Reviewerly/exHarmony huggingface dataset:
Accurate Facial Image Parsing at Real-Time Speed

In this paper, we propose a design scheme for deep learning networks in face parsing task with promising accuracy and real-time inference speed. By analyzing the differences between general image parsing task and face parsing task, we first revisit the structure of traditional FCN and make improvements to adapt to the unique properties of face parsing task. Especially, the concept of Normalized Receptive Field is proposed to give more insights on designing the network. Then a novel loss function called Statistical Contextual Loss is introduced, which integrates richer contextual information and regularizes features during training. For further model acceleration, we propose a semi-supervised distillation scheme that effectively transfers the learned knowledge to a lighter network. Extensive experiments on LFW and Helen dataset demonstrate the significant superiority of the new design scheme on both efficacy and efficiency.
Strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional Neural Networks

This paper describes a new optimization technique to perform an embedded implementation of convolutional neural networks (CNN). In this case, only the inference of convolutional neural networks is discussed. As known that both pooling layer and strided convolution can be used to summarize the data. So, the proposed technique aims to replace only max pooling layers by a strided convolution layers using the same filter size and stride of the old pooling layers in order to reduce the model size and improve the accuracy of a CNN. Also, pooling layer is parameter less. However, convolution layer has weights and biases to optimize. Then, the CNN can learn how to summarize the data. By replacing max pooling layers with strided convolution layers enhance the CNN accuracy and reduce the model size. This technique is proposed in order to build a CNN accelerator for real time application and embedded implementation. The proposed optimizations are applied on some state-of-the-art CNN models and the obtained results are compared with the original ones. The proposed optimization is demonstrated for reducing the memory occupation of the model and achieving accuracy enhancement. The proposed technique enables possibility of the implementation of the convolutional neural network models in embedded systems.
Multi-Writer Searchable Encryption

Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.
Human Motion Understanding for Selecting Action Timing in Collaborative Human-Robot Interaction

In the industry of the future, so as in healthcare and at home, robots will be a familiar presence. Since they will be working closely with human operators not always properly trained for human-machine interaction tasks, robots will need the ability of automatically adapting to changes in the task to be performed or to cope with variations in how the human partner completes the task. The goal of this work is to make a further step toward endowing robot with such capability. To this purpose, we focus on the identification of relevant time instants in an observed action, called dynamic instants, informative on the partner’s movement timing, and marking instants where an action starts or ends, or changes to another actions. The time instants are temporal locations where the motion can be ideally segmented, providing a set of primitives that can be used to build a temporal signature of the action and finally support the understanding of the dynamics and coordination in time. We validate our approach in two contexts, considering first a situation in which the human partner can perform multiple different activities, and then moving to settings where an action is already recognized and shows a certain degree of periodicity. In the two contexts we address different challenges. In the first one, working in batch on a dataset collecting videos of a variety of cooking activities, we investigate whether the action signature we compute could facilitate the understanding of which type of action is occurring in front of the observer, with tolerance to viewpoint changes. In the second context, we evaluate online on the robot iCub the capability of the action signature in providing hints to establish an actual temporal coordination during the interaction with human participants. In both cases, we show promising results that speak in favour of the potentiality of our approach.
Capitalizing on digital literacy skills for capacity development of people who are not in education, employment or training in South Africa

Most people from rural and township areas in South Africa do not have skills in utilizing information and communication technology (ICT), particularly those who are not in education, employment or training (NEET). Addressing the gap of ICT skills is one of the fundamental options to grow and develop the capacity of people to effectively participate in economic activities. The general objective of this qualitative study was to explore how digital literacy skills can advance the lives of people currently searching for employment. Data were obtained using semi-structured interviews with participants who were not employed and with diverse school backgrounds. The results indicate a need to strengthen existing digital literacy programmes for NEET people. Most of the interviewed NEET people experienced challenges when seeking work on digital platforms, because they are not skilled in digital literacy. Analysis of the data from interview transcripts revealed that there is an array of challenges which prevent NEET people from efficiently using ICTs when searching for work opportunities. These include the high cost of Internet connections and the lack of knowledge of how to use these digital platforms. The study concludes that digital literacy is one of the skills which remains critical for work seekers to increase their chances of achieving continuous work opportunities, and eventually obtain employment or become employers themselves. It is hoped that this study will contribute to the continuous discussions about the lack of digital literacy skills among young people who are economically inactive.
Quality control of diffuse solar radiation component with satellite-based estimation methods

In order to analyze solar systems, it is very important to determine the accurate solar radiation components belonging to any region. These components are defined as the beam and diffuse. The diffuse radiation component may not always be determined accurately since it is mostly affected by many geographical factors and climatic features in addition to the location parameters. Therefore, this work aims to compare and evaluate the validity of global and diffuse estimation methods for nine locations in Turkey. The satellite-based estimation methods (HELIOSAT, Meteonom, and PVGIS) mentioned in the literature were used to estimate global and diffuse radiation. These selected methods were statistically tested with accurate ground measured data. For HELIOSAT, the global radiation is varying −0.02 and 0.03, and 0.010 and 0.035, respectively, Relative MBE and Relative RMSE. Similarly, the diffuse radiation values are −0.020 and 0.025 for Relative MBE, and 0.015 and 0.030 for Relative RMSE. The goodness of the fit values for the HELIOSAT method shows a very good agreement with at least 97% confidence level of global and 91% of diffuse radiation values. Thus, HELIOSAT method shows a trustworthy alternative to the ground measurement data when reliable global and diffuse radiation data are not available.
Referenceless Quality Evaluation of Tone-Mapped HDR and Multiexposure Fused Images

Nowadays, the standard dynamic range (SDR) image acquired at a fixed exposure exposes weakness in portraying fine-grained details of real scenes. The high dynamic range (HDR) image and other types of SDR images generated by multiexposure fusion techniques provide us new choices for scene representation. To display on SDR screens, an HDR image must be tone-mapped to an SDR one. Since different tone-mapping/fusion algorithms produce images with varying visual quality levels, it naturally desires a quality evaluation model for comparison. This article proposes an effective model in the absence of the reference image. By analyzing the characteristics of tone-mapped HDR and multiexposure fused images, we first extract multiple quality-sensitive features from the following aspects: 1) colorfulness; 2) exposure; and 3) naturalness. Then, the model is built by bridging all extracted features and associated subjective ratings via support vector regression. Extensive experiments on publicly available databases prove the superiority of our model over the state-of-the-art referenceless quality evaluation ones.
What Happens After You Leak Your Password

Phishing has been a big concern due to its active roles in recent data breaches and state-sponsored attacks. While existing works have extensively analyzed phishing websites and their operations, there is still a limited understanding of the information sharing flows throughout the end-to-end phishing process. In this paper, we perform an empirical measurement on the transmission and sharing of stolen login credentials. Over 5 months, our measurement covers more than 179,000 phishing URLs (47,000 live phishing sites). First, we build a measurement tool to feed fake credentials to live phishing sites. The goal is to monitor how the credential information is shared with the phishing server and potentially third-party collectors on the client side. Second, we obtain phishing kits from a subset of phishing sites to analyze how credentials are sent to attackers and third-parties on the server side. Third, we set up honey accounts to monitor the post-phishing exploitation activities from attackers. Our study reveals the key mechanisms for information sharing during phishing, particularly with third-parties. We also discuss the implications of our results for phishing defenses.
Survey of Formal Verification Methods for Smart Contracts on Blockchain

Due to the immutable nature of distributed ledger technology such as blockchain, it is of utter importance that a smart contract works as intended before employment outside test network. This is since any bugs or errors will become permanent once published to the live network, and could lead to substantial economic losses; as manifested in the infamous DAO smart contract exploit hack in 2016. In order to avoid this, formal verification methods can be used to ensure that the contract behaves according to given specifications. This paper presents a survey of the state of the art of formal verification of smart contracts. Being a relatively new research area, a standard or best practice for formal verification of smart contracts has not yet been established. Thus, several different methods and approaches have been used to perform the formal verification. The survey presented in this paper shows that some variant of model checking or theorem proving methodology seems to be most successful. However, as of today, formal verification is only successful on simple contracts, and does not support more advanced smart contract syntax.
Real-Time Image Super-Resolution Using Recursive Depthwise Separable Convolution Network

In recent years, deep convolutional neural networks (CNNs) have been widely used for image super-resolution (SR) to achieve a range of sophisticated performances. Despite the significant advancement made in CNNs, it is still difficult to apply CNNs to practical SR applications due to enormous computations of deep convolutions. In this paper, we propose two lightweight deep neural networks using depthwise separable convolution for the real-time image SR. Specifically, depthwise separable convolution divides the standard convolution into depthwise convolution and pointwise convolution to significantly reduce the number of model parameters and multiplication operations. Moreover, recursive learning is adopted to increase the depth and receptive field of the network in order to improve the SR quality without increasing the model parameters. Finally, we propose a novel technique called Super-Sampling (SS) to learn more abundant high-resolution information by over-sampling the output image followed by adaptive down-sampling. The proposed two models, named SSNet-M and SSNet, outperform the existing state-of-the-art real-time image SR networks, including SRCNN, FSRCNN, ESPCN, and VDSR, in terms of model complexity, and subjective and PSNR/SSIM evaluations on Set5, Set14, B100, Urban100, and Manga109.
Revisiting Distillation and Incremental Classifier Learning

One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks. This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system. In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain conditions. To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature. We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e., it often leads to bias in classifiers. Finally, we propose a dynamic threshold moving algorithm that is able to successfully remove this bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results. Our implementation is available at: https://github.com/Khurramjaved96/incremental-learning .
Automated Segmentation Of Cervical Nuclei In Pap Smear Images Using Deformable Multi-Path Ensemble Model

Pap smear testing has been widely used for detecting cervical cancers based on the morphological properties of cell nuclei in microscopic image. An accurate nuclei segmentation could thus improve the success rate of cervical cancer screening. In this work, a method of automated cervical nuclei segmentation using Deformable Multipath Ensemble Model (D-MEM) is proposed. The approach adopts a U-shaped convolutional network as a backbone network, in which dense blocks are used to transfer feature information more effectively. To increase the flexibility of the model, we then use deformable convolution to deal with different nuclei irregular shapes and sizes. To reduce the predictive bias, we further construct multiple networks with different settings, which form an ensemble model. The proposed segmentation framework has achieved state-of-the-art accuracy on Herlev dataset with Zijdenbos similarity index (ZSI) of 0.933±014, and has the potential to be extended for solving other medical image segmentation tasks.
Quantum fractional revival on graphs

Fractional revival is a quantum transport phenomenon important for entanglement generation in spin networks. This takes place whenever a continuous-time quantum walk maps the characteristic vector of a vertex to a superposition of the characteristic vectors of a subset of vertices containing the initial vertex. A main focus will be on the case when the subset has two vertices. We explore necessary and sufficient spectral conditions for graphs to exhibit fractional revival. This provides a characterization of fractional revival in paths and cycles. Our work builds upon the algebraic machinery developed for related quantum transport phenomena such as state transfer and mixing, and it reveals a fundamental connection between them.
Privacy-preserving lightweight face recognition

Face recognition based on deep learning has become one of the mainstream identity authentication technologies. In recent years, many well-developed deep convolutional neural networks have emerged. But most of these network structures are very complicated, the training processes are very difficult and face recognition based on these network structures are too large to apply to mobile terminals. To solve this problem, we propose a lightweight face recognition algorithm: LightFace, which is based on depthwise separable convolution. In the process of training LightFace, the triplet loss algorithm is used to optimize model. However, LightFace requires a large amount of training data and the parameters of this model will record the users' data, which can be recovered by attackers. To address this problem, we propose an applicable approach to providing strong privacy guarantees for LightFace. In our approach, the generated data and additional noise are used to slightly disturb the original data distribution, so that the attackers cannot correctly predict the training data, which can improve the security of the model. Besides, in the process of training LightFace, ensemble learning increases the randomness of the original data distribution and enhances the robustness of the model. Within the differential privacy framework, we analyzed the privacy loss of the algorithm theoretically and conduct experiments on different face recognition datasets to demonstrate the effectiveness of our privacy preservation method. The experiment results show that LightFace with privacy preservation still has good recognition accuracy while protecting data privacy.
Dynamic index and LZ factorization in compressed space

In this paper, we propose a new dynamic compressed index of O(w) space for a dynamic text T, where w=O(min(zlogNlog∗M,N)) is the size of the signature encoding of T, z is the size of the Lempel–Ziv77 (LZ77) factorization of T, N is the length of T, and M≥N is the maximum length of T. Our index supports searching for a pattern P in T in O(|P|fA+logwlog|P|log∗M(logN+log|P|log∗M)+occlogN) time and insertion/deletion of a substring of length y in O((y+logNlog∗M)logwlogNlog∗M) time, where occ is the number of occurrences of P in T and fA=O(min{loglogMloglogwlogloglogM,logwloglogw}). Also, we propose a new space-efficient LZ77 factorization algorithm for a given text T, which runs in O(NfA+zlogwlog3N(log∗N)2) time with O(w) working space.
Investigation of the requirements change management challenges in the domain of global software development

Abstract The phenomenon of global software development (GSD) has been adopted by a majority of the software development firms to achieve the significant benefits it offers. However, there are many challenges faced by the GSD organizations, which are mainly related to requirements change management (RCM). The key objective of this study is to identify the challenges of RCM process in GSD domain. The systematic literature review (SLR) approach has been used to investigate the challenges of RCM activities, and a total of 30 challenges were identified. We have further classified the identified challenges in the domain of client and vendor GSD organizations, aiming to provide a clear understanding of the RCM process and its challenges in the context of both types of GSD organizations. The identified challenges were also categorized into three core types according to the organization size (small, medium sized, or large), which highlights the significance of each challenge for a specific organizational size. In addition, the criticality of the identified challenges was assessed using the criteria of challenges having a frequency greater than or equal to 50%. According to the findings of this study, a framework is provided that could help GSD organizations address the problems related to RCM in a GSD environment.
Robust Sequential Trajectory Planning Under Disturbances and Adversarial Intruder

Provably safe and scalable multivehicle trajectory planning is an important and urgent problem. Although this problem has been studied in the past, there has not been a method that guarantees both goal satisfaction and safety for vehicles with general nonlinear dynamics, while taking into account disturbances and potential adversarial agents, to the best of our knowledge. Hamilton-Jacobi (HJ) reachability is the ideal tool for guaranteeing goal satisfaction and safety under such scenarios, which has been successfully applied to many small-scale problems; however, its direct application in most cases becomes intractable when there are more than two vehicles due to the exponentially scaling computational complexity with respect to system dimension. In this paper, we take advantage of the guarantees provided by HJ reachability and eliminate the computation burden by assigning a strict priority ordering to vehicles under consideration. Under this sequential trajectory planning (STP) scheme, vehicles reserve “space-time” portions in the airspace. The space-time portions guarantee dynamic feasibility, collision avoidance, and optimality of trajectories given the priority ordering. With a computation complexity that scales quadratically when accounting for both disturbances and an intruder, and linearly when accounting for only disturbances, the STP can tractably solve the multivehicle trajectory planning problem for vehicles with general nonlinear dynamics in a practical setting. We demonstrate our theory in representative simulations.
Stability Based Filter Pruning for Accelerating Deep CNNs

Convolutional neural networks (CNN) have achieved impressive performance on the wide variety of tasks (classification, detection, etc.) across multiple domains at the cost of high computational and memory requirements.Thus, leveraging CNNs for real-time applications necessitates model compression approaches that not only reduce the total number of parameters but reduce the overall computation as well.In this work, we present a stability-based approach for filter-level pruning of CNNs.We evaluate our proposed approach on different architectures (LeNet, VGG-16, ResNet, and Faster RCNN) and datasets and demonstrate its generalizability through extensive experiments.Moreover, our compressed models can be used at run-time without requiring any special libraries or hardware.Our model compression method reduces the number of FLOPS by an impressive factor of 6.03X and GPU memory footprint by more than 17X, significantly outperforming other state-of-the-art filter pruning methods.