Reviewerly/exHarmony huggingface dataset:
Insider Threat Modeling: An Adversarial Risk Analysis Approach

Insider threats entail major security issues in many organizations. Game theoretic models of insider threats so far proposed do not take into account important factors such as the organizational culture and whether the attacker was detected or not. They also fail to model defensive mechanisms already put in place by an organization to mitigate insider attacks. We propose two new models which incorporate these settings and, hence, are more realistic, and use adversarial risk analysis to find their solutions. Our models and solutions are general and can be applied to most insider threat scenarios. A data security example illustrates the discussion.
Comparisions on KNN, SVM, BP and the CNN for Handwritten Digit Recognition

Handwritten digit recognition technology refers to the automatic identification of handwritten numbers through computers or other equipment, and it has a greater application prospect in letter postal identification and financial statements and bank bill processing. This paper takes the MNIST handwritten digit database as samples, discusses algorithms KNN, SVM, BP neural network, CNN and their application in handwritten digit recognition. In the training process, this work rewrites KNN with Python, SVM with scikit-learn library, and BP, CNN with Tensorflow, and fine-tunes the algorithm parameters to get the best results for each algorithm. Finally, by comparing the recognition rate and recognition duration of the four algorithms, the advantages and disadvantages of the four algorithms in handwriting recognition are analyzed.
Multimodel Feature Reinforcement Framework Using Moore–Penrose Inverse for Big Data Analysis

Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore-Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from ∼ 1 K to ∼ 2 M show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods.
Blockchain for Collaborative Businesses

Abstract Blockchain applications have continuously improved ever since its first debut on cryptocurrency. From then on, its uses have branched out from the financial realm, finding their way into numerous industries such as health, environmental, and governmental. Businesses are starting to take advantage of the intrinsic traits that made blockchain so notorious into their operations, such as security, integrity, and transparency. Blockchain’s versatility allows companies to cooperate on a shielded environment with business partners safely. This paper details how permissioned blockchain networks can accommodate collaborative business models securely to provide thriving business alliances. Examples of cooperative business models and business relationship orientation are described here, as well as how they generate value when paired with permissioned blockchain networks - a more business-oriented variety of blockchain. To support this study’s endeavors, business use cases are presented to highlight how simple it is to put in place a permissioned blockchain to businesses to achieve tighter bonds with business partners. The use cases contain particular goals that enterprises seek to accomplish by partnering up with other companies, as well as how, with the employment of blockchain, they can attain them.
Fostering Sustainable Quality Assurance Practices in Outcome-Based Education: Lessons Learned from ABET Accreditation Process of Computing Programs

Education is an important enabler for economic uplift of a society and academic institutions need to deliver quality education to equip students with required skills to excel in their professional careers. Due to international initiatives such as Washington and Seoul accords, outcome-based education has gained significant interest from industry, academia, governments, accreditation bodies and students. Outcome-based education is a paradigm shift form conventional education approach and its successful adoption requires sustainable quality practices by higher education institutions. Fostering quality assurance processes for outcome-based education requires careful planning and active collaboration among stakeholders. However, due to the sparse body of knowledge about quality processes in outcome-based education, many academic institutions rely on ad hoc practices, resulting in a trial and error approach. In this paper, we present set of guidelines which can help academic institutions to deploy sustainable practices in their academic programs. We document important guidelines to deliver outcome-based education based on our longitudinal work of ABET accreditation process of three different computing programs (Computer Science, Computer Information Systems, and Cyber Security and Digital Forensics). The successful application of proposed guidelines helps to foster sustainable quality practices in academic programs.
Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation

Existing review-based recommendation models mainly learn long- term user and item representations from a set of reviews. Due to the ignorance of rich side information of reviews, these models suffer from two drawbacks: 1) they fail to capture short-term changes of user preferences and item features reflected in reviews and 2) they cannot accurately model high-order user-item collaborative signals from reviews. To overcome these limitations, we propose a multi-view approach named Set-Sequence-Graph (SSG), to augment existing single-view (i.e., view of set) methods by introducing two additional views of exploiting reviews: sequence and graph. In particular, with reviews organized in forms of set, sequence, and graph respectively, we design a three-way encoder architecture that jointly captures long-term (set), short-term (sequence), and collaborative (graph) features of users and items for recommendation. For the sequence encoder, we propose a short-term priority attention network that explicitly takes the order and personalized time intervals of reviews into consideration. For the graph encoder, we design a novel review-aware graph attention network to model high-order multi-aspect relations in the user-item graph. To combat the potential redundancy in captured features, our fusion module employs a cross-view decorrelation mechanism to encourage diverse representations from multiple views for integration. Experiments on public datasets demonstrate that SSG significantly outperforms state-of-the-art methods.
Jollity Chatbot- A contextual AI Assistant

Chatbot is a software application that can stimulate a conversation via text, instead of direct contact with a live human through messaging applications, websites and mobile applications. Chatbot applications help to make interactions between people and services by enhancing the customer experience. Chatbot is widely used in the areas of food ordering, ecommerce and transportation, etc. Practically it is not possible to find a permanent companion to make us happy all the time. Hence, this paper has planned to design a jollity chatbot to talk with the human users and make sure that it entertains and give suggestion and motivation in tough times. The jollity chatbot is implemented in Rasa, an open -source conversational AI framework and it is easy to customize. The proposed method has added 12 intents with each more than 8 text examples constituting a total of 100 input samples in nlu.md and their response in domain.yml. The flow of interactions is given in stories.md. The jollity chatbot is deployed in Telegram using ngrok and the server URL details and the access token are given in the credentials.yml. The system is experimented with various evaluation measures like accuracy of the intents, accuracy of the stories and the confusion matrix to shows that the proposed jollity chatbot system is more robust and can identify the user intents appropriately.
3D CNN Acceleration on FPGA using Hardware-Aware Pruning

There have been many recent attempts to extend the successes of convolutional neural networks (CNNs) from 2-dimensional (2D) image classification to 3-dimensional (3D) video recognition by exploring 3D CNNs. Considering the emerging growth of mobile or Internet of Things (IoT) market, it is essential to investigate the deployment of 3D CNNs on edge devices. Previous works have implemented standard 3D CNNs (C3D) on hardware platforms, however, they have not exploited model compression for acceleration of inference. This work proposes a hardware-aware pruning approach that can fully adapt to the loop tiling technique of FPGA design and is applied onto a novel 3D network called R(2+1)D. Leveraging the powerful ADMM, the proposed pruning method achieves simultaneous high accuracy and significant acceleration of computation on FPGA. With layer-wise pruning rates up to 10× and negligible accuracy loss, the pruned model is implemented on a Xilinx ZCU102 FPGA board, where the pruned model achieves 2.6× speedup compared with the unpruned version, and 2.3× speedup and 2.3× power efficiency improvement compared with state-of-the-art FPGA implementation of C3D.
Sickle-cell disease diagnosis support selecting the most appropriate machine learning method: Towards a general and interpretable approach for cell morphology analysis from microscopy images

In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocytesIDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers.
Human Identification and Interaction Detection in Cross-View Multi-Person Videos with Wearable Cameras

Compared to a single fixed camera, multiple moving cameras, e.g., those worn by people, can better capture the human interactive and group activities in a scene, by providing multiple, flexible and possibly complementary views of the involved people. In this setting the actual promotion of activity detection is highly dependent on the effective correlation and collaborative analysis of multiple videos taken by different wearable cameras, which is highly challenging given the time-varying view differences across different cameras and mutual occlusion of people in each video. By focusing on two wearable cameras and the interactive activities that involve only two people, in this paper we develop a new approach that can simultaneously: (i) identify the same persons across the two videos, (ii) detect the interactive activities of interest, including their occurrence intervals and involved people, and (iii) recognize the category of each interactive activity. Specifically, we represent each video by a graph, with detected persons as nodes, and propose a unified Graph Neural Network (GNN) based framework to jointly solve the above three problems. A graph matching network is developed for identifying the same persons across the two videos and a graph inference network is then used for detecting the human interactions. We also build a new video dataset, which provides a benchmark for this study, and conduct extensive experiments to validate the effectiveness and superiority of the proposed method.
Detection of geochemical anomalies using a fractal-wavelet model in Ipack area, Central Iran

The purpose of this study is to detect geochemical anomalies utilizing a Wavelet-Number (W-N) fractal model based on combination of a two-dimensional Discrete Wavelet Transformation (DWT) signal analysis and a Concentration-Area (C-A) fractal modeling. The DWT was carried by Sym8 as a selected wavelet pattern (by energy matching method) for Zn and Pb based on rock samples collected from the Ipack area, Central Iran. In addition, the DWT was decomposed by continuous wavelet coefficients at five levels and a final image was synthesized. Furthermore, the DWT data were classified using a fractal-wavelet model for delineation of both Zn and Pb anomalies from background levels in this area. Moreover, a Spectrum-Area (S-A) fractal modeling and classical statistics analysis were carried out and their results were compared with the wavelet-fractal modeling. As a result, the main Zn and Pb anomalies are located in the central and southern parts of the Ipack area. The results obtained by the proposed fractal-wavelet modeling are in connection with drilling for Zn and Pb exploration.
Box Guided Convolution for Pedestrian Detection

Occlusions, scale variation and numerous false positives still represent fundamental challenges in pedestrian detection. Intuitively, different sizes of receptive fields and more attention to the visible parts are required for detecting pedestrians with various scales and occlusion levels, respectively. However, these challenges have not been addressed well by existing pedestrian detectors. This paper presents a novel convolutional network, denoted as box guided convolution network (BGCNet), to tackle these challenges simultaneously in a unified framework. In particular, we proposed a box guided convolution (BGC) that can dynamically adjust the sizes of convolution kernels guided by the predicted bounding boxes. In this way, BGCNet provides position-aware receptive fields to address the challenge of large variations of scales. In addition, for the issue of heavy occlusion, the kernel parameters of BGC are spatially localized around the salient and mostly visible key points of a pedestrian, such as the head and foot, to effectively capture high-level semantic features to help detection. Furthermore, a local maximum (LM) loss is introduced to depress false positives and highlight true positives by forcing positives, rather than negatives, as local maximums, without any additional inference burden. We evaluate BGCNet on popular pedestrian detection benchmarks, and achieve the state-of-the-art results, with the significant performance improvement on heavily occluded and small-scale pedestrians.
Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes

Temporal event sequence alignment has been used in many domains to visualize nuanced changes and interactions overtime. Existing approaches align one or two sentinel events. Overview tasks require examining all alignments of interest using interaction and time or juxtaposition of many visualizations. Furthermore, any event attribute overviews are not closely tied to sequence visualizations. We present SEQUENCE BRAIDING, a novel overview visualization for temporal event sequences and attributes using a layered directed acyclic network. SEQUENCE BRAIDING visually aligns many temporal events and attribute groups simultaneously and supports arbitrary ordering, absence, and duplication of events. In a controlled experiment we compare SEQUENCE BRAIDING and IDMVis on user task completion time, correctness, error, and confidence. Our results provide good evidence that users of SEQUENCE BRAIDING can understand high-level patterns and trends faster and with similar error. A full version of this paper with all appendices; the evaluation stimuli, data, and analysis code; and source code are available at osf.io/mq2wt.
CRNet: A Center-aware Representation for Detecting Text of Arbitrary Shapes

Existing scene text detection methods achieve state-of-the-art performance by designing elaborate anchors or complex post-processing. Nonetheless, most methods still face the dilemma of detecting adjacent texts as one instance and long text with large character spacing as multiple fragments. To tackle these problems, we propose an anchor-free scene text detector leveraging Center-aware Representation to achieve accurate arbitrary-shaped scene text detection namely CRNet. Firstly, we propose a center-aware location algorithm to explicitly learn center regions and center points of text instances, which is able to separate adjacent text instances effectively. Then, a multi-scale context extraction module capable of extracting local context, long-range dependencies and global context adaptively is designed to effectively perceive long text with large character spacing. Finally, a low-level features enhancement block is introduced to enhance the geometric information of text. Extensive experiments conducted on several benchmarks including SCUT-CTW1500, Total-Text, ICDAR2015, ICDAR2017 MLT, and MSRA-TD500 demonstrate the effectiveness of our method. Specifically, without any anchor and complicated post-processing, our CRNet achieves 84.2% and 85.1% on CTW1500 and MSRA-TD500 in F-measure, outperforming all state-of-the-art anchor-based and anchor-free methods.
Black Re-ID

Person re-identification (Re-ID) aims at retrieving an input person image from a set of images captured by multiple cameras. Although recent Re-ID methods have made great success, most of them extract features in terms of the attributes of clothing (e.g., color, texture). However, it is common for people to wear black clothes or be captured by surveillance systems in low light illumination, in which cases the attributes of the clothing are severely missing. We call this problem the Black Re-ID problem. To solve this problem, rather than relying on the clothing information, we propose to exploit head-shoulder features to assist person Re-ID. The head-shoulder adaptive attention network (HAA) is proposed to learn the head-shoulder feature and an innovative ensemble method is designed to enhance the generalization of our model. Given the input person image, the ensemble method would focus on the head-shoulder feature by assigning a larger weight if the individual insides the image is in black clothing. Due to the lack of a suitable benchmark dataset for studying the Black Re-ID problem, we also contribute the first Black-reID dataset, which contains 1274 identities in training set. Extensive evaluations on the Black-reID, Market1501 and DukeMTMC-reID datasets show that our model achieves the best result compared with the state-of-the-art Re-ID methods on both Black and conventional Re-ID problems. Furthermore, our method is also proved to be effective in dealing with person Re-ID in similar clothing. Our code and dataset are avaliable on https://github.com/xbq1994/.
Weakly-Supervised Image Hashing through Masked Visual-Semantic Graph-based Reasoning

With the popularization of social websites, many methods have been proposed to explore the noisy tags for weakly-supervised image hashing.The main challenge lies in learning appropriate and sufficient information from those noisy tags. To address this issue, this work proposes a novel Masked visual-semantic Graph-based Reasoning Network, termed as MGRN, to learn joint visual-semantic representations for image hashing. Specifically, for each image, MGRN constructs a relation graph to capture the interactions among its associated tags and performs reasoning with Graph Attention Networks (GAT). MGRN randomly masks out one tag and then make GAT to predict this masked tag. This forces the GAT model to capture the dependence between the image and its associated tags, which can well address the problem of noisy tags. Thus it can capture key tags and visual structures from images to learn well-aligned visual-semantic representations. Finally, the auto-encoders is leveraged to learn hash codes that can preserve the local structure of the joint space. Meanwhile, the joint visual-semantic representations are reconstructed from those hash codes by using a decoder. Experimental results on two widely-used benchmark datasets demonstrate the superiority of the proposed method for image retrieval compared with several state-of-the-art methods.
Recognition of Pashto Handwritten Characters Based on Deep Learning

Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto's hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, "Poha", for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.