Reviewerly/exHarmony huggingface dataset:
Accentuating Illocutionary Forces: Emoticons as Speech Act Realization Strategies in a Multicultural Online Communication Environment

The global acceptance of emoticons has acknowledged the development of digital symbols in a communication setting when language alone can become a barrier in expressing certain intentions and feelings. This paper discusses how emoticons help indicate the illocutionary forces in texts and serve as part of various conversation strategies in the online communication environment. To achieve the research objective, a documentation of naturally occurring conversations on Facebook was made over a 12-month period to compile daily updates and conversations posted by youngsters in Malaysia. 120 online users were identified using a purposive sampling technique. A corpus of 324 362 words was established and processed. This whole set of naturally occurring conversation was then analysed based on Searle’s (1976) five categorisations of illocutionary acts using Content Analysis and Wordsmith Tools 5.0. T he findings demonstrate some emoticons that accentuated illocutionary forces of speech acts in the online communication environment. Discussion of the findings also explores the purposes and functions of emoticons in Malaysian digital communication platform and the way users of a multicultural society employ emotion-symbols to achieve social cohesion and embrace cultural diversity. Keywords: emoticon; speech act; illocutionary force; online communication; indirectness; multicultural society
A Study on Deep Learning for Breast Cancer Detection in Histopathological Images

Pathological examination is the most accurate method for the diagnosis of cancer. Breast cancer histopathology evaluation analyses the chemical and cellular characteristics of the cells of a suspicious breast tumor. A computer-aided automatic classifier with the help of machine learning can improve the diagnosis system in terms of accuracy and time consumption. These types of system can automatically distinguish a benign and malignant pattern in a breast histopathology image. It can reduce the workload of pathologists and can provide a more accurate process. In recent years, like in other areas, deep networks have also attracted for histopathology image analysis. Convolution Neural Network has become a preferred choice for images analysis including breast histopathology. In this paper, we review various deep learning concepts applied to breast cancer histopathology analysis and summarizes contributions to this field. We present a summary of the recent developments and a discussion about the best practices done using deep in breast histopathology analysis and improvements that can be done in future research.
Real-time 3D object proposal generation and classification using limited processing resources

The task of detecting 3D objects is important in various robotic applications. The existing deep learning-based detection techniques have achieved impressive performances. However, these techniques are limited to being run on a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources, we propose an efficient detection method based on 3D proposal generation and classification. The proposal generation is based mainly on point segmentation, while proposal classification is performed by a lightweight convolution neural network (CNN). KITTI datasets are then used to validate our method. It takes only 0.082 s for our method to process one point block with one core of the central processing unit (CPU). In addition to efficiency, the experimental results also demonstrate the capability of the proposed method of producing a competitive performance in object recall and classification.
DIGITAL EDUCATIONAL RESOURCES IN TECHNICAL EDUCATION

Digital educational resources in education represent a modern trend of providing educational content to pupils. These resources can take various forms in practice. It is virtually impossible to create requirements that would closely respect the nature of all types of digital materials and resources that can be used in education or created specifically for educational purposes. Digital educational resources are understood as materials that are in digital form and are intended to support the development of digital literacy in education. The article describes the integration of digital educational resources as modern tools of education into primary school education in accordance with current education trends and educational policy strategies of the Czech Republic. Part of this work are partial results of the research, which was carried out among primary school teachers proving the state and starting points mentioned in the article. The main question is whether the use of digital educational resources is realized in the teaching of schools and, if so, to what extent, in which educational subjects and whether their use has positive effects (benefits) for teaching. The article describes some concepts of efficiency of these sources and some attempts to measure their effects. The results are evaluated and confronted with educational reality.
A radio burst detection method based on the Hough transform

ABSTRACT We present a simple and fast method for incoherent dedispersion and fast radio burst (FRB) detection based on the Hough transform, which is widely used for feature extraction in image analysis. The Hough transform maps a point in the time–frequency data to a straight line in the parameter space and points on the same dispersed f−2 curve to a bundle of lines all crossing at the same point, thus the curve is transformed to a single point in the parameter space, enabling an easier way for the detection of radio burst. By choosing an appropriate truncation threshold, in a reasonably radio quiet environment, i.e. with radio frequency interferences present but not dominant, the computing speed of the method is very fast. Using simulation data of different noise levels, we studied how the detected peak varies with different truncation thresholds. We also tested the method with some real pulsar and FRB data.
A Framework for Adversarially Robust Streaming Algorithms

We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chosen adaptively by an adversary that observes the outputs of the algorithm along the stream and can react in an online manner. While deterministic streaming algorithms are inherently robust, many central problems in the streaming literature do not admit sublinear-space deterministic algorithms; on the other hand, classical space-efficient randomized algorithms for these problems are generally not adversarially robust. This raises the natural question of whether there exist efficient adversarially robust (randomized) streaming algorithms for these problems. In this work, we show that the answer is positive for various important streaming problems in the insertion-only model, including distinct elements and more generally $F_p$-estimation, $F_p$-heavy hitters, entropy estimation, and others. For all of these problems, we develop adversarially robust $(1+\varepsilon)$-approximation algorithms whose required space matches that of the best known non-robust algorithms up to a $\text{poly}(\log n, 1/\varepsilon)$ multiplicative factor (and in some cases even up to a constant factor). Towards this end, we develop several generic tools allowing one to efficiently transform a non-robust streaming algorithm into a robust one in various scenarios.
Towards human activity recognition for ubiquitous health care using data from awaist-mounted smartphone

Understanding human activities is a newly emerging paradigm that is greatly involved in developing ubiquitous health care (u-Health) systems. The aim of these systems is to seamlessly gather knowledge about the patient?s health and, after collecting knowledge, make suggestions to the patient according to his/her health profile. For this purpose, one of the most important ubiquitous communication trends is the smartphone, which has drawn the attention of both professionals and caregivers for monitoring the aging population, childcare, fall detection, and cognitive impairment. Recognizing human actions in a ubiquitous environment is very challenging and researchers have extensively investigated different methods to recognize human activities in the past decade. However, this field of research still needs further exploration in order to improve the accuracy and reduce the computational cost of these health care systems. Therefore, for expediting the existing system, this research work investigated a novel approach based on feature selection and classification. In the proposed work, sparse Bayesian multinomial logistic regression (SBMLR) is used for feature subset selection and a multiclass support vector machine (SVM) is adapted for the classification of six human daily activities (laying down, walking up stairs, walking down stairs, sitting, standing, and walking). For identifying the best features among the features returned by the SBMLR, a tuned threshold value is used for the selection of the features. Further, other classification algorithms including K-nearest neighbor, decision tree, and naive Bayes and different feature selection methods such as principal component analysis and random subset feature selection are also used for evaluation and comparison. The dataset used for testing is obtained from the UCI Machine Learning Repository. It is collected by using a smartphone embedded with an accelerometer and gyroscope. The experimental results show that the highest accuracy of 99.40% can be achieved by using the proposed method. Moreover, the paired sample two-tailed t-test over the significance level of 0.05 reveals that the performance difference between the proposed technique and a competing technique is statistically significant.
On the Existence of Tacit Assumptions in Contextualized Language Models

Humans carry stereotypic tacit assumptions (STAs) (Prince, 1978), or propositional beliefs about generic concepts. Such associations are crucial for understanding natural language. We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs. Our prompts are based on human responses in a psychological study of conceptual associations. We find models to be profoundly effective at retrieving concepts given associated properties. Our results demonstrate empirical evidence that stereotypic conceptual representations are captured in neural models derived from semi-supervised linguistic exposure.
Solving the scalarization issues of Advantage-based Reinforcement Learning algorithms

In this research, some of the issues that arise from the scalarization of the multi-objective optimization problem in the Advantage Actor Critic (A2C) reinforcement learning algorithm are investigated. The paper shows how a naive scalarization can lead to gradients overlapping. Furthermore, the possibility that the entropy regularization term can be a source of uncontrolled noise is discussed. With respect to the above issues, a technique to avoid gradient overlapping is proposed, while keeping the same loss formulation. Moreover, a method to avoid the uncontrolled noise, by sampling the actions from distributions with a desired minimum entropy, is investigated. Pilot experiments have been carried out to show how the proposed method speeds up the training. The proposed approach can be applied to any Advantage-based Reinforcement Learning algorithm.
An Approach for Efficient and Secure Data Encryption Scheme for Spatial Data

This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary all-encompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user’s area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.
The Prime state and its quantum relatives

The Prime state of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> qubits, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:msub><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">P</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msub><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo fence="false" stretchy="false">⟩</mml:mo></mml:mrow></mml:mrow></mml:math>, is defined as the uniform superposition of all the computational-basis states corresponding to prime numbers smaller than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup></mml:math>. This state encodes, quantum mechanically, arithmetic properties of the primes. We first show that the Quantum Fourier Transform of the Prime state provides a direct access to Chebyshev-like biases in the distribution of prime numbers. We next study the entanglement entropy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:msub><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">P</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msub><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo fence="false" stretchy="false">⟩</mml:mo></mml:mrow></mml:mrow></mml:math> up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>30</mml:mn></mml:math> qubits, and find a relation between its scaling and the Shannon entropy of the density of square-free integers. This relation also holds when the Prime state is constructed using a qudit basis, showing that this property is intrinsic to the distribution of primes. The same feature is found when considering states built from the superposition of primes in arithmetic progressions. Finally, we explore the properties of other number-theoretical quantum states, such as those defined from odd composite numbers, square-free integers and starry primes. For this study, we have developed an open-source library that diagonalizes matrices using floats of arbitrary precision.
Top Benefits and Hindrances to Cloud Computing Adoption in Saudi Arabia: A Brief Study

Cloud computing is an emerging concept of information technology that in many countries has an influence on many companies. The research was conducted to evaluate cloud computing adoption in Saudi Arabia; Benefits and hindrances for small and medium-sized enterprises (SMEs). The qualitative research approach is performed by interviews with the management of a variety of SMEs active in the information and communication technology (ICT) industry. This paper illustrates a significant positive correlation between the use of cloud computing and organizational quality performances. The paper concluded that the knowledge level of SMEs on the accessibility of cloud services is below average scale. The greatest challenges about the cloud service are privacy and security in the cloud among providers and users for the Saudi Arabian firms.
rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking

Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.
Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model

Reinforcement learning (RL) tasks have often assumed a Markov decision process, which is to say, state information is ‘complete’, hence there is no need to learn what to learn from. However, recent advances—such as visual reinforcement learning—have enabled the tasks typically addressed using RL to expand to include significant amounts of partial observability. This implies that the representation needs to support multiple forms of memory, thus credit assignment needs to: find efficient ways to encode high dimensional data, as well has, determining under what conditions to save and recall specific pieces of information, and for what purpose. In this work, we assume the tangled program graph (TPG) formulation for genetic programming, where this has already demonstrated competitiveness with deep learning solutions to multiple RL tasks (under complete information). In this work, TPG is augmented with indexed memory using a probabilistic formulation of a write operation (defines long and short term memory) and an indexed read. Moreover, the register information specific to the programs co-operating within a program is used to provide the low dimensional encoding of state. We demonstrate that TPG can then successfully evolve a behaviour for a hero bot in the Dota 2 game engine when playing in a single lane 1-on-1 configuration with the game engine hero bot as the opponent. Specific recommendations are made regarding the design of an appropriate fitness function. We show that TPG without indexed memory completely fails to learn any useful behaviour. Only with indexed memory are useful hero behaviours discovered.
An Importance Aware Weighted Coding Theorem Using Message Importance Measure

There are numerous scenarios in source coding where not only the code length but the importance of each value should also be taken into account. Different from the traditional coding theorems, by adding the importance weights for the length of the codes, we define the average cost of the weighted codeword length as an importance-aware measure of the codes. This novel information theoretical measure generalizes the average codeword length by assigning importance weights for each symbol according to users' concerns through focusing on user's selections. With such definitions, coding theorems of the bounds are derived and the outcomes are shown to be extensions of traditional coding theorems.
A HPC Co-scheduler with Reinforcement Learning

Although High Performance Computing (HPC) users understand basic resource requirements such as the number of CPUs and memory limits, internal infrastructural utilization data is exclusively leveraged by cluster operators, who use it to configure batch schedulers. This task is challenging and increasingly complex due to ever larger cluster scales and heterogeneity of modern scientific workflows. As a result, HPC systems achieve low utilization with long job completion times (makespans). To tackle these challenges, we propose a co-scheduling algorithm based on an adaptive reinforcement learning algorithm, where application profiling is combined with cluster monitoring. The resulting cluster scheduler matches resource utilization to application performance in a fine-grained manner (i.e., operating system level). As opposed to nominal allocations, we apply decision trees to model applications' actual resource usage, which are used to estimate how much resource capacity from one allocation can be co-allocated to additional applications. Our algorithm learns from incorrect co-scheduling decisions and adapts from changing environment conditions, and evaluates when such changes cause resource contention that impacts quality of service metrics such as jobs slowdowns. We integrate our algorithm in an HPC resource manager that combines Slurm and Mesos for job scheduling and co-allocation, respectively. Our experimental evaluation performed in a dedicated cluster executing a mix of four real different scientific workflows demonstrates improvements on cluster utilization of up to 51% even in high load scenarios, with 55% average queue makespan reductions under low loads.
Revisiting Memory-Efficient Incremental Coreference Resolution.

We explore the task of coreference resolution under fixed memory by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Our algorithm creates explicit representations for each entity, where given a new sentence, spans are proposed and subsequently scored against each entity representation, leading to emergent clusters. Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts.