Reviewerly/exHarmony huggingface dataset:
Transfer learning for video anomaly detection

Anomaly detection from crowd is a widely addressed problem in the field of computer vision. It is an essential part of video surveillance and security. In surveillance videos, very little information about anomalous behaviors is available, so it becomes difficult to identify such activities. In th is work, transfer learning technique is used to train the network. A convolutional neural network (CNN) based VGG16 pre-trained model is used to learn spatial level appearance features for anomalous and normal patterns. Two approaches are explored to detect anomalies, i) homogeneous approach and ii) hybrid approach. In homogeneous approach, pre-trained network is used to fine-tune CNN for each dataset, while testing, single dataset is considered. Whereas, in hybrid approach, pre-trained network is used to fine-tune CNN on one dataset and it is further used to fine-tune another dataset. The performance of proposed system is verified on standard benchmark datasets such as UCSD and UMN available for anomaly detection, also the results of proposed system are compared with existing deep learning approaches.
Open-access policy and data-sharing practice in UK academia

Data sharing can be defined as the release of research data that can be used by others. With the recent open-science movement, there has been a call for free access to data, tools and methods in academia. In recent years, subject-based and institutional repositories and data centres have emerged along with online publishing. Many scientific records, including published articles and data, have been made available via new platforms. In the United Kingdom, most major research funders had a data policy and require researchers to include a ‘data-sharing plan’ when applying for funding. However, there are a number of barriers to the full-scale adoption of data sharing. Those barriers are not only technical, but also psychological and social. A survey was conducted with over 1800 UK-based academics to explore the extent of support of data sharing and the characteristics and factors associated with data-sharing practice. It found that while most academics recognised the importance of sharing research data, most of them had never shared or reused research data. There were differences in the extent of data sharing between different gender, academic disciplines, age and seniority. It also found that the awareness of Research Council UK’s (RCUK) Open-Access (OA) policy, experience of Gold and Green OA publishing, attitudes towards the importance of data sharing and experience of using secondary data were associated with the practice of data sharing. A small group of researchers used social media such as Twitter, blogs and Facebook to promote the research data they had shared online. Our findings contribute to the knowledge and understanding of open science and offer recommendations to academic institutions, journals and funding agencies.
Moving Deep Learning into Web Browser: How Far Can We Go?

Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.
Bridging the Gap Between Computational Photography and Visual Recognition

What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressing need for the development of algorithms that are designed for the joint problem of improving visual appearance and recognition, which will be an enabling factor for the deployment of visual recognition tools in many real-world scenarios. To address this, we introduce the UG 2 dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we propose a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduce six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG 2 Challenge workshop held at CVPR 2018. Under the proposed evaluation regime, we present an in-depth analysis of these algorithms and a host of deep learning-based and classic baseline approaches. From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
Statistical Arbitrage in Cryptocurrency Markets

Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy.
Energy Aware Virtual Machine Scheduling in Data Centers

Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.
Label-free classification of cells based on supervised machine learning of subcellular structures

It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.
Bayesian Optimization for Whole-Body Control of High-Degree-of-Freedom Robots Through Reduction of Dimensionality

This letter aims to achieve automatic tuning of optimal parameters for whole-body control algorithms to achieve the best performance of high-DoF robots. Typically, the control parameters at a scale up to hundreds are often hand-tuned yielding sub-optimal performance. Bayesian optimization (BO) can be an option to automatically find optimal parameters. However, for high-dimensional problems, BO is often infeasible in realistic settings as we studied in this letter. Moreover, the data is too little to perform dimensionality reduction techniques, such as principal component analysis or partial least square. We hereby propose an alternating BO algorithm that iteratively learns the parameters of sub-spaces from the whole high-dimensional parametric space through interactive trials, resulting in sample efficiency and fast convergence. Furthermore, for the balancing and locomotion control of humanoids, we developed techniques of dimensionality reduction combined with the proposed ABO approach that demonstrated optimal parameters for robust whole-body control.
Investigating the feasibility of finger identification on capacitive touchscreens using deep learning

Touchscreens enable intuitive mobile interaction. However, touch input is limited to 2D touch locations which makes it challenging to provide shortcuts and secondary actions similar to hardware keyboards and mice. Previous work presented a wide range of approaches to provide secondary actions by identifying which finger touched the display. While these approaches are based on external sensors which are inconvenient, we use capacitive images from mobile touchscreens to investigate the feasibility of finger identification. We collected a dataset of low-resolution fingerprints and trained convolutional neural networks that classify touches from eight combinations of fingers. We focused on combinations that involve the thumb and index finger as these are mainly used for interaction. As a result, we achieved an accuracy of over 92% for a position-invariant differentiation between left and right thumbs. We evaluated the model and two use cases that users find useful and intuitive. We publicly share our data set (CapFingerld) comprising 455,709 capacitive images of touches from each finger on a representative mutual capacitive touchscreen and our models to enable future work using and improving them.
Forty years of attacks on the RSA cryptosystem: A brief survey

AbstractAbstractRSA public key cryptosystem is the de-facto standard use in worldwide technologies as a strong encryption/decryption and digital signature scheme. RSA successfully defended forty years of attack since invention. In this study we survey, its past, present advancements and upcoming challenges that needs concrete analysis and as a counter measure against possible threats according to underlying algebraic structure. Past studies shows us some attacks on RSA by inspecting flaws on relax model using weak public/private keys, integer factorization problem, and some specific low parameter selection attacks. Such flaws can not hamper the security of RSA cryptosystem by at large, but can explore possible vulnerabilities for more deep understanding about underlying mathematics and improper parameter selection. We describe a brief survey of past findings and detail description about specific attacks. A comprehensive survey of known attacks on RSA cryptosystem shows us that a well implemented algorithm is unbreakable and it survived against a number of cryptanalytic attacks since last forty years.Keywords: RSA cryptanalysisLattice reduction attackCoppersmith's methodImplementation attacks
Multinational oil companies in Nigeria and corporate social responsibility in the HIV/AIDS response in host communities

We assess the impact of corporate social responsibility (CSR) of multinational oil companies (MOCs) on HIV/AIDS prevalence in Nigeria’s oil-producing communities. One thousand, two hundred households were sampled across the rural communities of Niger Delta. Using logit model, the main result indicates that General Memorandum of Understandings (GMoUs) have not significantly impacted on factors behind the spread of HIV/AIDs in rural communities. This implies that the impact of the disease on MOCs business, employees and their families, contractors, business partners and the oil communities have not inclined downward. The findings suggest that CSR offers an opportunity for MOCs to help address HIV/AID prevalence through a business case for stakeholders’ health in the region. It calls for MOCs to improve GMoUs health intervention on sensitisation campaigns, funding testing and counselling centres, subsidising anti-retroviral drugs, prevention of mother-to-child transmission, rehabilitation of orphaned and vulnerable children and other cares for people living with AIDS.
(R)CCA Secure Updatable Encryption with Integrity Protection

An updatable encryption scheme allows a data host to update ciphertexts of a client from an old to a new key, given so-called update tokens from the client. Rotation of the encryption key is a common requirement in practice in order to mitigate the impact of key compromises over time. There are two incarnations of updatable encryption: One is ciphertext-dependent, i.e. the data owner has to (partially) download all of his data and derive a dedicated token per ciphertext. Everspaugh et al. (CRYPTO'17) proposed CCA and CTXT secure schemes in this setting. The other, more convenient variant is ciphertext-independent, i.e., it allows a single token to update all ciphertexts. However, so far, the broader functionality of tokens in this setting comes at the price of considerably weaker security: the existing schemes by Boneh et al. (CRYPTO'13) and Lehmann and Tackmann (EUROCRYPT'18) only achieve CPA security and provide no integrity protection. Arguably, when targeting the scenario of outsourcing data to an untrusted host, plaintext integrity should be a minimal security requirement. Otherwise, the data host may alter or inject ciphertexts arbitrarily. Indeed, the schemes from BLMR13 and LT18 suffer from this weakness, and even EPRS17 only provides integrity against adversaries which cannot arbitrarily inject ciphertexts. In this work, we provide the first ciphertext-independent updatable encryption schemes with security beyond CPA, in particular providing strong integrity protection. Our constructions and security proofs of updatable encryption schemes are surprisingly modular. We give a generic transformation that allows key-rotation and confidentiality/integrity of the scheme to be treated almost separately, i.e., security of the updatable scheme is derived from simple properties of its static building blocks. An interesting side effect of our generic approach is that it immediately implies the unlinkability of ciphertext updates that was introduced as an essential additional property of updatable encryption by EPRS17 and LT18.
Postural instability in an immersive Virtual Reality adapts with repetition and includes directional and gender specific effects

The ability to handle sensory conflicts and use the most appropriate sensory information is vital for successful recovery of human postural control after injury. The objective was to determine if virtual reality (VR) could provide a vehicle for sensory training, and determine the temporal and spatial nature of such adaptive changes. Twenty healthy subjects participated in the study (10 females). The subjects watched a 90-second VR simulation of railroad (rollercoaster) motion in mountainous terrain during five repeated simulations, while standing on a force platform that recorded their stability. The immediate response to watching the VR movie was an increased level of postural instability. Repeatedly watching the same VR movie significantly reduced both the anteroposterior (62%, p < 0.001) and lateral (47%, p = 0.001) energy used. However, females adapted more slowly to the VR stimuli as reflected by higher use of total (p = 0.007), low frequency (p = 0.027) and high frequency (p = 0.026) energy. Healthy subjects can significantly adapt to a multidirectional, provocative, visual environment after 4-5 repeated sessions of VR. Consequently, VR technology might be an effective tool for rehabilitation involving visual desensitisation. However, some females may require more training sessions to achieve effects with VR.
Search in My Way: Practical Outsourced Image Retrieval Framework Supporting Unshared Key

The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead but also cannot well protect the image and query privacy in multi-user scenarios. To solve the above problems, we first propose a basic privacy-preserving content-based image retrieval (CBIR) framework which significantly reduces storage and communication overhead compared with the previous works. Furthermore, we design a new efficient key conversion protocol to support unshared key multi-owner multi-user image retrieval without losing search precision. Moreover, our framework supports unbounded attributes and can trace malicious users according to leaked secret keys, which significantly improve the usability of multi-source data sharing. Strict security analysis shows that the user privacy and outsourced data security can be guaranteed during the image retrieval process, and the performance analysis using real-world dataset shows that the proposed image retrieval framework is efficient and feasible for practical applications.
A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

In this paper, a novel context sensitive energy curve based Masi entropy for image segmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution for image segmentation but the performance is not satisfactory. However, better results can be obtained using the concept of energy curve for Masi entropy, but it consumes more time and also the complexity level for selecting suitable thresholds is high. MSA is a newly developed stochastic meta-heuristic optimization algorithm introduced after observing, mimicking and modeling the life cycle of moth swarm. It is used to simplify the problem of extensive exploration for finding the optimum threshold values and to increase the quality of the images. Experiments on standard daily-life color images are showed to establish the usefulness of the presented approach. The Energy-Masi-MSA technique is examined intensively regarding visual quality and quantitative matrices are considered to evaluate the results of the Energy-Masi-MSA scheme compared to existing methods. Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost. Experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation

One of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In this paper, to improve the FCNN performance for prostate MRI segmentation, we analyze various structures of shortcut connections together with the size of a deep network and suggest eight different FCNNs-based deep 2D network structures for automatic MRI prostate segmentation. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyze the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation on publicly available data, without any further post-processing.
2.6 A 2 ×30k-Spin Multichip Scalable Annealing Processor Based on a Processing-In-Memory Approach for Solving Large-Scale Combinatorial Optimization Problems

The last decade has seen impressive progress in the development of a new computer architecture, commonly known as annealing processor [1, 2]. An annealing processor provides a fast means for finding the ground state of an Ising model; thus, it can efficiently solve NP-hard combinatorial optimization problems [3]. In addition to quantum annealers based on superconducting circuits [1], annealing processors based on CMOS technology have received increased interest and are being developed on the basis of simulated annealing (SA) [2]. However, these CMOS annealing processors (CMOS-APs) have room for improvement, such as: i) expanding the bit widths of coefficients, and ii) increasing the number of spins handled by the processor. To address these challenges, a CMOS-AP based on the processing-in-memory approach (where CMOS circuits and an SRAM are tightly coupled [4]) has been developed. Its key features are threefold: a spin operator (processing local memory) which provides coefficients with expandable bit width and fast parallel spin updates according to the Gibbs distribution; a low-latency inter-chip interface (I/F) connecting two Ising chips, resulting in an increased number of spins; and a highly integrated spin circuit which directly connects the spin operator with the SRAM cell. Installed in a 2×30k spin system, the CMOS-AP demonstrates the capability for multi-chip operation with energy efficiency 1.75×105 higher than running SA on a CPU.