Reviewerly/exHarmony huggingface dataset:
Collaborative Filtering Algorithm Based on Improved Time Function and User Similarity

Abstract As a typical representative of information filtering technology in the era of big data, a recommendation system is an important means to solve the problem of information overload. A collaborative filtering recommendation algorithm is one of the important technologies to realize the recommendation system, but the traditional collaborative filtering algorithm only considers the similarity of ratings between users. As the number of users and the number of items increases, it faces user interest drift and reduced recommendation Precision, and other issues. In this regard, a collaborative filtering algorithm based on improved time function and user similarity is proposed. First, considering that user interests will dynamically change over time, this paper introduces an improved time function in the traditional scoring similarity; secondly, considering the impact of the number of items evaluated by users on the calculation of similarity measurement, this paper takes the Pearson correlation coefficient weighted scoring into account; Finally, the fusion recommendation is based on the improved time function and the weighted Pearson correlation coefficient to improve the Precision of recommendation prediction. This paper conducts simulation experiments on MovieLens 100K and 1M data sets respectively. The results show that, compared with the traditional collaborative filtering recommendation algorithm, combining the improved time function and the weighted Pearson correlation coefficient can effectively improve the recommendation Precision.
Research on Computer Network Information Security and Protection Strategy Based on Internet of Things

With the continuous development of network technology and information technology, network has become an indispensable part of people's daily life and production, and with the increasing demand for network information. The rapid development of computer network brings convenience to people's lives, but it also produces a series of computer network security problems, which infringe on people's privacy and interests. In view of this, based on the Internet of Things, this paper discusses the security of computer network information in detail, mainly analyzes the security problems existing in the current computer network information, and puts forward some targeted protection strategies, in order to ensure the security performance of computer network information and ensure the reliable, continuous and stable operation of computer network system.
Simplified Approach for Predicting Emotions of Multi-Turn Textual Utterances

Sentiment analysis is a well-known topic in Natural Language Processing (NLP). After a lot of research in this sector, the Emotion Analysis of Conversations prompted out as a trending topic. The main idea behind the Emotion Analysis of Conversations is to extract and classify the emotions expressed through a conversation. The scope of this research is adopted from "EmoContext" (SemEval 2019 - Task 3), a competition conducted by Codalab - Microsoft. There are some models that achieve a significant accuracy in this section but they are too much complicated to understand. There is a need for a model that achieves significant accuracy with a simplified approach for many uses. Here, we present a model that has achieved a microF1 score of 0.742 with our simple CNN-BiLSTM model and customized FastText word embedding, which comfortably betters the 3rd Quartile value of 0.7317 and stands up into the top quarter of the leader board of EmoContext competition.
Low Quality for High Quality: Exploiting 2K Frames for Supporting Efficient 4K-Quality Pervasive Video Streaming Applications

Emerging cloud-based video streaming applications suggest limitations on current video streaming systems and emphasize the need to preserve and exchange high-quality contents while minimizing processing latency, given that both are critical in perpetuating a high user-perceived quality. This article presents 4KODEC, a codec for supporting real-time high-quality videos for cloud-based video streaming. 4KODEC is designed to induce minimal latency when encoding and decoding 4K video frames, by exploiting the fact that many regions (tiles) of a 4K frame can be recovered from their 2K counterparts given the low-complexity embedded in most tiles. Furthermore, 4KODEC is designed to effectively identify complex tiles, so that original 4K representations can be selectively exchanged to maintain high quality. We present evaluations using four different 4K-quality online games to show that 4KODEC reduces the processing latency by nearly half while improving perceptual quality when compared to widely used H.264/265 codec-based 4K video streaming implementations.
The Ideal Block Ciphers - Correlation of AES and PRESENT in Cryptography

In this digital era, the usage of technology has increased rapidly and led to the deployment of more innovative technologies for storing and transferring the generated data. The most important aspect of the emerging communication technologies is to ensure the safety and security of the generated huge amount of data. Hence, cryptography is considered as a pathway that can securely transfer and save the data. Cryptography comprises of ciphers that act like an algorithm, where the data is encrypted at the source and decrypted at the destination. This paper comprises of two ciphers namely PRESENT and AES ciphers. In the real-time applications, AES is no more relevant especially for segmenting the organizations that leverage RFID, Sensors and IoT devices. In order to overcome the strategic issues faced by these organization, PRESENT ciphers work appropriately with its super lightweight block figure, which has the equivalent significance to both security and equipment arrangements. This paper compares the AES (Advance encryption standard) symmetric block cipher with PRESENT symmetric block cipher to leverage in the industries mentioned earlier, where the huge consumption of resources becomes a significant factor. For the comparison of different ciphers, the results of area, timing analysis and the waveforms are taken into consideration.
gRosSo: Mining Statistically Robust Patterns from a Sequence of Datasets

Pattern mining is a fundamental data mining task with applications in several domains. In this work, we consider the scenario in which we have a sequence of datasets generated by potentially different underlying generative processes, and we study the problem of mining statistically robust patterns, which are patterns whose probabilities of appearing in transactions drawn from such generative processes respect well defined conditions. Such conditions define the patterns of interest, describing the evolution of their probabilities through the datasets in the sequence, which may, for example, increase, decrease, or stay stable, through the sequence. Due to the stochastic nature of the data, one cannot identify the exact set of the statistically robust patterns analyzing a sequence of samples, i.e., the datasets, taken from the generative processes, and has to resort to approximations. We then propose GRosSo, an algorithm to find a rigorous approximation of the statistically robust patterns that does not contain false positives with high probability. We apply our framework to the mining of statistically robust sequential patterns. Our extensive evaluation on pseudo-artificial and real data shows that GRosSo provides high-quality approximations for the problem of mining statistically robust sequential patterns.
Recognition and Visualization of Facial Expression and Emotion in Healthcare

To make the SenseCare KM-EP system more useful and smart, we integrated emotion recognition from facial expression. People with dementia have capricious feelings; the target of this paper is measuring and predicting these facial expressions. Analysis of data from emotional monitoring of dementia patients at home or during medical treatment will help healthcare professionals to judge the behavior of people with dementia in an improved and more informed way. In relation to the research project, SenseCare, this paper describes methods of video analysis focusing on facial expression and visualization of emotions, in order to implement an “Emotional Monitoring” web tool, which facilitates recognition and visualization of facial expression, in order to raise the quality of therapy. In this study, we detail the conceptual design of each process of the proposed system, and we describe our methods chosen for the implementation of the prototype using face-api.js and tensorflow.js for detection and recognition of facial expression and the PAD space model for 3D visualization of emotions.
Data Analysis for Corruption Indications on Procurement of Goods and Services

Corruption occurs in many developing countries and is very difficult to detect because of weak legal awareness, lack of good governance, and integrity. In Indonesia, there are seven types of corruption cases handled by the Corruption Eradication Commission (KPK). One of the corruption cases which is detrimental to the state occurs in public procurement, such as the procurement of goods/services. This case is the second most corrupt crime after bribery in Indonesia. In this research, we try to identify potential corruption from auction data on goods/services procurement in government. By using Big Data technology, it is expected that the process can be carried out immediately to assist the KPK in identifying potential corruption in goods/services procurement auctions.
On the Purity of Resolutions of Stanley-Reisner Rings Associated to Reed-Muller Codes

Following Johnsen and Verdure (2013), we can associate to any linear code C an abstract simplicial complex and in turn, a Stanley-Reisner ring $$R_C$$ . The ring $$R_C$$ is a standard graded algebra over a field and its projective dimension is precisely the dimension of C. Thus $$R_C$$ admits a graded minimal free resolution and the resulting graded Betti numbers are known to determine the generalized Hamming weights of C. The question of purity of the minimal free resolution of $$R_C$$ was considered by Ghorpade and Singh (2020) when C is the generalized Reed-Muller code. They showed that the resolution is pure in some cases and it is not pure in many other cases. Here we give a complete characterization of the purity of graded minimal free resolutions of Stanley-Reisner rings associated with generalized Reed-Muller codes of an arbitrary order.
Dataset Building for Handwritten Kannada Vowels Using Unsupervised and Supervised Learning Methods

In the era of automation, recognition of the Kannada handwritten characters is an inevitable task as it has widespread applications in the digitization of documents in government offices, public sectors, and other domains like banking and post offices. Hence, the need for Optical Character Recognizers (OCR) for the Indian languages like Kannada is vital. This paper presents the recognition and labeling of offline Handwritten Kannada Vowels using the feature extraction techniques like Local Binary Pattern (LBP), Run Length Count (RLC), Chain Code (CC) and Histogram of Oriented Gradients (HOG) and feeding the features to the supervised and unsupervised machine learning algorithms with pure and hybrid features is presented. The comparative study of supervised learning algorithms on the data collected from 500 people. Also, the objective of this work is to label the unlabeled data by automation without manual labor. This has been achieved by feeding the features initially to the unsupervised learning algorithm, i.e., KMeans Clustering algorithm. The classified and misclassified vowels, then became the train and test sets respectively for supervised learning algorithms and a combined recognition rate has been presented.
Breast Cancer Prediction System: A novel approach to predict the accuracy using Majority-Voting Based Hybrid Classifier (MBHC)

Breast Cancer is the frequently recognized cancer growth among ladies and a significant explanation behind the expanded death rate among ladies. It takes more time to diagnose manually and very few automated prediction systems were present, leads to building up a new prediction framework that identifies the tumors in early-stage with more prediction accuracy. Machine Learning algorithms are using numerous classification algorithms were applied to classify whether the tumors are either benign or malignant in nature. Classification algorithms are well trained from the previous data and these can predict the new pattern from the current data received. Existing automated Breast Cancer prediction system gave lesser prediction accuracy from the Breast Cancer data set and the dataset is only about the image attributes which is not much convenient for the users. To enhance the accuracy of various prediction models, a novel Majority-Voting Based Hybrid Classifier (MBHC) is proposed to overcome the problems of predicting Breast Cancer. The experimental results of Breast Cancer Prediction System which use MBHC produced an accuracy score of 79%.
People movement tracking based on estimation of ages and genders

In this paper, a system for tracking people in a limited environment and creating a trajectory of their movement has been implemented. This includes detecting a person, estimating gender and age, and tracking location in the selected area. The system can instantly locate people based on their current position, estimate gender and age, monitor their movements and visually display it, but also to do statistics of their detention in certain parts of the building.
An Improved SIFT Algorithm for Large Tilt Angle Images

Image feature point extraction and matching is one of the important fields of image processing and understanding. SIFT is a classical and important algorithm for image feature point matching and widely used because of its advantages such as scale, translation and rotation invariance. However, SIFT does not have complete affine invariance. In terms of camera angle direction, SIFT can only maintain partial invariance so that it may not be able to extract enough matching feature points for large tilt angle images. As a remedy, the ASIFT algorithm is proposed and has been proved to be complete affine invariant for large tilt angle images. ASIFT mainly perform multiple affine transformations on images simulating the longitude and latitude angles taken by the camera. The process of affine transformation leads to a significant reduction of processing speed and some images formed in ASIFT with an uncommon angle are unlikely to appear in real situations. Based on this observation, this paper directly improves the SIFT algorithm and preprocess matching images to get only one perspective matrix. Through the perspective matrix, the large tilt angle image can be transformed to have the same perspective as the other image. Extracting feature points from the perspective transformed image, the number of matching feature points can be increased. Simulation results show the improved performance of the proposed method over SIFT in terms of the detected number of matching feature points and over ASIFT in terms of processing speed.
AI2VIS4BigData: Qualitative Evaluation of an AI-Based Big Data Analysis and Visualization Reference Model

AI2VIS4BigData is a reference model for Artificial Intelligence (AI) - based Big Data Analysis and Visualization that provides uniform terminology and logical entity-relationships to scientists and professionals working in this application domain. It thereby enables re-utilization of concepts and software, prevents reinventing the wheel and facilitates collaboration scenarios. AI2VIS4BigData was systematically derived from two foundation reference models utilizing reasoned assumptions. These assumptions required subjective decisions which were not evaluated right away. This publication targets to change that through presenting two qualitative evaluation approaches that were conducted in the course of an official satellite workshop of an international conference. Selected scientific and industrial experts participated thereby in an expert round table workshop and a survey. The validation results confirm the reference model’s practical applicability and legitimate the substantial majority of subjective decisions that were taken in the course of the reference model derivation. This publication concludes with outlining five research fields for future work that comprise the non-validated subjective decisions.
MFFSSD: An Enhanced SSD for Underwater Object Detection

The performance of deep neural networks in object detection is impressive, however, direct use of existingalgorithms may not obtain satisfactory resultsdue to the characteristics of underwater images. In this paper, a multi-scale feature fusion Single Shot Multibox Detector (MFFSSD) based on the SSD algorithm is proposed to improve the accuracy of underwater object detection. There are two main improvements: introducing attention module and perform multiscale feature fusion. By introducing attention module, the features of the input image obtained through convolution are weighted in both channel and space aspect, so as to highlight the useful informationto overcomes the problem of small and blur objects which are common in underwater images. By extracting a lower level feature map forward, more details of the lower level image can be obtained to adapt to the underwater object with simple shape, so that low-level features have a higher contribution to the recognition object; at the same time, the high-level feature map is enlarged by using deconvolution, and then fused with the low-level feature map to introduce more contextual information. The fusion of the features extracted from the shallow and high level networks ensures the efficiency of the features to the greatest extent. Experiments on URPC show that MFFSSD significantly improved the underwater object recognitionaccuracy compared to the original algorithm.
Towards a visual Sign Language dataset for home care services

We present our work towards creating a dataset, which is intended to be used for the implementation of a home care services system for the deaf. The dataset includes recorded realistic scenarios of interactions between deaf patients and mental health experts in their native sign language. The scenarios allow for contextualized representations, in contrast to typical datasets presenting isolated signs or sentences. It includes continuous videos in RGB and depth, which are challenging to analyze and closely resemble real-life scenarios. The research on representation of signs is supported by providing the hand shapes and trajectories for every video using hand and skeleton models, as well as facial features. Furthermore, the dataset may be used for studying the emotional context, since such conversations are typically emotionally charged.
Comparative Analysis of Mean-Shift Based Object Tracking Using Simulated Annealing and Locust Search Algorithm Approaches

Tracking process is a process to find the convergence value of 2 ROI boxes, i.e. object target ROI and candidate ROI. In other word, convergence is a situation where the value of those 2 ROI boxes has a high similarity value. Because the main process in tracking is update the object's position continuously, so it is important to pay attention to the processing time needed to reach the convergence point of the object target on each video frame while searching for ROI. Mean-Shift tracking algorithm has a deficiency in its technique during the ROI convergent search process. To overcome the deficiency of the Mean-Shift technique in searching for convergent ROI, the optimization algorithm is used. This research aims to produce a Mean-Shift based object tracking system with faster processing time performance to reach the converging point of the object target on each video frame. This research will provide analysis of optimization algorithm i.e. Simulated Annealing and Locust Search Algorithm in the process of finding optimum ROI point. Performance evaluation using one tail t-test testing technique with different variant assumptions. The performance result of both optimization algorithms will be shown in form of chart and t-test tables. The result summary obtained by using optimization algorithm due to searching for convergence point of ROI shows that Locust Search algorithm produce the better performance. The process time needed for Locust Search algorithm is 151.4 milliseconds, faster than Mean-Shift with 275.2 milliseconds and Simulated Annealing with 172.9 milliseconds.
A Knowledge-Intensive Decision Support System for Industrial Machines Maintenance

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.
Thickness and Refractive Index Measurement System for Multilayered Samples

In order to simultaneously obtain the thickness and refractive index for each layer of multilayered samples, this paper proposes a novel measurement system with a simple structure by using geometric optics. The key point of the overall structure is that the binary linear equation relation between the upper and lower planes of the layer can be known by the distances between laser spots reflected from the layer. Using two optical paths with different incident angles of laser beams, the CCD sensors capture the spot signal for image processing binarization and data sampling. After the obtained laser spots' spacing is substituted into the equations, the thicknesses and refractive indexes of the multilayered samples can be calculated and measured. The proposed measurement system is characterized numerically using simulations on the commercial software program Zemax and then experimentally tested using a laboratory-built prototype. The experiment results show that the refractive indexes and the thicknesses of three-layer samples were measured with high accuracy (with maximum measurement errors of 2.4% and 2% for a refractive index n and thickness t, respectively).