Reviewerly/exHarmony huggingface dataset:
Implementasi Metode HOT FIT pada Evaluasi Tingkat Kesuksesan Sistem Pengisian KRS Terkomputerisasi

This study aims to: (1) determine the success rate of computerized study plan card filling systems at the University Gunadarma using the HOT Fit method based on the results of evaluations from users and; (2) knowing the effects of measurement characteristics of the implementation of the HOT Fit method in evaluating the success rate of computerized study plan card filling systems at Gunadarma University. The method used for assessing this system uses the HOT Fit method which consists of 7 characteristics, namely System Quality (Ks), Information Quality (Ki), Service Quality (Kl), System Users (Ps), User Satisfaction (Kp), Benefits (Mn) and Organizational Structure (So) by using purposive sampling technique. The discussion chapter is deepened by triangulation, namely interviews, documentation and observation. The distribution of the questionnaire was carried out to users, namely Information Systems students in the 2018-2019 academic year as many as 96 samples. The analysis is done through a test case of each HOT Fit character. The results of this study indicate that: (1) computerized KRS filling system as a whole has a good interpretation according to the HOT Fit method with a value of 2.91; (2) the quality of the final result of the Ks character with an interpretation value of 2.98 (good), Ki with a value of 2.89 (good), Kl with a value of 2.9 (good), Ps with a value of 2.92 (good), Ks with a value of 2.64 (good), Mn with a value of 2.74 (good) and So with a value of 3.27 (very good) based on the HOT Fit method. Keywords: HOT fit, success rate of system, evaluation, characteristic measurement
WordPress: A Multi-Functional Content Management System

Most websites nowadays are driven by various content management systems (CMS), which allow website managers to make updates to their websites without having any understanding of web development tools. WordPress, Drupal, and Joomla are all prominent open source Content Management system platforms, but growth of WordPress has been accepted in popularity over the last several years.We cover the key features of WordPress in this study, as well as the pros and drawbacks of using WordPress as a content management system.The goal is to provide newbie users with instructions on how to utilise the Word Press Content Management System Tool.
Improved Decomposition Strategy for Joint Entity and Relation Extraction

The joint entity and relation extraction task detects entity pairs along with their relations to extract relational triplets. A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity. However, this strategy suffers from two major problems. First, if the HE detection task fails to find a valid head-entity, the model will then miss all related triplets containing this head-entity in the head role. Second, as Yu et al. (2020) stated, their model cannot solve the entity pair overlap (EPO) problem. For a given head-entity, the TER extraction task predicts only a single relation between the head-entity and a tail-entity, even though this entity pair can hold multiple relations. To address these problems, we propose an improved decomposition strategy that considers each extracted entity in two roles (head and tail) and allows a model to predict multiple relations (if any) of an entity pair. In addition, a corresponding model framework is presented to deploy our new decomposition strategy. Experimental results showed that our approach significantly outperformed the previous approach of Yu et al. (2020) and achieved state-of-the-art performance on two benchmark datasets.
Estimating Gibbs partition function with quantum Clifford sampling

Abstract The partition function is an essential quantity in statistical mechanics, and its accurate computation is a key component of any statistical analysis of quantum systems and phenomena. However, for interacting many-body quantum systems, its calculation generally involves summing over an exponential number of terms and can thus quickly grow to be intractable. Accurately and efficiently estimating the partition function of its corresponding system Hamiltonian then becomes the key in solving quantum many-body problems. In this paper we develop a hybrid quantum–classical algorithm to estimate the partition function, utilising a novel quantum Clifford sampling technique. Note that previous works on the estimation of partition functions require <?CDATA $\mathcal{O}(1/{\epsilon}\sqrt{{\Delta}})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mi>ϵ</mml:mi> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="normal">Δ</mml:mi> </mml:mrow> </mml:msqrt> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> -depth quantum circuits (Srinivasan et al 2021 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) pp 112–22; Montanaro 2015 Proc. R. Soc. A 471 20150301), where Δ is the minimum spectral gap of stochastic matrices and ϵ is the multiplicative error. Our algorithm requires only a shallow <?CDATA $\mathcal{O}(1)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> -depth quantum circuit, repeated <?CDATA $\mathcal{O}(n/{{\epsilon}}^{2})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mrow> <mml:mi>ϵ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> times, to provide a comparable ϵ approximation. Shallow-depth quantum circuits are considered vitally important for currently available noisy intermediate-scale quantum devices.
“To just steal the name of a character”

The Sherlock Holmes character first appeared in “A Study in Scarlet” in Beeton’s Christmas Annual in 1887. Serialized stories in The Strand Magazine from 1891 onwards gave the detective figure widespread popularity. By the 1920s, he had spawned a transmedia universe, consisting of adaptations (films, dramas, literary parodies) and journalistic paratexts; these originated in the UK and other European countries as well as in the US. Arthur Conan Doyle did relatively little to assert his intellectual property rights over adaptations, but paratexts regularly lauded him as the creator of the Great Detective. This chapter focuses on the early years of Sherlock Holmes. First, it traces the development of the character from the initial textual instantiations to the international transmedia universe of the early twentieth century. Textual analysis employed here is based on a character template consisting of demographics, psychological traits, habitual behaviors, speech patterns, physical appearance, relationships with secondary characters and geographical and temporal setting. Second, it explores the role of copyright and authorial authority. Third, it contrasts the long nineteenth-century character template with that of the early twenty-first century. It argues that these distinctions arise from the very different industrial underpinnings of the nineteenth- and twenty-first centuries’ transmedia universes.
Additional opportunities for using computer technologies in expert analysis of laboratory data

The presented work reflects the author’s algorithm of mathematical processing and possible interpretations of the results obtained when creating expert analytical systems using computer technology. As initial parameters for the construction of neural networks of the second level, it is proposed to use individual results of calculations of the ratios of a number of laboratory indicators capable of forming a single functional cluster (leukogram, indicators of water-electrolyte balance, protein fractions, etc.). Further, using the proposed calculation algorithm and the archive database, structural features in the ′deformation′ of the ratio panel were compared against the background of the growth of each indicator, a matrix table was built reflecting the degree (correlation coefficients, KCr) of the coincidence of the features of the formation of the structure of the obtained panels of the parameters determined in individual observations. At high values of KCr, a conclusion is made about their (determined parameters) unified participation in the mechanism of emerging disorders. Thus, the system allows you to establish a leading complex of associated relationships by changes in the structure of ratios in the selected panel, corresponding to the dynamics of deviation of the target (analyzed) indicator, thereby differentiating the different mechanisms of formation of pathological deviations in this patient, the manifestation and balance of their values in the system-wide response, to evaluate the specific value of the absolute indicator as the final result of such interaction at the time of examination of the patient. The approach used, proposed by the authors, allows us to evaluate not only quantitative relationships, but also the features of the manifestation of the functional properties of the evaluated indicators. All of the above significantly expands the informativeness of the obtained laboratory data, allowing us to build a reasonable paradigm of the connections of the emerging complex of pathological disorders in each individual case. In the list of references, the authors cite publications as concrete examples of the use of the proposed approach in assessing a complex of disorders with different pathological processes using different panels in calculations.
Dephasing superchannels

The authors introduce dephasing superchannels, which describe environmental noises that act only on the quantum properties of a gate and leave the classical parts untouched. They develop a formalism to explore various properties of these superchannels, such as the effects they have on channel coherence, and propose a physical realization based on pre- and postprocessing operations with memory.
SwordNet: Chinese Character Font Style Recognition Network

Chinese characters have been created into many font styles, such as official script, running script, and regular script. Other than that, some famous calligraphers, such as Ouyang Xun and Yan Zhenqing, have produced fonts with their style. Being able to detect and recognize these font styles quickly and accurately has essential applications in graphic design, page layout, handwriting identification, and other use cases. Distinguishing between font styles requires professional knowledge, which almost inevitably leads to errors for unprofessional people. Therefore, this paper presents a sword-like model based on a convolutional neural network with a sword structure to recognize font styles for Chinese characters. This model includes 15 convolutional layers. For each layer, we gradually increase the number of convolutional kernels to better extract the classification features of the input image. This paper uses four downsampling layers in the model. For each downsampling operation, the length and width of the image become half of their original values while the number of channels gradually increases, leading to a sword-like shape. As a result, we name our model as SwordNet. We also created a Chinese font dataset called the Nankai Chinese Font Style dataset and made it available on Github. Using the above dataset, we compared the accuracy of our model with six other state-of-the-art network models. The experiments showed that SwordNet could achieve an average recognition accuracy of 99.03% in multiple experiments, while the other six models can only achieve accuracy up to 94.91%. So we can conclude that SwordNet could perform better in font style recognition than other models.
Fusion of Depth and Thermal Imaging for People Detection

The methodology presented in this paper covers the topic of automatic detection of humans based on two types of images that do not rely on the visible light spectrum, namely on thermal and depth images. Various scenarios are considered with the use of deep neural networks being extensions of Faster R-CNN models. Apart from detecting people, independently, with the use of depth and thermal images, we proposed two data fusion methods. The first approach is the early fusion method with a 2-channel compound input. As it turned out, its performance surpassed that of all other methods tested. However, this approach requires that the model be trained on a dataset containing both types of spatially and temporally synchronized imaging sources. If such a training environment cannot be setup or if the specified dataset is not sufficiently large, we recommend the late fusion scenario, i.e. the other approach explored in this paper. Late fusion models can be trained with single-source data. We introduce the dual-NMS method for fusing the depth and thermal imaging approaches, as its results are better than those achieved by the common NMS.
Efficient Network Traffic Classification and Visualizing Abnormal Part Via Hybrid Deep Learning Approach : Xception + Bidirectional GRU

Due to a rapid development in the field of information and communication, the information technologies yielded novel changes in both individual and organizational operations. Therefore, the accessibility of information became easier and more convenient than before, and malicious approaches such as hacking or spying aimed at various information kept increasing. With the aim of preventing malicious approaches, both classification and detecting malicious traffic are vital. Therefore, our research utilized various deep learning and machine learning models for better classification. The given dataset consists of normal and malicious data and these data types are png files. In order to achieve precise classification, our experiment consists of three steps. Firstly, only vanilla CNN was used for the classification and the highest score was 86.2%. Second of all, for the hybrid approach, the machine learning classifiers were used instead of fully connected layers from the vanilla CNN and it yielded about 87% with the extra tree classifier.At last, the Xception model was combined with the bidirectional GRU and it attained a 95.6% accuracy score, which was the highest among all.
Optical Reservoir-based Noise Compensation in a Space Division Multiplexing Transmission System

We propose a nonlinear-optical loop mirror (NOLM)-based reservoir computing scheme to perform the noise compensation for a space-division multiplexing (SDM) transmission system, achieving the maximum Q 2 of the recovered signal up to 65.52dB.
Rationales for Sequential Predictions

Sequence models are a critical component of modern NLP systems, but their predictions are difficult to explain.We consider model explanations though rationales, subsets of context that can explain individual model predictions.We find sequential rationales by solving a combinatorial optimization: the best rationale is the smallest subset of input tokens that would predict the same output as the full sequence.Enumerating all subsets is intractable, so we propose an efficient greedy algorithm to approximate this objective.The algorithm, which is called greedy rationalization, applies to any model.For this approach to be effective, the model should form compatible conditional distributions when making predictions on incomplete subsets of the context.This condition can be enforced with a short finetuning step.We study greedy rationalization on language modeling and machine translation.Compared to existing baselines, greedy rationalization is best at optimizing the sequential objective and provides the most faithful rationales.On a new dataset of annotated sequential rationales, greedy rationales are most similar to human rationales.
Parametricity for Nested Types and GADTs

This paper considers parametricity and its consequent free theorems for nested data types. Rather than representing nested types via their Church encodings in a higher-kinded or dependently typed extension of System F, we adopt a functional programming perspective and design a Hindley-Milner-style calculus with primitives for constructing nested types directly as fixpoints. Our calculus can express all nested types appearing in the literature, including truly nested types. At the level of terms, it supports primitive pattern matching, map functions, and fold combinators for nested types. Our main contribution is the construction of a parametric model for our calculus. This is both delicate and challenging. In particular, to ensure the existence of semantic fixpoints interpreting nested types, and thus to establish a suitable Identity Extension Lemma for our calculus, our type system must explicitly track functoriality of types, and cocontinuity conditions on the functors interpreting them must be appropriately threaded throughout the model construction. We also prove that our model satisfies an appropriate Abstraction Theorem, as well as that it verifies all standard consequences of parametricity in the presence of primitive nested types. We give several concrete examples illustrating how our model can be used to derive useful free theorems, including a short cut fusion transformation, for programs over nested types. Finally, we consider generalizing our results to GADTs, and argue that no extension of our parametric model for nested types can give a functorial interpretation of GADTs in terms of left Kan extensions and still be parametric.
IBD: A Feedback Framework with Deep-learning for IoT-generated Big Data Processing

Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNNs)have the ability to find the accurate result in images and text respectively. The best classification results are still awaited due to the high cost of computation and high memory requirements of CNN and RNN. Our work suggests a framework that improves the quality of data at various layers by providing feedback to suggested system. The proposed framework leads to an error free processing system.
Career Counseling Chatbot on Facebook Messenger using AI

Increasingly we observe that newly graduated university students struggle to find employment, often ending up in fields completely different from those that they studied in, and employees reporting low levels of satisfaction in the careers that they choose. One of the reasons for this could be a lack of adequate, useful career guidance when an individual is in university or recently graduated. Chatbots are useful resources and a topic of interest in the realm of Computer Science and AI for their ability to mimic experts in different applications, as well as being able to replicate human interaction to varying degrees.
Service Cost Effective and Reliability Aware Job Scheduling Algorithm on Cloud Computing Systems

Nowadays, increasing number of services are provided to individuals and organizations through cloud computing systems in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pay-as-you-use</i> model. This business service paradigm encounters several cloud Quality of Service (QoS) challenges, such as reliability, cost, and response time. The most common mechanism to improve cloud service reliability is a primary/backup (PB) fault-tolerant technique. However, this reliability enhancement technique inevitably results in multiple replications, which lead to high service cost. In recognition of these challenges, we first build a cloud computing systems resources management architecture. Then, we analyze the cloud service execution reliability on the physical resources of a VM and used a CUDA (Compute Unified Device Architecture)-enabled parallel two-dimensional long short-term memory neural network to predict the software faults of a cloud VM. Third, we propose an effective primary/backup cloud service cost calculation approach. To overcome the cloud service response time constraint, we integrate a response time slack factor into this method. Fourth, we formulate the cloud service reliability and cost aware job scheduling problem, which aims at minimizing the total cloud service cost and rejection rate, and improving the system reliability. Fifthly, a heuristic greedy reliability and cost aware job scheduling (RCJS) algorithm is proposed. Finally, a performance evaluation is conducted and the experimental results demonstrate that our proposed RCJS algorithm significantly outperforms optimal redundant VM placement (OPVMP), MIN-MIN algorithms in terms of average service cost and rejection rate. This algorithm also demonstrates good trade-off of reliability when compared to the other two algorithms and is suitable for cloud services with high reliability and low-cost requirements.
Finetuning Pretrained Transformers into RNNs

Transformers have outperformed recurrent neural networks (RNNs) in natural language generation.But this comes with a significant computational cost, as the attention mechanism's complexity scales quadratically with sequence length.Efficient transformer variants have received increasing interest in recent works.Among them, a linear-complexity recurrent variant has proven well suited for autoregressive generation.It approximates the softmax attention with randomized or heuristic feature maps, but can be difficult to train and may yield suboptimal accuracy.This work aims to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy.Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.With a learned feature map, our approach provides an improved tradeoff between efficiency and accuracy over the standard transformer and other recurrent variants.We also show that the finetuning process has lower training cost relative to training these recurrent variants from scratch.As many models for natural language tasks are increasingly dependent on large-scale pretrained transformers, this work presents a viable approach to improving inference efficiency without repeating the expensive pretraining process. 1
The Concept of an Adaptive Trainer and Assessing Its Effectiveness in a Mathematical Application

Presented is a mathematical model of the self-learning adaptive trainer intended for adaptive learning and providing task selection. The approach in question is an alternative to the adaptive technologies based on the Item Response Theory. Possibility to take into account temporal dynamics of solution ability as well as smaller number of tasks that must be performed by a subject to provide the given results are among the features of the methods in use. To assess the effectiveness of the adaptive trainer concept under consideration, its web-implementation intended for training school students to solve mathematical tasks covered by the school curriculum was employed. The analysis performed revealed both high efficiency of the adaptive trainer (the mean test rating has increased 1.54 times owing to its use) and proven statistically significant influences of the adaptive training factor on the observed mathematical test results.