Reviewerly/exHarmony huggingface dataset:
Research and Prospect of Image Recognition Based on Convolutional Neural Network

Abstract This paper compares common image recognition technology, introduces three image recognition and classification technologies, and explains the most popular deep learning image recognition algorithm based on CNN in detail. As a new method of ANN, CNN combines ANN and deep learning technology. In the area of image recognition, the advantage of CNN is particularly prominent. Compared with the traditional image processing algorithm, CNN has higher recognition accuracy and speed, and can directly process the original image to avoid the complicated pre-processing image data. CNN is the first choice for image recognition.
A Novel Improved K-Means Algorithm Based on Parameter Adaptive Selection

Abstract As a classical clustering algorithm, K-means has been widely applied due to its features of simple mathematical thinking, fast convergence rate, less complexity, and easy to implementation. However, K-means algorithm always requires users to set the desired number of clusters in advance, and the initial cluster centers are usually generated in a random way. When dealing with unknown datasets that users do not have enough domain-assisted knowledge, such parameters setting strategies not only increases the burden on users, but also makes clustering quality difficult to guarantee. Therefore, in view of the high sensitivity of K-means clustering process to initial parameters, this paper propose an improved DDWK-means (Distance-Density-Weight K-means) algorithm. Based on the distance-density feature and the method of inertia weight of particle swarm optimization algorithm, the optimal initial cluster centers not only can be determined adaptively according to the structural characteristics of the dataset itself without introducing artificial parameters, but also can be adjusted dynamically due to the threshold change of clustering quality metric. We make an experimental study with five standard datasets from UCI (University of California Irvine), and the results indicate that the DDWK-means algorithm exhibits a significantly improvement in clustering efficiency and stability.
An artificial bee colony algorithm for mixture model-based clustering

Finite mixture model-based clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectation-maximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarm-based algorithms have gained popularity in a number of optimization scenarios. One such swarm-based optimization procedure, the artificial bee colony, incorporates both local searching and multiple solution generating to explore the parameter space. Herein, we propose hybridizing these algorithms for the task of fitting a multivariate Gaussian mixture model. This algorithm’s performance is contrasted with both the traditional approach and another hybrid algorithm on simulated and real data.
A steganography method based on decomposition of the Catalan numbers

In this chapter, we present a new method of steganography using decomposition of Catalan numbers. The proposed steganographic method consists of two segments: the first segment refers to the process of embedding data and generation of a complex stego key, whereas the second segment refers to the process of extracting a hidden message based on the generated stego key. Both segments consist of several phases. The proposed method provides that the data carrier retains its original shape. At the end of the chapter, security testing and steganalysis of the proposed method are conducted. Implementation in Java programming language successfully proves the work of the proposed method.
Suntracker on Rocker-Bogie Mechanism

This paper introduces an all-terrain robot, i.e., robot with the capability to traverse all kinds of terrain. For this purpose, the rocker-bogie mechanism was chosen for the robot design and differential gear mechanism is used to stabilize the platform. Another focus of ours in this paper is to increase the efficiency of the solar energy absorbed by the solar panels on the robot to make the robot self-powering. For this, a dual-axis sun-tracking mechanism is used. All parts of the robot were designed using CAD modeling software. The robot can be controlled via a Bluetooth joystick app of an Android phone making it extremely easy to change its speed and direction.
Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction

Theories of discourse argue that comprehension depends on the coherence of the learner’s mental representation. Our aim is to create a reliable automated representation to estimate readers’ level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner’s productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students’ reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features.
Knock Knock: A Binary Human–Machine Interactive Channel for Smartphone with Accelerometer

A binary human–machine interactive channel for smartphone with accelerometer is proposed in this paper. By comparing and analyzing the advantages and disadvantages of the four binary gestures, the knock gesture is selected as the interactive gesture. Subsequently, we elaborate on the knock gesture-oriented binary human–computer interaction channel. The accelerometer signal is sampled during the interaction process. Three methods including the heuristic algorithm, the support vector machine algorithm, the online sliding window and bottom-up algorithm are used to cut the sampled data into bit signal segment. Three machine learning algorithms including the decision tree, the support vector machine, and the naïve Bayesian are separately adopted to transform the cut signal segment into bit information. Finally, our binary human–computer interaction channel is verified by experiments. A higher recognition rate can be achieved only by using traditional machine learning methods.
The Pinisi : A Study on the Development of Boat Technology

Abstract Two main problems studied in this research; traditional boat-making and the modern one. This research aimed at studying the changes taking place in the boat-making and the factors causing these changes so that the modern boat-making. Research use of historical method. The data were collected by library research, documentation, interview and observation. The skill in the making of the traditional pinisi is considered as a skill of the boat makers inherited from their ancestors from generation to generation. The traditional boat-making has very simple working relation, namely, between leader, crew members and prospective crew members. The rising demand of boats have resulted in the development of working relations. The change has also occured in the traditional boat with the introduction of motorization, in which machine are use as the moving power. The traditional tools have also developed with introduction of modern ones as a step toward the process of adaptation and the speed in the production.
Implementation of Linear Congruential Generator (LCG) Algorithm, Most Significant Bit (MSB) and Fibonacci Code in Compression and Security Messages Using Images

Abstract The use of technology makes information spread more quickly compared to conventional methods that humans used before they knew technology. However, there is information that is only intended for certain people or institutions because the information is very confidential. The obstacle in exchanging information is the problem of information / data security. Because of that, an electronic message security algorithm which is quite well-developed is cryptographic (cryptography) and steganographic (steganography) algorithms. The development of increasingly advanced technology and the addition of more and more computer users also cause data to accumulate and transfer data from one device to another. The data is generally compressed first so that the exchange process does no take too long. Therefore, we need a system that can secure messages while reducing image data to be more efficient in the process of transmission and delivery. To implement this problem the system is designed using the Linear Congruential Generator (LCG) algorithm and the Most Significant Bit (MSB) as a security medium and using the Fibonacci Code algorithm as compression of the image. In implementation the system has a weakness in compressing images that not dominate colors because the size of the data becomes larger. In addition, the compression ratio on the variation of text length has decreased and has increased in the image variation experiments where if the compression ratio decreases, indicating better compression.
Assessment of Temperature Effects in Interior Orientation Parameters Calibration of Optoelectronic Devices

A digital micromirror device (DMD) micromirrors periodic spatial structure is a measuring scale in interior orientation parameters calibration of optoelectronic devices problems, when using a DMD as a testobject. It is important that DMD micromirrors periodic spatial structure remains constant. Change in a DMD micromirrors spatial structure may occur due to heating. In addition to heating a DMD, an optoelectronic device photodetector is also subject to heating and, accordingly, change in its spatial structure. It is necessary to estimate change in a spatial structure of DMD micromirrors and an optoelectronic device photodetector. A DMD micromirrors spatial drift and a DMD micromirrors spatial drift together with a digital camera photodetector pixels spatial drift for operation 4 h are analyzed. The drift analysis consisted in the points array position assessing formed by a DMD and projected onto a digital camera. When analyzing only a DMD micromirrors drift, a digital camera was turned on only for shooting time for exclude digital camera influence. A digital camera did not have time to significantly heat up, during this time. After a digital camera it cooled to a room temperature. Average drift of all DMD micromirrors determines the accuracy of interior orientation parameters calibration of optoelectronic devices using a DMD in time. Maximum drift of all micromirrors after switching on is observed. Minimum DMD warm-up time is 60 min for average drift of all micromirrors less than 1 μm is necessary. Minimum DMD warm-up time is 120 min when using a DMD together with a digital camera is necessary. A DMD expansion uniformity determines the accuracy of interior orientation parameters calibration of optoelectronic devices using a DMD, because irregular expansion disturbs micromirrors periodicity. The average change in distance of neighboring points is less than 0.1 μm for every 20 min. Thus, a DMD can be used as a test-object in interior orientation parameters calibration of optoelectronic devices. The results can be used as compensation coefficients of change in DMD micromirrors spatial structure due to temperature effects during operation, if more accurate are necessary.
Algorithm for Modeling Location Signals Reflected from the Edge of Various Underlying Surfaces

the paper proposes an algorithm for modeling location signals reflected from the edge of underlying surfaces that differ in the reflection characteristics of location signals. The land-sea edge, the sea-ice cover, the grassy cover-forest, etc. can serve as underlying surfaces. The features of modeling such signals in the analysis of algorithms for determining the outline of the studied edges on a computer using mathematical modeling methods, in particular the shape of the coastal zone, are considered. Simulation algorithms for edge echo signals take into account not only the laws of probability distribution of reflected signals, but also their correlation and spectral characteristics.
SemSeq: A Regime for Training Widely-Applicable Word-Sequence Encoders

A sentence encoder that can be readily employed in many applications or effectively fine-tuned to a specific task/domain is highly demanded. Such a sentence encoding technique would achieve a broader range of applications if it can deal with almost arbitrary word-sequences. This paper proposes a training regime for enabling encoders that can effectively deal with word-sequences of various kinds, including complete sentences, as well as incomplete sentences and phrases. The proposed training regime can be distinguished from existing methods in that it first extracts word-sequences of an arbitrary length from an unlabeled corpus of ordered or unordered sentences. An encoding model is then trained to predict the adjacency between these word-sequences. Herein an unordered sentence indicates an individual sentence without neighboring contextual sentences. In some NLP tasks, such as sentence classification, the semantic contents of an isolated sentence have to be properly encoded. Further, by employing rather unconstrained word-sequences extracted from a large corpus, without heavily relying on complete sentences, it is expected that linguistic expressions of various kinds are employed in the training. This property contributes to enhancing the applicability of the resulting word-sequence/sentence encoders. The experimental results obtained from supervised evaluation tasks demonstrated that the trained encoder achieved performance comparable to existing encoders while exhibiting superior performance in unsupervised evaluation tasks that involve incomplete sentences and phrases.
Virtual Reality in Support of Maritime Rescue Training

One of the main missions of the maritime authorities is to provide safety for people at sea. This task often involves specialized trained professionals either lifeguards at the beaches, as well as divers assigned to search & rescue (SAR) ships and helicopters. Training professionals for rescue operations at sea is a demanding process due to the challenging requirements of the real scenarios, frequently involving harsh weather conditions. Nevertheless, such training is critical to ensure the safety of the team and ensure the success of future rescue missions. The current paper describes the process followed in the design and development of an interactive environment using virtual reality, designed for the training of maritime rescue teams. The process resorted to interdisciplinary domains, such as user experience, gamification and storytelling, for delivering a product usable, motivating, efficiency and fun. The final validation of the solution is performed through usability tests with a sample of lifeguards and divers that experienced the VR solution. The rescue professionals are submitted to a survey in order to evaluate users' effectiveness, satisfaction and ease of learn. Results are analyzed to assessing the degree of compliance of the final product with the requirements and gather requirements for the next development iteration.
Strategic Behavior Discovery of Multi-agent Systems Based on Deep Learning Technique

Intelligent agents in multi-agent systems may have different strategies to reach the common goal. Discovering these strategies gives an advantage to the opponent systems. Deep Learning algorithms were applied to find parameters of pre-defined types of agent strategies. Different types of models were compared to reach discovery accuracy rate. Numerical experiments show that Deep Learning technique may be successfully applied to discover agent strategies. Simulation shows as well that it may be used effectively to optimize parameters so that strategy discovery of the opponent system will be much more challenging.
A Simple Study of Pleasing Parallelism on Multicore Computers

Pleasingly parallel computations are those that involve completely independent work. We investigate these in the context of a problem we call AllPageRank. The AllPageRank problem involves computing a subset of accurate PageRank entries for each possible seeded PageRank vector. AllPageRank is representative of a wider class of possible computational procedures that will run a large number of experiments on a single graph structure. Our study involves computing the AllPageRank vectors for a multi-million node graph within a reasonable timeframe on a modern shared memory, high-core count computer. For this setting, we parallelize over all of the seeded PageRank vector computations, which are all independent. The experiments demonstrate that there are non-trivial complexities in obtaining performance even in this ideal situation. For instance, threading computational environments gave scaling problems with a shared graph structure in memory. Also sparse matrix ordering techniques and multivector, or SIMD, optimizations were required to get a total runtime of a few days. We also show how different algorithms for PageRank that have different algorithmic advances and memory access patterns behave to guide future investigation of similar problems.
Partition-Aware Graph Computing System

Graph partition quality affects the overall performance of distributed graph computing systems. The quality of a graph partition is measured by the balance factor and edge cut ratio. A balanced graph partition with small edge cut ratio is generally preferred since it reduces the high network communication cost. However, through an empirical study on Giraph, we find that the performance over well partitioned graph might be even two times worse than simple random partitions. The reason is that the systems only optimize for the simple partition strategies and cannot efficiently handle the increasing workload of local message processing when a high-quality graph partition is used. In this chapter, we introduce a novel partition-aware graph computing system named PAGE, which equips a new message processor and a dynamic concurrency control model. The new message processor concurrently processes local and remote messages in a unified way. The dynamic model adaptively adjusts the concurrency of the processor based on the online statistics. The experimental studies demonstrate the superiority of PAGE over the graph partitions with various qualities.
Unobtrusive Interfaces for Historical and Culturally Sensitive Places

Unobtrusive and interactive interfaces for historical and culturally sensitive places in public, urban environments are still under-researched. Yet, the need for calm and unobtrusive technology is evidently considering its ubiquity as well as the multitude of user groups and needs that it should comply to. In my research, I am looking into potential design elements and materials to develop new interfaces with the aforementioned properties. Current use cases focus on installations at public places, cemeteries as well as museums. Traces of use are a potential design strategy for indicating interactive areas while also incorporating the property of unobtrusiveness due to their ubiquity. With my research, I want to contribute to making urban environments more connected and engaging while keeping the ambience of historical sites or green areas as-is. Thereby, users should identify more with their surroundings while dealing its history and culture.
Descriptive statistics analysis of the variable in the data of toothbrushing simulator system modelling

Abstract The controller of a system can be designed by constructing the model from the known the input and output. Modelling the dynamic of the system can help to visualize the real behavior of the system to develop the suitable controller parameter. The measured data from the real system need to be analyzed statistically to support that the current system needs a controller to improve the system performance. This study analyzed the normality and linearity of the variables in the research data of Toothbrushing Simulator System Modelling which are Speed (RPM) as the output(Y) and Voltage (V) as the input (X). There were five different data sets with 1000 observations respectively. All the data had been analyzed by using IBM SPSS Statistics 23 in which it will be explained by graphical method of histogram, scatter plot and descriptive measures of coefficient f determination between variables. The results for this study turn out that all the data sets were not normally distributed and not linear. Hence, the result from this statistical analysis has proven that the controller development is very crucial for the toothbrushing simulator system to improvise the system performance.
Determination of Granting Appropriateness Credit at “Daruzzakah Rensing” Cooperative Using the Support Vector Machine (SVM) Algorithm

Abstract Credit is the main product of savings and loan cooperatives to increase profitability. The greater the credit issued, the greater the benefits obtained by cooperatives. Each cooperative will package credit products in such a way as to attract the attention of every customer. However, cooperatives can find problems in the process of lending, such as the “Daruzzakah Rensing” Cooperative located in “Desa Rensing, Kecamatan Sakra Barat, Lombok Timur-NTB-Indonesia”. The main products of the Cooperative “Daruzzakah Rensing” are savings and loans. In distributing credit, the cooperative always decides based on statistical data. This data is sometimes not useful if the supporting methods used to predict and classify the data are not appropriate. Therefore, this research requires a method that can classify and predict problematic and non-problematic customers. To answer this question, using the SVM (Support Vector Machine) algorithm to find out the level of accuracy in analyzing creditworthiness proposed by prospective debtors. The SVM algorithm is used to predict, classify, evaluate, and analyze credit. From the results of data processing carried out using the SVM algorithm (Support Vector Machine), it can be categorized as an excellent method, with an accuracy of 90.42% and AUC at 0.957. Accuracy of 90.42% means the SVM algorithm can provide decisions about feasible or not feasible in granting credit to customers who apply for loans.
Down Sampling using Simulated Annealing

Robotic systems have been gathering more data as the field advances, allowing them to complete much more difficult tasks. This data has the potential to cause issues in the system once it reaches too large a size, and therefore must be preprocessed to manageable levels. This paper proposes a method to remove redundant data points from a data set based on a model formed from radial basis function (RBF) approximation. An optimization problem based off the Euclidean distance between the complete and reduced model with an additional least absolute shrinkage and selection operator (LASSO) component is formulated. A solution to the problem is then solved using simulated annealing. Finally, two simulations are set up to assess the performance of the algorithm.