Reviewerly/exHarmony huggingface dataset:
Comparison of Deep Learning Approaches for Sentiment Classification

Word embeddings are used to convert the unstructured text to numerical values for further analysis. Nowadays, prediction based embedding models like Continuous Bag Of Words (CBOW) and Skip grams are used in comparison to frequency based embeddings. Unlike frequency based embeddings, prediction based embeddings are able to model the semantics of the terms present in a sentence. Sentiment Analysis (SA) is a field of study that aims to automatically extract opinions from the data and to further classify them as positive and negative. The application of sentiment analysis in almost all the domains stands as a motivating factor for this work. It suffers from the problem of non-availability of sufficient labeled data to train the model. Due to the scalability and ability of deep learning models to perform automatic feature extraction from the data, they can be introduced to address this problem. They are also used for various applications due to its capability to extract hierarchical structures from complex data. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of three deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) for classifying the book reviews. From the experiments conducted, it is found that LS TM model performs better than CNN and simple RNN for sentiment classification.
Roles and their three facets: A foundational perspective

Roles remain nebulous entities, notwithstanding their extensive interdisciplinary research. This paper argues through a meta-ontological conceptual tool of grounding that there are three key facets of roles: a role position, a role specification, and a role potential. A foundational perspective on roles can be specified by “role choices” as to which facet of roles is primary. Role choices are illustrated with theories of roles that are built in compliance with four well-known upper ontologies: GFO, DOLCE, BFO, and UFO. The relationship between such three facets of roles and the GFO-based three kinds of roles (relational, processual, and social) is closely examined. These three facets are also comparatively studied from linguistic (e.g. ‘have a role’ versus ‘play a role’) and methodological (realism versus conceptualism regarding ontology design) perspectives. Furthermore, the family resemblance view of roles as “epistemic trackers” is proposed: the general notion of role is merely (partially) unified by its three facets and helps to keep track of some entity with respect to its role-related aspects. Finally, defining characteristics of roles in conceptual modeling are considered in terms of the three-facet theory. This work provides the grist for future practical development of an ontological module for generic role representation.
Challenges of being a digitally savvy principal

Purpose The purpose of this study was to better understand the challenges faced by leaders who have demonstrated excellence in integrating technology into teaching and learning in P-12 schools in the United States. Design/methodology/approach This case study of technology savvy P-12 school principals provides insights into how building leaders overcome digital technology innovation challenges. In the summer of 2017, the authors interviewed 12 of the 18 recipients of the NASSP Digital Principal Award. These principals serve as examples of how to lead schools in the digital age. Findings Using Bolman and Deal's (2013) conceptual framework, the authors analyzed the data around the four frames (i.e., political, structural, human resources, and symbolic) to understand the challenges of being a digital principal. Bolman and Deal posited leaders who function predominantly in a single frame may miss essential organizational change elements. Research limitations/implications The authors recognize several limitations in this study. First, the nominating process for the NASSP Digital Principals award involves an application process. Thus, while these principals were recognized for meeting these criteria, it is possible that these awardees were selected based on their nomination materials rather than on actual longitudinal evidence. Second, this study's data were gathered through interviews. The authors did not gather data through student work samples, teacher and staff interviews, or other data points, but rather the single data point of principal perspectives through interviews. Originality/value One silver lining from the pandemic is that leading schools cannot be detangled from the digital needs of diverse stakeholders. As such, digital principalship has become the new norm where the principal leads on a screen, teachers teach on a screen, and students learn on a screen. The award-winning digital principals in this study played an integral role in how they message their school's story, how they navigate and design structures, how they overcome political realities, and how they invest in addressing the needs of individuals.
Kernel Choice in One-Class Support Vector Machines for Novelty and Outlier Detection

Novelty and outlier detection are both used for anomaly detection. This paper works through the method of One-Class support vector machine (SVM) which could estimate the contour of initial observations and can be applied in the problems of anomaly detection. In this paper, the experiments on both artificial and real-world data sets are performed to demonstrate the importance of kernel and kernel parameter choice and the corresponding sensitivity of the algorithm. Due to the desire for general regulations of kernel parameter selection, some general methods of selecting the kernel bandwidth parameter of RBF kernel are therefore investigated, including median heuristic method and Bayesian kernel learning method. Then, the experiments based on these methods are conducted to observe their effect. Consequently, some new discoveries together with current issues on these methods, as well as some applicable situations of these methods, are found in this paper. Furthermore, this paper also confirms the effectiveness of RBF kernel and that the initial value of its bandwidth parameter can be set by those two methods.
Speed racing industry information management system based on machine learning and data mining

Horse racing is different from other competitive sports. It is an entertainment sport which combines commercialization and sports spirit. Therefore, horse racing has the characteristics of sports and commercial events. In addition, horse racing also has the nature of social welfare, and part of the funds for holding horse racing will be used for fund-raising. Now horse racing has become a complete industry and an important part of the tertiary industry. Horse racing, as a new industry, has developed rapidly. However, there are still some defects in the management of horse racing events. The main problem is the lack of information management of horse racing industry. The introduction of information technology into the management of horse racing industry can realize the efficient management of industrial information, which is more professional and orderly for horse racing.
Realization of Robot Fish with 3D Hologram Fish using Augmented Reality

The present research designed a tracking system of fish with 3D holographic augmented reality (AR). To track the fish, OpenCv through a camera with an algorithm was used. 3D holographic fish followed color marks that were used to track the fish. If the marks disappeared, 3D holographic fish stopped swimming. This process was repeated until the algorithm stopped. This experiment showed that 3D holographic fish followed the real fish.
Synthesis of Emotional Speech by Prosody Modification of Vowel Segments of Neutral Speech

Speech is viewed as a combination of voiced and unvoiced regions. Voiced speech is produced due to vibration of the vocal cords. The vibrating pattern of vocal cords is different in different emotions. During production of some consonant sound units, vocal cords do not vibrate. Therefore, consonants are less effective for emotion generation in speech signal. In this paper, we have considered only vowel regions for emotion synthesis using three prosody parameters duration, intensity and pitch patterns. Vowel like regions (VLR) is identified using vowel onset and offset points. Onset and offset points are starting and ending points of the vowel like regions. It is observed that during emotional synthesis from neutral speech mainly vowel regions of speech utterance are modified significantly. Our experimental result shows that the emotion synthesis using only prosody modification of VLR is significantly better than emotion synthesis of prosody modification at syllable level and it is also very effective in time consideration. The average mean opinion score is calculated using only vowel level prosody modification. The average mean opinion scores for angry, happy and fear emotional speeches are 3.85, 3.60 and 4.03, respectively. These mean opinion scores are better than syllable level prosody modification which are 3.56, 3.17 and 3.92 for angry, happy and fear emotions, respectively.
High Dimensional Model Explanations

Complex black-box machine learning models are regularly used in critical decision-making domains. This has given rise to several calls for algorithmic explainability. Many explanation algorithms proposed in literature assign importance to each feature individually. However, such explanations fail to capture the joint effects of sets of features. Indeed, few works so far formally analyze high dimensional model explanations. In this paper, we propose a novel high dimension model explanation method that captures the joint effect of feature subsets.
Relational schema optimization for RDF-based knowledge graphs

Characteristic sets (CS) organize RDF triples based on the set of properties associated with their subject nodes. This concept was recently used in indexing techniques, as it can capture the implicit schema of RDF data. While most CS-based approaches yield significant improvements in space and query performance, they fail to perform well when answering complex query workloads in the presence of schema heterogeneity, i.e., when the number of CSs becomes very large, resulting in a highly partitioned data organization. In this paper, we address this problem by introducing a novel technique, for merging CSs based on their hierarchical structure. Our method employs a lattice to capture the hierarchical relationships between CSs, identifies dense CSs and merges dense CSs with their ancestors. We have implemented our algorithm on top of a relational backbone, where each merged CS is stored in a relational table, and therefore, CS merging results in a smaller number of required tables to host the source triples of a dataset. Moreover, we perform an extensive experimental study to evaluate the performance and impact of merging to the storage and querying of RDF datasets, indicating significant improvements. We also conduct a sensitivity analysis to identify the stability and any possible weaknesses of our algorithm, and report on our results.
Physical Layer Anonymous Communications

In the era of the big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication, encryption, routing and protocols are applied at higher layers of networks, and ignore the fact that physical layer (PHY) also contains privacy-critical information, such as the signalling patterns and the inherent characteristics of channel fading. These can be used to identify traffic patterns and reveal users' identities, inflicting an unprecedented vulnerability to potential anonymity-violating behavior. Hence, privacy threats start from the acquisition of data, which necessitates complementary privacy solutions that reside at PHY. In this paper, we introduce the concept of PHY anonymity, and reveal the fact that the receiver is able to unmask the sender's identity by only analysing the PHY information. We first propose a novel sender detection strategy at the receiver, and then we develop a corresponding anonymous precoding design to address sender's anonymity while guaranteeing high receive signal-to-interference-plus-noise ratio (SINR) for communications. Simulation verifies that the proposed anonymous precoder is able to preserve anonymity and simultaneously guarantee high receive performance for communication purpose, opening a new dimension on anonymous designs at PHY.
Multi-scale channel network based on filer pruning for image super-resolution

Image super-resolution is an important image restoration technology, which is widely used in optical system, optical communication and other fields. Deep convolutional neural networks (CNNs) have made a great breakthrough in accuracy of single image super-resolution. Nevertheless, as the depth and width of the networks increase, CNNs methods face the challenge of memory consumption and computational complexity. To solve this problem, in this paper, we introduce a multi-scale channel network (MSCN) based on filter pruning to reconstruct the high-resolution (HR) image from the original low-resolution (LR) image. Firstly, we propose a multi-scale channel block (MSCB) to obtain the fine features based on the convolution kernels with different receptive fields. Channel split operator is used in MSCB to reduce the model parameters. The wider MSCN with more filters is trained to achieve a better performance. Then, we calculate the rank of feature maps and prune filters with low-rank feature information. Finally, the compressed multi-scale channel network with fewer parameters can achieve similar accuracies. The experimental results validate the effectiveness of our approaches.
Semantic image inpainting based on Generative Adversarial Networks

Semantic image inpainting is different from traditional methods. Although traditional image inpainting methods can achieve good results, they ignore the context, which makes the inpainting images look less natural. Semantic image inpainting does better in this aspect. In this paper, we propose a new method about semantic image inpainting by improving the model of Generative Adversarial Networks. The generator of our network model refers to Boundary Equilibrium Generative Adversarial Networks and adds spectral normalization to improving unstable training. Then, our network chooses discriminator of Self-Attention Generative Adversarial Networks which is more concise. After training, the network model is limited to generate image encoding by context loss and prior loss to make it as similar as possible to the missing image encoding. Then, the generated images encoding corresponding to the without missing parts of the real images are obtained. After fusing the generated missing parts with the unmissed parts of the real image, it is the inpainting image. By the comparisons of vision and quantization, it shows that our method can well complete the task of large missing regions.
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics

There is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether linguistic knowledge is a necessary condition for the good performance of modern language models, which we call the rediscovery hypothesis.&#x0D; In the first place, we show that language models that are significantly compressed but perform well on their pretraining objectives retain good scores when probed for linguistic structures. This result supports the rediscovery hypothesis and leads to the second contribution of our paper: an information-theoretic framework that relates language modeling objectives with linguistic information. This framework also provides a metric to measure the impact of linguistic information on the word prediction task. We reinforce our analytical results with various experiments, both on synthetic and on real NLP tasks in English.
Density ratio model with data-adaptive basis function

In many applications, we collect samples from multiple interconnected populations. These population distributions share some latent structure, so it is advantageous to jointly analyze the samples to make efficient inferences on the multiple distributions and their functionals. One effective way to connect the distributions is the density ratio model (DRM). A key ingredient of the DRM is that the log density ratios are linear combinations of prespecified functions; the vector formed by these functions is called the basis function. The benefit of DRM relies on correctly specifying the basis function to a large degree. In applications, the user may not have a complete knowledge to enable a suitable choice of the basis function, and many discussions have been devoted to this topic. In this article, we consider the still open problem of a data-adaptive choice of the basis function that can alleviate the risk of severe model misspecification. We propose a data-adaptive approach to the choice of basis function based on functional principal component analysis. Under some conditions, we show that this approach leads to consistent basis function estimation. Our simulation results show that the proposed adaptive choice can achieve an efficiency gain. We use a real-data example from economics to demonstrate the efficiency gain and the ease of our approach.
Characterising As–Bi–Co–Cu-bearing minerals at Scar Crags and Dale Head North, Lake District, UK

Abstract Scar Crags and Dale Head North in the English Lake District host mineralised veins enriched in ‘Energy Critical Elements’ (ECEs) specifically, bismuth, cobalt and copper. A limited number of studies in the 1970s investigated the mineralogy and inferred the genesis of these veins as being related to the intrusion of the Lake District batholith. This study investigates the geology, mineralogy, composition and paragenesis of these two mineralised areas. The results highlight the ubiquitous presence of Co–Fe–Ni-sulfarsenides in both deposits and the presence of some mineral species, hitherto unreported. Scar Crags samples contained high concentrations of cobalt, commonly present within arsenopyrite, whereas cobalt is of minor importance at Dale Head North, where copper and arsenic are the primary metals. A sequence of events, with As–Co–Ni-bearing fluids infilling the veins after an initial stage of quartz and chlorite precipitation is the most striking resemblance between the two mineralised systems, potentially indicating a common process for Co-rich vein-type deposits in the area. If so, understanding such processes could prove vital in aiding exploration in other terranes.
Sentimental Analysis based on hybrid approach of Latent Dirichlet Allocation and Machine Learning for Large-Scale of Imbalanced Twitter Data

Emotions classification in large amount of Twitter's data is very effective to analyze the users’ mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract meaningful features from a burst of raw tweets as emotions are subjective with limited fuzzy boundaries. These subjective features can be expressed in different terminologies and perceptions. In this paper, we proposed a hybrid approach of LDA and machine learning to predict emotions for large scale of imbalanced tweets. First, the raw tweets are preprocessed using tokenization method for capturing useful features without noisy information. Second, the local and global feature's importance is estimated by applying TFIDF statistical technique. Third, the Latent Dirichlet Allocation (LDA) topic modeling method is used to extract topics from these features. These topics explain concepts of related tweet which is really helpful for classification. Fourth, the Adaptive Synthetic (ADASYN) class balancing technique is applied to oversample the data and balance each class of topic. Finally, the K-Nearest Neighbor (KNN) machine learning algorithm is applied to predict the emotions in extracted topics. The class balancing method increase the significance of minor classes and solve the problem of class imbalance. The proposed approach is evaluated on two different Twitters’ emotions datasets. It is proved that, this methodology outperformed as compared to the popular state of the art methods in terms of precision, recall, f-measure and classification accuracy.
Towards Realizing Sign Language to Emotional Speech Conversion by Deep Learning

This paper proposes a framework of sign language to emotional speech conversion based on deep learning to solve communication disorders between people with language barriers and healthy people. We firstly trained a gesture recognition model and a facial expression recognition model by a deep convolutional generative adversarial network (DCGAN). Then we trained an emotional speech acoustic model with a hybrid long short-term memory (LSTM). We select the initials and the finals of Mandarin as the emotional speech synthesis units to train a speaker-independent average voice model (AVM). The speaker adaptation is applied to train a speaker-dependent hybrid LST-M model with one target speaker emotional corpus from AVM. Finally, we combine the gesture recognition model and facial expression recognition model with the emotional speech synthesis model to realize the sign language to emotional speech conversion. The experiments show that the recognition rate of gesture recognition is 93.96%, and the recognition rate of facial expression recognition in the CK+ database is 96.01%. The converted emotional speech not only has high quality but also can accurately express the facial expression.
Awareness and Perception of Agile in Saudi Software Industry

Over the last two decades, Agile software development methodology has garnered significant interest in both software industry and research with several studies investigating the level of awareness, perception and use of Agile in software engineering practice. However, most of these studies have focused on Agile practice in developed countries, with only a handful having been conducted in developing countries, especially in the Middle East. This is problematic given the strategic and economic importance of software industry in countries such as Saudi Arabia. This paper aims to start addressing this research gap with an empirical investigation of the awareness and perceptions of Agile among software practitioners in Saudi Arabia and the extent to which they are adopting Agile methods. To this end, we conduct four semi-structured interviews with expert software practitioners and a survey with 31 respondents, all from the mobile development software industry. Our findings indicate that there it seems to be a low level of awareness and usage of Agile in the country. However, we also find that those who use Agile, are appreciative of its benefits which include project management flexibility, rapid response to change, and a positive effect on team morale and communication.
Towards a Requirements Engineering Framework based on Semantics

Requirements engineering is one of the most important issues in systems development. Whether it is software or hardware systems or embedded systems, the need for well-defined requirements remains the same. The ultimate success or failure of developing a system stems largely from the initial definition and management of its requirements. However, despite the efforts that have been made, a coherent and easily understood process that leads from the requirements to correct implementations is still an open research issue, which seeks alternative promising approaches. To this end, in this paper, we propose a requirements engineering approach based on Semantics. It provides a novel mechanism that combines semantics, ontologies, and appropriate NLP techniques. The ultimate goal is to propose a framework that will include the minimum consistent set of formalities and languages to determine the requirements and perform the necessary verifications.