Reviewerly/exHarmony huggingface dataset:
16-QAM Sequences with Good Periodic Autocorrelation Function

Sequences that attain the smallest possible absolute sidelobes (SPASs) of periodic autocorrelation function (PACF) play fairly important roles in synchronization of communication systems, Large scale integrated circuit testing, and so on. This letter presents an approach to construct 16-QAM sequences of even periods, based on the known quaternary sequences. A relationship between the PACFs of 16-QAM and quaternary sequences is established, by which when quaternary sequences that attain the SPASs of PACF are employed, the proposed 16-QAM sequences have good PACF.
Analysis and Vulnerability Assessment of Various Models and Frameworks in Cloud Computing

Cloud Computing is a technology which provides us a convenient way of an on-demand network-based access to available shared pools of pre configurable system resources and higher-level services. The increasing data breaches demand security assurance inside the cloud computing system. In this research work we have studied different types of tools/models available for cloud computing accordingly we proposed a vulnerability assessment framework/process for Cloud computing system based on the Common Vulnerability Scoring System, i.e., CVSS 2.0 or 3.0 which generated or published by the NVD at regular interval. Since the proposed model/process is built with the progressive security automation protocols for Cloud computing, it has the capability of automobilists and interoperability with the other existing applications and models and also has the capabilities to address all the prospective cloud vulnerabilities which are still not identified. The proposed model/process addresses the vulnerability issues on the basis of CVSS which provides the new dimensions for effective handling of unknown vulnerabilities.
Learning with Dynamic Architectures for Artificial Neural Networks - Adaptive Batch Size Approach

In this research we explore the performance of ADANET framework by using custom search space for an image-classification dataset using tensorflow libraries in combination with adaptive batch sizes for learning. In one experiment we classified fashion MNISET data and MNIST data of handwritten digits and obtained favorable results in terms of training time as well as accuracy by alternating learning batch sizes dynamically. Our testing was applied using simple deep neural network (DNN) and also with convolutional neural network (CNN).
Improved Swarm Intelligence Optimization using Crossover and Mutation for Medical Classification

Early diagnoses helps in curing most diseases or in making them more bearable, it is vital to enhance the accuracy of predicting chronic diseases. Extreme Learning Machine (ELM) is a classifier which can be efficiently used to predict diseases. Artificial Bee Colony algorithm (ABC) and Dragonfly Algorithm (DA) have been efficiently used in several optimization problems, including the optimization of ELM settings. Evolutionary Computation is a type of optimization algorithm, which has biological operators to find desired solutions. Two of these operators are crossover and mutation (CM) that are used to generate new solutions from old ones, and can be integrated with swarm intelligence algorithms to enhance their results. In this paper, models that make use of ABC and DA to optimize the number of hidden neurons and weights of ELM are presented. Moreover, crossover and mutation are combined with the swarm search of ABC and DA for chronic diseases forecasting, in models called ELM-ABC-CM and ELM-DA-CM. Using 4 real datasets to evaluate the proposed models, and compare their results with the results of standard ABC and DA, and other well-known classifiers, including regular ELM, using different evaluation metrics. The results show that crossover and mutation improved the outcome of ABC and DA. Moreover, ELM-DA-CM proved its efficiency over ELM-ABC-CM.
Use Product Segmentation to Enhance the Competitiveness of Enterprises in the IoT

With the development of technology, the world of Internet of Things (IoT) is more and more developed, resulting in the rapid growth of diversified data and the formation of Big Data. Extract suitable data from a pile of seemingly useless materials, and apply different analysis and processing methods to form new and valuable data for the enterprise to enhance the competitiveness of the enterprise. Therefore, in this paper, the SOM (Self-Organization Map) will be used to aggregate the samples with similar characteristics from the product. In addition, RFM data analysis technology is used, we find out the more valuable customers in each cluster to solve the problem that the RFM total score has a large difference in different product attributes. After identifying the customers who are more valuable to the company, they then observe the products they purchased based on their past transaction data, perform the FP-Growth algorithm and construct the FP-tree. Finally, find out the frequent itemsets of the products through FP-tree and observe their relevance to provide companies with more accurate marketing strategies.
An Efficient Kick Strategy for Agents in the 2D Simulation League

This paper aims to answer the following research question: "How to build an efficient kick strategy for agents in the 2D Simulation League?". The robot soccer provides an opportunity for students and professionals to apply their concepts of intelligent agent development. One of the main challenges of this game is to decide when a player must kick the ball to the goal. The proposed solution to solve this question is a data mining approach. The solution consists of three components: 1) use of the Random Forest technique as a classifier, 2) enrichment of the database through the construction of new variables and 3) Features Selection. In order to validate the proposed solution, a comparative study between the original kick strategy of a base team and the solution proposed was conducted. Experiments showed that the proposed approach delivers a performance superior. The results showed that the proposed policy reached a winning rate of 65% against 28% of the original.
Explaining Hierarchical Multi-linear Models

We are interested in the explanation of the solution to a hierarchical multi-criteria decision aiding problem. We extend a previous approach in which the explanation amounts to identifying the most influential criteria in a decision. This is based on an influence index which extends the Shapley value on trees. The contribution of this paper is twofold. First, we show that the computation of the influence grows linearly and not exponentially with the depth of the tree for the multi-linear model. Secondly, we are interested in the case where the values of the alternatives are imprecise on the criteria. The influence indices become thus imprecise. An efficient computation approach is proposed for the multi-linear model.
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head

As a concise and classic framework for object detection and instance segmentation, Mask R-CNN achieves promising performance in both two tasks.However, considering stronger feature representation for Mask R-CNN fashion framework, there is room for improvement from two aspects. On the one hand, performing multi-task prediction needs more credible feature extraction and multi-scale features integration to handle objects with varied scales. In this paper, we address this problem by using a novel neck module called SA-FPN (Scale Aware Feature Pyramid Networks). With the enhanced feature representations, our model can accurately detect and segment the objects of multiple scales. On the other hand, in Mask R-CNN framework, isolation between parallel detection branch and instance segmentation branch exists, causing the gap between training and testing processes. To narrow this gap, we propose a unified head module named EJ-Head (Effective Joint Head) to combine two branches into one head, not only realizing the interaction between two tasks, but also enhancing the effectiveness of multi-task learning. Comprehensive experiments show that our proposed methods bring noticeable gains for object detection and instance segmentation. In particular, our model outperforms the original Mask R-CNN by 1~2 percent AP in both object detection and instance segmentation task on MS-COCO benchmark. Code will be available soon.
Practice of a New Model Fusion Structure in Short Video Recommendation

In recent years, the popular short video content understanding and recommendation technology has become a research hotspot. This paper presents a new mixed short video recommendation algorithm based on the content, the use of FM, FMM, DeepFM DeepDCN model) and the short video content (visual, text, user interaction), and other characteristics of the training, at the same time, using a combination of two hybrid model of strategy for learning, for users to return to a short video recommend high quality results. In order to evaluate the proposed algorithm, experiments were carried out on the short video content understanding and recommendation competition data set on Biendata open competition platform, which was derived from the TikTok (douyin overseas edition) short video APP owned by bytedance company, which included two tracks: yayi and yayi. Participants were asked to model a user's interest through a video and user interaction data set, and then predict the user's click behavior on another video data set. Based on the analysis of root race results, the algorithm proposed in this paper effectively improves the performance of short video recommendation algorithm.
A dynamical-structure neural network model specified for representing logical relations with inhibitory links and fewer neurons

To make ANNs have the ability of logical processing in order to fulfil the urgent requirement that computers can automatically judge according to numerous specific conditions, researches have been carried out to design novel neural network models for representing logical relations. Recently, a new ANN model for representing logical relations is proposed. In the model, six components are designed to simulate the operations of logic gates. The work provides a novel way for constructing logical relations running in a neural-like manner. However, the components are still complex and indirect for the representation since more extra neurons and links are needed to simulate logic gates. In order to represent logical relations more directly, this paper defines new neurons and multiple kinds of links to represent logic gates directly, and they can be combined to represent complex logical relations in a simpler neural network structures with fewer neurons. Additionally, this ANN model can dynamically create links on demand instead of the fixed full connections. It can constantly adjust its network structure when getting the data continuously. It can be used for the establishment of the rule library of the intelligent information system in the form of the neural network structure.
SCS: Style and Content Supervision Network for Character Recognition with Unseen Font Style

There is a significant style overfitting problem in traditional content supervision models of character recognition: insufficient generalization ability to recognize the characters with unseen font styles. To overcome this problem, in this paper we propose a novel framework named Style and Content Supervision (SCS) network, which integrates style and content supervision to resist style overfitting. Different from traditional models only supervised by content labels, SCS simultaneously leverages the style and content supervision to separate the task-specific features of style and content, and then mixes the style-specific and content-specific features using bilinear model to capture the hidden correlation between them. Experimental results prove that the proposed model is able to achieve the state-of-the-art performance on several widely used real world character sets, and it obtains relatively strong robustness when the size of training set is shrinking.
Detecting Voice Features for Criminal Case

Emotion detection research area is very popular in medical informatics and criminal technology. The voice is the one of the ways to detect the human inner emotions such as general feelings or psychiatric feelings. Humans has been expressed their emotions by varying different vocal features during speech generation. Voice and speech analysis help to become secure communications among people and human cannot easily lie their emotions to other. At the law court, emotion analysis can detect a culprit speak was lie or truth. The propose system is used to detect the emotions (normal, sad) of human through speech analysis by using Pitch, Mel Frequency Cepstral Coefficients (MFCC) and Short Time Energy (STE) feature extraction techniques to increase the accuracy and reliability. This system will help in court of law, when the lawyer questioning to the witness or plaintiff or defense to get the truth of the crime scene.
Use of the Visual Information Processing and Display System as Part of the Monitoring and Control Panel of the ISS RS Simulation Complex

The objects of research are the following: the space simulator (SS) as a training system; modules of the simulation complex (SC) of the ISS RS; information display system (IDS) of the monitoring and control panel (M&amp;CP) of the SC of the ISS RS as an object and video information as a subject of research; the members of the Interdepartmental Examining Board (IDEB). The subjects of research are the following: video information displayed on the IDS of the M&amp;CP and the upgraded visual information processing and display system (VIP&amp;DS) as part of the M&amp;CP IDS of the ISS RS Simulator Complex.
Using Machine Learning Technique for Effort Estimation in Software Development

Estimates in software projects aim to help practitioners predict more realistic values on software development, impacting the quality of software process activities regarding planning and execution. However, software companies have difficulties when carrying out estimations that represent adequately the real effort needed to execute the software project activities. Although, the literature presents techniques to estimate effort, this activity remains complex. Recently, Machine Learning (ML) techniques are been applied to solve this problem. Through ML techniques it is possible to use databases of finished projects (datasets) to help get more precisely estimations. This research aims to propose a methodology to estimate effort using a ML technique based on decision trees: XGBoost. To evaluate our methodology, we conducted tests with four datasets using two metrics: Mean Magnitude Relative Error and Prediction(25). The preliminary results show consistent results for this methodology for software effort estimation based on the employed metrics, which indicates that our methodology is promising. As further work, new datasets must be analyzed using our methodology, and also an approach using synthetic data to improve the ML training.
Wyner-Ziv Scheme Based Multiple Description Coding for Robust Video Transmission over Path Diversity Networks

In this paper, we propose a novel multiple description coding method based on the Wyner-Ziv scheme for robust video transmission. In the proposed method, the motion vector (MV) field is encoded into two descriptions, which are transmitted over different paths to the decoder. Unlike the conventional methods, the correlation between descriptions in the proposed method is generated without using any additional redundant data since it can be estimated based on the high spatial correlation of MVs themselves in the MV field. At the decoder, this correlation is then employed as the side information in the Wyner-Ziv scheme to effectively improve the performance of the proposed method. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better objective and subjective performance than other conventional MDC methods.
Effectiveness of Realia Media: A Case Study in SMP Negeri 1 Likupang Barat as a Partner in KKN-PPM Program

Abstract Communities in Talise Island face various problems, indeed children which has an impact on the high level of school dropouts. Children after school must help the work of parents who generally work as traditional farmers, breeder, or fishermen. Other factors that influence the dropout rate are the learning process and infrastructure which can be said is below national standards. To help alleviate the problems, an activity called Kuliah Kerja Nyata – Pembelajaran Pemberdayaan Masyarakat (KKN-PPM) has been carried out to reduce the number of school dropouts. The real steps taken are to optimize the utilization of existing infrastructure of SMP Negeri 1 Likungan Barat which has a land area of about 2 hectares for livestock farming and revitalize the learning process in schools by applying learning media suitable for the situation and conditions. Through this KKN-PPM activity, college students from the educational study program have implemented media realia in the learning process. Realia media are assistive devices or real objects that are used as teaching materials that can be presented in classrooms or observed directly according to their location outside the classroom. This article will describe the effectiveness of using realia media in SMP Negeri 1 Likupang Barat .
Ensemble Classifier Generation Using Class-Pure Cluster Balancing

Clustering based ensemble of classifiers have shown a significant improvement in classification accuracy in many real-world applications. Most of the existing clustering-based ensemble approaches generate and use predefined number of data clusters. However, datasets have different spatial structure that depends on number of characteristics for example class labels. Therefore, using a predefined set of hyperparameters to generate a clustering-based ensemble classifier is not an effective methodology. In this paper we propose a methodology to overcome this limitation by generating dataset dependent strong and balanced data clusters per class. This ensures that any spatial information that is inherent in the dataset can be exploited to train an ensemble classifier that can surpass the classification accuracy plateau. An ensemble classifier framework is proposed that benefits from this methodology and trains base classifiers on generated strong and balanced data clusters. We have evaluated the proposed approach on 8 benchmark datasets from UCI repository. Detailed experiments and results are presented in the paper, and it is evident from the results that varying the number of clusters per class does have an impact on the overall classification accuracy of the ensemble.
Multi-source Knowledge Fusion: A Survey

Multi-source knowledge fusion can effectively promote the research and development of knowledge graphs in the related fields such as Big Search in Cyberspace, natural language processing, and so on, effectively promote the construction of domain knowledge graphs, and bring enormous social and economic benefits. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open source knowledge fusion, multi-knowledge graphs fusion, information fusion within knowledge graphs and multi-modal knowledge fusion. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.
Automated Composition of Picture-Synched Music Soundtracks for Movies

We describe the implementation of and early results from a system that automatically composes picture-synched musical soundtracks for videos and movies. We use the phrase picture-synched to mean that the structure of the automatically composed music is determined by visual events in the input movie, i.e. the final music is synchronised to visual events and features such as cut transitions or within-shot key-frame events. Our system combines automated video analysis and computer-generated music-composition techniques to create unique soundtracks in response to the video input, and can be thought of as an initial step in creating a computerised replacement for a human composer writing music to fit the picture-locked edit of a movie. Working only from the video information in the movie, key features are extracted from the input video, using video analysis techniques, which are then fed into a machine-learning-based music generation tool, to compose a piece of music from scratch. The resulting soundtrack is tied to video features, such as scene transition markers and scene-level energy values, and is unique to the input video. Although the system we describe here is only a preliminary proof-of-concept, user evaluations of the output of the system have been positive.
Using Active Learning Sampling Strategies for Ensemble Generation on Opinion Mining

The lack of labeled data and the need to update the learning model are the two main challenges when performing Opinion Mining with data streams. Usual Opinion Mining approaches are not able to deal with these challenges, nor with the characteristics brought by this kind of data. Moreover, the occurrence of changes (drifts) in the concepts and/or opinions is another issue. Possible alternative solutions to these problems are: the use of Semi-supervised learning, such as Active Learning, which labels selected data rather than the entire data set; or Multiple Classifiers Systems, which combines different classifiers and are well suited to deal with drifts. In this study, we combined these two approaches, proposing the use of eight Active Learning sampling strategies as a generation method for Multiple Classifiers Systems. The Active Learning approach requires the choice of a strategy to select the instances, and each strategy results in the creation of a distinct classification model. Our method was evaluated in 14 Twitter stream data sets and the results showed that it can be better for Opinion Mining with data streams than popular ensemble generation methods present in the literature, such as Bagging and AdaBoost.
A Metaheuristic-Based Tool for Function Minimization

During the last decade, metaheuristic algorithms have occupied an important place in the field of optimization. Function minimization is of importance to researchers since many real-world problems can be modeled mathematically and be solved effectively through metaheuristic algorithms. Due to the growing scientific interest in the field of optimization and the good performances shown by the algorithms on function minimization, the practical and quick implementation concept is necessary to select the most appropriate algorithms on function minimization, and to assist researchers in analyzing the performance of the algorithms. In this study, a tool is developed to minimize user-defined functions in a specified range according to the chosen metaheuristic algorithms, which allows analyzing the algorithms in the general experimental environment. The tool, which has a user-friendly interface, can provide single and comparative solutions by simultaneously executing the algorithms. Each solution and computational time obtained by the algorithms is given numerically, and the convergence behavior of the algorithms is shown graphically in the tool interface. Minimization of functions can be made fast, easily and effectively through the developed tool.