Reviewerly/exHarmony huggingface dataset:
Ofelimos: Combinatorial Optimization via Proof-of-Useful-Work

Minimizing the energy cost and carbon footprint of the Bitcoin blockchain and related protocols is one of the most widely identified open questions in the cryptocurrency space. Substituting the proof-of-work (PoW) primitive in Nakamoto’s longest-chain protocol with a proof of useful work (PoUW) has been long theorized as an ideal solution in many respects but, to this day, the concept still lacks a convincingly secure realization. In this work we put forth Ofelimos, a novel PoUW-based blockchain protocol whose consensus mechanism simultaneously realizes a decentralized optimization-problem solver. Our protocol is built around a novel local search algorithm, which we call Doubly Parallel Local Search (DPLS), that is especially crafted to suit implementation as the PoUW component of our blockchain protocol. We provide a thorough security analysis of our protocol and additionally present metrics that reflect the usefulness of the system. DPLS can be used to implement variants of popular local search algorithms such as WalkSAT that are used for real world combinatorial optimization tasks. In this way, our work paves the way for safely using blockchain systems as generic optimization engines for a variety of hard optimization problems for which a publicly verifiable solution is desired.
RAGAN: Regression Attention Generative Adversarial Networks

Despite surrounding by Big Data, we still need to learn from insufficient data in many scenarios. Building an accurate regression model for a small amount of data is a pretty tricky and exciting problem. At present, it is a promising solution to augment limited real data by generated data through Generative Adversarial Networks (GAN). However, when GAN is used to generate labeled data in regression modeling, it lacks attention to the relationship between independent and dependent variables, resulting in poor performance of regression modeling. This paper proposes a novel regression attention GAN (RA-GAN) for augmented regression modeling. Regression attention mechanisms are introduced into network parameters learning of both generator and discriminator in RA-GAN to establish a known relationship between variables. This makes RA-GAN restore the regression information during data generation. In addition, an indicator called cross regression score is designed to describe the quality of the generated data before augmented regression modeling, effectively evaluating data augmentation performance in advance. The effectiveness and superiority of the proposed methods are verified in an actual industrial soft-sensing case and a diabetes prediction case through data augmentation regression applications.
Requirements on Explanations: A Quality Framework for Explainability

Explainability has been acknowledged as a fundamental requirement for modern information systems. However, there are currently only few guidelines available to assist software professionals in dealing with this requirement and integrating it into systems. More precisely, there is a lack of frameworks and guidelines that help to define and operationalize explainability requirements. To address this need, we present a quality framework that aggregates external dependencies, characteristics of explanations, and evaluation methods to facilitate the analysis, operationalization, and evaluation of explainability requirements. We conducted a literature study to construct the framework and demonstrated its applicability by using it as a guideline for incorporating explanations into an existing navigation system. Finally, we evaluated the quality and effect of the explanations through an experiment within our case study. Our results show that the quality framework is applicable and beneficial in an industrial context and leads to the construction of explanations that increase usage frequency, system acceptance and user satisfaction.
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation

Unsupervised domain adaptation is one of the challenging problems in computer vision. This paper presents a novel approach to unsupervised domain adaptations based on the optimal transport-based distance. Our approach allows aligning target and source domains without the requirement of meaningful metrics across domains. In addition, the proposal can associate the correct mapping between source and target domains and guarantee a constraint of topology between source and target domains. The proposed method is evaluated on different datasets in various problems, i.e. (i) digit recognition on MNIST, MNISTM, USPS datasets, (ii) Object recognition on Amazon, Webcam, DSLR, and VisDA datasets, (iii) Insect Recognition on the IP102 dataset. The experimental results show our proposed method consistently improves performance accuracy. Also, our framework can be incorporated with any other CNN frameworks within an end-to-end deep network design for recognition problems to improve their performance.
An Evaluation Framework for Vision-in-the-Loop Motion Control Systems

Industrial applications and processes such as quality inspections, pick and place operations, and semiconductor manufacturing require accurate positioning control for achieving the high throughput of the assembly machines. Vision-based sensing is considered to be a potential means to achieve robust positioning control which is referred to as a vision-in-the-loop (VIL) system. In such motion systems, the point-of-control and the point-of-interest are often different due to several physical factors. In this case, validation of a system is done only when a machine prototype is available. A physical prototype is often expensive and infeasible in real-life. This paper proposes an evaluation framework for VIL systems targeting a predictable multi-core embedded platform. The presented framework offers model-in-the-loop (MIL), software-in-the-loop (SIL), and processor-in-the-loop (PIL) simulation features for evaluating the closed-loop performance of industrial motion control systems. As a deployment platform, we consider a predictable embedded platform CompSOC. The predictable nature of the CompSOC platform guarantees periodic and deterministic execution of the control applications and allows verification of the timing properties and performance of the VIL system. Additionally, the framework offers automatic code generation feature targeting the CompSOC platform. Closed-loop simulation setup models the system dynamics and camera position in the CoppeliaSim physics simulation engine and simulates the system software in C and MATLAB. CoppeliaSim runs as a server and MATLAB as a client in synchronous mode. We show the effectiveness of our framework using a vision-based motion control example.
Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation

Abstract Large pretrained language models (PLMs) are often domain- or task-adapted via finetuning or prompting. Finetuning requires modifying all of the parameters and having enough data to avoid overfitting while prompting requires no training and few examples but limits performance. Instead, we prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs. This difference is expressed in terms of model weights and sublayer structure through our proposed dynamic low-rank reparameterization and learned architecture controller. Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over direct finetuning or preparation via domain-adaptive pretraining. Ablations show our task-adaptive reparameterization (TARP) and model search (TAMS) components individually improve on other parameter-efficient transfer like adapters and structure-learning methods like learned sparsification.
Cryptanalysis of Reduced Round SPEEDY

SPEEDY is a family of ultra low latency block ciphers proposed by Leander, Moos, Moradi and Rasoolzadeh at TCHES 2021. Although the designers gave some differential/linear distinguishers for reduced rounds, a concrete cryptanalysis considering key recovery attacks on SPEEDY was completely missing. The latter is crucial to understand the security margin of designs like SPEEDY which typically use low number of rounds to have low latency. In this work, we present the first third-party cryptanalysis of SPEEDY-r-192, where $$r \in \{5, 6, 7\}$$ is the number of rounds and 192 is block and key size in bits. We identify cube distinguishers for 2 rounds with data complexities $$2^{14}$$ and $$2^{13}$$ , while the differential/linear distinguishers provided by designers has a complexity of $$2^{39}$$ . Notably, we show that there are several such cube distinguishers, and thus, we then provide a generic description of them. We also investigate the structural properties of 13-dimensional cubes and give experimental evidence that the partial algebraic normal form of certain state bits after two rounds is always the same. Next, we utilize the 2 rounds distinguishers to mount a key recovery attack on 3 rounds SPEEDY. Our attack require $$2^{17.6}$$ data, $$2^{25.5}$$ bits of memory and $$2^{52.5}$$ time. Our results show that the practical variant of SPEEDY, i.e., SPEEDY-5-192 has a security margin of only 2 rounds. We believe our work will bring new insights in understanding the security of SPEEDY.
Qanaat

Today's large-scale data management systems need to address distributed applications' confidentiality and scalability requirements among a set of collaborative enterprises. This paper presents Qanaat , a scalable multi-enterprise permissioned blockchain system that guarantees the confidentiality of enterprises in collaboration workflows. Qanaat presents data collections that enable any subset of enterprises involved in a collaboration workflow to keep their collaboration private from other enterprises. A transaction ordering scheme is also presented to enforce only the necessary and sufficient constraints on transaction order to guarantee data consistency. Furthermore, Qanaat supports data consistency across collaboration workflows where an enterprise can participate in different collaboration workflows with different sets of enterprises. Finally, Qanaat presents a suite of consensus protocols to support intra-shard and cross-shard transactions within or across enterprises.
Low Computational Vehicle Lane Changing Prediction Using Drone Traffic Dataset

Safe autonomous driving assistance systems are actively being developed based on Convolutional Neural Network (CNN). Unlike understanding the road environment through the image viewed from the existing vehicle, it has the advantage of a drone image that can see a large area at once. It is used as safe driving assistance information by understanding the movements of various vehicles and predicting movement information according to time. In this paper, vehicle movement is predicted using LSTM by extracting vehicle time series information. Use YOLOv5 to detect the vehicle on the road. Road areas are collected as drone flight images. YOLOv5 is learned by labeling the vehicle through the collected image. Time-series vehicle movement information is extracted from the detected vehicle and the movement of each vehicle is predicted using the LSTM model. The predicted vehicle information is represented by an error through the MSE.
Learning Adaptive Differential Evolution by Natural Evolution Strategies

Adaptive parameter control is critical in the design and application of evolutionary algorithm (EA), so does in differential evolution. In the past decade, many adaptive evolutionary algorithms have been proposed, in which online information collected until current generation during the evolutionary search procedure is used to determine the algorithmic parameters for the next generation. Recent studies often assume that the algorithmic parameters follow some distributions, while the distributions' parameters (called hyper-parameters) are updated by the collected information. Performances of these adaptive EAs depend highly on the hyper-parameters. Notice that the experiences obtained from optimizing some related problems could provide useful guidelines on how to adaptively control the distributions' parameters. However, few existing studies sufficiently used such experiences. To fill the gap, we propose a general framework for adaptive parameter control by modeling its evolution procedure as a Markov decision process. In the framework, a neural network is employed to act as the controller. The natural evolution strategies is applied to train the neural network. The proposed framework is applied on two well-known differential evolutions (DEs), namely JADE and LSHADE. By incorporating the learned controller, two DEs, named JADE/AC and LSHADE/AC, are formed. Experimental results on the CEC 2018 benchmark suite show that in general JADE/AC and LSHADE/AC perform significantly better than their counterparts. Moreover, in comparison with some well-known EAs including three suggested best DEs in a review paper (including LSHADE, cDE and CoBiDE), the championship algorithm in the CEC 2018 competitions, a recently-developed learnable DE and recently proposed DEs, our study shows that LSHADE/AC performs the best amongst them without sacrificing much computation time.
Computer Network Security Technology and Prevention Strategy Analysis

The use of computers has brought convenience to people's work and life, but also caused network security problems. How to identify network security risks and ensure users' personal privacy has become very important. With the continuous development of the current era, social science and economy have achieved rapid development. The integration of Internet intelligence has also brought efficiency and convenience to people's life. However, under this good development background, there are also some potential security problems in computer networks, it is a great threat to the security of the computer network. It is very important to study the development of computer network security technology. based on this, the article will be the computer network security technology of the influencing factors and preventive measures. this requires us to improve our understanding of network information security technology. This paper aims at how to effectively carry out network security technology and seek scientific prevention strategies, hoping to provide strong support for Internet security.
Efficient One-Stage Video Object Detection by Exploiting Temporal Consistency

Recently, one-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. However, in the field of video object detection (VOD), most existing VOD methods are still based on two-stage detectors. Moreover, directly adapting existing VOD methods to one-stage detectors introduces unaffordable computational costs. In this paper, we first analyse the computational bottlenecks of using one-stage detectors for VOD. Based on the analysis, we present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD by exploiting the temporal consistency in video frames. Specifically, our method consists of a location-prior network to filter out background regions and a size-prior network to skip unnecessary computations on low-level feature maps for specific frames. We test our method on various modern one-stage detectors and conduct extensive experiments on the ImageNet VID dataset. Excellent experimental results demonstrate the superior effectiveness, efficiency, and compatibility of our method. The code is available at https://github.com/guanxiongsun/vfe.pytorch.
Align Representations with Base: A New Approach to Self-Supervised Learning

Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives. Departure from these methods which maximize mutual information of two generated views, along either instance or feature dimension, the proposed paradigm introduces intermediate variables at the feature level, and maximizes the consistency between variables and representations of each view. Specifically, the proposed intermediate variables are the nearest group of base vectors to representations. Hence, we call the proposed method ARB (Align Representations with Base). Compared with other symmetric approaches, ARB 1) does not require negative pairs, which leads the complexity of the overall objective function is in linear order, 2) reduces feature redundancy, increasing the information density of training samples, 3) is more robust to output dimension size, which out-performs previous feature-wise arts over 28% Top-1 accuracy on ImageNet-100under low-dimension settings.
Hugmon: Exploration of Affective Movements for Hug Interaction using Tensegrity Robot

Since ancient times, a hug has been one of the most basic ways to express emotions and has played an important role in building relationships between people. On the other hand, social robots that are designed to provide mental health care to patients have been attracting great attention, and hugging between humans and robots is becoming more and more popular. In this study, we propose a huggable robot that allows intimate interactions between humans and robots. Our robot is based on a tensegrity structure, which is composed of rigid elements connected by springs, and the structure allows the robot to flexibly respond to external force from a hugging interaction, and express various emotions through its movements. In addition, we conducted user experiments and explored an interaction design for the affective movement of the proposed robot. Through the experiments, we confirm that a robot with a tensegrity structure can be used for a hug interaction and it has a large possibility for emotional interaction.
A comparative study on the effect of data imbalance on software defect prediction

In the current stage, software defect prediction is suffering the imbalanced data problem. Traditional methods are insensitive to defect-prone modules and tend to predict defect-prone modules as defect-free modules. To deal with this problem, sampling techniques are adopted to rebalance the defect-prone and defect-free data to train the predictive model in order to improve the performance. However, it is not clear on the combined effect of the sampling techniques and the machine learning classifiers on the performance of software defect prediction. The intent of the paper is to study the performance impact on defect prediction incurred by different combinations of sampling techniques and machine learning classifiers. Specifically, we investigate three types of sampling techniques as resampling, spread subsampling and SMOTE (Synthetic Minority Over-sampling Technique), and five types of machine learning classifiers as C4.5, naive Bayes, logistic regression, support vector machine and deep learning to study their combined effect on defect prediction. By using the Friedman test and Nemenyi test, we find that there isn't an optimal method among all the 12 combinations in defect prediction. However, support vector machine and deep learning have produced the best performance stably among all the investigated projects. With ANOVA analysis, we find that the sampling techniques have great impact on the outcomes of defect prediction because they produce different data distributions for model training. Nevertheless, the sampling proportion has significant impacts on TPR (True Positive Ratio) and FPR (False Positive Ratio) while it can merely influence the AUC (Area under Curve) and Balance of logistic regression. We explain the experimental results in the paper.
HD-YOLO: Using radius-aware loss function for head detection in top-view fisheye images

People detection is commonly used in computer vision systems, particularly for video surveillance and passenger flow statistics. Unlike standard cameras, fisheye cameras offer a large field of view and reduce occlusions when mounted overhead. However, due to the orientation variation of people in fisheye images, head detection models suffer from severe distortion when applied to fisheye images captured by top-view fisheye cameras. This work develops an end-to-end head detection method named HD-YOLO against complex situations in top-view fisheye images. The radius-aware loss function is designed to make HD-YOLO adapt to the impact of fisheye distortion, and the channel attention module is added to the model. We have also created new fisheye-image datasets for evaluation. Experiments showed that HD-YOLO outperforms other baseline methods on public and self-built datasets.
A novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer for feature selection

Existing personality detection methods based on user-generated text have two major limitations. First, they rely too much on pre-trained language models to ignore the sentiment information in psycholinguistic features. Secondly, they have no consensus on the psycholinguistic feature selection, resulting in the insufficient analysis of sentiment information. To tackle these issues, we propose a novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer (GWO) for feature selection (IDGWOFS). Specifically, we introduced the Gaussian Chaos Map-based initialization and neighbor search strategy into the original GWO to improve the performance of feature selection. To eliminate the bias generated when using mutual information to select features, we adopt symmetric uncertainty (SU) instead of mutual information as the evaluation for correlation and redundancy to construct the fitness function, which can balance the correlation between features–labels and the redundancy between features–features. Finally, we improve the common Spark-based parallelization design of GWO by parallelizing only the fitness computation steps to improve the efficiency of IDGWOFS. The experiments indicate that our proposed method obtains average accuracy improvements of 3.81% and 2.19%, and average F1 improvements of 5.17% and 5.8% on Essays and Kaggle MBTI dataset, respectively. Furthermore, IDGWOFS has good convergence and scalability.
Technology equipment lending in an academic library: Understanding patron usage and proficiency through quantitative assessment

Clemson University Libraries offers a variety of technology equipment for loan by the campus community. The collection includes cameras, video camcorders, audio equipment, projectors, and VR/AR headsets. The program is popular with patrons, with technology equipment items accounting for 21 % of all library loans in 2021. This paper examines patron usage and proficiency with the technology equipment through a user survey and usage data. The results reveal differences in proficiency and usage across the types of equipment, types of patrons, and patron departmental affiliation. These results will help the program develop instruction and make collection decisions.