Reviewerly/exHarmony huggingface dataset:
Visible-Assisted Infrared Image Super-Resolution Based on Spatial Attention Residual Network

Infrared images have a wide range of applications in military and civilian fields, including night vision, surveillance, and robotics. However, the most commonly used infrared images are low-resolution (LR), which lack texture details, and existing infrared image super-resolution (SR) algorithms are limited by the lack of spatial information utilization. To solve the above problems, a spatial attention residual network (SAResNet) is proposed. Specifically, the network consists of spatial attention residual block (SARB) with several short skip connections (SSCs). The SARB contains 20 spatial attention blocks (SAB), which adaptively adjusts weights of different spatial regions by considering interdependence between spatial features. Meanwhile, the visible images are considered as complementary sources; thus, a visible-assisted training strategy is designed for the infrared SR process, promoting details preservation. Furthermore, the spatial attention (SA) mechanism is utilized, which focuses more on spatial characteristics of the image and refines the main objects and target boundaries. Experimentally, the proposed method, SAResNet, is compared with existing SR methods, and the effectiveness of the proposed method is demonstrated based on both quantity and quality analyses.
Cybersecurity Threats in FinTech

The rise of digital information has given birth to cyber frauds such as fraudulent transactions, extortion, denial of service attacks, and credit card fraud. The number of cyber incidents is growing rapidly. In order to curb these incidents, it is imperative to understand what are the cyber threats and what motivations drive these cyber incidents. There are several threat categories that pose severe cyber challenges to FinTech industry across the globe. The threat actors are politically, financially, and religiously motivated to launch cyberattacks. Threat modeling is the key to getting rid of cyber threats. FinTech threat modeling follows a structural approach that focuses on attacks, attackers, software, and assets. Several threat modeling methodologies such as STRIDE, Trike, VAST, and PASTA, can be applied to protect FinTech institutions. However, PASTA is the best fit for FinTech owing to the type of threat intelligence required for FinTech.
ACDNet with ASPP for Camouflaged Object Detection

Abstract This paper proposes a camouflage identification network ACDNet, which can perform end-to-end search and identification simply and efficiently. Use ASPP (Atrous Spatial Pyramid Pooling) to enhance the image features of camouflaged object, and then connect the Parallel Decoder (PD) module to get the global map, and then use the Group-Reversal Block (GRB) module to connect sequentially, refine the details, and finally perform deep supervision learning got result. Compared with the current 13 methods, ACDNet has a significant performance improvement in accuracy and similarity, and it is one of the potential solutions for camouflage identification.
The Importance of Clinical Data for the Diagnosis of Breast Tumours in North Afghanistan

This study was performed in knowledge of the increasing gap between breast disease treatment in countries with restricted resources and developed countries with increasingly sophisticated examination methods.The authors present the analysis of a breast disease register consisting of diagnostic cases from Mazar e Sharif and Herat in 2018 and 2019. The study comprises a total of 567 cases, which were presented to experts via telemedicine for final diagnosis. 62 cases (10.9%) were excluded due to inacceptable data or insufficient image quality. These data provided by daily diagnostic classification were used for the built-up of a profile for each frequent breast disease and a breast cancer register. All images and cases were seen by at least 3 independent experts. The diagnoses were made in 60% of cases by cytology of fine needle aspiration and in 40% by histological images.For each entity of breast diseases (e.g., fibroadenoma), a profile of context variables was constructed allowing to assist medical decisions, as "wait and see," elective surgery or immediate surgical intervention with R0 (complete) resection. These "profiles" could be described for fibroadenoma, mastitis, galactocele, fibrous-cystic disease, and invasive breast cancer.The presented preliminary data set could serve as a cost-effective basis for a North Afghan breast cancer registry, with option to extent to a national model. These preliminary data are transformed in profiles of breast diseases, which are used by the local physicians in charge of breast disease patients. Each new case can be compared by the local treating physician with the profile of all preceded cases with the same diagnosis.
Attention U-Net Architecture for Dental Image Segmentation

Dental image segmentation is a very challenging task due to the presence of multiple teeth in the X-Ray and due to its different size in each human being. U-Net Architecture has been used in the Pancreas Segmentation [1] and Lung Segmentation [2] but the feature map of dental image is different from that of the lungs and pancreas. Technology used in the screening of the lungs and pancreas is CT Scan which is different form the standard X-Ray. To adapt the dental image feature we have proposed a U-Net Architecture [3] along with the attention gate [4]. U-Net Architecture [3] created by Olaf Ronneberger, Thomas Brox and Philipp Fischer is generally used in biomedical segmentation of images. It uses encoder and decoder architecture for the image segmentation but still there may be information losses while encoding the layers for feature map and unnecessary information is passed using the skip connection while decoding in standard U-Net Architecture. Limitation of the plain skip connection can be solved using the Attention block. Using Attention block in the U-Net model will further increase the sensitivity and prediction accuracy with minimum computational overhead. Model trained with attention block self-learn themselves. Any irrelevant regions in the input image are suppressed, and hence only the important features are highlighted. In our network architecture we have achieved the accuracy of 92.8 percent and 94.7 percent dice score.
Quantitative evaluation of footwear evidence: Initial workflow for an end‐to‐end system

In the United States, footwear examiners make decisions about the sources of crime scene shoe impressions using subjective criteria. This has raised questions about the accuracy, repeatability, reproducibility, and scientific validity of footwear examinations. Currently, most footwear examiners follow a workflow that compares a questioned and test impression with regard to outsole design, size, wear, and randomly acquired characteristics (RACs). We augment this workflow with computer algorithms and statistical analysis so as to improve in the following areas: (1) quantifying the degree of correspondence between the questioned and test impressions with respect to design, size, wear, and RACs, (2) reducing the potential for cognitive bias, and (3) providing an empirical basis for examiner conclusions by developing a reference database of case-relevant pairs of impressions containing known mated and known nonmated impressions. Our end-to-end workflow facilitates all three of these points and is directly relatable to current practice. We demonstrate the workflow, which includes obtaining and interpreting outsole pattern scores, RAC comparison scores, and final scores, on two scenarios-a pristine example (involving very high quality Everspry EverOS scanner impressions) and a mock crime scene example that more closely resembles real casework. These examples not only demonstrate the workflow but also help identify the algorithmic, computational, and statistical challenges involved in improving the system for eventual deployment in casework.
‘Choice Is Yours’: Anatomy of a Lesson Plan from University V

Abstract This chapter aims to explore education as posthuman practice via the anatomy of a lesson plan. The lesson is narrated through the methodological device of speculative fiction. It is a fabulation set in the future but with roots that tangle with the past. Dark histories and futures are set to flicker here. Deception, de-identification and datafication lurk everywhere. If you are squeamish, you may wish to read no further. The datafication of people, their reduction to numbers, bytes and, most fatally of all, words, is laid out here in gory detail. If you do wish to read on, however, then you need nothing: just come as you are, and be assured as always that as the reader, choice is yours.
Attention-like feature explanation for tabular data

A new method for local and global explanation of the machine learning black-box model predictions by tabular data is proposed. It is implemented as a system called AFEX (Attention-like Feature EXplanation) and consisting of two main parts. The first part is a set of the one-feature neural subnetworks, which aim to get a specific representation for every feature in the form of a basis of shape functions. The subnetworks use shortcut connections with trainable parameters to improve the network training performance. The second part of AFEX produces shape functions of features as the weighted sum of the basis shape functions where weights are computed by using an attention-like mechanism. The most important advantage of AFEX is that it identifies pairwise interactions between features based on pairwise multiplications of shape functions corresponding to different features. A modification of AFEX with incorporating an additional surrogate model, which approximates the black-box model, is proposed. AFEX is trained end-to-end on a whole dataset only once such that it does not require to train neural networks again in the explanation stage. Numerical experiments with synthetic and real data illustrate AFEX. The corresponding code implementing the method is publicly available.
Relational Analysis of Sensor Attacks on Cyber-Physical Systems

Cyber-physical systems, such as self-driving cars or autonomous aircraft, must defend against attacks that target sensor hardware. Analyzing system design can help engineers understand how a compromised sensor could impact the system's behavior; however, designing security analyses for cyber-physical systems is difficult due to their combination of discrete dynamics, continuous dynamics, and nondeterminism. This paper contributes a framework for modeling and analyzing sensor attacks on cyber-physical systems, using the formalism of hybrid programs. We formalize and analyze two relational properties of a system's robustness. These relational properties respectively express (1) whether a system's safety property can be influenced by sensor attacks, and (2) whether a system's high-integrity state can be affected by sensor attacks. We characterize these relational properties by defining an equivalence relation between a system under attack and the original unattacked system. That is, the system satisfies the robustness properties if executions of the attacked system are appropriately related to executions of the unattacked system. We present two techniques for reasoning about the equivalence relation and thus proving the relational properties for a system. One proof technique decomposes large proof obligations to smaller proof obligations. The other proof technique adapts the self-composition technique from the literature on secure information-flow, allowing us to reduce reasoning about the equivalence of two systems to reasoning about properties of a single system. This technique allows us to reuse existing tools for reasoning about properties of hybrid programs, but is challenging due to the combination of discrete dynamics, continuous dynamics, and nondeterminism. To validate the usefulness of our relational properties and proof techniques, we present three case studies motivated by real design flaws in existing cyber-physical systems.
Ensemble learning with siamese networks for visual tracking

• An end-to-end trainable framework is proposed that allows Siamese models to learn to each other. To the best of our knowledge, this is the first successful demonstration of collaborative learning for visual tracking tasks. • Two new loss functions are introduced for the EL framework to address the challenges mentioned before. In particular, we propose a KL divergence based objective, named Hard-example Aware Loss, which solves the imbalanced classification between background and foreground. Besides, a novel Response Map Loss and adaptation layer are designed for hint knowledge transformation and distillation. RMLoss allows each student to learn a better neurons distribution in intermediate layers from each other. • We comprehensively validate the positive improvement on multiple public benchmarks. The EL is performed on four Siamese networks (SiamFC, SiamRPN, SiamFC+, and SiamRPN+) to prove the feasibility and effectiveness of our innovation. It is worth mentioning that the proposed framework is only used in the training phase and brings no additional cost during the deployment. Ensemble learning (EL) is an effective and commonly used technique to improve visual tasks’ accuracy, such as classification and detection. However, EL is rarely used in visual tracking. To fill this knowledge gap, we first have completed some research to investigate why knowledge distillation was ineffective in visual tracking tasks. Comparing the difference between the classification and visual tracking, conclusions are given: (i) Numerous simple negative examples are redundant, while only a few hard negative samples are valid for visual tracking knowledge distillation. (ii) The hint knowledge flows differently between classification and visual tracking. To solve the above problems, we design two new loss functions and integrate them into the proposed Ensemble Learning (EL) framework that can be employed in Siamese architectures such as SiamFC, SiamRPN, SiamFC+, and SiamRPN+. The EL treats two Siamese networks as students and enables them to learn collaboratively. A better solution is yielded by the EL framework than training students individually. Experiments on OTB-2013 , OTB-2015 , VOT2015 , VOT2016 , VOT2017 , VOT2018 , LaSOT and TrackingNet have verified the effectiveness of our proposed technique on boosting the performance for the four Siamese algorithms. The EL-SiamRPN+ achieves leading performance in the challenges.
An Open Source Power System Simulator in Python for Efficient Prototyping of WAMPAC Applications

An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance.The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows:First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.
Towards Automated Detection of Contradictory Research Claims in Medical Literature Using Deep Learning Approach

In Evidence-Based Medicine (EBM), medical literature is an essential resource used by clinicians and researchers. It contains research claims that summarize the critical findings of a study. However, research claims on the same topic can be contradicting. Given a clinical question, if two claims that answer the question have conflicting assertion values (Yes or No), they are considered contradictory claims. Hence, discovering the claim assertion value of a research claim is the key to detecting contradictory research claims in medical literature. In this study, we explored the usage of deep neural network (DNN) to recognize contradictory research claims in medical literature. The DNN model should determine the assertion value of a research claim against its clinical question. The model was evaluated using a publicly available corpus containing contradictory research claims from 24 systematic reviews on cardiovascular topic. Different DNN techniques such as the Global Vectors for Word Representation (GLoVe), bidirectional Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) were implemented in building the claim assertion model. The evaluation results suggest that the BERT model performs better than LSTM and GloVe model and outperforms the previous studies’ models.
High-speed simultaneous measurement of depth and normal for real-time 3D reconstruction

Depth measurement and normal measurement have an advantage in different spatial frequency and are complementary in 3D shape measurement. However, conventional measurement of depth and normal is performed exclusively or in time-division manner and high-speed simultaneous measurement has not been achieved. In this paper, we propose a new optical system setup for high-speed simultaneous measurement of depth and normal with an active stereo method and a photometric stereo method. Furthermore, we propose a high-speed 3D shape reconstruction method using GPU, which combines complementary information obtained from the two measurements. We evaluated the throughput of the prototype system and the result shows high-speed depth and normal simultaneous measurement and 3D shape reconstruction are performed at 500fps.
Engaging locals in rural areas: value correspondence in placemaking through mobile augmented reality

This research proposes value correspondence, which posits that the value shared in correspondence between stakeholders is essential for the development of placemaking in rural areas. By investigating location-based media; particularly mobile augmented reality (MAR) apps aimed at both reviving local tourism in rural townships and soliciting opinions from major stakeholders, the author has determined that local residents play a vital role in the placemaking process. However, research in this field has often ignored local residents and instead focused on tourists’ perceptions and augmented reality technology. Various MAR apps have been designed as forms of self-service technology that benefits tourists but, to some extent, also hinders dialog between tourists and locals; this results in value noncorrespondence, which may lead to the failure of placemaking. Thus, the author advocates that more attention should be paid to how locals can generate value in correspondence with other stakeholders via the use of MAR.
Machine learning forensics to gauge the likelihood of fraud in emails

Digitization leads to unarguable high speed processing but our data is becoming the soft target for many types of cybercrimes. Even though digital forensic investigation is growing to recover volatile and non-volatile data from suspicious locations, manual investigation for evidence acquisition and proving the admissible evidential artifacts is still tedious and time consuming. Automation in this field of digital forensic investigation is the need of the hour to reduce large interactions with humans and to avoid slowing down of investigation process in the pace of growing digital crimes. The human-like behavior depicted by the Machine Learning (ML) to support automation in digital forensic investigation and to help forensic investigators at various stages of digital investigation is the motivation for the proposed work. This paper proposes a machine learning forensics prototype to predict the fraudulent emails in advance to avoid victimization of cyber-crimes. The model applies supervised machine learning technique through an algorithm of Enhanced Forensics Fuzzy C-Means Clustering (EFFCM). The fraudulent components are identified and chain of custody is reported in the training and testing phases performed on Enron email dataset through Weka tool in Cloud environment. The results are comparatively presented in the variations of accuracy in training and testing data partitions and in email application. The prototype presented in this paper can greatly aid the automation in digital forensic investigation for the prediction of fraudulent emails.
A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training

Facial manipulation enables facial expressions to be tampered with or facial identities to be replaced in videos. The fake videos are so realistic that they are even difficult for human eyes to distinguish. This poses a great threat to social and public information security. A number of facial manipulation detectors have been proposed to address this threat. However, previous studies have shown that the accuracy of these detectors is sensitive to adversarial examples. The existing defense methods are very limited in terms of applicable scenes and defense effects. This paper proposes a new defense strategy for facial manipulation detectors, which combines a passive defense method, bilateral filtering, and a proactive defense method, joint adversarial training, to mitigate the vulnerability of facial manipulation detectors against adversarial examples. The bilateral filtering method is applied in the preprocessing stage of the model without any modification to denoise the input adversarial examples. The joint adversarial training starts from the training stage of the model, which mixes various adversarial examples and original examples to train the model. The introduction of joint adversarial training can train a model that defends against multiple adversarial attacks. The experimental results show that the proposed defense strategy positively helps facial manipulation detectors counter adversarial examples.
Interpretable Trade-offs Between Robot Task Accuracy and Compute Efficiency

A robot can invoke heterogeneous computation resources such as CPUs, cloud GPU servers, or even human computation for achieving a high-level goal. The problem of invoking an appropriate computation model so that it will successfully complete a task while keeping its compute and energy costs within a budget is called a model selection problem. In this paper, we present an optimal solution to the model selection problem with two compute models, the first being fast but less accurate, and the second being slow but more accurate. The main insight behind our solution is that a robot should invoke the slower compute model only when the benefits from the gain in accuracy outweigh the computational costs. We show that such cost-benefit analysis can be performed by leveraging the statistical correlation between the accuracy of fast and slow compute models. We demonstrate the broad applicability of our approach to diverse problems such as perception using neural networks and safe navigation of a simulated Mars rover.