Reviewerly/exHarmony huggingface dataset:
A Tool for Navigating and Editing 360 Video of Social Conversations into Shareable Highlights

We present ConvCut, an interactive tool for efficiently navigating and editing 360 video of social conversations into shareable video highlights of the memorable moments. ConvCut starts by obtaining a high-quality transcript of the conversation and uses it to segment the video into one 360 video clip per line of speech. It then applies audio, video and text analysis to label the clips with information including the spatial location of faces, the current speaker, the topics of conversation, instances of laughter and extreme changes in volume, facial expression, or gestural motion. The resulting structure lets users navigate the video using keyword search over the transcript and labels to quickly find memorable moments. Users can mark the lines corresponding to these moments and ConvCut edits together the corresponding video clips, automatically choosing a regular field of view (RFOV) framing that emphasizes the speaker of each line. If desired, users can modify the automatic edit to include alternative framings or reactions from others in the group. We demonstrate that with ConvCut , first-time users can easily edit long social conversations (25-60 min) into short highlight videos (0.27-2 min) and share them with others. The resulting highlights include jokes, reactions to pranks, funny stories and interactions with children.
Deepfakes : How a pervert shook the world

Recently a software has made it easy to create hyper-realistic face swaps in videos that leaves little-to-no traces of manipulation, in what is known as “deepfake” videos. Scenarios, where these AI manipulated/generated videos, are used for political distress, blackmail or even terrorism are easily envisioned as a near dystopia. This paper explores the various aspects of deepfake videos including its consequences and newly developed innovations in detecting deepfakes.
Novel Approach towards Arabic Question Similarity Detection

In this paper we are addressing the automatic detection of Arabic question similarity, which is an essential issue in a variety of NLP/NLU applications such as question answering systems, virtual assistants, chatbots ... etc. We are proposing and experimenting a rule-based approach that relies on lexical and semantic similarity between questions with the utilization of supervised learning algorithms. Our approach categorizes questions semantically according to their type and scope; this categorization is based on hypothetical rules that have been validated empirically, for example, a Timex Factoid question (a question asking about time) is less likely similar to an Enamex Factoid question (a question asking about a named entity). This article details the procedures of question pairs preprocessing, lexical analysis, feature extraction and selection and most importantly the similarity measures. According to the experiment we have conducted, our approach achieved promising precision and accuracy based on a test data of 1450 question pairs.
A new dynamical repulsive fractional potential for UAVs in 3D dynamical environments

In recent years, applications for drones have increased, from surveillance, exploration, rescue to transport applications. UAVs are more and more autonomous, therefore real-time trajectory planning is necessary and can be achieved with potential fields. A study is proposed to better scale attractive and repulsive forces which has always been problematic when dealing with artificial potential fields. The purpose of this article is to develop a new dynamical fractional potential repulsive field usable in a 3D environment by taking into account the obstacle dynamics (position and speed) and their dangerousness. Obstacle avoidance robustness is guaranteed, both from a safety point of view and from a trajectory optimization point of view. The potential fields are based on the relative position and speed of the drone in relation to the target for the attractive potential field or to the obstacle for the repulsive one.
High-Speed Searching Target Data Traces Based on Statistical Sampling for Digital Forensics

As technology of manufacturing storage medium advances, data storage capacity has been increasing exponentially. This pervasiveness has made a forensic examination time-consuming and difficult. If a file system of data storage remains intact, an examiner can find files that would be important evidence by analyzing hierarchy, name, time information, etc. of files and folders. However, as anti-forensic techniques such as metadata destruction and disk format are widely known, the data search based on the file system becomes more impractical. Besides, significant evidences could be stored in the unallocated area; investigating the entire area of data storage is still important. The famous methods of exploring the existence of evidence are hash comparison and random sampling. The hash comparison that calculates hash for all sectors and compares them can detect all fragments of the evidence. However, it requires an enormous amount of time and computing resources. Whereas the random sampling takes much less time as it exploits a portion of data storage, but it involves the risk of false-negative; this fact is critical to forensic examiners. In this paper, we blend the merits of both methods to make false-negative zero and to reduce the processing time extremely at the same time. We use 16-byte values in a sector instead of traditional hash to filter out the unmatched sector. The values are statistically selected based on the frequency of occurrence according to offset. The effectiveness of our methodology is evaluated through several experiments.
Graph of pseudo degree zero generated by FTTMnk

Fuzzy topological topographic mapping (FTTM) is a set of topological spaces and algorithms which is applied in solving neuro magnetic inverse problem. The special characteristic of FTTM, namely the homeomorphisms between its components, allow the construction of new FTTM from components of different of FTTM. These generated FTTMs can be represented in the form of a pseudo graph. A graph of pseudo degree zero is a special type of pseudo graph where each of the FTTM components version differ to components adjacent to it. Previous studies have investigated combinations of graph with pseudo degree zero for n number of components and k number of version by using a computer algorithm. In this paper one conjecture from the previous study namely the conjecture on the number of graphs with pseudo degree zero generated by a sequence of FTTM with three versions is proven analytically by using new definitions of FTTM namely FTTM grid as an alternative to FTTM sequence of polygon. This paper highlights some new theorems derived from the new definitions.
Cultivating Students’ Interest and Positive Attitudes towards Indonesian Language through Phenomenon-Text-Based Information Literacy Learning

This study was aimed at describing phenomenon-text based information literacy in Indonesian language learning and the students' response to the teaching.The subjects were 5 lecturers of Indonesian language and 302 students of Universitas Pendidikan Ganesha (Undiksha).Data on the teaching and learning activities were collected by observation and interview, while data on the students' responses were collected by questionnaire.The result showed that phenomenon-text based information in Indonesian language teaching and learning was done in six steps: 1) communicating the phenomenon that occurred in the society (through pictures or video), 2) discussing the phenomenon, 3) reading a phenomenon text, 4) searching information from various sources, 5) writing the results of the search and 6) communicating the results.This syntax of teaching and learning was then called as six steps of phenomenon information literacy.A survey showed that 77.65% of the students felt very happy, 19.91% of the students were happy, 2.44% of the students were less happy, and none of them said that they were unhappy with teaching and learning.It can be concluded that phenomenon-text based information literacy can attract the students' interest and develop students' positive attitude.
Approaching the chaotic regime with a fully connected superconducting quantum processor

The task of sampling the output distribution of random quantum circuits will drive a multiqubit system into chaos, reaching the Porter-Thomas distribution where small multiqubit probabilities appear most often. Such a task is hard to be computed classically and has been proposed as an important measure for quantum supremacy. An equivalent route to reach chaos is the controlled dynamics uniformly exploring the large Hilbert space of coupled qubits, and a proof-of-concept experiment was recently demonstrated by Neill et al. [Science 360, 195 (2018)] in a chain of nine qubits featuring only neighboring couplings. Here, we show numerically that a faster convergence to chaos is possible when full connectivity, instead of partial connectivity, is present among the qubits, i.e., cycles or time required to reach chaotic regime increase only slightly with the number of qubits. We validate the numerical result by carrying out an experiment using up to 12 qubits in a fully connected superconducting quantum processor. Based on a machine learning model, we quantify the evolution error rate to be less than (0.5%/cycle)/qubit, which is mostly determined by qubit dephasing.
Collective Embedding-based Entity Alignment via Adaptive Features.

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.
Data Compression Algorithms in Analysis of UI Layouts Visual Complexity

Measuring visual complexity (VC) of human-computer user interfaces (UIs) sees increasing development, as VC has been found to affect users’ cognitive load, aesthetical impressions and overall performance. Spatial allocation and ordering of UI elements is the major feature manipulated by an interface designer, and in our paper we focus on perceived complexity of layouts. Algorithmic Information Theory has justified the use of data compression algorithms for generating metrics of VC as lengths of coded representations, so we consider two established algorithms: RLE and Deflate. First, we propose the method for obtaining coded representations of UI layouts based on decreasing of visual fidelity that roughly corresponds to the “squint test” widely used in practical usability engineering. To confirm applicability of the method and the predictive power of the compression algorithms, we ran two experimental surveys with over 4700 layout configurations, 21 real websites, and 149 participants overall. We found that the compression algorithms’ metrics were significant in VC models, but the classical purely informational Hick’s law metric was even more influential. Unexpectedly, algorithms with higher compression ratios that presumably come closer to the “real” Kolmogorov complexity did not explain layouts’ VC perception better. The proposed novel UI coding method and the analysis of the compression algorithms’ metrics can contribute to user behavior modeling in HCI and static testing of software UIs.
Natural Human Intelligence - the Object of Research for Artificial Intelligence Creation

Intelligence is an algorithm of solving problems established by the consciousness (the author's definition). Intelligence is a non-material entity. In the material world, intelligence is also non-material, although its carriers are of material nature. Depending on what task a person should accomplish and what goal he has chosen, intelligence forms step by step the way to achieve it, i.e. creates algorithms of its solution. The algorithms a person forms determine the level of intelligence. If the algorithm is of high quality, it indicates a high level of intelligence, if of poor quality - low level. Human intelligence is limited.
MongoDB Features and Installation

This chapter describes NoSQL databases, various features of MongoDB, and how to interact with MongoDB. We are going to focus on the following topics:
Wearable camera for fall detection embedded system

The fall is one of the major problems that threaten the health of the elderly. According to world statistics, between 28% and 35% of seniors aged over 65 suffer from at least one fall per year. Continuous monitoring and early detection of critical events such as falls, allow for rapid response and immediate medical management. For this, several fall detection devices have been designed by the researchers. In this paper, we propose a fall detection system based on a portable camera. The camera is worn by the user, which allows him to follow his activity wherever he moves, whether indoors or outdoors. In addition, since it is the environment and not the individual that is being observed, the privacy issue is mitigated. The proposed fall detection method is a new approach based on the original Histogram of Oriented Gradient version (HOG) that we combine with the Optical Flow to improve system performance in detecting true falls. Our method has been implemented on a Raspberry embedded on the developed device, which allows us to parallelize the calculations and thus to answer the real time constraint. The results of the 20 tests performed on 09 subjects; show that the falls can be detected with a sensitivity of 95% from the standing position. The combination of optical flow with HOG has improved the specificity, for the rotation scenario, by increasing it by 50% compared to the use of HOG alone. As a result, we have achieved a specificity of 90%. Experimental results show the success of the proposed method.
LARA PORTAL: A TOOL FOR TEACHERS TO DEVELOP INTERACTIVE TEXT CONTENT, AN ENVIRONMENT FOR STUDENTS TO IMPROVE READING SKILL

LARA PORTAL: A TOOL FOR TEACHERS TO DEVELOP INTERACTIVE TEXT CONTENT, AN ENVIRONMENT FOR STUDENTS TO IMPROVE READING SKILL
Exploring and Identifying Malicious Sites in Dark Web Using Machine Learning

In recent years, various web-based attacks such as Drive-by-Download attacks are becoming serious. To protect legitimate users, it is important to collect information on malicious sites that could provide a blacklist-based detection software. In our study, we propose a system to collect URLs of malicious sites in the dark web. The proposed system automatically crawls dark web sites and collects malicious URLs that are judged by using VirusTotal and the Gred engine. We also predict dangerous categories of collected web sites that are potentially malicious using a document embedding with a gradient boosting decision tree model. In the experiments, we demonstrate that the proposed system can predict dangerous site categories with 0.82 accuracy in F1-score.
Fashion Libraries as a Means for Sustainability Education—An Exploratory Case Study of Adolescents’ Consumer Culture

Adolescents often do not consider sustainability in their consumption behaviours. Sustainability issues are challenging to communicate through course content alone. Against this background, the concept of fashion libraries (FLs) was introduced to adolescents at a non-denominational school in a small German city. The purpose was to explore whether the students’ attitudes would change over time in response to distinct interventions and project-based learning, such as the foundation of an FL, upcycling workshops and lectures on sustainability. The sample comprised 10 female adolescents in the age between 13 and 16 years, who volunteered to participate in the clothing library project. An exploratory mixed-methods approach, including a questionnaire, two interventions and pre-and post-intervention in-depth interviews, was utilized. The findings hint at an incremental change in the students’ attitudes towards an appreciation of garments and a change in behaviour. They started to reflect their behaviour and consequently reduced the frequency and quantity of their clothing purchases. To the best of the authors’ knowledge, this is the first article in which an FL was founded to investigate the consumption behaviour of adolescents.
Scene-and-Process-Dependent Spatial Image Quality Metrics

Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS) and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured by traditional methods. The authors present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ Scene-and-Process-Dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal transfer and noise scene dependency, respectively. The authors also investigate implementing contrast detection and discrimination models that account for scene-dependent visual masking. Also, three leading camera metrics are revised to use the above scene-dependent measures. All metrics are validated by examining correlations with the perceived quality of images produced by simulated camera pipelines. Metric accuracy improved consistently when the SPD-MTFs and SPD-NPSs were implemented. The novel metrics outperformed existing metrics of the same genre.
Mutual Information Gradient Estimation for Representation Learning

Mutual information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful representation. However, most of existing methods are not capable of providing accurate estimation of MI with low-variance when the MI is large. We argue that estimating gradients of MI is more appealing for representation learning than directly estimating MI due to the difficulty of estimating MI. Therefore, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on score estimation of implicit distributions. It exhibits a tight and smooth gradient estimation of MI in the high-dimensional and large-MI setting. We expand the applications of MIGE in both unsupervised learning of deep representations based on InfoMax and the Information Bottleneck method. Experimental results have indicated the remarkable performance improvement in learning useful representation.
WVD: A New Synthetic Dataset for Video-Based Violence Detection

Violence detection is becoming increasingly relevant in many areas such as for automatic content filtering, video surveillance and law enforcement. Existing datasets and methods discriminate between violent and non-violent scenes based on very abstract definitions of violence. Available datasets, such as “Hockey Fight” and “Movies”, only contain fight versus non-fight videos; no weapons are discriminated in them. In this paper, we focus explicitly on weapon-based fighting sequences and propose a new dataset based on the popular action-adventure video game Grand Theft Auto-V (GTA-V). This new dataset is called “Weapon Violence Dataset” (WVD). The choice for a virtual dataset follows a trend which allows creating and labelling as sophisticated and large volume, yet realistic, datasets as possible. Furthermore, WVD also avoids the drawbacks of access to real data and potential implications. To the best of our knowledge no similar dataset, that captures weapon-based violence, exists. The paper evaluates the proposed dataset by utilising local feature descriptors using an SVM classifier. The extracted features are aggregated using the Bag of Visual Word (BoVW) technique to classify weapon-based violence videos. Our results indicate that SURF achieves the best performance.
Tell Them from Me: An Encrypted Application Profiler

Profiling internet users associated with encrypted applications has been a long-standing challenging issue that helps to identify targeted users’ interests. This paper proposes a machine-learning based solution for creating encrypted application signatures without relying on any certain assumptions on the underlying network infrastructure such as IP address, port number, network flow characteristics. These applications signatures can later be used with passive network monitoring for profiling targeted users in terms of selected application usage such as Facebook, Tor. We propose a proof of concept (PoC) framework with effective features to identify (i) encrypted payloads from any network traffic, and (ii) targeted applications such as ToR, Skype for what the model is trained for. Our study shows that using classical Shannon’s entropy alone can help recognize encrypted payload, but may not help identify particular application payloads. We design features based on standard encoding e.g., UTF-8, entropy e.g., Shannon entropy, BiEntropy, and payload size, so that machine learning algorithms can be used to identify encrypted applications.
On Asymptotic Properties of the GMDH Criteria

The paper analyses the problem of the convergence investigation for typical GMDH criteria based on the data sample division into two subsamples. This study is significant for additional justification of applying the GMDH-based inductive modeling in case of large samples. The inductive modeling problem is defined and main external criteria are presented. An approach to the asymptotic investigations is explained based on assumptions of strong regularity of regressors. The study results demonstrate the convergence of any external criterion to some finite values in probability. Moreover, the minimal value of an accuracy criterion at infinity corresponds to the true model. This means that such criteria are consistent.
Natural Language Processing

In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or categorical. Generally, not much processing is required in those datasets because computers are great with standardized and structured data. While trying to apply those machine learning techniques to human language though, it opens up a whole new box of challenges as language is not precise and holds different meanings in different contexts. Even the basic structure changes when we move from one language to another. Hence language needs special consideration during the creation of intelligent applications, and this is grouped under the domain of Natural Language Processing (NLP).