Reviewerly/exHarmony huggingface dataset:
LightNet: A Lightweight Neural Network for Image Classification

Image Classification is widely used in the field of computer vision that focuses on classifying the object in a given image. Lately, image classification techniques are not only restricted to computer applications but are also famous for edge devices. Convolutional Neural Networks play a crucial role in building a good image classifier. However, in order to achieve high accuracy, CNN algorithms lead to use a large number of layers that results in increasing the number of parameters and makes it difficult to implement on edge devices. To overcome this problem, a lightweight image classifier "LightNet" is proposed in this paper that makes use of different scales of receptive fields to extract more feature maps with fewer parameters. To examine the efficacy of the proposed lightweight classifier, it is tested on the CIFAR-10 dataset and got 90% accuracy by using only .26M parameters, which shows that the proposed Lightnet is very effective to be implemented on edge devices.
Artificial Neural Network: Models, Applications, and Challenges

Neural networks are the mathematical models that process information or any signal based on biological neuron. The structure of these networks is very complex as it consists of interconnected neurons that make problem-solving less complicated. Since artificial neural networks (ANN) can be used in various applications specifically in the field of computer science and electronics; the researchers are designing artificial neural networks to find solutions to problems like recognition of pattern, optimization, prediction, associative memory, and control. This chapter gives the basic knowledge of artificial neural networks, its general architecture, and various categories. The chapter focuses on different models, their mathematical proof, and applications in real life. It also covers the detailed information about the use of ANN in different sectors.
Increasing Quantum Correlations Based on Measurement-Induced Disturbance via a Swapping Procedure with Two-Qubit Mixed States

In this paper, quantum correlation (QC) swapping for certain separable two-qubit mixed states is treated. A QC quantifier, measurement-induced disturbance (MID) (Luo in Phys Rev A 77:022301, 2008), is employed to characterize and quantify QCs in the relevant states. Properties of all QCs in the swapping process are revealed. Particularly, it is found that MID can be increased through QC swapping for certain separable two-qubit mixed states.
Pseudo-Bayesian Classified Mixed Model Prediction

We propose a new classified mixed model prediction (CMMP) procedure, called pseudo-Bayesian CMMP, that uses network information in matching the group index between the training data and new data, whose characteristics of interest one wishes to predict. The current CMMP procedures do not incorporate such information; as a result, the methods are not consistent in terms of matching the group index. Although, as the number of training data groups increases, the current CMMP method can predict the mixed effects of interest consistently, its accuracy is not guaranteed when the number of groups is moderate, as is the case in many potential applications. The proposed pseudo-Bayesian CMMP procedure assumes a flexible working probability model for the group index of the new observation to match the index of a training data group, which may be viewed as a pseudo prior. We show that, given any working model satisfying mild conditions, the pseudo-Bayesian CMMP procedure is consistent and asymptotically optimal both in terms of matching the group index and in terms of predicting the mixed effect of interest associated with the new observations. The theoretical results are fully supported by results of empirical studies, including Monte-Carlo simulations and real-data validation.
Agile–CMMI Alignment: Contributions and To-Dos for Organizations

We discuss the extent to which Capability Maturity Model Integration (CMMI) v2.0 addresses Agile alignment challenges. We identify the two most significant CMMI v2.0 artifacts for integration, and highlight important issues that organizations need to tackle.
Bitcoin: A New Digital Gold Standard in the 21st Century?

Since a mysterious creator under the alias Satoshi Nakamoto (a pseudonym) launched first successful cryptocurrency in January 2009, he (or could be she) also opened the door for never-ending criticism, claims, arguments, plethora of articles, and media frenzy all contemplating what Bitcoin is. This paper concludes that Bitcoin is not a currency to be used for every-day transactions like all fiat currencies. Furthermore, Bitcoin is not a stable cryptocurrency and it is useless (and impractical) of arguing how Bitcoin meets or fails basic functions of money; to make it clear, Bitcoin satisfies all three functions of money (not to the extent of fiat currencies, nonetheless it does); store of value, unit of account, and medium of exchange. As discussed in this article, our proposal of Bitcoin as a supranational central bank reserve currency not only will put an end to debates and the frequently asked old question of “what is Bitcoin?”, it will also make Bitcoin’s design flaws disappear instantly. Under the new Bitcoin standard (Gold 2.0), inefficiencies become trivial because Bitcoin is capable of easily handling the volume of operations among central banks. Bitcoin’s extensively discussed weaknesses become no issue; high latency (a block of transactions is validated every 10 minutes), low processing speed (3-7 transactions per second), huge power cost (mining of 1 bitcoin requires 1,825 kWh of electricity, equivalent to 63 days of power usage by a U.S. household), and low scalability (the maximum number of bitcoins is fixed at 21 million). Key elements of the proposed new Bitcoin Standard include: Bitcoin will be a supranational reserve currency used by central banks only; ownership, trade, and other uses of bitcoins by citizens and various entities will be prohibited; the fixed supply of bitcoins (21 million) will remain unchanged; every country’s financial authority will switch to central bank digital coins (CBDC); CBDCs will be defined in bitcoin by a corresponding exchange rate for each country; 100% of CBDCs will be 100% backed by bitcoins; any increase in the digital money supply by a central bank will require additional bitcoins; and the value of each bitcoin will be raised to a price level to cover 100% of the total money in circulation (approximately $75 trillion).
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020

Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge are required to design effective architectures, and the results of GNN models have high variance with different training setups, which limits the application of existing GNN models. In this paper, we present AutoHEnsGNN, a framework to build effective and robust models for graph tasks without any human intervention. AutoHEnsGNN won first place in the AutoGraph Challenge for KDD Cup 2020, and achieved the best rank score of five real-life datasets in the final phase. Given a task, AutoHEnsGNN first applies a fast proxy evaluation to automatically select a pool of promising GNN models. Then it builds a hierarchical ensemble framework: 1) We propose graph self-ensemble (GSE), which can reduce the variance of weight initialization and efficiently exploit the information of local and global neighborhoods; 2) Based on GSE, a weighted ensemble of different types of GNN models is used to effectively learn more discriminative node representations. To efficiently search the architectures and ensemble weights, we propose AutoHEnsGNN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gradient</inf> , which treats the architectures and ensemble weights as architecture parameters and uses gradient-based architecture search to obtain optimal configurations, and AutoHEnsGNN <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive</inf> , which can adaptively adjust the ensemble weight based on the model accuracy. Extensive experiments on node classification, graph classification, edge prediction and KDD Cup challenge demonstrate the effectiveness and generality of AutoHEnsGNN.
A Hybrid Machine Learning Method for Text Analysis to Determine Job Titles Similarity

This paper introduces a hybrid method based on the combination of two machine learning methods (k-NN and SVM). The novel method is designed to find job titles that are similar based on their description and industry. This method improves the accuracy and time-efficiency of a complex process like selecting the best candidates for a job.
Evaluating Stability of Post-hoc Explanations for Business Process Predictions

Predictive process analytics uses advanced machine learning techniques to accurately predict future states of running business processes. Given the complexity of these predictive models, explainable AI techniques are also required to enable informed decision-making. However, few studies evaluate the quality of explanations provided by existing methods to explain business process predictions. In this paper, we attempt to evaluate the consistency of explanations produced for process predictions by two popular explainable methods. We propose that methods and metrics to assess feature selection algorithms can be used to evaluate explanation stability. We use these metrics to assess explanations produced by LIME and SHAP. Our findings indicate that explanation stability may depend on dataset characteristics, feature construction methods and predictive model characteristics. In addition, we also find that, though stable explanations are needed for informed decision-making, unexpected behaviour in explanation stability can act as a diagnostic tool to determine model quality.
Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

The existing motion recognition system has a low athlete tracking recognition accuracy due to the poor processing effect of recognition algorithm for edge detection. A machine vision-based gymnast pose-tracking recognition system is designed for the above problem. The software part mainly optimizes the tracking recognition algorithm and uses the spatiotemporal graph convolution algorithm to construct the sequence graph structure of human joints, completes the strategy of label subset division, and completes the pose tracking according to the change of information dimension. The results of the system performance test show that the designed machine vision-based gymnast posture tracking recognition system can enhance the accuracy of tracking recognition and reduce the convergence time compared with the original system.
Two-stream Convolutional Neural Network for Image Source Social Network Identification

The identification of the source social network from an image is a relatively new research area in the image forensic domain. The classification of the source social network can be a crucial element for the growing number of cases of social-media related crimes, such as cyberbullying. This paper takes into consideration the state-of-the-art approaches addressing this problem and proposes a new methodology to improve the results obtained to date. Our identification technique is based on the idea that social networks perform some processing on the uploaded images, such as resizing or recompression, and leave some artifacts on them. We propose to use discrete cosine transform features and image noise residual analysis to detect such artifacts. A two-stream convolutional neural network, which combines the inputs from these two artifact domains, is trained to classify the source social network of images coming from three different datasets. This paper explores the two domains, proposes strategies for managing unbalanced datasets, provides details about the proposed two-stream convolutional neural network, and presents the results achieved by our method compared with the current state-of-the-art approaches.
Skin-tone modified emoji and first-person indexicality

Beginning in 2015, people have been able to transform many emoji – typically 18 byte, 12 × 12-pixel images inserted into digital text – with “skin-tone modifiers.” Racialized aspects of self-presentation have a long history of being marked in various ways in semiotic practice. However, this article argues that, parallel to other systems of social indexicals like honorifics and gendered speech, skin-tone modified emoji represent a robust example of the complex ways language and culture are bound together dialectically. Based on the views of 451 anglophone American respondents to a survey, I demonstrate that the selection of emoji – even yellow emoji – can appear as a social and political choice for certain speech communities. For these individuals, the addition of skin-tone modifiers in the emoji set may remove the possibility of remaining outside this system of author identification when using signs that have the potential to bear such modifiers.
AR based Assistance for the Tool Change of Cyber-Physical Systems

The Department of Computer Integrated Design at the TU Darmstadt deals with new innovative interaction possibilities between workers and Cyber-Physical Systems on the shop floor. In this paper, technologies of Augmented Reality play an essential role. Thus, a digital workflow between product development, planning and production can be established. For this purpose, a concept for AR-based assistance for the tool change of a CNC machine was developed. Based on the processed instructions for a workpiece, information for a complete tool change is progressively provided to the operator. The concept was implemented and validated using a Microsoft HoloLens and a 3-axis CNC milling machine.
Using Scrollytelling to Explain Voting Power in Ecuador

We present a scrollytelling visualization that explains the concept of “voting power”, which refers to the influence that a group of voters have, relative to the geographical area they live in, on an election’s outcome. We explain this in the context of Ecuador, a country with electoral districts of varied sizes and populations (and, thus, varied voting power). Our visualization is designed to explain that a bigger territory does not necessarily imply more votes. Understanding this concept is particularly important in Ecuador, a country that officially depicts elections results through maps that use color hue to indicate the winning candidate of a given geographical area.
Trusted Artificial Intelligence: Technique Requirements and Best Practices

The development and adoption of artificial intelligence (AI) have brought numerous problems and potential threats, such as information cocoons, algorithmic collusion, algorithmic bias and privacy, issues. To combat these issues, governments all over the world, international organizations and giant tech companies all have taken actions and reached an agreement that AI should be trusted. However, there is no uniform definition on trusted AI. In the paper, we make a survey on related works on artificial intelligence principles and ethical guidelines. Then analyze the ethical foundations of trusted AI and give a definition on trusted AI. We propose specific requirements of trusted AI from a technical perspective and give explanations on these technique requirements. Finally, best practices are recommended to achieve trusted AI and promote the responsible use of AI.
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors

Hierarchical models with gamma hyperpriors provide a flexible, sparsity-promoting framework to bridge and regularizations in Bayesian formulations to inverse problems. Despite the Bayesian motivation for these models, existing methodologies are limited to maximum a posteriori estimation. The potential to perform uncertainty quantification has not yet been realized. This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement. In addition, it lends itself naturally to conduct model selection for the choice of hyperparameters. We illustrate the performance of our methodology in several computed examples, including a deconvolution problem and sparse identification of dynamical systems from time series data.
Cloud Native Computing for Industry 4.0: Challenges and Opportunities

Cloud-based architectures are advantageous in aspects such as scalability, reliability and resource utilization efficiency, to name just a few, thus being considered one of the pillars of Industry 4.0. However, in this domain, cloud computing platforms are subject to specific requirements, namely in what concerns real-time performance, determinism and fault-tolerance. This paper focuses on cloud native computing, which is an emerging and promising cloud-computing paradigm, specifically addressing its applicability to real-time systems. Firstly, it introduces the architecture of cloud native applications, discussing their principles, potential advantages and challenges. Then it addresses the opportunities and constraints of such technologies when applied to industrial real-time systems.
How to Delegate Computations: The Power of No-Signaling Proofs

We construct a 1-round delegation scheme (i.e., argument-system) for every language computable in time t = t ( n ), where the running time of the prover is poly ( t ) and the running time of the verifier is n · polylog ( t ). In particular, for every language in P we obtain a delegation scheme with almost linear time verification. Our construction relies on the existence of a computational sub-exponentially secure private information retrieval ( PIR ) scheme. The proof exploits a curious connection between the problem of computation delegation and the model of multi-prover interactive proofs that are sound against no-signaling (cheating) strategies , a model that was studied in the context of multi-prover interactive proofs with provers that share quantum entanglement, and is motivated by the physical principle that information cannot travel faster than light. For any language computable in time t = t ( n ), we construct a multi-prover interactive proof ( MIP ), that is, sound against no-signaling strategies, where the running time of the provers is poly ( t ), the number of provers is polylog ( t ), and the running time of the verifier is n · polylog ( t ). In particular, this shows that the class of languages that have polynomial-time MIP s that are sound against no-signaling strategies, is exactly EXP . Previously, this class was only known to contain PSPACE . To convert our MIP into a 1-round delegation scheme, we use the method suggested by Aiello et al. (ICALP, 2000), which makes use of a PIR scheme. This method lacked a proof of security. We prove that this method is secure assuming the underlying MIP is secure against no-signaling provers.
Modeling design and control problems involving neural network surrogates

We consider nonlinear optimization problems that involve surrogate models represented by neural networks. We demonstrate first how to directly embed neural network evaluation into optimization models, highlight a difficulty with this approach that can prevent convergence, and then characterize stationarity of such models. We then present two alternative formulations of these problems in the specific case of feedforward neural networks with ReLU activation: as a mixed-integer optimization problem and as a mathematical program with complementarity constraints. For the latter formulation we prove that stationarity at a point for this problem corresponds to stationarity of the embedded formulation. Each of these formulations may be solved with state-of-the-art optimization methods, and we show how to obtain good initial feasible solutions for these methods. We compare our formulations on three practical applications arising in the design and control of combustion engines, in the generation of adversarial attacks on classifier networks, and in the determination of optimal flows in an oil well network.
ChaCha20-Poly1305 Crypto Core Compatible with Transport Layer Security 1.3

The security of the information represents a vital part of all communications protocols. In computer networks, Transport Layer Security (TLS) represents the majority of the use of secure channels for end-to-end communications. However, the efforts are directed only to optimize the software implementations. This paper shows an Authenticated Encryption with Associated Data (AEAD) hardware implementation of ChaCha20-Poly1305 compatible with TLS 1.3. Compared to a software implementation in a RISC-V environment, the performance increase by 7. The AEAD implementation reaches a speed of 21.5-cycle/byte.×The design is implemented in Xilinx Virtex-7 XC7VX485T Field-Programmable Gate Array (FPGA), using 7897 Look-Up Tables (LUT) and 4840 Flip-Flops (FF), represented in 26% of ChaCha20 and 54% of Poly1305.
Semi-Supervised Knowledge Distillation for Cross-Modal Hashing

Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to its low storage consumption and fast retrieval speed. Supervised cross-modal hashing methods have achieved substantial advancement by incorporating semantic information. However, to a great extent, supervised methods rely on large-scale labeled cross-modal training data which are laborious to obtain. Moreover, most cross-modal hashing methods only handle two modalities of image and text, without taking the scene of multiple modalities into consideration. In this paper, we propose a novel semi-supervised approach called semi-supervised knowledge distillation for cross-modal hashing (SKDCH) to overcome the above-mentioned challenges, which enables guiding a supervised method using outputs produced by a semi-supervised method for multimodality retrieval. Specifically, we utilize teacher-student optimization to propagate knowledge. Furthermore, we improves triplet ranking loss to better mitigate the heterogeneity gap, which increases the discriminability of our proposed approach. Extensive experiments executed on two benchmark datasets validate that the proposed SKDCH surpasses the state-of-the-art methods.
Pedestrian detection and counting method based on YOLOv5+DeepSORT

To alleviate the spread of the epidemic, most public places have begun to limit the number of trips. Therefore, this article proposes a pedestrian counting scheme based on YOLOv5 and DeepSORT for multi-target detection and tracking. Using the network weights trained by the coco data set and combining the YOLOv5 detector and the DeepSORT tracker, the pedestrians are detected and tracked, and the number of people entering and leaving is calculated, thereby realizing the control of the number of floating people. Through experiments on streets and subway stations, it is proved that this algorithm is suitable for tracking and counting high-density people, and based on ensuring the real-time performance of the system, it provides high system accuracy and robustness.