Reviewerly/exHarmony huggingface dataset:
Towards a New Paradigm for Intuitive Theatrical Lighting Control

A simplified model of a lighting process applied in theatrical productions is one that involves two key players.The first is that of the lighting designer, to produce a set of intentions and plans for the scenes that define the show.The second, the lighting technician, has the job of translating these designs into practice using control equipment, luminaires, and other technical instruments.The lighting design often becomes a 'working document' subject to change and adaptation as the physical reality of the design becomes apparent, and the input of other stakeholders is considered.This process can be a valuable creative tool, and also a difficult technical hurdle to overcome, depending on a varied number of factors.A common frustration with this process is that either the complexity of the task, or difficulty in communication can make it difficult for the final creative vision to be effectively realised.Strains may also arise in the case of small, often touring, theatre companies where the lighting designer and technician may be the same person, and frequently one of the performers as well.Considering the design aspect, there can be challenges in ensuring efficacy of lighting plans between venues in touring productions, with 2D lighting sketches or even 3D computer simulations confined to the paper or screen.From a technical perspective, the role of the lighting technician in theatres and performance situations has included the operation of lighting control equipment during shows.The equipment has evolved over time but has, until recently, been grounded upon the basis of faders and the mixing desk.It is argued that this paradigm has failed to keep pace with the change in other interactive technologies.The on-going research described in this paper explores existing and upcoming technologies in the field, whilst also seeking to understand the roles and communication workflows of those involved in theatrical lighting to find the best areas to seek improvement, adopting principles of user-centred design.The intention of this research is to develop a new paradigm, and manifestation of it, using a control method for lighting or projection that allows a more intuitive form of operation in theatre productions, which will be scalable and flexible.Lighting design.Pre-visualisation.
Spectrum-Based Bug Localization of Real-World Java Bugs

The localization of software bug is one of the most expensive tasks of program repair technology. Hence, there is a great demand for automated bug localization techniques that allow a programmer to be monitored up to the location of the error with little human arbitration. Spectrum-based bug localization helps software developers to quickly discover errors by investigating a program’s trace summary and creating a ranking list of most modules that may be in error. We used the real-world Apache Commons Math and Apache Commons Lang Java projects to examine the accuracy using spectrum-based bug localization metric. Our findings show that the higher performance of the specific similarity coefficients used to examine the spectra information is more effective in locating individual bugs.
The STNRW-ECF estimator for stable distributions with application in ECG beat

The α-stable distribution can describe a degree of concentration of the observations around the mean as well as their asymmetry, regardless of sample size. As the α stability index is the most interesting parameter in the applications, several estimators have been proposed for α. Here we develop an estimator for α (called STNRW-ECF) based on the empirical characteristic function and the Seismic Trace Noise Reduction by Wavelets and Double Threshold Estimation method (STNRW). We analyze the proposed estimator using Monte Carlo simulations and prove its asymptotic Gaussian distribution. Electrocardiography (ECG) is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. The time intervals between its various peaks, may contain useful information about the nature of disease afflicting the heart. To analyze this kind of data can be tiring and more prone to errors when interpreted by human beings, since there is a huge amount information to be processed. Here we propose the STNRW-ECF estimator to be an additional diagnostic tool that may provide an indication of cardiac arrhythmia, and we also propose a test based on the principal wavelet shrinkage to confirm whether or not the STNRW-ECF estimator should be used for this purpose.
Introducing the Deep Learning for Digital Age

Deep learning is playing vital role with greater success in various applications, such as digital image processing, human-computer interaction, computer vision and natural language processing, robotics, biological applications, etc. Unlike traditional machine learning approaches, deep learning has effective ability of learning and makes better use of data set for feature extraction. Because of its repetitive learning ability, deep learning has become more popular in the present-day research works.
Experience on Learning Through an AR-Based Course

Augmented Reality (AR) technology is one of rapidly growing areas in the digital field. There are numerous applications in the market. Our research focus on applying AR technology to support learning for students. In particular, the course materials are provided and prepared by applying AR technology. The AR contents are created and the learning style is purposed. The case study is performed by having a group of participants experienced with AR-based course. The pre- and post- test learning scores are used to assess the students’ satisfaction and attitudes.
Bit-Plane Specific Selective Histogram Equalization for Image Enhancement and Representation

Images acquired in poor acquisition conditions which are unavoidable and cannot be ignored in many visual applications require enhancement to achieve high contrast for better representation and interpretation of image objects. For efficient image storage and communications, compression and preserving of image details is required apart from contrast enhancement. A popular histogram equalization approach leaves undesirable artefacts in enhanced images and increases its data size due to excessive contrast enhancement. In this paper, we present a bit-plane specific selective histogram equalization technique which narrows the range of enhanced image than original image and keeps image details intact without unnoticeable artefacts with reduced image data size. The qualitative and quantitative measures as visual perception, histogram, edge details, entropy, data storage size, mean intensity and mean square error obtained for different images demonstrate the effectiveness of the selective bit-plane specific histogram equalization over to other methods of histogram equalization.
Training Dataset Extension Through Multiclass Generative Adversarial Networks and K-nearest Neighbor Classifier

The performance of deep learning architectures, where all the feature extraction stages are learned within the artificial neural network, requires a large number of labeled examples to model the variability of the different possible inputs. This issue is also present in other classification tasks with a large number of features where the number of examples can limit the number of possible input features, involving the creation of handcraft feature sets. Typical solutions in computer vision and document analysis and recognition increase the size of the database with additional geometric transformations (e.g. shift and rotation) and random elastic deformations of the original training examples. In this paper, we propose to evaluate the impact of additional images created through generative adversarial networks (GANs), which are deep neural network architectures. We study the addition of images created through a multiclass GAN in different databases of handwritten numerals from different scripts (Latin, Devanagari, and Oriya). The contributions of this paper are related to the use of multiclass GANs to extend the size of the training database after filtering the images with a k-nearest neighbor classifier where the k nearest neighbors must all agree on the decision to validate a GAN generated image. The accuracy is evaluated with the original training dataset, the GAN generated images, and the combination of the original training images and the GAN generated images. The results support the conclusion that GAN generated images through a multiclass paradigm can provide a robust and fully data driven solution for enlarging the size of the training database for improving the accuracy on the test dataset.
Near-optimal pitch for the optical implementation of dynamic visual cryptography

Dynamic visual cryptography is a technique for the encryption of secret visual information in such a way that the decryption is absolutely visual. The secret is encoded into the moiré grating using complex computational algorithms, but the decoding can be performed simply by observing oscillating encoded cover image by a naked eye. The law of the oscillations is always preselected before the encoding procedure. However, if the secret image is encoded into the harmonic moiré grating and the oscillation of the cover image is performed on the computer screen, the implementation of harmonic oscillations is not possible due to some constrains related with technical characteristics of the digital screen. The main aim of this investigation is to determine near-optimal pair of pitches of the moiré grating for the case when oscillations according to a rectangular waveform function are used for image hiding in dynamic visual cryptography.
Fluid-measurement technology using flow birefringence of nanocellulose

We propose a potential fluid-measurement technology aimed at supporting biomechanics research of water sports using fluid simulation and motion analysis. Cellulose nanofibers introduced into the water as tracer particles to visualize the movement of water. An optical property of nanofibers, called flow birefringence, makes water flows brighter than their surroundings when placed between right and left circularly polarized plates. We tested the capability of the technology in a water tank and succeeded in using an existing particle-tracking method-particle image velocimetry (PIV)-to measure the flows from a pump in the tank.
A spatial clustering approach by combining the geo-social-comment relationships of POIs

Existing spatial clustering approaches mainly consider the location distance (or/and social relationship) between spatial objects while ignore the user comment relationship between them. This paper proposes a spatial clustering method which combines the location, social, and comment relationships of spatial objects. First, the spatial object's Geographic-Social-Comment (GSC) relation model is constructed. And then, the methods for evaluating the social relationships and comment similarities between spatial objects are proposed, respectively. To improve the efficiency of the spatial clustering algorithm, a novel index structure and the corresponding implementation algorithm are also presented. The experimental results demonstrate that the spatial clustering results obtained by our method are more reasonable, the social connections and comment similarities within the cluster are more closely.
The Effect of Reward and Security Awareness on User Intention of Knowledge Portal Service for ROK Army

As the importance of Knowledge Management System (KMS) in the military increases, Republic of Korea Army (ROK Army) developed Army Knowledge Portal. Although the members in the military are encouraged to use the portal, few members currently use it. This study was conducted to find variables to predict the user’s intention to use the portal, which contributes to activating the use of Army Knowledge Portal in the army. On the basis of Technology Acceptance Model (TAM), ten variables such as perceived ease of use, general information security awareness, information security awareness, expectation for external rewards, expectation for relationships, sense of self-worth, attitude toward compliance with security policy, attitude toward knowledge sharing, intention of non-combat knowledge sharing, and intention of combat knowledge sharing were considered as independent variables. 105 participants on active duty who currently use or have experience to use the portal participated in this study. The results indicated that general information security awareness and information security awareness increases compliance with the information security policy. In addition, the attitude toward knowledge sharing is enhanced by expectations for relationship and sense of self-worth. Based on the results, the authors propose the need for policy alternatives to reinforce the reward system and security policy, which activates the use of Knowledge Portal Service for ROK Army.
AES Overhead Mitigation Using OpenMP

The Advanced Encryption Standard (AES) has been extensively used in many systems to provide security.However, many of the implementations are susceptible to Side Channel Attacks (SCA).To address the susceptibility, several researchers have proposed a number of countermeasures.Masking scheme is one of the countermeasures that are commonly used to counteract such attacks.In this paper, we investigate the overhead of the countermeasure in terms of execution time of the first order-masking scheme for the AES "Encryption part only".To achieve that, we have implemented the sequential algorithm of the AES with single processor, and after that used OpenMP directives to reduce the overhead of the countermeasure.Subsequently, the result of the sequential algorithm has been compared with its parallel implementations using 2, 4, 6, and 8 processors.We show how parallel implementation of the AES offers better performance yet flexible enough for cryptographic algorithms.Compared to the sequential unmasked AES implementation, the best masking scheme for the first order using data parallelism shows a performance in terms of speed up around 5x when 8 threads are used.
A coding theoretic approach to the uniqueness conjecture for projective planes of prime order

An outstanding folklore conjecture asserts that, for any prime p, up to isomorphism the projective plane $$PG(2,\mathbb {F}_p)$$ over the field $$\mathbb {F}_p := \mathbb {Z}/p\mathbb {Z}$$ is the unique projective plane of order p. Let $$\pi $$ be any projective plane of order p. For any partial linear space $${\mathcal {X}}$$ , define the inclusion number $$\mathbf{i}({\mathcal {X}},\pi )$$ to be the number of isomorphic copies of $${\mathcal {X}}$$ in $$\pi $$ . In this paper we prove that if $${\mathcal {X}}$$ has at most $$\log _2 p$$ lines, then $$\mathbf{i}({\mathcal {X}},\pi )$$ can be written as an explicit rational linear combination (depending only on $${\mathcal {X}}$$ and p) of the coefficients of the complete weight enumerator (c.w.e.) of the p-ary code of $$\pi $$ . Thus, the c.w.e. of this code carries an enormous amount of structural information about $$\pi $$ . In consequence, it is shown that if $$p > 2^ 9=512$$ , and $$\pi $$ has the same c.w.e. as $$PG(2,\mathbb {F}_p)$$ , then $$\pi $$ must be isomorphic to $$PG(2,\mathbb {F}_p)$$ . Thus, the uniqueness conjecture can be approached via a thorough study of the possible c.w.e. of the codes of putative projective planes of prime order.
Detection and Analysis of Video Inconsistency Based on Local Binary Pattern (LBP)

In the Human life, videos popularity has increased day by day. With the help of high qualityvideo camera and video editing tools peoples easily manipulate video for malicious intent. Sonow a day, this is becoming significant topic in the recent year with more difficulties. The proposed of research work focus in texture features by using Local Binary Pattern (LBP). The object of this work is to detect video inconsistency. The work is addressing in various steps as sequentially such as preprocessing, feature extraction, matching and decision. The evaluation of propose system by using True positive Rate (TPR) and False Positive Rate (FPR). The experimental results were executives on famous dataset REWIND database, which has 20 videos which give efficiency results with evaluation parameter TPR with $$0.0258\%$$ , FPR with $$0.0786\%$$ and Area Under Curve (AUC) = $$0.7903\%$$ .
Help Me Invest

Making an investment is very important. According to a recent study, people used to hold a commodity for about 8 years, but now the time period has reduced to about 4 months. The market is really volatile; with the help of internet, news travels really fast and is reachable to everyone in every part of the world. Buying any commodity cannot be easier, as the number of options available is very large. Different investment options bring different types of profit and risk. One of the major issues involved is the security in using such tools and applications. A good investor always makes an investment decision by calculating and analyzing various factors that could affect his profits. The advancement in artificial intelligence and computation plays an important role while analyzing these factors. Few simple lines of code can analyze the market and help evaluate various investment options. Most of these tools are available online, but they all come with security issues associated with them.
Nigerian Prisons Service and Internal Security Management in Nigeria

Globally, the principle behind the operation of prisons by a modern state is not only custody for criminals who constitute a security threat to the state but also reformatory for them. Thus, prison service ought to contribute to national security of a state significantly by ensuring that persons sent to prisons come back into the society as rehabilitated citizens. To ensure the attainment of this was the reason behind the establishment of Nigerian Prisons Service (NPS) to manage Nigerian prisons. Unfortunately, in Nigeria, persons sent to prisons come back becoming hardened criminals. This challenge is the fulcrum of attention of this research. Research questions that guided the study were the following: How did NPS evolve? To what extent has NPS contributed to the national security of Nigeria in its management of prisons in Nigeria? What are the challenges faced by NPS in the management of prisons for internal security of Nigeria? Data and information for this study were gathered from primary and secondary sources. Primary information was sourced from a senior prison official in Yola, Adamawa State and an ex-inmate in one of the prisons in the state. Furthermore, secondary data were collected from Federal Office of Statistics (FOS), National Bureau of Statistics (NBS) and other relevant textual materials like books, journal articles, online reports, especially those of the NPS, and newspapers. The thesis of the chapter is that NPS has a significant role to play in the management of the internal security of the Nigerian state. However, the institution is yet to perform this role effectively due to challenges such as inadequate funds, poor rehabilitation programmes, the mixture of minor offenders with hardened criminals, the problem of awaiting trial persons (ATPs) and the philosophy of operating Nigerian prisons as punitive rather than reformative systems. As a result, Nigerian prisons have become avenues for criminalisation of inmates rather than reformation centres, thus complicating Nigeria’s national security. The chapter recommends addressing the deteriorating conditions of Nigerian prisons through adequate funding, de-emphasizing imprisonment as a form of punishment except where it is absolutely necessary, addressing the problem of ATPs through judicial reform and separation of minor and major offenders during imprisonment.
Security Funding, Accountability and Internal Security Management in Nigeria

The thesis view of the study is that although more resources have been allocated for security in Nigeria, Tthe country has not attained security commensurate to funds disbursed. This is due largely to lack of transparency and accountability in the process of disbursement and utilisation of security funds in Nigeria. The chapter ends with a recommendation that the channels of disbursement and utilisation of security funds in Nigeria be reviewed and transparency and accountability be incorporated into it for effective utilisation.
Two-Encoder Pointer-Generator Network for Summarizing Segments of Long Articles

Usually long documents contain many sections and segments. In Wikipedia, one article can usually be divided into sections and one section can be divided into segments. But although one article is already divided into smaller segments, one segment can still be too long to read. So, we consider that segments should have a short summary for readers to grasp a quick view of the segment. This paper discusses applying neural summarization models including Seq2Seq model and pointer generator network model to segment summarization. These models for summarization can take target segments as the only input to the model. However, in our case, it is very likely that the remaining segments in the same article contain descriptions related to the target segment. Therefore, we propose several ways to extract an additional sequence from the whole article and then combine with the target segment, to be supplied as the input for summarization. We compare the results against the original models without additional sequences. Furthermore, we propose a new model that uses two encoders to process the target segment and additional sequence separately. Our results show our two-encoder model outperforms the original models in terms of ROGUE and METEOR scores.
Nonlinear semi-classical 3D quantum spin

In an effort to provide an alternative method to represent a quantum spin, a precise 3D nonlinear dynamics method is used. A two-sided torque function is created to mimic the unique behavior of the quantum spin. A full 3D representation of the magnetic field of a Stern–Gerlach device was used as in the original experiment. Furthermore, the temporarily driven nonlinear damped model exhibits chaos, but struggles to be consistent through azimuthal angles in reproducing the well known quantum spin statistics.
Edge Detection in Roof Images Using Transfer Learning in CNN

Edge Detection in image processing is very important due to large number of applications it offers in variety of fields that extend from medical imaging to text and object detection, security, mapping of roads, real time traffic management, image inpainting, video surveillance and many more. Traditional methods for edge detection mostly rely on gradient filter based algorithms which usually require excessive pre-processing of the images for noise reduction and post-processing of the generated results in order to get fine edges. Moreover, traditional algorithms are not reliable generally because; as the noise in images increases their efficiency is affected largely due to escalation of mask size which also makes the system computationally expensive. In this paper, we will employ transfer learning in CNN method to detect edges of roof images. Incorporating CNN into edge detection problem makes the whole system simple, fast, and reliable. Moreover, with no more extra training requirement and without any additional feature extraction, CNN can process input images of any size. This technique employs feature map of the image using Visual Geometry Group (VGG) CNN network followed by application of Roberts, Prewitt, Scharr and Sobel edge operators separately to compute required edges. Interpretations of ground truths were obtained using manual techniques on roof images for performance comparison, and PNSR value of computed results via multiple operators against the ground truths is calculated.