Reviewerly/exHarmony huggingface dataset:
Analisis Kesalahan Berbahasa dalam Karangan Narasi Siswa Kelas V SDN Cilengkrang Kabupaten Sumedang

This research was intended to describe the errors in narrative essays on 5th grade students of SDN Cilengkrang, Sumedang Regency. Observation results showed some of the problems, such as students found it difficult to express ideas so that it was difficult to write essays, students didn’t respond well so that they looked unhappy and less active in learning. Based on those problems, the impact on student outcomes was greater which causes errors at the level of morphology, phonology, syntax, semantics, and spelling. Errors that could occur due to students' lack of understanding of good and correct spelling. This research was a preliminary study specifically designed to describe errors in narrative essays on elementary school students in Sumedang Regency. The long-term goals of the results of this study, could be used in the learning process by using models and learning methods that could minimize the problems done by students. In addition, the results of this study could create teaching materials that could bring out student’s motivation in writing essays.The method used in this research was descriptive. This method explained the recently phenomena through the collection of data obtained. The results of the analysis obtained data from six primary schools namely, SDN Cilengkrang.
Optimal Combination of Imitation and Reinforcement Learning for Self-driving Cars

The two steps in human intelligence development, namely, mimicking and tentative application of expertise, are reflected by imitation learning (IL) and reinforcement learning (RL) in artificial intelligence (AI).However, the RL process does not always improve the skills learned from expert demonstrations and enhance the algorithm performance.To solve the problem, this paper puts forward a novel algorithm called optimal combination of imitation and reinforcement learning (OCIRL).First, the concept of deep q-learning from demonstrations (DQfD) was introduced to the actor-critic (A2C) model, creating the A2CfD model.Then, a threshold was estimated from a trained IL model with the same inputs and reward function with the DOfD, and applied to the A2CfD model.The threshold represents the minimum reward that conserves the learned expertise.The resulting A2CfDoC model was trained and tested on self-driving cars in both discrete and continuous environments.The results show that the model outperformed several existing algorithms in terms of speed and accuracy.
Investigating backtranslation for the improvement of English-Irish machine translation

In this paper, we discuss the difficulties of building reliable machine translation systems for the English-Irish (EN-GA) language pair. In the context of limited datasets, we report on assessing the use of backtranslation as a method for creating artificial EN-GA data to increase training data for use state-of-the-art data-driven translation systems. We compare our results to earlier work on EN-GA machine translation by Dowling et al (2016, 2017, 2018) showing that while our own systems do not compare in quality with respect to traditionally reported BLEU metrics, we provide a linguistic analysis to suggest that future work with domain specific data may prove more successful.
Forking-path routines for plot advancement and problem solving in narrative composition and dramatic writing

This paper investigates how plot-advancement routines, i.e. ‘what-if’, ‘if-do’, and forking-path story alternatives, are generated in narrative composition and dramatic writing by exploring cognitive representations such as schemata and problem-solving mechanisms. The complexity of narrative synthesis and problem-solving involves the integration, organisation and recall of data relations in a systemic way through identification of logical inconsistencies. Cognitively, the advancement of plot is not based solely on the consolidation of narrative information. Authors have to base their strategic decisions also on architectural and structural, goal-oriented routines and sub-routines, all of which are investigated in this paper through a comparative analysis of topics from behavioural and cognitive psychology, cognitive narratology and narrative theory, artificial intelligence and narrative causality, aiming to elucidate a multifaceted process during dramatic writing and narrative composition.
The Pilato Lake (Sibillini Mts., Central Italy): first results of a study on the supposed variations of its hydrogeological conditions induced by the seismic sequence 2016-2017

The Pilato Lake has glacial origin, is located in the Sibillini Mountains, Central Italy, and is characterized by the endemism of the small crustaceous Chirocephalus marchesonii. In the context of a research agreement with the Monti Sibillini National Park, the ISPRA Geological Survey of Italy is carrying on some studies aimed to evaluate the supposed effects of the 2016–2017 Central Italy earthquakes on the hydrogeological conditions controlling the lake’s evolution. The study, started in July 2018, aims primarily at the reconstruction of the geological and hydrogeological subsoil setting, beneath the valley hosting the lake. In order to define the conceptual model of groundwater circulation and, thus, to evaluate the emptying and recharge seasonal cycle of the lake, hydrogeological surveys and geophysical investigations were performed. The research program is still in progress and only preliminary results may be proposed. The hydrogeological surveys demonstrate the absence of geomorphological evidence of seismic-induced surface fractures generated by the seismic sequence 2016-2017. Consequently, the complete drying of the lake, occurred in summer 2017, was probably due to meteoclimatic reasons and/ or to not yet verified variations in the permeability characteristics of the surficial deposits and/or bedrock affecting the infiltration towards the subsoil, caused by the severe seismic shaking. The low electrical conductivity values of the lake’s waters are in accordance with the prevailing origin of the lake’s recharge by snow melting and direct rainfall. As concerns the first geophysical surveys in the Pilato Lake area, seismic surveys by surface wave method assessed that the maximum thickness of debris here estimated is about 12.5-14 m. As a whole, the factors responsible for the progressive lowering of the lake level are: i) the water overflow through the Fonte del Lago spring, ii) the evaporation from the lake surface and iii) the drainage through the scarcely permeable glacial deposits towards the Basal Calcareous Aquifer, hosted within the limestone at lower altitude.
Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.
Isolated Word-Based Spoken Dialogue System Using Odia Phones

Aims: To develop Spoken Dialogue System in Indian language with Voice response and voice based biometric feature. Backgrounds: Most of research works in spoken dialogue system are carried out in U.S. and Europe and currently, few government funding projects on spoken dialogue system (SDS) are carried out in Indian academic institutes. Objective: We have tried to use our developed spoken language system to eliminate the desktop clutter. It is very normal tendency of computer users to place the most frequently used files, folders, applications shortcuts on their computer’s desktop. Cluttering of desktop not only slows down the productivity of computer but also looks very messy and very difficult to find files as well. Therefore, we tried to use the spoken dialogue system to eliminate the desktop clutters in painless manner and the services are provided to the computer users by opening the files, folders and frequently used application of users in spoken command mode with voice response. Methods: In this research article, we have attempted to utilize an Indian spoken language for communication with spoken dialogue system. We have adopted a statistical machine learning algorithm called Hidden Markov Model for development of speech recognition engine. The speaker verification module is developed using fuzzy c-means algorithm. Speech synthesis is carried out using diphone corpus. Results: The speaker verification module has yielded satisfactory results with average accuracy of 66.2% using FCM algorithm. It is also seen that fundamental frequency and formant frequency carry the distinctive characteristics of speaker verification over Indian spoken language. The vital module of SDS i.e. speech recognition engine is developed by using HMM, a statistical algorithm. It is observed that word accuracy of ASR engine is 78.22 % and 62.31 % for seen and unseen users respectively. The voice response is given to the user in terms of synthesized speech. The audio quality of synthesized speech is measured using the MOS test. The MOS test value is found as 3.8 and 3.6 over two distinct groups of listeners. Conclusion: In this research paper, we have developed a spoken dialogue system based on Odia language phone set. We have integrated speaker verification module in order to provide additional biometric based security.
Ease and Level of use of Smartphone for Learning Amongst Pre-Service Teachers Colleges of Education in North-West, Nigeria

This study “ease and level of use of smartphone for learning amongst pre-service teachers colleges of education in North-West, Nigeria” was guided by two objectives, two research questions. The researchers used descriptive survey research design in the study. The researchers used mixed method (quantitative and qualitative) for data collection. The population of the study was 113,030. Random sampling technique was employed to select the sample. The total population of the sample is 32,300. The entire sample that was chosen is 371 for quantitative which is in line with Raosoft sample size and 6 for qualitative. The instruments used for data collection were adopted questionnaire prediction of user acceptance and adoption of smartphone for learning with technology acceptance model questionnaire (PUAASLTAMQ) by Sek, et al, (2010) and data-blank. The frequency and percentage were employed to analyze the respondents’ demographic information. To answer research question one (1), descriptive statistics (frequencies and percentages) were used. To answer research question two t-test was used. The summary of the major findings indicated that smartphone is very easy to use for learning by the pre-service teachers in Colleges of Education in North-West, Nigeria and male pre-service teachers in Colleges of Education in North-West, Nigeria use smartphone for learning more than their female counterpart. The research shows that smartphone may be accepted as instructional tool in future, because the mode of smartphone operation is not difficult.
Fusing Dynamic Images and Depth Motion Maps for Action Recognition in Surveillance Systems

Background: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Objective: Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Methods: Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. Results: This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.
Understanding EFL Linguistic Models through Relationship between Natural Language Processing and Artificial Intelligence Applications

Natural Language Processing (NLP) platforms have recently reported a higher adoption rate of Artificial Intelligence (AI) applications.The purpose of this research is to examine the relationship between NLP and AI in the application of linguistic tasks related to morphology, parsing, and semantics.To achieve this objective, a theoretical framework was designed to investigate the direct and indirect impact of the relationship between NLP platforms and AI applications, such as machine learning and deep learning.Theoretically, this study contributes to examining the relationship between NLP platforms and AI applications through selected linguistic models from the English as a Foreign Language (EFL) perspective.Practical implications are derived from syntactic and semantic variables when AI applications are used.The results of this study suggest that AI applications can use to support NLP tasks, particularly the adaptation of deep learning applications that can prove useful in extracting analytical inferences and enhancing NLP approaches applied to EFL texts.The conclusion drawn of this study is that if NLP caters to knowledge-rich AI techniques, it can make significant advances in the linguistics disciplines of morphology, parsing, and semantics.
Sistem Pakar Identifikasi Modalitas Belajar Siswa Menggunakan Metode Forward Chaining

Student learning modalities are important to be identified by teachers and students. Because the success of students in the field of academics is supported by the appropriate student learning modalities. Often occurs in the process of teaching and learning teachers do not know the modalities of student learning so that the material in teaching teachers difficult to accept by students. Appropriate learning modalities that are in accordance with the methods taught by the teacher need to be built in an Expert System. Expert System that is processed in this research is taken from the expertise of teachers of Senior High School Counseling Guidance 1 Tilatang Kamang by using Forward Chaining method. Learning modalities are processed using expert systems created with php programming languages ​​and mysql databases. Furthermore, this expert system can determine the modalities of visual learning, auditory and kinesthetic. The result of testing on this method is able to determine the learning modality in the students with the accuracy and the speed is good. Expert system test results have been able to determine student learning modalities clearly and can already be recommended to help teachers and students in improving the way students learn the right.&#x0D;
Comparison of Lexicon Performances on Unstructured Behavioral Data

Our challenge is gaining insight, analyzing and understanding implicit behavior behind text. Useful tools are machine learning and sentiment analysis. Text classification copes with content mass to extract actionable knowledge. We evaluate relative performance of four well-known lexicons implemented through Nave Bayes and bigram analysis. Results show Loughran Lexicon captures well predatory practices. Nave Bayes modeling provides means to compare lexicons. We obtain 87 % accuracy with Loughran. Cross Validation validates prediction accuracy. We support our hypothesis that lexicons are not sufficient for AI tools. Lexicons allow easier feature selection for Nave Bayes approach. Our goal is to understand sentiment and emotion behind complaints and provide clearly relevant training data as one attempts to automate decision making using AI tools.
Machine Learning Based Prediction of Complex Bugs in Source Code

During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.
High functionality reversible arithmetic logic unit

Energy loss is a big challenge in digital logic design primarily due to impending end of Moore’s Law. Increase in power dissipation not only affects portability but also overall life span of a device. Many applications cannot afford this loss. Therefore, future computing will rely on reversible logic for implementation of power efficient and compact circuits. Arithmetic and logic unit (ALU) is a fundamental component of all processors and designing it with reversible logic is tedious. The various ALU designs using reversible logic gates exist in literature but operations performed by them are limited. The main aim of this paper is to propose a new design of reversible ALU and enhance number of operations in it. This paper critically analyzes proposed ALU with existing designs and demonstrates increase in functionality with 56% reduction in gates, 17 % reduction in garbage lines, 92 % reduction in ancillary lines and 53 % reduction in quantum cost. The proposed ALU design is coded in Verilog HDL, synthesized and simulated using EDA (Electronic Design Automation) tool-Xilinx ISE design suit 14.2. RCViewer+ tool has been used to validate quantum cost of proposed design.
Selection of compression test images using variance-based statistical method

In this research paper, we used a variance-based statistical method to select 20 test images, according to these latter types (natural, satellite and medical) among a sample of 300 images (100 natural, 100 satellite and 100 medical). The images selection has been done using parameters of image quality, namely PSNR, MSSIM and VIF, which were applied on three compression algorithms (DWT+SPIHT, JPEG, and JPEG2000).
Securing speech signals by watermarking binary images in the wavelet domain

&lt;span&gt;Digital watermarking is the process of embedding particular information into other signal data in such a way that the quality of the original data is maintained and secured. Watermarking can be performed on images, videos, texts, or audio to protect them from copyright violation. Among all of these types of watermarking, audio watermarking techniques are gaining more interest and becoming more challenging because the quality of such signals is highly affected by the watermarked code. This paper introduces some efficient approaches that have capability to maintain the signals’ quality and preserves the important features of the audio signals. Moreover, the proposed digital audio watermarking approaches are performed in the transform domain. These approaches are gaining more attention due to their robustness or resistance to the attackers. These transform domains include discrete cosine transform (DCT), short-term Fourier transform (STFT), and digital wavelet transform (DWT). Furthermore, the most digital wavelet transforms were found to be applicable for speech watermarking are the Haar and the Daubechies-4. &lt;/span&gt;
Correction to: Extra-foveal Processing of Object Semantics Guides Early Overt Attention During Visual Search

In the published article “Extra-foveal Processing of Object Semantics Guides Early Overt Attention During Visual Search. Atten Percept Psychophys (2019). https://doi.org/10.3758/s13414-019-01906-1”, 1) the full name of the second author is given incorrectly.
The Comparative Analysis of NTRUCipher and NTRUEncrypt Encryption Schemes

The asymmetric encryption scheme NTRUEncrypt proposed in 1996 and is one of the fastest post-quantum encryption schemes. It is included in the ANSI X9.98-2010 standard and is the prototype of cryptosystems’ wide class with the same name, which security is based on the difficulty of finding short vectors in some lattices. The cryptographic properties of NTRUEncrypt encryption scheme are sufficiently explored and its latest modifications are presented at the current NIST competition to standardize post-quantum asymmetric encryption, key encapsulation and digital signature.One of the most important problem in the field of cryptology is the design of symmetric encryption schemes, whose security, similarly to the asymmetric one, is based on the complexity of solving only one particular problem (for example, for RSA this is the problem of factorization of numbers). Due to this, in 2017 the symmetric encryption scheme NTRUCipher based on NTRUEncrypt was proposed. For it, a preliminary security analysis was performed and a parameter selection algorithm was proposed. At the same time, there are essential errors in the proof of CPA-se­curity of the encryption algorithm NTRUCipher. Moreover, the problem of comparative analysis of NTRUCipher and NTRUEncrypt encryption schemes is not solved for security and practicality.The purpose of this article is to conduct a comparative analysis of the abovementioned encryption schemes and to prove correctly the conditions that ensure the CPA-security of the NTRUCipher encryption scheme. A certain result is analytical bounds of decryption failure probability in NTRUCipher encryption scheme. This result is important for the proper parameters’ choice of the encryption scheme in its practical implementation. It is shown that the decryption failure probability in the NTRUCipher varies from to while the value of this probability for the NTRUEncrypt encryption scheme varies from to . In addition, the obtained bounds are not based on any heuristic assumptions.
Suborbital graphs for a non-transitive action of the normalizer

In this paper, we investigate a suborbital graph for the normalizer of ?0(n) in PSL(2,R), where n will be of the form 32p2, p is a prime and p &gt; 3. Then we give edge and circuit conditions on graphs arising from the non-transitive action of the normalizer.