Reviewerly/exHarmony huggingface dataset:
Color Image Enhancement of Acute Leukemia Cells in Blood Microscopic Image for Leukemia Detection Sample

Leukemia is a type of cancer that affects the white blood cell. Early detection of leukemia is important to reduce the rate of mortality. In order to detect acute leukemia, conventional screening method based on microscopic image is used, where sample of blood cell will be taken from the suspected leukemia patient and manually white blood cell (WBC) condition is observed using microscope. The manual screening process is tedious, time consuming and usually prone to error due to low contrast between the nucleus and cytoplasm of WBCs. This report introduces a new enhancement method which is a combination of Particle swarm optimization (PSO) algorithm and contrast stretching, known as Hybrid PSO-Contrast stretching (HPSO-CS). The PSO has been used to optimize the fitness criterion in order to improve the contrast and detail in microscopic image by adapting the parameters as a contribution to enhancement technique. In this study, PSO algorithm is used to perform image segmentation to remove all the unwanted part such as red blood cell (RBC), platelet and also the background while retain the WBC part. The segmentation algorithm uses saturation S-component based on Hue, Saturation, Intensity (HSI) color model. After the segmentation is done, contrast stretching process is applied to the original image to stretch intensity of the pixel. Then the segmented image is combined with the resultant image that has been stretched to produce the enhanced image. The results of the proposed method are evaluated by using mean-square error (MSE), Peak-signal-to-noise-ratio (PSNR) and Absolute mean brightness error (AMBE). This proposed method is benchmarked by comparing against two image enhancement methods, global enhancement and Class Limited Adaptive Histogram Equalization (CLAHE). Based on the results, it can be concluded that quality of the enhanced image for the proposed method is much better with the lowest MSE (2067.651), AMBE (43.51827) and highest PSNR (14.98671) compared to the global and CLAHE method. The proposed method can improve the screening process and assist haematologist in leukemia screening and detection by improving the visual appearance of the WBC.
Data-flow-based adaption of the System-Theoretic Process Analysis for Security (STPA-Sec)

Security analysis is an essential activity in security engineering to identify potential system vulnerabilities and achieve security requirements in the early design phases. Due to the increasing complexity of modern systems, traditional approaches, which only consider component failures and simple cause-and-effect linkages, lack the power to identify insecure incidents caused by complex interactions among physical systems, human and social entities. By contrast, a top-down System-Theoretic Process Analysis for Security (STPA-Sec) approach views losses as resulting from interactions, focuses on controlling system vulnerabilities instead of external threats and is applicable for complex socio-technical systems. In this paper, we proposed an extension of STPA-Sec based on data flow structures to overcome STPA-Sec's limitations and achieve security constraints of information-critical systems systematically. We analyzed a Bluetooth digital key system of a vehicle by using both the proposed and the original approach to investigate the relationship and differences between both approaches as well as their applicability and highlights. To conclude, the proposed approach can identify more information-related problems with technical details and be used with other STPA-based approaches to co-design systems in multi-disciplines under the unified STPA process framework.
Analogical proportions

Abstract Analogy-making is at the core of human and artificial intelligence and creativity with applications to such diverse tasks as proving mathematical theorems and building mathematical theories, common sense reasoning, learning, language acquisition, and story telling. This paper introduces from first principles an abstract algebraic framework of analogical proportions of the form ‘ a is to b what c is to d ’ in the general setting of universal algebra. This enables us to compare mathematical objects possibly across different domains in a uniform way which is crucial for AI-systems. It turns out that our notion of analogical proportions has appealing mathematical properties. As we construct our model from first principles using only elementary concepts of universal algebra, and since our model questions some basic properties of analogical proportions presupposed in the literature, to convince the reader of the plausibility of our model we show that it can be naturally embedded into first-order logic via model-theoretic types and prove from that perspective that analogical proportions are compatible with structure-preserving mappings. This provides conceptual evidence for its applicability. In a broader sense, this paper is a first step towards a theory of analogical reasoning and learning systems with potential applications to fundamental AI-problems like common sense reasoning and computational learning and creativity.
Adversarial Confidence Estimation Networks for Robust Stereo Matching

Stereo matching aiming to perceive the 3-D geometry of a scene facilitates numerous computer vision tasks used in advanced driver assistance systems (ADAS). Although numerous methods have been proposed for this task by leveraging deep convolutional neural networks (CNNs), stereo matching still remains an unsolved problem due to its inherent matching ambiguities. To overcome these limitations, we present a method for jointly estimating disparity and confidence from stereo image pairs through deep networks. We accomplish this through a minmax optimization to learn the generative cost aggregation networks and discriminative confidence estimation networks in an adversarial manner. Concretely, the generative cost aggregation networks are trained to accurately generate disparities at both confident and unconfident pixels from an input matching cost that are indistinguishable by the discriminative confidence estimation networks, while the discriminative confidence estimation networks are trained to distinguish the confident and unconfident disparities. In addition, to fully exploit complementary information of matching cost, disparity, and color image in confidence estimation, we present a dynamic fusion module. Experimental results show that this model outperforms the state-of-the-art methods on various benchmarks including real driving scenes.
GPS Tracking App for Police to Track Ambulances Carrying COVID-19 Patients for Ensuring Safe Distancing

The outbreak of the SARS-CoV-2 virus is causing loss of lives and property all over the world. There have been more than 2.1 million cases of COVID-19 with a death of more than 1.2 lakh patients worldwide and the numbers are still rising. The virus spreads rapidly by the droplets coming out from the nose and mouth of an infected person (Sandoiu in Why does SARS-CoV-2 spread so easily? Medical news today, 2020 https://www.medicalnewstoday.com/articles/why-does-sars-cov-2-spread-so-easily ). In this situation, proper quarantining and monitoring of the already infected patients are very essential. In cases where patients need to be transferred to different locations by ambulances, monitoring of these ambulances by the traffic police can help to ensure distancing and faster movement of the vehicle inside the city. This paper presents the development of a Real-time Global Positioning System-based tracking app for the ambulances carrying COVID-19 patients which would help traffic police to ensure distancing the patients from the public.
Differentiable Mask for Pruning Convolutional and Recurrent Networks

Pruning is one of the most effective model reduction techniques. Deep networks require massive computation and such models need to be compressed to bring them on edge devices. Most existing pruning techniques are focused on vision-based models like convolutional networks, while text-based models are still evolving. The emergence of multi-modal multi-task learning calls for a general method that works on vision and text architectures simultaneously. We introduce a differentiable mask, that induces sparsity on various granularity to fill this gap. We apply our method successfully to prune weights, filters, subnetwork of a convolutional architecture, as well as nodes of a recurrent network.
Foundations of Fully Dynamic Group Signatures

Abstract Group signatures allow members of a group to anonymously sign on behalf of the group. Membership is administered by a designated group manager. The group manager can also reveal the identity of a signer if and when needed to enforce accountability and deter abuse. For group signatures to be applicable in practice, they need to support fully dynamic groups, i.e., users may join and leave at any time. Existing security definitions for fully dynamic group signatures are informal, have shortcomings, and are mutually incompatible. We fill the gap by providing a formal rigorous security model for fully dynamic group signatures. Our model is general and is not tailored toward a specific design paradigm and can therefore, as we show, be used to argue about the security of different existing constructions following different design paradigms. Our definitions are stringent and when possible incorporate protection against maliciously chosen keys. We consider both the case where the group management and tracing signatures are administered by the same authority, i.e., a single group manager, and also the case where those roles are administered by two separate authorities, i.e., a group manager and an opening authority. We also show that a specialization of our model captures existing models for static and partially dynamic schemes. In the process, we identify a subtle gap in the security achieved by group signatures using revocation lists. We show that in such schemes new members achieve a slightly weaker notion of traceability. The flexibility of our security model allows to capture such relaxation of traceability.
Earthquake Event Classification Using Multitasking Deep Learning

This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt.
HOTSPOT: Crossing the Air-Gap Between Isolated PCs and Nearby Smartphones Using Temperature

Air-gapped computers are hermetically isolated from the Internet to eliminate any means of information leakage. In this paper we present HOTSPOT - a new type of airgap crossing technique. Signals can be sent secretly from air-gapped computers to nearby smartphones and then on to the Internet - in the form of thermal pings. The thermal signals are generated by the CPUs and GPUs and intercepted by a nearby smartphone. We examine this covert channel and discuss other work in the field of air-gap covert communication channels. We present technical background and describe thermal sensing in modern smartphones. We implement a transmitter on the computer side and a receiver Android App on the smartphone side, and discuss the implementation details. We evaluate the covert channel and tested it in a typical work place. Our results show that it possible to send covert signals from air-gapped PCs to the attacker on the Internet through the thermal pings. We also propose countermeasures for this type of covert channel which has thus far been overlooked.
A novel approach to Steganography using pixel-based algorithm in image hiding

Steganography is the technique of hiding data under image to prevent it from being unintentionally accessed by anyone else. This process involves a plain text and an image file. By looking at the need of steganography we have proposed a new algorithm which will satisfy the aim of steganography. In our algorithm, we will have a cover image file and the message. Then the cover image’s pixel will be taken into consideration. In that we will embed each bit of secret text. This process will be continued until the last bit of secret text. After this step, the data is hidden under the image. Then we will send this image file to our client and client will have reverse process to retrieve original text from the image. We will then compare our algorithm with BLIND HIDE steganography algorithm on the basis of accuracy, precision, recall and f1-score. We will also check for the output image quality generated by both algorithms on structural similarity measure to reach proper consensus.
SafeTPU: A Verifiably Secure Hardware Accelerator for Deep Neural Networks

We present Safe-TPU, a framework for secure computations of Deep Neural Networks (DNNs) in untrusted hardware corrupted by Trojans or fault injection attacks. This work leverages previous advances on interactive proof (IP) systems for verifying, at run-time, the correctness of a neural network’s computations, and makes three new contributions: (1) We present a Trojan resilient DNN hardware accelerator based on interactive proofs; (2) We introduce new protocol enhancements that significantly reduce the space and time required to generate proofs; and (3) we propose an implementation of Safe-TPU with high parallelism and reuse of existing resources already deployed in the baseline DNN accelerator. We prototype Safe-TPU on an FPGA and analyze its security guarantees. Experimentally, we show that Safe-TPU’s area overhead is small (28%) over the baseline DNN accelerator and is 3.15× faster than state-of-the-art, while at the same time, Safe-TPU guarantees to catch, with high probability, any incorrect computations.
#DeleteFacebook: From Popular Protest to a New Model of Platform Capitalism?

In March 2018, a series of newspaper articles about the subversive use of the social media platform Facebook during various elections in 2016 were published. These revelations produced a backlash against the platform, which resulted in a popular hashtag, #DeleteFacebook.This article uses the #DeleteFacebook campaign as a case study to analyse the contemporary model of platform capitalism. Furthermore, by introducing emerging literature on data trusts – specifically, the notion of a bottom up data trust – this paper argues the #DeleteFacebook campaign reveals, both in its conception and in its ultimate failure, a desire for an alternative model of platform capitalism.By drawing on the #DeleteFacebook campaign, this article argues that a bottom up data trust designed to empower individual users can resolve many of the initial grievances, and barriers to success, experienced by the campaign.
Sensor tasking for search and catalog maintenance of geosynchronous space objects

The space object catalog provides orbital state and characteristic information of space objects for critical applications in space situational awareness. Maintaining accurate states for all objects in the catalog is essential, but it leads to large loads on sensors and limits the time available to search for new objects. This study proposes a novel sensor tasking method for search and catalog maintenance of space objects in Geosynchronous Earth Orbit (GEO). This new framework formulates sensor tasking as a multi-objective optimization problem. It seeks an optimal balance between sensor resources to search for new objects and to maintain precise state estimates for all objects in the catalog. In order to maintain custody of newly detected targets, an evidence-based decision-making method is used to effectively prompt follow-on tracking. The labeled multi-Bernoulli filter is employed to track existing and new space objects and provide refined orbital state estimation. Simulation results are presented, in which 100 cataloged GEO objects and 200 new GEO objects are tracked using a space-based sensor placed on a Sun-synchronous orbit.
Simple and precise multi-view camera calibration for 3D reconstruction

A precise calibration in multi-view camera environments allows to perform accurate 3D object reconstruction, precise tracking of objects and accurate pose estimation. Those techniques are of high value in the industry today in fields as quality control or automation. In the present work, an improvement of a simple existing multi-view camera calibration method is presented. The improved method employs a specially developed reference token to overcome some issues in the original algorithm. We prove that the new method overcomes those problems thus attaining a higher accuracy while keeping the process simple and the implementation costs low. This last aspects makes the method interesting for the industry but specially suitable for SMEs typical in traditional sectors.
Deep Audio-Visual Saliency: Baseline Model and Data

This paper introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed "DAVE" in conjunction with our efforts towards building an Audio-Visual Eye-tracking corpus named "AVE". Despite existing a strong relation between auditory and visual cues for guiding gaze during perception, video saliency models only consider visual cues and neglect the auditory information that is ubiquitous in dynamic scenes. Here, we propose a baseline deep audio-visual saliency model for multi-modal saliency prediction in the wild. Thus the proposed model is intentionally designed to be simple. A video baseline model is also developed on the same architecture to assess effectiveness of the audio-visual models on a fair basis. We demonstrate that audio-visual saliency model outperforms the video saliency models. The data and code are available at https://hrtavakoli.github.io/AVE/ and https://github.com/hrtavakoli/DAVE
Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results. Average aggregation is the most common aggregation method used in FCMs-based modelling for weighted interconnections between concepts. This paper proposes a new aggregation method using learning OWA (ordered weighted averaging) operators for aggregating FCM weights assigned by various stakeholders. Besides, we report a comparative analysis of 'OWA learning aggregation' with the conventional average aggregation method, while evaluating the theory of change for the world's most extensive poverty alleviation programme in India. The results of the FCMWizard web-based tool show that the proposed method provides an opportunity to policy-makers for evaluating outcomes of proposed policies while addressing social resilience and economic mobility.
A Coordinated Representation Learning Enhanced Multimodal Machine Translation Approach with Multi-Attention

In recent years, the application of machine translation has become more and more widely. Currently, the neural multimodal translation models have made attractive progress, which combines images into deep learning networks, such as Transformer and RNN. When considering images in translation models, they directly apply gate structure or image attention to introduce image feature to enhance the translation effect. We argue that it may mismatch the text and image features since they are in different semantic space. In this paper, we propose a coordinated representation learning enhanced multimodal machine translation approach with multimodal attention. Our approach accepts the text data and its relevant image data as the input. The image features are fed into the decoder side of the basic Transformer model. Moreover, the Coordinated Representation Learning is utilized to map the different text and image modal features into their semantic representations. The mapped representations are linearly related in a shared semantic space. Finally, the sum of the image and text representations, called Coordinated Visual-Semantic Representation (CVSR), will be sent to a Multimodal Attention Layer (MAL) in our Transformer based translation approach. Experimental results show that our approach achieves the state-of-art performance on the public Multi30k dataset.
Model-based information navigation for engineering documents

Engineering and the manner in which engineers think is largely visual and functional, and yet engineers are typically provided with search engines that are text-based. While software based on a visual and functional ethos exist (CAD for example), when searching for information engineers are still required to enter a text query into a search box. This process holds potential incompatibilities both with the nature of the data (i.e. 3D models) and with the way in which engineers think and work. Consequentially, the proposition tested in this paper is that a model-based approach to information access, i.e. a representation of an organisations information around a model of an artefact i.e. CAD model, can improve engineering information retrieval. In an A-B test with a traditional text-based search engine, and using study questions derived from real-world information seeking scenarios based on the activities of a world-leading aircraft manufacturer, the results presented in this paper suggest that there is merit to such an approach. Specifically, this paper shows that there is no significant difference in time to complete a search between a model-based and text-based interfaces in spite of the addition of a new stage in the search process (navigating a 3D model); that the system structure of the model-based interface allows for non-text based documents to be indexed, making up for the inherent limitations in traditional text-based search; and that participants enjoy using the model-based interface and find it intuitive, easy and simple to use. Further, this paper also finds that those with more experience/familiar with the product structure and those in managerial positions are more likely to find information using a model-based interface that those who are not, who perform better using a text-based interface.
Fringe projection decamouflaging

Unveiling the cheating mask of camouflage is of great significance in scientific and military research, but the acquisition of detailed information about camouflaged objects remains extremely difficult. This paper presents a fringe projection decamouflaging (FPDC) approach that can provide the position, outer rim profile, and shadow information about well-camouflaged objects in a complex scenario. FPDC requires no expensive instruments, just one camera and one projector for monitoring. A bionic detection field, namely, a phase-jump field, is established to detect the intrusion of camouflaged objects. Based on local variations in this field, multiple-parameter decamouflaging is achieved for the first time. Simulations and experiments show the superiority of this technique, which has potential applications in counterespionage, discovery of wild animals, and search-and-rescue.