Reviewerly/exHarmony huggingface dataset:
A Survey on Loss Functions for Deep Face Recognition Network

With the increased collaboration between humans and robots in daily life, face recognition becomes one of the essential aspects of human-robot interaction (HRI). The robot requires a highly accurate face recognition system to operate in different environments and conditions. The role of achieving high accuracy face recognition is the enhancement of learning discriminative feature representation, which is almost entirely around minimizing the intra-class distance and maximizing the inter-class distance. The loss function is used on deep Convolutional Neural Networks (CNNs) to enhance this discriminative power of the deeply learned features. Softmax loss is one of the most used loss functions in many CNNs. However, softmax loss did not have the sufficient discriminative power needed for face recognition. Recently, many researchers work on developing novel loss functions to improve discriminatory power mainly, the intra-class distance of deep features. This survey paper’s main objective is to compare the multiple loss functions used for deep face recognition networks showing the weakness for each loss function.
Pedestrian Detection Based on Improved YOLOv3 Algorithm

Pedestrian detection in security surveillance video is a hot spot in the field of AI (Artificial Intelligence) algorithms. Aiming at the influence of background information in different environments and the difficulty of small target detection, the HOG (Histogram of Oriented Gradient) feature extraction method of the improved algorithm is applicate as the preprocessing layer of YOLOv3 feature extraction part, which can highlight the pedestrian contour features, especially the small target pedestrian features, and reduce the interference caused by background information on the detection results. During training, the different data sets are mixed as inputs to increase the diversity of the sample data. Hybrid data is also used to validate the generalization capability of the algorithm during the final test phase.
Development of High-Pressure Washing Aerial Robot Employing Multirotor Platform with Add-on Planar Translational Driving System

We propose a multirotor aerial robot for high-pressure washing task. The robot consists of a multi rotor platform, an add-on planar translational driving system(ATD) and a high-pressure washing system. The ATD is equipped on the multirotor and consists of three ducted fans that can generate the force on horizontal plane. The ATD also allows multirotor to suppress the reaction force generated by nozzle of high-pressure washing system and spray water accurately. In this study, we focus on the development of the proposed aerial robot system. Through experiment, we show the successful water spraying by suppressing the reaction force while flying.
Evaluating Blockchain Protocols with Abusive Modeling

Strategic evaluations of blockchain systems allow a better understanding of the security of the mining process. In recent years, many researchers have focused on developing optimal strategies to evaluate the impact of an adversary on the mining process using different attack situations such as selfish mining, double-spending, feather-forking, Denial of Service. These strategies rely on the use of the Markov Decision Process (MDP) to find optimal settings that an adversary can exploit to earn maximum profit in every round. However, these strategies do not consider a case where adversaries turn abusive, and their only aim is to harm the mining process without profit. Motivated by this, a self-defying adversary model is proposed that uses ZEBRA (Zero Expectation-Based Reward Abuse) strategy to cause a maximum impact on the rewards of the honest players at lower settings. With the proposed method, the adversary itself may not be profitable, but has better control over the chain growth and causes maximum damage to reward by delaying the blocks and inducing forks subject to its compliance degree. The evaluations are demonstrated to show the reward control by the adversary along with the impact on delays and forks, followed by the possibilities of attacks using the hashing powers of different mining pools.
Probabilistic Density-based Adaptive Clustering for Streaming Data

The Internet of Things(IoT) has been applied in many real-world scenarios to provide intelligent solutions in recent years. The biggest challenge in these applications is the enormous volume of data generated from the devices. Adequate mechanisms are demanded to process this data in real-time to generate valuable information. In many IoT scenarios, a grouping of generated data is effective in deriving insights. Clustering techniques are beneficial for grouping data when the label is partially or fully not available. This paper proposes a probabilistic clustering algorithm for IoT data streams, where the number of clusters need not be defined in advance. In addition, the clusters are managed adaptively using a maintenance system. The model has experimented with two publicly available IoT data sets: weather and pollution data. We compared cluster quality with existing algorithms, and the results demonstrate the effectiveness of the proposed clustering scheme.
Self-calibration method for rotating Risley prism main sections based on chromatic dispersion characteristics of the Fourier spectrum

To address the main section misalignment of rotating Risley prisms in a camera imaging system, a zero calibration method for a Risley prism main section is proposed according to the dispersion characteristics of the prisms-camera imaging. The principal component analysis method and the Radon transform method are combined to establish an effective mapping relationship between the prism main section positions and the prismatic dispersion characteristics using the camera dispersion image as references. The estimation of the dispersion direction was realized by referring to the motion blur estimation method and verified by experiment. As a result, the target-free self-calibration of the prism main sections in the camera coordinate system can be carried out under a prisms-camera intact assembly mode.
Confidentiality Leakage Analysis of Database-Driven Applications

In a software system the database technology is one of the most important part that stores external information into the permanent storage and process them accordingly. However, the most prime concern of any information system is to protect the data in the persistent storage from any unauthorised access. Sometimes, the database technology is developed in such a way that sensitive information may be leaked directly or indirectly during information processing along with control structure of a software code. In this paper, we propose a dependency graph based approach to identify possible confidentiality leakage of a software code. We design a framework using the semantic based dependency analysis technique. It refines the existing syntax-based dependency graph by removing false alarms and generates more precise dependency information that leads to improved security analysis results.
Active Learning of Pattern Classification Based on PEDCC-Loss

Deep learning classifiers require a large number of labeled samples to train the model. Active learning reduces the dependence of classification model on labeled samples by gradually selecting high-quality samples for iterative training. In this article, an active learning method for pattern classification is proposed, which can use fewer labeled samples to achieve higher classification accuracy. The algorithm uses PEDCC-Loss to train the network model according to the predefined evenly-distributed class centroids (PEDCC) of the classification task, maximizing the distance between classes. Then, the maximum value of the cosine distance is selected to measure the sample uncertainty according to the PEDCC output characteristics. In the active learning algorithm iteration, samples with high uncertainty are selected for manual labeling, and samples with low uncertainty are given pseudo labels. At the same time, manual label samples and pseudo label samples are used to train the classification network to continuously optimize the classification boundary. Compared with CRL, LLAL and other methods, the experimental results on CIFAR100, CIFAR10, SVHN, and Animals-10 data sets show the effectiveness of this algorithm.
LLTI: Low-Latency Threshold Implementations

With the enormous increase in portable cryptographic devices, physical attacks are becoming similarly popular. One of the most common physical attacks is Side-Channel Analysis (SCA), extremely dangerous due to its non-invasive nature. Threshold Implementations (TI) was proposed as the first countermeasure to provide provable security in masked hardware implementations. While most works on hardware masking are focused on optimizing the area requirements, with the newer and smaller technologies area is taking a backseat, and lowlatency is gaining importance. In this work, we revisit the scheme proposed by Arribas et al. in TCHES 2018 to secure unrolled implementations. We formalize and expand this methodology, to devise a masking scheme, derived from TI, designed to secure hardware implementations optimized for latency named Low-Latency Threshold Implementations (LLTI). By applying the distributive property and leveraging a divide-and-conquer strategy, we split a non-linear operation in layers which are masked separately. The result is a more efficient scheme than the former TI for any operation of algebraic degree greater than two, achieving great optimizations both in terms of speed and area. We compare the performance of first-order LLTI with first-order TI in securing a cubic gate and a degree-7 AND gate without using any registers in between. We achieve a 137% increase in maximum frequency and a 60% reduction in area for the cubic gate, and 3131 times reduction in area in the case of a degree-7 AND gate compared to TI. To further illustrate the power of our scheme we take a low-latency PRINCE implementation from the literature and, by simply changing the secure S-box with the LLTI version, we achieve a 46% max. frequency improvement and a 38% area reduction. Moreover, we apply LLTI to a secure a low-latency AES implementation and compare it with the TI version, achieving a 6.9 times max. freq. increase and a 47.2% area reduction.
Revisiting the ODE Method for Recursive Algorithms: Fast Convergence Using Quasi Stochastic Approximation

Several decades ago, Profs. Sean Meyn and Lei Guo were postdoctoral fellows at ANU, where they shared interest in recursive algorithms. It seems fitting to celebrate Lei Guo's 60th birthday with a review of the ODE Method and its recent evolution, with focus on the following themes:
Technology-enabled online aggregated market for smallholder farmers to obtain enhanced farm-gate prices

Using scenario transformation methodology, we identified four scenarios that indicated a lack of trusted parties to sell harvest has forced smallholder farmers to sell the harvest to brokers who often collect the harvest at the farm gate at the lowest possible prices and sell in the market for large profits. As blockchain smart contracts provide a mechanism to reduce risk and establish trust between unknown trading partners, we transformed these into a scenario that establishes trust between farmer and unknown broker using smart contracts, generating a trust-enabled market. This scenario enables farmers to search for the optimum farm-gate price without relying on known brokers. The scenario is further enhanced to enable a Many-one-Many market linkage, facilitating automatic aggregated marketing. The paper presents the functional prototype of the scenario, explaining the functionality of the transformed system.
A New Approach for Finding Low-Weight Polynomial Multiples

We consider the problem of finding low-weight multiples of polynomials over binary fields, which arises in stream cipher cryptanalysis or in finite field arithmetic. We first devise memory-efficient algorithms based on the recent advances in techniques for solving the knapsack problem. Then, we tune our algorithms using the celebrated Parallel Collision Search (PCS) method to decrease the time cost at the expense of a slight increase in space. Both our memory-efficient and time-memory trade-off algorithms improve substantially the state-of-the-art. The gain is for instance remarkable for large weights; a situation which occurs when the available keystream is small, e.g. the Bluetooth keystream.
Development of Teaching Mode of Digital Electronic Technology Based on Virtual Simulation

With the rapid development of electronic technology, and society's requirements for talent training continue to increase. At present, the traditional teaching mode and teaching methods of the school have been difficult to meet the needs of teaching. At the same time, computer technology and software development technology are changing with each passing day, and the use of microcomputers is becoming wider and wider. Therefore, it is imperative to study how to apply VS technology to the teaching of electronic technology courses and use advanced teaching methods to change the past teaching mode. It is imperative to reform the school's teaching mode. As a local applied undergraduate college, a college in this city has a place to serve. It is based on a local, applied, open, and international orientation. At the same time, it has formulated a solid foundation, strong application, ethics, innovation, and high-quality the training specifications are in line with the new engineering talent training concept. "Digital electronic technology" is the core basic course of electronic information engineering. It is the basis for the subsequent courses of "Single-chip Microcomputer Principles and Interface Technology", "DSP and Sensor Principles" and so on. The traditional digital electronic technology teaching model has the disadvantages of fixed knowledge, aging teaching content, single teaching method and high experimental teaching cost. The traditional teaching mode of digital electronics technology has the disadvantages of fixed knowledge, aging teaching content, single teaching method and high cost of experimental teaching, which cannot meet the specific practical teaching requirements of the new engineering construction in China in the new era, to adapt to the changes in the current industrial development and to meet the needs of the future industrial development. Based on this, in order to respond to the teaching background of "new engineering", this paper designs and develops a new teaching model of digital electronic technology based on Virtual Simulation (VS). Research shows, in innovative teaching practices, students in the experimental classes will be able to fully understand and master the basic theory, basic methods and basic skills of knowledge, students' practical skills will be greatly enhanced, as well as their language skills. In addition, most students will be able to fully apply their knowledge and expertise in digital electronics in their first year of study.
FD-Net: A Fully Dilated Convolutional Network for Historical Document Image Binarization

Binarization is a key step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of the encoder-and-decoder architecture, such as fully convolutional network (FCN) and U-Net. Despite their success, they still suffer from two challenges: (1) max-pooling or strided convolution reduces the spatial resolution of the intermediate feature maps, which may lead to information loss, and (2) interpolation or transposed convolution attempts to restore the feature maps to the desired spatial resolution, which may also result in pixelation. To overcome these two limitations, we propose a fully dilated convolutional network, termed FD-Net, using atrous convolutions instead of downsampling or upsampling operations. We have conducted extensive experiments on the recent DIBCO (document image binarization competition) and H-DIBCO (handwritten document image binarization competition) benchmark datasets. The experimental results show that our proposed FD-Net outperforms other state-of-the-art techniques by a large margin. The source code and pre-trained models are publicly available at https://github.com/beargolden/FD-Net.
Business incubator assessment model

Abstract At the initial stage of economy innovative potential formation, the leading role belongs to small venture firms that provide market development of the intellectual activity results. Support for small businesses of this type is usually provided through the development of a business incubators system initiated by regional authorities and implemented through targeted budget funding. In this regard, it is important for incubators to develop an indicators system for evaluating the effectiveness of budget allocations and private investment. The paper proposes a two-level system of business incubator functioning and develops a management structure that allows analyzing the current state of the organization and the results of its work using a mathematical model. The incubator activity is determined by the state of the system at the time of starting the next cycle of work, the state of the incubated companies at each stage of incubation (pre-incubation, incubation, post-incubation) and planned tasks, taking into account resource and technological limitations. The proposed model can be used in the development of methods for continuous deviations monitoring from the planned performance indicators of the organization with the ability to pinpoint the problem and respond to it. The advantage of the proposed model is that it can be scaled up to the level of the innovation structure of the region.
Agent-mediation in RADT Framework for Self-Quarantine Monitoring

The technology advancement is beckoned by the recent pandemic crisis at all levels. With the situation of self-quarantined individuals found to be in public places against the rules, causing threat to the spread of the COVID-19 virus, the health industry and the whole nation started to revisit the monitoring capabilities in hand that runs parallel with the massive fight against the pandemic. Shortage of resources to curb the spread that is caused by stubborn self-quarantined individuals added to the pain. This paper presents a conceptual framework for agent-mediated self-quarantine monitoring to lessen the burden of the health officers. This framework consists of four main software agents performing tasks from data capture in sensor device to the analysis and transmission of alerts to health officers and appointed recipients. The name RADT framework is given for the main tasks the agents are assigned with, namely Retrieve, Analyse, Decide and Transmit. In general, the software agents perform their tasks in their environment, with the capabilities deemed possible at their level. As their services are required for only 14 days according to the self-quarantine procedure, the agents are programmed to self-terminate after that.
Negotiating Open Science: The Open Science Framework as a Technology-in-Practice

The open science movement promotes use of digital technology to increase the efficiency, inclusivity, and quality of scientific research. Developers of these platforms often advocate for open science on the grounds that it is in keeping with scientific values, specifically referencing Mertonian norms. However, many scientists are agnostic toward open science; as policies and technology enforce the movement’s aims of sharing openly, they seek to protect the research they view as their own. My dissertation work studies the enactment of open science by a variety of stakeholders in the Open Science Framework (OSF)—its developers, its users, and also its non-users. Through interviews, trace data collection, and observation of these various populations, I will explore how these stakeholders construct different technologies-in-practice. By taking the OSF not as a given, but as a technology whose purpose and effects are structured by the constraints and resources of its stakeholders, I will unpack the tensions among open science advocates, agnostics, and their use of technology. With these insights, science policy and open systems can be designed to better accommodate the diverse concerns of stakeholders.
IFR: Iterative Fusion Based Recognizer for Low Quality Scene Text Recognition

Although recent works based on deep learning have made progress in improving recognition accuracy on scene text recognition, how to handle low-quality text images in end-to-end deep networks remains a research challenge. In this paper, we propose an Iterative Fusion based Recognizer (IFR) for low quality scene text recognition, taking advantage of refined text images input and robust feature representation. IFR contains two branches which focus on scene text recognition and low quality scene text image recovery respectively. We utilize an iterative collaboration between two branches, which can effectively alleviate the impact of low quality input. A feature fusion module is proposed to strengthen the feature representation of the two branches, where the features from the Recognizer are Fused with image Restoration branch, referred to as RRF. Without changing the recognition network structure, extensive quantitative and qualitative experimental results show that the proposed method significantly outperforms the baseline methods in boosting the recognition accuracy of benchmark datasets and low resolution images in TextZoom dataset.
A Low Cost Virtual Guide for Next Generation Smart Museums

Museums are the place of wonders. Museum designers or curators work continuously to improve the visitor experience. The best way to create this wow effect is to use the most advanced technologies for our exhibits and displays. Nowadays Artificial Intelligence is one of the most discussed technological words used in the scientific community. Though it is yet to reach the masses, to give the flavour of AI to the general public and also to use the goodness of this technology in communicating science, a customized chatbot is developed to work as a museum guide. In order to achieve this, we have taken help from different AI and Internet of Things (IoT) based technologies. Finally, a virtual humanoid bot is developed with capabilities to operate and explain physical exhibits through interaction with visitors. This bot can replace museum guides for explaining different exhibits to the visitors
Towards a Robust Differentiable Architecture Search under Label Noise

Neural Architecture Search (NAS) is the game changer in designing robust neural architectures. Architectures designed by NAS outperform or compete with the best manual network designs in terms of accuracy, size, memory footprint and FLOPs. That said, previous studies focus on developing NAS algorithms for clean high quality data, a restrictive and somewhat unrealistic assumption. In this paper, focusing on the differentiable NAS algorithms, we show that vanilla NAS algorithms suffer from a performance loss if class labels are noisy. To combat this issue, we make use of the principle of information bottleneck as a regularizer. This leads us to develop a noise injecting operation that is included during the learning process, preventing the network from learning from noisy samples. Our empirical evaluations show that the noise injecting operation does not degrade the performance of the NAS algorithm if the data is indeed clean. In contrast, if the data is noisy, the architecture learned by our algorithm comfortably outperforms algorithms specifically equipped with sophisticated mechanisms to learn in the presence of label noise. In contrast to many algorithms designed to work in the presence of noisy labels, prior knowledge about the properties of the noise and its characteristics are not required for our algorithm.