Reviewerly/exHarmony huggingface dataset:
Turning Heads: Designing Engaging Immersive Video Experiences to Support People with Intellectual Disability when Learning Everyday Living Skills

As head mounted displays and 360° video cameras are becoming affordable, they offer opportunities to personalise immersive learning experiences to local contexts and individuals. In this paper, we present lessons learnt from a participatory design process focused on understanding engagement and preferences of users with intellectual disability viewing 360° videos. Over 4 iterations involving re-designs informed by interviews and observations of two to four participants with intellectual disability, we have established that: participants are more comfortable with using the technology if they are first introduced to a familiar scene before seeing anything new, they prefer to be 'accompanied' by an in-video facilitator, and participants engage more with the immersive visual environment when prompted to look around from within the video. We have also established a number of guidelines for filming with a 360° camera with regards to movement and viewpoint.
Augmented Reality in Teaching Descriptive Geometry, Engineering and Computer Graphics – Systematic Review and Results of the Russian Teachers’ Experience

The relevance and feasibility of this study are determined by the absence of serious, scientific research, as well as teaching materials, when it comes to the use of Augmented Reality (AR) in teaching students and future teachers Descriptive Geometry, Engineering and Computer Graphics (DGECG). The purpose of the study is to examine the current state of knowledge and practice of existing courses, which use the AR concept; to conduct a pedagogical experiment by teaching students how to create an information model of a building structure using the AR concept; to study the impact of the AR technology on students, lecturers, on the quality of students’ design works and project presentation. The research methods used were a set of various, complementing each other methods, which can be divided into two groups: 1) theoretical: analysis of the teachers’ and psychologists’ works on the point of the research, analysis of methodological and educational literature; empirical: observation, statement, pedagogical experiment. The authors synthesized qualitative and quantitative AR research in the field of education. A team of students from Saint-Petersburg Mining University, Kazan (Volga region) Federal University and Financial University under the Government of the Russian Federation solved a design problem using AR and created an informational 3D-model of the structure. Existing methods of teaching students were supplemented and updated by the method of graphical presentation of the results, with due regard for AR-technologies. It has been found that at the present moment, the concept of AR has gained popularity not only among designers and planners, but also among schoolteachers, as well as among teachers at engineering universities. The absence of scientifically substantiated and proven programs and training materials for training students of DGECG using AR has also been confirmed. The necessity of further scientific research in the field of AR for DGECG has been substantiated. The article materials could prove to be useful for lecturers, schoolteachers and parents.
SCARY DARK SIDE OF ARTIFICIAL INTELLIGENCE: A PERILOUS CONTRIVANCE TO MANKIND

Purpose of Study: The purpose of the study is to investigate the dark side of artificial intelligence followed by the question of whether AI is programmed to do something destructive or AI is programmed to do something beneficial?&#x0D; Methodology: A study of different biased Super AI is carried out to find the dark side of AI. In this paper SRL (system review of literature approach methodology is used and the data is collected from the different projects of MIT’s media lab named “Norman AI”, “Shelley” and AI-generated algorithm COMPAS.&#x0D; Main Finding: The study carried out the result if AI is trained in a biased way it will create havoc to mankind.&#x0D; Implications/Applications: The article can help in developing super-AIs which can benefit the society in a controlled way without having any negative aspects.&#x0D; Novelty/originality of the study: Our findings ensure that biased AI has a negative impact on society.
BHIN2vec

The goal of network embedding is to transform nodes in a network to a low-dimensional embedding vectors. Recently, heterogeneous network has shown to be effective in representing diverse information in data. However, heterogeneous network embedding suffers from the imbalance issue, i.e. the size of relation types (or the number of edges in the network regarding the type) is imbalanced. In this paper, we devise a new heterogeneous network embedding method, called BHIN2vec, which considers the balance among all relation types in a network. We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network. After splitting the skip-gram loss into multiple losses corresponding to different tasks, we propose a novel random-walk strategy to focus on the tasks with high loss values by considering the relative training ratio. Unlike previous random walk strategies, our proposed random-walk strategy generates training samples according to the relative training ratio among different tasks, which results in a balanced training for the node embedding. Our extensive experiments on node classification and recommendation demonstrate the superiority of BHIN2vec compared to the state-of-the-art methods. Also, based on the relative training ratio, we analyze how much each relation type is represented in the embedding space.
MrMep: Joint Extraction of Multiple Relations and Multiple Entity Pairs Based on Triplet Attention

This paper focuses on how to extract multiple relational facts from unstructured text.Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs.However, these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs.In this work, we propose a novel architecture that augments the encoder and decoder in two elegant ways.First, we apply a binary CNN classifier for each relation, which identifies all possible relations maintained in the text, while retaining the target relation representation to aid entity pair recognition.Second, we perform a multihead attention over the text and a triplet attention with the target relation interacting with every token of the text to precisely produce all possible entity pairs in a sequential manner.Experiments on three benchmark datasets show that our proposed method successfully addresses the multiple relations and multiple entity pairs even with complex overlapping and significantly outperforms the state-of-theart methods.All source code and documentations are available at https://github.com/chenjiayu1502/MrMep.
Target Tracking Algorithm for System With Gaussian/Non-Gaussian Multiplicative Noise

The paper considers target tracking system with multiplicative noise. Gaussian white, colored and non-Gaussian multiplicative noise are taken into account. We present optimal filtering algorithms to settle tracking system contaminated by Gaussian white and colored multiplicative noise and a suboptimal filtering algorithm to tackle the non-Gaussian case. The proposed optimal filtering algorithms are derived by using projection theorem and the suboptimal filtering algorithm is deduced by applying variational Bayesian inference. Simulation results verify the effectiveness of these presented algorithms.
Multistage fuzzy comprehensive evaluation of landslide hazards based on a cloud model

To accurately study the risk assessment of landslide disasters, firstly, the environmental conditions of induced landslide disasters are regarded as a fuzzy system, and the landslide risk factors in the multi-level analysis system are constructed to build a multi-level fuzzy evaluation index system. Then, the cloud model theory is introduced to improve the importance scale and membership degree involved in the evaluation process, and the multi-level fuzzy comprehensive evaluation method of landslide risk improved by a cloud model is proposed. Thus, a multi-level fuzzy evaluation cloud model for evaluating landslide risk is established. Finally, using the improved cloud model method, a multistage fuzzy comprehensive evaluation of landslide risk is conducted for the K112+210~K112 +630 section of the Long Chuan to Huaiji Highway Project in Guangdong Province. The results show that the improved cloud model can solve the problem of uncertainty in the process of landslide preparation and occurrence, greatly improve the effectiveness of landslide evaluation results, and provide an effective reference for landslide disaster prevention.
Neptune

Distributed dataflow systems allow users to express a wide range of computations, including batch, streaming, and machine learning. A recent trend is to unify different computation types as part of a single stream/batch application that combines latency-sensitive ("stream") and latency-tolerant ("batch") jobs. This sharing of state and logic across jobs simplifies application development. Examples include machine learning applications that perform batch training and low-latency inference, and data analytics applications that include batch data transformations and low-latency querying. Existing execution engines, however, were not designed for unified stream/batch applications. As we show, they fail to schedule and execute them efficiently while respecting their diverse requirements.
Generating Diverse Story Continuations with Controllable Semantics

We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs.We focus on the setting of generating the next sentence of a story given its context.As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters.Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics.We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system.
SOSD: A Benchmark for Learned Indexes

A groundswell of recent work has focused on improving data management systems with learned components. Specifically, work on learned index structures has proposed replacing traditional index structures, such as B-trees, with learned models. Given the decades of research committed to improving index structures, there is significant skepticism about whether learned indexes actually outperform state-of-the-art implementations of traditional structures on real-world data. To answer this question, we propose a new benchmarking framework that comes with a variety of real-world datasets and baseline implementations to compare against. We also show preliminary results for selected index structures, and find that learned models indeed often outperform state-of-the-art implementations, and are therefore a promising direction for future research.
Wearable Motion Attitude Detection and Data Analysis Based on Internet of Things

With the development of the Internet of Things technology and the continuous improvement of manufacturing processes, smart devices continue to evolve.In the existing research on human motion posture detection, there are relatively few portable wearable devices.Therefore, research on human motion posture detection based on smart wearable devices will be an important direction for future development.In this paper, a smart phone with a built-in triaxial acceleration sensor is used as a data acquisition facility to simulate the wearable device's experiment on the daily movement of the human body.The three-dimensional acceleration data during the motion is collected, and the related methods are proposed to detect the human body motion posture, and further analyze the transition between various postures, and detect the special fall posture.Starting from the two aspects of time domain and frequency domain, the analysis algorithm of human motion attitude detection is expounded.Using the time domain algorithm to detect the current motion posture of the human body, the current motion posture of the human body can be quickly detected.However, if we simply use time-domain-based detection methods, it is easy to cause errors in attitude detection.In order to improve this detection error, we combine the frequency domain analysis method, use the acceleration modulus algorithm and perform FFT (Fast Fourier Transformation) to analyze the frequency domain characteristics of the signal to distinguish the human body's motion posture and improve the basic daily posture detection of the human body.The results show that the proposed method can eliminate large noise interference and reduce the missed rate and false positive rate of human attitude detection. INDEX TERMS Motion attitude detection,
Communication trade-offs for Local-SGD with large step size

Synchronous mini-batch SGD is state-of-the-art for large-scale distributed machine learning. However, in practice, its convergence is bottlenecked by slow communication rounds between worker nodes. A natural solution to reduce communication is to use the \emph{``local-SGD''} model in which the workers train their model independently and synchronize every once in a while. This algorithm improves the computation-communication trade-off but its convergence is not understood very well. We propose a non-asymptotic error analysis, which enables comparison to \emph{one-shot averaging} i.e., a single communication round among independent workers, and \emph{mini-batch averaging} i.e., communicating at every step. We also provide adaptive lower bounds on the communication frequency for large step-sizes ($ t^{-\alpha} $, $ \alpha\in (1/2 , 1 ) $) and show that \emph{Local-SGD} reduces communication by a factor of $O\Big(\frac{\sqrt{T}}{P^{3/2}}\Big)$, with $T$ the total number of gradients and $P$ machines.
Implicit Life Event Discovery From Call Transcripts Using Temporal Input Transformation Network

Customer-agent conversations (i.e. call transcripts) are invaluable source for companies as they convey direct information from their customers implicit and explicit behaviour. Identifying customer-related events is an important task in customer services which is possible from the call transcripts. However, call centers produces a vast amount of transcripts which makes the manual or semi-manual processing of such raw datasets quite challenging. Furthermore, customer-agent call transcripts tend not to explicitly denote events that might be beneficial to customer services. Albeit being highly researched across multiple domains in the literature, event detection, especially implicit life event detection have not been well examined from call transcripts due to a lack of proper large-scale dataset. In this research, we propose a novel deep learning approach based on latent topic modeling and deep recurrent neural networks with memory units to automatically detect implicit events from a customer's history of call transcripts. These implicit events are detected prior to the report date of that event thereby not containing any explicit topic/feature. We provide a case study on a real-life, large-scale data of more than 800K call transcripts from a large financial services company in the U.S. to examine the practical features and challenges of this problem. The evaluation results demonstrate the potential applicability of our implicit life event detection as it achieves a macro-recall score of 53 (macro-f1 of 47.5) on a highly imbalanced test set, negative samples are 95% of the data. Our model beats the the state-of-the-art text classification benchmarks by macro-f1 score of 5.6 and macro-recall of 8.8 on average, and performs better than the ensemble of all single-document and sequential classification benchmarks albeit being significantly less complex. The comparison results show the importance as well as our model's capability of capturing the mutual information of a sequence of call transcripts in detecting the implicit life events.
Impact of photon addition and subtraction on nonclassical and phase properties of a displaced Fock state

Various nonclassical and quantum phase properties of photon added then subtracted displaced Fock state have been examined systematically and rigorously. Higher-order moments of the relevant bosonic operators are computed to test the nonclassicality of the state of interest, which reduces to various quantum states (having applications in quantum optics, metrology and information processing) in different limits ranging from the coherent (classical) state to the Fock (most nonclassical) states. The nonclassical features are discussed using Klyshko's, Vogel's, and Agarwal-Tara's criteria as well as the criteria of lower- and higher-order antibunching, sub-Poissonian photon statistics and squeezing. In addition, phase distribution function and quantum phase fluctuation have been studied. These properties are examined for various combinations of number of photon addition and/or subtraction and Fock parameter. The examination has revealed that photon addition generally improves nonclassicality, and this advantage enhances for the large (small) values of displacement parameter using photon subtraction (Fock parameter). The higher-order sub-Poissonian photon statistics is only observed for the odd orders. In general, higher-order nonclassicality criteria are found to detect nonclassicality even in the cases when corresponding lower-order criteria failed to do so. Photon subtraction is observed to induce squeezing, but only large number of photon addition can be used to probe squeezing for large values of displacement parameter. Further, photon subtraction is found to alter the phase properties more than photon addition, while Fock parameter has an opposite effect of the photon addition/subtraction. Finally, nonclassicality and non-Gaussianity is also established using $Q$ function.
El Aprendizaje Basado en Proyectos (ABP) en la formación metodológica del profesorado del Grado de Educación Primaria.

The objective of this research is to take knowledge the opinion of the students of the Degree in Primary Education about the teaching methods they acquire during their university education. The followed methodology has allowed us to make a descriptive study of the student’s conceptions based on the reading of a text about Project Based Learning (PBL) and four qualitative answers, as well as a quantitative questionnaire to analyze the methodologies and strategies of work they think would be more appropriate in their future teaching practice. Regarding the results of the sample (N = 162 students), they point out that undergraduates do not know the didactic models to be used in their classes and the acquisition of tools for teaching Social Sciences. As conclusion of the study we emphasize that the learning of the students of the Degree of Primary Education must be professionalized, mainly, from their university education. Likewise, this research work is integrated into the innovation and educational research project of the University of Córdoba entitled «Building History from Educational Competencies in Primary Education» (Cod. 2017-1-4012), where the undersigned is a coordinator.
VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge

The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition dataset from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and workshop held at Interspeech 2019 in Graz, Austria. This paper outlines the challenge and provides its baselines, results and discussions.
ENERGY USAGE, INTERNET USAGE AND HUMAN DEVELOPMENT IN SELECTED WESTERN AFRICAN COUNTRIES

The examination of energy usage and ICT usage in terms of their role in the improvement of human development was the study's objective. A panel analysis is carried out on World Bank data (2000-2014) from selected Western African countries, with notable energy usage within the region. The study utilizes generalized least squares (GLS), the fixed effect (FEM) and the random effect (REM) econometric estimation techniques to determine the degree of relationship and impact existing between the variables of interest. The results indicate that internet usage and energy usage affect human development in the selected West African countries. The policy implication from the findings posit that it is expedient that the government and private sector, exert financial and non-financial contributions to ensure that energy and ICT facilities are readily available for use. This would improve the level of the two major human development components (health and education).Keywords: Energy usage, ICT usage, Human development, Western Africa.JEL Classification: Q4DOI: https://doi.org/10.32479/ijeep.7611
QuTiBench

Neural Networks have become one of the most successful universal machine-learning algorithms. They play a key role in enabling machine vision and speech recognition and are increasingly adopted in other application domains. Their computational complexity is enormous and comes along with equally challenging memory requirements in regards to capacity and access bandwidth, which limits deployment in particular within energy constrained, embedded environments. To address these implementation challenges, a broad spectrum of new customized and heterogeneous hardware architectures have emerged, often accompanied with co-designed algorithms to extract maximum benefit out of the hardware. Furthermore, numerous optimization techniques are being explored for neural networks to reduce compute and memory requirements while maintaining accuracy. This results in an abundance of algorithmic and architectural choices, some of which fit specific use cases better than others. For system-level designers, there is currently no good way to compare the variety of hardware, algorithm, and optimization options. While there are many benchmarking efforts in this field, they cover only subsections of the embedded design space. None of the existing benchmarks support essential algorithmic optimizations such as quantization, an important technique to stay on chip, or specialized heterogeneous hardware architectures. We propose a novel benchmark suite, QuTiBench , that addresses this need. QuTiBench is a novel multi-tiered benchmarking methodology ( Ti ) that supports algorithmic optimizations such as quantization ( Qu ) and helps system developers understand the benefits and limitations of these novel compute architectures in regard to specific neural networks and will help drive future innovation. We invite the community to contribute to QuTiBench to support the full spectrum of choices in implementing machine-learning systems.