Reviewerly/exHarmony huggingface dataset:
A dynamic framework for tuning SVM hyper parameters based on Moth-Flame Optimization and knowledge-based-search

In the real world, most of the collections of data are dynamic in nature, i.e. their size may grow with time. This dynamic nature of the data not only reduces the performance of the classifiers but also demands more optimized models for retaining the performance. Due to this, machine learning models developed in a static environment cannot be deployed efficiently to solve the real-world problems. Nowadays, maximum existing works consider only the static behaviour of the data for the training of machine learning models where the size of the collection of training data does not change over time. This paperwork imposes Support Vector Machine (SVM) in a dynamic environment. It has been identified that shifting of the optimum values of two hyper-parameters C (Penalty Parameter) and γ (Kernel Parameter) in the search space is one of the primary reasons for the performance degradation of SVM in dynamic environment. This paper proposes a novel framework that uses a new optimization module Knowledge-Based-Search (KBS) along with Moth –Flame Optimization (MFO) to optimize C and γ in a dynamic environment to train SVM efficiently. KBS uses knowledge gathered at various instances of time, which are the bi-products of MFO. MFO in our framework is the base optimization algorithm which works underneath KBS. The experiments have shown that KBS helps in controlling the exponential growth of the time complexity of the optimization process where only MFO is used to optimize C andγ. Integration of KBS with MFO brings down the time complexity to a large extent. To validate the proposed framework we have used a simulated dynamic environment for profit/loss classification problem for organizations. The experiments have also shown that KBS's integration with MFO outperforms integration of KBS with other modern optimization techniques such as Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), Grey-Wolf Optimization (GWO), Cuckoo Search (CS), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Fire-Fly Algorithm (FFA) and Salp Swarm Algorithm (SSA).
Multiple air route crossing waypoints optimization via artificial potential field method

Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization, capacity and resilience in dealing with air traffic congestion and delay. However, research is lacking on the optimization of multiple Crossing Waypoints (CWPs) in the fragmented airspace separated by Prohibited, Restricted and Dangerous areas (PRDs). To tackle this issue, this paper proposes an Artificial Potential Field (APF) model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles. An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs, air route segments and PRDs. Based on the framework, an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost. The proposed model is applied to a busy controlled airspace. And the obtained results show that after optimization the safety-related indicators: conflict number and controller workload reduced by 7.75% and 6.51% respectively. As for the cost-effectiveness indicators: total route length, total air route cost and non-linear coefficient, declined by 1.74%, 3.13% and 1.70% respectively. While the predictability indicator, total flight delay, saw a notable reduction by 7.96%. The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.
Can computer forensic tools be trusted in digital investigations?

This paper investigates whether computer forensic tools (CFTs) can extract complete and credible digital evidence from digital crime scenes in the presence of file system anti-forensic (AF) attacks. The study uses a well-established six stage forensic tool testing methodology based on black-box testing principles to carry out experiments that evaluate four leading CFTs for their potential to combat eleven different file system AF attacks. Results suggest that only a few AF attacks are identified by all the evaluated CFTs, while as most of the attacks considered by the study go unnoticed. These AF attacks exploit basic file system features, can be executed using simple tools, and even attack CFTs to accomplish their task. These results imply that evidences collected by CFTs in digital investigations are not complete and credible in the presence of AF attacks. The study suggests that practitioners and academicians should not absolutely rely on CFTs for evidence extraction from a digital crime scene, highlights the implications of doing so, and makes many recommendations in this regard. The study also points towards immediate and aggressive research efforts that are required in the area of computer forensics to address the pitfalls of CFTs.
Ultrasound Video Summarization Using Deep Reinforcement Learning

Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn’t received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. We evaluate our method on videos from fetal ultrasound screening, where commonly only a small amount of the recorded data is used diagnostically. We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Characterizing Community Guidelines on Social Media Platforms

Social media platforms use community guidelines to enact governance and moderate content, but the limitation in their moderation capacity forces them to choose the types of misbehavior they focus more on. In this work, we analyze these choices through a content analysis of the community guidelines of 11 major social media platforms. We find 66 different types of rules across their community guidelines, with great variability in the coverage of these rules across different platforms. Our research reveals the types of misbehavior that platforms chose to focus on, and motivates further inquiries into policymaking and content moderation in specific problem areas such as inciting violence and voter suppression.
Multivariate spatial data visualization: a survey

Multivariate spatial data play an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis, and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research.
Key-Point Sequence Lossless Compression for Intelligent Video Analysis

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate the spatial and temporal redundancies of key points in videos. Multiple prediction modes with an adaptive mode selection method are proposed to handle key-point sequences with various structures and motion. Experimental results validate the effectiveness of the proposed scheme on four types of widely used key-point sequences in video analysis.
Learning to denoise astronomical images with U-nets

ABSTRACT Astronomical images are essential for exploring and understanding the Universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope (HST), are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes denoising a mandatory step in post-processing the data before further data analysis. In order to maximize the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose Astro U-net, a convolutional neural network for image denoising and enhancement. For a proof-of-concept, we use HST images from Wide Field Camera 3 instrument UV/visible channel with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover $95.9{{\ \rm per\ cent}}$ of stars with an average flux error of $2.26{{\ \rm per\ cent}}$. Furthermore, the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least three input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns.
Maximum number of modes of Gaussian mixtures

Abstract Gaussian mixture models are widely used in Statistics. A fundamental aspect of these distributions is the study of the local maxima of the density or modes. In particular, it is not known how many modes a mixture of $k$ Gaussians in $d$ dimensions can have. We give a brief account of this problem’s history. Then, we give improved lower bounds and the first upper bound on the maximum number of modes, provided it is finite.
Efficient pedestrian detection in top-view fisheye images using compositions of perspective view patches

Pedestrian detection in images is a topic that has been studied extensively, but existing detectors designed for perspective images do not perform as successfully on images taken with top-view fisheye cameras, mainly due to the orientation variation of people in such images. In our proposed approach, several perspective views are generated from a fisheye image and then concatenated to form a composite image. As pedestrians in this composite image are more likely to be upright, existing detectors designed and trained for perspective images can be applied directly without additional training. We also describe a new method of mapping detection bounding boxes from the perspective views to the fisheye frame. The detection performance on several public datasets compare favorably with state-of-the-art results.
iVisit: Digital Interactive Construction Site Visits Using 360-Degree Panoramas and Virtual Humans

Site visits or field trips are an integral part of construction management education, providing students with experiential learning of jobsite conditions. However, these types of real-world opportunities are difficult to obtain within the current educational framework based on classroom instruction. To expose students to jobsite spatiotemporal contexts (spatial, temporal, or social situations), field trips must be organized at locations that are often inaccessible, dangerous, or expensive to reach. To address field trip barriers, this research proposes the use of iVisit—a proof-of-concept platform for guided interactive site visits that leverages 360-degree panoramas and virtual humans. In this paper, the technical requirements for the creation of digital site visit experiences and resulting educational platform are explained in detail. Additionally, a pilot study was conducted to assess the iVisit platform in terms of usability, presence, and student knowledge gains. A masonry materials' site visit learning experience was designed and tested with 10 participants at introductory level construction courses. It was found that students perceived the iVisit guided tour as easy to use (SUS Usability Score – Mean = 86%; STD = 8.8%) and highly realistic (SUS Presence Score – Mean = 68.4%; STD = 14.4%). However, students answer approximately one-third of the presented knowledge questions correctly (Student Knowledge Score – Mean = 31.7%; STD = 25%). These outcomes in student knowledge gains were linked to low image quality in the 360-degree captures and augmented pictures within the digital site. Supporting feedback provided by the study participants suggested that future improvements to iVisit require higher image quality and some refinements to its user-interfaces to increase presence and knowledge gains.
Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs. Whilst various stereo algorithms have been deployed on these platforms, usually cut down to better match the embedded architecture, certain key parts of the more advanced algorithms, e.g. those that rely on unpredictable access to memory or are highly iterative in nature, are difficult to deploy efficiently on FPGAs, and thus the depth quality that can be achieved is limited. In this paper, we leverage a FPGA-CPU chip to propose a novel, sophisticated, stereo approach that combines the best features of SGM and ELAS-based methods to compute highly accurate dense depth in real time. Our approach achieves an 8.7% error rate on the challenging KITTI 2015 dataset at over 50 FPS, with a power consumption of only 5W.
A chi-square goodness-of-fit test for continuous distributions against a known alternative

The chi square goodness-of-fit test is among the oldest known statistical tests, first proposed by Pearson in 1900 for the multinomial distribution. It has been in use in many fields ever since. However, various studies have shown that when applied to data from a continuous distribution it is generally inferior to other methods such as the Kolmogorov-Smirnov or Anderson-Darling tests. However, the performance, that is the power, of the chi square test depends crucially on the way the data is binned. In this paper we describe a method that automatically finds a binning that is very good against a specific alternative. We show that then the chi square test is generally competitive and sometimes even superior to other standard tests.
AR-Loupe: Magnified Augmented Reality by Combining an Optical See-Through Head-Mounted Display and a Loupe

Head-mounted loupes can increase the user's visual acuity to observe the details of an object. On the other hand, optical see-through head-mounted displays (OST-HMD) are able to provide virtual augmentations registered with real objects. In this article, we propose AR-Loupe, combining the advantages of loupes and OST-HMDs, to offer augmented reality in the user's magnified field-of-vision. Specifically, AR-Loupe integrates a commercial OST-HMD, Magic Leap One, and binocular Galilean magnifying loupes, with customized 3D-printed attachments. We model the combination of user's eye, screen of OST-HMD, and the optical loupe as a pinhole camera. The calibration of AR-Loupe involves interactive view segmentation and an adapted version of stereo single point active alignment method (Stereo-SPAAM). We conducted a two-phase multi-user study to evaluate AR-Loupe. The users were able to achieve sub-millimeter accuracy ( 0.82 mm) on average, which is significantly ( ) smaller compared to normal AR guidance ( 1.49 mm). The mean calibration time was 268.46 s. With the increased size of real objects through optical magnification and the registered augmentation, AR-Loupe can aid users in high-precision tasks with better visual acuity and higher accuracy.
SAVE: Sparsity-Aware Vector Engine for Accelerating DNN Training and Inference on CPUs

General Matrix Multiplication (GEMM) is the key operation in Deep Neural Networks (DNNs). While dense GEMM uses SIMD CPUs efficiently, sparse GEMM is much less efficient, especially at the modest levels of unstructured sparsity common in DNN inference/training. Thus, most DNNs use dense GEMM.In this paper, we propose SAVE, a novel vector engine for CPUs that efficiently skips ineffectual computation due to sparsity in dense DNN implementations. SAVE's hardware extensions to the vector pipeline are transparent to software. SAVE accelerates FP32 and mixed-precision kernels with unstructured sparsity from both weights and activations. Further, SAVE is not DNN-specific and can potentially speed-up any vector workload with sparsity. To evaluate SAVE, we use simulations of a 28-core machine and run VGG16, ResNet-50, and GNMT, with and without pruning. With realistic sparsity, SAVE accelerates inference by 1.37x-1.68x and end-to-end training by 1.28x-1.64x.
Rusty Variation: Deadlock-free Sessions with Failure in Rust

Rusty Variation (RV) is a library for session-typed communication in Rust which offers strong compile-time correctness guarantees.Programs written using RV are guaranteed to respect a specified protocol, and are guaranteed to be free from deadlocks and races.
Use of a Multitheoretic Model to Understand and Classify Juvenile Computer Hacking Behavior

Criminological inquiry has identified a range of risk factors associated with juvenile delinquency. However, little research has assessed juvenile computer hacking, despite the substantial harm and opportunities for delinquent behavior online. Therefore, understanding the applicability of criminological risk factors among a cross-national sample of juvenile hackers is important from a theoretical and applied standpoint. This study aimed to address this gap using a logistic regression and latent class analysis (LCA) of risk factors associated with self-reported hacking behavior in a sample of more than 60,000 juveniles from around the globe. Results demonstrated support for individual- and structural-level predictors of delinquency, although distinct risk factors for hacking among three subtypes are identified in the LCA. This study examines criminological risk factors for juvenile hacking in a cross-national sample and provides insight into the distinct risk factors of hacking, so more tailored prevention and treatment modalities can be developed.
DeepSearch: a simple and effective blackbox attack for deep neural networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches.
HeaL: A blockchain-envisioned signcryption scheme for healthcare IoT ecosystems

The paper proposes a light-weight blockchain (BC)-envisioned scheme HeaL, that ensures secure and trusted exchange of electronic health records (EHR) over open wireless channels, with minimum encryption and signing overheads. HeaL operates in two phases. In the first phase, proximity sensor nodes (PSN) are mounted over the patient to form a wireless body area network (WBAN). The nodes elect a cluster-head (CH) in its vicinity based on resource capability to forward data to gateway sensor nodes (GSN) in WBAN, with EHR meta-information recorded in BC. Then, in the second phase, GSN executes a lightweight signcryption scheme that combines encryption and signing of data among authorized stakeholders. The data is exchanged over open channels and is accessible to authorized personals by fetching secure keys from Inter-planetary file systems (IPFS). The mined information is finally stored as block ledgers in global chain structures to reduce computational overheads. The proposed scheme HeaL is compared against existing schemes in terms of parameters like transaction and signing cost, transactional throughput, and computational and communication costs. In simulation, HeaL achieves an average signing cost and verifying cost of 3.32 seconds (s) and 6.52 s, respectively. For 200 blocks, the mining latency is 3.325 s, compared to traditional schemes with a latency of 7.8 s. The obtained transactional throughput is 142.78 Mbps, compared to traditional 102.45 Mbps. In security evaluation, the computation cost (CC) is 45.80 milli-seconds (ms), and communication cost (CCM) is 97 bytes, which indicates the viability of the proposed scheme against other state-of-the-art approaches.
Generate to Adapt: Resolution Adaption Network for Surveillance Face Recognition

Although deep learning techniques have largely improved face recognition, unconstrained surveillance face recognition is still an unsolved challenge, due to the limited training data and the gap of domain distribution. Previous methods mostly match low-resolution and high-resolution faces in different domains, which tend to deteriorate the original feature space in the common recognition scenarios. To avoid this problem, we propose resolution adaption network (RAN) which contains Multi-Resolution Generative Adversarial Networks (MR-GAN) followed by a feature adaption network. MR-GAN learns multi-resolution representations and randomly selects one resolution to generate realistic low-resolution (LR) faces that can avoid the artifacts of down-sampled faces. A novel feature adaption network with translation gate is developed to fuse the discriminative information of LR faces into backbone network, while preserving the discrimination ability of original face representations. The experimental results on IJB-C TinyFace, SCface, QMUL-SurvFace datasets have demonstrated the superiority of our method compared with state-of-the-art surveillance face recognition methods, while showing stable performance on the common recognition scenarios.
Daily Stand-Up Meetings: Start Breaking the Rules

Members of high performing software teams collaborate, exchange information and coordinate their work on a frequent, regular basis. Most teams have the daily stand-up meeting as a central venue for these activities. Although this kind of meeting is one of the most popular agile practices, it has received little attention from researchers. We observed 102 daily stand-ups and interviewed 60 members of 15 teams in five countries. We found that the practice is usually challenging to conduct in a way that benefits the whole team. Many team members have a negative experience from conducting the meeting, which reduces job satisfaction, co-worker trust and well-being. However, the practice can be adjusted and improved to empower teams. In this article, we describe key factors that affect the meeting and propose four recommendations for improving the practice.