Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Sentiment Analysis of Algerian Dialect during Hirak 2019

Recent studies have used sentiment classification to analyze social media content using either semantic rule-based methods such as linguistic extraction models obtained from morpho-syntactic analysis, statistical methods based on Bag-of-word techniques or machine learning and deep learning models. This work uses the traditional Machine Learning algorithms and the Deep Learning models (Convolutional Neural Networks CNN and Recurrent Neural Networks RNN) applying on corpus collected from social media (Facebook, YouTube and Twitter) about the Hirak_19 (popular protest in Algeria during 2019) written in Algerian Dialect to analyze sentiments and provide a deeper understanding of opinions. The corpus is built from several dialectal Arabic texts; it consists of 7800 comments about political Hirak topics. CNN and RNN have been applied in several applications; in this paper, we show their power of detecting and classifying opinions about a social issue and analyzing sentiments. The results are positive demonstrated by the accuracy scheme 63.28% (CNN) and 60.97 (RNN) of cross-validation tests.
Blockchain Use Cases Revisited: Micro-Lending Solutions for Retail Banking and Financial Inclusion

Abstract While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has remarkable practical effects [1] . Against this background, we present and discuss a case study at length on the impact of this technology in the concrete setting of small short-term loans in retail banking. We propose to banks a robust and scalable blockchain technology with proof of stake and limited energy consumption used to streamline their processes, resulting in lower transaction and administration costs. This is made possible by smart contracts. Thereby, we facilitate small scale lending at high frequencies and short-term duration as well as an easier and more efficient way to connect small borrowers and lenders.
Focused LRP: Explainable AI for Face Morphing Attack Detection

The following topics are dealt with: feature extraction; learning (artificial intelligence); object detection; image segmentation; neural nets; computer vision; face recognition; video signal processing; traffic engineering computing; convolutional neural nets.
An improved scheme for multifeature-based foreground detection using challenging conditions

Detecting an accurate foreground from a video frame is a critical task under different complex situations such as sudden changes in illuminations, relocation of background objects, shadow, low contrast videos and dynamic backgrounds (like waving tree, rippling of water etc.). Most of the existing schemes utilize a single feature-based foreground detection approach, which in turn is hard to apply under aforementioned complex situations. In order to mitigate this issue and properly exploit the different characteristics of the pixels, the present work proposes an efficient foreground detection scheme for better segmenting the foreground. In the proposed scheme, first the texture features are first extracted utilizing cross-diagonal texture matrix (CDTM), which essentially combines the merits of both the gray level co-occurrence matrix (GLCM) and the texture spectrum (TS) to provide a complete texture information about a frame. The color and gray value features of the pixel along with texture features are utilized for the feature vector generation. Second, during background modeling phase, the similarity distance measure is computed employing the Canberra distance between the mean feature vector of the current frame and the model. Finally, a method for adaptively selecting the threshold value is proposed, instead of choosing heuristically to correctly classify the foreground and background pixels under the dynamic background condition when background pixels changing frequently. Experiments are conducted using a wide variety of indoor and outdoor video sequences under various different challenging conditions and the results compared with that of the existing state-of-the-art methods. From the experimental results, it is shown that the proposed scheme outperforms the existing schemes in terms of the quantitative as well as qualitative measures.
Dialogic ItAlian: the creation of a corpus of Italian spontaneous speech

In recent decades, the interest in speech sciences for casual conversations has increased, and researchers have begun to investigate the characteristics of spontaneous and casual speech in different languages, such as German, Dutch, English, French, Spanish and Czech. These types of investigations are based on novel spontaneous speech corpora that allow the systematic research on large amounts of data and contribute to understand the empirical dimension of phonological competence. In this paper we present a new speech corpus of spontaneous Italian based on dialogic interactions (DIA - Dialogic ItAlian), representing the language variety spoken by both native and non-native speakers in a bilingual community. After discussing some of the methodological and theoretical issues concerning the role of speech corpora in the study of language variation, we illustrate how the corpus was created, from data collection to the construction of a database. We then offer an exploratory investigation on some indicators of spontaneity.
Robust Extraction and Super-Resolution of Low-Resolution Flying Airplane From Satellite Video

Extracting the flying airplane from the satellite video and enhancing its resolution are significant and demanding tasks in the remote sensing community. The challenge mainly lies in that the flying airplane target in the satellite video often suffers from detail loss due to complex background and limited spaceborne imaging device. In this article, a novel constructive model is proposed to model the airplane of low resolution for more complete extraction, and a new reflective symmetry shape prior is integrated into the super-resolution process to obtain the higher resolution result. Concretely, each frame can be decomposed as a linear combination of foreground and background with specific mixture ratios. With the assumption of uniform linear motion and the rigidity of the airplane, a periodic change of mixture ratios through frames is induced, which can construct the airplane as complete as possible by adopting the proposed iterative matting optimization. To further enhance the resolution of the extracted airplane, an improved alternating direction method of multipliers (ADMM) is utilized to solve the super-resolution problem with the reflective symmetry of the shape as prior. The effectiveness of our method with respect to extraction and super-resolution is borne out by the experiments on both synthetic and real data.
Deep visual Re-identification with confidence

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand the demand, i.e., better track moving agents (e.g., vehicles and pedestrians). Whether the cameras are mounted on drones, vehicles, or fixed in the built environments, they inevitably remain scatter. We need to develop the technology to re-identify the same agents across images captured from non-overlapping field-of-views, referred to as the visual re-identification task. State-of-the-art methods learn a neural network based representation trained with the cross-entropy loss function. We argue that such loss function is not suited for the visual re-identification task hence propose to model confidence in the representation learning framework. We show the impact of our confidence-based learning framework with three methods: label smoothing, confidence penalty, and deep variational information bottleneck. They all show a boost in performance validating our claim. Our contribution is generic to any agent of interest, i.e., vehicles or pedestrians, and outperform highly specialized state-of-the-art methods across 6 datasets. The source code and models are shared towards an open science mission.
DeepDT: Generative Adversarial Network for High-Resolution Climate Prediction

Climate prediction is susceptible to a variety of meteorological factors, and downscaling technology is used for high-resolution climate prediction. This technology can generate small-scale regional climate prediction from large-scale climate output information. Inspired by the concept of image super resolution, we propose to apply the convolutional neural network (CNN) to downscaling technology. However, some unpleasant artifacts always appear in the final climate images generated by existing CNN-based models. To further eliminate these unpleasant artifacts, we present a new training strategy for the generative adversarial network, termed DeepDT. The key idea of our DeepDT is to train a generator and a discriminator separately. More specifically, we apply the residual-in-residual dense block as the basic frame structure to fully extract the features of the input. Additionally, we innovatively use a CNN model to fuse multiple climate elements to generate trainable climate images, and build a high-quality climate data set. Finally, we evaluate the DeepDT using the proposed climate data sets, and the experiments indicate that DeepDT performs best compared to most CNN-based models in climate prediction.
CNN Application in Detection of Privileged Documents in Legal Document Review

Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.
Framing in Secret Sharing

Secret sharing, a well-known cryptographic technique, introduced 40 years ago as a private and reliable variant of classical storage, has now become a major cryptographic primitive with numerous real-world applications. In this paper we consider the digital forensics aspects of secret sharing. We investigate the problem of framing which occurs when a coalition is able to calculate the share of a participant who does not belong to it. In the extreme case one authorized coalition can calculate shares of another authorized coalition and use the secret in some way blaming another authorized coalition for their action. In this context seniority plays an important role. We define seniority, which comes natural in the context of hierarchical access structures. Roughly speaking, our work shows that in an ideal secret sharing scheme an authorized coalition cannot frame participants who are less senior than all members of the coalition and is able to frame a participant who is more senior than at least one pivotal member of the coalition. We show that for any monotone access structure there exists a (non-ideal) frameproof secret sharing scheme.
From Digital Humanities to Quantum Humanities: Potentials and Applications

Quantum computers are becoming real. Therefore, it is promising to use their potentials in different application areas, which includes research in the humanities. Due to an increasing amount of data that needs to be processed in the digital humanities the use of quantum computers can contribute to this research area. To give an impression on how beneficial such involvement of quantum computers can be when analysing data from the humanities, a use case from the media science is presented. Therefore, both the theoretical basis and the tooling support for analysing the data from our digital humanities project MUSE is described. This includes a data analysis pipeline, containing e.g. various approaches for data preparation, feature engineering, clustering, and classification where several steps can be realized classically, but also supported by quantum computers.
LSTM-based Automatic Modulation Classification

Recently, automatic modulation classification (AMC) has been studied by more and more researchers, and a host of methods based on deep learning have been proposed. Different from image data, signal data is a sequence that changes with time, and has temporal characteristics. Like the spatial feature, the temporal feature cannot be ignored. In this paper, we add a Long Short-Term Memory (LSTM) module to the convolutional neural network (CNN) to learn the time-related features of wireless signals. LSTM learns the dependency relationship between the current element and the elements before-after through the gating structure. Experimental results show that our proposed methods perform better than the model without LSTM. The proposed network Incept-LSTM has a classification accuracy of 97.5% at a high signal-to-noise ratio (SNR). Compared to the model without LSTM, the classification accuracy of our proposed model is improved by 0.1% to 5.7%. At the same time, we compared the classification accuracy under different SNRs to explore its impact on classification performance. We found the classification ability of LSTM is more prominent at the low SNRs.
Massive picture retrieval system based on big data image mining

The traditional picture retrieval system has a slow retrieval speed, poor retrieval accuracy, and a low recall when performing massive picture retrieval. In this paper, we design a massive picture retrieval system using the big data image mining technology. It is constructed with data processing layer, business logic layer and presentation layer and works through three steps of data segmentation, mining and merging. For instance, it runs the distributed file system module in a Master/Slave operation mode and designs file read and write requests according to user interaction. Next, it performs parallel computing of picture data sets based on Map Reduce module to solve the picture matching and similarity metrics and returns to the user sorted picture matching result. Then, it extracts the color and texture features of the target area to generate the final picture retrieval result. We select a large number of pictures on a big data platform as simulation test set. The results show that the system we designed has a good retrieval accuracy and a high retrieval speed, which greatly improves the recall of picture retrieval.
Group polarisation among location-based game players: an analysis of use and attitudes towards game slang

Purpose This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang. Design/methodology/approach The authors performed an exploratory data analysis on an original dataset of n = 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokémon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players' attitudes towards the word were further probed with a survey ( n = 185). Finally, selected participants ( n = 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes. Findings The players' teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation. Practical implications Game design with static teams and inter-team conflict influences players' social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base. Originality/value The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games.
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation

For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge. We formalize this as a bootstrapped self-supervised learning problem where a system is initially bootstrapped with supervised training on a labeled dataset and we look for a self-supervised training method that can subsequently improve the system over the supervised training baseline using only unlabeled data. In this work, we leverage temporal consistency between frames in monocular video to per-form this bootstrapped self-supervised training. We show that a well-trained state-of-the-art semantic segmentation network can be further improved through our method. In addition, we show that the bootstrapped self-supervised training framework can help a network learn depth estimation better than pure supervised training or self-supervised training.
On the Compression Performance of HEVC, VP9 and AV1 Encoders for Virtual Reality Videos

Thanks to advances in display technologies and affordable end user devices, interest in Virtual Reality (VR) and related multimedia content, namely 180° and 360° images and videos, has been rapidly increasing in recent years. For effective delivery of a video in such format, compression algorithms with high efficiency are vital. In this paper, the outputs of a subjective-based compression performance study of recent (HEVC and VP9) and emerging (AV1) video coding formats for 180°/360° videos are presented. Viewers' Quality of Experience (QoE) is assessed by subjective tests utilizing Absolute Category Rating with Hidden Reference (ACR-HR) protocol. In addition, these tests, wherein two different Head Mounted Devices (HMDs) from Oculus were used, are extended with a custom questionnaire that helps to better understand observers' experience and feelings with VR. Results revealed that not only the quality levels (bit rates) of the compressed 180°/360° videos have influence on the viewers' perceived QoE.
Ephemeral Keys Authenticated with Merkle Trees and Their Use in IoT Applications

Public key algorithms based on quasi-cyclic binary moderate-density parity-check codes (QC-MDPCs) and QC low-density parity-check codes (QC-LDPCs) codes for key encapsulation and encryption submitted to the NIST post-quantum competition (Bit Flipping Key Encapsulation (BIKE), QC-MDPC KEM, LEDA) are vulnerable against reaction attacks based on decoding failures. To protect algorithms, authors propose to limit the key usage, in the extreme (BIKE) to only use ephemeral public keys. In some authenticated protocols, we need to combine each key with a signature, which can lead to increased traffic overhead, especially given the large signature sizes of some of the proposed post-quantum signature schemes. We propose to combine ephemeral public keys with a simple Merkle tree to obtain a server authenticated key encapsulation/transport suitable for TLS-like handshake protocols. This allows a very simple public key verification on the client, leading to efficient protocols suitable for Internet of Things applications.
Massification of Online Education: A Holistic Strategy

Before 2020, online learning has not been very popular in Slovakia. Online courses were quite exceptional at most universities and factually non-existing at lower levels of schools. When the pandemic locked them down, they faced a shock because they have virtually no experience and had to improvise. This learned them about the importance of online education as a replacement for classroom education. During the short period (March-June 2020), they gained some positive results and experience. Nevertheless, many of their educational practices have not been coordinated and standardized; they still suffer from being far from an optimum. For years, the authors were educators at the sole Slovak university with a long-term strategy of online education. Every course has its online partner and all students can select between its online and on-site format. Such education has been demanding due to its necessity to develop and to perform both “soft” and “hard” teaching methodologies and practices. At the same time, in times of the pandemic they proved their value. Below we outline a strategy, which can be used for reaching a similar goal. We point to a variety of factors: the organization of the work, design and development of courses, staff development, teachers' personality, their knowledge, specific skills and expected experience. It guarantees the sustainability of top-quality teaching and learning. Another our aim is to demonstrate that such a holistic approach can lead to a global change in the Slovak educational system - to Open Education. For years, the authors were educators at the sole Slovak university with a long-term strategy of online education. Every course has its online partner and all students can select between its online and on-site format. Such education has been demanding due to its necessity to develop and to perform both “soft” and “hard” teaching methodologies and practices. At the same time, in times of the pandemic they proved their value. Below we outline a strategy, which can be used for reaching a similar goal. We point to a variety of factors: the organization of the work, design and development of courses, staff development, teachers' personality, their knowledge, specific skills and expected experience. It guarantees the sustainability of top-quality teaching and learning. Another our aim is to demonstrate that such a holistic approach can lead to a global change in the Slovak educational system - to Open Education. Below we outline a strategy, which can be used for reaching a similar goal. We point to a variety of factors: the organization of the work, design and development of courses, staff development, teachers' personality, their knowledge, specific skills and expected experience. It guarantees the sustainability of top-quality teaching and learning. Another our aim is to demonstrate that such a holistic approach can lead to a global change in the Slovak educational system - to Open Education.