Reviewerly/exHarmony huggingface dataset:
Behavior prediction for several seconds using human body model and non-contact sensor

Based on social background such as aging and diversification of individual's lives, we propose the concept of Biofied building. The Biofied building is a concept that provides a safe, secure and comfortable space for residents by using small robots. The small robots used in the Biofied building sense the human position and motion. However, in the current system it is difficult to prevent accidents such as falls. The robots are not designed not disturb residents. Therefore, in this research, in order to realize more natural space control of the robot, the purpose is to incorporate motion prediction in the system in addition to motion recognition. Many conventional motion prediction methods use RNN (Recurrent Neural Networks), which is considered to be difficult to implement due to the necessity of big data and the large calculation cost. Furthermore, in RNN, it was a problem that physical constraints of the human body were not taken into consideration. Therefore, this study aims to propose a simple motion prediction method using a human body model based on dynamics.
Qualitative and Quantitative Results of Enterprise Security Visualization Requirements Analysis through Surveying

In order to find gaps or missing points in any domain, examination of the literature work is necessary and provides a good amount of information. Doing a requirement analysis on top of this literature search incorporating the domain experts is a convenient way to find out ideas to fill out the detected gaps. The security visualization domain has been popular for the latest twenty years. There have been many designs. However, our literature analyses work resulted with the conclusion that the majority of the earlier security visualization work focuses a known set of use-cases, and these are trying to be validated using these small sets of vulnerabilities and some commonly known threats through a few case studies or experimental results. In this work, a security visualization requirement analysis survey with 30 information security experts is done. The paper presents the qualitative and quantitative results of this survey.
Transforma at SemEval-2019 Task 6: Offensive Language Analysis using Deep Learning Architecture

SemEval-2019 Task 6 (Zampieri et al., 2019b) requires us to identify and categorise offensive language in social media. In this paper we will describe the process we took to tackle this challenge. Our process is heavily inspired by Sosa (2017) where he proposed CNN-LSTM and LSTM-CNN models to conduct twitter sentiment analysis. We decided to follow his approach as well as further his work by testing out different variations of RNN models with CNN. Specifically, we have divided the challenge into two parts: data processing and sampling and choosing the optimal deep learning architecture. In preprocessing, we experimented with two techniques, SMOTE and Class Weights to counter the imbalance between classes. Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task. Given the quality and quantity of data we have been given, we found that the addition of CNN layer provides very little to no additional improvement to our model's performance and sometimes even lead to a decrease in our F1-score. In the end, the deep learning architecture that gives us the highest macro F1-score is a simple BiLSTM-CNN.
Design and Implementation of 32-Channel ADPCM CODEC

The paper constitutes of a design methodology and Implementation of a 32 channel ADPCM codec on an SoC which is used to convert and compress the digital data stream incoming at 64 Kbps to 16,24,32 or 40 Kbps ADPCM words for both A and U laws at a 125 usec time slot. The designed module is compatible with G.726 standards which was defined by ITU-T. The design uses a seven stage pipelining architecture. The proposed design uses the ADPCM speech compression technique which is used to predict the next sample based on the difference signal and the actual signal. This technique reduces redundancy and also gives a low bit rate as compared to standardized PCM technique. The encoder and the decoder were implemented on a 32-bit AMBER ARM processor.
Identification and Extraction of Digital Forensic Evidence from Multimedia Data Sources using Multi-algorithmic Fusion

With the enormous increase in the use and volume of photographs and videos, multimedia-based digital evidence has come to play an increasingly fundamental role in criminal investigations.However, given the increase in the volume of multimedia data, it is becoming time-consuming and costly for investigators to analyse the images manually.Therefore, a need exists for image analysis and retrieval techniques that are able to process, analyse and retrieve images efficiently and effectively.Outside of forensics, image annotation systems have become increasingly popular for a variety of purposes and major software/IT companies, such as Amazon, Microsoft and Google all have cloud-based image annotation systems.The paper presents a series of experiments that evaluate commercial annotation systems to determine their accuracy and ability to comprehensively annotate images within a forensic image analysis context (rather than simply single object imagery, which is typically the case).The paper further proposes and demonstrates the value of utilizing a multi-algorithmic approach via fusion to achieve the best results.The results of these experiments show that by existing systems the highest Average Recall was achieved by imagga with 53%, whilst the proposed multialgorithmic system achieved 77% across the selected datasets.These results demonstrate the benefit of using a multi-algorithmic approach.
A note on Contextual Blindness as extended to only

Abstract This paper discusses the Contextual Blindness principle as extended to the exclusive operator only . It focuses on the interaction between only and alternatives derived from a special category of contextual orders, generally referred to as “rank orders”. It submits problematic evidence for the principle and argues that access to contextual information is required in the relevant cases. Its conclusion is that, as things stand, these cases constitute an obstacle to the semantic generalization of scalar reasoning involving only .
Natural Language Interaction with Explainable AI Models

This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components -- namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG's predictions. In this work, we focus on the XAI model specified to interact with the user in natural language, whereas the AOG's predictions are considered given and represented by the corresponding parse graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides answers to the user's questions using the following types of reasoning: direct evidence (e.g., detection scores), part-based inference (e.g., detected parts provide evidence for the concept asked), and other evidences from spatio-temporal context (e.g., constraints from the spatio-temporal surround). We identify several correlations between user's questions and the XAI answers using Youtube Action dataset.
Encoding 3SUM

We consider the following problem: given three sets of real numbers, output a word-RAM data structure from which we can efficiently recover the sign of the sum of any triple of numbers, one in each set. This is similar to a previous work by some of the authors to encode the order type of a finite set of points. While this previous work showed that it was possible to achieve slightly subquadratic space and logarithmic query time, we show here that for the simpler 3SUM problem, one can achieve an encoding that takes $\tilde{O}(N^{\frac 32})$ space for inputs sets of size $N$ and allows constant time queries in the word-RAM.
Neuromorphic photonics: towards ultrafast brain-inspired computing systems (Conference Presentation)

Photonic techniques emulating the brain’s powerful computational capabilities are attracting considerable research interest as these offer promise for ultrafast operation speeds. In this talk we will review our approaches for ultrafast photonic neuronal models based upon Semiconductor Lasers, the very same devices used to transmit internet data traffic over fiber-optic telecommunication networks. We will show that a wide range of neuronal computational features, including spike activation, spiking inhibition, bursting, etc., can be optically reproduced with these devices in a controllable and reproducible way at sub-nanosecond time scales (up to 9 orders of magnitude faster than the millisecond timescales of biological neurons). Moreover, all our results are obtained using off-the-shelf, inexpensive Vertical-Cavity Surface Emitting Lasers (VCSELs) emitting at 1310 nm and 1550 nm; hence making our approach fully compatible with current optical communication technologies. Further, we will also introduce our recent work demonstrating the successful communication of sub-nanosecond spiking signals between interconnected artificial VCSEL-based photonic neurons and the potential of these systems for the ultrafast emulation of basic cortical neuronal circuits. These early results offer great prospects for novel neuromorphic (brain-like) photonic networks for brain-inspired ultrafast information processing systems going beyond traditional digital computing platforms.
Efficient Imputation Method for Missing Data Focusing on Local Space Formed by Hyper-Rectangle Descriptors

In real world data set, there might be missing data due to various reasons. These missing values should be handled since most data analysis methods are assuming that data set is complete. Data deletion method can be simple alternative, but it is not suitable for data set with many missing values and may be lack of representativeness. Furthermore, existing data imputation methods are usually ignoring the importance of local space around missing values which may influence quality of imputed values. Based on these observations, we suggest an imputation method using Hyper-Rectangle Descriptor (ܪܦ) which can focus on local space around missing values. We describe how data imputation can be carried out by using ܪܦ, named ܪܦ_݌ݑݐ, and validate the performance of proposed imputation method with a numerical experiment by comparing to imputation results without ܪܦ. Also, as a future work, we depict some ideas for further development of our work.
Jurisprudential and law Investigation into Bitcoins

Bitcoin, one of the realities is the economic system in contemporaneously, which, despite the opportunities, threats and risks it poses to the economic system of countries. At the moment, Bitcoin be considered, as the most used and most valuable virtual currency in the real world. Bitcoin is actually an internet innovation and with similar functions of paperless money or government money, a tool for transferring or storing value is considered with a decentralized nature, which has become widespread in the cyberspace and allows users to do so the entire process of publishing, processing and deals is made by users' networks and without any intermediary. There are many discussions about the nature of bitcoin. Indeed, bitcoin can be considered as a currency, capital or commodity? Extending the welcome to bitcoin has made it possible to examine Bitcoin's various dimensions and its use in the economic system on the agenda of research centers, jurisprudence centers, legislative assembly and central banks of many countries of the world That Iran is no exception. A look on the Bitcoin studies indicate that many experts believe Bitcoin can head certain functions in the real world that traditional cash and e-money play in the economy .Therefore, it does not contradict with Sharia law and Islamic principles, so as virtual currency accepted But the problems and challenges faced in this regard That must be The reliability and confidence in the exchanges by setting proper rules and strict monitoring
Application of Cortical Learning Algorithms to Movement Classification

Classifying the objects' trajectories extracted from Closed-Circuit Television (CCTV) feeds is a key video analytic module to systematize or rather help to automate both the real-time monitoring and the video forensic process.Machine learning algorithms have been heavily proposed to solve the problem of movement classification.However, they still suffer from various limitations such as their limited ability to cope with multi-dimensional data streams or data with temporal behaviour.Recently, the Hierarchical Temporal Memory (HTM) and its implementation, the Cortical Learning Algorithms (CLA) have proven their success to detect temporal anomalies from a noisy data stream.In this paper, a novel CLA-based movement classification algorithm has been proposed and devised to detect abnormal movements in realistic video surveillance scenarios.Tests applied on twenty-three videos have been conducted and the proposed algorithm has been evaluated and compared against several state-of-the-art anomaly detection algorithms.Our algorithm has achieved 66.29% average F-measure, with an improvement of 15.5% compared to the k-Nearest Neighbour Global Anomaly Score (kNN-GAS) algorithm.The Independent Component Analysis-Local Outlier Probability (ICA-LoOP) scored 42.75%, the Singular Value Decomposition Influence Outlier (SVD-IO) achieved 34.82%, whilst the Connectivity Based Factor algorithm (CBOF) scored 8.72%.The proposed models have empirically portrayed positive potential and had exceeded in performance when compared to state-of-the-art algorithms.
Student Empowerment in the Literacy Movement to Increase Interest in Reading School-Age Children [Pemberdayaan Mahasiswa Dalam Gerakan Literasi Untuk Meningkatkan Minat Baca Anak Usia Sekolah]

Education is an attempt to shape the quality of a nation. One important thing that supports the progress of the quality of education is how much interest in reading. Efforts to increase reading interest were carried out by the government assisted with the community, one of which was the literacy movement This service activity aims to empower mathematics education students at Serang Raya (Unsera) in the literacy movement to increase reading interest in school-age children. The implementation method is in the form of training, mentoring and discussion. The literacy movement activities in the form of collecting reading books will then make the reading garden in public places every Sunday in the cities of Serang, Cilegon, Pandeglang, and Rangkasbitung. The next activity is the literacy movement in several schools in the city of Serang. The results of the activity were good enthusiasm from the community towards the literacy movement with indicators of a large number of visitors to the reading park and the enthusiastic attitude of students in participating in literacy activities in partner schools.
A Review on Open Source Solution for Cloud Computing

Cloud Computing is the latest evolution of internet based computing which assures to provide more flexibility, less expense, and more efficiency in IT services to end users.It is believed that governments, businesses and researchers all can benefit from the adoption of cloud computing services.The aim of this paper is to study four main open source cloud computing platforms available in the market.This paper also highlights their features and limitations which will definitely help service providers and enterprises for selecting best open source cloud computing solutions for their organization according to their needs.
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals.

In this paper, several variants of two-stream architectures for temporal action proposal generation in long, untrimmed videos are presented. Inspired by the recent advances in the field of human action recognition utilizing 3D convolutions in combination with two-stream networks and based on the Single-Stream Temporal Action Proposals (SST) architecture, four different two-stream architectures utilizing sequences of images on one stream and sequences of images of optical flow on the other stream are subsequently investigated. The four architectures fuse the two separate streams at different depths in the model; for each of them, a broad range of parameters is investigated systematically as well as an optimal parametrization is empirically determined. The experiments on the THUMOS'14 dataset show that all four two-stream architectures are able to outperform the original single-stream SST and achieve state of the art results. Additional experiments revealed that the improvements are not restricted to a single method of calculating optical flow by exchanging the formerly used method of Brox with FlowNet2 and still achieving improvements.
Facial Expression Detection for video sequences using local feature extraction algorithms

Facial expression image analysis can either be in the form of static image analysis or dynamic temporal 3D image or video analysis.The former involves static images taken on an individual at a specific point in time and is in 2-dimensional format.The latter involves dynamic textures extraction of video sequences extended in a temporal domain.Dynamic texture analysis involves short term facial expression movements in 3D in a temporal or spatial domain.Two feature extraction algorithms are used in 3D facial expression analysis namely holistic and local algorithms.Holistic algorithms analyze the whole face whilst the local algorithms analyze a facial image in small components namely nose, mouth, cheek and forehead.The paper uses a popular local feature extraction algorithm called LBP-TOP, dynamic image features based on video sequences in a temporal domain.Volume Local Binary Patterns combine texture, motion and appearance.VLBP and LBP-TOP outperformed other approaches by including local facial feature extraction algorithms which are resistant to gray-scale modifications and computation.It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video sequences can increase accuracy and reduce the error rate.This can be achieved by removing unimportant information from the facial images.The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional patterns than holistic algorithms like GLCM and Linear Discriminant Analysis.The study proposes local binary pattern variant LBP-TOP, local directional patterns and support vector machines aided by genetic algorithms for feature selection.The study was based on Facial Expressions and Emotions (FEED) and CK+ image sources.
Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter

One of the most promising commercial applications of Human Computer Interface is the vision based Human fatigue detection systems. Most methods and algorithms currently rely heavily on movement of the head and the colorization of the eye ball. In this paper, a new algorithm for detecting human fatigue by relying primarily on eyelid movements as a facial feature is proposed. The features of the eye region and eyelid movement which are geometric in nature are processed alongside each other to determine the level of fatigue of a person. Haar classifiers are employed to detect the eye region and eyelid features. The eye region is, however processed to ascertain attributes of eyelid movement of each individual of interest. The eyelids are then detected as either opened, closed or in transition state. The movement or velocity of the eyelid is tracked using a Kalman filtered velocity function. This algorithm calculates a human blink cycle for each individual, and estimates the associated errors of the eye movement due to friction using the Kalman filter. The study has established human blink cycle calculation as a new classifier to characterize human fatigue and the calculation of the movement of eyelid using the Kalman filter in determining the level of fatigue.
Hate groups increase 30 percent in four years

There's been a 30 percent increase in hate groups in the United States over the last four years, and a 7 percent increase in hate groups in 2018 alone, according to research from the Southern Poverty Law Center. The group classified 1,020 organizations as hate groups in the year 2018. The SPLC defines a hate group as a group that has “beliefs or practices that attack or malign an entire class of people, typically for their immutable characteristics.” Those characteristics include race, religion, ethnicity, sexual orientation, and gender identity.
On the Sprague-Grundy function of compound games

The classical game of {\sc Nim} can be naturally extended and played on an arbitrary hypergraph $\cH \subseteq 2^V \setminus \{\emptyset\}$ whose vertices $V = \{1, \ldots, n\}$ correspond to piles of stones. By one move a player chooses an edge $H$ of $\cH$ and reduces arbitrarily all piles $i \in H$. In 1901 Bouton solved the classical {\sc Nim} for which $\cH = \{\{1\}, \ldots, \{n\}\}$. In 1910 Moore introduced and solved a more general game $k$-{\sc Nim}, for which $\cH = \{H \subseteq V \mid |H| \leq k\}$, where $1 \leq k &lt; n$. In 1980 Jenkyns and Mayberry obtained an explicit formula for the Sprague-Grundy function of Moore's {\sc Nim} for the case $k+1 = n$. Recently it was shown that the same formula works for a large class of hypergraphs. In this paper we study combinatorial properties of these hypergraphs and obtain explicit formulas for the Sprague-Grundy functions of the conjunctive and selective compounds of the corresponding hypergraph {\sc Nim} games.
Physics Enhanced Artificial Intelligence

We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem. Based on mean-variance portfolio theory and bias-variance trade-off analysis, we prove combining models from various domains produces a model that has lower risk, increasing user trust. We call such combined models - physics enhanced artificial intelligence (PEAI), and suggest use cases for PEAI.
An Identification of Learners' Confusion through Language and Discourse Analysis

The substantial growth of online learning, in particular, Massively Open Online Courses (MOOCs), supports research into the development of better models for effective learning. Learner 'confusion' is among one of the identified aspects which impacts the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of bewilderment and uncertainty of how to move forward. The majority of recent works neglect the 'individual' factor and measure the influence of community-related aspects (e.g. votes, views) for confusion classification. While this is a useful measure, as the popularity of one's post can indicate that many other students have similar confusion regarding course topics, these models neglect the personalised context, such as individual's affect or emotions. Certain physiological aspects (e.g. facial expressions, heart rate) have been utilised to classify confusion in small to medium classrooms. However, these techniques are challenging to adopt to MOOCs. To bridge this gap, we propose an approach solely based on language and discourse aspects of learners, which outperforms the previous models. We contribute through the development of a novel linguistic feature set that is predictive for confusion classification. We train the confusion classifier using one domain, successfully applying it across other domains.