Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images

Abnormality detection in medical images is a one-class classification problem for which existing methods typically involve variants of kernel principal component analysis or one-class support vector machines. However, existing methods rely on highly-curated training sets with full supervision, often using heuristics for model fitting or ignore the variances of the data within principal subspaces. In contrast, we propose novel methods that can work with imperfectly curated datasets using robust statistical learning, by extending the multivariate generalized-Gaussian distribution to a reproducing kernel Hilbert space (RKHS) and employing it within a mixture model. We propose a novel semi-supervised extension of our learning scheme, showing that a small amount of expert feedback through high-quality labeled data of the outlier class can boost performance. We propose expectation maximization for our semi-supervised robust mixture-model learning in RKHS, using solely the Gram matrix and without the explicit lifting map. Our methods incorporate optimal component means, principal directions, and variances for abnormality detection. Results on four large public datasets on retinopathy and cancer, compared against a variety of contemporary methods, show that our method gives benefits over the state of the art in one-class classification for abnormality detection.
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

The existing approaches for salient motion segmentation are unable to explicitly learn geometric cues and often give false detections on prominent static objects. We exploit multiview geometric constraints to avoid such shortcomings. To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features. We find dense trajectories, covering every pixel in the video, and propose trajectory-based epipolar distances to distinguish between background and foreground regions. Trajectory epipolar distances are dataindependent and can be readily computed given a few features' correspondences between the images. We show that by combining epipolar distances with optical flow, a powerful motion network can be learned. Enabling the network to leverage both of these features, we propose a simple mechanism, we call input-dropout. Comparing the motion-only networks, we outperform the previous state of the art on DAVIS-2016 dataset by 5.2% in the mean IoU score. By robustly fusing our motion network with an appearance network using the input-dropout mechanism, we also outperform the previous methods on DAVIS-2016, 2017 and Segtrackv2 dataset.
Multispectral Remote Sensing Image Matching via Image Transfer by Regularized Conditional Generative Adversarial Networks and Local Feature

Multispectral image matching is at the base for many remote sensing and computer vision applications. Due to the different imaging principles and spectra, there are significant nonlinear variations in intensity, texture, and style in multispectral images. This makes it difficult for many classic methods designed for the images of the same spectrum to achieve satisfactory matching performance. To cope with this problem, this letter proposes a new method based on image transfer and local feature for multispectral image matching. First, we propose a new regularized conditional generative adversarial network (GAN) for image transfer to preprocess the multispectral images. This step eliminates the differences in grayscale, texture, and style between the multispectral images. Then, we use a classic local feature to match the generated and original images. We evaluate our method on two commonly used data sets and compare with several state-of-the-art methods. The experiments show that our method performs well by significantly improving the matching accuracy and robustness, and slightly increasing the runtime.
Sub-Pixel Edge Detection Algorithm Based on Canny–Zernike Moment Method

In order to solve the problems of low efficiency and long running time caused by the traditional Zernike moment method for convolution calculation of the whole image, this paper combines the canny detection algorithm with the Zernike moment method. First, the canny edge detection algorithm, which combined with the Otsu threshold method, is used to extract the pixel edge of the image. Then an improved Hough transform method is used to fit the geometric edge in the image. Based on this, the Zernike moment method is applied to realize sub-pixel positioning of images. The algorithm improves the deficiencies of direct sub-pixel detection, improving accuracy and reducing running time. To verify the effectiveness of the proposed algorithm, the algorithm is applied to the dimension measurement experiment of T-type guide way. The results clearly show that the algorithm is superior to the traditional algorithm in accuracy.
Gaussian process regression with heteroscedastic noises — A machine-learning predictive variance approach

Gaussian process regression (GPR) is one of the most important data analytic tools in modelling processes. It has attracted increasing interest in chemical engineering applications due to its superior performance in dealing with complex modelling problems such as high-dimensional and nonlinear data. However, traditional GPR has the main limitation in that it considers an independent identically distributed (i.i.d.) noise at every sample point. Modern chemical processes typically have a more complex data structure and noise properties. The assumption of i.i.d. noise is not realistic. Thus, there is a growing interest in solving a heteroscedastic noise problem that does not satisfy the i.i.d. condition. The most common heteroscedastic noise is the noise with varying variance. This paper proposes a novel machine learning variance prediction method to solve the heteroskedastic GPR problem. By considering not only the input-dependent noise variance but also the input-output-dependent noise variance, a regression model based on support vector regression (SVR) and extreme learning machine (ELM) method is proposed for both noise variance prediction and smoothing. Compared with the existing weighted Gaussian process regression (W-GPR) of the literature, the proposed method not only expands the use of W-GPR but also improves the prediction performance of heteroscedastic GPR models. Finally, the proposed algorithm is verified by two numerical examples and tested in a real polyester polymerization process. The results all demonstrate the effectiveness of the proposed approach.
Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic

We present a processing pipeline for flow-based throughput classification based on a machine learning component using deep neural networks (DNNs) that is trained to predict the likely bit rate of a real-world network traffic flow ahead of time. The DNN is trained and evaluated on a flow data stream as well as on a reference dataset collected from a university data center. Predicted bit rates are quantized into three classes instead of the common binary classification into “mice” and “elephant” flows. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. We employ t-SNE (a state-of-the-art data visualization algorithm) to visualize network traffic data, thus enabling us to analyze and understand the characteristics of network traffic data and relations between communication flows at a glance. Additionally, an architecture for flow-based routing utilizing the developed pipeline is proposed as a possible use-case.
Detachable Body: The Impact of Binocular Disparity and Vibrotactile Feedback in Co-Presence Tasks

Detachable Body is a new concept of a robot arm wearable as an extended part of the body. It can be detached from the user's natural body; it can be attached not only to another person but also anywhere in the environment. Humans can eventually perform tasks that involve co-presence, or tasks that are concurrent and performed in two separate places, by utilizing the Detachable Body. In this letter, we design an information presentation interface to concurrently manage both the natural and detached bodies that are located in two separate locations. The interface consists of a vision presentation system that superimposes two environment images with binocular disparity, and a proprioception presentation system that provides somatosensory feedback of the detached arm's position. The usability of the proposed interface was evaluated by measuring work efficiency and subjective evaluation in a task involving co-presence. The results suggest the existence of the effects of the binocular disparity in the vision presentation system and the tactile information provided via feedback.
A Guess-And-Determine Attack On SNOW-V Stream Cipher

Abstract The 5G mobile communication system is coming with a main objective, known also as IMT-2020, that intends to increase the current data rates up to several gigabits per second. To meet an accompanying demand of the super high-speed encryption, EIA and EEA algorithms face some challenges. The 3GPP standardization organization expects to increase the security level to 256-bit key length, and the international cryptographic field responds actively in cipher designs and standard applications. SNOW-V is such a proposal offered by the SNOW family design team, with a revision of the SNOW 3G architecture in terms of linear feedback shift register (LFSR) and finite state machine (FSM), where the LFSR part is new and operates eight times the speed of the FSM, consisting of two shift registers and each feeding into the other, and the FSM increases to three 128-bit registers and employs two instances of full AES encryption round function for update. It takes a 128-bit IV, employs 896-bit internal state and produces 128-bit keystream blocks. The result is competitive in pure software environment, making use of both AES-NI and AVX acceleration instructions. Thus, the security evaluation of SNOW-V is essential and urgent, since there is scarcely any definite security bound for it. In this paper, we propose a byte-based guess-and-determine attack on SNOW-V with complexity $2^{406}$ using only seven keystream blocks. We first improve the heuristic guessing-path auto-searching algorithm based on dynamic programming by adding initial guessing set, which is iteratively modified by sieving out the unnecessary guessing variables, in order to correct the guessing path according to the cipher structure and finally launch smaller guessing basis. For the specific design, we split all the computing units into bytes and rewrite all the internal operations correspondingly. We establish a backward-clock linear equation system according to the circular construction of the LFSR part. Then we further simplify the equations to adapt to the input requirements of the heuristic guessing-path auto-searching algorithm. Finally, the derived guessing path needs modification for the pre-simplification and post-reduction. This is the first complete guess-and-determine attack on SNOW-V as well as the first specific security evaluation to the full cipher.
Application of image recognition technology based on embedded technology in environmental pollution detection

In recent years, with the aggravation of environmental pollution, relying on the traditional detection of environmental pollution has been unable to meet the current requirements for the environment, the use of advanced technology to detect environmental pollution has become crucial. In order to meet the requirements of today's era, with the rapid development of image recognition technology, it is necessary to study how to apply it to environmental pollution detection. The image recognition technology based on embedded technology has the characteristics of high discrimination, high acceptability and strong touch sense. It is of great significance for environmental monitoring to make full use of image recognition technology. Image recognition technology is to capture the same environment by digital camera, and then store the obtained image by computer. The HSI model is used to quantify the stored photos, and the variance of the gradient function is used to form photos and images with different levels of clarity, so as to analyze the environmental pollution according to the quantified results. In this paper, the ambient air quality is taken as the research object, and HSI model is used to quantify, so as to analyze the correlation of air detection results.
Capability-Based Non-fungible Tokens Approach for a Decentralized AAA Framework in IoT

The proliferation of IoT devices across various application domains led to a high level of heterogeneity which introduced new device management challenges. These challenges include, bringing the capability of the service delivery and the underlying accounting, authentication and authorization mechanisms. Moreover, IoT devices tend to no longer require a centralized authority to authenticate and authorize access to the services offered. In this work, we address this by introducing a decentralized Authentication, Authorization and Accounting (AAA) framework using Capability-based Tokens based on the ERC721 standard to provide secure authentication and authorization for IoT devices. The approach is tested on a private Ethereum Blockchain node to analyze performance factors related to access time, timeout ratio and overhead.
Non-cognitive Abilities of Exceptional Software Engineers

Important building blocks of software engineering concepts are without a doubt technical. During the last decade, research and practical interest for non-technicalities has grown, revealing the building blocks to be various skills and abilities beside pure technical knowledge. Multiple attempts to categorise these blocks have been made, but so far little international studies have been performed that identify skills by asking experts from both the industrial and academic world: which abilities are needed for a developer to excel in the software engineering industry? To answer this question, we performed a Delphi study, inviting 36 experts from 11 different countries world-wide, affiliated with 21 internationally renowned institutions. This study presents the 55 identified and ranked skills as classified in four major areas: communicative skills (empathy, actively listening, etc.), collaborative skills (sharing responsibility, learning from each other, etc.), problem solving skills (verifying assumptions, solution-oriented thinking, etc.), and personal skills (curiosity, being open to ideas, etc.), of which a comparison has been made between opinions of technical experts, business experts, and academics. We hope this work inspires educators and practitioners to adjust their training programs, mitigating the gap between the industry and the academic world.
Safeguarding the evidential value of forensic cryptocurrency investigations

Analyzing cryptocurrency payment flows has become a key forensic method in law enforcement and is nowadays used to investigate a wide spectrum of criminal activities. However, despite its widespread adoption, the evidential value of obtained findings in court is still largely unclear. In this paper, we focus on the key ingredients of modern cryptocurrency analytics techniques, which are clustering heuristics and attribution tags. We identify internationally accepted standards and rules for substantiating suspicions and providing evidence in court and project them onto current cryptocurrency forensics practices. By providing an empirical analysis of CoinJoin transactions, we illustrate possible sources of misinterpretation in algorithmic clustering heuristics. Eventually, we derive a set of legal key requirements and translate them into a technical data sharing framework that fosters compliance with existing legal and technical standards in the realm of cryptocurrency forensics. Integrating the proposed framework in modern cryptocurrency analytics tools could allow more efficient and effective investigations, while safeguarding the evidential value of the analysis and the fundamental rights of affected persons.
A Validated Scoring Rubric for Explain-in-Plain-English Questions

Previous research has identified the ability to read code and understand its high-level purpose as an important developmental skill that is harder to do (for a given piece of code) than executing code in one's head for a given input ("code tracing"), but easier to do than writing the code. Prior work involving code reading ("Explain in plain English") problems, have used a scoring rubric inspired by the SOLO taxonomy, but we found it difficult to employ because it didn't adequately handle the three dimensions of answer quality: correctness, level of abstraction, and ambiguity. In this paper, we describe a 7-point rubric that we developed for scoring student responses to "Explain in plain English'' questions, and we validate this rubric through four means. First, we find that the scale can be reliably applied with with a median Krippendorff's alpha (inter-rater reliability) of 0.775. Second, we report on an experiment to assess the validity of our scale. Third, we find that a survey consisting of 12 code reading questions had a high internal consistency (Cronbach's alpha = 0.954). Last, we find that our scores for code reading questions in a large enrollment (N = 452) data structures course are correlated (Pearson's R = 0.555) to code writing performance to a similar degree as found in previous work.
Facial attribute-controlled sketch-to-image translation with generative adversarial networks

Abstract Due to the rapid development of the generative adversarial networks (GANs) and convolution neural networks (CNN), increasing attention is being paid to face synthesis. In this paper, we address the new and challenging task of facial sketch-to-image synthesis with multiple controllable attributes. To achieve this goal, first, we propose a new attribute classification loss to ensure that the synthesized face image with the facial attributes, which the users desire to have. Second, we employ the reconstruction loss to synthesize the facial texture and structure information. Third, the adversarial loss is used to encourage visual authenticity. By incorporating above losses into a unified framework, our proposed method not only can achieve high-quality sketch-to-image translation, but also allow the users control the facial attributes of synthesized image. Extensive experiments show that user-provided facial attribute information effectively controls the process of facial sketch-to-image translation.
Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and Machine Learning

Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country's diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms; hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.
The differential spectrum of a ternary power mapping

A function f(x) from the finite field GF(pn) to itself is said to be differentially δ-uniform when the maximum number of solutions x∈GF(pn) of f(x+a)−f(x)=b for any a∈GF(pn)⁎ and b∈GF(pn) is equal to δ. Let p=3 and d=3n−3. When n>1 is odd, the power mapping f(x)=xd over GF(3n) was proved to be differentially 2-uniform by Helleseth, Rong and Sandberg in 1999. For even n, they showed that the differential uniformity Δf of f(x) satisfies 1≤Δf≤5. In this paper, we present more precise results on the differential property of this power mapping. For d=3n−3 with even n>2, we show that the power mapping xd over GF(3n) is differentially 4-uniform when n≡2(mod4) and is differentially 5-uniform when n≡0(mod4). Furthermore, we determine the differential spectrum of xd for any integer n>1.
Blockchain-Based Fair and Secure Electronic Double Auction Protocol

Double auction is an auction in which multiple buyers and sellers looking for a price where supply and demand balance. Since the electronic double auction based on secure multiparty computation (DABSMPC) cannot guarantee its fairness, we propose a blockchain-based fair and secure electronic double auction protocol (BFSDA). BFSDA modifies the data input and distribution mechanism of participants in DABSMPC, which improves the security of the protocol. Then, the BFSDA introduces and improves the blockchain-based fair and secure multiparty computation protocol (BFSMPC) to ensure fairness while increasing the success rate of secret recovery. In addition, BFSDA uses a fairer and more efficient protocol for secure two-party comparing to obtain the final marketing clearing price. The schema analysis result of BFSDA shows that: first, the private input data will not be revealed as long as data owner is not compromised, second, honest participants can get the result or economic compensation, and third, participants only need to pay the deposit once and a large amount of complicated verification operations are carried out off the chain, which ensures the efficiency of the protocol.
Frustum VoxNet for 3D object detection from RGB-D or Depth images

Recently, there have been a plethora of classification and detection systems from RGB as well as 3D images. In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system first detects objects in 2D (either RGB, or pseudo-RGB constructed from depth). The next step is to detect 3D objects within the 3D frustums these 2D detections define. This is achieved by voxelizing parts of the frustums (since frustums can be really large), instead of using the whole frustums as done in earlier work. The main novelty of our system has to do with determining which parts (3D proposals) of the frustums to voxelize, thus allowing us to provide high resolution representations around the objects of interest. It also allows our system to have reduced memory requirements. These 3D proposals are fed to an efficient ResNet-based 3D Fully Convolutional Network (FCN). Our 3D detection system is fast, and can be integrated into a robotics platform. With respect to systems that do not perform voxelization (such as PointNet), our methods can operate without the requirement of subsampling of the datasets. We have also introduced a pipelining approach that further improves the efficiency of our system. Results on SUN RGB-D dataset show that our system, which is based on a small network, can process 20 frames per second with comparable detection results to the state-of-the-art [16], achieving a 2× speedup.