Reviewerly/exHarmony huggingface dataset:
Research on Imbalanced Data Classification Based on Classroom-Like Generative Adversarial Networks

Most of the research on machine learning classification methods is based on balanced data; the research on imbalanced data classification needs improvement. Generative adversarial networks (GANs) are able to learn high-dimensional complex data distribution without relying on a prior hypothesis, which has become a hot technology in artificial intelligence. In this letter, we propose a new structure, classroom-like generative adversarial networks (CLGANs), to construct a model with multiple generators. Taking inspiration from the fact that teachers arrange teaching activities according to students' learning situation, we propose a weight allocation function to adaptively adjust the influence weight of generator loss function on discriminator loss function. All the generators work together to improve the degree of discriminator and training sample space, so that a discriminator with excellent performance is trained and applied to the tasks of imbalanced data classification. Experimental results on the Case Western Reserve University data set and 2.4 GHz Indoor Channel Measurements data set show that the data classification ability of the discriminator trained by CLGANs with multiple generators is superior to that of other imbalanced data classification models, and the optimal discriminator can be obtained by selecting the right matching scheme of the generator models.
Inference of Absolute Time Value from Temporal Expressions

In this paper, we explore and discuss a way to extract temporal information from natural language texts. The suggested method is divided into two parts: temporal expression recognition and temporal value inference. The former employs the conventional NER approach, using a BiLSTM-CRF architecture. The latter is implemented with a rule-based algorithm, which can be further developed in later work for better coverage of various temporal expressions. In terms of the corpus, we have selected 200 articles from one of the major Japanese newspaper companies to create an annotated corpus, classifying temporal expressions into five different types. As for the performance, we have achieved 0.866 in F-measure for the recognition of temporal expressions and 0.920 in accuracy for the inference of the absolute temporal values of the expressions. Combining the two modules and running them as an end-to-end system, we have attained 0.891 of F-measure.
Quality of Service of Quantum Entanglement in Mobile Networks

There are many problems in cellular communications cannot be resolved traditionally.The quantum communications can add new dimensions, safety, encryption and solution to the traditional networks because of its robust physical strength.However, it is not entirely realised how to adapt the quantum into the traditional communications because it is not entirely utilised.This paper addresses the necessary guidelines and assessments for future quantum solutions to the standard mobile cloud networks.In particular, using entanglement phenomenon to increase the performance of the X2 application (X2-AP) protocol by minimising the overhead signalling, represented by the time and energy consumption the conventional cloud encounters.We intended to offer a delay reduction while adapting the quantum technique into the cloud by modelling the latency of both paradigms.Finally, increasing the number of photons has decreased the delay to about 40% compared to the traditional network.In addition, the energy efficiency in the quantum case has been increased while decreasing the power consumption by about 10%.
Black-Box Accumulation Based on Lattices

Black-box accumulation (BBA) is a cryptographic protocol that allows users to accumulate and redeem points, e.g. in payment systems, and offers provable security and privacy guarantees. Loosely speaking, the transactions of users remain unlinkable, while adversaries cannot claim a false amount of points or use points from other users. Attempts to spend the same points multiple times (double spending) reveal the identity of the misbehaving user and an undeniable proof of guilt. Known instantiations of BBA rely on classical number-theoretic assumptions, which are not post-quantum secure. In this work, we propose the first lattice-based instantiation of BBA, which is plausibly post-quantum secure. It relies on the hardness of the Learning with Errors (LWE) and Short Integer Solution (SIS) assumptions and is secure in the Random Oracle Model (ROM).Our work shows that a lattice-based instantiation of BBA can be realized with a communication cost per transaction of about 199MB if built on the zero-knowledge protocol by (CRYPTO 2019) and the CL-type signature of (ASIACRYPT 2017). Without any zero-knowledge overhead, our protocol requires 1.8 MB communication.
Learning the two parameters of the Poisson–Dirichlet distribution with a forensic application

Abstract In forensic science, the rare type match problem arises when the matching characteristic from the suspect and the crime scene is not in the reference database; hence, it is difficult to evaluate the likelihood ratio that compares the defense and prosecution hypotheses. A recent solution consists of modeling the ordered population probabilities according to the two‐parameter Poisson–Dirichlet distribution, which is a well‐known Bayesian nonparametric prior, and plugging the maximum likelihood estimates of the parameters into the likelihood ratio. We demonstrate that this approximation produces a systematic bias that fully Bayesian inference avoids. Motivated by this forensic application, we consider the need to learn the posterior distribution of the parameters that governs the two‐parameter Poisson–Dirichlet using two sampling methods: Markov Chain Monte Carlo and approximate Bayesian computation. These methods are evaluated in terms of accuracy and efficiency. Finally, we compare the likelihood ratio that is obtained by our proposal with the existing solution using a database of Y‐chromosome haplotypes.
Path planning for Lunar polar exploration: Bayesian network-based sensor modeling of neutron spectrometers and sampling-based optimal motion planning

In this paper, we propose an exploration algorithm for a rover to search for water ice in the lunar polar region. It has been known that water ice is highly likely to exist in an area where there is no sunlight at all times, called a permanent shadowed region. During exploration in the permanent shadowed region, it is necessary that the rover must detect water ice efficiently while escaping to some illumination areas due to the limited amount of the rover's battery capacity. To this end, we first provide a suitable mathematical model of a sensor that aims at detecting water ice. In particular, the measurement model for the neutron spectrometer will be provided based on the Bayesian network. Then, based on the constructed sensor model and the RRT* algorithm, we provide an exploration algorithm such that the rover will search for water ice efficiently while escaping to the illumination area in finite time. We demonstrate the effectiveness of the proposed approach through numerical simulations.
A plain-text independent color image encryption system with multi-thread permutation and multi-channel diffusion

In this paper, we present a novel multi-threaded parallel permutation and channel-combined diffusion for image encryption which is independent of plain text. In our proposed method, the coupled map lattice is used to generate the key sequences for multi-thread permutation and diffusion. Then intra- and inter-thread permutations are achieved using multi-threading in combination with the tent mapping. For the subsequent diffusion, this paper introduces a method based on channel-combined diffusing which simultaneously diffuses three channels. Experimental results indicate a high encryption performance with the capability of effectively resisting the known plain text and differential attacks. Our proposed method also has a lower computational complexity which enables its applicability in practical scenarios.
Medical Text Classification System Based on Deep Learning

With the continuous development of the Internet, text information as the most common form of expression has naturally become a research hotspot, among which medical records and clinical diagnosis are important medical resources. However, due to data sparseness and high latitude, medical text classification is more challenging and difficult than other fields. This paper proposes a medical text classification model that combines an improved bidirectional long-term short-term memory (BI-LSTM) and attention mechanism. BI-LSTM is used to extract features and contains sentence information, and the attention mechanism is used to obtain different BI-LSTMs to encode sentence weights, and the attention mechanism is used to decode them. Finally, we use the SoftMax classifier to obtain the classification results. This article uses five public data sets and self-built medical data sets for experimental comparison and verification to verify the effectiveness of the method.
WITHDRAWN: Assessing and comparing the role of information and communication technology (ICT) and supply chain management (SCM) and its impact on contribution of e-commerce

This article has been retracted at the request of the Editor. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Entropy of Artificial Intelligence

We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy.
A Multi-window Bitcoin Price Prediction Framework on Blockchain Transaction Graph

Bitcoin, as one of the most popular cryptocurrency, has been attracting increasing attention from investors. Consequently, bitcoin price prediction is a rising academic topic. Existing bitcoin prediction works are mostly based on trivial feature engineering, that is, manually designed features or factors from multiple areas. Feature engineering not only requires tremendous human effort, but the effectiveness of the intuitively designed features can not be guaranteed. In this paper, we aim to mine the abundant patterns encoded in Bitcoin transactions, and propose k-order transaction graphs to reveal patterns under different scopes. We propose features based on a transaction graph to automatically encode the patterns. The Multi-Window Prediction Framework is proposed to train the model and make price predictions, which can take advantage of patterns from different historical periods. We further demonstrate that our proposed prediction method outperforms the state-of-art methods in the literature.
A Cross-Layer Fusion Multi-target Detection and Recognition Method Based on Improved FPN Model in Complex Traffic Environment

According to the low accuracy and speed of multi-target detection in complex traffic environment, this paper proposes a multeature Pyramid Networks) to improve the target detection accuracy and network generalization ability. Firstly, the five-layer architecture of ResNet101 is adopted to construct a top-down feature map by 2\(\times \)-sampling the spatial resolution, and the up-sampling map and the bottom-up feature map are combined by mixing of their elements to construct a feature layer, which has both integrating high-level semantic information and low-level geometric information. Secondly according to the imbalance of training samples in BBox regression, an improved Focal EIOU Loss function is proposed by using Efficient IOU Loss function and Focal Loss function. Finally, manual annotation of mixed data set is used for training by considering the actual situation of complex traffic environment. The experimental results show that this model has better detection effect than the other current models in complex traffic environment. The average detection accuracy and speed of the model are 2.5% and 2 FPS higher than FPN on KITTI test set, respectively. And the average detection accuracy and speed are 1.4% and 4 FPS higher than FPN on Cityscale test set.
Research on Fast Face Retrieval Optimization Algorithm Based on Fuzzy Clustering

Aiming at the problem of long retrieval time for massive face image databases under a given threshold, a fast retrieval algorithm for massive face images based on fuzzy clustering is proposed. The algorithm builds a deep convolutional neural network model. The model can be used to extract features from face photos to obtain a high-dimensional vector to represent the high-level semantic features of face photos. On this basis, the fuzzy clustering algorithm is used to perform fuzzy clustering on the feature vectors of the face database to construct a retrieval pedigree map. When the threshold is passed in for database retrieval of the target face photos, the pedigree map can be quickly retrieved. Experiments on the LFW face dataset and self-collected face dataset show that the model is better than the commonly used K-means model in face recognition accuracy, clustering effect, and retrieval speed and has certain commercial value.
An Optimizing Method Based On Combining Edge Computer And Long Short-Term Memory Networks Applied To Solar Power Forecasting

Along with the rapid development of solar energy sources on the global power system, the challenges related to the weather instability of this power type are also of increasing concern. Intending to solve these challenges, the study of solar power forecasting has become one of the most important problems in the power system field. Most of the published forecast algorithms aim to minimize prediction errors. However, in reality, convenience and data processing time are two necessary goals to commercialize a forecasting software. Recently, IoT is emerging as an effective solution to solve the above multi-objective problem. Especially, in the current trend of real-time forecasting, the application of Edge Computer in the IoT environment is bringing new opportunities for the goal of reducing data processing time while ensuring acceptable forecast errors. Moreover, Edge Computer also reduces the risk of network congestion due to its distributed processing characteristics. This paper introduces a new methodology using Edge Computer combined with the LSTM algorithm applied to the solar power forecasting problem. Real-time calculation results show that the accuracy can be improved by the good choice of the converted data type while the execution time is halved compared to without using Edge Computer.
Optimal Localized Trajectory Planning of Multiple Non-holonomic Vehicles

We present a trajectory planning method for multiple vehicles to navigate a crowded environment, such as a gridlocked intersection or a small parking area. In these scenarios, decoupled path planning techniques may not produce feasible solutions and a joint planning method is necessary to allow the vehicles to reach their destinations. We use multiple Reeds-Shepp (RS) non-holonomic dynamic models, and combine them into a single higher-dimensional non-holonomic multi-vehicle system. We then search for a simultaneous set of vehicle trajectories, represented by a single choreography curve in the higher-dimensional space, using the RRT&#x002A; graph-based search method. While variants of RRT are widely used, convergence is known only for the specific cases of holonomic systems and sub-Riemannian non-holonomic systems. We prove the algorithm&#x2019;s convergence in a more general setting and demonstrate the effectiveness of the approach through simulation and experimental studies on multiple robots. The proposed approach with guaranteed optimality can be used to locally resolve collisions among a small subset of vehicles in a large multi-vehicle trajectory planning problem in presence of obstacles.
Implementation and Analysis of various Encryption Techniques with Blowfish on various Data Files

In today's information technology era, data security is a big concern. Because data is stored in various locations throughout the world, it becomes especially dangerous in the cloud environment. Encryption is a technique, and various encryption methods play an imperative role to provide cloud data security. In cloud computing, encryption algorithms are employed to protect data security. The goal of data security is to limit access to just those who are concerned and permitted. In this work, we explain file size using AES, DES, BLOWFISH and Pailier, and compare their performance based on the examination of various data formats such as text files, image files, and word documents, among others, at various setups for individual users.
Deep Just-In-Time Defect Localization

During software development and maintenance, defect localization is an essential part of software quality assurance. Even though different techniques have been proposed for defect localization, i.e., information retrieval (IR)-based techniques and spectrum-based techniques, they can only work after the defect has been exposed, which can be too late and costly to adapt to the newly introduced bugs in the daily development. To assist developers to detect bugs in time and avoid introducing them, just-in-time (JIT) bug localization techniques have been proposed, which is targeting to locate suspicious buggy code after a change commit has been submitted. In this paper, we propose a novel JIT defect localization approach, named DeepDL (Deep Learning-based defect localization), to locate defect code lines within a defect introducing change. DeepDL employs a neural language model to capture the semantics of the code lines, in this way, the naturalness of each code line can be learned and converted to a suspiciousness score. The core of our DeepDL is a deep learning-based neural language model. We train the neural language model with previous snapshots (history versions) of a project so that it can calculate the naturalness of a piece of code. In its application, for a given new code change, DeepDL automatically assigns a suspiciousness score to each code line and sorts these code lines in descending order of this score. The code lines at the top of the list are considered as potential defect locations. Our tool can assist developers efciently check buggy lines at an early stage, which is able to reduce the risk of introducing bugs in time and improve the developers condence in the reliability of their software. We conducted an extensive experiment on 14 open source Java projects with a total of 11,615 buggy changes. We evaluate the experimental results considering four evaluation metrics. The experimental results show that our method outperforms the state-of-the-art by a substantial margin.
Recommendation and Prediction of Solar energy consumption for smart homes using machine learning algorithms

Solar Energy is a principal source of renewable energy generation. Solar intensity is directly proportionate to solar power generation and it is highly reliant on the weather. A model is proposed that predicts the amounts of solar radiation produced using weather information implemented using various machine learning techniques such as Gradient boosting, SVM, etc. The results allow us to make effective energy consumption plans for smart homes with efficient utilization of solar energy which may provide several economic benefits. Additionally, accurate forecasts would make users more prepared to switch between conventional and renewable sources as required. A comparison study is performed with various machine learning models to determine the best method for building a prediction model. The groundwork for constructing models that could be dispatched to various regions is laid out that will incorporate that geographic location’s weather data, and output accurate solar intensity predictions for that area. Furthermore, a recommendation system is proposed for the consumption of thus predicted energy.
A Comparative Study Between Different Treatments for Spider Phobia

The use of augmented reality (AR) environments to treat small animals phobia is an alternative that allows supporting therapy through the virtual, gradual and controlled exposure of the patient to the animal to which s/he is afraid. In this paper we compare four different treatments used in exposure therapy for spider phobia with thirty users, namely, a mobile haptic augmented reality system, an immersive and non-immersive augmented reality environment, and in-vivo direct interaction with a real spider. To compare these four conditions, all participants received a one-session treatment with all of them. The Fear of Spiders Questionnaire (FSQ) was applied at the beginning and end of the study to determine the impact of the treatments on participant´s arachnophobia, whereas the perception of usefulness and experience of use of each of the treatments were evaluated with a TAM-like on-exit questionnaire. The results showed that there is no significant difference with respect to the perception of usefulness among the three applications, while the haptic augmented reality system generated the least discomforting experience of use and helped to improve the performance of participants.
A Quantum 3D Convolutional Neural Network with Application in Video Classification

Quantum computing seeks to exploit the properties of quantum mechanics to perform computations at a fraction of the cost compared to the classical computing methods. Recently, quantum methods for machine learning have attracted the interest of researchers. Those methods aim to exploit, in the context of machine learning, the potential benefits that the quantum computers should be able to offer in the near future. A particularly interesting area of research in this direction, investigates the union of quantum machine learning models with Convolutional Neural Networks. In this paper we develop a quantum counterpart of a 3D Convolutional Neural Network for video classification, dubbed Q3D-CNN. This is the first approach for quantum video classification we are aware of.
Model digitalnog reflektivnog portfolija za strani jezik struke

In the conditions of online teaching and working in digital environment, digital portfolio is one of important tools in the process of improving the quality of teaching and the implementation of the student-oriented model of university teaching. The aim of this paper is to create a model of a reflective digital portfolio for foreign languages for specific purposes at the Teacher Education Faculty, University of Belgrade, with the aim of raising the level of language and digital literacy in the conditions of hybrid and online teaching. In addition to the modeling method, a case study was used which included students attending the academic course of Educational Technology at the Teacher Education Faculty, with a focus on interdisciplinary connections with foreign languages for specific purposes. The paper discusses the theoretical starting points, the possibilities and challenges of using a digital portfolio teaching foreign languages for specific purposes. The review and analysis concluded that a hybrid model is a starting model for an efficient digital portfolio development, and that the digital portfolio can be successfully used in both formative and summative evaluation of student achievement, with horizontal and vertical interdisciplinary connections playing a key role.