Reviewerly/exHarmony huggingface dataset:
Semi-supervised learning using adversarial training with good and bad samples

In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial networks (GANs) can be used for multiple purposes in SSL . Triple-GAN, which aims to jointly optimize model components by incorporating three players, generates suitable image-label pairs to compensate for the lack of labeled data in SSL with improved benchmark performance. Conversely, Bad (or complementary) GAN optimizes generation to produce complementary data-label pairs and force a classifier’s decision boundary to lie between data manifolds. Although it generally outperforms Triple-GAN, Bad GAN is highly sensitive to the amount of labeled data used for training. Unifying these two approaches, we present unified-GAN (UGAN), a novel framework that enables a classifier to simultaneously learn from both good and bad samples through adversarial training. We perform extensive experiments on various datasets and demonstrate that UGAN: (1) achieves competitive performance among other GAN-based models, and (2) is robust to variations in the amount of labeled data used for training.
Security Evaluation Methods and the used parameters for Some Cryptosystem

The evaluation process is a necessary process that must be performed by the designer to help in avoiding any weakness which happens during the design stages. There are many evaluation approaches followed for different cipher systems. The evaluation mainly based on the measure of the security of the ciphering algorithm. The security is related to the complexity of the algorithm. Hence the complexity can be considered as an evaluation parameter of the designed cipher system. Generally, it is possible to combine many different concepts to adapt the evaluation process to the tested cipher systems. This paper provides the evaluation of Security of cryptosystem using different approaches.
Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search

Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and related cell subtypes has assumed importance since it aids the laborious manual process of review and diagnosis. Several State-Of-The-Art (SOTA) methods developed using Deep Convolutional Neural Networks suffer from the problem of domain shift - severe performance degradation when they are tested on data (target) obtained in a setting different from that of the training (source). The change in the target data might be caused by factors such as differences in camera/microscope types, lenses, lighting-conditions etc. This problem can potentially be solved using Unsupervised Domain Adaptation (UDA) techniques albeit standard algorithms presuppose the existence of a sufficient amount of unlabelled target data which is not always the case with medical images. In this paper, we propose a method for UDA that is devoid of the need for target data. Given a test image from the target data, we obtain its `closest-clone' from the source data that is used as a proxy in the classifier. We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution. We propose a method in which a latent-variable generative model based on variational inference is used to simultaneously sample and find the `closest-clone' from the source distribution through an optimization procedure in the latent space. We demonstrate the efficacy of the proposed method over several SOTA UDA methods for WBC classification on datasets captured using different imaging modalities under multiple settings.
Detecting and understanding real-world differential performance bugs in machine learning libraries

Programming errors that degrade the performance of systems are widespread, yet there is very little tool support for finding and diagnosing these bugs. We present a method and a tool based on differential performance analysis---we find inputs for which the performance varies widely, despite having the same size. To ensure that the differences in the performance are robust (i.e. hold also for large inputs), we compare the performance of not only single inputs, but of classes of inputs, where each class has similar inputs parameterized by their size. Thus, each class is represented by a performance function from the input size to performance. Importantly, we also provide an explanation for why the performance differs in a form that can be readily used to fix a performance bug. The two main phases in our method are discovery with fuzzing and explanation with decision tree classifiers, each of which is supported by clustering. First, we propose an evolutionary fuzzing algorithm to generate inputs that characterize different performance functions. For this fuzzing task, the unique challenge is that we not only need the input class with the worst performance, but rather a set of classes exhibiting differential performance. We use clustering to merge similar input classes which significantly improves the efficiency of our fuzzer. Second, we explain the differential performance in terms of program inputs and internals (e.g., methods and conditions). We adapt discriminant learning approaches with clustering and decision trees to localize suspicious code regions. We applied our techniques on a set of micro-benchmarks and real-world machine learning libraries. On a set of micro-benchmarks, we show that our approach outperforms state-of-the-art fuzzers in finding inputs to characterize differential performance. On a set of case-studies, we discover and explain multiple performance bugs in popular machine learning frameworks, for instance in implementations of logistic regression in scikit-learn. Four of these bugs, reported first in this paper, have since been fixed by the developers.
A Secure and Distributed Construction Document Management System Using Blockchain

Construction Document Management Systems (CDMS) track, manage, and store a variety of documents such as 3D model files, schedules, specifications, and images that are large in size and have different ownerships. The primary objectives of CDMSs are to facilitate document approval workflows, document version management, and security properties such as integrity of data, audit-trail, and approvals. A popular approach for CDMS is to consolidate all the project documents into an on premise or cloud-based centralized location that is owned and managed by a project participant or a trusted third party. Such centralized approaches for electronic documents pose risks such as single points of failures causing loss or corruption of documents and deliberate blocking of access during disputes between the owner of CDMS and other project participants. This situation is particularly challenging in construction projects where project participants cannot fully trust each other due to its inherently fragmented project organizational structure. Therefore, in this paper, a distributed construction document management system using Blockchain and distributed content-addressable storage technologies is presented. Blockchain is a peer-to-peer technology that facilitates distributed computation and irreversible data recording through smart contract and blockchain ledger technologies respectively. Immutability of records and computational logic is facilitated through the unique cryptographic data structure of blockchain ledgers and probabilistic consensus algorithms. In this paper, smart contracts are deployed to facilitate document approval workflows to support processes such as design review and request for information in construction projects. A blockchain ledger data model for tracking workflows and document version management is proposed. Public-key cryptography is deployed to facilitate data confidentiality and integrity in endorsements. A cryptographic indexing structure to support blockchain ledger in document versioning and to validate the authenticity of document search results for CDMS is proposed. The proposed framework also deploys peer-to-peer content-addressable storage for preventing single points of failure and data integrity of documents through data partitioning, data replication, and cryptography. The proposed CDMS is a distributed yet unified platform for managing construction documents. A demonstration of the proposed CDMS is presented with a case-based scenario on request for information (RFI) management.
Age-Related Differences in Emoji Evaluation

Background For decades, the nature of emotions has been at the center of psychological research, particularly regarding the underlying mechanisms that enable people to perceive, recognize, and process emotional stimuli. Research has indicated that there are interindividual differences in the processing of emotions. This includes age, which underlies neurological changes that contribute to the specific processing of emotions. Increasing age seems to be associated with a more positive evaluation of emotional information, from perception itself to attention, memory, and decision-making.Method The current study aimed to investigate whether these differences can be found in highly artificial emotional faces. Since emojis are representatives of emotional faces in digital communication, we selected a subset of 13 emojis and asked 170 participants to evaluate them for their ability to represent different target emotions.Results An exploratory factor analysis revealed a two-factorial structure with positive and negative valence for most of the ratings for the evaluated emojis. Furthermore, a multilevel model analysis based on the individual factor scores indicated higher age to be associated with an increase in factor scores for negative valence compared to positive valence.Conclusion In the present study, a trend for an age-specific positivity bias could only be shown in the classical smiley, while other emojis were related to negative valence with increasing age. Thus, we revealed age-related differences in emotion classification, even for highly artificial stimuli such as emojis.
Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Abstract Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio‐technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy‐aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy‐preserving data visualization has witnessed progress along many of these dimensions. In this state‐of‐the‐art report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road‐map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio‐technical challenges involving data privacy with the help of visualization.
Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM

In this letter, we propose a deep learning-assisted approach for signal detection in uplink orthogonal frequency-division multiplexing (OFDM) systems over time-varying channels. In particular, we utilize a recurrent neural network (RNN) with bidirectional long short-term memory (LSTM) architecture to achieve signal detection. In addition, with the help of convolutional neural network (CNN) and batch normalization (BN), a new network structure CNN-BN-RNN Network (CBR-Net) is proposed to obtain better performance. The sequence feature information of the OFDM received signal is extracted from big data to successfully train a RNN-based signal detection model, which simplifies the architecture of OFDM systems and can adapt to the change of channel paths. Simulation results also demonstrate that the trained RNN model has the ability to recall the characteristics of wireless time-varying channels and provide accurate and robust signal recovery performance.
Computational Intelligence From Autonomous System to Super-Smart Society and Beyond

In this article the author discusses main implications of current autonomous system (AS) through the symbiotic autonomous system (SAS) and the symbiotic system science (SSS) towards the incoming super-smart society, by bringing to light SSS fundamental considerations, according to experience and talks gained on scientific system development in the past fifty years, and formulating the proposal for a new understanding of them, at an effective scientific and operative level towards a real super-smart society. SSS is a growing scientific area which is taking a leadership role in fostering consensus on how best to bring about symbiotic relationships between current AS and incoming SAS in a mixed or hybrid society, called super-smart society. In order to achieve an antifragile behavior, next generation human-made system must have a new fundamental component able to address and to face effectively the problem of multiscale ontological uncertainty management, in an instinctively sustainable way: active, practical wisdom by design!
Optimize Scheduling of Federated Learning on Battery-powered Mobile Devices

Federated learning learns a collaborative model by aggregating locally-computed updates from mobile devices for privacy preservation. While current research typically prioritizing the minimization of communication overhead, we demonstrate from an empirical study, that computation heterogeneity is a more pronounced bottleneck on battery-powered mobile devices. Moreover, if class is unbalanced among the mobile devices, inappropriate selection of participants may adversely cause gradient divergence and accuracy loss. In this paper, we utilize data as a tunable knob to schedule training and achieve near-optimal solutions of computation time and accuracy loss. Based on the offline profiling, we formulate optimization problems and propose polynomial-time algorithms when data is class-balanced or unbalanced. We evaluate the optimization framework extensively on a mobile testbed with two datasets. Compared with common benchmarks of federated learning, our algorithms achieve 210× speedups with negligible accuracy loss. They also mitigate the impact from mobile stragglers and improve parallelism for federated learning.
Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.
Topology-Guided Roadmap Construction With Dynamic Region Sampling

Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate how this guidance improves PRM's efficiency in building a roadmap that can answer multiple queries in both robotics and protein ligand binding applications.
Private Multi-Hop Accountability for Supply Chains

Today's supply chains are becoming increasingly flexible in nature. While adaptability is vastly increased, these more dynamic associations necessitate more extensive data sharing among different stakeholders while simultaneously overturning previously established levels of trust. Hence, manufacturers' demand to track goods and to investigate root causes of issues across their supply chains becomes more challenging to satisfy within these now untrusted environments. Complementarily, suppliers need to keep any data irrelevant to such routine checks secret to remain competitive. To bridge the needs of contractors and suppliers in increasingly flexible supply chains, we thus propose to establish a privacy-preserving and distributed multi-hop accountability log among the involved stakeholders based on Attribute-based Encryption and backed by a blockchain. Our large-scale feasibility study is motivated by a real-world manufacturing process, i.e., a fine blanking line, and reveals only modest costs for multi-hop tracing and tracking of goods.
The Forceful Reevaluation of Cash-Based Transactions by COVID-19 and Its Opportunities to Transition to Cashless Systems in Digital Urban Networks

Cash-based transactions have been losing in popularity due to the high cost associated to maintaining the cash system and due to its numerous risks. Through the advent of the fourth industrial revolution, it was believed that a rapid transition to a more flexible, secure, and open cashless system would be accelerated. However, numerous hurdles in regard to legislation, protocols, and resistance to change were noted, rendering a slow adoption rate. However, the advent of COVID-19 pandemic and the noted potential contamination of bank notes and coinage revealed a steep support toward cashless payments and supported an immediate need to amend legislations and protocols to maintain the efficiency and stability of banking systems while catering for the shift in user demand. This chapter surveys the shift created by the COVID-19 pandemic from cash-based to cashless systems in cities and unveils how payment giants and information and communication technology corporations have been active in providing the underlaying foundation that would be critical in supporting the transition.
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC

HTTP/2 has been explored widely for adaptive video streaming, but still suffers from Head-of-Line blocking, and three-way handshake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of QUIC and HTTP/2 on the performance of ABR algorithms. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High Efficiency Video Coding (SHVC).
0.36-mW, 52-Mbps True Random Number Generator Based on a Stochastic Delta–Sigma Modulator

This letter presents a true random number generator (TRNG) based on a stochastic delta-sigma modulator (DSM) with ring oscillator (RO) integrators. The TRNG leverages noise and jitter in the ROs as entropy source. A multibit nonreturn-to-zero (NRZ) digital-to-analog converter (DAC) in the feedback path ensures DSM noise dominates input swing seen by the front-end integrators. Hence, the DSM can concurrently operate as an ADC and TRNG. A 65-nm prototype achieves Shannon entropy > 0.999998 with lower-bound min-entropy > 0.995 at 52 Mb/s throughput while passing all NIST tests across multiple chips and voltage/temperature corners without needing calibration. The combined power consumption of ADC and TRNG is 0.36 mW which is the lowest among state-of-the-art RO TRNGs. Low power consumption, high entropy, and concurrent operation of the TRNG as an ADC are principal contributions of this letter.
An improved inverse finite element method for shape sensing using isogeometric analysis

The inverse finite element method (iFEM) is advisable as accurate and efficient method for structure health monitoring (SHM). The previous standard iFEM uses 0th-order and 1st-order shape functions to reconstruct displacement fields under concentrated load and distributed load, respectively. However, in engineering environmental loads, it is difficult to determine which external load is mentioned above. To solve this problem, this paper proposes an improved iFEM based on isogeometric analysis (IGA), which can achieve a unified description of the displacement fields under the effect of concentrated load, distributed load, or concentrated-distributed-hybrid load. Initially, B-spline functions of order two are employed to reconstruct the strain fields. Then, the shape function order of translations and rotations are deduced based on the section strain theory. Next, these deduced displacement shape functions are further used to reconstruct the deformation field using strain measurement. Finally, a wing structure is subjected to concentrated and distributed loads in simulation analysis and experimental tests. The result shows that improved iFEM is more accurate and effective than standard iFEM in displacement reconstruction.
Clava: C/C++ source-to-source compilation using LARA

This article presents Clava, a Clang-based source-to-source compiler, that accepts scripts written in LARA, a JavaScript-based DSL with special constructs for code queries, analysis and transformations. Clava improves Clang’s source-to-source capabilities by providing a more convenient and flexible way to analyze, transform and generate C/C++ code, and provides support for building strategies that capture run-time behavior. We present the Clava framework, its main capabilities, and how it can been used. Furthermore, we show that Clava is sufficiently robust to analyze, instrument and test a set of large C/C++ application codes, such as GCC.