Reviewerly/exHarmony huggingface dataset:
Face Recognition Using Hybrid HOG-CNN Approach

Face recognition is a biometric system used to identify a person from digital images; its application is primarily for security and monitoring purposes. Recently, there have been more research works on deep neural network for facial recognition achieving positive results. In this study, we propose an effective model to solve face recognition problem which could be applied to identify directly from camera. In particular, the study focuses on two phases: face detection and face identification. The face detection method that we propose uses HOG features and SVM linear classifier. The face recognition model is proposed based on CNN—convolution neural network. The efficiency of the model is evaluated on the FEI, LFW, UOF datasets, and the results show that the proposed model achieves high accuracy.
A New Robust Reference Image Hashing System

The authentication and content protection of multimedia data is a challenging task in the present scenario. One solution is to generate the perceptual hash which essentially authenticates the multimedia data and can also be dealt with image database search problems. In this article, a novel system for generating an image hash is presented. The proposed system utilizes the global and local features in the hash generation process. The local features are obtained from non-linear scale-space based KAZE features. These KAZE features have the ability to capture the most stable point under several content preserving distortions. In contrast, first the input image is converted to a normalized image, it is transformed to the log-polar coordinate system, and then a reference image using the local contrast in the wavelet domain is obtained to extrcat the global features. An intermediate hash sequence is achieved by extracting the significant information from the reference image using the singular value decomposition. The final hash sequence combines both the vectors followed by the randomization process. Extensive experiments are conducted to demonstrate the feasibility and the robustness of the proposed hashing system against a wide range of intentional/unintentional distortions. Further, the comparative analysis with some state-of-the-art techniques validates the better discrimination of the proposed work.
Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control

Mixed-integer convex programming (MICP) has seen significant algorithmic and hardware improvements with several orders of magnitude solve time speedups compared to 25 years ago. Despite these advances, MICP has been rarely applied to real-world robotic control because the solution times are still too slow for online applications. In this work, we present the CoCo (Combinatorial Offline, Convex Online) framework to solve MICPs arising in robotics at very high speed. CoCo encodes the combinatorial part of the optimal solution into a strategy. Using data collected from offline problem solutions, we train a multiclass classifier to predict the optimal strategy given problem-specific parameters such as states or obstacles. Compared to [1], we use task-specific strategies and prune redundant ones to significantly reduce the number of classes the predictor has to select from, thereby greatly improving scalability. Given the predicted strategy, the control task becomes a small convex optimization problem that we can solve in milliseconds. Numerical experiments on a cart-pole system with walls, a free-flying space robot, and task-oriented grasps show that our method provides not only 1 to 2 orders of magnitude speedups compared to state-of-the-art solvers but also performance close to the globally optimal MICP solution.
SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created when high spiking thresholds prevent neuronal activity. Here, we introduce sparsity into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to contribute to decision-making but suppressing information from neurons with high thresholds. This approach, which we term "SpaRCe," optimizes the sparsity level of the reservoir without affecting the reservoir dynamics. The read-out weights and the thresholds are learned by an online gradient rule that minimizes an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing interneuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe on classification problems and find that threshold learning improves performance compared to standard reservoir computing. SpaRCe alleviates the problem of catastrophic forgetting, a problem most evident in standard echo state networks (ESNs) and recurrent neural networks in general, due to increasing the number of task-specialized neurons that are included in the network decisions.
Effective Filter for Common Injection Attacks in Online Web Applications

Injection attacks against web applications are still frequent, and organizations like OWASP places them within the Top Ten of security risks to web applications. The main goal of this work is to contribute to the community with the design of an effective protection of web applications against common injection attacks. Our proposal is a validation filter of input fields that is based on OWASP Stinger, a set of regular expressions, and a sanitization process. It validates both fundamental characters (letters, numbers, dot, dash, question marks, and exclamation point) and complex statements (JSON and XML files) for each field. The procedure of deploying the proposed filter is detailed, specifying the sections and contents of the configuration file. In addition, the infrastructure for running the tests is described, including the setting of an attack tool, and the implementation of a controller. The attack tool is used as a security scanner for common injection attacks, and the controller is developed for routing the requests in two steps; first a request is addressed to the filter, and if it is valid, it will redirect to the web application itself. The proposal filter has been tested on three public as well as on a real private web application. An accuracy of 98,4% and an average processing time of 50 ms are achieved, based on wich it is possible to conclude the proposed filter is highly reliable and does not require additional computational resources.
A Systematic Literature Review on Implementing Non-functional Requirements in Agile Software Development: Issues and Facilitating Practices

Agile Software Development methods have become a widespread approach used by the software industry. Non-functional requirements (NFRs) are often reported to be a problematic issue for such methods. We aimed to identify (within the context of Agile projects): (1) the issues (challenges and problems) reported as affecting the implementation of NFRs; and (2) practices that facilitate the successful implementation of NFRs. We conducted a systematic literature review and processed its results to obtain a comprehensive summary. We were able to present two lists, dedicated to issues and practices, respectively. Most items from both lists, but not all, are related to the requirements engineering area. We found out that the issues reported are mostly related to the common themes of: NFR documentation techniques, NFR traceability, elicitation and communication activities. The facilitating practices mostly cover similar topics and the recommendation is to start focusing on NFRs early in the project.
DuroNet

Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of &lt;underline&gt;Du&lt;/underline&gt;al-&lt;underline&gt;ro&lt;/underline&gt;bust Enhanced Spatial-temporal Learning &lt;underline&gt;Net&lt;/underline&gt;work (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.
Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown that finger- vein- and fingerprint-based authentication methods are susceptible to wolf attacks, in which a wolf sample matches many enrolled user templates. In this work, we demonstrated that wolf (generic) faces, which we call “master faces,” can also compromise face recognition systems and that the master face concept can be generalized in some cases. Motivated by recent similar work in the fingerprint domain, we generated high-quality master faces by using the state-of-the-art face generator StyleGAN in a process called latent variable evolution. Experiments demonstrated that even attackers with limited resources using only pre-trained models available on the Internet can initiate master face attacks. The results, in addition to demonstrating performance from the attacker's point of view, can also be used to clarify and improve the performance of face recognition systems and harden face authentication systems.
Study on anatomical and functional medical image registration methods

The purpose of this paper is to give an overview of various well known medical image registration techniques with a special focus on registration between anatomical and functional medical images. Examples of anatomical medical images (AMI) are Computer Tomography (CT), Magnetic Resonance Images (MRI), X-ray radiographs and ultrasound etc. whereas Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT) and fMRI are examples of functional medical images (FMI). Irrespective of such types (AMI or FMI), every case can be considered as a medical imaging modality. All these modalities are widely used by clinicians to study the structure/functionality of human body parts for diagnosis and treatment. It is frequently required to combine PET/SPECT with CT/MRI to simultaneous study the metabolic and molecular information received through PET/SPECT with fine anatomical details observed by CT/MRI. This concurrent study will help in the diagnosis and localization of many diseases like cancer, blockage in coronary arteries and brain-related diseases like Parkinson, Alzheimer etc. Further, in many cases, clinicians are required to co-register one or more anatomical images with functional images, for example, ultrasound-guided biopsy fused with PET and MRI. This registration can be done either at the hardware level or at the software level. The introduction of integrated PET-CT machine increases the acceptability of hardware-based registration systems as compared to software-based methods among the medics. One possible reason for this is the lack of validation of results achieved through software-based registration methods. On the other hand, software-based registration methods also have many advantages over the hardware-based registration systems like lesser exposure to radiation and no need for new investment on hardware etc. In this paper, both the methods with their merits and demerits are discussed in detail.
Weakly Supervised Instance Segmentation by Deep Community Learning

We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset. The implementation of our algorithm is available on the project webpage: https://cv.snu.ac.kr/research/WSIS_CL.
A new perspective on personas and customer journey maps: Proposing systemic UX

This article explores user experience research within a consequentialist philosophical framework, where an optimal design depends on users' resultant levels of satisfaction and pleasure. Personas and customer journey maps can be pragmatic in indicating the requirements of a system by aggregating the overall pleasure of the majority of users. However, a revised approach, focused on minimising displeasures at the expense of reducing the pleasures of others, may level out individuals' satisfactory experiences with a view toward more holistic, systemic satisfaction for all users. We explore our philosophical thinking and illustrate these premises with a project set out to develop technological innovation for the rail industry. We conclude with recommendations of how designers could think about user experience to incorporate systemic views of pleasure when proposing innovations.
Proactive load balancing fault tolerance algorithm in cloud computing

Abstract As the demand for cloud computing infrastructure increased, availability and reliability have become more important as they are major features in real‐time computing systems. The chances of failure in a cloud computing system increase compared to other computing systems. To achieve high reliability and availability for the cloud computing infrastructure fault tolerance is one of the most essential modules. For a cloud computing infrastructure, a proactive fault tolerance algorithm with load balancing is proposed in this paper. The proposed methodology tolerates virtual machine CPU faults to achieve maximum cloud computing infrastructure reliability and availability. CPU faults can occur during virtual machine operation. The proposed model's main objective is to track changes in CPU utilization and make a decision once a high value of CPU utilization is identified. The decision is either to migrate the defective virtual machine to another “destination host”, or to manage loads on the destination host so that the faulty virtual machine can be migrated. A load is calculated for the destination host; if the destination host is overloaded, the subroutine for load balancing is launched. Load balancing is based on the technique of choosing a virtual machine.
“Talking a different Language”: Anticipating adversary attack cost for cyber risk assessment

Typical cyber security risk assessment methods focus on the system under consideration, its vulnerabilities, and the resulting impact in the event of a system compromise. Cyber security, however, increasingly requires anticipating the moves of intelligent adversaries, who make decisions based on a range of factors including the cost of their attacks. A study of current risk assessment literature and industry practice shows that consideration of this cost is a notable gap in the understanding of adversaries. The factors of cost experienced by an adversary are established in this paper as Time, Finance, and Risk, supported by a practical study undertaken with relevant security practitioners. Using these factors as a base, a framework is proposed and developed to support the probabilistic determination of cost incurred by an adversary. This framework is an important extension to existing cyber security risk assessments, and is demonstrated in the paper through the use of a case study.
Attention-Based Spatial Guidance for Image-to-Image Translation

The aim of image-to-image translation algorithms is to tackle the challenges of learning a proper mapping function across different domains. Generative Adversarial Networks (GANs) have shown superior ability to handle this problem in both supervised and unsupervised ways. However, one critical problem of GAN in practice is that the discriminator is typically much stronger than the generator, which could lead to failures such as mode collapse, diminished gradient, etc. To address these shortcomings, we propose a novel framework, which incorporates a powerful spatial attention mechanism to guide the generator. Specifically, our designed discriminator estimates the probability of realness of a given image, and provides an attention map regarding this prediction. The generated attention map contains the informative regions to distinguish the real and fake images, from the perspective of the discriminator. Such information is particularly valuable for the translation because the generator is encouraged to focus on those areas and produce more realistic images. We conduct extensive experiments and evaluations, and show that our proposed method is both qualitatively and quantitatively better than other state-of-the-art image translation frameworks.
HARP

Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis for knowledge acquisition. Therefore, this article introduces concept information derived from the lexical knowledge graph (e.g., Probase), and proposes a novel Hierarchical Attention model for Relation Prediction, which consists of entity-level attention mechanism and concept-level attention mechanism, to throughly integrate multiple semantic signals. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.
A fusion fall detection algorithm combining threshold-based method and convolutional neural network

Falls are one of the major causes of injury among the elderly. The fall detection solution can automatically detect the occurrence of a fall, reducing fall-related injuries. This paper presents a fusion fall detection algorithm combining threshold-based method (TBM) and convolutional neural network (CNN). The TBM of the fusion algorithm is executed on the wearable device named SHFFD. When an abnormal event occurs, the SHFFD exploits the TBM to preliminarily identify the event based on triaxial acceleration samples collected by the MPU6050. If this event is identified as a suspected fall event, a three-dimensional time series containing 300 triaxial acceleration samples is transmitted to the server, encoded as an RGB image, and finally the algorithm based on CNN is applied to classify the suspected fall event to determine whether it is a fall. In addition, the feature set at the TBM stage of the fusion algorithm is optimized. This ensure that all real fall events are transmitted to the server for processing, while minimizing the power consumed by transferring the acceleration samples. Experimental results reveal that the fusion algorithm achieves high sensitivity (98.04%), specificity (96.91%) and accuracy (97.46%).
Behavioral structure of users in cryptocurrency market

Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k -means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.
Towards Visually Explaining Video Understanding Networks with Perturbation

"Making black box models explainable " is a vital problem that accompanies the development of deep learning networks. For networks taking visual information as input, one basic but challenging explanation method is to identify and visualize the input pixels/regions that dominate the network's prediction. However, most existing works focus on explaining networks taking a single image as input and do not consider the temporal relationship that exists in videos. Providing an easy-to-use visual explanation method that is applicable to diversified structures of video understanding networks still remains an open challenge. In this paper, we investigate a generic perturbation-based method for visually explaining video understanding networks. Besides, we propose a novel loss function to enhance the method by constraining the smoothness of its results in both spatial and temporal dimensions. The method enables the comparison of explanation results between different network structures to become possible and can also avoid generating the pathological adversarial explanations for video inputs. Experimental comparison results verified the effectiveness of our method.