Reviewerly/exHarmony huggingface dataset:
UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

Abstract. Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.
The Effect of Teacher Feedback on the Simple Past Tense Acquisition in Senior High School Students' English Writing

Teacher feedback is an essential part which can not be overlooked in English writing teaching. Whether teacherfeedback is effective and which type of feedback is more effective in improving learners’ writing proficiency haveattracted more and more researchers’ attention. Direct feedback is the feedback that teachers correct students’ errorsdirectly under the form or the right using habit of target language. Indirect feedback is the feedback that teachersonly mark out or point out students’ errors but not correct errors. This study explores the effects of two differentkinds of feedback (direct feedback and indirect feedback) on the acquisition of English simple past tense in highschool students’ writing. And it attempts to answer the following three questions: 1) Do teachers’ direct and indirectfeedback in English writing influence senior high school students’ acquisition of simple past tense? 2) If so, whichkind of feedback (direct or indirect feedback) has the greater influence on improving students’ acquisition of simplepast tense in English writing? 3) What perceptions do students have towards teachers’ direct and indirect feedback inEnglish writing?The result showed that the two kinds of feedback have an influence on the acquisition of the target structure,however, there is no significant difference between direct and indirect feedback group, which states that indirectfeedback class was not better than direct feedback class.
OPT+: A Monotonic Alternativeto OPTIONAL in SPARQL

Due to the OPTIONAL operator, the core fragment of the SPARQL query language is non-monotonic. That is, some solutions of a query result can be returned to the user only after having consulted all ...
A COMPARATIVE ANALYSIS OF DIFFERENT FEATURE SET ON THE PERFORMANCE OF DIFFERENT ALGORITHMS IN PHISHING WEBSITE DETECTION

Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis.Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information.Many methods have been proposed in past for phishing detection.But the quest for better solution is still on.This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset.
Ego-Pose Estimation and Forecasting as Real-Time PD Control

We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos. The method learns directly from unsegmented egocentric videos and motion capture data consisting of various complex human motions (e.g., crouching, hopping, bending, and motion transitions). We propose a video-conditioned recurrent control technique to forecast physically-valid and stable future motions of arbitrary length. We also introduce a value function based fail-safe mechanism which enables our method to run as a single pass algorithm over the video data. Experiments with both controlled and in-the-wild data show that our approach outperforms previous art in both quantitative metrics and visual quality of the motions, and is also robust enough to transfer directly to real-world scenarios. Additionally, our time analysis shows that the combined use of our pose estimation and forecasting can run at 30 FPS, making it suitable for real-time applications.
Reading Motivation vis-s-vis Academic Performance

Abundant literature can be sourced out from different authorities about reading motivation, yet little work can be traced between this component and the academic achievement.Hypothesizing that motivation has a significant relationship to success, this study attempted to investigate such a particular connection.The researchers used a modified version of the Motivation for Reading Questionnaire (MRQ) administered to 82 Grade III pupils, of which the first half were required to answer the questions for pilot testing.For the correlation test, the raw scores of quarterly academic grades of pupils were utilized.Statistical results identified that reading motivation was not significantly related to the academic achievement of the pupils.One that is carried out is the development of learners as readers.In this manner, the researchers suggested that teachers should continue nurturing of enhancing the reading skills of pupils by implementing different strategies to improve the academic performance of the learners.
Data Protection and Tech Startups: The Need for Attention, Support, and Scrutiny

While discussions of data protection have focused on the larger, more established organisations, startups also warrant attention. This is particularly so for tech startups, who are often innovating at the ‘cutting-edge’ – pushing the boundaries of technologies that typically lack established data protection best-practices. Initial decisions taken by startups could well have long-term impacts, and their actions may inform (for better or for worse) how these technologies are implemented, deployed and perceived for years to come. Ensuring that the innovations and practices of tech startups are sound, appropriate and acceptable should therefore be a high priority.This paper explores the attitudes and preparedness of tech startups to issues of data protection. We interviewed a series of UK-based emerging tech startups as the EU’s General Data Protection Regulation (GDPR) came into effect, which revealed areas in which there is a disconnect between the approaches of the startups and the nature and requirements of the GDPR. We discuss the misconceptions and associated risks facing innovative tech startups, and offer a number of considerations for the firms and supervisory authorities alike. In light of our discussions, and given what is at stake, we argue that more needs to be done in order to help ensure that emerging technologies (and indeed, the practices of the companies that operate them) better align with the regulatory obligations. We conclude that tech startups warrant increased attention, support, and scrutiny in order to raise the standard of data protection for the benefit of us all.
Tracking Full-Body Motion of Multiple Fish with Midline Subspace Constrained Multicue Optimization

Capturing the body motion of fish has been gaining considerable attention from scientists of various fields. In this paper, we propose a method which is able to track the full-body motion of multiple fish with frequent interactions. We firstly propose to model the midline subspace of a fish body which gives a compact low-dimensional representation of the complex shape and motion. Then we propose a particle swarm-based optimization framework whose objective function takes into account multiple sources of information. The proposed multicue objective function is able to describe the details of fish appearance and is also effective through mutual occlusions. Excessive experimental results have demonstrated the effectiveness and robustness of the proposed method.
Accelerating Mini-batch SARAH by Step Size Rules

StochAstic Recursive grAdient algoritHm (SARAH), originally proposed for convex optimization and also proven to be effective for general nonconvex optimization, has received great attention due to its simple recursive framework for updating stochastic gradient estimates. The performance of SARAH significantly depends on the choice of step size sequence. However, SARAH and its variants often employ a best-tuned step size by mentor, which is time consuming in practice. Motivated by this gap, we proposed a variant of the Barzilai-Borwein (BB) method, referred to as the Random Barzilai-Borwein (RBB) method, to calculate step size for SARAH in the mini-batch setting, thereby leading to a new SARAH method: MB-SARAH-RBB. We prove that MB-SARAH-RBB converges linearly in expectation for strongly convex objective functions. We analyze the complexity of MB-SARAH-RBB and show that it is better than the original method. Numerical experiments on standard data sets indicate that MB-SARAH-RBB outperforms or matches state-of-the-art algorithms.
Acoustic Events Processing with Deep Neural Network

Safety is one of the society requirement, what we need for cheerful live. The principal purpose is to recognize potentially dangerous acoustic events (gun shooting and glass breaking). This document compares a Neural Network (NN) based on the detection system and a hidden Markov model based on the acoustic event detector. For both methods, the same database was used. The database consisted of shots, glass breaks and background noise. Proposed deep neural network processes an acoustic signal through two hidden layers. The whole process may divide into three parts. Training, testing and evaluation part. As the main resulting parameter accuracy has been chosen. This computation process uses a confusion matrix for reliable detection. Accuracy is compared with previous research in this area, as well.
A Population-Based Stochastic Coordinate Descent Method

This paper addresses the problem of solving a bound constrained global optimization problem by a population-based stochastic coordinate descent method. To improve efficiency, a small subpopulation of points is randomly selected from the original population, at each iteration. The coordinate descent directions are based on the gradient computed at a special point of the subpopulation. This point could be the best point, the center point or the point with highest score. Preliminary numerical experiments are carried out to compare the performance of the tested variants. Based on the results obtained with the selected problems, we may conclude that the variants based on the point with highest score are more robust and the variants based on the best point less robust, although they win on efficiency but only for the simpler and easy to solve problems.
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization

Modern deep neural networks are typically highly overparameterized. Pruning techniques are able to remove a significant fraction of network parameters with little loss in accuracy. Recently, techniques based on dynamic reallocation of non-zero parameters have emerged, allowing direct training of sparse networks without having to pre-train a large dense model. Here we present a novel dynamic sparse reparameterization method that addresses the limitations of previous techniques such as high computational cost and the need for manual configuration of the number of free parameters allocated to each layer. We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model. We further investigated the mechanisms underlying the superior generalization performance of the resultant sparse networks. We found that neither the structure, nor the initialization of the non-zero parameters were sufficient to explain the superior performance. Rather, effective learning crucially depended on the continuous exploration of the sparse network structure space during training. Our work suggests that exploring structural degrees of freedom during training is more effective than adding extra parameters to the network.
Review on the Role of Social Media for Dengue Prevention and Monitoring

With the emerging of social media as a platform of interaction, users are now connected with all around the world. This connection enhances dissemination of information from users. Examples of social platforms which are highly used among users are Facebook and Twitter. Recently WHO stated that dengue is currently rising among the countries in the World. In Malaysia, the number of dengue cases are currently rising at an alarming stage where it exceeds the number of last year’s dengue cases. Although traditional methods of distributing surveys and conducting awareness about dengue has been done, positive responses from those efforts are little. In this paper, we have surveyed the feasibility of engaging social media like Twitter for monitoring and preventing dengue. Tweets related to dengue will be analyzed using emotion detection techniques, aiming to identify tweets that generate positive emotion, which in return helpful for dengue monitoring and prevention. Since most of tweets are written in Malay language by Malaysians, a proper dictionary is needed to analyze the emotion of users. The outcome from this study will be beneficial for dengue prevention and monitoring by detecting how Malaysians are actually tweeting and reacting to dengue cases in Malaysia.
Bootstrapping an End-to-End Natural Language Interface for Databases

The ability to extract insights from data is critical for decision making. Intuitive natural language interfaces to databases provide non-technical users with an effective way to formulate complex questions and information needs efficiently and effectively. A recent trend in the area of Natural Language Interfaces for Databases (NLIDBs) has been the use of neural machine translation models to synthesize executable Structured Query Language (SQL) queries from natural language utterances. The main bottleneck in this type of approach is the acquisition of examples for training the model. Recent work has assumed access to a rich manually-curated training set for a given target database. However, this assumption ignores the large manual overhead required to curate the training set for any new database. As a result, NLIDB systems that can simply 'plug in' to any new database and perform effectively for naive users have yet to make their way into commercial products. Here we present DBPal, an end-to-end NLIDB framework in which a neural translation model is trained for any new database schema with minimal manual overhead. In addition to being the first off-the-shelf, neural machine translationbased system of its kind, the contributions of our project are 1) its use of a synthetic training set generation pipeline used to bootstrap a translation model without requiring manually curated data, and 2) its use of state-of-the-art multi-task and cross-domain learning techniques that increases the robustness of the translation model towards unseen linguistic phenomena in new domains. In experiments we show that our system can achieve competitive performance on the recently released benchmarks for nl-to-sql translation. Through ablation experiments we show the benefit of using cross-domain learning techniques on the performance of the system. In a user study we show that DBPal outperforms a well-known rule-based NLIDB and performs comparably to an approach using a similar neural model that relies on manually curated data.
Blame it on me

In this paper, we develop a formalisation of the main ideas of the work of Van de Poel on responsibility. Using the basic concepts through which the meanings of responsibility are defined, we construct a logic which enables to express sentences like “individual i is accountable for φ”, “individual i is blameworthy for φ” and “individual i has the obligation to see to it that φ”. This formalization clarifies the definitions of responsibility given by Van de Poel and highlights their differences and similarities. It also helps to assess the consistency of the formalisation of responsibility, not only by showing that definitions are not inconsistent, but also by providing a formal demonstration of the relation between three main meanings of responsibility (accountability, blameworthiness, and obligation). The formal account can be used to derive new properties of the concepts. With the help of the formalisation, we detect the occurrence of the problem of many hands (PMH) by defining a logical framework for reasoning about collective and individual responsibility. This logic extends the Coalition Epistemic Dynamic Logic (CEDL) by adding a notion of group knowledge (and generalize the definitions of individual responsibility to groups of agents), agent ability and knowing how to its semantics.
Raise to Speak

The two most common ways to activate intelligent voice assistants (IVAs) are button presses and trigger phrases. This paper describes a new way to invoke IVAs on smartwatches: simply raise your hand and speak naturally. To achieve this experience, we designed an accurate, low-power detector that works on a wide range of environments and activity scenarios with minimal impact to battery life, memory footprint, and processor utilization. The raise to speak (RTS) detector consists of four main compo- nents: an on-device gesture convolutional neural network (CNN) that uses accelerometer data to detect specific poses; an on-device speech CNN to detect proximal human speech; a policy model to combine signals from the motion and speech detector; and an off-device false trigger mitigation (FTM) system to reduce unin- tentional invocations trigged by the on-device detector. Majority of the components of the detector run on-device to preserve user privacy. The RTS detector was released in watchOS 5.0 and is running on millions of devices worldwide.
State Examination as a Way to Assess Postgraduate Students’ Teaching Competencies

The article discusses the system for developing and assessing teaching competencies of PhD program graduates. The experience of Russian universities in conducting the final state examination in postgraduate schools is considered. The authors analyze the options for the distribution of the competencies being assessed between the two parts of the final state certification of postgraduate students: the state examination and the defense of their research report. The system developed and implemented at Lobachevsky University for postgraduate students’ step-by-step training for a teaching career at a higher education institution is discussed in detail, including: 1) theoretical training in the framework of the discipline “Psychology and Pedagogy of Higher Education”, 2) teaching practice, 3) preparation of a study manual based on the results of the postgraduate student’s dissertation research, and its defense at the final state examination. Assessments of the practice-oriented format of the state examination by postgraduate students are presented.
Closure Ordinals of the Two-Way Modal $$\mu $$-Calculus

The closure ordinal of a $$\mu $$-calculus formula $$\varphi (x)$$ is the least ordinal $$\alpha $$, if it exists, such that, in any model, the least fixed point of $$\varphi (x)$$ can be computed in at most $$\alpha $$ many steps, by iteration of the meaning function associated with $$\varphi (x)$$, starting from the empty set. In this paper we focus on closure ordinals of the two-way modal $$\mu $$-calculus. Our main technical contribution is the construction of a two-way formula $$\varphi _n$$ with closure ordinal $$\omega ^n$$ for an arbitrary $$n\in \omega $$. Building on this construction, as our main result we define a two-way formula $$\varphi _\alpha $$ with closure ordinal $$\alpha $$ for an arbitrary $$\alpha <\omega ^\omega $$.
A New Floor Region Estimation Algorithm Based on Deep Learning Networks with Improved Fuzzy Integrals for UGV Robots

In this article, a new floor estimation algorithm based on multiple deep learning image segmentation and conventional texture segmentations using fuzzy integrals theory is proposed. The proposed algorithm combines an FCN-8s, a DeepLabv2, and Canny Edge Detection with superpixel segmentation, two deep learning networks, and one texture classifier to recognize a walkable floor area for UGV robots. The authors intersect three results with an Improved Fuzzy Integrals (IFI) method. The experimental results show that the combination algorithm accuracy can reach up to 97.63% on average without any other sensor assistance. In order to achieve real-time performance, the proposed algorithm has been implemented on an NVIDIA Jetson TX2 embedded platform with ROS compatible environment supporting.
A prototype mobile application for the Athens Numismatic Museum

The continuous development of mobile platforms provides the opportunity to integrate and improve existing applications or to introduce new features to make life better. The purpose of this paper is to investigate the use of mobile platforms in civilization (museums) and present the design and the implementation of a mobile application that satisfies various design criteria. Through this application the visitor can navigate and tour virtually in the museum through a smartphone. In addition, features have been included in the application that make it easier for a user to visit the museum. First, we present the operating parameters and the aesthetic presentation of the application, which delimits usability and ease of access through the interfaces of a smartphone. Then we highlight the cloud features that were exploited in the application. Then, an extended evaluation of the mobile application is presented, that proves its high applicability and user acceptability.
Hechth–Nielsen theorem for a modified neural network with diagonal synaptic connections

The work suggests a modified three-layer neural network with architecture that has only the diagonal synaptic connections between neurons; as a result we obtain the transformed Hecht-Nielsen theorem.This architecture of a three-layer neural network (m = 2n + 1 is the number of neurons in the hidden layer of the neural network, n is the number of input signals) allows us to approximate the function of n variables, with the given accuracy ε > 0, using one aggregation operation, whereas a three-layer neural network that has both diagonal and non-diagonal synaptic connections between neurons approximates the function of n variables by means of two aggregation operations.In addition, the matrix diagonalization of the synaptic connections leads to a decrease of computing resources and reduces the time of adjustment of the weight coefficients during the training of a neural network.
Towards Near-imperceptible Steganographic Text

We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text. We formally analyze them and empirically evaluate these assumptions. Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.