Reviewerly/exHarmony huggingface dataset:
Ambient Intelligence At-Home Laboratory for Human Everyday Life

The relation of artificial intelligence (AI) and human intelligence (HI) still needs better understanding. In this article, the authors develop the fourth brain concept where AI is considered as a specific form of HI evolution. The concept development considers evolutionary and ontogenetic insights on intelligence. The forth brain can be implemented using ambient intelligence (AMI) environments that surround humans in everyday life. AMI environment allows scaffolding and other assistance information services to compensate for the HI function decay. This article proposes an at-home laboratory (AHL) design where an AMI environment is deployed at home and using everyday devices. In this setting, AHL provides a functionally equivalent alternative to the professional healthcare laboratory, though less accurate and advanced in regard to data measurements and the assistance information value.
Using the Ensemble of Deep Neural Networks for Normal and Abnormal Situations Detection and Recognition in the Continuous Video Stream of the Security System

It is suggested to use the ensemble of deep neural networks to design an intellectual situation classifier that solves the problem of normal and abnormal situations detection and recognition in a continuous video stream of the security system. The testing of its work was made on the basis of modern hardware and software and computer vision methods, the result of which is the classification probabilities for each video fragment. A software module in Python was created for normal and abnormal situations detection and recognition.
The NN-Stacking: Feature weighted linear stacking through neural networks

Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by combining them linearly, as done by Breiman [1]. Even though this approach is useful from an interpretative perspective, it often does not lead to high predictive power. We propose the NN-Stacking method (NNS), which generalizes Breiman’s method by allowing the linear parameters to vary with input features. This improvement enables NNS to take advantage of the fact that distinct base models often perform better at different regions of the feature space. Our method uses neural networks to estimate the stacking coefficients. We show that while our approach keeps the interpretative features of Breiman’s method at a local level, it leads to better predictive power, especially in datasets with large sample sizes.
Obsidian

Blockchain platforms are coming into use for processing critical transactions among participants who have not established mutual trust. Many blockchains are programmable, supporting smart contracts , which maintain persistent state and support transactions that transform the state. Unfortunately, bugs in many smart contracts have been exploited by hackers. Obsidian is a novel programming language with a type system that enables static detection of bugs that are common in smart contracts today. Obsidian is based on a core calculus, Silica, for which we proved type soundness. Obsidian uses typestate to detect improper state manipulation and uses linear types to detect abuse of assets. We integrated a permissions system that encodes a notion of ownership to allow for safe, flexible aliasing. We describe two case studies that evaluate Obsidian’s applicability to the domains of parametric insurance and supply chain management, finding that Obsidian’s type system facilitates reasoning about high-level states and ownership of resources. We compared our Obsidian implementation to a Solidity implementation, observing that the Solidity implementation requires much boilerplate checking and tracking of state, whereas Obsidian does this work statically.
Distributed deep learning on data systems

Deep learning (DL) is growing in popularity for many data analytics applications, including among enterprises. Large business-critical datasets in such settings typically reside in RDBMSs or other data systems. The DB community has long aimed to bring machine learning (ML) to DBMS-resident data. Given past lessons from in-DBMS ML and recent advances in scalable DL systems, DBMS and cloud vendors are increasingly interested in adding more DL support for DB-resident data. Recently, a new parallel DL model selection execution approach called Model Hopper Parallelism (MOP) was proposed. In this paper, we characterize the particular suitability of MOP for DL on data systems, but to bring MOP-based DL to DB-resident data, we show that there is no single "best" approach, and an interesting tradeoff space of approaches exists. We explain four canonical approaches and build prototypes upon Greenplum Database, compare them analytically on multiple criteria (e.g., runtime efficiency and ease of governance) and compare them empirically with large-scale DL workloads. Our experiments and analyses show that it is non-trivial to meet all practical desiderata well and there is a Pareto frontier; for instance, some approaches are 3x-6x faster but fare worse on governance and portability. Our results and insights can help DBMS and cloud vendors design better DL support for DB users. All of our source code, data, and other artifacts are available at https://github.com/makemebitter/cerebro-ds.
Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.
Analysis of Machine Learning Techniques for Credit Card Fraud Detection

Rapid and easy transaction through credit card system has increased fallacious cases everywhere. Machine Learning algorithms has been applied for identifying fraudulent transactions. Fraud detection problem have two major issues, firstly, the legitimate and fraudsters consistently change behaviour and secondly, datasets are severely skewed. Implementation of the system littered with machine learning algorithms, excerpting variables and sampling accession on datasets. This paper investigates the performance of Logistic Regression, Random Forest, Decision Tree and SVM (Support Vector Machine). The proposed system shows results according to accuracy, sensitivity, specificity, precision of above techniques. Transactions in the dataset are heavily right skewed. Undersampling and oversampling is conducted on the data. The work is implemented in Python.
A novel method for creating an optimized ensemble classifier by introducing cluster size reduction and diversity

In this paper, a new method is proposed for creating an optimized ensemble classifier. The proposed method mitigates the issue of class imbalances by partitioning the input data into its various data classes. The partitions are then clustered incrementally to generate a pool of class pure data clusters. The generated data clusters are then balanced by adding samples from all classes which are closest to the cluster centroid. In this manner all generated data clusters are balanced and classifiers trained on such a data cluster are unbiased as well. This creates a diverse input space for training of base classifiers. The pool of clusters is then utilized to train a set of diverse base classifiers to generate the base classifier pool. The pool of classifiers is then treated as a combinatorial problem of optimization and an evolutionary algorithm is incorporated. The proposed approach generates an optimized ensemble classifier that can not only achieve the highest classification accuracy but also has a lower component size as well. The proposed approach is tested on 31 benchmark datasets from UCI machine learning repository and results are compared with existing state-of-the-art ensemble classifiers as well.
Position-Aware Participation-Contributed Temporal Dynamic Model for Group Activity Recognition

Group activity recognition (GAR) aiming at understanding the behavior of a group of people in a video clip has received increasing attention recently. Nevertheless, most of the existing solutions ignore that not all the persons contribute to the group activity of the scene equally. That is to say, the contribution from different individual behaviors to group activity is different; meanwhile, the contribution from people with different spatial positions is also different. To this end, we propose a novel Position-aware Participation-Contributed Temporal Dynamic Model (P2CTDM), in which two types of the key actor are constructed and learned. Specifically, we focus on the behaviors of key actors, who maintain steady motions (long moving time, called long motions) or display remarkable motions (but closely related to other people and the group activity, called flash motions) at a certain moment. For capturing long motions, we rank individual motions according to their intensity measured by stacking optical flows. For capturing flash motions that are closely related to other people, we design a position-aware interaction module (PIM) that simultaneously considers the feature similarity and position information. Beyond that, for capturing flash motions that are highly related to the group activity, we also present an aggregation long short-term memory (Agg-LSTM) to fuse the outputs from PIM by time-varying trainable attention factors. Four widely used benchmarks are adopted to evaluate the performance of the proposed P2CTDM compared to the state of the art.
Iterative Network for Image Super-Resolution

Single image super-resolution (SISR), as a traditional ill-conditioned inverse problem, has been greatly revitalized by the recent development of convolutional neural networks (CNN). These CNN-based methods generally map a low-resolution image to its corresponding high-resolution version with sophisticated network structures and loss functions, showing impressive performances. This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization. A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization. We first analyze the observation model of image SR problem, inspiring a feasible solution by mimicking and fusing each iteration in a more general and efficient manner. Considering the drawbacks of batch normalization, we propose a feature normalization (F-Norm, FN) method to regulate the features in network. Furthermore, a novel block with FN is developed to improve the network representation, termed as FNB. Residual-in-residual structure is proposed to form a very deep network, which groups FNBs with a long skip connection for better information delivery and stabling the training phase. Extensive experimental results on testing benchmarks with bicubic (BI) degradation show our ISRN can not only recover more structural information, but also achieve competitive or better PSNR/SSIM results with much fewer parameters compared to other works. Besides BI, we simulate the real-world degradation with blur-downscale (BD) and downscale-noise (DN). ISRN and its extension ISRN+ both achieve better performance than others with BD and DN degradation models.
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

The challenges of building knowledgegrounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously.This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task.In this method, a context filter and a knowledge filter are first built, which derive knowledge-aware context representations and context-aware knowledge representations respectively by global and bidirectional attention.Besides, the entries irrelevant to the conversation are discarded by the knowledge filter.After that, iteratively referring is performed between context and response representations as well as between knowledge and response representations, in order to collect deep matching features for scoring response candidates.Experimental results show that FIRE outperforms previous methods by margins larger than 2.8% and 4.1% on the PERSONA-CHAT dataset with original and revised personas respectively, and margins larger than 3.1% on the CMU DoG dataset in terms of top-1 accuracy.We also show that FIRE is more interpretable by visualizing the knowledge grounding process.
Algorithms for Calculating the Structural Reliability of a Mobile Communication System

In this study, algorithms for calculating the structural reliability of the mobile communication system were developed and the results of calculations were presented. At the same time, the calculation of the structural reliability of the situation centers of mobile communication stations in the center and outside the center was carried out.
Connecting the dots: Theorizing and mapping learning entanglement through archaeology and design

Abstract Designing for digitally enhanced learning has increased in complexity. In response, this paper calls for a reconceptualization of technology—as the reconfiguring of space, place, materials, time and social relations—enrolled and refashioned in emergent learning activity. Such reconceptualization requires analytical tools, methods and processes to map heterogeneous assemblages of people, tools and tasks. Educational researchers and designers are in need of approaches and theories that move beyond deterministic accounts about the utility of individual learning technologies, to connect theory, research and practice. Our research shows that in learning to distinguish between elements that are open to alteration through design and those that are not, educational designers gain deeper insights into the flows of matter, information and humans characteristic of productive networked learning environments. And this, in turn, gives rise to valued qualities of emergent learning activity in alignment with current theories about learning. In this paper, we present a series of methods adapted from archaeology and design, illustrating their power in tracing the complex webs of dependence characteristic of productive learning entanglement, with the aim of supporting future design for learning.
Exploring the Correlation Between Attention and Cognitive Load Through Association Rule Mining by Using a Brainwave Sensing Headband

In recent years, Internet of Things (IoT) technology has brought many applications and developments for wearable devices, and the use of non-invasive electroencephalography (EEG) instruments to measure attention has been a topic of discussion.However, the correlation between attention and cognitive load has rarely been analyzed by data mining.For this reason, this study used head-mounted noninvasive EEG instruments based on IoT technology to collect attention values related to two courses and extracurricular activities and used a cognitive load questionnaire to investigate the cognitive loads of subjects.Correlation analysis was carried out through data mining technology to find the correlation between attention and cognitive load.In addition, six short-term experiments and relaxation experiments were designed to measure the subjects' maximum attention and minimum attention values, so as to propose a strategy for setting the attention baseline.According to the results of the various experiments, subjects suffering from overload showed a state of inattention during the whole activity while subjects suffering a high load showed low sustained attention; only subjects with a medium load showed high sustained attention.Subjects with a low load showed inattention for nearly the entire activity.In this study, a strategy for setting an attention baseline was proposed to normalize the attention values from different EEG instruments.The correlation between attention value and cognitive load is analyzed using association rule mining technology so that the change of cognitive load could be effectively estimated by measuring the attention value instead of using questionnaire in the future.
Analysis of the Malicious Bots Market

Social media bots can pose a serious threat by manipulating public opinion. Attempts to detect bots on social networks have resulted in bots becoming more sophisticated. A wide variety of types of bots has appeared, which must be taken into account when developing methods for detecting them. In this paper, we present the classification of the types of bots, which we made after analyzing the offers from bot traders in the market. We studied 1657 offers from 7 companies for 5 social networks: VKontakte, Instagram, Telegram, YouTube, and TikTok. Based on this information and descriptions from bot-traders, we are aggregating the types of bots and their key features. We also perform price analysis for different types of bots for the Russian Internet segment. The results show that the main pricing factors are bot action and bot quality. At the same time for different social networks, they affect pricing in different ways. Also, for messengers and social networks in which recommendation algorithms take into account complex actions more strongly, there is a tendency for higher quality bots to perform more complex actions. While in other social networks, the complexity of the action and the quality of bots are not correlating. The results of this study can be useful for developers of tools for detecting bots and determining the cost of an attack.
Leveraging Local and Global Cues for Visual Tracking via Parallel Interaction Network

Despite that both local and context information are crucial for robust tracking, existing CNN-based and transformer-based methods mainly focus on one of these aspects. Consequently, the former fails to exploit rich global context information due to the limited receptive field, while the latter suffers from the deficiencies in constructing the local relationship among neighboring regions. To address this issue, we propose the SiamPIN tracker, based on our Parallel Interaction Network. It consists of two effective modules, namely Global Aggregation Block (GAB) and Local Process Block (LPB). GAB perceives the global context to capture the long-range spatial dependency through a transformer-based architecture. Meanwhile, LPB performs local information extraction using a CNN model to retain the detailed appearance information of the target. These two modules are connected consecutively to compose a Trans-Conv unit block, which transmits the global context information to the local feature extraction procedure, hence enables the interaction of global-local information flow. Several such blocks are cascaded so that our model can learn to aggregate local and context information interactively. The proposed tracker achieves state-of-the-art performance on six benchmark datasets, while maintaining a real time running speed.
A Framework for Evaluating Image Obfuscation under Deep Learning-Assisted Privacy Attacks

Computer vision applications such as object detection and recognition, allow machines to visualize and perceive their environments. Nevertheless, these applications are guided by learning-based methods that require capturing, storing and processing large amounts of images thus rendering privacy and anonymity a major concern. In return, image obfuscation techniques (i.e., pixelating, blurring, and masking) have been developed to protect the sensitive information in images. In this paper, we propose a framework to evaluate and recommend the most robust obfuscation techniques in a specific domain of application. The proposed framework reconstructs obfuscated faces via deep learning-assisted attacks and assesses the reconstructions using structural/identity-based metrics. To evaluate and validate our approach, we conduct our experiments on a publicly available celebrity faces dataset. The obfuscation techniques considered are pixelating, blurring and masking. We evaluate the faces reconstructions against five deep learning-assisted privacy attackers. The most resilient obfuscation technique is recommended with regard to structural and identity-based metrics.
Cauchy loss induced block diagonal representation for robust multi-view subspace clustering

With the rapid emergence of data that can be described by different feature sets or different “views”, multi-view subspace clustering has attracted considerable research attention. To uncover the common latent structure shared by multiple views, existing models usually impose the sparse or/and low-rank constraint on the coefficients of each view data, and use Frobenius norm or ℓ1-norm based metric to measure the residuals of multi-view data. However, the intuition behind the sparse or low-rank regularization is implicit. Besides, the Frobenius norm or ℓ1-norm based metric is suitable to handle either Gaussian noise or sparse noise, which are very sensitive to larger noise or outliers. When the data is contaminated by large noise or densely corrupted, performance of existing models is degraded dramatically. In this article, we propose a novel multi-view subspace clustering method to provide superior robustness against large noise or outliers embedded in multi-view data. Our method adopts a more direct and intuitive block diagonal regularization to preserve the underlying structure of each view, and meantime introduces the cauchy loss function to deal with large noise. The derived consensus representation matrix can effectively preserve the underlying common structure of multi-view data and be robust to large noise and data corruptions. Experimental results show that our method outperforms the state-of-the-arts on both synthetic and real-world benchmark datasets.
Bangla Handwritten Word Recognition System Using Convolutional Neural Network

In recent years, Machine Learning and Data Mining based research become prevalent and handwritten recognition is one of the hotcakes. Bangla handwritten word recognition and extraction acquired huge attention in many research sectors like Computer Vision, Image Processing, Machine Learning, and many others for a large field of applications. To tackle this challenging problem, a perfect segmentation and recognition method are described in this paper with a good percentage of accuracy. The main challenge was to introduce a sound segmentation system and merge multi-zoned characters. This paper proposes a multi-zoned character segmentation, and a merging method is also proposed, which can produce the handwritten term. Utilizing Convolutional Neural Network (CNN) for preparing 84% precision is accomplished for character level, and 82% precision is achieved in word level.