the storage cost up to 6,000x when compared to related works based on real-valued representations.
Counting Abelian Squares for a Problem in Quantum Computing

In a recent work I developed a formula for efficiently calculating the number of abelian squares of length $t+t$ over an alphabet of size $d$, where $d$ may be very large. Here I show how the expressiveness of a certain class of parameterized quantum circuits can be reduced to the problem of counting abelian squares over a large alphabet, and use the recently developed formula to efficiently calculate this quantity.
Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets.
Unsupervised Flow Refinement near Motion Boundaries

Unsupervised optical flow estimators based on deep learning have attracted increasing attention due to the cost and difficulty of annotating for ground truth. Although performance measured by average End-Point Error (EPE) has improved over the years, flow estimates are still poorer along motion boundaries (MBs), where the flow is not smooth, as is typically assumed, and where features computed by neural networks are contaminated by multiple motions. To improve flow in the unsupervised settings, we design a framework that detects MBs by analyzing visual changes along boundary candidates and replaces motions close to detections with motions farther away. Our proposed algorithm detects boundaries more accurately than a baseline method with the same inputs and can improve estimates from any flow predictor without additional training.