Reviewerly/exHarmony huggingface dataset:
An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection

Anomaly detection is a classic and crucial problem in the field of artificial intelligence, which aims to find instances that deviate so much from the main distribution of data or differ from known instances.This paper explores how to combine advanced deep learning techniques with traditional probabilistic statistical methods for anomaly detection.We propose a very effective and concise semi-supervised anomaly detection method named ''ORGAN-KDE'' based on the orthogonal generative adversarial network (O-GAN) and kernel density estimation.In the training phase, we use the encoder of O-GAN to learn the latent representation of normal data, namely the code of normal data, and then use the kernel density estimation to solve the probability density function of code.The code of normal sample obtained through the trained encoder can get a larger probability value when passing through the trained kernel density estimator, while the code of anomalous sample can get a smaller probability value, so as to achieve the purpose of anomaly detection.Compared with other anomaly detection methods based on GAN, our method has a very simple network structure, and experiments have proved that it performs well on both structured datasets and image datasets.
Latent Topic-Aware Multi-label Classification

In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels. In our study, we assume that there exists a common feature representation for labels in each topic. Then, feature-label correlation can be exploited in the latent topic space. This paper shows that the sample and feature exaction, which are two important procedures for removing noisy and redundant information encoded in training samples in both sample and feature perspectives, can be effectively and efficiently performed in the latent topic space by considering topic-based feature-label correlation. Empirical studies on several benchmarks demonstrate the effectiveness and efficiency of the proposed topic-aware framework.
Empirical method for modeling crystal lattice parameters of A2XY6 cubic crystals using grid search-based extreme learning machine

The lattice parameters of A2XY6 (A = K, Cs, Rb, and Tl; X = tetravalent cation; Y = F, Cl, Br, and I) cubic crystals play significant roles in designing materials for specific technological applications and uniquely describe the material crystal structure. Despite the importance of its lattice parameters, the experimental determination of these parameters requires special sophisticated equipment, while the first principle calculation consumes appreciable time and might need complex software packages. The existing empirical relation in the literature is characterized by large percentage deviation, and the recently proposed machine learning support vector regression method cannot be empirically implemented on new compounds. This present work fills the research gap through the development of empirical relation between the lattice parameters, electronegativity and ionic radii of the constituting ions using extreme learning machine (ELM) with the grid search (GS) hyper-parameters optimization method. The proposed model is developed through the analysis of atomic structural properties of 85 crystals that serve as representatives of the A2XY6 group. On the basis of a mean absolute percentage error, the developed GS-ELM model outperforms the existing Brik and Kityk [J. Phys. Chem. Solids 72(11), 1256–1260 (2011)] model with a percentage improvement of 58.37%, while it performs better than Alade et al. [J. Appl. Phys. 127(1), 15303 (2020)] model with the percentage enhancement of 37.90%. The outstanding performance of the proposed GS-ELM model coupled with its ease of implementation would be of great significance by enhancing the search for new materials tailored to targeted application and preventing lattice constant mismatch in thin film fabrication.
Optimal Control Design for a Group of Mobile Robots with Uncertainties

The paper considers the problem of optimal control design in the presence of uncertainties. Uncertainties of various nature are almost always appear in real objects. They can be caused by inaccuracy of the model, noise, uncertainty of the initial conditions and so on. It is proposed to use a new two-stage approach of synthesized optimal control, which at the first stage mathematically solves the problem of synthesis of the stabilization system and then the optimal trajectory of motion for the updated stabilized object is searched for. The implementation of such optimal control over a stabilized object makes it possible to level out model inaccuracies or other uncertainties much better than directly solving the optimal control problem, which is shown in a comparative example of solving the optimal control problem for a group of four mobile robots moving in a complex environment with phase constraints.
Reusing software engineering knowledge from developer communication

Software development requires many different types of knowledge, such as knowledge about software development processes, practices and techniques, and about the domain of an application. Software, developers often share knowledge in informal communication channels (e.g., instant messaging tools, e-mails, or online forums). Considering that this informal communication contains knowledge that may be potentially relevant for other developers and given that this knowledge is not necessarily captured and formally documented for reuse, in this work we propose (a) exploring whether developer communication (via instant messaging) is a suitable source of reusable software engineering knowledge; (b) investigating how to identify that knowledge using data mining; (c) and analysing through action research how to present it to developers in a useful way for reuse. The envisioned theories and solutions approaches will analyze existing software development data captured in communication, rather than data that were captured and stored specifically to be reused.
VAEPP: Variational Autoencoder with a Pull-Back Prior

Many approaches to training generative models by distinct training objectives have been proposed in the past. Variational Autoencoder (VAE) is an outstanding model of them based on log-likelihood. In this paper, we propose a novel learnable prior, Pull-back Prior, for VAEs by adjusting the density of the prior through a discriminator that can assess the quality of data. It involves the discriminator from the theory of GANs to enrich the prior in VAEs. Based on it, we propose a more general framework, VAE with a Pull-back Prior (VAEPP), which uses existing techniques of VAEs and WGANs, to improve the log-likelihood, quality of sampling and stability of training. In MNIST and CIFAR-10, the log-likelihood of VAEPP outperforms models without autoregressive components and is comparable to autoregressive models. In MNIST, Fashion-MNIST, CIFAR-10 and CelebA, the FID of VAEPP is comparable to GANs and SOTA of VAEs.
Tracy: Smartphone-based Contact Tracing Solution that Supports Self-investigation to Limit the Spread of COVID-19

The unprecedented spread of COVID-19 pandemic has become the main challenge for several countries around the world.One of the crucial measures taken to control and manage the diffusion of COVID-19 pandemic is contact tracing.The approach is based on tracing and identifying people who have been exposed to an infected individual to prevent onward transmission by alerting those who came in contact with the positive case; thus, isolation measures and suitable precautions can be taken.By using the communication and localization technologies embedded on the smartphones, those who made contact can be effectively traced by continuously collecting the timestamped locations and contacts of the owners.In this paper, an innovative privacy preserving smartphone-based contact tracing solution named 'Tracy' is proposed to help the health facilities to limit and control the spread of COVID-19 especially with the upcoming second wave of the virus.The proposed system consists of three main components: an intelligent application installed on smartphones, a data processing platform, and a website on which the different functions of those parts are integrated to provide the required services for the contact tracing solution.The system is designed to allow individuals to investigate the possibility of them contacting a person infected with the emerging coronavirus.A novel algorithm is designed to determine the effective location points in the individual's routes in which possible contacts can happen and based on these effective points, the prior contacts are decided.The system is also designed to provide an effective communication method with the local health facilities to receive medical advice and precautionary measures required for those who have discovered the possibility of contact with COVID-19.The reliability and scalability of the proposed solution is recognized by the usage of effective contact location points to determine the point of contact with the infected rather than using all the points of the individual's route.
Analysis of Crime Report by Data Analytics Using Python

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.
Unlabelled Gibbs partitions

We study random composite structures considered up to symmetry that are sampled according to weights on the inner and outer structures. This model may be viewed as an unlabelled version of Gibbs partitions and encompasses multisets of weighted combinatorial objects. We describe a general setting characterized by the formation of a giant component. The collection of small fragments is shown toconverge in total variation toward a limit object following a P\'olya-Boltzmann distribution.
Biometric Face Recognition Based on Landmark Dynamics

Face identification is one of the most widely adopted approach in several real scenarios. Unfortunately, in presence of occlusions or non-cooperative subjects nontrivial issues arise. This work explores a complementary approach to biometric face recognition, that is the dynamics of facial landmark during phonation while pronouncing sentences. These dynamics (both periocular and labial area) can be seen as a kind of signature that uniquely and unmistakably identifies an individual. The experimental results focus the attention on periocular area, which represents one of the most discriminating physiological characteristics after the whole face. Preliminary experimental results conducted on a public dataset and considering 14 periocular features showed an accuracy slightly below 80%, which confirms the robustness in uncontrolled scenarios.
Quantum integrated photonic circuits

Integrated quantum nanophotonics aims at combining optical elements on-chip to realize complex circuits with full quantum functionality. Examples are integrated boson sampling circuits and chips that generate 2D photonic cluster states. As such, this modern field of photonics opens up many exciting opportunities in quantum technology with the overarching goal of implementing large quantum networks and photonic quantum computers. This chapter provides an overview of important basics of integrated quantum nanophotonics, represents the state of the art, and provides an outlook on possible future developments. It includes an insight into numerical design and optimization processes and introduces modern manufacturing processes for quantum circuits. Based on this, corresponding circuits relying on homogeneous semiconductor structures are discussed, with a focus on elements with integrated quantum emitters. In addition, advanced concepts are also presented, which are enabled by the heterogeneous combination of different materials in order to develop fully integrated quantum chips. The chapter closes with an outlook on future developments and a summary.
Chatbots for Brand Representation in Comparison with Traditional Websites

&lt;p class="0abstractCxSpFirst"&gt;Chatbots have gained enormous popularity in recent years. IT giants such as Microsoft, Google and Facebook have taken an interest in automated conversations. Messaging apps like WhatsApp and Facebook Messenger are playing an increasingly important role in smartphone usage and communication in general - perfect conditions for chatbots. This paper provides an introduction of recent chatbot development in general and in customer environments. As part of this work, a chatbot was developed for the Austrian IT company CodeFluegel GmbH. The chatbot, named Theodore, provides information about CodeFluegel via Facebook Messenger and webchat, similar to the existing company website. The design process and implementation of the chatbot as well as architectural considerations are explained throughout this document. In a user study, participants perform typical tasks with the website and the chatbot. The usage of both platforms is evaluated in order to identify advantages and disadvantages as well as differences compared to the other technology and to draw conclusions. Findings to this study indicate promising prospects for chatbots as alternative touchpoints for customers and others and a way to replace and enhance traditional websites.&lt;/p&gt;
MathCloud: A Discrete Cloud Implementation to Enhance Learning Experience in Mathematics

Education systems all over the world have seen an unprecedented transformation, modernization, innovation and implementation of modern technological aids to enhance teaching experience. A modern classroom in higher education is not same as it used to be some 20 years back, needless to mention how much of transformation has taken place during last two decades of technological revolution especially in the area of information and communications technology. The implementation of cloud computing, big data, IoT, machine learning and other innovative technologies in educational system at different institutions all over the world has brought startling alterations to teaching and learning to yield more results and enhance teaching experience. Although the use of the modern technologies like cloud, big data and IoT is already seen in many premiere institutions and educational establishments all over the world however it is not as prevalent as expected and required in this golden age of information and communications technology. This research emphasizes the need for use of innovative technologies in teaching and learning and more specifically exhibits the challenges faced by teachers in higher education to make students comprehend the complex mathematical problems and use of technology like cloud computing to resolve and decrease the complexity of the problems. This research focuses more on the students of higher education in Sultanate of Oman and more specifically the problems that are faced by them in comprehending mathematical concepts and how innovative technologies can be provided as an aid to help cater these problems of the students. This research is a mere effort to use modern technology like cloud computing and machine learning to facilitate students and enhance teaching experience.
Monitoring of Gait Features during Outdoor Walking by Simple Foot Mounted IMU System

The world population is ageing. In the aged society, grasping the body condition of the elderly is helpful to choose appropriate rehabilitation. This research focuses on the outdoor walking analysis by use of the simple foot mounted IMU system. The walking feature values are estimated by the algorithm, and they are combined with the GPS data obtained by smartphone to compare the feature values of frail elderly and healthy subject. The differences of walking feature values that are calculated by this system show the usefulness of developed system. The developed system is simple, but useful to grasp the gait features changing of the user.
Spark-based adaptive Mapreduce data processing method for remote sensing imagery

Existing Hadoop-based remote sensing data processing approaches are insufficient for efficiently meeting the requirements of applications, especially when large remote sensing datasets are involved. This paper proposes an adaptive Spark-based remote sensing data processing method on the cloud that achieves improved efficiency and stability. The method includes a remote sensing data storage scheme on the cloud that employs the Hadoop Distributed File System (HDFS) and adaptive MapReduce mechanisms for use with remote sensing data; specifically, a mapping strategy for use with image tiles, a reducing strategy for use with adjacent tiles, and a mechanism for merging the results are proposed. An image classification experiment is conducted using Land Remote-Sensing Satellite System (Landsat) Thematic Mapper (TM) data, and the proposed method displays improved performance, stability and scalability compared to the existing Hadoop-based method. Hence, the proposed method is more suitable for processing large volumes of remote sensing data.
A multi-task representation learning approach for source code

Representation learning has shown impressive results for a multitude of tasks in software engineering. However, most researches still focus on a single problem. As a result, the learned representations cannot be applied to other problems and lack generalizability and interpretability. In this paper, we propose a Multi-task learning approach for representation learning across multiple downstream tasks of software engineering. From the perspective of generalization, we build a shared sequence encoder with a pretrained BERT for the token sequence and a structure encoder with a Tree-LSTM for the abstract syntax tree of code. From the perspective of interpretability, we integrate attention mechanism to focus on different representations and set learnable parameters to adjust the relationship between tasks. We also present the early results of our model. The learning process analysis shows our model has a significant improvement over strong baselines.
A New Dynamic Conditional Proxy Broadcast Re-Encryption Scheme for Cloud Storage and Sharing

Security of cloud storage and sharing is concerned for years since a semi-trusted party, Cloud Server Provider (CSP), has access to user data on cloud server that may leak users' private data without constraint. Intuitively, an efficient solution of protecting cloud data is to encrypt it before uploading to the cloud server. However, a new requirement, data sharing, makes it difficult to manage secret keys among data owners and target users. Therefore conditional proxy broadcast re-encryption technology (CPBRE) is proposed in recent years to provide data encryption and sharing approaches for cloud environment. It enables a data owner to upload encrypted data to the cloud server and a third party proxy can re-encrypted cloud data under certain condition to a new ciphertext so that target users can decrypt re-encrypted data using their own private key. But few CPBRE schemes are applicable for a dynamic cloud environment. In this paper, we propose a new dynamic conditional proxy broadcast reencryption scheme that can be dynamic in system user setting and target user group. The initialization phase does not require a fixed system user setup so that users can join or leave the system in any time. And data owner can dynamically change the group of user he wants to share data with. We also provide security analysis which proves our scheme to be secure against CSP, and performance analysis shows that our scheme exceeds other schemes in terms of functionality and resource cost.
A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition

Named Entity Recognition (NER) is an important task in many natural language processing applications. There are several studies that have focused on NER for the English language. However, there are some limitations when applying the current methodologies directly on the Arabic language text. Recent studies have shown the effectiveness of pooled contextual embedding representations and significant improvements in English NER tasks. This work investigates the performance of pooled contextual embeddings and bidirectional encoder representations from Transformers (BERT) model when used for NER on the Arabic language while addressing Arabic specific issues. The proposed method is an end-to-end deep learning model that utilizes a combination of pre-trained word embeddings, pooled contextual embeddings, and BERT model. Embeddings are then fed into bidirectional long-short term memory networks with a conditional random field. Different types of classical and contextual embeddings were experimented to pool for the best model. The proposed method achieves an F1 score of 77.62% on the AQMAR dataset, outperforming all previously published results of deep learning, and non-deep learning models on the same dataset. The presented results also surpass those of the wining system for the same task on the same data in the Topcoder website competition.
Entanglement estimation in non-optimal qubit states

In the last years, a relationship has been established between the quantum Fisher information (QFI) and quantum entanglement. In the case of two-qubit systems, all pure entangled states can be made useful for sub-shot-noise interferometry while their QFI meets a necessary and sufficient condition (Hyllus et al., 2010). In M-qubit systems, the QFI provides just a sufficient condition in the task of detecting the degree of entanglement of a generic state (Pezzé and Smerzi, 2009). In our work, we show analytically that, for a large class of one-parameter non-optimal two-qubit states, the maximally entangled states are associated with stationary points of the QFI, as a function of such parameter. We show, via numerical simulations, that this scenario is maintained for the generalisation of this class of states to a generic M-qubit system. Furthermore, we suggest a scheme for an interferometer able to detect the entanglement in a large class of two-spin states.
Improving Spectral Clustering Using the Asymptotic Value of the Normalized Cut

Spectral clustering (SC) is a popular and versatile clustering method based on a relaxation of the normalized graph cut objective. Despite its popularity, selecting the number of clusters and tuning the important scaling parameter remain challenging problems in practical applications of SC. Popular heuristics have been proposed, but corresponding theoretical results are scarce. In this article, we investigate the asymptotic value of the normalized cut for an increasing sample assumed to arise from an underlying probability distribution. Based on this, we find strong connections between spectral and density clustering. This enables us to provide recommendations for selecting the number of clusters and setting the scaling parameter in a data driven manner. An algorithm inspired by these recommendations is proposed, which we have found to exhibit strong performance in a range of applied domains. An R implementation of the algorithm is available from https://github.com/DavidHofmeyr/spuds. Supplementary materials for this article are available online.