Reviewerly/exHarmony huggingface dataset:
Privacy Enabled Immunity Credential System on Blockchain

Immunity credentials for individuals are essential to ensure safety and security of communities in the current pandemic-stricken world. Immunity credentials can be employed to permit movement of only a certain class of individuals that are least prone to spreading infections, thus reducing the risk for the society as a whole. While the authenticity of the immunity credentials is extremely critical, it is also important to guarantee privacy and confidentiality of the individuals’ personal health information. In this paper, we present a blockchain based immunity credential system that allows users to authenticate immunity credentials in a verifiable manner. We leverage BLS aggregate signature that facilitates credential holders to selectively disclose credentials in a privacy preserving manner. Blockchain acts as single source of trust and ensures authenticity of claims. Our system enables users to reuse the tamper-evident verification status recorded on blockchain to authenticate to same verifiers without need for repeated cryptographic proof verification process. The protocol restricts any intermediaries/third-parties to use the identity or proof without authorization of the user. The protocol is fully automated through smart contracts and makes sure that credential verification is settled in near real time. We chose permissioned blockchain, Hyperledger Fabric (HLF) to implement our protocol as it befits the requirements of this problem and ensures smart contract development that supports user privacy, confidentiality, automated proof generation and verification of immunity credentials.
Towards Intelligent Security for Unmanned Aerial Vehicles: A Taxonomy of Attacks, Faults, and Detection Mechanisms

In this extended paper we provide two mappings using a fault taxonomy as a pivot: first, we map attacks or faults scenarios to a class of well-known error types. Then, we categorize detection mechanisms by their ability to detect error types. By combining those two tables, a designer can confidently select a detection mechanism for identified attack or fault scenarios.
SNAS: Fast Hardware-Aware Neural Architecture Search Methodology<sup />

Recently, automated neural architecture search (NAS) emerges as the default technique to find a state-of-the-art (SOTA) convolutional neural network (CNN) architecture with higher accuracy than manually designed architectures for image classification. In this article, we present a fast hardware-aware NAS methodology, called S3NAS, reflecting the latest research results. It consists of three steps: 1) supernet design; 2) Single-Path NAS for fast architecture exploration; and 3) scaling and post-processing. In the first step, we design a supernet, superset of candidate networks with two features: one is to allow stages to have a different number of blocks, and the other is to enable blocks to have parallel layers of different kernel sizes (MixConv). Next, we perform a differential search by extending the Single-Path NAS technique to support the MixConv layer and to add a latency-aware loss term to reduce the hyperparameter search overhead. Finally, we use compound scaling to scale up the network maximally within the latency constraint. In addition, we add squeeze-and-excitation (SE) blocks and h-swish activation functions if beneficial in the post-processing step. Experiments with the proposed methodology on four different hardware platforms demonstrate the effectiveness of the proposed methodology. It is capable of finding networks with better latency–accuracy tradeoff than SOTA networks, and the network search can be done within 4 h using TPUv3.
Optimized performance of PV panels and site selection for a solar park in Pakistan

Information such as the optimal tilt and azimuth angle of the solar collectors’ location is needed to efficiently incorporate solar energy. Also, when building a solar park, it is important to know the most suitable and high-energy generating location, thus saving time and money. This study analyzed collector geometry for Karachi in particular and Pakistan in general. Karachi has the potential of 339.36 kWh·m −2 ·annum −1 at a fixed annual tilt of 26°. In case the collector geometry needed to be changed for Karachi, we adopted a range of 40° azimuth angle and 20° tilt angle from its maximum value. The difference in power produced with these modifications would only be 1%. Optimal yearly and monthly tilt for most of the potential solar park locations of Pakistan (300+) were calculated, and we found that the optimal tilt for Pakistan closely follows the value for latitude. Generally, changing the tilt angle monthly is recommended for areas that produce more energy, whereas a fixed annual tilt could be suitable for low energy-producing regions. The effects of temperature were also incorporated while examining the energy produced by the photovoltaic (PV) panels.
Predictive intelligence in evaluation of visual perception thresholds for visual pattern recognition and understanding

This paper will present how predictive evaluation can be applied in research on specifying perception thresholds in cognitive approach to understanding images. This approach will be based on predictive methods with cognitive inference for threshold re-production for image patterns. In particular, two basic parameters having an impact on perception thresholds evaluation, i.e. knowledge and expectations will be taken into consideration. These are the very important parameters on which perception thresholds depend. They can become lower with attempts to identify correctly or understand images by an observer, who has in-depth topical knowledge and far-going expectations concerning the image content.
Evaluating the Impact of Design Patterns on Software Maintainability: An Empirical Evaluation

Software maintainability is a critical quality attribute that refers to the efforts required to perform specific changes to the software after deployment. This research aims to prove that the effective use of design patterns leads to a better software maintainability value by empirically evaluating a simpler solution (a solution without applying design patterns) with its refined version (after applying appropriate design patterns). A scenario is used in the paper to generate two working solutions to evaluate both of their maintainability values and justify how the use of design patterns on the refined solution has significantly improved the quality of the software. Furthermore, a critical analysis is performed to compare the results of the refined solution with its equivalent simpler solution. The result of the empirical evaluation indicates that the use of design patterns brought significant improvement to the solution in reducing the complexity and improving the maintainability of the solution.
A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.
Forecasting Solar Radiation

Renewable energy, such as solar and wind, has been increasing in popularity for over a decade. This is especially true in rural, underdeveloped areas, and urban households that desire energy independence. Renewable energy sources, such as solar, provide enhanced environmental benefits while simultaneously minimizing the carbon footprint. One popular technology that can capture solar energy is solar panels. The demand for solar panels has been on the rise due to increases in energy conversion efficiency, long-term financial advantages, and contributions to decreasing fossil fuel usage. However, solar panels need a steady supply of sunlight. This can be challenging in many situations, geographies, and environments. This paper uses multiple machine learning (ML) algorithms that can predict future values of solar radiation based on previously observed values and other environmental features measured without the use of complex equipment with methods that are computationally efficient so that forecasting can be done on consumer premises.
Revisiting ARM Debugging Features: Nailgun and its Defense

Processors nowadays are consistently equipped with debugging features to facilitate program analysis. Specifically, the ARM debugging architecture involves a series of CoreSight components and debug registers to aid the system debugging, and a group of debug authentication signals are designed to restrict the usage of these components and registers. Meanwhile, the security of the debugging features is under-examined since it normally requires physical access to use these features in the traditional debugging model. However, ARM introduces a new debugging model that requires no physical access since ARMv7, which exacerbates our concern on the security of the debugging features. In this article, we perform a comprehensive security analysis of the ARM debugging features and summarize the security implications. To understand the impact of the implications, we also investigate a series of platforms with ARM-A architecture in different product domains (i.e., development boards, IoT devices, cloud servers, and mobile devices). We consider that the analysis and investigation expose a new attacking surface that universally exists in platforms with ARM-A architecture. To verify our concern, we further craft <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nailgun</small> attack, which obtains sensitive information (e.g., AES encryption key and fingerprint image) and achieves arbitrary payload execution in a high-privilege mode from a low-privilege mode via misusing the debugging features. This attack does not rely on software bugs, and our experiments show that almost all the platforms we investigated are vulnerable to the attack. Our analysis also indicates that ARM-R and ARM-M platforms may suffer from the same issue. To defend against the attack, we discuss potential mitigations from different perspectives in the ARM ecosystem. Finally, a practical defense mechanism based on ARM virtualization technology is presented, and the evaluation result shows that our defense can prevent <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nailgun</small> with a negligible performance penalty.
Crowd Anomaly Detection Systems Using RFID and WSN Review

The security and safety of public places have been a concern to more entities in recent decades. Video surveillance is widely used to guarantee the security and safety of public places. Yet, abnormal crowd movement detection and estimation are essential in video surveillance to avoid incidents like a stampede. The most challenging problem is detecting and locating people in moving dense crowds with obstacles like occlusion and blind spots where traditional video surveillance techniques fail. This paper presents an extensive review of state-of-the-art advances in detecting abnormal behavior in dense crowds approaches. The techniques are based on range-free localization for detecting the direction and speed of the crowd movement. Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN) are surveyed. RFID is used by analyzing the Received Signal Strength Indicator (RSSI) for detecting orientation and speed of crowd movement and provides information like the crowd density, movement velocity, flow rate, and the number of persons passing to detect the onset of a stampede.
Variation Analysis of Spectral Characteristics of Reclamation Vegetation in a Rare Earth Mining Area Under Environmental Stress

Large-area hyperspectral remote sensing monitoring of physiological parameters of reclaimed vegetation is a crucial means of environmental supervision in rare earth (RE) mining areas. The variation analysis of reclaimed vegetation characteristics under environmental stress in mining areas provides the necessary foundation for accurate dynamic monitoring of ecological restoration. In this article, the original spectral reflectance of six typical reclaimed vegetations in the RE mining area and their corresponding vegetations in the normal environment are measured. The spectral variations between reclaimed and normal vegetations are then compared and analyzed. To extract more hidden information in spectral features, the original spectrum is transformed using common derivative transform. In addition, wavelet transform (WT) and fractional differential (FD) transform are used to strengthen the detailed information of the original spectrum and explore the spectral features of reclaimed vegetation under environmental stress in RE mining areas. It can be deduced that in the first derivative spectrum, the reclaimed vegetations show a blueshift in the red edge position except for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wetland pine</i> . This indicates that reclaimed vegetation is affected by external factors, such as environmental stresses to different degrees in the mining area. WT can extract the original spectral features with a high degree of influence, highlighting the detailed information with obvious changes and sharp fluctuations in spectral information features, in which the reclaimed vegetations and normal vegetations had obvious differences at the d5 scale. These results provide technical support for process management and formulation of reclamation measures of reclaimed vegetation in RE mining areas, and therefore contribute to the ecological reconstruction of RE reclamation mining areas.
LanCon-Learn: Learning With Language to Enable Generalization in Multi-Task Manipulation

Robots must be capable of learning from previously solved tasks and generalizing that knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot assistance in the real world. While multi-task learning research has produced agents capable of performing multiple tasks, these tasks are often encoded as one-hot goals. In contrast, natural language specifications offer an accessible means both for (1) users to describe a set of new tasks to the robot and (2) robots to reason about the similarities and differences among tasks through language-based task embeddings. Until now, multi-task learning with language has been limited to navigation based tasks and has not been applied to continuous manipulation tasks, requiring precision to grasp and move objects. We present <small>LanCon-Learn</small>, a novel attention-based approach to language-conditioned multi-task learning in manipulation domains to enable learning agents to reason about relationships between skills and task objectives through natural language and interaction. We evaluate <small>LanCon-Learn</small> for both reinforcement learning and imitation learning, across multiple virtual robot domains along with a demonstration on a physical robot. <small>LanCon-Learn</small> achieves up to a 200&#x0025; improvement in zero-shot task success rate and transfers known skills to novel tasks faster than non-language-based baselines, demonstrating the utility of language for goal specification.
Visual secret sharing scheme with (n,n) threshold based on WeChat Mini Program codes

With the widespread use of WeChat Mini Programs, their security has attracted more and more attention. The WeChat Mini Program can be accessed by scanning a WeChat Mini Program code. We should protect the code thus to protect the Mini Program. In this paper, based on studying the function in each pattern of WeChat Mini Program codes, a visual secret sharing (VSS) scheme for WeChat Mini Program codes (MPCVSS) with (n,n)(n≥4) threshold is proposed to control and identify the users of Mini Program. MPCVSS combines the error-correcting characteristic of WeChat Mini Program codes with the theory of VSS. A secret WeChat Mini Program code is shared into n shared codes slightly modified from n cover codes. Each shared code is a valid code that can be scanned and decoded correctly. The secret code can be recovered by XORing n shares. The recovered code can be decoded as the same secret Mini Program. Theoretical analysis and experiments show that the proposed VSS scheme is practical and feasible.
A Research on Constructing Evaluative Expression Dictionaries for Cosmetics Based on Word2Vec

In recent years, there are various review sites on the Web. In online shopping, review sites are important because they strongly influence consumers’ purchasing decisions. We focus on cosmetic reviews and consider the skin type and usability of individual users. In order to realize the cosmetic recommendation system, we are working on the development of a review recommendation method by an automatic scoring system using an evaluative expression dictionary for each cosmetic item classification. Since cosmetic items have detailed classifications, it is necessary to build an evaluative expression dictionary for each cosmetic classification in order to perform automatic scoring. Therefore, it is desirable that the evaluative expression dictionary construction method be efficient and semi-automatic. In this paper, we try to improve the evaluative expression dictionary and examine the efficient method for developing evaluative expression dictionary based on Word2Vec for cosmetics.
Integrating contrastive learning with dynamic models for reinforcement learning from images

Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent’s policy or Q-function. In particular, methods based on contrastive learning that induce linearity of the latent dynamics or invariance to data augmentation have been shown to greatly improve the sample efficiency of the reinforcement learning algorithm and the generalizability of the learned embedding. We further argue, that explicitly improving Markovianity of the learned embedding is desirable and propose a self-supervised representation learning method which integrates contrastive learning with dynamic models to synergistically combine these three objectives: (1) We maximize the InfoNCE bound on the mutual information between the state- and action-embedding and the embedding of the next state to induce a linearly predictive embedding without explicitly learning a linear transition model, (2) we further improve Markovianity of the learned embedding by explicitly learning a non-linear transition model using regression, and (3) we maximize the mutual information between the two nonlinear predictions of the next embeddings based on the current action and two independent augmentations of the current state, which naturally induces transformation invariance not only for the state embedding, but also for the nonlinear transition model. Experimental evaluation on the Deepmind control suite shows that our proposed method achieves higher sample efficiency and better generalization than state-of-art methods based on contrastive learning or reconstruction.
Knowledge-Guided Fraud Detection Using Semi-supervised Graph Neural Network

Fraud detection is about finding unusual behaviors in the data, and it is essential for companies to detect fraudulent users to prevent unpredictable risks. The graph-based approaches model relationships into graphs for capturing the intricate characteristics of complex scenarios to detect fraudsters. However, it still faces the problem of data skew where labeled fraudsters are far fewer than unlabeled examples. Knowledge may help identify these unlabeled data, thus this paper combines domain knowledge with GNN and proposes a Knowledge-Guided Semi-supervised Graph Neural Network, namely KS-GNN, to address the problem of data skew. We utilize domain experts to design small amount of rules to roughly label unlabeled data as noisy and use a semi supervised method to train fraud detectors. By utilizing only 13 GFD rules conducted by domain experts, the performance of our method yields about 15% improvement over the state-of-the-art fraud detection methods CARE-GNN on banking transaction funds supervision datasets (BTFSD). Moreover, with some modification of the GFD rules on BTFSD, the performance of KS-GNN on other domain datasets such as IEEE-CIS Fraud Detection (https://www.kaggle.com/c/ieee-fraud-detection/data) and Yelp-Chi is also improved by about 5% on average compared with the state-of-the-art methods.
Gaze Tracking Based on Concatenating Spatial-Temporal Features

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.