Reviewerly/exHarmony huggingface dataset:
A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.
Fast Online Video Pose Estimation by Dynamic Bayesian Modeling of Mode Transitions

We propose a fast online video pose estimation method to detect and track human upper-body poses based on a conditional dynamic Bayesian modeling of pose modes without referring to future frames. The estimation of human body poses from videos is an important task with many applications. Our method extends fast image-based pose estimation to live video streams by leveraging the temporal correlation of articulated poses between frames. Video pose estimation is inferred over a time window using a conditional dynamic Bayesian network (CDBN), which we term time-windowed CDBN. Specifically, latent pose modes and their transitions are modeled and co-determined from the combination of three modules: 1) inference based on current observations; 2) the modeling of mode-to-mode transitions as a probabilistic prior; and 3) the modeling of state-to-mode transitions using a multimode softmax regression. Given the predicted pose modes, the body poses in terms of arm joint locations can then be determined more accurately and robustly. Our method is suitable to investigate high frame rate (HFR) scenarios, where pose mode transitions can effectively capture action-related temporal information to boost performance. We evaluate our method on a newly collected HFR-Pose dataset and four major video pose datasets (VideoPose2, TUM Kitchen, FLIC, and Penn_Action). Our method achieves improvements in both accuracy and efficiency over existing online video pose estimation methods.
Cluster Phenomenon to Determine Anomaly Detection of Flight Route

The study of Flight has anomaly begun 21 years ago. The focus of this research is to automatically resolve conflicts on flights through the flight route. The deciding factor to analyse flight phenomena is a flight data sourced from Automatic Dependent Surveillance-Broadcast (ADS-B) data sets. The main parameters to be referenced position (latitude, longitude); velocity (speed); and traveling time. This study aims to detect flight anomalies based on the call sign in a waypoint area. The proposed method is BSGVD, ie a) Building Segment based on waypoint range; b) Grouping object based on clustering; c) Determining the computing aspect of the clustering; d) Cluster Validity based on index value; and e) Distance measurement on cluster centroid for funding to find a potentially anomalous area. The results achieved in this study were to generate potential anomaly areas in the ADS-B data in the segment region. It is dominantly influenced by time parameters. Because, based on the analysis of the distance between the four aspects, obtained the longest distance is in the aspect (latitude, longitude, traveling time) with distance = 182.01 and aspects (speed, traveling time) with distance = 182.32. Based on the index value criteria, we get the aspect (speed, travel time) that has the cluster analysis. Then, the final are measurement validity results obtained: Dunn index value = 0.645; Silhouette index value = 0.89; and Davies-Bouldin index value = 14.81.
Deep and Wide Convolutional Neural Network Model for Highly Dense Crowd

In this study, a novel and efficient deep learning model are proposed to estimate the number of people in highly dense crowd images. We present a convolutional neural network model consisting of two parallel modules which focus on various specific features of the images. Thus, while the general density map is derived by obtaining lower-level features from the first module, it is possible to identify regions of the human body, such as head and upper body with the help of the higher-level features in the deeper second module. These two modules are then concatenated with a fully connected neural network. The proposed model was tested with the ShanghaiTech Part-A dataset. The mean square error and mean absolute error values are used as performance metrics. By comparing these metrics regarding recent studies, more successful results were obtained by using the proposed method.
Solar Resource Assessment and Potential in Indian Context

The demand for electricity is increasing day by day causing environmental problems due to the widespread burning of fossil fuels. Therefore, research has been promoted in solar energy to mitigate environmental pollution. The land of India receives a good amount of solar energy. This paper describes the assessment of solar radiation resources in India. All solar resource stations have advance measuring instruments of the same type to collect, store and send radiation and meteorological parameters at central station National Institute of Wind Energy (NIWE) Chennai. One-year solar data from January to December 2015 of DCRUST, Murthal center is assessed, which assist solar project developers for feasibility analysis and development of new solar projects in this area. Moreover, state-wise solar potential and installed capacity are also described. This study is significant for design, modeling and performance analysis of solar plants. In addition to this solar resources give impetus for the deployment of solar energy along with futuristic solar technologies in India.
Spatial Mixer

When workspaces for commercial music production are digitized they are generally configured based on existing analogue music production environments, using the "channel strip" metaphor to organize the multiplicity of devices involved in the process. The channel strip metaphor is derived from the traditional design of mixing consoles with parallel sliders acting as separate channels. An alternative to this is the "stage" metaphor, which visually represents sound elements as discrete actors on a stage for manipulating settings. To study cross-device interactions in music mixing, we designed the Spatial Mixer, an app that uses the stage metaphor, to work in concert with Ableton Live, an existing music composition program. A study of Spatial Mixer in use showed that the stage metaphor and spatial visualizations of music tracks and effects was intuitive for users to interact with and helped them work across both pieces of software to effectively create their musical composition.
An Ontological Approach for Service Adaptation in Context Aware Environment

Several research initiatives have investigated the potential of mobile learning as a new emerging technology to assist teaching and learning strategy that exploit learner’s contextual information to provide an adaptive learning experience tailored to learner’s personal characteristics and context information as context-aware. The main challenge in context-aware is context ontologies, it represents the most part for context modeling. In this paper, we propose an ontology-based approach for service adaptation in context aware environment.
Classification of teak wood production in Central Java using the C5.0 algorithm

Central Java has the largest teak land area in Java Island. It is about two-thirds of teak land area on Java Island is located in Central Java. The purpose of this study is to determine the classification of factors that influence the production of teak trees in Central Java with the Commercial Version 5.0 (C5.0) algorithm. This algorithm is a refinement of the ID3 and C4.5 algorithms. The C5.0 algorithm works by separating samples based on attributes that have the highest information gain. The results of the study show that there are three classification rules produced with an 81.8% accuracy rate.
On the Complexity of the LWR-Solving BKW Algorithm

Duc et al. applied the Blum-Kalai-Wasserman (BKW) algorithm to the learning with rounding (LWR) problem. The number of blocks is a parameter of the BKW algorithm. By optimizing the number of blocks, we can minimize the time complexity of the BKW algorithm. However, Duc et al. did not derive the optimal number of blocks theoretically, but they searched it for numerically. In this paper, we theoretically derive the asymptotically optimal number of blocks and show the minimum time complexity of the algorithm. Furthermore, we derive an equation that relates the Gaussian parameter $$\sigma $$ of the LWE problem and the modulus p of the LWR problem. When $$\sigma $$ and p satisfy the equation, the asymptotic time complexity of the BKW algorithm to solve the LWE and LWR problems are the same.
New nonexistence results on (m, n)-generalized bent functions

In this paper, we present some new nonexistence results on (m, n)-generalized bent functions, which improved recent results. More precisely, we derive new nonexistence results for general n and m odd or $$m \equiv 2 \pmod {4}$$, and further explicitly prove nonexistence of (m, 3)-generalized bent functions for all integers m odd or $$m \equiv 2 \pmod {4}$$. The main tools we utilized are certain exponents of minimal vanishing sums from applying characters to group ring equations that characterize (m, n)-generalized bent functions.
General Method to Design Reversible Universal n-Bit Up/Down Counters

With the growing trend towards reducing the size of electronic devices, reducing power consumption has become one of the major concerns of circuit designers, and designing reversible circuits is one of the approaches proposed for reducing power consumption. Although several studies have been done in the field of synthesizing combinational reversible circuits, little work has been done for designing reversible sequential circuits. Furthermore, many researches in this context use traditional designs which replace latches, flip-flops and associated combinational gates with their reversible counterparts. This traditional technique is not very promising, because it leads to high quantum cost (QC) and garbage outputs. Recently, researchers have proposed direct design of reversible sequential circuits using Reed Muller expressions to obtain next state output. Since most sequential circuits have multiple outputs, using common product terms between multiple outputs might decrease QC significantly. In this paper, a modular and low QC design for a synchronous reversible [Formula: see text]-bit up/down counter with parallel load capability is presented. In this design, the common terms among multiple outputs are used efficiently, which leads to a low QC for the counter. A general formula to evaluate the QC of our proposed reversible counter is presented. This result shows that in our proposed design by increasing the number of bits of counter ([Formula: see text], the QC increases linearly, while in previous works by increasing the number of bits of counter, the QC increases exponentially.
Neural Network Detection of Shilling Attack Based on User Rating History and Latent Features

The recommendation system is a significant field to solve the problem of information overload today. However, the recommendation system relies heavily on the user's behavior information, which is easy to suffer attacks. Therefore, how to prevent the recommendation system from effecting by the attacks is a major issue in the research field of protection recommendation system. In response to the above questions, we found that the shilling attack has the features of short rating history, large number of rating and other potential features compared with normal user profiles. Therefore, we propose a neural network structure to learn the latent features between users and items and we construct shilling attack detection model based on neural network. The experimental results show that the detection method of this paper has achieved the expected goal.
Research on K-Means Clustering Algorithm Over Encrypted Data

Aiming at the privacy-preserving problem in data mining process, this paper proposes an improved K-Means algorithm over encrypted data, called HK-means++ that uses the idea of homomorphic encryption to solve the encrypted data multiplication problems, distance calculation problems and the comparison problems. Then apply these security protocols to the improved clustering algorithm framework. To prevent the leakage of privacy while calculating the distance between the sample points and the center points, it prevents the attacker from inferring the cluster grouping of the user by hiding the cluster center. To some extent, it would reduce the risk of leakage of private data in the cluster mining process. It is well known that the traditional K-Means algorithm is too dependent on the initial value. In this paper, we focus on solving the problem to reduce the number of iterations, and improve the clustering efficiency. The experimental results demonstrate that our proposed, HK-Means algorithm has good clustering performance and the running time is also reduced.
Security of Related-Key Differential Attacks on TWINE, Revisited

In this paper, we revisit related-key security of TWINE block cipher with 80-bit and 128-bit keys. Using an MILP-aided automatic search algorithm, we point out the previous evaluation of TWINE with a 80-bit key is wrong, and give a corrected evaluation result. Besides, we show a first security evaluation of TWINE with a 128-bit key in the related-key setting, which was infeasible due to the high computation cost in the original proposal.
Information-based Particle Flow for High Uncertainty Estimation

This work presents a new formulation of the Gaussian particle flow filter derived using an information theoretic approach. The developed information flow addresses two problems with Gaussian particle flow: the lack of inherent meaning in the flow parameters and the inability to easily include modifications for a non-Bayesian update. Equivalency between Gaussian particle flow and information flow is established using a linear, Gaussian example. An orbit determination simulation with high initial uncertainty is used to demonstrate the consistent, robust performance of the information flow filter in situations where the extended Kalman filter fails.
The Changing Roles of Humans and Algorithms in (Process) Matching

Historically, matching problems (including process matching, schema matching, and entity resolution) were considered semiautomated tasks in which correspondences are generated by matching algorithms and subsequently validated by human expert(s). The role of humans as validators is diminishing, in part due to the amount and size of matching tasks. Our vision for the changing role of humans in matching is divided into two main approaches, namely Humans Out and Humans In. The former questions the inherent need for humans in the matching loop, while the latter focuses on overcoming human cognitive biases via algorithmic assistance. Above all, we observe that matching requires unconventional thinking demonstrated by advanced machine learning methods to complement (and possibly take over) the role of humans in matching.
Coping with Unbalanced Designs of Generalizability Theory: G String V

The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets. Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this situation. Thus, the software G String V will be introduced. First, the generalizability theory will be reviewed, followed by a description of the unbalanced synthetic data that was used to conduct the analysis using the software. Explanations are provided for installing the software, preparation of the data, and the step-by-step data analysis. Moreover, the interpretation of the data is also explained. Finally, the limitations of the software are shared.
A New Angular Measurement in Minkowski 3-Space

In Lorentz–Minkowski space, the angles between any two non-null vectors have been defined in the sense of the angles in Euclidean space. In this work, the angles relating to lightlike vectors are characterized by the Frenet frame of a pseudo null curve and the angles between any two non-null vectors in Minkowski 3-space. Meanwhile, the explicit measuring methods are demonstrated through several examples.
Fuzzy Logic Application to Link National Culture and Cybersecurity Maturity

National culture and cybersecurity maturity (CM) both play defining roles in the identity and security of a nation. This study examines the relationship that these two constructs play in determining whether certain cultural dimensions of a nation leads to riskier cybersecurity behaviours. Based on the confirmed affirmation of the relationship between national culture and CM, we develop a research model based on fuzzy logic to predict the level of influence that national culture has on a country's Cybersecurity Maturity Level (CML). The fuzzy model was generated based on three semantic steps: (1) conducting statistical correlation analyses based on datasets related to 65 countries; (2) synthesizing a fuzzy model based on the derived knowledge; and (3) evaluating the fuzzy model based on public domain CM data across three case study countries. Our statistical analyses reveal a strong correlation between the cultural dimensions of Power Distance (PDI), Long Term Orientation (LTO), and Individualism (IDV) with login strength, implicating that these dimensions also strongly impact CM. Consequently, the proposed fuzzy model takes PDI, LTO, and IDV as the inputs to derive a quantitative value to the Global Cybersecurity Index related to a particular country. Hence, the novelty of this research lies in its interdisciplinary approach of creating and validating a model that can be used to determine the influence that national culture has on CML. Furthermore, this study suggests that national culture must be factored in when determining cybersecurity policy and frameworks which govern individuals within a nation's CM development.
Generating Word and Document Embeddings for Sentiment Analysis

Sentiments of words can differ from one corpus to another. Inducing general sentiment lexicons for languages and using them cannot, in general, produce meaningful results for different domains. In this paper, we combine contextual and supervised information with the general semantic representations of words occurring in the dictionary. Contexts of words help us capture the domain-specific information and supervised scores of words are indicative of the polarities of those words. When we combine supervised features of words with the features extracted from their dictionary definitions, we observe an increase in the success rates. We try out the combinations of contextual, supervised, and dictionary-based approaches, and generate original vectors. We also combine the word2vec approach with hand-crafted features. We induce domain-specific sentimental vectors for two corpora, which are the movie domain and the Twitter datasets in Turkish. When we thereafter generate document vectors and employ the support vector machines method utilising those vectors, our approaches perform better than the baseline studies for Turkish with a significant margin. We evaluated our models on two English corpora as well and these also outperformed the word2vec approach. It shows that our approaches are cross-domain and portable to other languages.
A Novel Approach to Improve Face Recognition Process Using Automatic Learning

In real-life applications, the appearance of a face changes significantly due to variations in expression, lighting, aging, exposure, and occlusion, which makes face recognition difficult. We present in this article a new approach for facial recognition. This approach is based on a set of variants of the Ho-LBP descriptor that we have proposed. In fact, the presentation of the images using a set of variants of the Ho-LBP descriptor helps the classifier to learn better. In addition, these variants are combined to improve the performance of facial recognition. We evaluated the effectiveness of our approach on ORL, Extended Yale B, and Feret databases. The obtained results are very promising, especially when compared with those of existing approaches. They show that our approach improves the accuracy of facial recognition in a very efficient way and in particular to the variations of the poses and the changes of the luminance.
Privacy Preservation Quality of Service Model for Data Exposure

Mobile applications and smartphones are closely related to each other. Mobile application plays a crucial role among smartphone users. It has gained enormous support from the smartphone users ranging from young to elder generations. It is very common to download and install desired mobile applications from application store to aid in completing particular tasks with ease. However, this behavior exposes user towards privacy risk as it has become a concern in smartphone usage. In this research, we propose a mathematical model named Privacy Risk Model (PRiMo) that measures the risk score of a user in smartphone usage. We are approaching the privacy issues in mobile applications from the individual’s perspective. We conduct studies on different categories of mobile applications to determine the risk posed by the mobile applications. There is no standardization for residual risk. Therefore, by using the proposed model, we are exploring and creating a standardization for residual risk that becomes a benchmark for user to accept and live with that risk.
Wavelet Decomposition Based Authentication Scheme for Dental CBCT Images

Incorrect information or documentation of the patient details and a wrong direction provided by any personnel to the patient can largely influence the lab reports or any other confidential data. Once the medical image is verified as secure and safe by the authorized access, one can review the medical image and the patient information. This paper focuses on providing a helping tool to ensure higher accuracy as well as authentication of the medical data and the related information. It proposes an authentication scheme to address the issue of security and privacy preservation of medical details. This technique involves generating a secure identification pattern from the fusion of the patient’s information with the features of cone-beam CT images obtained through wavelet analysis without any tampering with the medical details. The patient’s information can be successfully restored at the authorized receiver’s end. This authentication scheme can be helpful and implemented in telecare medical information systems.