Reviewerly/exHarmony huggingface dataset:
Lightweight CNN based Meter Digit Recognition

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.
Bidirectional LSTM approach to image captioning with scene features

Image captioning involves generating a sentence that describes an image. More recently, it has been driven by encoderdecoder approaches where the encoder such as convolutional neural network (CNN) can extract the visual features of an image. The extracted visual features are passed to a decoder such as a long short-term memory (LSTM) network in order to generate a sentence that describes the image. One major challenge with this approach is to precisely include the scene of an image in the generated sentences. To resolve this challenge, visual scene features have been used with unidirectional LSTM decoders. However, for long sentences, this limits the precision of the generated text. This research proposes a novel approach to generate sentences using visual scene information with a bidirectional LSTM decoder. The encoder is based on Inception v3 to extract the object features and Places365 to extract the scene features. The decoder uses a bidirectional LSTM to generate a sentence. The encoder-decoder model is trained using the Flickr8k dataset. Results show improved performance for generating longer sentences with a 9% increase in BLEU-3 and a 12% increase in BLEU-4 scores compared to compared to other encoder-decoder methods that are limited to only using global image features. Visually impaired people that use screen readers would benefit from this research as they would get an enhanced description of an image that includes the background scene thereby creating a wholesome picture in the mind of the reader.
Neural network pruning for biomedical image segmentation

Instead of manual segmentation, Segmentation using Artificial Neural Networks is very useful in biomedical image analysis. However, deploying artificial neural networks requires large memory footprint and computational costs. In this work, we propose a pruning approach to alleviate these requirements for the U-Net, which is the most popular segmentation neural network for biomedical image. Our approach handles upsampling layers and skip connections, which are essential components in U-Net architecture. We show that our approach achieves 2x speedup, more than 7x size reduction with less than 2% loss in average intersection-over-union (IOU) on PhC- U373 and DIC-HeLa biomedical data set.
Do Foreign Direct Investments (FDI) Crowd In or Crowd Out Domestic Investment in Cote D’ivoire?

The study is conducted to analyse the impact of foreign direct investments on domestic investment in Cote d’Ivoire. To this end, annual time series data for the period of 1975-2018 is used. The model is based on the theoretical model of Agosin and Mayer (2001).The study employs the differenced error correction model of Hendry (2006). The results reveal that the speed of adjustment of the variables towards their long-run equilibrium path is 16%. Foreign direct investments crowd out domestic investment in the long run and in the short run economic growth increase gross fixed capital formation. Human capital is significantly positive in the short and long run. Human capital increases investments in Cote d’Ivoire. Policy recommendation emanating from the study is that foreign direct investments is important when Cote d’Ivoire has already a national sector dynamic and well developed domestic companies.
Segmentasi Citra Kanker Serviks Menggunakan Markov Random Field dan Algoritma K-Means

Cervical cancer is a dangerous disease caused by malignant tumors that grow on the cervix and has globally attacked many women. Pap smear test is one of the early prevention efforts for cervical cancer. Medical personnel often have difficulty identifying images of cervical cancer cells. Several studies have used the K-Means clustering method to identify cervical cancer cell images from Herlev dataset. This study uses the Herlev dataset with the K-Means clustering algorithm and also used the Markov Random Field parameter as a feature for the process of identifying cervical cancer cell images. This study compared the results of the proposed method with some differences in the preprocessing process. The experimental results show an accuracy of 74,51% for RGB channels without low pass filter. Accuracy of 75,11% is obtained from the segmentation process using RGB channels with low pass filter. A further increase in accuracy was obtained by 75,76% when the segmentation process used the grayscale channel with low pass Filter. Based on the segmentation experiment with the highest segmentation accuracy results, the classification process using K-Nearest Neighbor (KNN) gives an accuracy of 89,29%.
Research on English Translation Skills and Problems By Using Computer Technology

Abstract At present, network related technology and computer research content at home and abroad has become the focus of academic research. This is the inevitable stage of our economic modernization. The discussion of English language translation based on computer skills is also heating up. From the perspective of computer science, this is an important measure of innovative form of machine learning. From the perspective of English language, this is a modern reform of information combined linguistics. Of course, the emergence of a new technology in the early stage will inevitably encounter a lot of doubts. This paper also explains the skills and problems of computer English literal translation in view of some doubts in the current academic circles.
Understanding employees' information security identities: an interpretive narrative approach

Purpose The authors seek to understand the formation of control- and security-related identities among organizational employees through and interpretive narrative analysis. The authors also seek to identify how the identities form over time and across contexts. Several identities are identified as well as the changes that may occur in the identities. Design/methodology/approach Few interpretive or critical studies exist in behavioral information security research to represent employee perspectives of power and control. Using qualitative interviews and narrative analysis of the interview transcripts, this paper analyzes the security- and control-related identities and values that employees adopt in organizational settings. Findings Two major categories of behavioral security compliance identities were identified: compliant and noncompliant. Specific identities within the compliant category included: faithful follower vs the reasoned follower, and other-preserving versus the self-preserving identities. The noncompliant category included: anti-authority identity, utilitarian identity, trusting identity and unaware identity. Furthermore, three patterns of identity changes were observed. Research limitations/implications The authors’ narrative stories suggest that employee identities are complex and multi-faceted, and that they may be fluid and adaptive to situational factors. Future research should avoid assumptions that all employees are the same or that employee beliefs remain constant over time or in different contexts. Identities are also strongly rooted in individuals' rearing and other life experiences. Thus, security control is far broader than is studied in behavioral studies. The authors find that history matters and should be examined carefully. Practical implications The authors’ study provides insights that managers can use to enhance security initiatives. It is clear that different employees build different control-related identities. Managers must understand that their employees are unique and will not all respond to policies, punishments, and other forms of control in the same way. The narratives also suggest that many organizations lack appropriate programs to enhance employees' awareness of security issues. Originality/value The authors’ narrative analysis suggests that employee security identities are complex and multi-faceted, and that they are fluid and adaptive to situational factors. Research should avoid assumptions that all employees are the same or that their beliefs remain constant over time or in different contexts. Identities are also strongly rooted in individuals' rearing and other life experiences. Their history matters and should be examined carefully.
Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room

Intraoperative surgical video enables better surgical training, continued performance enhancement for surgeons and system-level quality improvement initiatives, however the capture of high-quality intraoperative video of open surgical procedures is difficult. Wearable cameras, typically in the form of a head-mounted action camera are frequently used for this purpose, although the video from these devices often contains significant motion artifact due to movement of the surgeon's head. When trying to compare the performance of various wearable cameras in the surgical setting, we could not find a motion sensor appropriate for this purpose. We therefore describe in this article the design, assembly and validation of a small sensor that can be attached to wearable cameras in the operating room to objectively quantify camera motion. The sensor incorporates an inertial measurement unit coupled to a microcontroller. Concurrent validity is established by comparing the positional sensing of the device to a geared tripod head that allows for fine, measured manipulations of the sensor in three orthogonal axes. The methodology of capturing, processing and reporting camera movement for a surgical procedure is also detailed.
Research on Route Planning based on improved Ant Colony Algorithm

Abstract In order to enhance the safe flight performance of UAV and better complete complex tasks, it is extremely necessary to study route planning. Firstly, the feature points of the landform landform are extracted as the flight track points of the UAV, and the track planning problem is transformed into the traveling salesman problem. Secondly, an adaptive pheromone update method is proposed, and an improved ant colony algorithm for optimizing the local pheromone is proposed to avoid the basic ant colony algorithm from falling into the local optimal solution and improve the global search ability. Finally, the proposed algorithm is verified by simulation experiments. The experimental results show that the improved ant colony algorithm is practical and superior, and can effectively solve the problem of route planning and obtain the optimal path of route planning.
Intelligent game recommendation system

The technologically advanced learning implies the adaptation of the recommendation systems to the specific wishes and capabilities of the users. The present document proposes an intelligent system designed for the encompassing education of students with special educational needs. The system integrates the capabilities of intelligent agents, the recommendation systems, and the game based learning in order to provide a personalized learning content. A Bayesian Classifier recommends educational games depending on the interests and the preferences of the users, and a multi-agent system provides individual needs by analyzing users’ behavior and by determining the need of acquisition of certain knowledge and skills appropriate for the level of learning.
Saturation channel extraction of HSV color space for segmenting Plasmodium parasite

Abstract Malaria is one of the dangerous diseases in the world. Despite it has been decreased every year, the number of patients for this disease is still reasonably a lot. Technical problems that arise in the malaria cases are due to malaria examination and diagnosis analysis, which are carried out manually. At the same time, there are still medical personnel scarcities in some rural areas including of some districts in Indonesia. Moreover, the required examination process demands accuracy and takes a long time. Thus, an effective solution is necessary to help parasitologist to reduce malaria cases. One of the alternative ways to solve those problems is by utilizing technology to assist medical personnel in conducting examinations and analyzing the diagnosis results. This research was conducted to reach this objective. In achieving this objective, an image segmentation approach for finding the Plasmodium parasites was developed. The proposed approach consisted of four stages, which are the pre-processing stage, the segmentation stage, the refinement of segmentation results stage, and the evaluation stage. This proposed method was performed on 25 digital microscopy images of the thin blood smear digital microscopic images. The experiment yielded a good performance with the accuracy, sensitivity, and specificity of 99.83%, 85.06%, and 99.90%, respectively. These results indicate that the proposed method has an outstanding performance in segmenting Plasmodium parasite and can be utilized as a preliminary project to assist doctors in examining or identifying the Plasmodium parasites location.
An Adaptive Location-Aware Swarm Intelligence Optimization Algorithm

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds’ classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds’ sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles’ speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles’ merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems.
Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM

Abstract Based on the actual monitoring historical data of photovoltaic power station, combined with the actual engineering demand of photovoltaic microgrid on the user side, the lightweight algorithm of ultra short-term photovoltaic power prediction is studied, which is conducive to improving the operation efficiency and economy of power system. In this paper, the ultra short-term power prediction of photovoltaic power station is carried out by combining the LSTM algorithm with attention mechanism. Firstly, Pearson correlation coefficient method is used to reduce the dimension of the data set. The data with low correlation between weather variables and power to be predicted and historical power are eliminated, and the algorithm model structure is simplified. Then, the attention mechanism is combined with LSTM network to improve the effectiveness of the prediction model for long time series input. The proposed model is trained and compared with the data of a photovoltaic power station. The results show that the model achieves good experimental results in different weather conditions, and can effectively improve the prediction accuracy.
Witness operator provides better estimate of the lower bound of concurrence of bipartite bound entangled states in $$d_{1}\otimes d_{2}$$-dimensional system

It is known that the witness operator is useful in the detection and quantification of entangled states. This motivated us for the construction of the family of witness operators that can detect many mixed entangled states. This family of witness operators is then used to estimate the lower bound of concurrence of the detected mixed entangled states. Our method of construction of witness operator is important in the sense that it will estimate a better lower bound of concurrence of the entangled states in arbitrary $$d_{1}\otimes d_{2} (d_{1}\le d_{2})$$ -dimensional system compared to the lower bound of the concurrence given in Chen et al. (Phys Rev Lett 95:040504, 2005). We have shown the significance of our constructed witness operator by detecting many bound entangled states that are not detected by the earlier methods, and then, we use the expectation value of the witness operator to estimate the lower bound of the concurrence of those bound entangled states.
Analysis and Design of UI and UX Web-Based Application in Maiproyek Startup Using User Centered Design Method in Information System Program of Telkom University

Abstract To develop a good application, the aspect that needs to be considered is to design a User Experience and User Interface that suits the needs of the users themselves. One of the design methods that can be used is User Centered Design method. This method focuses on designing the user’s needs where it aims to provide comfort for the user and provide a relevant interface for the user. The study was conducted in Information Systems Program of Telkom University. The design will then be tested on each lecturer and student in Information Systems Program of Telkom University. This test aims to test whether a design is in accordance with the desired objectives or not. The results of the research the author wants to achieve is to design a user interface and user experience design of a web-based application for a Maiproyek application. After conducting research, the results obtained by the author are usability scores obtained from the Maze.design application. The scores obtained are calculated in the medium category and show that the design of the application tested to each respondent already has a good usability value.
NEW EDUCATIONAL FORMAT OF PROFESSIONAL DEVELOPMENT: PERSONALIZED EDUCATIONAL TRAJECTORY OF THE STUDENT

The article proposes a new format of professional development of a university student – personalized training, the target orientation of which is psychological and pedagogical support of the personalized educational route of the student. The authors state that in the modern conditions of modernization and digital transformation of education, new educational formats are required that are adequate to the new situation, one of which is the development of personalized educational routes for students of the university.
Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency

In this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specific probabilistic metric: the continuous rank probability score (CRPS) as they give extensive information to compare the forecasting performance of both methodologies. The two solar probabilistic forecasting methodologies are used to produce intra-day solar forecasts with time horizons ranging from 1 h to 6 h. The first methodology is based on two steps. In the first step, we generated a point forecast for each horizon and in a second step, we use quantile regression methods to estimate the prediction intervals. The second methodology directly estimates the prediction intervals of the forecasted clear sky index distribution using past data as inputs. With this second methodology we also propose to add solar geometric angles as inputs. Overall, nine probabilistic forecasting performances are compared at six measurements stations with different climatic conditions. This paper shows a detailed picture of the overall performance of the models and consequently may help in selecting the best methodology.
Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

We analyzed cryptocurrency dynamics in the global U.S. dollar–denominated market and the emerging market economies (EMEs) with a view to ascertaining whether activities in these markets are predominantly shaped by reinforcement or substitution effect. Cryptocurrencies analyzed include the Bitcoins, Ethereum, Litecoin, Steller, Bitcoin Cash, and USD Tether. The results suggest that, on average, correlation between digital assets in the cryptocurrencies’ ecosystem is positive. However, there is evidence of an outlier with respect to the USD Tether (USDT) in the global market, revealing that the USDT is negatively associated with all other cryptocurrencies. This is supported by the dynamic regression results that provided evidence of reinforcement effect in favor of the USDT in the global crypto market, thus confirming the status of the USDT as “Stablecoin” as it is pegged 1:1 to USD. In the global market context, the results also revealed that USDT/USD returns had identical outliers that could portend lesser chances of extreme gains or losses compared with suggestions of extreme gains or losses in the EMEs. Furthermore, USDT did not seem to have similar evolution in the EMEs where it had relatively marginal influence in the markets. The vector error correction (VEC) estimate showed mixed results between Altcoins in all the markets; moreover, our finding showed that reinforcement effects hold in favor of Steller (XLM) both in the Russian ruble and Indian rupee crypto markets, whereas the Chinese yuan crypto market was predominantly characterized by substitution effect in favor of Bitcoin.