Reviewerly/exHarmony huggingface dataset:
An Enhanced Naïve Bayes Model for Crime Prediction using Recursive Feature Elimination

It is no secret that crime affects everyone and has devastating socio-economic impact with long lasting effects. For this reason, law enforcement agents work tirelessly to reduce crime. However, despite their best efforts, crime still prevails and is constantly on the rise. Predictive approaches have shown positive results in mitigating this problem, hence in this paper, we have adopted the well-known Naïve Bayes algorithm to tackle crime. In this work, we augmented the classic Naïve Bayes with recursive feature elimination and applied it to crime prediction. When compared with the original Naïve Bayes, it was observed that our approach indeed improved the performance of Naïve Bayes by about 30\%. We further bench-marked our Naïve Bayes variant with other predictive algorithms such as Random Forests and Extremely Randomized Trees, and obtained results showed that our variant was equally as good and even better in some instances..
Professional Deformations of Teachers at Regional Universities

The purpose of the research is to identify professional deformations of university teachers at the regional level. The paper examines the distinguishing features of the teaching profession at the present stage of higher education development; considers factors that form the basis for the emergence and intensification of teachers’ professional deformations at regional universities; sheds light on the most common manifestations of deformations and provides their brief characteristics. Scientific novelty of the work lies in the fact that the authors have studied the issue of professional deformations among the teaching staff of a number of regional universities, have analysed the empirical material obtained and have found an inverse relationship between the severity of deformations and job satisfaction on a teacher’s part, between the progression of teachers’ deformations and the quality of the student body, as well as the correlation between the severity of “burnout” and a teacher’s subject field. As a result, factors contributing to the emergence of teachers’ deformations at the regional level have been identified, as well as their manifestations, the stages of development of professional “burnout” have been outlined and the main risk groups have been identified. It has been noted that the greatest risk of professional deviations is characteristic of teachers who do not hold academic degrees and ranks, as well as those aged 40 and over.
MT-ORL: Multi-Task Occlusion Relationship Learning

Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image. We observe two key issues in existing works: firstly, lack of an architecture which can exploit the limited amount of coupling in the decoder stage between the two subtasks, namely occlusion boundary extraction and occlusion orientation prediction, and secondly, improper representation of occlusion orientation. In this paper, we propose a novel architecture called Occlusion-shared and Path-separated Network (OPNet), which solves the first issue by exploiting rich occlusion cues in shared high-level features and structured spatial information in task-specific low-level features. We then design a simple but effective orthogonal occlusion representation (OOR) to tackle the second issue. Our method surpasses the state-of-the-art methods by 6.1%/8.3% Boundary-AP and 6.5%/10% Orientation-AP on standard PIOD/BSDS ownership datasets. Code is available at https://github.com/fengpanhe/MT-ORL.
Performance Centric Primary Storage Deduplication Systems Exploiting Caching and Block Similarity

Data deduplication is a concept of physically storing a single instance of data by eliminating redundant copies to save the storage space by matching strong data hashes (e.g., fingerprints). The adoption of deduplication for primary storage has been hampered because of its complexities, such as random-access patterns to data and the need for quicker request response time. Most of the solutions designed for primary storage are offline and dependent on the concept of locality. This paper proposes an inline deduplication system with a Machine Learning based cache eviction policy to reduce the metadata overhead in the deduplication process, eliminate the redundant writes and improve the overall throughput in latency-sensitive storage workload.Caching of the fingerprints plays a vital role in improving performance during deduplication. A novel Machine Learning model for cache eviction is built based on the recency, frequency, Logical Block Address, and category of a data block. The experimental results show that 33% of redundant writes are eliminated, 54.5% of metadata overhead is reduced by exploiting block similarity, and the metadata cache hit rates based on the Machine Learning model are higher by 5.43% and 10.36% over systems with Least Recently Used eviction and Least Frequently Used eviction policy respectively. We achieved 14.4% better throughput with a workload-dependent Machine Learning-based cache eviction policy than a system with traditional cache eviction policy. The cache system learns the past evicted block I/O statistics and refines itself while choosing an eviction candidate. Our system was evaluated on real-world I/O traces in experiments.
Intelligent Fault Diagnosis Approach for PV Arrays Based on Sparrow Search Algorithm Optimized Kernel Extreme Learning Machine

In order to accurately identify the fault types of photovoltaic arrays, a fault diagnosis method for PV arrays based on the kernel extreme learning machine optimized by sparrow search algorithm is proposed. Firstly, the output characteristics of photovoltaic arrays in typical fault types are analyzed. Then, the parameters containing the information of the photovoltaic array are used as input variables, and the normal operation, open-circuit, short-circuit, abnormal aging and partial shadow are used as output vectors. The penalty factor and kernel parameter of the kernel extreme learning machine are optimized by the sparrow search algorithm to improve the fault diagnosis accuracy. The example simulation results show that the approach proposed improve the generalization ability and stability of extreme learning machine, and has higher recognition accuracy of photovoltaic array faults than other methods.
MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations

Abstract Modern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidence in the observation. We propose MV-Datalog and $\mathrm{MV-Datalog}^\pm$ as extensions of Datalog and $\mathrm{Datalog}^\pm$ , respectively, to the fuzzy semantics of infinite-valued Łukasiewicz logic $\mathbf{L}$ as languages for effectively reasoning in scenarios where such uncertain observations occur. We show that the semantics of MV-Datalog exhibits similar model theoretic properties as Datalog. In particular, we show that (fuzzy) entailment can be decided via minimal fuzzy models. We show that when they exist, such minimal fuzzy models are unique and can be characterised in terms of a linear optimisation problem over the output of a fixed-point procedure. On the basis of this characterisation, we propose similar many-valued semantics for rules with existential quantification in the head, extending $\mathrm{Datalog}^\pm$ .
Multiframe blind restoration with image quality prior

In dynamic environments, such as ground-based optic observations for space targets, anisotropic turbulence and random noise tend to produce different discriminate image degradations at every moment. These degradations severely reduce the quality of the target images and make restoration of these images very difficult. However, an inconsistent degradation implies that there is complementary information in such images. In this paper, a ranking network (Ranknet) is first proposed to ensure that input sequences have a consistent distribution upon degradation and squeeze the spatial distribution of the sample set. Then, an extraction-refinement neural network (ERnet) is proposed to extract complementary features and blindly reconstruct a clean image of the observation target. In ERnet, an extraction subnetwork (EN) uses 3D convolutions to extract discriminate features from multiframe input sequences, and a refinement subnetwork (RN) based on 2D convolution restores clean images by refining the effective features. In addition, a spatial–temporal attention module (STAM) is devoted to enhancing features through utilization of the high-quality features. Experimental results on the restorations of space target images and motion blur images confirm the superior performance of ERnet as compared with other state-of-the-art methods.
Recognition of Complex Human Activities for Wellness Management from Smartwatch using Deep Residual Neural Network

Activity-based wellbeing administration integrating health apps on intelligent wearables is becoming increasingly prominent. The accessibility of multimodal sensors in everyday wearable devices makes it feasible to measure human behavior and provide wellness services that are aware of the user's environment. In smartphones, smartwatches, and many other wearables, integrated sensors provide a wealth of information that can be utilized to recognize human movement. This research shows how effective deep learning algorithms can extract abstract information for human movement categorization in daily life. We came up with an approach to deal with this issue is what we name one-dimensional deep residual neural network (1D-ResNet). With an overall accuracy of 94.15%, we observed that the proposed 1D-ResNet outperforms standard deep learning models in categorizing complicated human activities such as reading, writing, eating, and other hand-oriented tasks.
A Machine Learning-Based Privacy-Preserving Model for COVID-19 Patient using Differential Privacy

Machine learning models are often trained on the dataset, which contains sensitive information. Private Aggregation of Teacher Ensembles is a helpful framework that can be used along with Differential privacy to preserve users' privacy. The teacher models are trained on a disjoint dataset, and the aggregated knowledge of the teacher model is then used to train the student model. The proposed method used the teacher models technique to detect whether a person's chest CT-Scan is affected by COVID-19 or not while preserving the user's privacy and compared the proposed privacy-preserving model with a standard CNN model to compare their accuracy.
Increasing Efficiency in Counting Number of Motorcycle with Object Relation Matching

Counting number of object is very important for data collection. This research had experimented in counting number of motorcycle with object detection and tracking technology by using YOLOv4 model for object detection and Deepsort model for object tracking. However, the experiment showed several errors in counting number of object detection and tracking. Therefore, the object relation matching concept was applied in an attempt to solve these errors. It was found that, after applying object relation matching concept, precision in counting number of object rose from 68.29 percent to 80.99 percent, or 12.7 percent increase
Multi Association Semantics-Based User Matching Algorithm Without Prior Knowledge

Cross-network user matching is one of the basic issues for realizing social network data integration. Existing research based on structure features provides a good matching method for nodes with high-degree, but ignores the matching of nodes with low values. This paper proposes an unsupervised cross-network user matching algorithm based on association semantics to solve the problem of cross-network user matching. It can effectively solve the low-degree user matching problem when the quality of the text type attributes of network users cannot be guaranteed. First, build a social network graph based on multiple types of user behaviors, and define different association semantics according to different behaviors; then, walk based on the association semantics to obtain user node sequences with association semantics, and then combine the network embedding model to learn the user feature vector representation is obtained; finally, the user similarity is measured based on the user vector similarity, and then cross-network user matching is realized. The highlights of this paper are two folds. (1) We treat the association as nodes. (2) Social network user graphs with association semantics can enrich structure feature; walks based on association semantics can obtain walks that are closer to natural language short sentences. The experimental results show that the proposed method can effectively match user with similar behavior characteristics without any prior knowledge, and can effectively achieve the matching of low-degree nodes in heterogeneous networks.
Anomaly Detection in File Fragment Classification of Image File Formats

File fragment classification is an important and challenging problem in many domains. Among all the different file formats in computer networks and storage devices, image file formats are of much importance and interest. However, image formats form only a small portion of the datasets used in the previous works in the field of file fragment classification. Additionally, the proposed methods in the literature are concentrated on limited feature sets, which are suitable only for the task at hand. A challenge in the area of image type classification is to distinguish image file fragments from any other file format. To date, all presented approaches share the same hypothesis that each sample belongs to one of the training class labels. In most applications, this assumption is wrong, since a variety of file formats are transferred or being stored in a given environment. This proves the importance of discriminating image formats from other file types. In this paper, using a feature vector with length 577, we have classified image fragments of 10 different file formats. We have proposed to include an anomaly detection step before the classification phase to determine the non-image file fragments. Semi-supervised anomaly detection methods from other research domains are used as image discriminators. To evaluate the proposed method, three different anomaly detection methods are explained and designed. The result is evaluated with three different file format families considered anomalies: audio, video, and text.
Looking on Efficiency of Content Moderation Systems from the Lens of Reddit's Content Moderation Experience During COVID-19

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Fine-grained Urban Flow Inference with Incomplete Data

Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city given the coarse-grained urban flow observations, is critically important to various smart city related applications such as urban planning and public safety. Previous works assume that the urban flow monitoring sensors are evenly distributed in space for data collection and thus the observed urban flows are complete. However, in real-world scenarios, sensors are usually unevenly deployed in space. For example, the traffic cameras are mostly deployed at the crossroads and central areas of a city, but less likely to be deployed in suburb. The data scarcity issue poses great challenges to existing methods for accurately inferring the fine-grained urban flows, because they require all urban flow observations to be available. In this paper, we make the first attempt to infer fine-grained urban flows based on the incomplete coarse-grained urban flow observations, and propose a Multi-Task urban flow Completion and Super-Resolution network (MT-CSR for short) to simultaneously complete the coarse-grained urban flows and infer the fine-grained flows. Specifically, MT-CSR consists of the data completion network (CMPNet for short) and data super-resolution network (SRNet for short). CmpNet is composed of a local spatial information based data completion module LocCmp and an auxiliary information based data completion module AuxCmp to consider both the local geographical and global semantic correlations for urban flow data completion. SRNet is designed to capture the complex associations between fine-and coarse-grained urban flows and upsample the coarse-grained data by stacking the designed super-resolution blocks. To gain an accurate inference, two parts are jointly conducted under a multi-task learning framework, and trained in an end-to-end manner using a two-stage training strategy. Extensive experiments on four large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.
DOCR-CAPTCHA: OCR Classifier based Deep Learning Technique for CAPTCHA Recognition

The objective of this research is to analyze the security flaws of CAPTCHA generating model in order to build more resilient CAPTCHAs without such risks associated with human attempt and fail attempts. In this work, a more efficient DOCR-CAPTCHA model has presented which is deep learning approach based on an optical character recognition (OCR) to address the concerns of lower efficiency and inadequate performance of existing CAPTCHA detection algorithms. First, the DOCR-CAPTCHA model preprocesses the images to enhance the quality by following gray scale conversion, cropping and resizing the image. Second, it extracts the character to create a dictionary and mapping each character with labeling. Next, it performs classification using OCR technique and train the model. It also performed the validation on the same data. The simulation has done on the CAPTCHA images dataset. The recognition rate of this simulated model results achieved a high accuracy rate of 99.98 percent and minimized error rate of 0.0051 for the CAPTCHA train dataset. It has also compared with the existing YOLO technique and found that it has outperformed than YOLO.
Data Cleaning of Sound Data with Label Noise Using Self Organizing Map

The noise label of data is a problem that can cause low performance of deep learning. It is difficult to manually relabel due to huge amounts of data. In addition, there are much more problems due to the similarity of sounds that are difficult to manually distinguish and label sound data. We proposed a data cleaning method using SOM (Self-Organizing Map), one of the unsupervised learning methods. In order to extract compact features from audio, densely connected layer with log scaled Mel-spectrogram is used. Data selection is performed based on the Euclidean distance of each Best matching unit (BMU) derived through the SOM. We also experiment with various grid sizes for SOM to find an efficient grid size. In addition, an appropriate distance finding experiment is conducted. This method is evaluated in sound classification using a pre-trained DenseNet model.
A Social Recommendation Framework for Chinese Long-stayers in Chiang Mai

With the tremendous development of the Internet, e-commerce and social networks, research on Social Recommendation Systems has become a hot topic to solve information overload and respond to diverse individual demands. However, many of the existing social recommendation methods lack an in-depth analysis of long-stay users from cross-cultural backgrounds, which causes insufficient social recommendations, since the social attributes and requirements of these users differ from those of short-stay tourists, local organisations or residents. Therefore, taking Chinese long-stayers in Chiang Mai as a case study, this paper follows the Knowledge Management process with the aim of producing a social recommendation framework based on social networks to provide Chinese long-stayers with filtered and efficient social recommendations. The research employs qualitative and quantitative methods to investigate the research problems and conducts online surveys and in-depth interviews to collect the individual and collective data and analyse them using Social Network Analysis. The proposed social recommendation framework aims to benefit Chinese long-stayers and any other organisations that need social recommendations or innovative business and management strategies in relation to Chinese long-stayers in Chiang Mai. The preliminary work will contribute to the current accumulative knowledge of the impact of social networks of Chinese long-stayers on social recommendation performance in a cross-cultural context.
Multi-stage network for single image Demoireing

The moiré pattern refers to the interference fringes generated by two equal amplitude sinusoids with close frequencies. In digital imaging, images collected in some scenes are vulnerable to moiré, such as images taken from knitted fabrics and images taken on LED screens, the visual quality of which is always damaged seriously. The difficulty of demoiréing lies in the moiré patterns distributed through different bands of frame frequencies and vary in colors and shapes. To fully learn the global information of the moiré images and remove the moiré patterns in a wide range of frequency bands, we proposed a multi-stage and multi-patch network, which can recover non-homogeneous moire images by aggregating the features of different spatial regions of patches in different stages. To increase the receptive field, we also introduce a novel Atrous Fusion Module in different atrous rates to learn multi-scale information. By taking advantage of these improvements, our proposed network can achieve superior accuracy than state-of-the-art approaches on the public dataset in the NTIRE2020 Single Image Demoiréing Challenge.
Optimal recovery of unsecured debt via interpretable reinforcement learning

This paper addresses the issue of interpretability and auditability of reinforcement-learning agents employed in the recovery of unsecured consumer debt. To this end, we develop a deterministic policy-gradient method that allows for a natural integration of domain expertise into the learning procedure so as to encourage learning of consistent, and thus interpretable, policies. Domain knowledge can often be expressed in terms of policy monotonicity and/or convexity with respect to relevant state inputs. We augment the standard actor–critic policy approximator using a monotonically regularized loss function which integrates domain expertise into the learning. Our formulation overcomes the challenge of learning interpretable policies by constraining the search to policies satisfying structural-consistency properties. The resulting state-feedback control laws can be readily understood and implemented by human decision makers. This new domain-knowledge enhanced learning approach is applied to the problem of optimal debt recovery which features a controlled Hawkes process and an asynchronous action–feedback relationship.