Reviewerly/exHarmony huggingface dataset:
Average utility driven data analytics on damped windows for intelligent systems with data streams

In industrial areas, most of databases are dynamic databases, and the volume of the databases has grown with the passage of time. Especially, pattern mining for incremental database needs different approaches from static database because the profit or the accuracy of the previously inserted data can be reduced. Since data is time- sensitive, the recent data has a relatively higher value than the old data. In this paper, we suggest the damped window based average utility driven data analytics for intelligent systems, which the damped window reflects the importance according to the arrival time of the transactions. The proposed mining approach adopts novel data structure, which modify the importance of item as the passage of time, and it improves mining efficiency with several pruning strategies and without generating candidate patterns. To evaluate the performance of the proposed mining approach, we conducted various experiments using several real and synthetic data sets. The result of the experiments presented that the suggested method performs better in terms of runtime and memory usage than the other state-of-the-art mining techniques. Moreover, through the scalability experiments, which changed the number of different items or transactions, we verified that the proposed algorithm maintained a stable performance under various environmental changes.
Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

As one of the disruptive technologies in the fourth industrial revolution, Blockchain has irrevocably changed how people live and work. Many scholars have studied the Blockchain, and the number of related publications has increased over time. Bibliometrics is an excellent method to study the development trend of a specific field. However, few scholars have used bibliometrics to research Blockchain; the inheritance and evolution among its different research themes have not yet been fully understood. This study emphasizes the scientific evolution and research focus of Blockchain through bibliometric analysis. We analyzed 2993 publications from 2014 to 2020 using co-word analysis and cluster analysis with bibliometric tools such as the SciMAT. The most productive author, country, institutions and leading research areas were identified, outlining the Blockchain field's fundamental developments. The results show the changes in keywords regarding Blockchain summarize the research hotspots in different periods. The study discusses the evolution process of various themes in each period, providing a meaningful reference for scholars to identify research hotspots and future research directions in the Blockchain field.
A Neural Transition-based Model for Argumentation Mining

The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts.Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance.Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation.Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations.Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints.Experimental results show that our model achieves the best performance on two public datasets of different structures.
Known-plaintext attack (KPA) on an image encryption scheme using enhanced skew tent map (ESTM) and its improvement

Recently, the Skew Tent Map (STM) is enhanced and used for designing the image encryption scheme based on confusion-diffusion. It is claimed that this scheme is secure against various cryptanalytical attacks. In this paper, KPA is applied to this scheme, and the cipher image is decrypted successfully with the help of only one known plain/ cipher image pair in real-time. This attack is implemented in MATLAB and the results are demonstrated. Further, the reasons for the applicability of this attack are found. Finally, the scheme is improved in such a way that it resists KPA as well as maintains the properties of the original scheme.
Luminance Learning for Remotely Sensed Image Enhancement Guided by Weighted Least Squares

Low/high or uneven luminance results in low contrast of remotely sensed images (RSIs), which makes it challenging to analyze their contents. In order to improve the contrast and preserving fine weak details of RSIs, this letter proposes a novel enhancement framework to correct luminance guided by weighted least squares (WLS), including the following key parts. First, an image is separated into a base layer and a detail layer by employing the WLS. Then, a learning network is proposed to correct luminance for the base layer enhancement. Next, an enhancement operator for improving the detail layer is computed by using the original image and the enhanced base layer. Finally, the output image is obtained with a fusion of the enhanced base and detail components. Both quantitatively and qualitatively experimental results verify that the proposed method performs better than the state of the arts in contrast improvement and detail preservation.
A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems

With a growing interest in high-performing work teams and how to form them, a new computational challenge, denominated Team Formation Problem (TFP), has emerged. After almost two decades of research on this problem, many works continue to raise particular conceptions of what a TFP is. Any new practitioner, unfamiliar with the problem, may be hindered in discerning what is essential and what is particular in each proposal. Until now, there was a lack of a document serving as a guide, synthesizing and framing what has been done to date. In this review, we mainly introduce two things: (1) a taxonomy proposal for the TFPs and (2) the description of the main components of a TFP. Additionally, we present and discuss some applications, complexity, and resolution methods. Finally, we conclude by describing some perspectives on this topic for future studies, where we give some insight into open problems and research opportunities. The main goal of this review is to facilitate the generalization of research, identify knowledge gaps, and support the development of a theory for the TFP.
Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things (IoT), machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT. By feeding previous power electronic data into the learning model, accurate information is drawn, and the quality of IoT-based power services is improved. Generally, the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology. The efficient execution of this data-intensive Power Workflow (PW) needs massive computing resources, which are available in the cloud infrastructure. Nevertheless, the execution efficiency of PW decreases due to inappropriate sub-task and data placement. In addition, the power consumption explodes due to massive data acquisition. To address these challenges, a PW placement method named PWP is devised. Specifically, the Non-dominated Sorting Differential Evolution (NSDE) is used to generate placement strategies. The simulation experiments show that PWP achieves the best trade-off among data acquisition time, power consumption, load distribution and privacy preservation, confirming that PWP is effective for the placement problem.
Hey Google, Do You Have a Personality? Designing Personality and Personas for Conversational Agents

Conversational agents designed to interact through natural language are often imbued with human-like personalities. At times, the agent might also have a distinct persona with traits such as gender, age, or a backstory. Designing such personality or persona for conversational agents has become a common design practice. In this work, we review the emerging literature on designing agent persona or personality, and reflect on these approaches, along with the personas that are created for common conversational agents. We discuss open questions with regards to three aspects: meeting user needs, the ethics of deception, and reinforcing social stereotypes through conversational agents. We hope this work can provoke researchers and practitioners to critically reflect on their approach for designing personality or persona of conversational agents.
Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution

A master face is a face image that passes face-based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the LFW identities (over 40%) with less than 10 master faces, for three leading deep face recognition systems.
A Model-Driven Deep Dehazing Approach by Learning Deep Priors

Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks. Single image dehazing is an ill-posed inverse problem that has attracted much attention in recent years. Generally, current single dehazing methods can be categorized into the traditional prior-based methods and the data-driven deep learning methods that respectively investigate haze-related image priors and deep architectures. In this paper, we propose a novel model-driven deep learning approach that combines the advantages of both kinds of methods. First, we build an energy model for single image dehazing with physical constraints in both color image space and haze-related feature space (implemented as dark channel space in this work), regularized by haze-related image priors. Then, we design an iterative optimization algorithm for solving the proposed dehazing energy model based on the half-quadratic splitting algorithm, and the priors are transformed to their corresponding proximal operators. Finally, inspired by the optimization algorithm, we design a deep dehazing neural network, dubbed as proximal dehaze-net, by learning the proximal operators for haze-related image priors using CNNs. Our network incorporates physical model constraints of hazes and haze-related prior learning into a novel deep architecture. Extensive experiments show that our method achieves promising performance for single image dehazing.
BALFA: A brain storm optimization-based adaptive latent factor analysis model

Information overload in recent years has tremendously sparked recommender systems (RSs). An RSs usually recommends valuable information for users based on historical experience high-dimensional and incomplete (HDI) data. Since each user cannot mark whole items, extracting latent factors (LF) learned by the stochastic gradient descent (SGD) optimization method is frequently used. However, interference from the adjustment of parameters induces an inferior convergence rate and low efficiency. To address this issue, the paper proposes a brain storm optimization (BSO)-based adaptative latent factor analysis (BALFA) model consisting of the following three essential ideas: 1) divergent mechanism to avoid premature convergence, 2) particle retention technique to prevent invalid search, and 3) self-adaptive multidimensional leaning rate for more efficient application on varying data. Moreover, the convergence of the modified BSO used in BALFA is proofed based on the Markov chain. Comparison experiments on six HDI datasets in the SGD-based LF model indicate that the BALFA achieves state-of-art convergence rate and computational efficiency for HDI data analysis.
Advanced Long-Context End-to-End Speech Recognition Using Context-Expanded Transformers

This paper addresses end-to-end automatic speech recognition (ASR) for long audio recordings such as lecture and conversational speeches. Most end-to-end ASR models are designed to recognize independent utterances, but contextual information (e.g., speaker or topic) over multiple utterances is known to be useful for ASR. In our prior work, we proposed a context-expanded Transformer that accepts multiple consecutive utterances at the same time and predicts an output sequence for the last utterance, achieving 5-15% relative error reduction from utterance-based baselines in lecture and conversational ASR benchmarks. Although the results have shown remarkable performance gain, there is still potential to further improve the model architecture and the decoding process. In this paper, we extend our prior work by (1) introducing the Conformer architecture to further improve the accuracy, (2) accelerating the decoding process with a novel activation recycling technique, and (3) enabling streaming decoding with triggered attention. We demonstrate that the extended Transformer provides state-of-the-art end-to-end ASR performance, obtaining a 17.3% character error rate for the HKUST dataset and 12.0%/6.3% word error rates for the Switchboard-300 Eval2000 CallHome/Switchboard test sets. The new decoding method reduces decoding time by more than 50% and further enables streaming ASR with limited accuracy degradation.
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients

Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting. When using Bayesian models in continual learning, knowledge from previous tasks can be retained in two ways: 1). posterior distributions over the parameters, containing the knowledge gained from inference in previous tasks, which then serve as the priors for the following task; 2). coresets, containing knowledge of data distributions of previous tasks. Here, we show that Bayesian continual learning can be facilitated in terms of these two means through the use of natural gradients and Stein gradients respectively.
Dadu-Eye: A 5.3 TOPS/W, 30 fps/1080p High Accuracy Stereo Vision Accelerator

Stereo vision is widely deployed on robots and drones to enable depth estimation at a low cost. The combination of lightweight deep neural network (DNN) and cost volumes algorithm is proved to possess the advantages of both high depth estimation accuracy and speed. However, currently there is no accelerator architecture compatible with both efficient DNN inference and cost generation algorithms such as stereo matching. This work proposes a stereo vision accelerator called Dadu-eye, dedicated to real-time processing of high-resolution image streams. The proposed architecture adopts a pipelined hardware design with the techniques of operation approximation and scheduling-level optimization. First, a cost estimation block is designed to generate cost volumes from both luminance and color information. Second, a super pipelined multiplication and accumulation array with a row scan-based fused-layer convolution scheduling is proposed to perform the encoding and decoding neural network efficiently. Finally, an optical flow block is designed and cooperates with the array to approximately predict half of the frames’ depth to achieve real-time (30fps) processing on 1080p view. Based on the SMIC 40 nm CMOS process, this stereo vision accelerator achieves 5.3 TOPS/W power efficiency and significantly reduces 81% off-chip memory access.
Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study

Inter-release defect prediction (IRDP) is a practical scenario that employs the datasets of the previous release to build a prediction model and predicts defects for the current release within the same software project. A practical software project experiences several releases where data of each release appears in the form of chunks that arrive in temporal order. The evolving data of each release introduces new concept to the model known as concept drift, which negatively impacts the performance of IRDP models. In this study, we aim to examine and assess the impact of feature selection (FS) on the performance of IRDP models and the robustness of the model to concept drift. We conduct empirical experiments using 36 releases of 10 open-source projects. The Friedman and Nemenyi Post-hoc test results indicate that there were statistical differences between the prediction results with and without FS techniques. IRDP models trained on the data of most recent releases were not always the best models. Furthermore, the prediction models trained with carefully selected features could help reduce concept drifts.
Deepfake video detection: YOLO-Face convolution recurrent approach

Recently, the deepfake techniques for swapping faces have been spreading, allowing easy creation of hyper-realistic fake videos. Detecting the authenticity of a video has become increasingly critical because of the potential negative impact on the world. Here, a new project is introduced; You Only Look Once Convolution Recurrent Neural Networks (YOLO-CRNNs), to detect deepfake videos. The YOLO-Face detector detects face regions from each frame in the video, whereas a fine-tuned EfficientNet-B5 is used to extract the spatial features of these faces. These features are fed as a batch of input sequences into a Bidirectional Long Short-Term Memory (Bi-LSTM), to extract the temporal features. The new scheme is then evaluated on a new large-scale dataset; CelebDF-FaceForencics++ (c23), based on a combination of two popular datasets; FaceForencies++ (c23) and Celeb-DF. It achieves an Area Under the Receiver Operating Characteristic Curve (AUROC) 89.35% score, 89.38% accuracy, 83.15% recall, 85.55% precision, and 84.33% F1-measure for pasting data approach. The experimental analysis approves the superiority of the proposed method compared to the state-of-the-art methods.
Non-Degraded Adaptive HEVC Steganography by Advanced Motion Vector Prediction

Current video steganography operates with either the decoded frame images or the compression coding parameters, which could cause quality degradation of the reconstructed frames. In this letter, by exploiting the advanced motion vector prediction (AMVP) technique of High Efficiency Video Coding (HEVC) standard, we propose a non-degraded adaptive steganographic approach for H.265/HEVC videos. The index value in the candidate list of the prediction unit (PU) is used for embedding. Experimental results demonstrate the superiority of the proposed steganographic approach against both hand-crafted feature-based and deep learning network-based steganalytic detectors. Our work explores a new embedding space that is not previously studied. It is a significant development in finding new ways to escape from video quality change-based steganalysis.
Asymmetric 3D Context Fusion for Universal Lesion Detection

Modeling 3D context is essential for high-performance 3D medical image analysis. Although 2D networks benefit from large-scale 2D supervised pretraining, it is weak in capturing 3D context. 3D networks are strong in 3D context yet lack supervised pretraining. As an emerging technique, \emph{3D context fusion operator}, which enables conversion from 2D pretrained networks, leverages the advantages of both and has achieved great success. Existing 3D context fusion operators are designed to be spatially symmetric, i.e., performing identical operations on each 2D slice like convolutions. However, these operators are not truly equivariant to translation, especially when only a few 3D slices are used as inputs. In this paper, we propose a novel asymmetric 3D context fusion operator (A3D), which uses different weights to fuse 3D context from different 2D slices. Notably, A3D is NOT translation-equivariant while it significantly outperforms existing symmetric context fusion operators without introducing large computational overhead. We validate the effectiveness of the proposed method by extensive experiments on DeepLesion benchmark, a large-scale public dataset for universal lesion detection from computed tomography (CT). The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion. To facilitate open research, our code and model in PyTorch are available at https://github.com/M3DV/AlignShift.
Improving Knowledge Graph Embeddings with Ontological Reasoning

Knowledge graph (KG) embedding models have emerged as powerful means for KG completion. To learn the representation of KGs, entities and relations are projected in a low-dimensional vector space so that not only existing triples in the KG are preserved but also new triples can be predicted. Embedding models might learn a good representation of the input KG, but due to the nature of machine learning approaches, they often lose the semantics of entities and relations, which might lead to nonsensical predictions. To address this issue we propose to improve the accuracy of embeddings using ontological reasoning. More specifically, we present a novel iterative approach ReasonKGE that identifies dynamically via symbolic reasoning inconsistent predictions produced by a given embedding model and feeds them as negative samples for retraining this model. In order to address the scalability problem that arises when integrating ontological reasoning into the training process, we propose an advanced technique to generalize the inconsistent predictions to other semantically similar negative samples during retraining. Experimental results demonstrate the improvements in accuracy of facts produced by our method compared to the state-of-the-art.
Human activity recognition with AutoML using smartphone radio data

Participants of the fourth edition of SHL recognition challenge 2021 aim to recognize eight locomotion and transportation activities in a user-independent manner based on radio data, including GPS reception, GPS location, WiFi reception, and GSM cell tower scans. Team "DD" proposes applying Google's AutoML Tables service to preprocess data, train, and evaluate the model. During this challenge, we showed the advantages and disadvantages of AutoML Tables. In addition, we have employed additional complimentary publically available datasets. AutoML Tables helped to train an artificial neural network model using the AdaNet algorithm. As a result, it has shown an ability to recognize classes with a precision of 81.2% and recall of 78.2%. Also, we opened the source code of the required feature engineering and published it on GitHub: https://github.com/dbalabka/shl-activity-recognition-2021.