Reviewerly/exHarmony huggingface dataset:
Overcoming an Annotation Hurdle: Digitizing Pen Annotations from Whole Slide Images

The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations - manually drawn by pathologists in digital slide viewers - is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling.We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool.Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations.We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
Assessing the implications of digital contact tracing for COVID-19 for human rights and the rule of law in South Africa

The article argues that the establishment of centralised and aggregated databases and applications enabling mass digital surveillance, despite their public health merits in the containment of the COVID-19 pandemic, is likely to lead to the erosion of South Africa's constitutional human rights, including rights to equality, privacy, human dignity, as well as freedom of speech, association and movement, and security of the person. While derogation clauses have been invoked, thereby limiting International Covenant on Civil and Political Rights clauses and enabling the mass collection of location data only for contact tracing purposes under the Disaster Management Act, a sustained breach of these rights may pose an impending threat to the human rights framework in South Africa. Any proposed digital contact tracing technologies in their design, development and adoption must pass the firm legal muster and adhere to human rights prescripts relating to user-centric transparency and confidentiality, personal information, data privacy and protection that have recently been enacted through the latest development on Protection of Personal Information Act.
Obfuscation of logic schemes of pseudo-random number generators based on linear and non-linear feedback shift registers

The paper describes methods of protection against reverse engineering of logic circuits of pseudo-random number generators (PRNG) on linear and non-linear feedback shift registers. These methods are based on the use of additional logic elements in the structure of the generator to hide its original functionality. Obfuscation of the generator logic circuit changes its design such that the device works correctly only if the signals at the additional key inputs of the generator take on the correct values. It is shown that even with a small bit capacity of generators, it is possible to provide a huge number of PRNG implementations with different numbers of states and different properties. The concept of a (М + 1)-sequence generator is introduced. The possibility of transforming (M – 1)- and (M – 3)-sequences generators into (M + 1)-sequences generators is demonstrated. The proposed methods can be used to ensure the security of IoT devices.
Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach

Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it provides decision-makers with uncensored public views on products, government policies, and programs. Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. The challenge encountered in machine learning method-based sentiment classification still remains the abundant amount of data available, which makes it difficult to train the learning algorithms in feasible time. This eventually degrades the classification accuracy of the algorithms. From this assertion, the effect of training data sizes in classification tasks cannot be overemphasized. This study statistically assessed the performance of Naive Bayes, support vector machine (SVM), and random forest algorithms on sentiment text classification task. The research also investigated the optimal conditions such as varying data sizes, trees, and kernel types under which each of the respective algorithms performed best. The study collected Twitter data from Ghanaian users which contained sentiments about the Ghanaian Government. The data was preprocessed, manually labeled by the researcher, and then trained using the aforementioned algorithms. These algorithms are three of the most popular learning algorithms which have had lots of success in diverse fields. The Naive Bayes classifier was adjudged the best algorithm for the task as it outperformed the other two machine learning algorithms with an accuracy of 99%, F1 score of 86.51%, and Matthews correlation coefficient of 0.9906. The algorithm also performed well with increasing data sizes. The Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data.
Implementation of modified GSO based magic cube keys generation in cryptography

Over the last few decades, tremendous and exponential expansion in digital contents together with their applications has emerged. The Internet represents the essential leading factor for this expansion, which provides low-cost communication tools worldwide. However, the main drawback of the Internet is related to security problems. In order to provide secure communication, enormous efforts have been spent in the cryptographic field. Recently, cryptographic algorithms have become essential for increasing information safety. However, these algorithms require random keys and can be regarded as compromised when the random keys are cracked via the attackers. Therefore, it is substantial that the generation of keys should be random and hard to crack. In this paper, this is guaranteed via one of the most efficient nature-inspired algorithms emerged by inspiring the movements of stars, galaxies, and galaxy superclusters in the cosmos that can be utilized with a mathematical model (magic cube) for generating hardly cracking random number keys. In the proposed cryptographic system, the Modified Galactic Swarm Optimization (GSO) algorithm has been utilized in which every row and column of magic cube faces are randomly rotated until reaching the optimal face, and the optimal random elements are selected as optimal key from the optimal face. The generated optimized magic cube keys are used with several versions of RC6 algorithms to encrypt various secret texts. Furthermore, these generated keys are also used for encrypting images using the logical XOR operation. The obtained results of NIST tests proved that the generated keys are random and uncorrelated. Moreover, the security of the proposed cryptographic system was proved
Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.
Compressed Particle Methods for Expensive Models With Application in Astronomy and Remote Sensing

In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the last years, in order to obtain parameter inversion, model selection, or uncertainty quantification. Bayesian inference requires the approximation of complicated integrals involving (often costly) posterior distributions. Generally, this approximation is obtained by means of Monte–Carlo (MC) methods. In order to reduce the computational cost of the corresponding technique, surrogate models (also called emulators) are often employed. Another alternative approach is the so-called approximate Bayesian computation (ABC) scheme. ABC does not require the evaluation of the costly model but the ability to simulate artificial data according to that model. Moreover, in ABC, the choice of a suitable distance between real and artificial data is also required. In this work, we introduce a novel approach where the expensive model is evaluated only in some well-chosen samples. The selection of these nodes is based on the so-called compressed MC scheme. We provide theoretical results supporting the novel algorithms and give empirical evidence of the performance of the proposed method in several numerical experiments. Two of them are real-world applications in astronomy and satellite remote sensing. (Corresponding author: Luca Martino.)
Hierarchically Attending Time-Frequency and Channel Features for Improving Speaker Verification

Attention-based models have recently shown powerful representation learning ability in speaker recognition. However, most of the attention mechanism based models primarily focus on pooling layers. In this work, we present an end-to-end speaker verification system which leverage time-frequency and channel features hierarchically. To further improve system performance, we employ Large Margin Cosine Loss to optimize the model to determine the optimal loss function. We carry out experiments on the VoxCeleb1 datasets to evaluate the effectiveness of our methods. The results suggest that our best system outperforms the i-vector + PLDA and x-vector system by 53.3% and 7.6%, respectively.
Weakly-supervised video object localization with attentive spatio-temporal correlation

Weakly-supervised video object localization is a challenging yet important task. The system should spatially localize the object of interest in videos, where only the descriptive sentences and their corresponding video segments are given in the training stage. Recent efforts propose to apply image-based Multiple Instance Learning (MIL) theory in this video task, and propagate the supervision from the video into frames by applying different frame-weighting strategies. Despite their promising progress, the spatio-temporal correlation between different object regions in videos has been largely ignored. To fill the research gap, in this work we introduce a simple but effective feature expression and aggregation framework, which utilizes the self-attention mechanism to capture the latent spatio-temporal correlation between multimodal object features and design a multimodal interaction module to model the similarity between the semantic query in sentences and the object regions in videos. We conduct extensive experimental evaluation on the YouCookII and ActivityNet-Entities datasets, which demonstrates significant improvements over multiple competitive baselines.
An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Storing images consumes a lot of storage space due to the large number of bits used to represent them.These bits are comprised of pixels that make up the image.These heavy images are also very difficult to be transmitted over channels due to their great size.Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image.There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images.This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)based techniques for satellite image compression.The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region.The areas with higher bits are reduced and compression is achieved.A hybrid methodology of Agile and Waterfall model was used in this approach.The model was implemented using MATLAB and satellite images gotten from a public repository.The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression.The Mean Square Error (MSE) was 2.51 which is low.This study will be beneficial to remote sensor companies, Graphic designers and the research community.
CONSTRUCTION OF A SOMATOSENSORY INTERACTIVE SYSTEM BASED ON COMPUTER VISION AND AUGMENTED REALITY TECHNIQUES USING THE KINECT DEVICE FOR FOOD AND AGRICULTURAL EDUCATION

A somatosensory interactive system based on computer vision and augmented reality (AR) techniques using the Kinect device is proposed, on which a game of harvesting three kinds of fruit can be played for food and agricultural education. The Kinect is used to capture users’ motion images, the Unity3D is used as the game engine, and the Kinect SDK is used for developing programs, to implement the tasks of face detection and tracking, hand-gesture recognition, and body-model matching and tracking involved in fruit-harvesting activities. AR-based photos of the harvest result can be taken and downloaded as souvenirs. The system was exhibited and observations of the users’ performances as well as interviews with experts and the users were conducted. The collected opinions were used to evaluate the effectiveness of the system, reaching the following conclusions: 1) the interactive experience of using this system is simple and intuitive; 2) the use of body movements for man-machine interaction is given positive reviews; 3) the introduction of somatosensory interactive education can arouse participants’ interest, achieving the effect of edutainment; and 4) the experience of taking commemorative photos can achieve the publicity and promotion effect of food and agricultural education through sharing on social media.
Improving transfer of expressivity for end-to-end multispeaker text-to-speech synthesis

The main goal of this work is to generate expressive speech in different speaker's voices for which no expressive speech data is available. The presented approach conditions Tacotron 2 speech synthesis with latent representations extracted from text, speaker identity, and reference expressive Mel spectrogram. We propose to use multiclass N-pair loss in the end-to-end multispeaker expressive Text-To-Speech (TTS) for improving the transfer of expressivity to the target speaker's voice. We have jointly trained the end-to-end (E2E) TTS with multiclass N-pair loss to discriminate between various emotions. This augmentation of the loss function during training paves the way to enhance the latent space representation of emotions. We have experimented with two different neural network architectures for expressivity in the encoder, namely global style token (GST) and variational autoencoder (VAE). We transferred the expressivity using the mean of latent representation extracted from the expressivity encoder for each emotion. The obtained results show that adding multiclass N-pair loss based deep metric learning in the training process improves expressivity in the desired speaker's voice.
Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

&lt;p class="Abstract"&gt;&lt;span id="docs-internal-guid-d3fe8e21-7fff-17fc-df0e-00893428243c"&gt;&lt;span&gt;The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
COVID-19: cross-border contact tracing in Germany, February to April 2020

IntroductionThe Robert Koch Institute (RKI) managed the exchange of cross-border contact tracing data between public health authorities (PHA) in Germany and abroad during the early COVID-19 pandemic.AimWe describe the extent of cross-border contact tracing and its challenges.MethodsWe analysed cross-border COVID-19 contact tracing events from 3 February to 5 April 2020 using information exchanged through the European Early Warning Response System and communication with International Health Regulation national focal points. We described events by PHA, number of contacts and exposure context.ResultsThe RKI processed 467 events, initiating contact to PHA 1,099 times (median = 1; interquartile range (IQR): 1-2) and sharing data on 5,099 contact persons. Of 327 (70%) events with known exposure context, the most commonly reported exposures were aircraft (n = 64; 20%), cruise ships (n = 24; 7%) and non-transport contexts (n = 210; 64%). Cruise ship and aircraft exposures generated more contacts with authorities (median = 10; IQR: 2-16, median = 4; IQR: 2-11) and more contact persons (median = 60; IQR: 9-269, median = 2; IQR: 1-3) than non-transport exposures (median = 1; IQR: 1-6 and median = 1; IQR: 1-2). The median time spent on contact tracing was highest for cruise ships: 5 days (IQR: 3-9).ConclusionIn the COVID-19 pandemic, cross-border contact tracing is considered a critical component of the outbreak response. While only a minority of international contact tracing activities were related to exposure events in transport, they contributed substantially to the workload. The numerous communications highlight the need for fast and efficient global outbreak communication channels between PHA.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient

Metrics optimized in complex machine learning tasks are often selected in an ad-hoc manner. It is unknown how they align with human expert perception. We explore the correlations between established quantitative segmentation quality metrics and qualitative evaluations by professionally trained human raters. Therefore, we conduct psychophysical experiments for two complex biomedical semantic segmentation problems. We discover that current standard metrics and loss functions correlate only moderately with the segmentation quality assessment of experts. Importantly, this effect is particularly pronounced for clinically relevant structures, such as the enhancing tumor compartment of glioma in brain magnetic resonance and grey matter in ultrasound imaging. It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training. To cope with this challenge, we propose a novel strategy employing techniques of classical statistics to create complementary compound loss functions to better approximate human expert perception. Across all rating experiments, human experts consistently scored computer-generated segmentations better than the human-curated reference labels. Our results, therefore, strongly question many current practices in medical image segmentation and provide meaningful cues for future research.
A UAV Maneuver Decision-Making Algorithm for Autonomous Airdrop Based on Deep Reinforcement Learning

How to operate an unmanned aerial vehicle (UAV) safely and efficiently in an interactive environment is challenging. A large amount of research has been devoted to improve the intelligence of a UAV while performing a mission, where finding an optimal maneuver decision-making policy of the UAV has become one of the key issues when we attempt to enable the UAV autonomy. In this paper, we propose a maneuver decision-making algorithm based on deep reinforcement learning, which generates efficient maneuvers for a UAV agent to execute the airdrop mission autonomously in an interactive environment. Particularly, the training set of the learning algorithm by the Prioritized Experience Replay is constructed, that can accelerate the convergence speed of decision network training in the algorithm. It is shown that a desirable and effective maneuver decision-making policy can be found by extensive experimental results.
Science communication for social inclusion: exploring science &amp; art approaches

Engaging communities at risk of social exclusion poses a big challenge for science communicators. We schematize a framework for projects using science &amp; art to promote social inclusion, composed of 3 phases — design, plan and collaboration; implementation; and evaluation. We present a case study that aimed to engage with a community of migrant senior women, mostly illiterate. Our findings suggest high engagement was achieved by building trust, involving emotions, choosing a relatable topic and following participatory practices. Inclusive activities occurred on the short-term, but for medium-term impact, community insiders need to be regarded as a second audience.
A Reinforcement Learning Assisted Eye-Driven Computer Game Employing a Decision Tree-Based Approach and CNN Classification

Human-Machine Interfaces employing biosignal-based inputs are hard to translate to real-life applications, in part because of the difficulty of developing generalized models to classify physiological events representing a user's actions. In the proposed framework, an Electrooculography (EOG)-based game is operated through a pipeline of decision methods. These include a user-independent classification model of eye movements using a Convolutional Neural Network (CNN), which is fed with images created from signal windows, and an Ensemble of Utility Decision Networks (EUDN), which moderates the impact of oftentimes conflicting ocular events while enabling a more natural level of control over the interface. The CNN and the EUDN replace the normally used feature-based ocular event detection methods for EOG. Finally, a Reinforcement Learning-based game actuation approach simultaneously updates multiple (State, Action) pairs for each rewarded outcome, intervenes to mitigate the consequences of wrongful game Commands, and can be used as part of a “shared-control” paradigm based on EOG. Results show a positive impact of Reinforcement Learning both in improving participants' game performance as well as in reducing some of their subjective workload indicators.