Reviewerly/exHarmony huggingface dataset:
Image Emotion Analysis Based on the Distance Relation of Emotion Categories via Deep Metric Learning

Existing deep learning-based image emotion analysis methods regard image emotion classification as a usual classification task in which the semantics of categories are clear. Nevertheless, the semantics of emotion categories are fuzzy, leading to that people are ambiguous between emotions of similar semantic distance when observing images. Considering the semantic distance of emotion categories, that is, far or near distance relations between them, we design a similarity decline rule to first pre-process the similarities of sample pairs making them comparable. Then, image emotion analysis is performed through deep metric learning. For key issues in deep metric learning, that is, sampling and weighting, we design adaptive decision boundaries for sampling and a double-weighted mechanism for sampled pairs which is integrated in our proposed emotion constraint loss, which learns more information contributing to update model by boasting the weights. Therefore, more expressive embedding features are learned from embedding space. Thus, the similarity of pairs from adjacent categories is larger than that from far away ones. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods. In addition, the ablation experiments show that it is necessary to consider the semantic distance of emotion categories in image emotion analysis.
A Study on the Efficacy of Model Pre-Training In Developing Neural Text-to-Speech System

In the development of neural text-to-speech systems, model pre-training with a large amount of non-target speakers’ data is a common approach. However, in terms of ultimately achieved system performance for target speaker(s), the actual benefits of model pre-training are uncertain and unstable, depending very much on the quantity and text content of training data. This study aims to understand better why and how model pre-training can positively contribute to TTS system performance. It is postulated that the pre-training process plays a critical role in learning text-related variation in speech, while further training with the target speaker’s data aims to capture the speaker-related variation. Different test sets are created with varying degrees of similarity to target speaker data in terms of text content. Experiments show that leveraging a speaker-independent TTS trained on speech data with diverse text content can improve the target speaker TTS on domain-mismatched text. We also attempt to reduce the amount of pre-training data for a new text domain and improve the data and computational efficiency. It is found that the TTS system could achieve comparable performance when the pre-training data is reduced to 1/8 of its original size.
Solving generalized eigenvalue problems by ordinary differential equations on a quantum computer

Many eigenvalue problems arising in practice are often of the generalized form <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>A</mml:mi><mml:mrow><mml:mtext mathvariant="bold">x</mml:mtext></mml:mrow><mml:mo>=</mml:mo><mml:mi>λ</mml:mi><mml:mi>B</mml:mi><mml:mrow><mml:mtext mathvariant="bold">x</mml:mtext></mml:mrow></mml:math> . One particularly important case is symmetric, namely <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>A</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi></mml:math> are Hermitian and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>B</mml:mi></mml:math> is positive definite. The standard algorithm for solving this class of eigenvalue problems is to reduce them to Hermitian eigenvalue problems. For a quantum computer, quantum phase estimation is a useful technique to solve Hermitian eigenvalue problems. In this work, we propose a new quantum algorithm for symmetric generalized eigenvalue problems using ordinary differential equations. The algorithm has lower complexity than the standard one based on quantum phase estimation. Moreover, it works for a wider case than symmetric: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>B</mml:mi></mml:math> is invertible, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>B</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mi>A</mml:mi></mml:math> is diagonalizable and all the eigenvalues are real.
Composing the Network with Streams

We present Aleatora, an early-stage framework for building compositions from lazy, effectful streams. Aleatora’s streams, which may be combined by sequential, parallel, or functional composition, are well-suited to expressing interactive and aleatoric musical compositions. Aleatora includes a networking module which aids in writing compositions for the Internet of Sounds using network data sources such as OSC, external APIs, and Internet sound repositories (e.g. Freesound). This paper describes the design and implementation of Aleatora and demonstrates how it can facilitate weaving external input sources, such as network streams, into compositions.
Differential Evolution Algorithm for Multimodal Optimization: A Short Survey

Most of the real-world problems are multimodal in nature. Several algorithms have been proposed to solve multimodal optimization problems. Classical gradient-based methods fail for optimization problems in which functions are either discontinuous or non-differentiable. Differential Evolution (DE) is simple to implement population-based heuristic method used for solving optimization problems even if the function is discontinuous or non-differentiable. It is proved to have one of the fastest rates of convergence toward the optima. The search behavior of DE algorithm is governed by its parameters. DE has won top ranks in many IEEE CEC competitions as it has outperformed its competitors in solving real parameter space optimization problems. DE and its variants have also been applied to solve various engineering optimization problems. This paper aims to cover the work done in the area of real parameter single objective multimodal optimization using differential evolution algorithm.
Picket: guarding against corrupted data in tabular data during learning and inference

Data corruption is an impediment to modern machine learning deployments. Corrupted data can severely bias the learned model and can also lead to invalid inferences. We present, Picket, a simple framework to safeguard against data corruptions during both training and deployment of machine learning models over tabular data. For the training stage, Picket identifies and removes corrupted data points from the training data to avoid obtaining a biased model. For the deployment stage, Picket flags, in an online manner, corrupted query points to a trained machine learning model that due to noise will result in incorrect predictions. To detect corrupted data, Picket uses a self-supervised deep learning model for mixed-type tabular data, which we call PicketNet. To minimize the burden of deployment, learning a PicketNet model does not require any human-labeled data. Picket is designed as a plugin that can increase the robustness of any machine learning pipeline. We evaluate Picket on a diverse array of real-world data considering different corruption models that include systematic and adversarial noise during both training and testing. We show that Picket consistently safeguards against corrupted data during both training and deployment of various models ranging from SVMs to neural networks, beating a diverse array of competing methods that span from data quality validation models to robust outlier detection models.
Topic Detection for Video Stream based on Geographical Relationships and its Interactive Viewing System

During the recent years of Internet TV spread, researches on recommending relevant information for TV programs have been actively conducted. The NHK’s Hybridcast provides a service that recommends relevant information on the same screen during the broadcast of a TV program. However, there is currently no service for recommending supplementary information based on the users’ viewing behavior. Based on this research background, we first extract geographic words (location names) and topics of each scene using closed captions of TV programs. Next, we analyze the user’s viewing behavior to extract the scenes selected by the user in the sequence. After that, we can detect the topics of the user’s selected scenes. Therefore, the supplementary information is recommended by generating queries based on geographical relationships using geographical words and topics. In this paper, we discuss our proposed system for supporting interactive viewing of TV programs, which is based on the viewing behavior of users and geographic relationships.
Day-Ahead Solar Irradiance Forecasting Model

The article describes the development of a day-ahead solar irradiance forecasting model in an hourly resolution. A statistical model based on the method of artificial neural networks with a multilayer perceptron architecture was chosen as a forecasting technique. The ground measurements of solar radiation and the weather archive, consisting of the main meteorological quantities, were used for training the model. The selecting of optimal input data combination and hyperparameter tuning were carried out according to the criterion of the minimum error. The developed model on the basis of the multilayer perceptron consists of 5 inputs, 64 neurons in a hidden layer with 1 neuron in the output layer. In the process of predicting the hourly average solar irradiance on a tilted surface, numerical weather forecast data (total cloudiness, air temperature, relative humidity, atmospheric pressure) provided by two different weather services and the cosine of the solar incidence angle as a geometric/temporal parameter were used as the input variables.
Novel Image Encryption and Compression Scheme for IoT Environment

Latest advancements made in the processing abilities of smart devices have resulted in the designing of Intelligent Internet of Things (IoT) environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assisted environment since it contains visual sensors that examine the surroundings from a number of overlapping views by capturing the images incessantly. Since IoT devices generate a massive quantity of digital media, it is therefore required to save the media, especially images, in a secure way. In order to achieve security, encryption techniques as well as compression techniques are employed to reduce the amount of digital data, being communicated over the network. Encryption Then Compression (ETC) techniques pave a way for secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presents a new ETC technique to accomplish image security in IoT environment. The proposed model involves three major processes namely, IoT-based image acquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA) abbreviated as ST-NMWOA. The optimal key generation of signcryption technique takes place with the help of NMWOA. Besides, the presented model also uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed model. The obtained values infer that the presented model is superior in terms of both compression efficiency and data secrecy in resource-limited IoT environment.
Phase manipulated two-mode entangled state from a phase-sensitive amplifier

The phase manipulation of the two-mode entangled state, which can flexibly control the combination of quadrature components on demand, is important for continuous variable (CV) quantum information and quantum metrology. Here, we experimentally demonstrate the phase manipulation of entangled state by using a phase-sensitive amplifier (PSA) based on four-wave mixing (FWM) process. The entanglement with different phase space squeezing orientations can be generated by directly changing the phase of the PSA. Our scheme is concise and can be expanded to generate multi-parties entangled states on demand. Our results here pave the way to realize a phase-coded quantum key distribution protocol and squeezing-enhanced Raman spectroscopy.
Indexing structures for the PLS blockchain

Abstract This paper studies known indexing structures from a new point of view: minimisation of data exchange between an IoT device acting as a blockchain client and the blockchain server running a protocol suite that includes two Guy Fawkes protocols, PLS and SLVP. The PLS blockchain is not a cryptocurrency instrument; it is an immutable ledger offering guaranteed non-repudiation to low-power clients without use of public key crypto. The novelty of the situation is in the fact that every PLS client has to obtain a proof of absence in all blocks of the chain to which its counterparty does not contribute, and we show that it is possible without traversing the block’s Merkle tree. We obtain weight statistics of a leaf path on a sparse Merkle tree theoretically, as our ground case. Using the theory we quantify the communication cost of a client interacting with the blockchain. We show that large savings can be achieved by providing a bitmap index of the tree compressed using Tunstall’s method. We further show that even in the case of correlated access, as in two IoT devices posting messages for each other in consecutive blocks, it is possible to prevent compression degradation by re-randomising the IDs using a pseudorandom bijective function. We propose a low-cost function of this kind and evaluate its quality by simulation, using the avalanche criterion.
The MaLET Model — Maturity Levels for Exploratory Testing

Based on multiple series of interviews and workshops, this paper presents the MaLET model – a representation of the typical evolution path for companies who successfully adopt exploratory testing. The model provides a step-by-step approach to systematically improve exploratory testing over time, and shows how and why capabilities may regress. The MaLET model was validated through a series of interviews with 20 interviewees from eight case study companies in separate industry segments. The interviews also revealed examples on improvement initiatives that had failed in the companies, showing that improvement initiatives tend to fail if they are not planned in an order corresponding to the maturity levels in the model. The MaLET model was well received by the interviewees during the validation, describing the model as sound, relevant and useful in practice.
Dynamic Gain Military Game Algorithm Based on Episodic Memory

In recent years, the development of deep reinforcement learning has become a new research frontier in the field of artificial intelligence. In the complex and changeable battlefield environment of military confrontation, how to use reinforcement learning methods to assist in formulating a set of efficient military game strategies has become a new research direction. The military game confrontation environment usually uses static reward settings ignoring the importance of captured target in the tactical arrangement. This paper starts from the use of episodic memory Q-network model to train drone swarm confrontation and uses expert knowledge to design a local state target threat assessment method. By analyzing the threat indicator of killed target, it provides drones with dynamic rewards to help drones comb local situation. Aiming at the problem that the static weight of episodic memory is very inefficient by manual adjustment in complex military confrontation environment, we use dynamic weight adjustment strategy inspired by multitask learning. Through the improvement of above method, we establish a dynamic gain drones confrontation model based on episodic memory with methods of dynamic reward mechanism and dynamic weight adjustment. The effectiveness of model is verified by machine-to-machine confrontation which provides thoughts for the analysis of military game decision-making in the complex battlefield environment.
Informative Planning in the Presence of Outliers

Informative planning seeks a sequence of actions that guide the robot to collect the most informative data to build a large-scale environmental model or learn a dynamical system. Existing work in informative planning mainly focuses on proposing new planners and applying them to various robotic applications such as environmental monitoring, autonomous exploration, and system identification. The informative planners optimize an objective given by a probabilistic model, e.g., Gaussian process regression (GPR). In practice, the ubiquitous sensing outliers can easily affect the model, resulting in a misleading objective. A straightforward solution is to filter out the outliers in the sensing data stream using an off-the-shelf outlier detector. However, informative samples are also scarce by definition so they might be falsely filtered out. In this paper, we propose a method to enable the robot to re-visit the locations where outliers were sampled besides optimizing the informative planning objective. The robot can collect more samples in the vicinity of outliers and update the outlier detector to reduce the number of false alarms. We achieve this by designing a new objective for the Pareto Monte Carlo tree search (MCTS). We demonstrate that the proposed framework performs better than applying an outlier detector naively.
Emotion Detection Using Bi-directional LSTM with an Effective Text Pre-processing Method

In a real-life scenario, extracting emotion from unstructured text is an active and challenging area of research. It has diverse applications in various aspects of our daily life To overcome various challenges involved in detecting emotion from text, researchers from diverse fields applied various machine learning algorithms. However, deep learning methods such as long short-term memory is effective to detect emotion by maintaining the sequence structure of the text. In this work, we use Bi-directional long short-term memory with attention layer for emotion detection for better accuracy for prediction. In addition, we employ a text preprocessing method to improve further results. We perform the experiments on three data sets and the models are evaluated based on the classification accuracy.
Short Paper: Debt Representation in UTXO Blockchains

We provide a UTXO model of blockchain transactions that is able to represent both credit and debt on the same blockchain. Ordinarily, the UTXO model is solely used to represent credit and the representation of credit and debit together is achieved using the account model because of its support for balances. However, the UTXO model provides superior privacy, safety, and scalability when compared to the account model. In this work, we introduce a UTXO model that has the flexibility of balances with the usual benefits of the UTXO model. This model extends the conventional UTXO model, which represents credits as unmatched outputs, by representing debts as unmatched inputs. We apply our model to solving the problem of transparency in reverse mortgage markets, in which some transparency is necessary for a healthy market but complete transparency leads to adverse outcomes. Here the pseudonymous properties of the UTXO model protect the privacy of loan recipients while still allowing an aggregate view of the loan market. We present a prototype of our implementation in Tendermint and discuss the design and its benefits.
Deep Robot Path Planning from Demonstrations for Breast Cancer Examination

In 2020, breast cancer affected around two million people worldwide. Early cancer detection is, therefore, needed to save many lives and reduce treatment costs. Nowadays, mammography and self- palpation are the most popular monitoring methods. The high number of cases and the difficulty of correct self-diagnosis has prompted this research work to design a fully autonomous robot to perform breast palpation. Specifically, this study focuses on learning the path for a successful breast examination of a silicone model. Learning from demonstrations proved to be the most suitable approach to reproduce the desired path. We implemented a teleoperation control between two Franka Emika Panda robots with tactile and force feedback to perform palpation on both simple and complex shapes. Moreover, we created a dataset of simple palpation strategy. Finally, we developed and tested different sequential neural networks such as Recurrent Neural Network (RNN), Long short-term memory (LSTM), Gated recurrent unit (GRU) and Temporal Convolutional Network (TCN) to learn the stochastic behaviour of the acquired palpation trajectories. The results showed that TCN is capable of reproducing the desired behaviour with more accuracy and stability than the other models.
Curvature Aware Motion Planning with Closed-Loop Rapidly-exploring Random Trees

The road's geometry strongly influences the path planner's performance, critical for autonomous navigation in high-speed dynamic scenarios (e.g., highways). Hence, this paper introduces the Curvature-aware Rapidly-exploring Random Trees (CA-CL-RRT), whose planning performance is invariant to the road's geometry. We propose a transformation strategy that allows us to plan on a virtual straightened road and then convert the planned motion to the curved road. It is shown that the proposed approach substantially improves path planning performance on curved roads as compared to prior RRT-based path planners. Moreover, the proposed CA-CL-RRT is combined with a Local Model Predictive Contour Controller (LMPCC) for path tracking while ensuring collision avoidance through constraint satisfaction. We present quantitative and qualitative performance results in two navigation scenarios: dynamic collision avoidance and structured highway driving. The results demonstrate that our proposed navigation framework improves the path quality on curved highway roads and collision avoidance with dynamic obstacles.
Cloud Technology Dual Data Center Information System Based on Disaster Recovery Platform

In this paper, the complete design process of cloud technology dual data center information system based on disaster recovery platform is introduced, and the key steps are described in detail. The main feature of the system is to use the module embedding technology to realize the organic combination of the system and the platform. At the same time, it supports a variety of replication modes to ensure the consistency of data. It improves the data security and reduces the bandwidth requirements through encryption and compression transmission. In addition, it provides a disaster recovery mechanism to ensure that the master data system can be easily migrated and moved back in case of disaster. Both theory and experiment prove that the data disaster recovery system can ensure the data reliability and availability.