Reviewerly/exHarmony huggingface dataset:
A New Model-Agnostic Method and Implementation for Explaining the Prediction on Finance Data

Artificial neural networks (ANNs) are widely used in critical mission systems such as healthcare, self-driving vehicles and the army, which directly affect human life, and in predicting data related to these systems. However, the black-box nature of ANN algorithms makes their use in mission-critical applications difficult, while raising ethical and forensic concerns that lead to a lack of trust. The development of the Artificial Intelligence (AI) day by day and gaining more space in our lives have revealed that the results obtained from these algorithms should be more explainable and understandable. Explainable Artificial Intelligence (XAI) is a field of AI that supports a set of tools, techniques, and algorithms that can create high-quality interpretable, intuitive, human-understandable explanations of artificial intelligence decisions. In this study, a new model-agnostic method that can be used for the financial sector has been developed by considering the stock market data for explainability. This method enables us to understand the relationship between the inputs given to the created model and the outputs obtained from the model. All inputs were evaluated individually and combined, and the evaluation results were shown with tables and graphics. This model will also help create an explainable layer for different machine learning algorithms and application areas.
Hybrid Transform Based Speech Band Width Enhancement Using Data Hiding

The limited narrow band (LNB) speech signal spread in the range of 300 to 3400Hz used in public switched telephone networks results in poor-quality telephony speech. Bandwidth extension techniques are performed to expand the frequency range from LNB speech to a clear wideband (CWB) speech signal range of 50Hz-7000Hz over existing public telephone networks. In this paper, a novel robust speech bandwidth extension algorithm by Discrete Wavelet Transform- Discrete Cosine Transform- Based Data Hiding (DWT-DCT-DH) Hybrid transform model was used to spread the out-of-band (3400Hz to 7000Hz) speech frequencies over the LNB speech. In this proposed technique the out-of-band speech frequencies are embedded in LNB speech and imperceptibly spread over the network. These Embedded out-of-band speech frequencies are recovered steadily at the receiver end to generate a restored CWB telephony speech of considerably better quality. The proposed technique simulation results show more intelligible and better-quality telephony speech generated compared to the other bandwidth extension techniques.
Optimized Piccolo Lightweight Block Cipher: Area Efficient Implementation

Piccolo algorithm is one of the lightweight block ciphers designed specifically for low-resource devices which present physical constraints in terms of area, power, and memory. Various hardware architectures for Piccolo block cipher have been proposed in recent years with the aim of obtaining a more appropriate low-resource design for specific constrained applications. The latter must meet real-time processing constraints without affecting the need for hardware resources. Finding a good compromise between computation time and implementation resource consumption is a major consideration in the design process. In this paper, we suggest six serial hardware architectures for Piccolo lightweight algorithm with a 128 bits key length. Proposed architectures are compared to existing designs based on hardware resource occupancy, latency, and throughput. Also, we tested the security of the Piccolo algorithm, and the obtained results show the good robustness of the Piccolo block cipher against statistical attacks. Thus, we can use the Piccolo algorithm in lightweight applications that require a high level of privacy.
Improving the Grade 9 Students’ Engagement in Online Synchronous Classes in English Through Interactive Games

This classroom-based action research aimed to improve the Grade 9 students’ level of engagement in online synchronous classes in English through games. The participants of the study were the 39 Grade 9 students who were officially enrolled at the Mariano Marcos State University – Laboratory High School, Batac Campus, for the School Year 2021-2022. The data on students’ level of engagement were gathered using the survey questionnaire patterned after the Student Engagement Survey by Singh and Srivastava (2014). Frequency, percentage distribution, means and mean difference were used to treat the data. Findings reveal that the Grade 9 have varied levels of engagement before and after the application of the intervention. Likewise, there is a significant change on the students’ pre-survey and post-survey scores. Moreover, this study found that the use of games such as Four Pics One Word and Deal or No Deal was effective in improving the students’ level of engagement in their online synchronous classes in English.
PROFIL MOTIVASI BERPRESTASI SISWA SMA NEGERI 1 NGAMPRAH

The purpose of this study was to describe the achievement motivation of class X students of SMA Negeri 1 Ngamprah. The subjects of this study consisted of 30 students of class X SMA Negeri 1 Ngamprah, while the sampling technique used purposive sampling technique, namely the technique of determining the sample with certain considerations, this study used quantitative descriptive research methods. The data collection technique is through interviews and observations, while the data collection tool used is the distribution of questionnaires using the grand theory indicators from Mc. Clelland. The results of the study obtained a picture of student achievement motivation, namely the need for achievement, which is 68.75% with a moderate categorization, where the need for power is 45.73% with a low categorization, and the need for affiliation (need for power). of affiliation) that is the need to obtain good social relations by 63.46% with moderate categorization. Based on the results of this study, it can be concluded that most of the students' achievement motivation is still classified as moderate, with an average of 64.33%.
BIMBINGAN KELOMPOK DENGAN TEKNIK SELF-MANAGEMENT UNTUK MENGATASI KECEMASAN SISWA MTS TERHADAP KEGIATAN E-LEARNING.

The implementation of e-learning during the pandemic is a new breakthrough for the learning system in Indonesia, but this new thing is certainly a burden for students and teachers because it takes a long time to adapt. E-learning makes it easier for students to get material flexibly and be able to evaluate their own learning anywhere. However, behind this convenience, e-learning makes students misuse the learning system and learning time to play. This happens because students consider the tasks and materials given by the teacher to be unimportant, even the learning atmosphere is considered saturated. In the end, the negative impact formed an anxiety, especially for the students of MTs Darussalam to make their achievement decline. Self-management technique group guidance is an alternative solution for BK teachers to stimulate students' anxiety into positive energy. This research was conducted using descriptive qualitative method with research subjects from two sources, namely six students of Class 8B and one BK teacher. The results of this study prove that the self-management technique group guidance service carried out with the stages of self-awareness, self-evaluation and self-reinforcement, this service succeeded in reducing anxiety levels and helping students manage their anxiety. This is a new reference for BK MTs Darussalam teachers to provide similar services so that all students can minimize anxiety levels during e-learning.
GAMBARAN PERILAKU MEROKOK SISWA MTS DI PURWAKARTA

The purpose of this study was to identify the number of smokers and the description of the causes of smoking behavior in adolescent students, especially class VIII MTs. This research used qualitative descriptive method involving 6 students as research samples taken from 30 students of class VIII. Data collection techniques and sample selection through the distribution of questionnaires. Based on the resulted of the research related to the description of smoking behavior in class VIII MTs in Purwarkarta, from total 30 students. the researcher can draw the conclusion that it was found that a total of 6 students (20%) smoke and 24 students (80%) did not smoke. The students who have a level of addiction to cigarettes are categorized from the number of smokers, 4 students (66%) are heavy smokers and 2 students (34%) are moderate smokers. Factors behind this include peers, curiosity, the community and family who smoke.
Time-optimal trajectory generation in joint space for 6R industrial serial robots using Cuckoo search algorithm

The trajectory planning problem in industrial robotic applications has recently attracted the great attention of many researchers. In this paper, an optimal trajectory planning approach is proposed based on optimal time by utilizing the interpolation spline method. The method including a combination of cubic spline and 7th order polynomial is used for generating the trajectory in joint space for robot manipulators. Cuckoo Search (CS) optimization algorithm is chosen to optimize the joint trajectories based on objective, including minimizing total traveling time along the whole trajectory. The spline method has been applied to the PUMA robot for optimizing the joint trajectories with the CS algorithm based on the objective. With the trajectory planning method, the joint velocities, accelerations, and jerks along the whole trajectory optimized by CS meet the requirements of the kinematic constraints in the case of the objective. Simulation results validated that the used trajectory planning method based on the proposed algorithm is very effective in comparison with the same methods based on the algorithms proposed by earlier authors. Keywords: CS; industrial robots; interpolation; spline methods; trajectory planning.
Analysis of Accounting Information Systems in the Cash Flow Expenditure Cycle at UD

At this time there have been many companies that are growing rapidly.Every company that develops and competes cannot be separated from the role of technological development.One example of the role of technological advances in the company is the use of information systems.Accounting information system is an information system that is needed by the company.Every company is required to have an accounting information system.The accounting information system is useful as a process of structuring procedures so that the processes carried out are more orderly and obtain consistent results.Cash flow activities are very important and also vital for the company because all transaction processing activities carried out by the company are related to the purchase and payment processes.In accordance with its designation, cash flow has a liquid nature that is very easy to change hands.So it is necessary to do security by utilizing an accounting information system.The proposed accounting information system in the company is expected to assist the company in completing the effectiveness of the work obtained, especially in the field of cash flow.Errors and omissions in the process that have been carried out so far can be resolved by using an accounting information system.The research is based on descriptive research conducted by conducting a literature review of the studies that have been carried out.The proposed accounting information system in the company is expected to be able to assist the company in completing the effectiveness of the work obtained, especially in the cash flow field.Errors and omissions in the processes carried out so far can be resolved by using an accounting information system.
Early Days and Further Development

Abstract We present here the key people supporting the university development, the legacy of the Russian educational system combined with the novelty of the project. The past and the future live in Innopolis in a unique blend. As a private university, there are also challenges.
PENDA: Privacy ENhanced Data Aggregator

Data is spread across different organizations and must be combined to get valuable analytics or train machine learning models.Sensitive features such as identification numbers, as columns, are common in these data, and organizations can only link if they share these columns.However, data protection regulations prevent these organizations from revealing the values in these columns to others.This paper proposes a technique, PENDA (Privacy ENhanced Data Aggregator), to encrypt columns of a database table or spreadsheet so that a central aggregation server can join them without decrypting using XOR-based homomorphism.We implement our PENDA system and demonstrate how organizations taking part in the process can encrypt and merge data.The experimental results show that the system can handle very large data files and scale to multiple organizations.
Feature Completion Using Correlation-Preserved Autoencoder

Missing value existing in various datasets always causes tremendous obstacles to data mining in the real world.In recent years, autoencoder has been a popular deep learning model in data imputation due to the simple structure and efficient training period.In this paper, we developed a model that combined advantages of multiple imputations using denoising autoencoder (MIDA) and tracking-remove autoencoder (TRAE), integrating the idea of tracking-remove into MIDA.We introduce KNN to pre-impute the dataset after the input was denoising, and then we put the pre-imputed input data into both MIDA and MIDA with tracking-remove and ensemble the outputs by linear combination corresponding to the "multiple imputation" thought.The model called correlation-preserved autoencoder (CPAE) is applied to the completion of brain tissue feature data in ADNIMERGE (ADNI database).Experiments show that CAPE has a better performance than MIDA, TARE, and other autoencoders.
EFFECT OF COGKLAK GAME METHOD TO STUDENT’S MATHEMATICS LEARNING OUTCOME AT STATE ELEMENTARY SCHOOL

This study aims to examine the effect of congklak game method on student learning outcomes and to determine student learning outcomes before and after the application of congklak game method in math subject. The method uses in this research is a quantitative field research. The research conducts using a true experimental design research with a one group pretest-posttest design model. The sample in this study is 15 students of class IV B. The results showed that, firstly, the results of hypothesis testing are obtained to &gt; ttable at the significance level of 5% or 0.05 and the significance level of 1% or 0.01, namely 1.76 &lt;37,31&gt; 2,14 which means that there is a significant effect of congklak game method on student learning outcomes. Secondly, the average pre-test score of students is 30, and the average post-test score of students is 80. Based on these data, it can be concluded that learning using congklak game method can improve student learning outcomes in the math subject of lowest common multiple (LCM) and greatest common factor (GCF).
Evaluasi Aktifitas Belajar Mahasiswa Berbasis Teknologi Informasi Pada Masa Pandemi Covid 19 di Fakultas Teknik Universitas Batanghari Jambi

Information technology is an information service that is used as a form of interaction used by students during the COVID-19 pandemic. One of them as a medium of learning or communication. This study using quantitative methods that are analytical observational through the distribution of questionnaires through a google form. The distribution of the sample was 323 respondents with a research design in the form of a cross-sectional study approach. The results of data analysis based on univariate stated that 50.8% of respondents stated that learning dared to support learning activities but did not experience a significant increase, while bivariate analysis of information technology supported Unbari students' interactions with learning activities. during the covid 19 pandemic.
Quadratic improvement on accuracy of approximating pure quantum states and unitary gates by probabilistic implementation

Pure quantum states are often approximately encoded as classical bit strings such as those representing probability amplitudes and those describing circuits that generate the quantum states. The crucial quantity is the minimum length of classical bit strings from which the original pure states are approximately reconstructible. We derive asymptotically tight bounds on the minimum bit length required for probabilistic encodings with which one can approximately reconstruct the original pure state as an ensemble of the quantum states encoded in classical strings. We also show that such a probabilistic encoding asymptotically halves the bit length required for "deterministic" ones. This is based on the fact that the accuracy of approximating pure states by using a given subset of pure states can be increased quadratically if we use ensembles of pure states in the subset. Moreover, we show that a similar fact holds when we consider the approximation of unitary gates by using a given subset of unitary gates. This improves the reduction rate of the circuit size by using probabilistic circuit synthesis compared to previous results. This also demonstrates that the reduction is possible even for low-accuracy circuit synthesis, which might improve the accuracy of various NISQ algorithms.
Distributed quantum phase sensing for arbitrary positive and negative  weights

Estimation of a global parameter defined as a weighted linear combination of unknown multiple parameters can be enhanced by using quantum resources. Advantageous quantum strategies may vary depending on the weight distribution, requiring the study of optimal schemes achieving a maximal quantum advantage for a given sensing scenarios. In this work, we propose an optimal distributed quantum phase sensing scheme using Gaussian states with zero displacement for an arbitrary distribution of the weights with positive and negative signs. The estimation precision of the optimal scheme is derived, and shown to be achievable by using squeezed states injected into linear beam-splitter networks and performing homodyne detection on them in the absence of loss. Interestingly, the optimal scheme exploits entanglement of Gaussian states only among the modes assigned with equal signs of the weights, but separates the modes with opposite weight signs. We also provide a deeper understanding of our finding by focusing on the two-mode case, in comparison with the cases using non-Gaussian probe states. We expect this work to motivate further studies on quantum-enhanced distributed sensing schemes considering various types of physical parameters with an arbitrary weight distribution.
Explainable nonlinear modelling of multiple time series with invertible neural networks

A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modelled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the back-propagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.
ICT and Educational Innovation: Demands and Expectations

In the development of the research project "Strengthening and expanding the coverage of postgraduate training in Educational Innovation with ICTs through virtual education strategies relevant to the socio-cultural context of the region", the meaning of Educational Innovations for teachers was investigated. Through 7 focus groups, with the participation of 168 teachers from urban and rural schools, public and private schools, as well as teachers in training, schoolteachers and university professors, from 3 different departments in southwestern Colombia, it was identified, through the use of the grounded theory analysis method, that teachers associate educational innovations with changes in the way they teach in their classes. In this way, the incorporation of ICTs is seen as important, since it facilitates teaching processes, motivates learning and improves the quality and pertinence of education.
2D vs. 3D LiDAR-based Person Detection on Mobile Robots

Person detection is a crucial task for mobile robots navigating in human-populated environments. LiDAR sensors are promising for this task, thanks to their accurate depth measurements and large field of view. Two types of LiDAR sensors exist: the 2D LiDAR sensors, which scan a single plane, and the 3D LiDAR sensors, which scan multiple planes, thus forming a volume. How do they compare for the task of person detection? To answer this, we conduct a series of experiments, using the public, large-scale JackRabbot dataset and the state-of-the-art 2D and 3D LiDAR-based person detectors (DR-SPAAM and CenterPoint respectively). Our experiments include multiple aspects, ranging from the basic performance and speed comparison, to more detailed analysis on localization accuracy and robustness against distance and scene clutter. The insights from these experiments highlight the strengths and weaknesses of 2D and 3D LiDAR sensors as sources for person detection, and are especially valuable for designing mobile robots that will operate in close proximity to surrounding humans (e.g. service or social robot).
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles

We consider a step search method for continuous optimization under a stochastic setting where the function values and gradients are available only through inexact probabilistic zeroth- and first-order oracles. Unlike the stochastic gradient method and its many variants, the algorithm does not use a pre-specified sequence of step sizes but increases or decreases the step size adaptively according to the estimated progress of the algorithm. These oracles capture multiple standard settings including expected loss minimization and zeroth-order optimization. Moreover, our framework is very general and allows the function and gradient estimates to be biased. The proposed algorithm is simple to describe and easy to implement. Under fairly general conditions on the oracles, we derive a high probability tail bound on the iteration complexity of the algorithm when it is applied to non-convex, convex, and strongly convex (more generally, those satisfying the PL condition) functions. Our analysis strengthens and extends prior results for stochastic step and line search methods.