Reviewerly/exHarmony huggingface dataset:
Towards Efficient Superconducting Quantum Processor Architecture Design

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, this paper proposes an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform IBM's general-purpose design schemes with better Pareto-optimal results.,0
Coordination artifacts in Agile Software Development

Agile Software Development is characterized by collaborative social interactions and fast-paced and iterative changes in project requirements. Much of the extant Agile development literature focuses on team and organizational aspects of Agile development projects. However, coordination mechanisms used within Agile projects have received less attention. Particularly, existing research lacks a discussion on how artifacts are applied in Agile software development to support project coordination, and how their potential for information richness and mutability impact their ability to support coordination. To address these issues, we conducted a case study of an Agile development project that involved customer and vendor organizations. Our case follows the interactions between the two organizations, identifies the different types of artifacts that they utilized to coordinate their joint activities throughout the various phases of the project, and traces the enactment of these artifacts' informational potential. Our study reveals that the organizations applied four different types of coordination artifacts - Foundational, Projective, Exposition and Indicative artifacts – to address different coordination challenges that arose throughout the project. The four types of artifacts were characterized by varying levels of information richness and mutability, which were shaped to support the Agile activities within which they were used. Our study enriches the understanding of the role of coordination artifacts in Agile projects, provides insights on the informational potential of the artifacts, and highlights some of the pitfalls than can result from misunderstanding object use.
Digitalization as a New Direction in Education Sphere

Digitalization in education sphere is the new paradigm of high technologies development. The article proves that digital technologies are relevant and widely used in various areas of society: management, economic relations, science and education. However, digitalization of the educational process is of particular importance, because the several factors such an advance of the quality and relevant experience in this sphere. Today, important transformation processes are taking place in the field of education: electronic textbooks, Internet portals, databases of information needs are spreading, systems of online courses and distance learning are actively developing.
A Domain-Specific Generative Chatbot Trained from Little Data

Accurate generative chatbots are usually trained on large datasets of question–answer pairs. Despite such datasets not existing for some languages, it does not reduce the need for companies to have chatbot technology in their websites. However, companies usually own small domain-specific datasets (at least in the form of an FAQ) about their products, services, or used technologies. In this research, we seek effective solutions to create generative seq2seq-based chatbots from very small data. Since experiments are carried out in English and morphologically complex Lithuanian languages, we have an opportunity to compare results for languages with very different characteristics. We experimentally explore three encoder–decoder LSTM-based approaches (simple LSTM, stacked LSTM, and BiLSTM), three word embedding types (one-hot encoding, fastText, and BERT embeddings), and five encoder–decoder architectures based on different encoder and decoder vectorization units. Furthermore, all offered approaches are applied to the pre-processed datasets with removed and separated punctuation. The experimental investigation revealed the advantages of the stacked LSTM and BiLSTM encoder architectures and BERT embedding vectorization (especially for the encoder). The best achieved BLUE on English/Lithuanian datasets with removed and separated punctuation was ~0.513/~0.505 and ~0.488/~0.439, respectively. Better results were achieved with the English language, because generating different inflection forms for the morphologically complex Lithuanian is a harder task. The BLUE scores fell into the range defining the quality of the generated answers as good or very good for both languages. This research was performed with very small datasets having little variety in covered topics, which makes this research not only more difficult, but also more interesting. Moreover, to our knowledge, it is the first attempt to train generative chatbots for a morphologically complex language.
KBPearl

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in-time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end-to-end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state-of-the-art techniques, through extensive experiments on real-world datasets.
Statistical Multi-Objects Segmentation for Indoor/Outdoor Scene Detection and Classification via Depth Images

With the advancement of technology, intelligence capabilities of machines are growing day by day. Researchers are committed to equip the machines with the capability of thinking humanly. Currently, the machines can sense and process information gathered from sensors. However, still there is a huge gape to improve the capability of thinking and understanding real scenes. Scene understanding is fiery area of research now a day. Therefore, we have proposed a model to understand and recognize a scene using depth data to make machines capable of interpreting the real time scenes like humans. The proposed recognition technique is a novel segmentation framework that uses statistical multi object segmentation to learn robust scene model and segregate the objects in the scene. Then, the unique features are extracted from these segregated objects to further process for recognition using linear SVM. Finally, multilayer perceptron is provided with the features and weights for the recognition of the scene. Our system demonstrated significant improvement over state-of-the-art systems. The proposed system is effective in autonomous vision systems like robotic vision, GPS based location finder, sports and security.
Elastic exponential linear units for convolutional neural networks

Activation functions play important roles in determining the depth and non-linearity of deep learning models. Since the Rectified Linear Unit (ReLU) was introduced, many modifications, in which noise is intentionally injected, have been proposed to avoid overfitting. Exponential Linear Unit (ELU) and their variants, with trainable parameters, have been proposed to reduce the bias shift effects which are often observed in ReLU-type activation functions. In this paper, we propose a novel activation function, called the Elastic Exponential Linear Unit (EELU), which combines the advantages of both types of activation functions in a generalized form. EELU has an elastic slope in the positive part, and preserves the negative signal by using a small non-zero gradient. We also present a new strategy to insert neuronal noise using a Gaussian distribution in the activation function to improve generalization. We demonstrated how EELU can represent a wider variety of features with random noise than other activation functions, by visualizing the latent features of convolutional neural networks. We evaluated the effectiveness of the EELU approach through extensive experiments with image classification using the CIFAR-10/CIFAR-100, ImageNet, and Tiny ImageNet datasets. Our experimental results show that EELU achieved better generalization performance and improved classification accuracy over conventional activation functions, such as ReLU, ELU, ReLU- and ELU-like variants, Scaled ELU, and Swish. EELU produced performance improvements in image classification using a smaller number of training samples, owing to its noise injection strategy, which allows significant variation in function outputs, including deactivation.
Comparative Performance of Machine Learning and Deep Learning Algorithms for Arabic Hate Speech Detection in OSNs

Nowadays, Online Social Networks (OSNs) are the most popular and interactive media that used to express feelings, communicate and share information between people. However, along with useful and interesting content, sometimes unsuitable or abusive content can be published on these networks, such as hate speech and insults. Hate speech includes any type of online abuse concepts like cyberbullying, discrimination, abusive language, profanity, flaming, toxicity, and harassment. Most of the Hate speech detection attempts have concentrated on the English text, while work on the Arabic text is sparse. In this paper, we constructed a standard Arabic dataset that can be used for hate speech and abuse detection. In contrast to most previous work the datasets were collected from one platform, the proposed dataset is collected from more social network platforms (Facebook, Twitter, Instagram, and YouTube). To validate the effectiveness of the proposed datasets twelve machine learning algorithms and two deep learning architecture were used. Recurrent Neural Network (RNN) outperformed other classifiers with an accuracy of 98.7%.
CHARM-Deep: Continuous Human Activity Recognition Model Based on Deep Neural Network Using IMU Sensors of Smartwatch

In the present paper, an attempt was made to achieve high-performance continuous human activity recognition (CHAR) using deep neural networks. The present study focuses on recognizing different activities in a continuous stream, which means `back-to-back' consecutive set of activities, from only inertial measurement unit (IMU) sensors mounted on smartwatches. For that purpose, a new dataset called `CHAR-SW', which includes numerous streams of daily activities, was collected using smartwatches, and feature representations and network architectures were designed. Experimental comparisons using our own dataset and public datasets (Aruba and Tulum) have been performed. They demonstrated that cascading bidirectional long short-term memory (Bi-LSTM) with featured data performed well in offline mode from the viewpoints of accuracy, computational time, and storage space required. The input to the Bi-LSTM is a descriptor which composed of a stream of the following features: autocorrelation, median, entropy, and instantaneous frequency. Additionally, a novel technique to operate the CHAR system online was introduced and shown to be effective. Experimental results can be summarized as: the offline CHARM-Deep enhanced the accuracy compared with using raw data or the existing approaches, and it reduced the processing time by 86% at least relative to the time consumed in executing the Bi-LSTM classifier directly on the raw data. It also reduced storage space by approximately 97.77% compared with using raw data. The online evaluation shows that it can recognize activities in real-time with an accuracy of 91%.
Code smell detection using multi-label classification approach

Code smells are characteristics of the software that indicates a code or design problem which can make software hard to understand, evolve, and maintain. There are several code smell detection tools proposed in the literature, but they produce different results. This is because smells are informally defined or subjective in nature. Machine learning techniques help in addressing the issues of subjectivity, which can learn and distinguish the characteristics of smelly and non-smelly source code elements (classes or methods). However, the existing machine learning techniques can only detect a single type of smell in the code element that does not correspond to a real-world scenario as a single element can have multiple design problems (smells). Further, the mechanisms proposed in the literature could not detect code smells by considering the correlation (co-occurrence) among them. To address these shortcomings, we propose and investigate the use of multi-label classification (MLC) methods to detect whether the given code element is affected by multiple smells or not. In this proposal, two code smell datasets available in the literature are converted into a multi-label dataset (MLD). In the MLD, we found that there is a positive correlation between the two smells (long method and feature envy). In the classification phase, the two methods of MLC considered the correlation among the smells and enhanced the performance (on average more than 95% accuracy) for the 10-fold cross-validation with the ten iterations. The findings reported help the researchers and developers in prioritizing the critical code elements for refactoring based on the number of code smells detected.
Learning Content-Weighted Deep Image Compression

Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance, and requires to cope with the spatial variation of image content and contextual dependence among learned codes. Traditional entropy models can spatially adapt the local bit rate based on the image content, but usually are limited in exploiting context in code space. On the other hand, most deep context models are computationally very expensive and cannot efficiently perform decoding over the symbols in parallel. In this paper, we present a content-weighted encoder-decoder model, where the channel-wise multi-valued quantization is deployed for the discretization of the encoder features, and an importance map subnet is introduced to generate the importance masks for spatially varying code pruning. Consequently, the summation of importance masks can serve as an upper bound of the length of bitstream. Furthermore, the quantized representations of the learned code and importance map are still spatially dependent, which can be losslessly compressed using arithmetic coding. To compress the codes effectively and efficiently, we propose an upper-triangular masked convolutional network (triuMCN) for large context modeling. Experiments show that the proposed method can produce visually much better results, and performs favorably against deep and traditional lossy image compression approaches.
Domain Adaptation on Graphs by Learning Aligned Graph Bases

A common assumption in semi-supervised learning is that the class label function has a slow variation on the data graph, while in many problems, the label function may vary abruptly in certain graph regions, resulting in high-frequency components. Although semi-supervised learning is an ill-posed problem, it is often possible to find a source graph on which the label function has similar frequency content to the target graph where the actual classification problem is defined. In this paper, we propose a method for domain adaptation on graphs based on learning the spectrum of the label function in a source graph with many labels, and transferring the spectrum information to the target graph. When transferring the frequency content, it is not easy to share graph Fourier coefficients directly between the two independently constructed graphs, since no match exists between their Fourier bases. We solve this by learning a transformation between the Fourier bases of the two graphs that flexibly “aligns” them. The unknown class label function on the target graph is then reconstructed from the learnt spectrum while retaining consistency with the available labels. Comparative experiments suggest that the proposed algorithm often outperforms recent domain adaptation methods in various data classification applications.
A Novel Blockchain Framework for Industrial IoT Edge Computing

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.
Alignment-Length Synchronous Decoding for RNN Transducer

We present a beam decoding strategy for recurrent neural network transducers which has the characteristic that all competing hypotheses within the beam have the same alignment length (number of output symbols plus BLANK symbols). We contrast the proposed technique with time-synchronous decoding where the competing hypotheses within the beam correspond to the same input frames (but can have different length output sequences). Experiments on the Switchboard 2000 hours corpus show that alignment-length synchronous decoding (ALSD) is 25% faster than time-synchronous decoding (TSD) for the same accuracy because ALSD performs 42% fewer joint network evaluations and hypothesis expansions during the search. Additionally, we discuss the benefit of caching and batching the prediction and joint network evaluations, of using prefix trees instead of full output vocabulary expansions, and of performing hypothesis recombination after pruning. With open beam decoding, we reach a 6.2% / 10.9% word error rate on the Switchboard and CallHome Hub5 2000 evaluation testsets which compares favorably to other published single-model results on this corpus.
An efficient certificateless public key encryption scheme with authorized equality test in healthcare environments

The challenges confronting the privacy of outsourcing medical data have become a significant concern for patients and healthcare providers. The encryption of this sensitive data before outsourcing to the healthcare server present an effective solution for protection. However, searching on different ciphertexts while protecting the privacy of data remains a research challenge. Indeed, most of the existing schemes are inefficient, particularly for deployment on mobile devices due to limited resources (e.g. computing power, battery lifetime, and storage capacity). In this paper, we propose a CertificateLess Public-Key Encryption with Authorized Equality Test (CLPKE-AET) scheme. With the CLPKE-AET scheme, an authorized cloud server is permitted to check the equivalence of two different ciphertexts consisting of the same messages. Moreover, We provide four types of authorizations, base on which an authorized user can directly search on different user's ciphertext using different methods while enhancing the privacy of the user's data. We present our construction from bilinear pairing and demonstrate its security under modified Bilinear Diffie-Hellman (mBDHI) assumption in the Random Oracle Model (ROM). Finally, compared with other's similar schemes, the performance analysis shows that our CLPKE-AET scheme is practical and more suitable for healthcare environments.
Visualization Techniques in Augmented Reality: A Taxonomy, Methods and Patterns

In recent years, the development of Augmented Reality (AR) frameworks made AR application development widely accessible to developers without AR expert background. With this development, new application fields for AR are on the rise. This comes with an increased need for visualization techniques that are suitable for a wide range of application areas. It becomes more important for a wider audience to gain a better understanding of existing AR visualization techniques. In this article we provide a taxonomy of existing works on visualization techniques in AR. The taxonomy aims to give researchers and developers without an in-depth background in Augmented Reality the information to successively apply visualization techniques in Augmented Reality environments. We also describe required components and methods and analyze common patterns.
Image Denoising using Deep Learning: Convolutional Neural Network

It has become an important task to remove noise from the image and restore a high-quality image in order to process image further for the purpose like object segmentation, detection, tracking etc. This paper presents denoising of image using the convolutional neural network (CNN) model in deep learning. This analysis is done by adding 1% to 10% Gaussian white noise to the image and then applying CNN model to denoise it. Further, qualitative and quantitative analysis of the denoised image is performed. Under qualitative analysis comes the quality of image where edge factor, texture, uniform region and non-uniform region, smoothness, structure of objects is considered. The quantitative analysis is done using the three metrics which are PSNR (peak signal to noise ratio), SSIM (structural similarity index measurement), and MSE (mean square error) in which the CNN based method's results are compared with the traditional or standard methods of image denoising. The results from the analysis and experiment show that the CNN model can efficiently remove a lot of Gaussian noise and restore the image details and data than any other traditional/standard image filtering techniques.