Reviewerly/exHarmony huggingface dataset:
Leveraging Extended Chat History through Sentence Embedding in Multi-turn Dialogue toward Increasing User Engagement

Multi-turn dialogue is the major manifestation of a conversation. Compared with single-turn dialogue, response selection is more complex as the context varies. We stress the importance of dialogue history and apply the pre-trained model BERT to assign proper weight to each utterance of a dialogue. Previous works take all the dialogue history as context to measure the matching degree of a context-response pair, causing the quadratic computational cost and truncation of longer sequences exceeding the length limitation of BERT. We propose a sentence-based method to deal with the aforementioned problems, obtaining the sentence embedding of a single unit utterance of dialogue and forming a classification token of a context-response pair. We discuss how to obtain a sentence embedding with high quality and to design the input representations in response selection. The results show that the average of the first-last layer output exhibits the best performance for obtaining a sentence representation. The proposed method, concatenating the sentence embeddings of context with the token embeddings of response candidates, is nearly on a par with the token embedding based SOTA method. Notably, the processable length of dialogue history is enlarged about ten times with a low computational cost, potentially reducing chatbot response time and inspiring user engagement.
Qualitative Analysis of Single Object and Multi Object Tracking Models

Tracking the object(s) of interest in the real world is one of the most salient research areas that has gained widespread attention due to its applications. Although different approaches based on traditional machine learning and modern deep learning have been proposed to tackle the single and multi-object tracking problems, these tasks are still challenging to perform. In our work, we conduct a comparative analysis of eleven object trackers to determine the most robust single object tracker (SOT) and multi-object tracker (MOT). The main contributions of our work are (1) employing nine pre-trained tracking algorithms to carry out the analysis for SOT that include: SiamMask, GOTURN, BOOSTING, MIL, KCF, TLD, MedianFlow, MOSSE, CSRT; (2) investigating MOT by integrating object detection models with object trackers using YOLOv4 combined with DeepSort, and CenterNet coupled with SORT; (3) creating our own testing videos dataset to perform experiments; (4) performing the qualitative analysis based on the visual representation of results by considering nine significant factors that are appearance and illumination variations, speed, accuracy, scale, partial and full-occlusion, report failure, and fast motion. Experimental results demonstrate that SiamMask tracker overcomes most of the environmental challenges for SOT while YOLOv+DeepSort tracker obtains good performance for MOT. However, these trackers are not robust enough to handle full occlusion in real-world scenarios and there is always a trade-off between tracking accuracy and speed.
SDA-UNet: a hepatic vein segmentation network based on the spatial distribution and density awareness of blood vessels

Abstract Objective. Hepatic vein segmentation is a fundamental task for liver diagnosis and surgical navigation planning. Unlike other organs, the liver is the only organ with two sets of venous systems. Meanwhile, the segmentation target distribution in the hepatic vein scene is extremely unbalanced. The hepatic veins occupy a small area in abdominal CT slices. The morphology of each person’s hepatic vein is different, which also makes segmentation difficult. The purpose of this study is to develop an automated hepatic vein segmentation model that guides clinical diagnosis. Approach. We introduce the 3D spatial distribution and density awareness (SDA) of hepatic veins and propose an automatic segmentation network based on 3D U-Net which includes a multi-axial squeeze and excitation module (MASE) and a distribution correction module (DCM). The MASE restrict the activation area to the area with hepatic veins. The DCM improves the awareness of the sparse spatial distribution of the hepatic veins. To obtain global axial information and spatial information at the same time, we study the effect of different training strategies on hepatic vein segmentation. Our method was evaluated by a public dataset and a private dataset. The Dice coefficient achieves 71.37% and 69.58%, improving 3.60% and 3.30% compared to the other SOTA models, respectively. Furthermore, metrics based on distance and volume also show the superiority of our method. Significance. The proposed method greatly reduced false positive areas and improved the segmentation performance of the hepatic vein in CT images. It will assist doctors in making accurate diagnoses and surgical navigation planning.
Adaptive spatial discretization using reinforcement learning

Abstract A well-known challenge for deformation monitoring is the spatial discretization, i.e. the choice of monitoring points at which measurements are to be taken. Well-chosen monitoring points employ prior knowledge to yield a significant amount of information about a certain aspect of the monitored object. However, the choice of such a set of points is typically made to be practically expedient or left to the measurement instrument itself. We aim to derive adaptive discretization strategies that implicitly incorporate domain knowledge about the monitored object via a cycle of interaction and learning. In those strategies, previous measurements impact the locations of subsequent ones. We formulate the choice of monitoring points as a decision theoretical problem and review the framework of reinforcement learning which formalizes the problem of deriving optimal sequential decisions under uncertainty. Iterative algorithms produce solution schemes for this optimal control task. We benchmark the performance of reinforcement learning and compare its results to random, pseudorandom, and numerically designed discretization strategies on several geodetically motivated examples. Advantages, disadvantages, and practical feasibility of the approach are evaluated and reveal a significant boost in efficiency of the data collection scheme compared to classical approaches.
Multi-modal Transformer for Indoor Human Action Recognition

Indoor human action recognition is used in various fields. For example, we can use it to recognize exercise movements in the fitness industry, which can significantly help improve the health of modern people. With the development of sensors, it has become possible to easily acquire multiple data modalities of RGB, IR, depth, and skeleton in the same scene. Since each data modality is complementary, proper fusion is beneficial in recognizing human action. However, existing studies have limitations in utilizing the advantages of each modality. Therefore, we propose a Multi-Modal Transformer (MMT) to use RGB and skeleton data simultaneously in this work. Using the transformer-based structure, MMT can capture the correlation between non-local joints in skeleton data modality. In addition, MMT does not require additional training phases or multiple trained networks as the number of people on the scene changes. In experiments on public benchmark datasets, MMT shows comparable results using only eight input frames.
Blind Watermarking Using DCT and Fractional-Order Lorenz System

This paper presents a new blind watermarking system based on the Discrete Cosine Transform (DCT). The system’s security is increased by encrypting the watermark image using the fractional-order Lorenz system. After converting the cover image to the YCbCr color domain, DCT is applied on the Y channel and embedding of the encrypted watermark is performed in the frequency domain. The fractional-order Lorenz system has more parameters than the integer order system, which increase the length of the system key and make it secure against brute-force attacks. Although blind detection of the watermark is not easy, the proposed algorithm successfully detects the hidden watermark by using statistical properties of the DCT coefficients. Standard imperceptibility and robustness measures are used to evaluate the proposed system, and the results are good.
Z property for the shuffling calculus

ABSTRACT This paper gives a new proof of confluence for Carraro and Guerrieri’s call-by-value lambda calculus λ v σ with permutation rules. We adapt the compositional Z theorem to λ v σ .
Quantifying Dark Web Shops’ Illicit Revenue

The Dark Web, primarily Tor, has evolved to protect user privacy and freedom of speech through anonymous routing. However, Tor also facilitates cybercriminal actors who utilize it for illicit activities. Quantifying the size and nature of such activity is challenging, as Tor complicates indexing by design. This paper proposes a methodology to estimate both size and nature of illicit commercial activity on the Dark Web. We demonstrate this based on crawling Tor for single-vendor Dark Web Shops, i.e., niche storefronts operated by single cybercriminal actors or small groups. Based on data collected from Tor, we show that just in 2021, Dark Web Shops generated at least 113 million USD in revenue. Sexual abuse is the top illicit revenue category, followed by financial crime at a great distance. We also compare Dark Web Shops’ activity with a large Dark Web Marketplace, showing that these are parallel economies. Our methodology contributes towards automated analysis of illicit activity in Tor. Furthermore our analysis sheds light on the evolving Dark Web Shop ecosystem and provides insights into evidence-based policymaking regarding criminal Dark Web activity.
Deep Domain Adaptation Using Cascaded Learning Networks and Metric Learning

How can we bridge efficiently distribution difference between the source and target domains in an isomorphic latent feature space by using metric learning.This study introduces metric learning on manifolds which combine a cascaded learning network and a metric learning model to form a Unified Domain Adaptation Model.Our approach is based on formulating a transfer from the source to the target as a geometric mean metric learning problem on manifolds.The solution of symmetric positive definite covariance matrices not only reduces the statistical differences between the source and target domains, but also the underlying geometry of the source and target domains using diffusions on the underlying source and target manifolds.By retaining both the nonlinear structure of the Riemannian geometry of the open cone of symmetric positive definite matrices and cascaded learning networks, we improve the state-of-the-art results on the Amazon (A), Caltech256 (C), DSLR (D), Webcam (W) and VisDA benchmark datasets by knowledge transfer, while achieving comparable performances to competing methods on domain adaptation modeling.
Self-Coherent Receiver Based on a Recurrent Optical Spectrum Slicing Neuromorphic Accelerator

Coherent technology supporting higher order modulation formats is indispensable for fulfilling the ever-increasing need for higher capacity. However, coherent receivers require heavy digital signal processing (DSP) which increases the power envelope of coherent puggables well above the Intensity Modulation and Direct Detection (IM/DD) counterparts. Although self-coherent or phase retrieval methods have gained significant attention, they still lack in efficiency and suffer from multiple drawbacks in comparison with the standard coherent receiver. Here, we propose and numerically simulate a neuromorphic receiver based on recurrent optical spectrum slicing (ROSS) for detection and equalization of coherent modulation formats, utilizing minimal DSP and low bandwidth components. Through spectral slicing, the receiver can support up to 800 Gb/s/λ net data rate in single polarization configuration for short reach scenarios.
Recursive Visual Explanations Mediation Scheme Based on DropAttention Model With Multiple Episodes Pool

In some DL applications such as remote sensing, it is hard to obtain the high task performance (e.g. accuracy) using the DL model on image analysis due to the low resolution characteristics of the imagery. Accordingly, several studies attempted to provide visual explanations or apply the attention mechanism to enhance the reliability on the image analysis. However, there still remains structural complexity on obtaining a sophisticated visual explanation with such existing methods: 1) which layer will the visual explanation be extracted from, and 2) which layers the attention modules will be applied to. 3) Subsequently, in order to observe the aspects of visual explanations on such diverse episodes of applying attention modules individually, training cost inefficiency inevitably arises as it requires training the multiple models one by one in the conventional methods. In order to solve the problems, we propose a new scheme of mediating the visual explanations in a pixel-level recursively. Specifically, we propose DropAtt that generates multiple episodes pool by training only a single network once as an amortized model, which also shows stability on task performance regardless of layer-wise attention policy. From the multiple episodes pool generated by DropAtt, by quantitatively evaluating the explainability of each visual explanation and expanding the parts of explanations with high explainability recursively, our visual explanations mediation scheme attempts to adjust how much to reflect each episodic layer-wise explanation for enforcing a dominant explainability of each candidate. On the empirical evaluation, our methods show their feasibility on enhancing the visual explainability by reducing average drop about 17% and enhancing the rate of increase in confidence 3%.
An Unequal Caching Strategy for Shared-Memory Graph Analytics

Recent advances in computer architecture significantly enhance the computational capacity of multicore systems. It allows large-scale graphs to be processed inside a single machine. Nevertheless, the irregular processing pattern of graph-structured data constrains the hardware resources from being productively utilized. In this paper, we investigate the constraints in two aspects: workload imbalance and parallel inefficiency. When a graph analytics algorithm is multithreaded, the thread time is highly diversified, indicating an uneven work distribution. Also, the intensive thread contention lowers the computing capacity of CPU cores, thereby hindering the effective utilization of CPU resources. To address these challenges, we present a proactive graph caching strategy that unequally segments graph components into cache-able subsets of varying sizes, namely Syze. First, the computational loads of cache-sized subgraphs are estimated. Then, the demanding subgraphs are further subdivided until certain threshold is met. Moreover, during the propagation of updates, a fraction of vertex ID (i.e., several bits) are encoded to facilitate the communication between subgraphs. As a result, Syze is able to balance the workloads amongst the logical cores by shortening the longest thread execution time. Meanwhile, it alleviates thread contention and thus elevates the parallel efficiency of multicores. Compared with well-optimized Ligra, Gemini and GPOP, Syze achieves accelerations by up to <inline-formula><tex-math notation="LaTeX">$17.76\times$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$11.67\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$2.81\times$</tex-math></inline-formula> respectively. Additionally, the side effects of Syze are evaluated, including raised cache misses and memory accesses. They play a trivial role in deciding the overall performance, as their costs are far outweighed by the gains from the even distribution of workloads and the improved utilization of multicores. <!--?query id="Q2"--> Author: Please confirm or add details for any funding or financial support for the research of this article. ?>
Blockchain applications in drug data records

Blockchain is a data storage technique in the form of blocks where the hash system and blocks that cannot be manipulated make blockchain suitable for storing important data. One of them is drug data, where currently in drug data, data manipulation often occurs which leads to drug counterfeiting. From previous research, blockchain in the medical world has been applied in storing patient history by utilizing smart contracts. Goals of blockchain application, drug data from manufacturers can be directly viewed and purchased by buyers. The research more towards innovation, blockchain as a database for storing drug data. Ethereum blockchain can be implemented and stored data can be well integrated with Smart Contract. The existence of smart contracts that support and facilitate transactions between producers and buyers. To maintain data security, system will be implementing access permissions in smart contracts to maintain data integrity and security. Eventually maintaining privacy, decentralization, transparency, and authentication in drug data and every drug transaction can be implemented properly.
Computer network design and implementation using load balancing technique with per connection classifier (PCC) method based on MikroTik router

A computer network system in a company or agency generally uses more than one Internet Service Provider (ISP) to maintain the quality of its internet services. When one of the ISP's services that are used is interrupted and the internet connection is lost, then the techniques that can be used are load balancing and failover. This study aims to establish a network of two ISPs using load balancing methods and failover techniques with MikroTik. Load balancing to equalize traffic loads and stabilize internet connections for each client and apply failover techniques with 2 ISPs. The method used is the Network Development Life Cycle which redesignates the new network topology by analyzing Quality of Service (QoS) based on the THIPON standard. Based on the test results, the QoS value obtained is 3.75 during busy internet usage hours and the QoS value is 4 at normal hours. Overall, the implementation of computer networks using load balancing techniques with the PCC method showed good changes, this was concluded based on the results of distributing questionnaires before and after implementation from the previous score of 0.4 rising to 0.89.
SkeletonCLIP: Recognizing Skeleton-based Human Actions with Text Prompts

Human action recognition has been a hot research for decades, and mainstream supervised frameworks include a feature extraction backbone and a softmax classifier to predict daily human actions. When the number of classes applied to the dataset changes, we must retrain the classifier on the well-trained backbone. This pipeline restricts the generalization and transfer ability of the model due to an extra training period. Moreover, replacing action labels with simple number labels discards useful semantic information and can only receive a meaningless classifier at last. In this work, we present a model SkeletonCLIP for skeleton-based human action recognition. We add an alternative text encoder to extract semantic information from labels while keeping the original sequence encoder. We use dot production to measure the similarities of sequence-text pairs in place of traditional classifier head and cross-entropy loss. Experiments from three human action datasets show that our framework can reach a higher recognition accuracy with the help of semantic information when training the network from scratch. The code has been shown at eunseo-v/SkeletonCLIP.
Japanese language learning game “Miryoku” using android-based speech recognizer API

The goal of this research is to produce a game that uses speech recognition technology that can be used to learn basic Japanese and to find out whether there is an increase in basic Japanese skills for beginner students who use game media for learning. The research method includes the pre-test and the final test in the form of a quiz, with questions addressed to players to determine the improvement achieved after playing the game “Miryoku” for five days. The Android-based SpeechRecognizer API will be implemented as the core of the algorithm. Data collection was carried out using the distribution of a survey form to 31 participants with minimal skills in Japanese and or who wish to learn Japanese. The result was an increase in the average score obtained by the participants from 64.77 out of 100 on the initial test to a score of 87.77 out of 100 on the final test. It can be concluded that game media is in demand and effective for learning Japanese as seen from the improvement.
Visual-Inertial Odometry Priors for Bundle-Adjusting Neural Radiance Fields

We present bundle-adjusting Neural Radiance Fields (BARF) with motion priors. Neural Radiance Field (NeRF) has opened up tremendous potential for neural volume rendering and 3D scene representations in recognition of their ability to synthesize photo-realistic novel views. BARF mitigates NeRF’s reliance on accurate 6-DoF camera poses, enabling scene learning with inaccurate camera poses. However, initializing estimates far from an optimal solution, such as BARF, can easily fall into local minima. We utilize Visual-Inertial Odometry Motion Priors to the BARF, which jointly optimizes 3D scene representations and camera poses, providing higher accuracy in view synthesis and a more stable motion estimate. The proposed method achieves results that outperform original BARF in real-world data, demonstrating the effectiveness of motion priors to knowledge use.
Autonomous Indoor Proximity Flight of a Quadcopter Drone Using a Directional 3D Lidar and VIO Sensor

This paper describes the implementation and the flight experiment result of the autonomous indoor proximity flight of a MAV(Micro Aero Vehicle) quadcopter. As the visual sensors for autonomous flight of the drone, light-weighted commercial products were used. For the flight control and visual sensor processing, several open softwares were used. After system structures are explained, the data processing procedures in software are explained briefly from the sensor input to the control of the drone. The flight simulation and real flight test were performed and their results are described.
Facial Expression Recognition based on Convolutional Neural Network with Sparse Representation

Facial Expression Recognition (FER) in the wild using Convolutional Neural Networks (CNNs) has been a challenge for years because of the significant intra-class variances and interclass similarities. In contrast, facial expression recognition in the wild is vital for human-computer interactions and has numerous applications. Enhancing the discriminative features extraction ability is one approach to solving this issue. In this work, a sparse transform is used to improve a CNN’s ability to extract features without adding to the network’s computational load. We use a sparse representation layer that is built by the Haar wavelet transform or shearlet transform prior to the convolutional layers of a standard CNN. With the proposed sparse representation layers, we introduce a VGGNet and an AlexNet architecture and conduct experiments on the FER2013 dataset without the use of additional training data. The experimental results demonstrated that the wavelet transform’s sparse representation layer can improve FER performance without increasing an excessive computational burden. We achieved testing accuracy of 73.25 percent on the FER2013 dataset using VGGNet paired with a sparse representation layer built inside a wavelet transform, which is among the best results for a single network.