Reviewerly/exHarmony huggingface dataset:
VLSI Implementation of AES Algorithm in Cryptography Developed in Xilinx

In the present era of information processing through computers and access to private information over the internet like bank account information, even the transaction of money, and business deals through video conferencing, encryption of the messages in various forms has become inevitable. There are mainly two types of encryption algorithms, a private key (also called a symmetric key having a single key for encryption and decryption) and a public key(a separate key for encryption and decryption). In terms of computational complexity, a private key algorithm is less complex than a public key algorithm. The simple architecture of the private key algorithm attracts the VLSI implementation through the basic digital components like basic gates and flip-flops. Moreover, the high throughput architecture can be realized for the encryption of very large amounts of data, e.g., images and videos, in real-time. The National Institute of Standards and Technology (NIST) adopted Advanced Encryption Standard (AES) as the standard for the encryption and decryption of blocks of data. The draft is published under the name FIPS-197 (Federal Information Processing Standard number 197). AES is an asymmetric key block cipher. It encrypts data of block size 128 bits. The AES algorithm is used in diverse application fields like WWW servers, automated teller machines (ATMs), cellular phones, and digital video recorders
頭部運動機能特徴に基づく対話者の主観的印象の予測・説明モデルの構築

An explainable model is proposed to automatically explain when and what kind of behaviors affected interlocutors’ impressions in group discussions. Focusing on self-reported scores on the impressions such as atmosphere and enjoyment felt during the discussions, we tackle a new problem that identifies both influential behaviors and their timings that were related to impression formation. To that end, this paper formulates the problem as the identification of behavioral features, which contributed to the impression prediction and the detection of the timings when such behaviors occurred very frequently. To solve this problem, this paper presents a two-fold framework consisting of a prediction model using random forest regressors, followed by an explanation model using a SHAP analysis. The prediction part employs functional head-movement features, which can capture the various aspects of interactive conversational behaviors. The key feature of the latter explanation part is the temporal decomposition of features’ contributions by integrating the SHAP values and the temporal distributions of the occurrence probabilities of the behavioral features, which were computed by a kernel density estimation method from the detected samples of head movements and their functions. To derive this process, we exploit the dual additivity in the SHAP values and the functional head-movement features over time. Finally, the influential behaviors and their timings are identified from the temporal local maximums of the features’ contribution distributions. Targeting 17-group 4-female discussions included in a SA-Off corpus, this paper shows some case studies and the results of quantitative evaluations, which compared the reported overall scores and two-minute-wise scores at the timings found by the model. These results confirmed the predictability and explainability of the proposed model.
Learning Pixel Perception for Identity and Illumination Consistency Face Frontalization in the Wild

A frontal and realistic face image was synthesized from a single profile face image. It has a wide range of applications in face recognition. Although the frontal face method based on deep learning has made substantial progress in recent years, there is still no guarantee that the generated face has identity consistency and illumination consistency in a significant posture. This paper proposes a novel pixel-based feature regression generative adversarial network (PFR-GAN), which can learn to recover local high-frequency details and preserve identity and illumination frontal face images in an uncontrolled environment. We first propose a Reslu block to obtain richer feature representation and improve the convergence speed of training. We then introduce a feature conversion module to reduce the artifacts caused by face rotation discrepancy, enhance image generation quality, and preserve more high-frequency details of the profile image. We also construct a 30,000 face pose dataset to learn about various uncontrolled field environments. Our dataset includes ages of different races and wild backgrounds, allowing us to handle other datasets and obtain better results. Finally, we introduce a discriminator used for recovering the facial structure of the frontal face images. Quantitative and qualitative experimental results show our PFR-GAN can generate high-quality and high-fidelity frontal face images, and our results are better than the state-of-art results.
Wider Depth Dynamic Range Using Occupancy Map Correction for Immersive Video Coding

The MPEG Immersive Video (MIV) standard for immersive video coding provides users with an immersive sense of 6 degrees of freedom (6DoF) of view position and orientation by efficiently compressing multiview video acquired from different positions in a limited 3D space. In the MIV reference software called Test Model for Immersive Video (TMIV), the number of pixels to be compressed and transmitted is reduced by removing inter-view redundancy. Therefore, the occupancy information that indicates whether each pixel is valid or invalid must also be transmitted to the decoder for viewport rendering. The occupancy information is embedded in a geometry atlas and transmitted to the decoder side. At this time, to prevent occupancy errors that may occur during the compression of the geometry atlas, a guard band is set in the depth dynamic range. Reducing this guard band can improve the rendering quality by allowing a wider dynamic range for depth representation. Therefore, in this paper, based on the analysis of occupancy error of the current TMIV, two methods of occupancy error correction which allow depth dynamic range extension in the case of computer-generated (CG) sequences are presented. The experimental results show that the proposed method gives an average 2.2% BD-rate bit saving for CG compared to the existing TMIV.
Image-to-Image Translation for Data Augmentation on Multimodal Medical Images

Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still a difficult task in the medical field. For this reason, research in the field of image-to-image translation is combined with computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to medical images. In this paper, we try to perform data augmentation on unimodal data. The designed StarGAN V2 based network has high performance in augmenting the dataset using a small number of original images, and the augmented data is expanded from unimodal data to multimodal medical images, and this multimodal medical image data can be applied to the segmentation task with some improvement in the segmentation results. Our experiments demonstrate that the generated multimodal medical image data can improve the performance of glioma segmentation.
Measurement of frequency characteristics under various tuning conditions in violin performances

Scordatura, a method of changing the tone color by changing the tuning interval, is sometimes used for playing string instruments. Resonance is one of the important factors in playing stringed instruments. In this study, the frequency response was demonstrated to depend on the difference between tuning temperaments such as Pythagorean tuning and equal temperament. In addition, the decay time was found to increase or decrease with changes in tuning conditions. It was also observed that the inharmonicity of higher-order harmonics was due to the tuning frequency.
A practical method for generating whispers from singing voices: Application of improved phantom silhouette method

The previously proposed phantom silhouette method is promising for converting ordinary speech into whispered speech. It is a simple parametric method that uses high-quality vocoder-type speech analysis and synthesis. An ordinary speech sample is first analyzed using the WORLD vocoder. Then, based on the extracted spectral envelope, spectral features are manipulated so that the voice sounds like a whisper. The target speech is synthesized by driving it with white noise instead of the vocal source signal to make the whole speech sound voiceless. In this study, this method was applied to singing voices to generate whisper voices. In addition to actual singing voices, virtual singers' voices were generated using a Vocaloid voice synthesizer, and AI singers' voices synthesized using a NEUTRINO neural singing synthesizer were also tested to generate whisper voices from singing voices.
Convolution Block Feature Addition Module (CBFAM) for Lightweight and Fast Object Detection on Non-GPU Devices

This letter proposes a novel lightweight deep learning object detector named LW-YOLOv4-tiny, which incorporates the convolution block feature addition module (CBFAM). The novelty of LW-YOLOv4-tiny is the use of channel-wise convolution and element-wise addition in the CBFAM instead of utilizing the concatenation of different feature maps. The model size and computation requirement are reduced by up to 16.9 Mbytes, 5.4 billion FLOPs (BFLOPS), and 11.3 FPS, which is 31.9%, 22.8%, and 30% smaller and faster than the most recent version of YOLOv4-tiny. From the MSCOCO2017 and PASCAL VOC2012 benchmarks, LW-YOLOv4-tiny achieved 40.2% and 69.3% mAP, respectively.
話し方種別情報を含むテキスト対話を活用した表現豊かなテキスト音声合成

This paper aims to generate expressive speech for integration with a robot and AI character dialogue systems. To generate expressive speech, some researchers have proposed using labels that express specific dialogue acts and emotions (i.e., speaking style information). Our approach is to use the speaking style information as an intermediate representation and to train a model for inferring the speaking style information from the text and a speech synthesis model independently. Using a model that infers speaking style information from text, we construct a method that can generate expressive speech for text in the dialogue domain, outside the scope of speech synthesis training. The method first estimates the labels corresponding to the speaking style information for the input text. Then, the estimated labels and the input text are used to generate speech using a speech synthesis model. Experiments show that our method effectively improves the accuracy of text classification of speaking style labels. Subjective evaluation experiments show that our method can produce more expressive speech than conventional methods.
知識グラフに基づく質問応答システムにおける論理形式パターンを用いた論理形式検索の改善

In recent years, various applications such as semantic search, question answering, and dialogue systems using large-scale knowledge graphs such as Wikidata and DBpedia have been studied. This study focuses on Question Answering over Knowledge Graphs (KGQA). To promote and evaluate KGQA studies, multiple question answering data sets based on large-scale knowledge graphs have been constructed. CSQA is a question answering benchmark based on Wikidata. CSQA targets interactive question answering, and there are 10 question types, some of which require inference, such as comparison and set operation. In the previous study, a multi-task semantic parsing model that converts utterances to logical forms that express operations on a database can answer with high accuracy. The conversion to a logical form is performed by defining the grammar for deriving the logical form and predicting the order in which the grammar is applied from the utterance. To learn a model for the prediction, it is necessary to search for a logical form that can answer the question from logical forms that can be generated by applying the grammar. However, since the search space is enormous, depending on the search method, problems such as a decrease in the success rate of the search and incorrect logical forms occur, which may adversely affect the learning of the model. In this research, we propose a method for searching logical forms with a high success rate of the search in a short time by using patterns of logical forms. The proposed method consists of logical form pattern search unit, logical form search unit, and logical form determination unit. The logical form pattern search unit searches for a logical form pattern that can generate a logical form that can answer questions. The logical form search unit searches only logical forms that can be created from logical form patterns obtained. The logical form determination unit decides which logical form to use for learning. For each logical form pattern, count the number of questions that can be answered by the logical form generated from that pattern. For each logical forms searched for each question, select a logical form generated from the logical form pattern with the largest number. In the evaluation, we searched the logical format for each of the 10 question types in the dataset used, with 5 000 questions each, and compared the search success rate with the search time. We also compared the accuracy of question answering when learning the existing system using the searched questions. The search success rate increased for 9 out of 10 question types in the dataset used in the experiment. The search time was reduced for 7 out of 10 question types. The question answering accuracy improved for 8 out of 10 question types.
A Structural, Time Aware, Coordinated Tag Generation Based on Transformer Network

The content quality of shared knowledge in Stack Overflow (SO) is critical in supporting software developers with their programming problems. Thus, it allows its users to suggest editing the software to improve the quality of a post. However, existing all research shows that many suggested edits in SO are rejected due to undesired contents or violating editing guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. we propose Semantically Tag and Score Recommendation, with the use of the deep learning-based approach that automatically recommends tags or grades or scores through learning the semantics of both tags, score, grade and questions in such software CQA. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multitasking, the core modules of Semantically Tag and Score Recommendation, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent-based algorithm for classification
Android based Attendance Management using QR Code

Until today, most of the lectures in universities are found still using the conventional methods of taking student’s attendance either by calling out the student names or by passing around an attendance sheet for students to sign conforming their presence. In addition to the time-consuming issue, such method is also at high risk of having students cheating about their attendance, especially in a large classroom. Therefore, a method of taking attendance by employing an application running the Android platform is proposed in this paper. This application once installed can be used to download the students list from a designated web server. Based on the downloaded list of students, the device will then act like a scanner to scan each of the students card one by one to confirm and verify the student’s presence. The device camera will act as a sensor that will read the QR code printed on the student’s card. The updated attendance list is then uploaded to an online database and can also be saved as a file to be transferred to a PC later on.
Selective Learning of Human Pose Estimation Based on Multi-Scale Convergence Network

Pose estimation is a research hot spot in computer vision tasks and the key to computer perception of human activities. The core concept of human pose estimation involves describing the motion of the human body through major joint points. Large receptive fields and rich spatial information facilitate the keypoint localization task, and how to capture features on a larger scale and reintegrate them into the feature space is a challenge for pose estimation. To address this problem, we propose a multi-scale convergence network (MSCNet) with a large receptive field and rich spatial information. The structure of the MSCNet is based on an hourglass network that captures information at different scales to present a consistent understanding of the whole body. The multi-scale receptive field (MSRF) units provide a large receptive field to obtain rich contextual information, which is then selectively enhanced or suppressed by the Squeeze-Excitation (SE) attention mechanism to flexibly perform the pose estimation task. Experimental results show that MSCNet scores 73.1% AP on the COCO dataset, an 8.8% improvement compared to the mainstream CMUPose method. Compared to the advanced CPN, the MSCNet has 68.2% of the computational complexity and only 55.4% of the number of parameters.
3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate Volumetric Medical Image Segmentation

Convolutional neural networks (CNNs) have become popular in medical image segmentation. The widely used deep CNNs are customized to extract multiple representative features for two-dimensional (2D) data, generally called 2D networks. However, 2D networks are inefficient in extracting three-dimensional (3D) spatial features from volumetric images. Although most 2D segmentation networks can be extended to 3D networks, the naively extended 3D methods are resource-intensive. In this paper, we propose an efficient and accurate network for fully automatic 3D segmentation. Specifically, we designed a 3D multiple-contextual extractor to capture rich global contextual dependencies from different feature levels. Then we leveraged an ROI-estimation strategy to crop the ROI bounding box. Meanwhile, we used a 3D ROI-attention module to improve the accuracy of in-region segmentation in the decoder path. Moreover, we used a hybrid Dice loss function to address the issues of class imbalance and blurry contour in medical images. By incorporating the above strategies, we realized a practical end-to-end 3D medical image segmentation with high efficiency and accuracy. To validate the 3D segmentation performance of our proposed method, we conducted extensive experiments on two datasets and demonstrated favorable results over the state-of-the-art methods.
Physical modeling of Japanese temple bells using thin cylindrical shells involving dynamics of clappers &lt;i&gt;Shumoku&lt;/i&gt;

In this paper, we present a physical model of Japanese temple bells based on the elasticity theory of thin cylindrical shells. The proposed model involves the dynamics of the clapper, called shumoku, thereby constructing a total physical system of a temple bell. The governing equation of a temple bell is solved semi-analytically using the Fourier series expansion in the circumferential direction. The finite difference method (FDM) is applied to the governing equation in the axial direction. These modeling and numerical treatment are an attempt to reduce computational costs while retaining the physical essence of temple bells. Shumoku is physically modeled as a 3D beam, and numerically analyzed by the finite element method (FEM). Simulation results show that the fundamental frequencies calculated by the proposed method are close to those of existing FDM calculations and measured data, while differences in higher-order partial frequencies are observed, indicating that the differences are attributed to the details of the structural features of the bell omitted in the proposed model. Changing the material of shumoku affects the spectrogram of the vibration of the bell, suggesting the importance of physical modeling of clappers to precisely simulate bell systems.
Multi-Scale Correspondence Learning for Person Image Generation

A generative model is presented for two types of person image generation in this paper. First, this model is applied to pose-guided person image generation, i.e., converting the pose of a source person image to the target pose while preserving the texture of that source person image. Second, this model is also used for clothing-guided person image generation, i.e., changing the clothing texture of a source person image to the desired clothing texture. The core idea of the proposed model is to establish the multi-scale correspondence, which can effectively address the misalignment introduced by transferring pose, thereby preserving richer information on appearance. Specifically, the proposed model consists of two stages: 1) It first generates the target semantic map imposed on the target pose to provide more accurate guidance during the generation process. 2) After obtaining the multi-scale feature map by the encoder, the multi-scale correspondence is established, which is useful for a fine-grained generation. Experimental results show the proposed method is superior to state-of-the-art methods in pose-guided person image generation and show its effectiveness in clothing-guided person image generation.
Explainable Natural Language Inference in the Legal Domain via Text Generation

Natural language inference (NLI) in the legal domain is the task of predicting entailment between the premise, i.e. law, and the hypothesis, which is a statement regarding a legal issue. Current state-of-the-art approaches to NLI with pre-trained language models do not perform well in the legal domain, presumably due to a discrepancy in the level of abstraction between the premise and hypothesis and the convoluted nature of legal language. Some of the difficulties specific to the legal domain are that 1) the premise and hypothesis tend to be extensive in length; 2) the premise comprises multiple rules, and only one of the rules is related to the hypothesis. Thus only small fractions of the statements are relevant for determining entailment, while the rest is noise, and; 3) the premise is often abstract and written in legal terms, whereas the hypothesis is a concrete case and tends to be written with more ordinary vocabulary. These problems are accentuated by the scarcity of such data in the legal domain due to the high cost.
Procedure for calculating fluctuation strength for marimba tremolo

Our previous study described a procedure for calculating fluctuation strength (FS) from tremolo produced with a mandolin. However, the forms of the amplitude envelope on sounds played on the mandolin vary so little that how the forms affect the perception of fluctuation has not yet been investigated. Therefore, we present a procedure for calculating FS by considering the form of the amplitude envelope on a tremolo produced with a marimba, whose forms of the acoustic waveform on the tremolo vary due to the stiffness of the head of the mallet. We obtained acoustic features of a marimba tremolo as a set of parameters and calculated FS using the parameters. We found that the procedure observing three features concerning constant fluctuation, the deviation for time and amplitude, and deformation on the envelope can calculate FS correctly.