Reviewerly/exHarmony huggingface dataset:
Interpretable Concept Drift

In a dynamic environment, models tend to perform poorly once the underlying distribution shifts. This phenomenon is known as Concept Drift. In the last decade, considerable research effort has been directed towards developing methods capable of detecting such phenomenons early enough so that models can adapt. However, not so much consideration is given to explain the drift, and such information can completely change the handling and understanding of the underlying cause. This paper presents a novel approach, called Interpretable Drift Detector, that goes beyond identifying drifts in data. It harnesses decision trees’ structure to provide a thorough understanding of a drift, i.e., its principal causes, the affected regions of a tree model, and its severity. Moreover, besides all information it provides, our method also outperforms benchmark drift detection methods in terms of false-positive rates and true-positive rates across several different datasets available in the literature.
Learning of monotone functions with single error correction

Abstract Learning of monotone functions is a well-known problem. Results obtained by V. K. Korobkov and G. Hansel imply that the complexity φ M ( n ) of learning of monotone Boolean functions equals <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable rowspacing="4pt" columnspacing="1em"> <m:mtr> <m:mtd> <m:mstyle displaystyle="true"> <m:msubsup> <m:mi>C</m:mi> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo fence="false" stretchy="false">⌊</m:mo> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> <m:mo fence="false" stretchy="false">⌋</m:mo> </m:mrow> </m:msubsup> </m:mstyle> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} \displaystyle C_n^{\lfloor n/2\rfloor} \end{array}$ + <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable rowspacing="4pt" columnspacing="1em"> <m:mtr> <m:mtd> <m:mstyle displaystyle="true"> <m:msubsup> <m:mi>C</m:mi> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo fence="false" stretchy="false">⌊</m:mo> <m:mi>n</m:mi> <m:mrow class="MJX-TeXAtom-ORD"> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> <m:mo fence="false" stretchy="false">⌋</m:mo> <m:mo>+</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msubsup> </m:mstyle> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} \displaystyle C_n^{\lfloor n/2\rfloor+1} \end{array}$ ( φ M ( n ) denotes the least number of queries on the value of an unknown monotone function on a given input sufficient to identify an arbitrary n -ary monotone function). In our paper we consider learning of monotone functions in the case when the teacher is allowed to return an incorrect response to at most one query on the value of an unknown function so that it is still possible to correctly identify the function. We show that learning complexity in case of the possibility of a single error is equal to the complexity in the situation when all responses are correct.
A Convolutional Neural Network Model for Road Flow Direction Detection

It is an important work area to determine realtime characteristics of roads where vehicles are in motion in critical areas where artificial intelligence is effectively used, such as driverless vehicles. The purpose of this article work is to present a deeper learning method that will allow a vehicle in motion to detect the direction of flow in the path. Convolutional Neural Networks (KSA) have been used as deep learning models for the determination of the direction of flow (YAY) in the study. The YAY-KSA model developed for flow direction detection is applied on 587 real road images in the CMU VASC image database. To compare the performances of the prepared model, Cifar model which is a common KSA model was applied on the same data. According to the classification results obtained, it was seen that the designed YAY-KSA model correctly determined flow direction at 80.1% level.
Attribute and Identity Are Equally Important: Person Re-identification with More Powerful Pedestrian Attributes

Person re-identification (ReID) technology aims to identify characteristic people from different perspectives taken by different surveillance cameras. Due to the large changes in external light and viewing angle, the traditional ReID may completely fail. In response to this, we propose a joint recognition model with pedestrian attributes. Specifically, first, from the perspective of machine attention, we comprehensively consider the mutual exclusion and dependence between attributes, and propose a multi-task attribute recognition method that limits the attentional area of the neural network. Then we combine the pedestrian attribute and the person identity characteristics as the complete pedestrian feature information, to measure the similarity. In particular, we treat attribute information the same as identity information, instead of treating it as an attachment to identity information, in order to more effectively correct the errors of identity recognition. The experiments on the DukeMTMC-reID and Market1501 datasets prove the superiority of our method.
A Survey on Knowledge-Driven Multi-Agent Computer Simulations for Disaster Management

Protecting humans from disasters has been an active mission of governments and experts through the deﬁnition of disaster management plans. Deﬁning disaster response strategies are crucial to reduce the number of victims and the economic impact. An efﬁcient preparedness depends on the cycle which aims at planning and organizing disaster response, experimenting the plans through training and exercise, and Assessment of the planning based on the experiments. Therefore, plan assessment is an essential step in the preparedness to identify potential problems and planning errors. However, the plan assessment requires a plan experiment step. There are two ways to experiment plans: exercises and computer simulations. This survey focuses on preparedness phases for plan experimentation and effectiveness assessment by providing details about last work on mulit-agent simulation methods and knowledge engineering approaches. This survey answers the following question: how do multi-agent simulations and semantic representations of disaster management provide an effective way to assess disaster management plans? How to measure the quality of disaster management plans? Indeed, the plan’s effectiveness must be measured to support its improvement.
ACKF filtering algorithm based on exponential weighting

In order to optimally track the tracking and exploration targets, it is based on the performance of the traditional volume Kalman filter (CKF) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]–[2]</sup> in practical applications and the characteristics of uncertain and unknown time-varying system noise statistical characteristics. This paper proposes an ACKF algorithm that combines maximum a posteriori estimation and exponential weighting with CKF filtering. After using Sage-Husa's suboptimal unbiased maximum posterior estimator to estimate the unknown time-varying or inaccurate noise statistics in real time <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[9]</sup> , and combining it with the CKF algorithm to obtain the ACKF recursive formula, and then use the progressive. The combination of memory index weighting and limited memory index weighting continuously corrects the noise statistics, and strengthens the robustness of the filter by reducing the state estimation error.
Neuromorphic Photonics

Neuromorphic photonic applies concepts extracted from neuroscience to develop photonic devices behaving like neural systems and achieve brain-like information processing capacity and efficiency. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and paves the way to ultrafast, power efficient and low cost and complex signal processing. We explore the use of semiconductor lasers with optoelectronic feedback operating in self-pulsating mode as photonic neuron that can deliver flexible control schemes with narrow optical pulses of less than 30 ps pulse width, with adjustable pulse intervals of −2 ps/mA to accommodate specific Pulse Position Modulation (PPM) coding of events to trigger photonic neuron firing as required. The analyses cover in addition to self-pulsation performance and controls, the phase noise and jitter characteristics of such solution.
Human Activities Analysis Using Machine Learning Approaches

Human activity recognition has always been a challenging issue. Many researches have been carried out on activity recognition as it is playing a vital role in many surveillance systems, healthcare systems, smart homes, etc. So, the requirement for analyzing the human activities has now become a crucial part of life. Human activity recognition can be done using data collected from sensors, smartphones and images. Earlier, data collection process was difficult as the sensors and smartphones were not that much affordable, but now this problem has been resolved. A human activity recognition model has been proposed on UCI HAR dataset using four machine learning algorithms, namely logistic regression, decision tree, random forest classifier and LinearSVC in this paper, and the study found that among all the four machines learning algorithms LinearSVC achieved best accuracy.
Informational Dialogue Generation based on Hierarchical Attention Mechanism and Knowledge Distillation

Knowledge-driven dialogue has attracted widespread attention in academia and industry, and has achieved remarkable results. However, many researches cannot select knowledge that is closely related to the text and generate inconsistent response semantics. In order to solve these problems, this paper proposes a dialogue model based on hierarchical attention mechanism and knowledge distillation, HAMKD. The model uses knowledge distribution to select knowledge closely related to conversation context, and dynamically updates knowledge attention according to decoder state during decoding. For the semantic inconsistency of generated responses in the model, we used self-attention in the decoder to solve the inconsistency of generated responses. In order to further improve the effect of the model, the knowledge distillation [1] method is used in the training phase. Experimental results show that the proposed model is better than the comparative model.
OblivShare: Towards Privacy-Preserving File Sharing with Oblivious Expiration Control

People have personal and/or business need to share private and confidential documents; however, often at the expense of privacy. Privacy aware users demand that their data is secure during the entire life cycle, and not residing in clouds indefinitely. A trending feature in industry is to set download constraints of shared files - a file can be downloaded for a restricted number of times and/or within a limited time framework. Metadata privacy becomes concerning with web services and applications providing such additional level of security control but not hiding the metadata. There is no prior research focusing on privacy-preserving expiration control, hence we propose OblivShare, a privacy-preserving file sharing scheme to proactively fill the gap. The scheme is based on ORAM for secure computation that 1) supports file expiration at users’ control, 2) hides expiration metadata from the server, 3) server is fully oblivious of file access pattern and expiration state of a file. We demonstrate that our protocol has a complexity poly-logarithmic to the number of files while achieving privacy of metadata.
StrGAN for Generating Enhanced Samples

Most studies use the negative effect of perturbation to mislead the neural network, such as adversarial examples, while ignoring the positive effect of improving neural networks’ performance. In this work, we use enhanced samples with positive perturbation to improve target classifiers’ performance and propose an algorithm of strong generative adversarial networks (StrGAN) to generate enhanced samples. StrGAN directly generates enhanced samples of unlabeled data. Since StrGAN and the target classifier are independent of each other, it can effectively reduce the classifier’s computing resources and training time while improving the performance. The experiment shows that the enhanced samples generated by StrGAN have higher accuracy than original samples, and its accuracy can increase by up to 28.6%.
A Dynamic Group Signature Scheme for Blockchain-Based Traceability Bulletin Board

The traceability system plays a vital role in tracing the supply records of products with quality problems. The application of blockchain in the traceability system solves the problems of easy information tampering and centralization of data storage during the traceability process, but it also brings problems such as easy information leakage, i.e., the real identity of the corresponding user can be found through the address. The characteristics of data integrity, fairness, verifiability, anonymity, and irrevocability in group signatures are of great significance for protecting the privacy of blockchain traceable transaction data. Therefore, this paper proposes a blockchain-based traceability bulletin board group signature scheme, where the bulletin board provides a direct interaction platform for users and departments. In the group signature scheme proposed in this paper, the role is re-divided for traceability, in which department members can dynamically join or withdraw, and the signature information of department members is stored on the traceability bulletin board of the blockchain and hidden department personnel’s signature information on traceability documents.
Dynamic Weighted Anytime Bounded Cost Search Algorithm

The best first algorithm like A* can find the optimal path if the time and memory is enough. However, it is usually difficult to do it in many cases. Moreover, there is always not enough time to support algorithm to find the optimal solution. On the other hand, users usually need an available solution rather than the optimal solution. Based on this, we propose Dynamic weighted Anytime Bounded Cost Search(DW-ABCS) algorithm. It combines the Bounded sub-optimal search(BSS) with the Bounded cost search(BCS) and improves the problem that BCS may not find a solution if the cost bound set is less than the optimal solution. Furthermore, in order to improve the efficiency of search, DW-ABCS algorithm dynamically modify the cost bound by considering the depth of nodes in the search process. In this way, the nodes considered become less and less and the complexity of algorithm will be lower and lower along the depth of search increasing, which improves the search efficiency significantly. In addition, the idea of anytime algorithm is used to find a better solution in every iteration. In this way, the algorithm can quickly find a sub-optimal path within suboptimal bound, and then reducing the sub-optimal bound to optimize it until the time out. If time permitted, the algorithm can converge to the optimal solution. Specially, we conducted a rigorous theoretical analysis. The various gird maps are used to verify the performance of DW-ABCS. Experimental results show that DW-ABCS algorithm can effectively identify the sub-optimal solution in different scenes.
Evaluation Model of Power Staff Skill State Based on Improved Analytic Hierarchy Process_Fuzzy Comprehensive Evaluation

In view of the problem that the State Grid Corporation cannot visually reflect the comprehensive level of power employees’ skills in skill evaluation, this paper proposes a power employee skill status evaluation model based on improved analytic hierarchy process_fuzzy comprehensive evaluation. Researchers establish a power employee skill evaluation index system based on the research situation and the construction principles of the index system. Aiming at the problem that the traditional analytic hierarchy process adopts a single weight calculation method, it is easy to cause a large difference in weighting due to different method selections, and an improved analytic hierarchy process is proposed to assign weights to indicators. The improved analytic hierarchy process uses geometric average method, arithmetic average method, eigenvector method and least square method to calculate weights respectively and takes average value instead of single method to calculate weights. The grading standard calculates the membership degree of the index. The researcher obtains the evaluation result of the skill status of the electric power staff through the corresponding fuzzy evaluation calculation. The researcher applies the improved analytic hierarchy process_fuzzy comprehensive evaluation to the comprehensive evaluation of the skills of electric power employees. The effectiveness of the model is verified by calculating the actual data of employees in a training center of a certain province of the State Grid.
Random Projection Through the Lens of Data Complexity Indicators

Random projection (RP) is a simple and efficient tool for dimensionality reduction and has been applied in many machine learning algorithms. Theoretical guarantees ensure that the method approximately preserves the pairwise Euclidean distances with high probability. However, worst-case theoretical bounds on the required target dimension of the projection are not sufficiently tight for direct practical use, so for a large data set most often much lower values are being used in practice. While theory also suggests that this may be due to the low complexity structure of the underlying data support, this structure is typically unknown in practice. Here we investigate the use of a previously proposed battery of data complexity indicators (DCIs) to gain insight into how random projection changes the data complexity for classification problems. Our experimental results show that complexity increases with compression, but in most cases we can project data onto fairly low dimensions while maintaining nearly the same level of data complexity as that of the original data. This may offer some guidance for the choice of the target dimension in practice, although it should be interpreted with caution.
Enhancing the generalization of feature construction using genetic programming for imbalanced data with augmented non-overlap degree

Genetic programming (GP) has a significant achievement in feature construction and non-overlap degree can help to improve the generalization ability of GP based feature construction. However, the non-overlap degree is biased towards the majority class. In this paper, a novel GP based feature construction method with augmented non-overlap degree is proposed to enhance the generalization ability for imbalanced data. And the constructed features are evaluated by a novel function based on the combination of the area under the ROC curve metric and the augmented non-overlap degree. The generalization performance is evaluated not only by a particular classification algorithm, but also by six widely used classification algorithms. The experiments conducted on five imbalanced biomedical datasets with different imbalance rates show that the proposed GP-AANO method can achieve superior generalization performance for classification.
Multi-branch Semantic Learning Network for Text-to-Image Synthesis

In this paper, we propose a multi-branch semantic learning network (MSLN) to generate image according to textual description by taking into account global and local textual semantics, which consists of two stages. The first stage generates a coarse-grained image based on the sentence features. In the second stage, a multi-branch fine-grained generation model is constructed to inject the sentence-level and word-level semantics into two coarse-grained images by global and local attention modules, which generate global and local fine-grained image textures, respectively. In particular, we devise a channel fusion module (CFM) to fuse the global and local fine-grained features in the multi-branch fine-grained stage and generate the output image. Extensive experiments conducted on the CUB-200 dataset and Oxford-102 dataset demonstrate the superior performance of the proposed method. (e.g., FID is reduced from 16.09 to 14.43 on CUB-200).
A Constructivist Ontology Relation Learning Method

From the perspective of philosophy, ontology relations denote ultimate semantic relations of related knowledge concepts. Beyond doubt, it is still a very difficult problem on how to automatically depict and construct ontology relations because of its high abstractness. Some latest research attempted to realize ontology relation learning by learning abstract hierarchies or similarities among knowledge concepts. Inspired by the requirements of associative semantic cognition like in the human brain, a constructivist ontology relation learning (CORL) method is put forward in this study by borrowing the idea of the constructivist learning theory. Wherein, two following points are supposed: 1) each symbol knowledge is looked as a token of representing certain abstract pattern and 2) each pattern denotes a type of relation structures on other patterns, or a directly observed event data, such as physical sensing data, natural image, sound data, text word etc. So, ontology relation could be considered as the associative support degrees from other knowledge concepts to the target concept, which reflects how one knowledge ontology can be demarcated by other knowledge concepts. Then, the knowledge network can be employed to represent an entire domain knowledge system. Meanwhile, an associative random walk mechanism (ARWM) on knowledge network can be considered to explain the semantic generative process of every document. Thus, CORL can be realized by integrating ARWM into an extended latent Dirichlet allocation (LDA) model. Some theoretical and experimental analysis are done. The corresponding results demonstrate that CORL can obtain effective associative semantic relations among concept words, and gain some novel characteristics in better representing knowledge ontology than existing methods.
Entity Relation Fusion for Real-Time One-Stage Referring Expression Comprehension

Referring Expression Comprehension (REC) is the task of grounding object which is referred by the language expression. Previous one-stage REC methods usually use one single language feature vector to represent the whole query for grounding and no reasoning between different objects is performed despite the rich relation cues of objects contained in the language expression, which depresses their grounding accuracy. Additionally, these methods mostly use the feature pyramid networks for multi-scale visual object feature extraction but ground on different feature layers separately, neglecting the connections between objects with different scales. To address these problems, we propose a novel one-stage REC method, i.e. the Entity Relation Fusion Network (ERFN) to locate referred object by relation guided reasoning on different objects. In ERFN, instead of grounding objects at each layer separately, we propose a Language Guided Multi-Scale Fusion (LGMSF) model to utilize language to guide the fusion of representations of objects with different scales into one feature map.For modeling connections between different objects, we design a Relation Guided Feature Fusion (RGFF) model that extracts entities in the language expression to enhance the referred entity feature in the visual object feature map, and further extracts relations to guide object feature fusion based on the self-attention mechanism. Experimental results show that our method is competitive with the state-of-the-art one-stage and two-stage REC methods, and can also keep inferring in real time.