Reviewerly/exHarmony huggingface dataset:
A comparative research of different granularities in Korean text classification

Text classification is a process, which can make the specified documents group into several categories, predefined at the beginning through learning a series of rules or under the guidance of the goal function. This paper compared the subword-level, spacing-level of Korean and the word-level, then analyzed the influence of the preprocessing of different granularities on the text classification task of Korean. After that, analyzed the results of classification linguistically. Thus we can choose the proper granularity as the input to improve the classification effect. Firstly, cut the corpus according to different granularities; then, used Glove word embedding. Finally, used self-attention classification mechanism to verify the effect of the corpus which are preprocessed through different granularities. And using the accuracy and loss as the index. After experimental and comparison, the best result is selected when the accuracy is 83.46% through the spacing-level. Experiments show that the research in this paper can improve the effect of text classification.
Analysis of Machine Sensing of Hate Speech on Twitter in Nigeria

The Advent Of Social Media Has No Doubt Liberated The Communication Space, Deepened Participatory Democracy And Granted Citizens Platform To Ventilate Their Anger On National And Important Discourse. Twitter Have Gained Prominence In Recent Years Because Of Nature And Quality Of Information Available On Them. Policy Statements That Are Sourced On The Conventional Media Are Replete On Twitter. However, The Platform Has Also Become Breeding Ground For Obfuscation Of Many Absurdities. Therefore, This Study Investigates The Prevalence Of Hate Speech On Twitter In Nigeria From 2015 To 2019. Using Twitter Api As Data Mining Method, The Study Content Analyzed The Mined Tweets By Categorizing The Tweets Under Political, Religious, Ethnic, Region, Gender And Disability Categories. Findings Of The Study Show That Hate Speech On Twitter Is More Frequent During The Electioneering Years. Also, Unverified Handles On Twitter Tend To Tweet More Hate Speech Than The Verified Handles. Findings Show That Twitter Users Tend To ‘Like’ Hate Tweets Than ‘Retweet’ Them. Similarly, Political Hate Speeches Are Dominant On Twitter. The Study Therefore Recommend For Regulatory Legislation On Hate Speech Within The Confinement Of Freedom Of Speech In Order Not To Infringe On The Fundamental Human Right Of The People.
Pengembangan Budaya Literasi melalui Pojok Baca di SMPN 55 Merangin, Jambi

The reading corner is a school literacy cultural activity carried out by students in their spare time between class hours to read books available on the shelves in the reading corner. Reading is an important activity in the teaching and learning process, because reading is the ability to absorb knowledge through text or reading material. This reading corner activity itself is carried out as an effort to increase students' reading interest which is applied as a habit. And aims to develop a literacy culture in schools through reading corner activities made in the library. The reading corner activity that was created in the library was carried out with the students of SMPN 55 Merangin aimed at restoring the role of the library as a center for empowering students to develop literacy-based potential. This service is carried out through preparation, socialization of reading corners in libraries, and implementation of reading corners. The result of the dedication to the formation of a reading corner at SMPN 55 Merangin. It is hoped that the establishment of this reading corner can improve school literacy culture.
Hyper-chaos-based image encryption and cryptanalysis on a non-RGB channel

This paper proposes a novel hyper-chaos-based colour image encryption technique and uses cryptanalysis to examine its resistance to intruders. The encryption technique uses hyper-chaotic sequence-dependent diffusion, combined with multi-layered scrambling utilising Local Bit Scrambling (LBS), Global Bit Scrambling (GBS), and pixel scrambling with Arnold Cat Map (ACM) to increase the security level. In contrast to the commonly used RGB channel, we opted for the YCbCr-channel for encrypting the colour image. More layers of diffusion and scrambling have been performed on the higher informative Y channel (luminance channel). On the contrary, the other channel (the less informative 'chroma channels', i.e., Cb, Cr) goes through fewer stages. It minimises computing complexity in comparison to other state-of-the-art works without compromising security. Several commonly used security-testing parameters and the NIST test evaluate the security strength. A comparative study with current and related research works is performed to show the proposed algorithm's superiority in security.
Research on Internet of things control system based on EEG signal

The brain computer interface technology using EEG signals is widely used in the fields of emotion recognition, state monitoring, medical rehabilitation, etc. The brain computer interface technology which "builds a bridge between the human brain and the outside world" fits perfectly with the Internet of things control system. In this paper, the TGAM module is used as the acquisition module, and the EEG signal acquisition system integrating acquisition, transmission and storage is built by using Bluetooth technology and MATLAB; EMD and ICA technologies are selected to construct the noise reduction system in the collected original signals; Based on SVM (support vector machine), EMD algorithm is used to extract data features and build classification system; Finally, based on STM32, Bluetooth technology is used to realize the connection between the upper and lower computers, the lower computers and the controlled objects, and the Internet of things control system based on motion imagination and using brain computer interface technology is realized.
Hierarchical Topic Model Inference by Community Discovery on Word Co-occurrence Networks

The most popular topic modelling algorithm, Latent Dirichlet Allocation, produces a simple set of topics. However, topics naturally exist in a hierarchy with larger, more general super-topics and smaller, more specific sub-topics. We develop a novel topic modelling algorithm, Community Topic, that mines communities from word co-occurrence networks to produce topics. The fractal structure of networks provides a natural topic hierarchy where sub-topics can be found by iteratively mining the sub-graph formed by a single topic. Similarly, super-topics can by found by mining the network of topic hyper-nodes. We compare the topic hierarchies discovered by Community Topic to those produced by two probabilistic graphical topic models and find that Community Topic uncovers a topic hierarchy with a more coherent structure and a tighter relationship between parent and child topics. Community Topic is able to find this hierarchy more quickly and allows for on-demand sub- and super-topic discovery, facilitating corpus exploration by researchers.
A 1800μm<sup>2</sup>, 953Gbps/W AES Accelerator for IoT Applications in 40nm CMOS

A compact and energy-efficient AES accelerator for area and power-constrained IoT applications was fabricated in a 40nm CMOS process. By eliminating the need of intermediate data registers for MixColumns and ShiftRows in our proposed AES accelerator, we were able to reduce the total flip-flops to only 269 bits. Further, by reusing functional blocks and swapping the D flip-flops in data storage with scan flip-flops, our chip occupies only a tiny area of $1800 \mu \text{m}^{2}$ with an extremely low number of 657 gates. In addition, clock gating method and near-threshold voltage were used in our design. Thus, our accelerator consumes only $3.2 \mu \text{W}$ with an operation efficiency of 953 Gbps/W using a 0.48 V supply voltage. Compared with prior arts, our design has savings of 53% on area and 55% on the number of gates. When operated with a supply voltage of 0.48 V at 25°C, we can also achieve lower energy efficiency.
A Demonstration of Loose Ends, A Mixed-Initiative Narrative Instrument

We present a demonstration of Loose Ends, a mixed-initiative creative interface for playful storytelling that assists players in managing plot threads to achieve storytelling goals related to high-level story structure. From a design perspective, Loose Ends is an example of a narrative instrument: an expression-oriented playable system that can be played to produce narrative, in much the same way that musical instruments are played to produce music.
Robust Hand Detection Based on Convolutional Neural Network and Attention Module

The hands are essential parts, helping people to contact and communicate with the surrounding environment. Hand gesture and position detection is an interesting topic in computer vision field, it was applied in the areas such as action recognition, Human-Computer Interaction, Human-Robot Interaction, control systems, etc. With the strong emergence of artificial neural networks and computer hardware devices, it becomes easier to apply hand detection in practice. Based on the benefits of convolutional neural network (CNN) and bottleneck attention module, this paper proposes a robust CNN for hand detection. The proposed network achieved 95.52% of average precision (AP) on the Egohands test set and 59.07 frames per second (FPS) on the Intel Core I7–4770 @ 3.40 GHz CPU in real-time testing.
Exploring Gender Disparity in Capable Guardianship Against Identity Theft: A Focus on Internet-Based Behavior

Despite the growing literature on identity theft, limited studies have been conducted to explore demographic differences between individuals in levels of self-protection measures. Additionally, previous research indicates that men are less likely to employ self-protection measures compared to women, but the potential mechanisms to explain this gender difference remain unclear. Using the 2016 Identity Theft Supplement of the National Crime Victimization Survey in the U.S., the current study examined the interrelationship between gender, Internet-based behaviors (e.g., online shopping, credit card use, and debit card use), and the use of self-protection measures. Our logistic regression models revealed that the use of debit cards to make purchases online mediates the relationship between gender and the decision to employ self-protection measures.
Data-Driven Haptic Modeling and Rendering of Viscoelastic Behavior Using Fractional Derivatives

Data-driven modeling and rendering is a general approach in haptics aiming to provide highly accurate haptic perceptual experiences simulating complex real physical dynamics, such as deformable or textured objects. A prevalent problem in the present methods for data-driven haptics is that the computational cost for modeling grows rapidly, even becoming intractable, as the interaction complexity or the number of data increases. This paper proposes one data-driven method featured with greatly improved computational efficiency for modeling viscoelastic deformable objects. This advantage is enabled by the use of fractional derivatives for modeling features and regression forests for data-interpolation models. For the benchmark of normal interaction on deformable objects, we describe a computational framework for data-driven haptic modeling and rendering. Its performance is validated by physical experiments for modeling accuracy and cost and a perceptual experiment for the similarity between real and virtual objects. The experiments demonstrate that our method offers highly realistic haptic perceptual experiences with markedly better modeling cost (at least ten times) than other state-of-the-art methods.
Artificial Text Detection with Multiple Training Strategies

As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate product reviews, fake news, and even fake political content. The paper proposes a solution for the Russian Artificial Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish which model within the list is used to generate this text. We introduce the DeBERTa pre-trained language model with multiple training strategies for this shared task. Extensive experiments conducted on the RuATD dataset validate the effectiveness of our proposed method. Moreover, our submission ranked second place in the evaluation phase for RuATD 2022 (Multi-Class).
Fast Inference for Quantile Regression with Tens of Millions of Observations

While applications of big data analytics have brought many new opportunities to economic research, with datasets containing tens of millions of observations, making usual econometric inferences based on extreme estimators would require huge computing powers and memories that are often not accessible. In this paper, we focus on linear quantile regression employed to analyze ``ultra-large'' datasets such as U.S. decennial censuses. We develop a fast inference framework based on the stochastic sub-gradient descent (S-subGD) updates. The cross-sectional data are treated sequentially into the inference procedure: (i) the parameter estimate is updated when each ``new observation'' arrives, (ii) it is aggregated as the \emph{Polyak-Ruppert average}, and (iii) a pivotal statistic for inference is computed using a solution path only. We leverage insights from time series regression and construct an asymptotically pivotal statistic via random scaling. Our proposed test statistic is computed in a fully online fashion and the critical values are obtained without any resampling methods. We conduct extensive numerical studies to showcase the computational merits of our proposed inference. For inference problems as large as $(n, d) \sim (10^7, 10^3)$, where $n$ is the sample size and $d$ is the number of regressors, our method can generate new insights beyond the computational capabilities of existing inference methods. Specifically, we uncover the trends in the gender gap in the U.S. college wage premium using millions of observations, while controlling over $10^3$ covariates to mitigate confounding effects.
Lightweight Image Compression Based on Deep Learning

Deep learning based image compression (DLIC) algorithms have achieved higher compression gain than conventional algorithms. However, the large parameters and float-point operations (FLOPs) of DLIC severely limit their application on mobile devices. To reduce the parameters and FLOPs while maintaining the superior compression gain, this paper proposes lightweight algorithms especially for the feature analysis, synthesis, and fusion modules in DLIC networks. Firstly, based on the observation that there are highly correlated pairing convolution kernels in the analysis/synthesis modules, a new Dynamic Concatenated Convolution (DCC) is proposed to discard half of pairing convolution kernels, which are then restored by the affine transformation of the remaining convolution kernels. Secondly, a novel Depthwise Separable Residual Block (DSRB), utilizing improved depthwise separable convolutions and skipped connections, is proposed to simplify the stacks of residual blocks in feature fusion modules, significantly reducing parameters and FLOPs. Extensive experimental results demonstrate that the proposed lightweight algorithms have fewer parameters/FLOPs and better image compression gain compared with the existing state-of-the-art lightweight algorithms.
HFedMTL: Hierarchical Federated Multi-Task Learning

Federated learning is an effective way to enable artificial intelligence over massive distributed nodes with security and communication efficiency. Some previous works primarily focus on learning a single global model for a unique task across the network, which is less competent to handle multi-task scenarios with stragglers and fault, after adopting the general gradient update methods in a federated environment. Others aim to learn a distinct model for each node, which is expensive in terms of the computation and communication cost. Using hierarchical network to reduce communication cost is becoming a new candidate. Thus, we propose a primal-and-dual method-based hierarchical federated multi-task learning system, supported with HFedMTL algorithm that allows massive nodes from distributed areas to join in the federated multi-task learning process. Empirical experiments verify the analysis and demonstrate the benefits of improving the learning performance and convergence rate.
Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm

Based on data obtained from WHO, stroke is a disease that ranks as the second most deadly disease. The cause of a stroke is when a blood vessel is hit or ruptured, resulting in a part of the brain not getting the blood supply that carries the oxygen it needs, leading to death. By utilizing technology in the health sciences, especially in the health sector, machine learning models can adjust and make it easier for users to predict certain diseases. Previous studies have had problems with low accuracy when used in healthcare. The purpose of this research is to increase accuracy by proposing the application of one of the ensemble learning algorithms, namely the Xtreme Gradient Boosting algorithm. This stroke prediction research uses the Xtreme Gradient Boosting Algorithm; the application of this method with split data Training data and 70/30 test data, 70% of the training data is 3582, 30% of the test data is 1536, and the results are 96% accuracy with these results having good results. This study increase accuracy in predicting stroke cases and get better accuracy than previous studies.
Game Engine Comparative Anatomy

Video game developers use game engines as a tool to manage complex aspects of game development. While engines play a big role in the success of games, to the best of our knowledge, they are often developed in isolation, in a closed-source manner, without architectural discussions, comparison, and collaboration among projects. In this work in progress, we compare the call graphs of two open-source engines: Godot 3.4.4 and Urho3D 1.8. While static analysis tools could provide us with a general picture without precise call graph paths, the use of a profiler such as Callgrind allows us to also view the call order and frequency. These graphs give us insight into the engines’ designs. We showed that, by using Callgrind, we can obtain a high-level view of an engine’s architecture, which can be used to understand it. In future work, we intend to apply both dynamic and static analysis to other open-source engines to understand architectural patterns and their impact on aspects such as performance and maintenance.
Pivot-Guided Embedding for Domain Generalization

Neural networks have suffered from a distribution gap between training and test data, known as domain shift. Domain generalization (DG) methods aim to learn domain invariant representations only with limited source domain data to cope with unseen target domains. The main assumption is that the model trained to extract semantically consistent features without any domain specific information is highly adaptable to the unseen target domain. Metric learning allows embedding representations to be class-separated and domain-mixed, which is an optimal condition for DG but has been downplayed in recent works. Even the most popular triplet embedding has limitations in forming an optimal embedding space for DG due to instability. In this paper, we present a novel deep metric learning method for domain invariant representations. Specifically, we propose Pivot-Guided Embedding (PGE), which explicitly forms the entire feature distribution of the embedding space with a novel pivot-guided attraction-repulsion mechanism, to address the instability problem that triplet embedding has. In particular, we leverage pivot features representing a coarse distribution of the entire space as reference points to guide other features toward domain invariant feature distribution. To this end, a pivot selection algorithm is presented to reliably reflect the entire feature distribution. Furthermore, we define Guide-Field, a subspace spanned by a subset of pivots chosen for individual samples, to guide each sample to domain invariant feature space. In a nutshell, the attraction-repulsion mechanism based on pivots, the reliable set of features representing the entire feature distribution, enables the model to extract domain invariant feature representations and also settles the instability problem of triplet loss. Experimental results on three different benchmarks validate the performance advantages of the proposed method over the state-of-the-art DG techniques.
Dual-Tree Genetic Programming for Deadline-Constrained Dynamic Workflow Scheduling in Cloud

Dynamic workflow scheduling (DWS) aims to allocate abundant cloud resources to process a large number of heterogeneous workflows in order to minimize total operation cost and the penalty for violating deadline constraints. Instead of using manually designed heuristics that cannot work effectively across different problem instances, we develop a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to automatically design scheduling heuristics for a newly formulated deadline-constrained dynamic workflow scheduling in cloud (DCDWSC) problem. Different from previous works, our GPHH algorithm can design a pair of rules for Virtual Machine selection and task selection. A new dual-tree representation is proposed to jointly evolve the rule pair, enabling the algorithm to effectively control the inter-dependencies of the two rules. Experimental results show that our new algorithm can significantly outperform three baseline algorithms on a wide range of testing scenarios.
Audio Watermarking Algorithm Based on WSOLA Resisting Speed Change

Digital audio watermarking algorithm is a technology that embeds the watermark signal with special significance into the original audio signal through some method, and then extracts the watermark signal when necessary. It has important applications in the fields of copyright protection, content authentication, secure communication and bill anti-counterfeiting. With the popularity of various multimedia software and audio editing software, it becomes more and more convenient to modify audio. Common audio editing operations such as editing, shifting, tone sandhi and splicing are widely used, but most of the existing digital audio watermarking algorithms can not resist shifting operations. Therefore, this paper proposes an audio watermarking algorithm for audio variable speed attack. The algorithm is based on WSOLA principle. In the process of audio speed change, the spread spectrum watermark is embedded into the non overlapping region.
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions

Decision support systems seek to enable informed decision-making. In the recent years, automated planning techniques have been leveraged to empower such systems to better aid the human-in-the-loop. The central idea for such decision support systems is to augment the capabilities of the human-in-the-loop with automated planning techniques and enhance the quality of decision-making. In addition to providing planning support, effective decision support systems must be able to provide intuitive explanations based on specific user queries for proposed decisions to its end users. Using this as motivation, we present our decision support system RADAR-X that showcases the ability to engage the user in an interactive explanatory dialogue by first enabling them to specify an alternative to a proposed decision (which we refer to as foils), and then providing contrastive explanations to these user-specified foils which helps the user understand why a specific plan was chosen over the alternative (or foil). Furthermore, the system uses this dialogue to elicit the user's latent preferences and provides revised plan suggestions through three different interaction strategies.