Reviewerly/exHarmony huggingface dataset:
The Impact of Humanoid Robots on Australian Public Libraries

‘Humanoid robots’ or ‘social robots’ are an emerging generation of robots that have abilities to perceive their environment, recognise faces, read emotions and communicate with people. They have been introduced into Australian public libraries over the past few years but their impact on library practice has remained unknown. This research therefore aims to fill this gap. It explores the influence of these robots on Australian public libraries through the view of librarians and the experience of library clients. Ten in-depth individual interviews with librarians in Australian public libraries, who are directly involved in designing and running humanoid robot programmes, were carried out. Observations of interaction between library patrons and humanoid robots at five events in five public libraries were also conducted. Interview transcripts and field notes were analysed using a thematic analysis approach. The analysis revealed four themes describing the way humanoid robots impact Australian public libraries. Humanoid robots are considered as a community builder, a teacher, an aide, and a challenger. This research provides an evidence-based understanding that supports the research, planning and implementation of humanoid robot initiatives in Australian public libraries in order to enhance the experience of library user communities.
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://github.com/hchoi71/AMC-Loss.
A Wireless-controlled 3D printed Robotic Hand Motion System with Flex Force Sensors

Hand gesture recognition is an emerging field of technology in robotics and human-computer interaction. It has tremendous applications in daily life activities and intelligent workplaces. In this study, a system which could help people to work and operate without directly using hands or contacting by hands, is proposed and demonstrated. This system composed of a glove with flexible force sensors and a 3D printed robotic forearm. The user wearing the glove could control the action of the 3D printed robotic forearm. The 3D printed forearm simultaneously acted following the motion of the glove. The 3D printed forearm was composed of 46 individual parts that were printed with white biodegradable polylactic acid (PLA). Electronic components in the system are five flex sensors, a master Arduino Nano, a slave Arduino Nano, a wireless NRF24L01 transmitter module banding on the glove, a second wireless NRF24L01 receiver module in the forearm and five motors. The five flex sensors on the fingers of the glove detected and collected the signals reflecting the movements of the hands. The Arduino Nano processed the signals from the flex sensors and sent them through the wireless transmitter module to the slave Arduino Nano. In order to control the action of the robotic forearm, it was embedded with a slave Arduino Nano as a control kernel, a wireless NRF24L01 receiver module and five actuators. The slave Arduino Nano received and processed the signals through the wireless receiver module. After that, the signals were sent to the actuators- servo motors. The fingers' action in the robotics arm was executed with the actuators. After carefully testing the system, the robotic arm followed the action correctly with a maximum 0.133 ms time delay all the time. This system could be really useful for the users who work in dangerous conditions, hazardous environment or require remote operation for safety reasons.
Inclusion of Unicode Standard seamless characters to expand Arabic text steganography for secure individual uses

The need to protect confidential data stored on personal computers (PCs) or sent to other parties has increased significantly. Therefore, a secure, high-capacity strategy is needed to cover the data contained within media and make it difficult to detect. Due to the limited number of research studies that have achieved these ends through text steganography, we present an innovative approach for hiding secret bits in Arabic text with Unicode Standard seamless. Our method uses the contextual forms of Arabic characters to hide certain secret bits. Extra characters such as Zero-Width Joiners (ZWJ), Kashida, Medium Mathematical Spaces (MMSPs), and Zero-Width Non-Joiners (ZWNJ) are also used to further enhance the method's capacity without diminishing the data's security. Our experimental results show that this technique outperforms similar methods with an average improvement in capacity of more than 50%. This technique also has a higher security ratio than the other methods in this study, with the exception of Method 5, and is robust against electronic text modifications such as copying, pasting, and text formatting. Moreover, our algorithm can be widely adopted in related languages, such as Urdu and Farsi, due to its utilization of Unicode characters, which is the encoding standard used in most of the world's writing systems.
Context from within: Hierarchical context modeling for semantic segmentation

Conditional Random Fields (CRFs) have been widely adopted in conjunction with Fully Convolutional Networks (FCNs) to model and integrate contextual information in the semantic segmentation procedure. In contrast to existing approaches applying CRFs in parallel or in cascade with FCNs, we propose a new paradigm to incorporate CRFs deeper inside the architecture of FCNs to model the context exhibited within the middle layers of an FCN. We approximate the mean-field inference process of a dense CRF as a multi-dimensional Gated Recurrent Unit (GRU) layer, termed CRF-GRU layer, effectively extracting intermediate context within an FCN. More importantly, multiple CRF-GRU layers can be injected into an FCN to model hierarchical contexts presented in multiple middle layers, showing competitive results on the PASCAL VOC 2012 and PASCAL-Context datasets. Secondly, we contribute a new approach to automatically learn, from the training data, the optimal segmentation architecture of the FCN with multiple CRF-GRU layers injected. The proposed approach relies on Genetic Evolution Strategies to allow the existing architecture to iteratively evolve towards higher accuracy instances. The discovered network not only outperforms state-of-the-art segmentation techniques, but also provides exciting new insights into the design of the segmentation networks.
Semi-Immersive Virtual Reality as a Tool to Improve Cognitive and Social Abilities in Preschool Children

Virtual reality (VR) creates computer-generated virtual environments where users can experience and interact in a similar way as they would do in real life. VR systems are increasingly being used for rehabilitation goals, mainly with adults, but also with children, extending their application to the educational field. This report concerns a study of the impact of a semi-immersive VR system in a group of 25 children in a kindergarten context. The children were involved in several different games and activity types, specifically developed with the aim of learning specific skills and foster team collaboration. Their reactions and behaviors were recorded by their teachers and by trained psychologists through observation grids addressing task comprehension, participation and enjoyment, interaction and cooperation, conflict, strategic behaviors, and adult-directed questions concerning the activity, the device or general help requests. The grids were compiled at the initial, intermediate and final timepoint during each session. The results show that the activities are easy to understand, enjoyable, and stimulate strategic behaviors, interaction and cooperation, while they do not elicit the need for many explanations. These results are discussed within a neuroconstructivist educational framework and the suitability of semi-immersive, virtual-reality-based activities for cognitive empowerment and rehabilitation purposes is discussed.
Natural Language Processing: History, Evolution, Application, and Future Work

It is quite hard to imagine a smart system like a voice assistant or a chat-bot or a recommender system without natural language processing (NLP). It all starts with an initial unit that first interprets the data (audio or text) provided and then start making sense of the data, and after proper processing of the data, the actual steps are followed by the machine to throw some replies or get the work done. NLP does not fall under a discipline; rather it is a part of several different disciplines, i.e., computer science, information engineering, artificial intelligence (AI), and linguistics. The concern of NLP is the interaction between a computer and human languages. NLP areas include speech recognition, machine translation, automatic text summarization, part-of-speech tagging, etc. Generally, NLP is used in many real-time applications like smart homes, smart offices like Alexa, Cortana, Siri, and Google Assistant. The history of NLP generally started in the 1950s and has come a long way from then and improved a lot. This paper discusses the history of NLP, its evolution, its tools and techniques, and its applications in different fields. The paper also discusses the role of machine learning and artificial neural networks (ANNs) to improve NLP.
Encryption of 3D medical images based on a novel multiparameter cosine number transform

In this paper, a multiparameter cosine number transform is proposed. Such a transform is obtained using the fact that the basis vectors of the three-dimensional cosine number transform (3D-CNT) constitute a possible eigenbasis for the Laplacian of the cubical lattice graph evaluated in a finite field. The proposed transform is identified as three-dimensional steerable cosine number transform (3D-SCNT) and is defined by rotating the 3D-CNT basis vectors, using a finite field rotation operator. We introduce a 3D medical image encryption scheme based on the 3D-SCNT, which uses the rotation angles as secret parameters. By means of computer experiments, we have verified that the scheme is resistant against the main cryptographic attacks.
Design-Space Exploration of Quantum Approximate Optimization Algorithm under Noise

Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve NP-hard problems in near-term gate-based noisy quantum devices. In QAOA, the gate parameters of a parameterized quantum circuit (PQC) are varied by a classical optimizer to generate a quantum state with a significant support to the optimal solution. The existing analysis fails to consider nonidealities in the qubit quality i.e., short lifetime and imperfect gate operations in a realistic quantum hardware. In this article, we study the impact of various noise sources on the performance of QAOA both in simulation and on a real quantum computer from IBM. Our analysis indicates that QAOA performance is noise-sensitive (especially higher-depth QAOA instances). Therefore, the optimal number of stages (p-value) for any QAOA instance is limited by the noise in the target hardware as opposed to the current perception that QAOA will provide monotonically better performance with higher-depth. We show that the two-qubit gate error has to be decreased by more than 75% of the current state-of-the-art levels to attain a performance within 10% of the maximum value for the lowest-depth QAOA.
Quantum image scrambling algorithm based on discrete Baker map

Quantum image processing has become a significant aspect within the field of quantum information processing because the image is an essential carrier of information, and quantum computation has powerful image processing ability. Image scrambling algorithms are often required as initial image operations in quantum image processing applications such as quantum image encryption and watermarking. However, the efficiency of existing quantum image scrambling algorithms needs to be improved urgently, especially in terms of periodicity. Therefore, a novel quantum image scrambling algorithm based on discrete Baker map is proposed in this paper, which can be implemented by swapping qubits with low circuit complexity. The quantum version of discrete Baker map is deduced and the corresponding quantum circuit is designed. The simulation results show that the scrambling algorithm has the characteristic of long period, which can further enhance the security of quantum image encryption algorithms. Besides, for generalized discrete Baker maps, the conditions that they can be implemented by swapping qubits are given. Moreover, the number of discrete Baker maps satisfying the conditions is also calculated.
Migrating from Monolithic to Serverless: A FinTech Case Study

Serverless computing is steadily becoming the implementation paradigm of choice for a variety of applications, from data analytics to web applications, as it addresses the main problems with serverfull and monolithic architecture. In particular, it abstracts away resource provisioning and infrastructure management, enabling developers to focus on the logic of the program instead of worrying about resource management which will be handled by cloud providers. In this paper, we consider a document processing system used in FinTech as a case study and describe the migration journey from a monolithic architecture to a serverless architecture. Our evaluation results show that the serverless implementation significantly improves performance while resulting in only a marginal increase in cost.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records

Abstract Background Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP2018 organizers have made the first attempt to annotate 1068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. Methods We developed models using traditional machine learning and deep learning approaches. For the post challenge, we focused on two models: the Random Forest and the Encoder Network. We applied sentence embeddings pre-trained on PubMed abstracts and MIMIC-III clinical notes and updated the Random Forest and the Encoder Network accordingly. Results The official results demonstrated our best submission was the ensemble of eight models. It achieved a Person correlation coefficient of 0.8328 – the highest performance among 13 submissions from 4 teams. For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~ 13%. During the challenge task, no end-to-end deep learning models had better performance than machine learning models that take manually-crafted features. In contrast, with the sentence embeddings pre-trained on biomedical corpora, the Encoder Network now achieves a correlation of ~ 0.84, which is higher than the original best model. The ensembled model taking the improved versions of the Random Forest and Encoder Network as inputs further increased performance to 0.8528. Conclusions Deep learning models with sentence embeddings pre-trained on biomedical corpora achieve the highest performance on the test set. Through error analysis, we find that end-to-end deep learning models and traditional machine learning models with manually-crafted features complement each other by finding different types of sentences. We suggest a combination of these models can better find similar sentences in practice.
MLCQ

Context Research on code smells accelerates and there are many studies that discuss them in the machine learning context. However, while data sets used by researchers vary in quality, all which we encountered share visible shortcomings---data sets are gathered from a rather small number of often outdated projects by single individuals whose professional experience is unknown.
Negation scope detection for sentiment analysis: A reinforcement learning framework for replicating human interpretations

Textual materials represent a rich source of information for improving the decision-making of people, businesses and organizations. However, for natural language processing (NLP), it is difficult to correctly infer the meaning of narrative content in the presence of negations. The reason is that negations can be formulated both explicitly (e.g., by negation words such as “not”) or implicitly (e.g., by expressions that invert meanings such as “forbid”) and that their use is further domain-specific. Hence, NLP requires a dynamic learning framework for detecting negations and, to this end, we develop a reinforcement learning framework for this task. Formally, our approach takes document-level labels (e.g., sentiment scores) as input and then learns a negation policy based on the document-level labels. In this sense, our approach replicates human perceptions as provided by the document-level labels and achieves a superior prediction performance. Furthermore, it benefits from weak supervision; meaning that the need for granular and thus expensive word-level annotations, as in prior literature, is replaced by document-level annotations. In addition, we propose an approach to interpretability: by evaluating the state-action table, we yield a novel form of statistical inference that allows us to test which linguistic cues act as negations.
Security and privacy issues of blockchain technology

Blockchain technology is currently one of the most popular topics in the field of information technology. Blockchain is often associated with cryptocurrency or electronic cash, but it can also be extended to any interconnected information blocks. Now blockchain finds application in areas such as financial transactions, user identification, or the creation of cybersecurity technologies. So, in this framework we shall talk about that despite the fact that blockchain technology is reliable and supportive, the security issues and challenges of this technology cannot be left out especially when it comes to building personal privacy protection.
Chinese Text Sentiment Analysis Based on BI-GRU and Self-attention

Humans have been trying to get machines to understand human emotional states, and using natural language processing techniques for sentiment analysis is the mainstream solution. In order to solve the problem of high complexity and low efficiency in Chinese LSTM-based Chinese text sentiment analysis, this paper uses Bi-GRU (bidirectional GRU neural network) and attention mechanism model to analyze Chinese text sentiment. First of all, the model can extract deep features of the text, and combine the context of the sentence to learn the text features more accurately. Secondly, the model introduces Multi-Head Self-Attention, which reduces the dependence on external parameters, assigns weights to word vectors, highlights text features, and pays more attention to the internal dependencies on sentences. Experiments show that the Chinese text sentiment analysis based on the model can get 87.1% accuracy.
TemporalGAT: Attention-Based Dynamic Graph Representation Learning

Learning representations for dynamic graphs is fundamental as it supports numerous graph analytic tasks such as dynamic link prediction, node classification, and visualization. Real-world dynamic graphs are continuously evolved where new nodes and edges are introduced or removed during graph evolution. Most existing dynamic graph representation learning methods focus on modeling dynamic graphs with fixed nodes due to the complexity of modeling dynamic graphs, and therefore, cannot efficiently learn the evolutionary patterns of real-world evolving graphs. Moreover, existing methods generally model the structural information of evolving graphs separately from temporal information. This leads to the loss of important structural and temporal information that could cause the degradation of predictive performance of the model. By employing an innovative neural network architecture based on graph attention networks and temporal convolutions, our framework jointly learns graph representations contemplating evolving graph structure and temporal patterns. We propose a deep attention model to learn low-dimensional feature representations which preserves the graph structure and features among series of graph snapshots over time. Experimental results on multiple real-world dynamic graph datasets show that, our proposed method is competitive against various state-of-the-art methods.
Artificial intelligence and the future of professional work

Considering the implications of the influence of artificial intelligence given previous industrial revolutions.
A systematic review of the dark side of CRM: the need for a new research agenda

Technology provides businesses with the opportunity to better engage with customers, but the speed and scope of transformation in customer management have triggered a dark side, affecting people, companies and societies. Research is needed to structure and manage the dark side concept more systematically, considering aspects of dark side behaviours in relation to customer relationship management. Marketers frequently cite research on surveillance, cyber resilience, the dark web, theft by customers/staff, the illicit monetisation of data to third parties, and many other topics as examples of the dark side. The paper reviews the current state of the research and practice regarding the dark side of CRM and explores the dimensions and consequences. A conceptual framework is devised, which captures four important drivers of the dark side – namely unfairness, distrust, opportunism and lack of transparency. The theoretical and managerial implications are discussed.