Reviewerly/exHarmony huggingface dataset:
Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data

Shrinkage methods for linear regression were developed over the last ten years to reduce the weakness of ordinary least squares (OLS) regression with respect to prediction accuracy.And, high dimensional data are quickly growing in many areas due to the development of technological advances which helps collect data with a large number of variables.In this paper, shrinkage methods were used to evaluate regression coefficients effectively for the high-dimensional multiple regression model, where there were fewer samples than predictors.Also, regularization approaches have become the methods of choice for analyzing such high dimensional data.We used three regulation methods based on penalized regression to select the appropriate model.Lasso, Ridge and Elastic Net have desirable features; they can simultaneously perform the regulation and selection of appropriate predictor variables and estimate their effects.Here, we compared the performance of three regular linear regression methods using cross-validation method to reach the optimal point.Prediction accuracy using the least squares error (MSE) was evaluated.Through conducting a simulation study and studying real data, we found that all three methods are capable to produce appropriate models.The Elastic Net has better prediction accuracy than the rest.However, in the simulation study, the Elastic Net outperformed other two methods and showed a less value in terms of MSE.
Individualization of Learning: An Investigation on Educational Technologies

The paper discusses various approaches to the concept of “individualization of learning” and their relevant changes in connection with the development of educational technologies. An analysis of the relevant literature shows that a more precise content of this concept in each specific case depends on what goals are meant when talking about individualization. There are large differences found in different countries, among different authors, and in everyday educational practice when this concept is used. More than that, the authors demonstrate that the very notion of “individualization of learning” is replaced by “individual learning,” i.e. by the concept of “independently performing learning tasks.” The research also clearly shows that a number of opportunities for better individualization of training are limited in conditions of traditional didactics, as well as in terms of group work and the existing system of lectures and seminars. The authors argue that “individualized learning” is a part of “personalized learning,” since personalization is the realization of a person’s desire to be a person. And personality is a systemic quality of an individual. This quality appears in three spaces, one of which is the space of a person’s individual life. Another claim is that full individualization of potentials existing in education can only be achieved through a didactic-technological paradigm based on web-technology and application of intelligent robots in education. The second requirement implies administration of both education and educational process management. The authors additionally provide a case study of the “LUNCH Intellectual Information System” in order to support their arguments.
Sparse Selective Encryption for HEVC 4K Video Using Spatial Error Spread

This paper proposes a universal HEVC SE method named Sparse Selective Encryption (SSE), which encrypts a few of bits on the bitstream without coupling with the encoder. High efficiency of the SSE results from using spatial correlation of prediction units to propagate the error from encrypted units. The sparse selection of key units is decided by two spatial influence models, which are designed according to the angular intra prediction mode and the motion vector prediction direction respectively. SSE allows user to customize the encryption density of the bitstream by adjusting the proportion of selected coding units. Additionally, the format compliance of the bitstream is kept by performing bit encryption on the bits of cipherable syntax elements of selected units using the modified AES CTR cipher. The paper presents the comprehensive security analysis and efficiency performance of the SSE, including visual perception, cryptography attack and computation cost. Achieved experiment results confirm that SSE is secure and effective to protect HEVC 4K videos and backwards compatible with lower resolution videos.
Fuzzy Cognitive Mapping Approach for Assessing Industry 4.0 Tendency

The correct understanding of the conceptual and practical counterpart of Industry 4.0 is of great importance because global competition has made technology-based production a necessity. However, Industry 4.0 studies have not sufficient explanatoriness in terms of their understanding. The aim of this study is to propose a model that will predict the companies' existing and predicted Industry 4.0 levels.The changes of the concepts are examined and interpreted for 3 different hypothetically prepared scenarios. In the first scenario, an organization that is poorly managed in terms of the development of Industry 4.0 is considered. The industry 4.0 tendency was obtained as 0.04 reaching steady state after 12 time periods using the FCM algorithm. Moderate and well-managed organizations are considered in Scenario 2 and 3 respectively. The industry 4.0 tendency reached 0.12 after 15 time periods for Scenario 2. The tendency is calculated as 0.95 at the end of 5 iterations in the third scenario, which has well-managed concept values in the current situation. In addition to the scenario analysis, strategy and organization, smart operation, and smart factory concepts are found to provide the most significant contribution over the industry 4.0 level as a result of static analysis section.
Deep Residual Network Based Medical Image Reconstruction

Recent years have witnessed the development of medical imaging technology. However, the process of imaging, storage and transmission often makes the image quality reduced and affects the visual and post-processing effect. The degradation of medical image often leads to the interference of noise and the decrease of resolution. In order to reconstruct the degraded medical image, a deep residual network combined with perceptual loss and mean square error (MSE) loss is proposed to enhance image quality. As a result, a single network can handle de-noising and super-resolution in same time. By using the residual network, the number of network layers can be deepen while the gradient dispersion problem can be avoided. More image edges and details can be reconstructed with the joint loss. Experiments on one medical image data set TCIA show that the proposed method can jointly perform de-noising and super-resolution to restore more medical image texture details and get better visual effect, especially for restraining noise in the low-dose CT image.
Provably Secure Identity-Based Encryption and Signature over Cyclotomic Fields

Identity-based cryptography is a type of public key cryptography with simple key management procedures. To our knowledge, till now, the existing identity-based cryptography based on NTRU is all over power-of-2 cyclotomic rings. Whether there is provably secure identity-based cryptography over more general fields is still open. In this paper, with the help of the results of collision resistance preimage sampleable functions (CRPSF) over cyclotomic fields, we give concrete constructions of provably secure identity-based encryption schemes (IBE) and identity-based signature schemes (IBS) based on NTRU over any cyclotomic field. Our IBE schemes are provably secure under adaptive chosen-plaintext and adaptive chosen-identity attacks, meanwhile, our IBS schemes are existentially unforgeable against adaptively chosen message and adaptively chosen identity attacks for any probabilistic polynomial time (PPT) adversary in the random oracle model. The securities of both schemes are based on the worst-case approximate shortest independent vectors problem (SIVP γ ) over corresponding ideal lattices. The secret key size of our IBE (IBS) scheme is short—only one (two) ring element(s). The ciphertext (signature) is also short—only two (three) ring elements. Meanwhile, as the case of NTRUEncrypt, our IBE scheme could encrypt n bits in each encryption process. These properties may make our schemes have more advantages for some IoT applications over postquantum world in theory.
New Temperature Dependent Models for Estimating Global Solar Radiation across the Coastal Climatic Zone of Nigeria

In this study, five different temperature dependent models were proposed and compared with three existing temperature dependent models (Chen, Hargreaves and Samani (HS) and Garcia) using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological parameters during the period of thirty one (1980 – 2010) years. The comparison assessment using seven different statistical validation indices of&nbsp;coefficient of determination (R2),&nbsp;Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) was employed to determine the most accurate model for estimating global solar radiation in the five tropical locations situated in the coastal climatic zone of Nigeria. It was found that in each of the locations under investigation the proposed temperature dependent models were reported more suitable for global solar radiation estimation. In Akure, Ogoja, Ikeja, Benin and Owerri; the proposed quadratic logarithmic, linear exponential, multiple linear, quadratic (exponential 2) and quadratic (exponential 1) temperature dependent models respectively were found more accurate and can be applied to locations with the comparable weather condition, if properly calibrated against the local data. The HS and Garcia temperature dependent models evaluated in this study for Ikeja were compared with those available in literature and was found more accurate. The figures comparing the measured and estimated temperature dependent models indicates that the most accurate models in each location exhibit the best fitting with the measured global solar radiation data.
Spatial-aware Online Adversarial Perturbations Against Visual Object Tracking.

Adversarial attacks of deep neural networks have been intensively studied on image, audio, natural language, patch, and pixel classification tasks. Nevertheless, as a typical, while important real-world application, the adversarial attacks of online video object tracking that traces an object's moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a \textit{spatial-aware} basic attack by adapting existing attack methods, i.e., FGSM, BIM, and C&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the spatial-aware online incremental attack (a.k.a. SPARK) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, SPARK quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than basic attacks. The in-depth evaluation on state-of-the-art trackers (i.e., SiamRPN++ with AlexNet, MobileNetv2, and ResNet-50, and SiamDW) on OTB100, VOT2018, UAV123, and LaSOT demonstrates the effectiveness and transferability of SPARK in misleading the trackers under both UA and TA with minor perturbations.
Can a Blockchain-Based Maas Create Business Value?

In this paper two contemporary technological novelties are combined to introduce the concept of a blockchain-based MaaS, with the aim of pinpointing where and how business value can be created through data-based services of such a system. Towards this purpose, an integrated version of the Business Model Canvas is deployed, combining the advantages of the Lean Canvas and the Ethics Canvas. The overview of data flows among the versatile system stakeholders are outlined to highlight the potential benefits for diverse industries through sharing and collaboration.
Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS)

This research discusses prediction pass rates the certification microsoft office specialist 2013 version (word and excel) aimed to provide information concerning to pass rates and certification give alternative solutions to determine the program certificationi appropriate to chosen before test certification. Naive bayes used for the classification certification graduation where participants know what information pass and did not finish. Naive bayes is a classification with the probability and statistics to predict opportunities in the future based on the Provided before. In this study, system development CRISP-DM to use of the become more ordered and testing done with the BlackBox to test each function is on the application built. From the study, produce values probability of 0.001042 the accuracy of 99 %. These results, proving that naïve bayes method can be used to assist in a prediction graduation rates participants (word and excel), because it produces quite high accuracy. So participants were able to determine the certification program proper chosen before test certification.
Research on Facial Expression Recognition Based on Voting Model

Abstract In order to improve the recognition rate of real-time classification of facial expressions, we proposed a method of facial expression recognition based on voting mechanism. Firstly, different neural network models are constructed to learn facial features. Then, the extracted features are fed into the classifier to obtain the posterior probability of various features. Finally, through the voting mechanism, the optimal decision-making level fusion is achieved to complete the facial expression classification. Experiments show that the average recognition rate of fer2013, CK+ and JAFFE database is 74.58%, 100% and 100% respectively. Compared with other recognition methods, experiental data show that this method has superior performance, improves the recognition rate and robustness of the algorithm, and ensures the universality of the algorithm.
TEACHING ENGLISH SPEAKING THROUGH INFORMATION GAP TECHNIQUE (A Classroom Action Research at the 8th Grade Students of Madrasah Tsanawiyah Al-Husna Depok)

: Speaking is an important skill in learning English. However, the teacher still need an appropriate technique to teach speaking. So that, the writer used Information Gap technique in learning English speaking . This study aims to apply Information-Gap techniques in teaching speaking English in 8th grade students at MTs.Al-Husna Depok. In this study, the writer uses A Classroom Action Research (CAR). The writer analyzes and collects data during the teaching process through; interviews, observation, literature study, and the internet. Moreover, in order to collect the result of the significant differences before and after of using Information Gap, the writer uses pre-test and post-test during the observation. The research findings include: 1) Information Gap is an interesting technique in learning speaking, so that the students can enjoy the learning process. 2) With the Information Gap technique can make students more active in communication, and increase the courage and confidence of students. 3) In addition, by applying this technique students do not feel bored, because there are many different and interesting activities in Information Gap technique. So, it can be concluded that the Information Gap technique is very effective in teaching English speaking to students. Keyword: Teaching, Speaking, Information Gap Technique Abstract : Speaking is an important skill in learning English. However, the teacher still need an appropriate technique to teach speaking. So that, the writer used Information Gap technique in learning English speaking . This study aims to apply Information-Gap techniques in teaching speaking English in 8th grade students at MTs.Al-Husna Depok. In this study, the writer uses A Classroom Action Research (CAR). The writer analyzes and collects data during the teaching process through; interviews, observation, literature study, and the internet. Moreover, in order to collect the result of the significant differences before and after of using Information Gap, the writer uses pre-test and post-test during the observation. The research findings include: 1) Information Gap is an interesting technique in learning speaking, so that the students can enjoy the learning process. 2) With the Information Gap technique can make students more active in communication, and increase the courage and confidence of students. 3) In addition, by applying this technique students do not feel bored, because there are many different and interesting activities in Information Gap technique. So, it can be concluded that the Information Gap technique is very effective in teaching English speaking to students. Keyword: Teaching, Speaking, Information Gap Technique
A depth estimation framework based on unsupervised learning and cross-modal translation

In recent years, with the vigorous development of artificial intelligence and autonomous driving technology, the importance of scene perception technology is increasing. Unsupervised deep learning based methods have demonstrated a certain level of robustness and accuracy in some challenging scenes. By inferring depth from a single input image without any ground truth label, a lot of time and resources can be saved. However, unsupervised depth estimation has defects in robustness and accuracy under complex environment which could be improved by modifying network structure and incorporating other modal information. In this paper, we propose an unsupervised, monocular depth estimation network achieving high speed and accuracy, and a learning framework with our depth estimation network to improve depth performance by incorporating transformed images across different modalities. The depth estimator is an encoder-decoder network to generate the multi-scale dense depth map. The sub-pixel convolutional layer is adopted to obtain depth super-resolution by replacing the up-sample branches. The cross-modal depth estimation using near-infrared image and RGB image enhances the performance of depth estimation than pure RGB image. The training mode is to transfer both images to the same modality and then carry out super-resolved depth estimation for each stereo camera pair. Compared with the initial results of depth estimation using only RGB images, the experiment verifies that our depth estimation network with the cross-modal fusion system designed in this paper achieves better performance on public datasets and a multi-modal dataset collected by our stereo vision sensor.
Assessment of Reference Architectures and Reference Models for Ambient Assisted Living Systems

The innovation and development of software systems in the Ambient Assisted Living (AAL) domain have brought huge challenges for academia and software industry as well. Despite the existence of architectural models that can be used as references to build AAL systems, their selection for new AAL projects is a difficult task. In this work, the authors present the state of the art on Reference Architectures (RA) and Reference Models (RM) found through the conduction of a systematic literature review. The authors identified, analyzed, and assessed 24 existing RA&amp;RM for AAL domain, and, as result, the authors spotted interesting research directions that should be further explored to improve existing and future RA&amp;RM and software systems for that domain.
Prototype Alat Pengantar Makanan Berbasis Arduino Mega

The development of Revolution 4.0 is now bringing change to the culinary business process, making culinary entrepreneurs or restaurant owners challenged to be more creative and innovative in order to thrive and survive Industrial Revolution 4.0. One of the culinary world business processes in the 4.0 Industrial Revolution is to provide a contemporary service that is one of them is using robots. This study aims to create a prototype Arduino based food Introduction tool Mega is a new breakthrough in the culinary world with the methods of research among other methods of data collection, methods of designing tools and methods of testing tools. So this prototype can be used to deliver food by using RFID (Radio Frequency Identification) based on instructions from Arduino Mega as a microcontroller, with the command RFID will read the ID on the table and Stepper Motors as a tool drive. So this prototype can be used as a food delivery robot not only as a tool to facilitate the work as well as a tool for the appeal of a restaurant that uses it.
A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence

As a result of the continuously growing volume of information available, browsing and querying of textual information in search of specific facts is currently a tedious task exacerbated by a reality where data presentation very often does not meet the needs of users. To satisfy these ever-increasing needs, we have designed an solution to provide an adaptive and intelligent solution for the automatic answer of multiple-choice questions based on the concept of mutual information. An empirical evaluation over a number of general-purpose benchmark datasets seems to indicate that this solution is promising.
A basic smart linear Kalman filter with online performance evaluation based on observable degree

The observable degree can be used to directly explain the system filtering performance (or filtering accuracy) of Kalman filtering (KF) to some extent. The effective observable degree can not only be obtained before filtering but also be used to measure the system filtering performance. In applications, the exact knowledge of the system parameters and models is always unavailable. A basic smart Kalman filter (SKF) with online performance evaluation is proposed based on the observable degree in this paper. Since the collection of observations is limited in initial alignment with complex situations, mobile sensor networks are introduced. To improve the filtering performance with inaccuracy system parameters, the relatively optimal smart adjusting factor is iteratively selected by an optimized observable degree with autonomous learning function. The self-assessment function is also available for real-time performance evaluation. Finally, simulation examples are demonstrated to validate the proposed smart Kalman filter.
Saliency Guided 2D-Object Annotation for Instrumented Vehicles

Instrumented vehicles can produce huge volumes of video data per vehicle per day that must be analysed automatically, often in real time. This analysis should include identifying the presence of objects and tagging these as semantic concepts such as car, pedestrian, etc. An important element in achieving this is the annotation of training data for machine learning algorithms, which requires accurate labels at a high-level of granularity. Current practise is to use trained human annotators who can annotate only a limited volume of video per day. In this paper, we demonstrate how a generic human saliency classifier can provide visual cues for object detection using deep learning approaches. Our work is applied to datasets for autonomous driving. Our experiments show that utilizing visual saliency improves the detection of small objects and increases the overall accuracy compared with a standalone single shot multibox detector.