Reviewerly/exHarmony huggingface dataset:
Thinking with Digital Images in the Post-Truth Era: A Method in Critical Media Literacy

Abstract This article introduces a new method to support critical media literacy, learning and research in higher education. It acts as a response to an unprecedented profusion of visual information across digital media that contributes to the contemporary post-truth era, marked by fake news and uncritical consumption of the media. Whereas much has been written about the reasons behind and the character of the post-truth, less space has been dedicated to how educators could counteract the uncritical consumption of images from the perspective of semiotics. This article adopts a unique semiotic approach to address the stated gap. It discusses in depth the meaning making of pictures, digital photographs and material objects that photographs can embody. It does so by focusing on three aspects of a pictorial sign: (1) the materiality of its representation and representational elements, (2) its object (what the sign refers to) and (3) its descriptive interpretations. These three aspects inform the signification analysis within the proposed production-signification-consumption (PSC) method, exemplified with digital photographs. Understanding and analysing images via the PSC method draw attention to how humans create, interpret, (re)use, consume and respond to online and offline communication signs. The method can contribute to the development of critical media literacy as an engagement with postdigital semiotics, much needed in an age of global ecological and social crises, uncertainty and fast consumption of digital content.
Remote teaching for equal and inclusive education in rural areas? An analysis of teachers’ perspectives on remote teaching

Purpose The aim of this study is to explore equality and inclusion as an aspect of remote teaching in rural areas. Moreover, the aim is to explore teachers' pedagogical digital competence (PDC) and school organizational support as conditions for developing remote teaching. Design/methodology/approach A mixed method approach with both qualitative and quantitative data was used. Findings According to this study, remote teaching can solve many problems for school organizations and offer pupils new opportunities to learn in rural areas. Remote teaching expands the learning environment and provides pupils with equal access to qualified teachers and a wider range of learning solutions for different needs. However, the learning context needs to be redesigned with flexibility to meet the needs of individual pupils, whereas the remote teaching format itself can contribute to difficulties in teachers' flexibility. In meeting these challenges teachers' PDC and digital relational competencies are becoming increasingly important. Moreover, teachers' access to communities and school contexts where remote teaching is collaboratively discussed and elaborated on. Research limitations/implications The study is limited to a region in Sweden, with ten participants. Practical implications The practical implications are that equal and inclusive remote teaching is dependent on technological as well as pedagogical competence from teachers as well as from organizations. Social implications If sufficient professional development for teachers is provided as well as organizational structure are in place, remote teaching is an option for equal access to education in sparsely populated areas. This means inclusive education can be provided to areas otherwise lacking in teacher competence. Originality/value The study is one of few that investigates how remote teaching teachers perceive the teaching form and the competencies and support required to develop and use it in rural areas.
Blob Detection and Deep Learning for Leukemic Blood Image Analysis

In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators’ workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art.
An Automatic System for Unconstrained Video-Based Face Recognition

Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of modules for face/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.
Pay as You Decrypt: Decryption Outsourcing for Functional Encryption Using Blockchain

The concept of functional encryption (FE) has been introduced to address the shortcomings of public-key encryption (PKE) in many emerging applications which require both data storage and data sharing (e.g., cloud storage service). One of the major issues existing in most FE schemes is the efficiency, as they are built from bilinear pairings of which the computation is very expensive. A widely accepted solution to this problem is outsourcing the heavy workloads to a powerful third party and leaving the user with the light computation. Nevertheless, it is impractical to assume that the third party (e.g., the cloud) will provide free services. To our knowledge, no attention has been paid to the payment procedure between the user and the third party in an FE with outsourced decryption (FEOD) scheme under the assumption that neither of them should be trusted. Leveraging the transactions on cryptocurrencies supported by the blockchain technology, in this paper, we aim to design FE with payable outsourced decryption (FEPOD) schemes. The payment in an FEPOD scheme is achieved through a blockchain-based cryptocurrency, which enables the user to pay a third party when it correctly completes the outsourced decryption. We define the adversarial model for FEPOD schemes, and then present a generic construction of FEPOD schemes. Also, we evaluate the performance of the proposed generic construction by implementing a concrete FEPOD scheme over a blockchain platform.
Review on Augmented Reality in Oral and Cranio-Maxillofacial Surgery: Toward “Surgery-Specific” Head-Up Displays

In recent years, there has been an increasing interest towards the augmented reality as applied to the surgical field.We conducted a systematic review of literature classifying the augmented reality applications in oral and cranio-maxillofacial surgery (OCMS) in order to pave the way to future solutions that may ease the adoption of AR guidance in surgical practice.Publications containing the terms ''augmented reality'' AND ''maxillofacial surgery'', and the terms ''augmented reality'' AND ''oral surgery'' were searched in the PubMed database.Through the selected studies, we performed a preliminary breakdown according to general aspects, such as surgical subspecialty, year of publication and country of research; then, a more specific breakdown was provided according to technical features of AR-based devices, such as virtual data source, visualization processing mode, tracking mode, registration technique and AR display type.The systematic search identified 30 eligible publications.Most studies (14) were in orthognatic surgery, the minority (2) concerned traumatology, while 6 studies were in oncology and 8 in general OCMS.In 8 of 30 studies the AR systems were based on a head-mounted approach using smart glasses or headsets.In most of these cases (7), a video-see-through mode was implemented, while only 1 study described an optical-seethrough mode.In the remaining 22 studies, the AR content was displayed on 2D displays (10), full-parallax 3D displays (6) and projectors (5).In 1 case the AR display type is not specified.AR applications are of increasing interest and adoption in oral and cranio-maxillofacial surgery, however, the quality of the AR experience represents the key requisite for a successful result.Widespread use of AR systems in the operating room may be encouraged by the availability of ''surgery-specific'' head-mounted devices that should guarantee the accuracy required for surgical tasks and the optimal ergonomics.
An Object Detection Technique For Blind People in Real-Time Using Deep Neural Network

This paper proposed an object detection technique for blind people in real time to detect objects on any device running this model. We use convolutional neural network along with single shot multi-box detector algorithm to develop the proposed model. This model is composed of multiple layers to classify the given objects into any of the defined classes. Due to recent advancement in deep learning with image processing, enable us to develop this model. Our model takes color images as input and train the mode until the error rate is less. We have tested our model by supplying some sample images. To increase the computational performance of the model we have used single shot multi-box detector algorithm along with the help of architecture of faster region convolutional neural network. We also have calculated the accuracy for detecting the objects and found that the accuracy is acceptable compare to other existing scheme. For that, we have considered different parameter like mean average precision (mAP) and frames per second (FPS). The single shot multi-box detector algorithm uses standard VOC and COCO datasets. This model integrates the audio device, which will be helpful for the blind people.
Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)

We propose a new approach to video face recognition. Our component-wise feature aggregation network (C-FAN) accepts a set of face images of a subject as an input, and outputs a single feature vector as the face representation of the set for the recognition task. The whole network is trained in two steps: (i) train a base CNN for still image face recognition; (ii) add an aggregation module to the base network to learn the quality value for each feature component, which adaptively aggregates deep feature vectors into a single vector to represent the face in a video. C-FAN automatically learns to retain salient face features with high quality scores while suppressing features with low quality scores. The experimental results on three benchmark datasets, YouTube Faces [39], IJB-A [13], and IJB-S [12] show that the proposed C-FAN network is capable of generating a compact feature vector with 512 dimensions for a video sequence by efficiently aggregating feature vectors of all the video frames to achieve state of the art performance.
Multimodal Encoder-Decoder Attention Networks for Visual Question Answering

Visual Question Answering (VQA) is a multimodal task involving Computer Vision (CV) and Natural Language Processing (NLP), the goal is to establish a high-efficiency VQA model.Learning a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions is the heart of VQA.In this paper, a novel Multimodal Encoder-Decoder Attention Networks (MEDAN) is proposed.The MEDAN consists of Multimodal Encoder-Decoder Attention (MEDA) layers cascaded in depth, and can capture rich and reasonable question features and image features by associating keywords in question with important object regions in image.Each MEDA layer contains an Encoder module modeling the self-attention of questions, as well as a Decoder module modeling the question-guided-attention and self-attention of images.Experimental evaluation results on the benchmark VQA-v2 dataset demonstrate that MEDAN achieves state-of-the-art VQA performance.With the Adam solver, our best single model delivers 71.01%overall accuracy on the test-std set, and with the AdamW solver, we achieve an overall accuracy of 70.76% on the test-dev set.Additionally, extensive ablation studies are conducted to explore the reasons for MEDAN's effectiveness.
Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks

Recommender systems help users to find information that fits their preferences in an overloaded search space. Collaborative filtering systems suffer from increasingly severe data sparsity problem because more and more products are sold in commercial websites, which largely constrains the performance of recommendation algorithms. User clustering has already been applied to recommendation on sparse data in the literature, but in a completely different way. In most existing works, user clustering is directly used to identify the similar users of the target user to whom we want to make recommendation. More specifically, the users who are clustered in the same group of the target user are considered as similar users. However, in this paper we use user clustering to reconstruct the user-item bipartite network such that the network density is significantly improved. The recommendation made on this dense network thus can achieve much higher accuracy than on the original sparse network. The experimental results on three benchmark data sets demonstrate that, when facing the problem of data sparsity, our proposed recommendation algorithm based on node clustering achieves a significant improvement in accuracy and coverage of recommendation.
One-to-Many Multilingual End-to-End Speech Translation

Nowadays, training end-to-end neural models for spoken language translation (SLT) still has to confront with extreme data scarcity conditions. The existing SLT parallel corpora are indeed orders of magnitude smaller than those available for the closely related tasks of automatic speech recognition (ASR) and machine translation (MT), which usually comprise tens of millions of instances. To cope with data paucity, in this paper we explore the effectiveness of transfer learning in end-to-end SLT by presenting a multilingual approach to the task. Multilingual solutions are widely studied in MT and usually rely on “target forcing”, in which multilingual parallel data are combined to train a single model by prepending to the input sequences a language token that specifies the target language. However, when tested in speech translation, our experiments show that MT-like target forcing, used as is, is not effective in discriminating among the target languages. Thus, we propose a variant that uses target-language embed-dings to shift the input representations in different portions of the space according to the language, so to better support the production of output in the desired target language. Our experiments on end-to-end SLT from English into six languages show important improvements when translating into similar languages, especially when these are supported by scarce data. Further improvements are obtained when using English ASR data as an additional language (up to +2.5 BLEU points).
Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.
Agile vs. Traditional Approach in Project Management: Strategies, Challenges and Reasons to Introduce Agile

Agility is a very topical issue today, however, it can be found most frequently in literature related to software development. The agile software development methodology is based on a gradual approach that is repeated. It focuses on the flexibility, acceptability of change, continuous advancement and strong interaction. There is a slowly emerging recognition that agile have something to offer within the wider project landscape and motivation for this research came from the fact that not many empirically grounded studies at the moment exist. Based on the aforementioned this study aims in the first instance, to provide academics and practitioners with a coherent overview of the strategies to introduce agile in traditional project management environment, recommended in literature. In the second instance, through empirical research to show which are the reasons for introduction of agile and challenges in its application in and beyond software development. Originally developed questionnaire was distributed through Project Management Institute worldwide network.
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions

We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent and accelerated gradient (AG) methods for minimizing strongly convex functions when the gradient has random errors in the form of additive white noise. With gradient errors, the function values of the iterates need not converge to the optimal value; hence, we define the robustness of an algorithm to noise as the asymptotic expected suboptimality of the iterate sequence to input noise power. For this robustness measure, we provide exact expressions for the quadratic case using tools from robust control theory and tight upper bounds for the smooth strongly convex case using Lyapunov functions certified through matrix inequalities. We use these characterizations within an optimization problem which selects parameters of each algorithm to achieve a particular trade-off between rate and robustness. Our results show that AG can achieve acceleration while being more robust to random gradient errors. This behavior is quite different than previously reported in the deterministic gradient noise setting. We also establish some connections between the robustness of an algorithm and how quickly it can converge back to the optimal solution if it is perturbed from the optimal point with deterministic noise. Our framework also leads to practical algorithms that can perform better than other state-of-the-art methods in the presence of random gradient noise.
MANDOLA

In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents. In this article, we present MANDOLA, a big-data processing system that monitors, detects, visualizes, and reports the spread and penetration of online hate-related speech using big-data approaches. MANDOLA consists of six individual components that intercommunicate to consume, process, store, and visualize statistical information regarding hate speech spread online. We also present a novel ensemble-based classification algorithm for hate speech detection that can significantly improve the performance of MANDOLA’s ability to detect hate speech. To present the functionality and usability of our system, we present a use case scenario of real-life event annotation and data correlation. As shown from the performance of the individual modules, as well as the usability and functionality of the whole system, MANDOLA is a powerful system for reporting and monitoring online hate speech.
Fine-grained vehicle type detection and recognition based on dense attention network

Intelligent transportation is an indispensable part of the smart city and the primary development direction of the future transportation systems. Vehicle detection and recognition, which is one of the most important aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recognition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly similar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe information attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model's detection ability. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.