Reviewerly/exHarmony huggingface dataset:
The Concept of Applying a SIFT Algorithm and Orthophotomaps in SAR-based Augmented Integrity Navigation Systems

This paper presents the concept of utilizing a Scale-Invariant Feature Transform (SIFT) in the applications of SAR-based augmented integrity systems. SAR-based navigation systems are used for platform navigation corrections in the case of a lack of global navigation satellite system (GNSS) signals. In such a case, SAR images collected in real-time are matched with on-board image databases. In the past, some algorithms have been proposed based on the correlation of characteristic objects in images, such as roads, buildings, etc. [1, 2]. In this paper, existing algorithms for SAR images matched with optical databases, including neural networks, are revised and analyzed. The application of the SIFT method in SAR-based navigation system applications was also proposed, which seems to be an efficient approach to solve the problem of matching characteristic points in SAR and optical images. The application of the SIFT algorithm was intensively tested and verified using real SAR images. Selected results of these tests are presented in this paper.
Sequence Pairs With Asymptotically Optimal Aperiodic Correlation

The Pursley-Sarwate criterion of a pair of finite complex-valued sequences measures the collective smallness of the aperiodic autocorrelations and the aperiodic cross-correlations of the two sequences. It is known that this quantity is always at least 1 with equality if and only if the sequence pair is a Golay pair. We exhibit pairs of complex-valued sequences whose entries have unit magnitude for which the Pursley-Sarwate criterion tends to 1 as the sequence length tends to infinity. Our constructions use different carefully chosen Chu sequences.
Context-Aware Layered Learning for Argumentation Based Multiagent Collaborative Recognition

Multiagent recognition based on argumentation is highly concerned with the utilization of the advantages offered by argument games for the purpose of justifying and explaining the decision results of intelligent systems. However, arguing agents for collaborative recognition tasks are often encountered with disagreement due to different abstract levels of object categories. To cope with this category level inconsistent problem in argumentation based multiagent recognition, we propose CALL, a context-aware layered learning method for conflict resolution among multiple agents. Context-awareness is explored, in this paper, to investigate how structured contextual knowledge can facilitate dynamic arguments constructing in argumentation. The main contribution provided by the proposed method is that it not only can achieve natural conflict resolution in multiagent collaborative recognition systems, but also give consistent explanations with easily assimilated reasoning of multi-party argument games. Preliminary experimental results demonstrate the effectiveness of our method with significant improvements over state-of-the-art, especially in presence of noise.
Modified weight initialization in the self-organizing map using Nguyen-Widrow initialization algorithm

Self-organizing map (SOM) is a neural network trained using unsupervised learning for clustering. The clustering result and learning speed of SOM, however, is dependent on the initial weights which are randomly initialized with a low close to zero value from the range of vectors within the given input space data negatively affecting training speed and clustering result. The study used the Nguyen-Widrow initialization algorithm to initialize the weights of SOM and speed up the training process. The cluster error rate and the number of iterations to converge to final clustering is recorded and compared with the traditional SOM to determine the performance of the modified SOM. The result reveals that the modified SOM algorithm produces better cluster results and improved training speed as compared to traditional SOM. Hence, Nguyen-Widrow algorithm for initialization of weights in the SOM yields better cluster result and improved training speed of the algorithm in terms of the number of iterations.
Beyond the Audience Seat: The recording and production of immersive opera and interactive concerto programmes for VR experiences

New modes of expression have always defined a central attribute within the ambitions of classical music.The extended realities (XR) hold the potential to expand this expressive repertoire by transporting audiences into the middle of the action within an immersive music performance.We argue that implementing these innovative formats in contemporary opera performance, composition, production, and recording opens up new aesthetical paradigms that can reciprocally inform the artistic disciplines and reach a wider demographic of audiences.To our knowledge, this paper presents the first ever interactive virtual reality (VR) production of an entire opera, "She" (2017) and "Croak" (2018).The recordings devise 3-dimensional 360-degree high-resolution video and 3rd-order Ambisonics audio from multiple perspectives within a bespoke software interface.Following a discussion of both operas from the composer's and engineer's perspective, the paper will present an enhanced rendition of "CroakVR", a 6-degrees-of-freedom (6DoF) volumetric simulation of its corresponding opera, that raises the bar of interactivity by enabling audiences to freely explore various mimetic and diegetic perspectives of its staging.Furthermore, we discuss on-going work on "Climb!VR", a 6DoF volumetric VR production devising multiple Ambisonics recordings of an interactive electro-acoustic piano concerto "Climb!" based on ludonarrative structures.It is argued that VR recordings can amend the life span of musical compositions by offering listeners a concert experience that can extend beyond the walls of the physical concert hall.Opera.Virtual reality.6DoF.
OOCQM: Object Oriented Code Quality Meter

Source code is core of Software engineering. Source code of good quality can be maintained and upgraded easily. Source code quality can be broken down in factors like maintainability, reusability, change proneness, testability and stability. These quality factors are high level representation of code quality and cannot be measured directly. Research in field of source code quality has proposed enormous amount of code metrics that measure quality in different aspects like complexity, size, coupling, cohesion and abstractness etc. This study proposes a framework named Object Oriented Code Quality Meter (OOCQM) for measuring source code quality of object-oriented code using low level code metrics and high-level quality factors. Code metrics has a proven relationship with quality factors. This relationship is used to calculate a numerical value for quality factors based on metric values. It is observed that all selected metrics has negative correlation with mapped quality factor. Quality factors scores are aggregated and used to depict quality of code in numerical form. A PHP based tool is developed to validate the results. Framework results are compared with Maintainability Index (MI) which is popular quality measure in terms of code maintainability. OOCQM measures code quality correctly as quality results are correct according to MI. This framework provides more detail at individual quality factors level. OOCQM is compared with few other tools developed for quality measurement. Comparison shows that this tool supports more quality factors analysis than other tools.
The Impact of Texting on Standard English: The Students’ Perspective

Overindulgence in social networking, in general, and texting, in particular, is much in practice.It is cutting across various population boundaries and has almost assumed an endemic proportion.Its consequential impact on the standard language has acquired greater importance.This paper aims to determine the perceptions and attitudes of English Second Language (ESL) learners at Aligarh Muslim University towards the consequences of texting on Standard English.The data were collected through a five-point scale questionnaire from ninety students who were enrolled at Aligarh Muslim University during the academic year 2010-2011.The respondents completed a 16-item questionnaire.The students from which the data were collected were grouped according to their levels.The results indicate the negative impact of this new usage of the language in breaking the rules of English language and influencing their literacy.Moreover, the questionnaire results from respondents' point of view show that regardless of their heavy use of texting, most respondents have a negative attitude towards texting and they viewed it as a threat to Standard English.
3D Human Pose Estimation with Grouping Regression

Most of the methods for predicting the 3D human pose from single picture are to first extract the 2d joint position in the image, and then use the 2d joint coordinates to get the 3d joint position. This type of method focuses on learning the mapping from 2d to 3d, and neglects the kinematic properties of the joints. Combined with the independent characteristics of human limb joint motion, we adopt the structure of grouping regression. The limbs and trunk were divided into different joint groups, and the 3d position of the joints in each group was predicted separately. In order to make the prediction results are closer to the real human posture, we use BiLSTM to design a human joint self-constrained network to adjust prediction results and improve accuracy. Experiments show that the method of grouping regression combined with self-constrained network is superior to the current advanced method.
A Guidance Application for Historical Routes and Location Area with Augmented Reality

Abstract Indonesia is a cultural and historical diversity country. The Indonesian national culture is an analogous multicultural, rooted in older societies and interethnic relations caused the national culture is most easily observed in cities but aspects of it now reach into the countryside as well, for example, capital of Sumatera Utara, Medan. Indonesia’s borders are those of the Netherlands East Indies, though Dutch imperialism caused nation and many of its institutions should be able to preserve and introduce their culture and history to future generations. One way that can be done by involving the development of Augmented Reality. Augmented Reality (AR) is a technology that can be developed by creating 3D (Dimensions) of each created object such as a historic place. In this study, the object to be constructed is the 3D object of the historic building in Medan city which is a historic city in North Sumatra. In addition to historical sites there is a city tour service with the location of the historic building nearby with the location point of reference based on the current location. In addition, the user interaction can be done with 3D objects using Augmented Reality technology and information about historic buildings and the use of virtual reality technology in creating 3D visuals of extinct historic locations. This research is expected to attract users of heritage traveller applications and educate the community in preserving the cultural heritage with the help of Augmented Reality (AR) and Virtual Reality (VR) technology.
Context-aware recommendations via a tree-based ensemble framework

Incorporating contextual information is very useful to improve the accuracy of personalized recommendations. However, how to utilize the contexts more efficiently are always confusing researchers, the existing work ignores the connections among different contexts, and even suffers from data sparsity. Cross-context behavior transfer has been tested to help overcome the problems. In this paper, we propose a tree-based ensemble framework of exploiting behavioral relations between different contexts to make context-aware recommendations. Specifically, our framework is to make recommendations for the target context based an ensemble of different user/item feature vectors learnt from other contexts with a regularization term. The empirical result and analysis on real-life data demonstrate that our framework achieves a significant increase in recommendation accuracy.
Deep Learning for Online Display Advertising User Clicks and Interests Prediction

In this paper, we propose a deep learning based framework for user interest modeling and click prediction. Our goal is to accurately predict (1) the probability that a user clicks on an ad, and (2) the probability that a user clicks a specify type of campaign ad. To achieve the goal, we collect page information displayed to users as a temporal sequence, and use long-term-short-term memory (LSTM) network to learn latent features representing user interests. Experiments and comparisons on real-world data shows that, compared to existing static set based approaches, considering sequences and temporal variance of user requests results in an improvement in performance ad click prediction and campaign specific ad click prediction.
Implementation of Vector Algebra and The Hybrid Pseudo Random Number Generator in Android Game of Kuaci

Abstract Kuaci is a game property made of plastic and has various unique shapes. Two to ten people usually play Kuaci game in turn. The game starts with determining how many Kuaci will be at stake, and then each player puts the Kuaci into a box. At a distance of one foot from the box, a first straight line is made, and at a distance of two meters from the first line, the second line is created. From the second line, the kuaci master is thrown to the first line to determine the turn. The player’s turn is sorted from the closest one to the first line to the furthest from the first line. The first player tosses the Kuaci master into the box until the Kuaci master and the regular pieces located inside the box are out, then from the place where the Kuaci was thrown, the player aims at the opponent to eliminate it. If other players have been eliminated, then the player has won the game. The game was built on Android smartphone that has recently become the biggest contributor to the technological development of the traditional games. The authors implemented Vector Algebra in determining the direction of the computer player Kuaci master and The Hybrid Pseudo Random Number Generator on the determination of the throwing power of the computer player Kuaci master so the computer player can balance the game with the user. These methods implementation is to assure the user to play the game without eliminating the essence of the game such as managing the strategy to defeat the opponent. As a result, the performance of the opponent’s Kuaci master can run according to the difficulty of each game.
Teaching Self-Balancing Trees Using a Beauty Contest

Trees data structures and their performance is one of the main topics to teach in a data structures course. Appreciating the importance of tree structure and tree height in software performance is an important concept to teach. In this paper, a simple and amusing activity is presented. It demonstrates to students the importance of a well-balanced tree by comparing the height of a binary search tree to a balanced (AVL) tree build upon some personal data to find the "prettiest" tree (minimum height). The activity highlights the fact that, irrelevant of your data sequence, a balanced tree guarantees a height of O(log n) and everyone "wins" the beauty contest.
Accurately and robustly estimating three-dimensional rigid transformation using an effective local reference frame

We present an accurate and efficient transformation estimation algorithm called local reference frame-based maximum consensus statistic (LRF-MCS). Moreover, a local reference frame (LRF) is proposed for effectively implementing our LRF-MCS. The proposed LRF uses a small local region to calculate its <italic>z</italic> axis and employs a salient local point to determine its <italic>x</italic> axis for improving the robustness to incomplete region. The proposed LRF-MCS first efficiently calculates the consensus set connected to each input correspondence by an LRF-based transformation technique. Then a switch program is used to select an appropriate consensus set for calculating the optimal transformation, which ensures the strong robustness to the seriously corrupted input correspondences. Extensive experiments are implemented on four standard datasets for comprehensively evaluating the performance of the proposed LRF and LRF-MCS techniques. The experimental results show that our LRF and LRF-MCS techniques outperform the state-of-the-art algorithms in terms of accuracy, efficiency, and robustness, achieving the best overall performance.
Determining the Optimal Random-Padding Size for Rabin Cryptosystems

Rabin encryption and a secure ownership transfer protocol based on the difficulty of factorization of a public key use a small public exponent. Such encryption requires random number padding. The Coppersmith's shortpad attack works effectively on short padding, thereby allowing an adversary to extract the secret message. However, the criteria for determining the appropriate padding size remains unclear. In this paper, we derived the processing-time formula for the shortpad attack and determined the optimal random-padding size in order to achieve the desired security.
Forensic Analysis of Microblogging Sites Using Pinterest and Tumblr as Case Study

The growth in the popularity of microblogging and online social network (OSN) websites such as Facebook, Twitter, Tumblr, Pinterest, etc. in this modern age progresses relentlessly. Accompanying this growth in popularity is the increase in the use of these websites by individuals, companies, and even industries at large. Aside from social networking, microblogging and OSN websites have been exploited by many profitable companies as a medium for advertisement of their products, information dissemination and building connections. This increasing usage for various purposes has also opened doors to a wide range of criminal and malicious acts. This chapter studies two particular microblogging sites, Pinterest and Tumblr, both of which have gained great popularity today. Specifically, we seek to contribute to the forensics field by finding the artifacts associated with these websites that would be of interest should the need arise, as in criminal and civil cases, by replicating the actions of a standard user while using these sites in the Internet Explorer, Firefox and Chrome Browsers, and then, analyzing this self-created evidence to find such artifacts. Also, the paper exposes and exploits most of the top forensic tools employed today in the investigation of cyber-criminal cases, while making use of forensically sound methodologies in our analysis.
Strong Gaussian approximation of the mixture Rasch model

We consider the famous Rasch model, which is applied to psychometric surveys when $n$ persons under test answer $m$ questions. The score is given by a realization of a random binary $n\times m$-matrix. Its $(j,k)$th component indicates whether or not the answer of the $j$th person to the $k$th question is correct. In the mixture, Rasch model one assumes that the persons are chosen randomly from a population. We prove that the mixture Rasch model is asymptotically equivalent to a Gaussian observation scheme in Le Cam’s sense as $n$ tends to infinity and $m$ is allowed to increase slowly in $n$. For that purpose, we show a general result on strong Gaussian approximation of the sum of independent high-dimensional binary random vectors. As a first application, we construct an asymptotic confidence region for the difficulty parameters of the questions.
Investigation of initialization strategies for the Multiple Instance Adaptive Cosine Estimator

Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, previously investigated approach is the Multiple Instance Adaptive Cosine Estimator (MI-ACE). In this paper, a number of new initialization techniques for MI-ACE are proposed and evaluated using their respective performance and speed. The cross validated learned signatures, as well as learned background statistics, are used with Adaptive Cosine Estimator (ACE) to generate confidence maps, which are clustered into alarms. Alarms are scored against a ground truth and the initialization approaches are compared.
Deep Random Based Key Exchange Protocol Resisting Unlimited MITM

We present a protocol enabling two legitimate partners sharing an initial secret to mutually authenticate and to exchange an encryption session key. The opponent is an active Man In The Middle (MITM) with unlimited computing and storage capacities. The resistance to such an unlimited MITM is obtained through the combined use of Deep Random secrecy, introduced in former work and proven as unconditionally secure against passive opponent for key exchange, and universal hash techniques. We prove the resistance to MITM interception attacks, and show that (i) upon successful completion, the protocol leaks as few residual information as desired about the current value of the shared secret to the opponent, and (ii) that any interception attempt results into unsuccessful completion and is detectable by the legitimate partners. We also discuss implementation techniques.
Automatic Risk Detection System for Farmer’s Health Monitoring Based on Behavior of Pesticide Use

Abstract The Indonesian farmers usually use the agrochemical technology (i.e. pesticide) to prevent or control not only pests but also weeds. Unfortunately, this technique is often applied without adhering to the Occupational Health and Safety (OHS) standards, so that the risks to the pesticide exposure cannot be avoided. Nowadays, the stakeholder has the difficulties for detecting the risks. The current detection system is still performed manually by involving the related experts to assess the farmers’ behavior of pesticide use. This process often spends more times, so that it is impacted on late awareness of the pesticide exposure risk. Due to this, this paper proposed the automatic early detection (ArDeFarm) system with applying Certainty Factor method. This system uses the certainty values acquired from the related experts in order to generate the percentage of the pesticide exposure risk based on the farmers’ behavior in the pesticide mixing, loading and application. It is tested by using farmers’ behavior data collected from 100 orange farmers in Karo District. Based on the experiment, it is able to identify the pesticide exposure risk faced on farmers in the form of the risk percentage automatically, without relying on the experts. Besides, the result shows that it has been appropriate to the expert assessment.
iPads in the Classroom

Handheld digital devices, especially iPads, have become increasingly popular in educational institutions surrounded by debates between advocates and skeptics. This chapter examines the perceptions of middle school teachers on the use of iPads in the classroom. A review of the existing literature on the digital devices and on iPads use in the classroom was conducted. The participants, 53 teachers, responded to a Likert scale type online survey asking them questions about how they felt about the 1:1 iPad initiative at their school. Data analysis included open and axial coding for identification of themes and patterns. The findings showed mixed findings, although the majority of teachers believed that the iPads played a significant role in the teaching-learning process to engage students in the classroom. Some participants, however, responded with concerns that iPads caused student distraction and allowed off-task behaviors in the classroom. The findings also suggest that teachers need targeted professional development training on pedagogical and practical use of iPad to be able to successfully integrate the iPad into their practice. Implications for educators, app designers, and for future research are discussed.