Reviewerly/exHarmony huggingface dataset:
Multi-task learning on the edge: cost-efficiency and theoretical optimality

This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mixtures, (ii) computationally cheap and scalable. Supporting experiments on synthetic and real benchmark data demonstrate that significant energy gains can be obtained with no performance loss.
Non-autoregressive neural machine translation with auxiliary representation fusion

Recently, many efforts have been devoted to speeding up neural machine translation models. Among them, the non-autoregressive translation (NAT) model is promising because it removes the sequential dependence on the previously generated tokens and parallelizes the generation process of the entire sequence. On the other hand, the autoregressive translation (AT) model in general achieves a higher translation accuracy than the NAT counterpart. Therefore, a natural idea is to fuse the AT and NAT models to seek a trade-off between inference speed and translation quality. This paper proposes an ARF-NAT model (NAT with auxiliary representation fusion) to introduce the merit of a shallow AT model to an NAT model. Three functions are designed to fuse the auxiliary representation into the decoder of the NAT model. Experimental results show that ARF-NAT outperforms the NAT baseline by 5.26 BLEU scores on the WMT’14 German-English task with a significant speedup (7.58 times) over several strong AT baselines.
Perancangan Museum Vulkanologi Di Kabupaten Boyolali Dengan Konsep Green Architecture

AbstractBecause of ist location at the foot of Mount Merapi, Boyolali Regancy is often affected by the affects of the eruption of Mount Merapi, This is what underlies the making of the Boyolali Regency Volcanology Museum. The Volcanology Museum of Boyolali Regency can be used as a means of education, dissemination of information on volcanic aspects in particular and other geological disasters that are recreational-educative for thewider community with the aim of providing insight and understanding of scientific, as well as socio-cutural and other aspects relating to volcanoes and other sources of geological disaster. The Volcanology Museum is expected to be an alternative solution as a very important and potential tool as a center for volcano information services in an effort to educate people’s lives, as well as a media is raising public awareness and alerrness about the benefits and treaths of volcanic eruption and other geological disasters.Keywords: boyolali, museum, education, benefits, geology
Klasifikasi Rasa Susu Almond Berdasarkan Minat Pelanggan Menggunakan Algoritma C4.5 di Home Made MamiNia Ice Pematangsiantar

Almond milk is a plant-based milk drink made from almonds. Almond juice has a paler color and a thicker texture than regular milk. As for the benefits of almond milk in the world of health, namely to increase breast milk production, prevent high blood pressure, strengthen immunity, protect bone health, maintain baby's heart health, prevent free radicals, facilitate digestion, make skin bright after childbirth. Data mining is part of the Knowledge Discovery in Database (KDD) process stage. Therefore, the authors provide a classification solution in Data Mining is done using the Algorithm C4.5. With the Algorithm C4.5 you will get a Decision Tree that is easy to understand and easy to understand. Thus, it can help the owner determine the almond milk production plan that is most in demand and without worrying about excess goods or shortages of ingredients.
An Investigation on the Impact of Age Group and Gender on the Authentication Performance of Keystroke Dynamics

Keystroke dynamics is a biometric that has been explored as a means of making user authentication more secure.However, studies have indicated that the performance of such a system might be influenced by the demography of the user population.The purpose of this study is to investigate the relationship between the age and gender of the users of a keystroke dynamics-based mobile phone user authentication and the performance of the scheme.Using a mobile keystroke dynamics dataset containing the age and gender information of the participants, an anomaly detector algorithm was used to test whether an impostor user would have been recognised or not.A False Acceptance Rate (FAR) is calculated for the genuine user and impostors' combination.A Two-Way Analysis of Variance (ANOVA) was used to test the hypotheses whether there are significance differences and interaction between the FARs obtained with respect to the age group and gender categories.The result suggests that the age and gender of the users of a keystroke dynamics user authentication system on a mobile phone is not expected to have significant impact on the performance of such a system.Unlike previous studies that were based on keystroke dynamics data from desktop computer users, this investigation focused on keystroke dynamics for mobile phones.The results obtained in this paper has further improved our understanding that demographic bias relating to age and gender may be eliminated from the concerns that may arise from the use of a keystroke dynamics user authentication on a mobile phone.
Meningkatkan Hasil Belajar Matematika Dengan Media Konkrit Kelas II SDN 6 Baturetno Kecamatan Baturetno Tahun Pelajaran 2019/2020

The purpose of this study is to find out a positive relationship between the use of concrete object media and mathematics learning outcomes for second grade students at SDN 6 Baturetno Baturetno District for the 2019/2020 school year and to find out the contribution of the use of concrete object media to mathematics learning outcomes. This research is a quantitative and qualitative research. The initial condition of the mathematics scores of students who scored above the KKM was only 23% or as many as 3 children out of 13 students. After the end of the first cycle, the student's score was above the KKM 77%, then the second cycle was obtained or 13 children scored above the KKM or 100%. The success indicator of this research is the average grade of 82.30, the percentage of students who are above the KKM is 100% of the 13 students. Based on the analysis and discussion of the research results, it can be concluded that there is a positive relationship between the use of concrete object media and mathematics learning outcomes
Toward a Human-Level Video Understanding Intelligence

We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly challenging task, and evaluation methods for adequately measuring and analyzing the progress of AI agent are lacking as well. In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents. We define a general format and procedure of the Video Turing Test and present a case study to confirm the effectiveness and usefulness of the proposed test.
Fine-Grained Control of Artistic Styles in Image Generation

Recent advances in generative models and adversarial training have enabled artificially generating artworks in various artistic styles. It is highly desirable to gain more control over the generated style in practice. However, artistic styles are unlike object categories -- there are a continuous spectrum of styles distinguished by subtle differences. Few works have been explored to capture the continuous spectrum of styles and apply it to a style generation task. In this paper, we propose to achieve this by embedding original artwork examples into a continuous style space. The style vectors are fed to the generator and discriminator to achieve fine-grained control. Our method can be used with common generative adversarial networks (such as StyleGAN). Experiments show that our method not only precisely controls the fine-grained artistic style but also improves image quality over vanilla StyleGAN as measured by FID.
Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation

Deep learning struggles to generalize well to an unseen target domain of interest.Current domain adaptation methods simultaneously learn a classifier and an adversarial game for invariant representations but inadequately align local structures, while the underlying process is hard to interpret.We propose a new interpretable adversarial domain architecture, matching local manifold approximations across domains.Evaluated against related networks, the approach is competitive, while the adaptation process can be visually verified.
Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking

On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets.To this end, we propose a browser-based benchmarking tool for researchers and challenge organizers, with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA.Our tool helps test generalization capabilities of models across multiple datasets, evaluating not just accuracy, but also performance in more realistic real-world scenarios such as robustness to input noise.Additionally, we include metrics that measure biases and uncertainty, to further explain model behavior.Interactive filtering facilitates discovery of problematic behavior, down to the data sample level.As proof of concept, we perform a case study on four models.We find that state-of-the-art VQA models are optimized for specific tasks or datasets, but fail to generalize even to other in-domain test sets, for example they cannot recognize text in images.Our metrics allow us to quantify which image and question embeddings provide most robustness to a model.All code 1 is publicly available.
On Solving the Minimum Common String Partition Problem by Decision Diagrams

In the Minimum Common String Partition Problem (MCSP), we are given two strings on input, and we want to partition both into the same collection of substrings, minimizing the number of the substrings in the partition. This combinatorial optimization problem has applications in computational biology and is NP-hard. Many different heuristic and exact methods exist for this problem, such as a Greedy approach, Ant Colony Optimization, or Integer Linear Programming. In this paper, we formulate the MCSP as a Dynamic Program and develop an exact solution algorithm based on Decision Diagrams for it. We also introduce a restricted Decision Diagram that allows to compute heuristic solutions to the MCSP and compare the quality of solution and runtime on instances from literature with existing approaches. Our approach scales well and is suitable for heuristic solution of large-scale instances.
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks

In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts. In this paper, we introduce Sub-bit Neural Networks (SNNs), a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are inspired by an empirical observation, showing that binary kernels learnt at convolutional layers of a BNN model are likely to be distributed over kernel subsets. As a result, unlike existing methods that binarize weights one by one, SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Specifically, our method includes a random sampling step generating layer-specific subsets of the kernel space, and a refinement step learning to adjust these subsets of binary kernels via optimization. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs. For instance, on ImageNet, SNNs of ResNet-18/ResNet-34 with 0.56-bit weights achieve 3.13/3.33 times runtime speed-up and 1.8 times compression over conventional BNNs with moderate drops in recognition accuracy. Promising results are also obtained when applying SNNs to binarize both weights and activations. Our code is available at https://github.com/yikaiw/SNN.
WeTS: A Benchmark for Translation Suggestion

Translation Suggestion (TS), which provides alternatives for specific words or phrases given the entire documents translated by machine translation (MT) \cite{lee2021intellicat}, has been proven to play a significant role in post editing (PE). However, there is still no publicly available data set to support in-depth research for this problem, and no reproducible experimental results can be followed by researchers in this community. To break this limitation, we create a benchmark data set for TS, called \emph{WeTS}, which contains golden corpus annotated by expert translators on four translation directions. Apart from the human-annotated golden corpus, we also propose several novel methods to generate synthetic corpus which can substantially improve the performance of TS. With the corpus we construct, we introduce the Transformer-based model for TS, and experimental results show that our model achieves State-Of-The-Art (SOTA) results on all four translation directions, including English-to-German, German-to-English, Chinese-to-English and English-to-Chinese. Codes and corpus can be found at https://github.com/ZhenYangIACAS/WeTS.git.
Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput.

The vast amounts of data used in social, business or traffic networks, biology and other natural sciences are often managed in graph-based data sets, consisting of a few thousand up to billions and trillions of vertices and edges, respectively. Typical applications utilizing such data either execute one or a few complex queries or many small queries at the same time interactively or as batch jobs. Furthermore, graph processing is inherently complex, as data sets can substantially differ (scale free vs. constant degree), and algorithms exhibit diverse behavior (computational intensity, local or global, push- or pull-based). This work is concerned with multi-query execution by automatically controlling the degree of parallelization, with overall objectives including high system utilization, low synchronization cost, and highly efficient concurrent execution. The underlying concept is three-fold: (1) sampling is used to determine graph statistics, (2) parallelization constraints are derived from algorithm and system properties, and (3) suitable work packages are generated based on the previous two aspects. We evaluate the proposed concept using different algorithms on synthetic and real world data sets, with up to 16 concurrent sessions (queries). The results demonstrate a robust performance in spite of these various configurations, and in particular that the performance is always close to or even slightly ahead of the performance of manually optimized implementations. Furthermore, the similar performance to manually optimized implementations under extreme configurations, which require either a full parallelization (few large queries) or complete sequential execution (many small queries), shows that the proposed concept exhibits a particularly low overhead.
Isogeny-based Group Signatures and Accountable Ring Signatures in QROM

We provide the first isogeny-based group signature (GS) and accountable ring signature (ARS) that are provably secure in the quantum random oracle model (QROM). We do so by building an intermediate primitive called openable sigma protocol and show that every such protocol gives rise to a secure ARS and GS. Additionally, the QROM security is guaranteed if the perfect unique-response property is satisfied. Our design, with the underlying protocol satisfying this essential unique-response property, is sophisticatedly crafted for QROM security. From there, with clever twists to available proving techniques, we obtain the first isogeny-based ARS and GS that are proven QROM-secure. Concurrently, an efficient construction was proposed by Beullens et al. (Eurocrypt 2022), but is only proven secure in the classical random oracle model (ROM). Our proposal seeks stronger QROM security, although it is less efficient due to the signature size quadratically scaling with the ring/group size.
Human Values in Mobile App Development: An Empirical Study on Bangladeshi Agriculture Mobile Apps

Given the ubiquity of mobile applications (apps) in daily lives, understanding and reflecting end-users' human values (e.g., transparency, privacy, social recognition etc.) in apps has become increasingly important. Violations of end users' values by software applications have been reported in the media and have resulted in a wide range of difficulties for end users. Value violations may bring more and lasting problems for marginalized and vulnerable groups of end-users. This research aims to understand the extent to which the values of Bangladeshi female farmers, marginalized and vulnerable end-users, who are less studied by the software engineering community, are reflected in agriculture apps in Bangladesh. Further to this, we aim to identify possible strategies to embed their values in those apps. To this end, we conducted a mixed-methods empirical study consisting of 13 interviews with app practitioners and four focus groups with 20 Bangladeshi female farmers. The accumulated results from the interviews and focus groups identified 22 values of Bangladeshi female farmers, which the participants expect to be reflected in the agriculture apps. Among these 22 values, 15 values (e.g., accuracy, independence) are already reflected and 7 values (e.g., accessibility, pleasure) are ignored/violated in the existing agriculture apps. We also identified 14 strategies (e.g., "applying human-centered approaches to elicit values", "establishing a dedicated team/person for values concerns") to address Bangladeshi female farmers' values in agriculture apps.
Research on Comprehensive Quality Evaluation of Teachers of Public English Course in Higher Vocational Education Based on AHP Model

A public course for evaluating accurately and improving higher vocational English teachers' comprehensive quality. Through comprehensive analysis in this paper, it was extracting core literacy evaluation index factors, and adopt Analytic Hierarchy Process (AHP) to carry out the questionnaire survey from the teaching ability of comprehensive knowledge quality practice innovation ability of higher vocational teachers. Eventually determined each evaluation index weight and built up the scientific and effective core of higher vocational teacher’s evaluation system. It will provide the beneficial reference for improve higher vocational teachers' professional level and teaching quality.
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

Matrix completion has attracted attention in many fields, including statistics, applied mathematics, and electrical engineering. Most of the works focus on the independent sampling models under which the observed entries are sampled independently. Motivated by applications in the integration of knowledge graphs derived from multi-source biomedical data such as those from Electronic Health Records (EHR) and biomedical text, we propose the {\bf B}lock-wise {\bf O}verlapping {\bf N}oisy {\bf M}atrix {\bf I}ntegration (BONMI) to treat blockwise missingness of symmetric matrices representing relatedness between entity pairs. Our idea is to exploit the orthogonal Procrustes problem to align the eigenspace of the two sub-matrices, then complete the missing blocks by the inner product of the two low-rank components. Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption. Simulation studies show that the method performs well under a variety of configurations. In the real data analysis, the method is applied to two tasks: (i) the integrating of several point-wise mutual information matrices built by English EHR and Chinese medical text data, and (ii) the machine translation between English and Chinese medical concepts. Our method shows an advantage over existing methods.
On Exploring and Improving Robustness of Scene Text Detection Models

It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. For systematically exploring this problem, we propose two datasets from which to evaluate scene text detection models: ICDAR2015-C (IC15-C) and CTW1500-C (CTW-C). Our study extends the investigation of the performance and robustness of the proposed region proposal, regression and segmentation-based scene text detection frameworks. Furthermore, we perform a robustness analysis of six key components: pre-training data, backbone, feature fusion module, multi-scale predictions, representation of text instances and loss function. Finally, we present a simple yet effective data-based method to destroy the smoothness of text regions by merging background and foreground, which can significantly increase the robustness of different text detection networks. We hope that this study will provide valid data points as well as experience for future research. Benchmark, code and data will be made available at \url{https://github.com/wushilian/robust-scene-text-detection-benchmark}.
PENGARUH BAHAN AJAR DAN MINAT TERHADAP PRESTASI BELAJAR (Studi Eksperimen pada Mata Pelajaran Kimia Siswa Kelas XII IPA di SMA Negeri 1 Pagar Alam)

This study aims to examine the difference between the learning achievement of students who study with e-module teaching materials and students who study using textbooks, the difference in learning achievement of students who have high interest and students who have low interest, the interaction between teaching materials and student interest. on learning achievement. The design used in this study is a quasi-experimental design with a 2 x 2 factorial design. The research population is all students of class XII MIPA SMA Negeri 1 Pagar Alam for the academic year 2020-2021 which consists of 6 classes. The class sampled in the experimental group was class XII MIPA 1 and the control class was class XII MIPA 3. The sample was selected using purposive random sampling technique. Data were collected by means of a learning achievement test in the form of multiple choice and a questionnaire for interest in learning. The collected data was statistically processed using the two-way analysis of variance (ANAVA) technique using a significance level of 5%. The results showed that: the learning achievement of students who were taught using e-module teaching materials was higher than that of textbooks, the learning achievement of students who had high interest in learning was higher than that of students who had low interest, there was an interaction between teaching materials. and interest in learning towards learning achievement