Reviewerly/exHarmony huggingface dataset:
From partial neural graph-based LTAG parsing towards full parsing

In this paper, we extend recent approaches to Lexicalized Tree Adjoining Grammar (LTAG) parsing that combine supertagging with dependency parsing. In other words, we assign supertags (= unanchored elementary trees) to lexical items and we compute substitution/adjunction arcs between them. Kasai et al. (2017, 2018) jointly predict these structures with a neural graph-based parser. Predicting 1-best supertags and dependency arcs (as in Kasai et al. (2017, 2018)) however leads only to partial parsing due to incompatibilities between elementary trees and derivation trees. We therefore extend the approach described in Kasai et al. (2017, 2018) to n-best supertags and k-best dependency arcs and combine it with a subsequent A*-parsing step that extends the TAG parser from Waszczuk (2017). We show that this architecture allows for efficient full TAG parsing while being sufficiently accurate. We test our architecture on an LTAG extracted from the French Treebank (FTB).
Forensic Facial Composites

For over four decades, witnesses to and victims of crime have created pictures of offenders they have seen at the scene of a crime. The resulting pictures, referred to as facial composites, are shown to law enforcement officers and members of the public with the aim that someone will name the person depicted and thereby help solve the crime. Considerable laboratory research and police field trials have investigated the effectiveness of facial composites, the result of which has allowed composite systems and forensic procedures to improve and evolve. This chapter will provide an overview of composite systems available to law enforcement—past, present and potential future. As will be seen, forensic systems have changed considerably over time. When tested using the ‘gold’ standard procedure that appropriately simulates real-world use of composites, it is evident that traditional approaches (based on selection of individual facial features) do not consistently produce faces that are identifiable to another person. Part of the reason for this unfortunate outcome is that the process used to create the face is at odds with the natural way that faces are recognised, as whole entities. However, more-recent advancements involve witnesses reflecting on the character of a target identity and selecting from arrays of whole faces (or whole-face regions); also, whole-face procedures have been developed to improve recognition of finished composites. Importantly, when such procedures are combined, composites can now be constructed that are identified with a very high level of success, a situation that seemed almost inconceivable even a decade ago.
Study on a security intelligence trading platform based on blockchain and IPFS

Security response centre (SRC) is an important solution for enterprises to ensuring their network security. The existing security response centres can be mainly divided into two types, the third-party vulnerability reporting platforms and xSRCs of each enterprise. Normally, hackers find and submit valuable information to a vulnerability reporting platform or xSRC. However, the hackers who submit vulnerabilities probably disagree with the assessment results of vulnerability level by enterprises or the third-party platform experts entrusted by enterprises, which may lead to some dangerous situations that can threat the enterprise’s network security. This paper proposes a security intelligence trading platform based on blockchain and IPFS (Inter Planetary File System), and applies it to a specific example. Due to the decentralization and immutability of blockchain technology and IPFS, it can make the vulnerability level assessment fair and just, which will protect the interests and privacy of both hackers and enterprises. The example proves that the proposed method is simple and feasible, and has theoretical and practical value to the exploration of security information transaction mechanism.
Effect of financial technology on cash holding in Nigeria

Purpose This paper aims to examine the effect of financial technology on cash holding in Nigeria. Design/methodology/approach The authors use Pesaran et al. ’s (2001) autoregressive distributed lag (ARDL) bounds test approach to cointegration to estimate the long-run relationship between four direct measures of financial technology (automated teller machine [ATM], Internet banking [IB], point of sale [POS] and mobile banking [MB]) and cash holding. Findings The authors find the presence of long-run negative relationship between cash holding and the four direct measures of financial technology. Practical implications Despite the negative effect of financial technology on cash holding, the descriptive results highlight increasing trajectory in cash holding. This suggests that structural factors such as ethical climate, literacy level, household characteristics, currency denomination structures, economic uncertainty and infrastructure deficit may account for the pervasive cash transactions in Nigeria and not necessarily the unwillingness of economic agents to use digital platform for financial transactions. Originality/value This study contributes to existing literature by augmenting the money demand function to accommodate direct measures of financial technology in examining the effectiveness of the policy on cash holding in Nigeria.
Detecting human emotions on Facebook comments

Human emotion detection plays a vital role in interpersonal relationships. From the early eras, automatic recognition of emotions has been an active research topic. Today, sharing emotions on social media is one of the most popular activities among internet users. However, when it comes to a specific domain like emotion detection in social media, it is still on a research-level. There are less number of applications have been developed to detect emotions online, using online comments and user comments. The aim of this research is to develop a system that identifies human emotions on Facebook comments. Among the different social media platforms, this research specifically focuses on Facebook comments written in the English language to narrow down the problem. The research is based on Semantic analysis, which comes under Natural Language Processing (NLP) and the system development consists of four major steps, including the extraction of Facebook comments via Graph API, preprocessing, classification and emotion detection. To classify the emotions, a classification model was created by using Naïve Bayes Algorithm. When it comes to marketing, emotions are what lead your onlookers to purchase. By using the detected emotions, marketers can promote their campaigns by changing online advertisements dynamically. The results obtained through testing the system show that it is capable of accurately identifying human emotions hidden in Facebook comments with an accuracy level of 80%, making it highly useful for marketing purposes.
Visual and Instrument Helicopter Approach Stability Analysis Using Data Fusion and Data Analytics

Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents in the U.S. occur during either the approach or landing phases of flight and that unstabilized approaches are a leading cause due to the associated increased potential for loss of control, loss of situational awareness, or controlled flight into terrain. In the present work, routine flight data records obtained as part of a voluntary helicopter flight data monitoring program are used to identify, analyze, and evaluate approach events in a variety of helicopter flights from different mission types, such as emergency medical services, flight training, and research. A semi-automated process is developed which enables detection of the approach phase, construction of a nominal approach path, analysis of approach stability, and knowledge discovery through data mining. The process was able to identify 3950 visual and 183 instrument approaches in a set of 3749 operational helicopter flight data records. Analysis of visual approach events suggests that typical approaches are higher and steeper than expected. Thus, the traditional definition of a stabilized approach may not align with typical operations. Additionally, atypical approaches can be classified as highly stabilized, marginally unstabilized, or unstabilized depending on their associated stability measures.
Arabic Dialect Identification Using BERT Fine-Tuning

In the last few years, deep learning has proved to be a very effective paradigm to discover patterns in large data sets. Unfortunately, deep learning training on small data sets is not the best option because most of the time traditional machine learning algorithms could get better scores. Now, we can train the neural network on a large data set then fine-tune on a smaller data set using the transfer learning technique. In this paper, we present our system for NADI shared Task: Country-level Dialect Identification, Our system is based on fine-tuning of BERT and it achieves 22.85 F1-score on Test Set and our rank is 5th out of 18 teams.
Fuzzy Logic Controller for Force Feedback Control of Quadcopter via Tether

Abstract Tethered drone systems can be used to perform long-endurance tasks such as area surveillance and relay stations for wireless communication. However, all the existing systems use tethers only for data and power transmission from a stationary point on the ground. This work presents a control strategy that enables a quadcopter to follow a moving tether anchor. A force feedback controller is implemented using Fuzzy Logic. Using force-based strategy provides effective compliance between the tether’s anchor and the drone. The drone can thus be controlled by mere physical movement/manipulation of tether. This enhances the safety of current tethered drone systems and simplifies the flying of drones. Fuzzy Logic provides an intuitive edge to the control of such systems and allows handling noise in force sensors. Extensive simulation results are presented in this paper showing the effectiveness of the proposed control scheme.
Deconstructing the Regularization of BatchNorm

Batch normalization (BatchNorm) has become a standard technique in deep learning. Its popularity is in no small part due to its often positive effect on generalization. Despite this success, the regularization effect of the technique is still poorly understood. This study aims to decompose BatchNorm into separate mechanisms that are much simpler. We identify three effects of BatchNorm and assess their impact directly with ablations and interventions. We find that preventing explosive growth at the final layer at initialization and during training explains most of BatchNorm's generalization boost. This regularization mechanism can lift accuracy by 2.9% for Resnet-50 on Imagenet without BatchNorm. We show it is linked to other methods like Dropout and recent initializations like Fixup. Surprisingly, this simple mechanism matches the improvement of 0.8% of the more complex Dropout regularization for the state-of-the-art Efficientnet-B8 model on Imagenet. This demonstrates the underrated effectiveness of simple regularizations and sheds light on directions to further improve generalization for deep nets.
YouTube as a Learning Tool to Improve Students’ Speaking Skills as Perceived by EFL Teachers in Secondary School

YouTube has been shown to be a rich source for teaching speaking in EFL contexts. However, the use of YouTube is developing speaking skills is still understudied in the Saudi EFL context. The full use and awardees of YouTube resources are not well articulated by many EFL teachers in Saudi Arabia. This study attempted to examine the perceptions of English instructors of using YouTube to improve the speaking skills of the students in the secondary stage in Saudi Arabia. The English instructors’ attitudes towards YouTube and the best ways to use YouTube in the class were examined. The descriptive and analytical approaches have been applied in order to design the research. For data collection, a questionnaire has been designed to addresses the research topic. It was distributed on a sample of English language instructors (30 EFL instructors) in the secondary stage in Riyadh city in Saudi Arabia. The findings of the study showed that English language instructors see that YouTube is a highly beneficial instructional tool in improving the speaking skills of the secondary school students. It was found that the instructors have positive attitudes towards YouTube. They expressed that YouTube lessens the students’ anxiety and allow the students to learn faster, retain attention for a long time and motivates the students to learn the language. On the other hand, the findings showed that YouTube can be used in different ways in the class such as asking the students to comment on a particular video or to give some details, discuss, pose questions and give answers. YouTube gives a real chance for the students to interact in the class and to speak freely and get improved in pronunciation. The study recommended using YouTube as an integral instructive tool in English language classes.
Layer-adaptive Sparsity for the Magnitude-based Pruning

Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on ``how to choose,'' the layerwise sparsities are mostly selected algorithm-by-algorithm, often resorting to handcrafted heuristics or an extensive hyperparameter search. To fill this gap, we propose a novel importance score for global pruning, coined layer-adaptive magnitude-based pruning (LAMP) score; the score is a rescaled version of weight magnitude that incorporates the model-level l2 distortion incurred by pruning, and does not require any hyperparameter tuning or heavy computation. Under various image classification setups, LAMP consistently outperforms popular existing schemes for layerwise sparsity selection. Furthermore, we observe that LAMP continues to outperform baselines even in weight-rewinding setups, while the connectivity-oriented layerwise sparsity (the strongest baseline overall) performs worse than a simple global magnitude-based pruning in this case.
Object based Human-Object Interaction (HOI) Recognition using Wrist-mounted Sensors

Detecting and recognizing objects with human interaction is very essential for wide-ranging applications, such as health monitoring, human behavior analysis, and access control. In the last few years, numerous approaches have been introduced for object detection and activity recognition using the image and video processing-based techniques. However, these approaches are exposed to privacy challenges and are computationally expensive. The ubiquitous sensing systems based on wearable inertial sensors, such as the accelerometer and gyroscope, provide an effective solution to these hurdles. Thus, in this research work, we present an approach for recognizing objects and human-object interactions based on wearable inertial sensors. In this aspect, we selected three objects with fourteen different interactions and recorded data from 15 different participants using sensors embedded in the commercially available wearable wristwatch (Metawear). In the first step, we extracted different time-domain features and performed object classification. Next, we undertook the object-dependent interaction recognition for classifying object interactions. For testing recognition results, we used three different classifiers (random forest (RF), k-nearest neighbors (KNN), and Naive Bayes (NB)), and achieved the best accuracy rate of 87%, 86.33%, 87.22% using RF classifier for laptop, mobile, and notebook, respectively.
Video Summarization Using Deep Neural Networks: A Survey

Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based analysis pipeline. Then, we suggest a taxonomy of the existing algorithms and provide a systematic review of the relevant literature that shows the evolution of the deep-learning-based video summarization technologies and leads to suggestions for future developments. We then report on protocols for the objective evaluation of video summarization algorithms and we compare the performance of several deep-learning-based approaches. Based on the outcomes of these comparisons, as well as some documented considerations about the suitability of evaluation protocols, we indicate potential future research directions.
Experience in creating technology for the teaching course “Psychology and Pedagogy”

We consider the reasons for technologization of the educational process at the university and a practical example of the creation of pedagogical technology within the framework of the teaching course “Psychology and Pedagogy”. The structure is presented and all stages of implementation of each of its components are described in detail. The interaction and continuity of technology components is shown, both in theoretical planning and when filling them with specific educational material and practical implementation. The implementation of the general concept at each subsequent stage is consistently described, which gives the technology integrity and helps to expediently fill the content and procedural parts. We also describe an example of using analysis of trends in education to update the content and procedural parts of technology. Specific examples of real and virtual space use for the implementation of technology, various software and hardware are given. The features of students transfer to subject-subject relations by providing them with an independent choice of tools for personal self-development, more focused on increasing educational motivation, are disclosed in detail. The role and functions of a teacher based on the principle of humanism declared in the concept are described. General results and conclusions on the practical implementation of this technology are presented.
Impact of dust events on the optimization of photovoltaic-wind hybrid system in desert

Reliable optimization of renewable energy systems has to be adapted to the installation site and its environmental factors. In dust event sources, the impact of dust on solar irradiance or on system’s production is not usually taken into consideration during optimization of photovoltaic (PV) or PV-wind hybrid systems. The first objective of this paper was to make observatory studies on wind velocity and lifting of Saharan dust that characterize Algerian desert which allowed us to extract a wind threshold for each event in our desert site. The second objective was to quantify this dust suspension and its negative impact, up to 47.46% on global solar irradiance (GSI) and 48.95% on photovoltaic yield effects were observed. The third and main objective of this paper was evaluating changes in optimized system when the dust impact was taken into account. To achieve that we adjusted GSI satellite data on HOMER software to dust presence, and then we run an optimization on HOMER with its GSI database and the new data, this data adjustment leads to a significant expansion of 13.70% on the size of PV generator, also 7.66% and 7.68% increase on the investment expenses and cost of energy respectively. Results obtained on this paper can be used to make PV-hybrid system’s technical and financial optimization in the main Algerian dust source trustworthy.
Optimization of Large-Scale Agent-Based Simulations Through Automated Abstraction and Simplification

Agent-based simulations of social media platforms often need to be run for many repetitions at large scale. Often, researchers must compromise between available computational resources (memory, run-time), the scale of the simulation, and the quality of its predictions.As a step to support this process, we present a systematic exploration of simplifications of agent simulations across a number of dimensions suitable for social media studies. Simplifications explored include sub-sampling, implementing agents representing teams or groups of users, simplifying agent behavior, and simplifying the environment.We also propose a tool that helps apply simplifications to a simulation model, and helps find simplifications that approximate the behavior of the full-scale simulation within computational resource limits.We present experiments in two social media domains, GitHub and Twitter, using data both to design agents and to test simulation predictions against ground truth. Sub-sampling agents often provides a simple and effective strategy in these domains, particularly in combination with simplifying agent behavior, yielding up to an order of magnitude improvement in run-time with little or no loss in predictive power. Moreover, some simplifications improve performance over the full-scale simulation by removing noise.We describe domain characteristics that may indicate the most effective simplification strategies and discuss heuristics for automatic exploration of simplifications.
A Framework to Preserve User Privacy for Machine Learning as a Service

Suffered from the contradiction between the limited capacity of local devices and large size of DNN models, a practical solution is transferring the heavy computational tasks from the local to the server side such as cloud. However, the untrusted server naturally requires all the user data to train neural networks and infer results, which causes the asset loss of the local and raises serious privacy concerns on user's sensitive information. To solve this problem in scenarios of machine learning as a service, we propose a general framework to balance the user privacy, model accuracy and training efficiency, simultaneously. Specifically, our representative subset selection algorithm takes the training value of data into account, selecting the most representative subset from the training data, in order to mitigate the loss of data assets, lower down the transmission overhead from the local to the server and lessen the training burden on the server at the same time. We also design a noisy representation transformation algorithm applying on the features extracted by neural networks to further perturb the data within the selected representative subset. Extensive experiments demonstrate that our framework can run locally with little sacrifice on the computation resource. It can not only protect private data before uploading, but also promote the training efficiency of servers.