Reviewerly/exHarmony huggingface dataset:
Associative unification in Maude

Semantic unification has been much studied since the early days of automated reasoning, and theories such as commutativity and associativity-commutativity (AC) have been built into a number of theorem provers and term rewriting engines. Associativity, which captures the notion of lists, might be considered even more basic than AC, which captures the notion of multisets. However despite being decidable, associative unification has not been widely implemented in general purpose systems, due to its infinitary nature and the high complexity of available algorithms. In this paper we describe the support for associative unification implemented in the Maude system, which is incomplete, but handles many useful cases and smoothly combines with other supported theories.
Investigation of spurious electromagnetic radiation in residential premises

The article discusses side electromagnetic radiation in residential premises, provides the results of measurements of their spectral components.
Small Files in HDFS and Their Impact on Hadoop Performance

In this paper, we discuss the issues that arise with fragmentation of data in big data platforms such as Hadoop when small files are processed. We examine the conditions under which small files cause problems, and review some approaches that have been made to mitigate these problems. We conduct experiments regarding the interaction between fragmentation and batch processing performance, and argue that with some care in application design and configuration, most of the fragmentation problems can be avoided without extensive changes to the Hadoop infrastructure.
Domain Adaptation with Stacked Convolutional Sparse Autoencoder

Domain adaptation aims to transfer knowledge from source domain to promote the learning task in target domain. The key problem of domain adaptation is how to learn more abstract and discriminative features for across domains. Recently, autoencoder based methods have achieved substantial performance in domain adaptation for the advantages of no label requirement and fast convergence speed. However, most of existing methods rely on the classical structure of autoencoder or integrate the regularization item into the objective function, which poses challenges for more abstract feature representation learning. To address this problem, we propose a domain adaptation method with Stacked Convolutional Sparse Autoencoder (short for SCSA). More specifically, more discriminative features are learned by convolutional lower layers with kernels learned by a sparse autoencoder. Meanwhile, the Reconstruction Independent Component Analysis (RICA) is introduced for independent component analysis of the input data. Extensive experimental results demonstrate the effectiveness of our proposed methods compared to several state-of-the-art domain adaptation methods.
Text-to-Speech Systems for Filipino Using Unit Selection and Deep Learning

There are several text-to-speech (TTS) systems developed and published for Philippine languages, particularly in Filipino and Cebuano. However, there is still a need to improve the performance of existing systems. Due to the limited amount of linguistic resources and lack of speech data available for Philippine languages, developing a reliable TTS system to support these languages becomes difficult. In this paper, we implement and evaluate the performance of two TTS systems for Filipino. We implemented two methods: unit selection using MaryTTS and deep learning approach using Tacotron-2. We tried applying modification on the F0 contour and duration of the unit selection system and used voice conversion to augment the training data of Tacotron-2. The unit selection system achieved a mean opinion score (MOS) of 3.05, however, the boundary-based F0 modification yielded perceivable distortions in the output and requires more enhancements to become more effective. On the other hand, the use of voice conversion to transform the original multi-speaker data into single-speaker data and producing more samples for training boosted the Tacotron-2 performance from an overall MOS of 1.51 to 2.01.
Using a Two-Stage GAN to Learn Image Degradation for Image Super-Resolution

Recent super-resolution (SR) methods based on generative adversarial networks (GANs) almost assume that the degradation process is known. Most of these works are to use bicubic or bilinear down-sampling to obtain low-resolution (LR) images, but for real-world images, these methods often can not recover the details well. Thus affecting the performance. In this paper, we propose to first build a self-attention gradient degradation GAN, then build a self-attention gradient super-resolution GAN to alleviate the above problem. Specifically, first of all, we learn the down-sampling process by self-attention gradient degradation GAN to approximate the real-world degradation of high-resolution (HR) images, and use unpaired HR and LR images in the training process. Then, we get the LR images by self-attention gradient degradation GAN, and send them into the self-attention gradient super-resolution GAN together with the corresponding original HR images to get the SR images. The experimental results show that our method is superior to other state-of-the-art methods in terms of FID and we get competitive results on PSNR. It also potentially means that our method can be used for other categories of images.
Improved Real-Time Traffic Congestion Detection with Automatic Image Cropping using Online Camera Images

Traffic congestion causes severe traffic delays, increases fuel wastage and monetary losses. Real-time information on traffic congestion can help road users avoid congested road segments and thus reduce commute time. In this paper, we propose an improved version of the real-time traffic congestion detection system using online camera images. To improve the detection accuracy, we propose an automatic image cropping method to identify the region of interest, based on the detected locations using the bounding boxes produced by YOLOv4. We improve the use of instantaneous intersection over union for multiple bounding boxes (mIOU) values to simplify the process of traffic status estimation, by taking the weighted average of the current and the previous instantaneous mIOU values. The experimental results show that it is feasible to define a congestion detection threshold for weighted mIOU when the time interval between the two images is greater than 2 seconds. With a threshold of 0.3, the average Precision and the average Recall Rate can achieve 89.84&#x0025; and 92.09&#x0025; in the manually labeled test data sets. The detected numerical traffic status results can be used for short-term traffic prediction.
The Impact of Feature Extraction on Multi-Source Sentiment Analysis

The rapid growth of internet users combined with the increasing dominance of online review sites and social media platforms, have given rise to the importance of sentiment analysis, also known as opinion mining, seeks to determine what other people believe and comment. Almost every enthusiastic or person who loves social platforms likely to articulate their ideas in the shape of comments on various social media platforms, and this is viewed as the main resource of sentiment analysis. These comments not only communicate people’s feelings, but also provide insight into their moods. Because the text on these media is unstructured, we must first preprocess it, employing six different preprocessing approaches, before extracting features from the pre-processed data. Some of the examples of feature extraction techniques are TF-IDF, word embedding, Bag of Words and word count, noun count are feature based natural language processing. Apart from the work that has already been done in text analytics, feature extraction in sentiment analysis is presently a hot topic of research. The impact of existing methodologies and approaches for feature extraction in sentiment analysis on the performance of various sentiment classification algorithms is discussed in this review study.
Semi-supervised Medical Image Segmentation with Distribution Calibration and Non-local Semantic Constraint

The available medical images with accurate segmentation masks are usually limited due to the expensive and time-consuming annotation cost. Many semi-supervised approaches tried to exploit a large amount of unlabeled data together with the small number of labeled data. However, their learned segmentation models can easily become overfitted on the biased labeled data, mainly because of the misalignment between the labeled and unlabeled data distribution. To address this challenging problem, we propose a novel semi-supervised model with distribution calibration and non-local semantic constraint for medical image segmentation. In specific, we explicitly calibrate the learned feature distributions of the labeled and unlabeled data to make them aligned. Meanwhile, we add a special nonlocal semantic loss to encourage the learned features to be more discriminative for the segmentation task at the same time. Consequently, our final segmentation networks have the advantage to better generalize on the unlabeled data in both training and test set. Experimental results on three popular medical image segmentation benchmarks demonstrate that our proposed model achieves superior performance over other state-of-the-art methods. We will release our source code in this URL.
An Approach to Scene Change Detection

Millions of videos are uploaded each day to Youtube and similar platforms. One of the many issues that these services face is the extraction of useful metadata. There are a lot of tasks that arise with the processing of videos. For example, putting an ad is better in the middle of a video, and as an advertiser, one would probably prefer to show the ad in between scene cuts, where it would be less intrusive. Another example is when one would like to watch only through the most interesting or important pieces of video recording. In many cases, it is better to have an automatic scene cut detection approach instead of manually labeling thousands of videos. The scene change detection can help to analyze video-stream automatically: which characters appear in which scenes, how many of them interact and for how long, their relations and importance, and also to track many more issues. The potential solution can rely on different facts: objects appeared, contrast or intensity changed, other colorization appeared, background changed, even sound changed. In this work, we propose the method for effective scene change detection, which is based on thresholding and fade-in/fade-out scene analysis. It uses computer vision and image analysis approaches to identify the scene cuts. The comparison with the PySceneDetect is included. Experimental results demonstrate the effectiveness of the proposed scene change detection approach.
Robot Arm Control Using Reward-Modulated Hebbian Learning

In recent years, soft robots with “softness” have been attracting much attention. Since soft robots have “softness”, they are expected to be able to perform delicate tasks that only humans can do. On the other hand, it is challenging to control. Therefore, in this research, we focused on reservoir computing with a biologically inspired learning algorithm. Reward-modulated Hebbian learning, one of the reservoir computing frameworks, is based on Hebbian learning rules and rewards and allows us to train the network without explicit teacher signals. The rewards are provided depending on the predicted and actual state of the environment influenced by the exploratory noise. We demonstrate that our model successfully controls the robot arm so that the tip position of the arm draws a given target trajectory.
Practical Research on Artificial Intelligence and Internet of Things in Smart Home

With the development of artificial intelligence, Internet of things and other information technology, people have higher requirements for the quality of life. The concept of smart home has become popular in recent years. In the scene of smart home, the smart speaker can accompany the elderly and children, the intelligent refrigerator can be responsible for people’s diet health, and the intelligent wardrobe can help people dress up. With the yearning for the ideal life scene in the smart family, I conceived the future smart family plan and considered the challenges in the future smart family development process. I believe that the technical solutions of smart home will be able to enter thousands of households in the future and bring more intelligent life to everyone.
Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.
Improved Ciphertext-Only Attack on GMR-1

The GEO-Mobile Radio Interface-1 (GMR-1) is a satellite communication standard used in Thuraya, a United Arab Emirates-based regional mobile satellite service provider. The specification of the encryption algorithm used in GMR-1 was not disclosed until it was uncovered by Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> in 2012 through reverse engineering. Given that A5-GMR-1, a stream cipher used in GMR-1, is primarily based on A5/2, Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> presented a ciphertext-only attack from the attacks on A5/2. Their ciphertext-only attack recovers the session key from multiple sets of 24 ciphertexts in an average of 32.1 min and requires 400 GB of pre-computed data. This study enhances Driessen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s ciphertext-only attack on A5-GMR-1 in all aspects of time, memory, and data. Our contributions are fourfold. First, we optimize the inefficient part of the previous attack. As a result, our ciphertext-only attack recovers the session key from multiple sets of 13 ciphertexts in less than 1 second and requires 400 MB of pre-computed data. Second, we propose novel memory-saving techniques. These techniques reduce the memory complexity to 216 ~ 289 MB without increasing the time and data complexity. Third, we present several time-memory-data tradeoff techniques. Using these techniques, we can present an attack that meets the desired conditions, such as memory minimization or data minimization. Furthermore, while the complexity of the previous attack is presented vaguely as “multiple sets” of 24 ciphertexts, these techniques allow us to accurately calculate the time, memory, and data complexity of the attack. Finally, we demonstrate that A5-GMR-1 can be attacked without frame numbers. To find out the frame number of each ciphertext, it is necessary to analyze and synchronize multiple channels. We present a plaintext recovery attack that does not require these processes.
Adaptive Learning Algorithms and Platforms: A Concise Overview

The distance education has become an indispensable teaching method, especially after the situation of Covid-19. For this reason, educational institutions are looking for e-learning platforms that offer better course management, ease of administration, user friendly, and achieving the learning objectives. Adaptive learning is considered an active research area, it enables to detect learning style of learners based on their behaviors and learning purposes in order to recommend relevant course materials. The objective of this article is to present an overview of personalization in the traditional learning system and the new developed systems as well as the approaches used to understand the learner’s individual needs. Furthermore, this work analyzes the problems in these systems and presents the prospect of development.
Exploiting Low-Rank Latent Gaussian Graphical Model Estimation for Visual Sentiment Distributions

Currently, an increasing number of applications and services has encouraged users to openly express their emotions via images. Unlike visual sentiment classification, visual sentiment distribution learning exploits the overall distribution to represent the relative importance of sentiment labels. Considering that most relevant studies have failed to completely model correlation structures or explicitly apply them to unknown instances, in this paper, we proposed a low-rank latent Gaussian graphical model estimation (LGGME) method for visual sentiment distribution learning tasks. There are three main characteristics of LGGME: 1) an integrated inverse covariance matrix whose parameters characterize the latent correlation structures between and within features and sentiments is estimated based on the sparse Gaussian graphical model; 2) a multivariate normal assumption is assigned on the concatenated latent feature representations and the estimated sentiment distributions instead of the original observations for a reasonable surrogate; and 3) the latent feature representations are projected from a low-rank subspace, which is also available for unseen instances, and the estimated sentiment distributions are evaluated by KL divergence to ensure a suitable setting for distribution learning. We further developed an effective optimization algorithm based on the alternating direction method of multipliers (ADMM) for our objective function. The experimental results obtained on three publicly available datasets demonstrate the superiority of our proposed method.
An A* based path planning approach for autonomous vehicles

This paper presents a path planning algorithm for an autonomous vehicle. The proposed method uses a tree to generate an obstacle-free path (branches of the tree) in the presence of static obstacles. The tree's growth is constrained by the vehicle's kinematic constraints, such as maximum turning angle and linear velocity. The tree generation method proposed in this work generates branches only in the direction of viable vehicle movement, making the proposed tree generation method efficient. The algorithm uses a heuristics-based A* algorithm which evaluates the tree to find an obstacle-free path. The A* algorithm uses the cost value defined at the tree branches to estimate the obstacle-free path. The developed algorithm is versatile and can be applied to autonomous vehicles, such as Autonomous Underwater Vehicle (AUV) and Autonomous Ground Vehicle (AGV).
Symbolic Reasoning About Quantum Circuits in Coq

A quantum circuit is a computational unit that transforms an input quantum state to an output one. A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it. However, when the number of qubits increases, the matrix dimension grows exponentially and the computation becomes intractable. In this paper, we propose a symbolic approach to reasoning about quantum circuits. It is based on a small set of laws involving some basic manipulations on vectors and matrices. This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq, as demonstrated with some typical examples.
Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model.
Using AI As A Techno-Regulatory Enforcement Tool

AI can be used in a variety of ways toward the advancement of our laws, including the enforcement of regulatory or legislated enactments. The seemingly mundane speed bump proffers a low-tech indication of such enforcement, beyond which AI will gradually be used in an akin high-tech manner. This raises important questions about techno-regulatory tools and their suitable uses.
Entities and Relations Aware Graph Convolutional Network for Knowledge Base Completion

Graph Neural Networks (GNNs) have recently been shown to be quite effective in modeling graph-structured data. Recent methods such as RGCN and SACN, have achieved the most advanced results in knowledge graph completion. However, previous efforts are mostly restricted to aggregating information given by neighboring nodes only, ignoring the information given by neighboring edges. This paper proposes a novel Entities and Relations Aware Graph Convolutional Network (ERA-GCN), with an encoder-decoder framework which jointly embeds both entities and relations in a multi-relation graph. In the encoder end, ERA-GCN uses a weighted graph convolutional network to capture both graph structure and neighborhood information. In the decoder end, we utilize Conv-TransE to retain the translational property between entity and relation embedding, leading to better link prediction performance. We evaluate our proposed method on standard FB15k-237 and WNISRR datasets, and achieve about 11% relative improvement compared to current state-of-the-art ConvE in terms of HITS@l, HITS@3 and HITS@10.