Reviewerly/exHarmony huggingface dataset:
Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing

kNN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain kNN search (CD-kNN) in multidomain space. Given a query location q in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD-kNN finds top-k data points with the minimum distance to q. This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.
Dynamic Gated Graph Neural Networks for Scene Graph Generation

We describe a new deep generative architecture, called Dynamic Gated Graph Neural Networks (D-GGNN), for extracting a scene graph for an image, given a set of bounding-box proposals. A scene graph is a visually-grounded digraph for an image, where the nodes represent the objects and the edges show the relationships between them. Unlike the recently proposed Gated Graph Neural Networks (GGNN), the D-GGNN can be applied to an input image when only partial relationship information, or none at all, is known. In each training episode, the D-GGNN sequentially builds a candidate scene graph for a given training input image and labels additional nodes and edges of the graph. The scene graph is built using a deep reinforcement learning framework: states are partial graphs, encoded using a GGNN, actions choose labels for node and edges, and rewards measure the match between the ground-truth annotations in the data and the labels assigned at a point in the search. Our experimental results outperform the state-of-the-art results for scene graph generation task on the Visual Genome dataset.
Human Activity Recognition Based on Convolutional Neural Network

It is increasingly essential to monitor clinical signs and physical activities of elderly, looking for early warning signs or to recognize abnormal situations, such as a fall. In recent years, the usage of wearable sensors has increased significantly. Data from wearable devices can be used to recognize human movement patterns while performing various activities. Accelerometers have been widely used in human activity recognition systems, however, instead of traditional techniques used for feature extraction, the scientific community is currently developing classifiers based on deep learning techniques, seeking better performance and lower computational cost. Convolutional neural networks (CNN) are the main deep learning technique used in this context. These networks adjust filter coefficients that are applied to small regions of the data, extracting local patterns and their variations. This paper presents a human activity recognition system based on convolutional neural networks to classify six activities—walking, running, walking upstairs, walking downstairs, standing and sitting—from accelerometer data. Results demonstrate the ability of the proposed CNN-based model to obtain a state-of-art performance, with accuracy of 94.89% and precision of 95.78% for the best configuration.
An Effective Clustering Method over CF+ Tree Using Multiple Range Queries

Many existing clustering methods usually compute clusters from the reduced data sets obtained by summarizing the original very large data sets. BIRCH is a popular summary-based clustering method that first builds a CF tree, and then performs a global clustering using the leaf entries of the tree. However, to the best of our knowledge, no prior studies have proposed a global clustering method that uses the structure of a CF tree. Therefore, we propose a novel global clustering method ERC (effective multiple range queries-based clustering), which takes advantage of the structure of a CF tree. We further propose a CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> tree, which optimizes the node split scheme used in the CF tree. As a result, the CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -ERC (CF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> tree-based ERC) method effectively computes clusters over large data sets. Furthermore, it does not require a predefined number of clusters to compute the clusters. We present in-depth theoretical and experimental analyses of our method. Experimental results on very large synthetic data sets demonstrate that the proposed approach is effective in terms of cluster quality and robustness and is significantly faster than existing clustering methods. In addition, we apply our clustering method to real data sets and achieve promising results.
Time-Aware Knowledge Graphs for Decision Making in the Building Industry

The phenomenal progress in the development of the Internet of Things (IoT) has already had tremendous impact on almost all industrial sectors and, finally, on our everyday life. The ongoing total digital transformation leads to entirely new time-aware services and creates new and more pro-active business opportunities. Knowledge graphs are becoming more and more popular in different domains, and also for the integration of sensor networks. In this project we make knowledge graphs time-aware through a set of general temporal properties relevant for the integration of sensing networks. Time-aware knowledge graphs enable us to do time series analysis, find temporal dependencies between events, and implement time-aware applications. The requirements for the temporal properties derive from a use case of residential real estate, with the aim to enable the occupants to interact with their houses.
How Does Grooming Fit into Social Engineering?

In this research, we propose to formallyZambrano, Patricio include the technique of grooming within the processes of social engineering, validating its insertion with the phases of socialTorres, Jenny engineering designed by Kevin Mitnick. Scientific evidenceFlores, Pamela has shown that grooming is the technique applied by child sexual harassers and research in this field has generated significant contributions in this study. Nevertheless, researchers do not interrelate the contributions generated by grooming and social engineering, which allows us to infer that the studies are isolated and incomplete and must be consolidated with complementary scientific evidence.
Product Phrase Extraction from e-Commerce Pages

Analyzing commercial pages to infer the products or services being offered by a web-based business is a task central to product search, product recommendation, ad placement and other e-commerce tasks. What makes this task challenging is that there are two types of e-commerce product pages. One is the single-product (SP) page where one product is featured primarily and users are able to buy that product or add to cart on the page. The other is the multi-product (MP) page, where users are presented with multiple (often 10-100) choices of products within a same category, often with thumbnail pictures and brief descriptions — users browse through the catalogue until they find a product they want to learn more about, and subsequently purchase the product of their choice on a corresponding SP page. In this paper, we take a two-step approach to identifying product phrases from commercial pages. First we classify whether a commercial web page is a SP or MP page. To that end, we introduce two different image recognition based models to differentiate between these two types of pages. If the page is determined to be SP, we identify the main product featured in that page. We compare the two types of image recognition models in terms of trade-offs between accuracy and latency, and empirically demonstrate the efficacy of our overall approach.
The Effectiveness of Distinctive Information for Cancer Cell Analysis Through Big Data

In the healthcare system, a huge volume of multi-structured patient data is generated from in-hospital clinical examinations, wearable body sensors, and doctor memos. These data play a deterministic role in finding the patient's cause of disease and corresponding cure. However, there are many challenges underlying healthcare system such as integration of different data format, appropriate selection of healthcare parameters and disease prediction. Besides these, concern whether this data is equally significant for decision making or not exists. To address these problems, we introduced the infinite latent feature selection (ILFS) to find highly informative gene from a gene pool and feed it to the classifier. We adopted a hybrid approach for gene classification. The hybrid scheme integrated two (sophisticated) algorithms: speed advantage of extreme learning machine (ELM) and accuracy advantage of sparse representation classifier (SRC). We validated our proposed model with 198 tumor samples of global cancer map (GCM) dataset and divided them into 14 common tumor categories having 11,370 expression level of genes. The performance of this system was measured using five different matrices (i.e., accuracy, sensitivity, specificity, precision, and F-score). This achieved a notable improvement of accuracies in both scenarios in selective (considering only highly expressive genes) and original genes (74.75% and 81.82% respectively).
An Efficient Surveillance Video Coding Scheme for Static Camera Based Captured Video Data

Now-a-days video surveillance system is highly used in different organizations for security purposes. A video surveillance system based on static camera continuously captures video of a particular region. Due to the bulkiness of video data, main challenge is to efficiently store that huge data in a limited storage space. Conventional video compression algorithms are not that much efficient in terms of compression aspect. In this paper, we have proposed an efficient approach for surveillance video coding which is captured through static camera. Background subtraction technique is used to reduce temporal redundancy drastically between frames. An adaptive GoP length technique based on number of objects in a frame is introduced to get better compression of video data. Object based motion estimation technique will further reduce the video size in a conventional way. Our approach is tested on different captured video data and produces better results in terms of effective compression.
Optimized Random Walk with Restart for Recommendation Systems

Many sophisticated recommendation methods have been developed to produce recommendations to the users. Among them, Random Walk with Restart (RWR) is one of the most widely used techniques. However, RWR has a large time complexity of $$O(k(n+m)^3)$$ and memory complexity of $$(O(n+m)^2)$$ . The change reducing the computational complexity is of great practical importance. In this paper, we propose an optimized version of random walk with restart, called the Optimized Random Walk with Restart (ORWR) and conduct theoretical and empirical studies on its performance. Mathematical analysis shows that using this technique the time complexity reduces to $$O(nm^2)$$ and the memory complexity to O(nm). Experiments on three different recommendation problems using real-world datasets confirms the proposed ORWR method improves both time and memory cost of the recommendation.
Deep learning for impulsive noise removal in color digital images

Deep learning has been widely applied in many computer vision tasks due to its impressive capability of automatic feature extraction and classification. Recently, deep neural networks have been used in image denosing, but most of the proposed approaches were designed for Gaussian noise suppression. Therefore, in this paper, we address the problem of impulsive noise reduction in color images using Denoising Convolutional Neural Networks (DnCNN). This network architecture utilizes the concept of deep residual learning and is trained to learn the residual image instead of the directly denoised one. Our preliminary results show that direct application of DnCNN allows to achieve significantly better results than the state-of-the-art filters designed for impulsive noise in color images.
A wearable system for unobtrusive mood detection

This paper provides an overview of a mood detection system using the Fitbit Ionic wearable smart watch. Wearable technologies have become more easily accessible and widely used in daily activities and even work settings. The proposed application uses heart rate and steps to capture mood from the individual. This knowledge can be used for health intervention or workplace improvement. The decision trees used were inaccurate in predicting the user's mood. In future work, we will add additional annotation to increase prediction accuracy.
Privacy Preserving of IP Address through Truncation Method in Network-based Intrusion Detection System

Network-based Intrusion Detection System (IDS) is gaining wide attention from the research community since the past decades. While having a precise classification model in separating the normal and malicious network traffics is still remain as the ultimate goal, the privacy protection for network traffic database cannot be ignore as well. The impetuous ignorance of database privacy will continue to restrain governments, organisations and individuals in releasing the real and ontological network traces. The common solution to tackle this matter is anonymising the database through the statistical approach. Anonymising can be referred to masking, hiding or removing certain sensitive information from the database. Thus, this will be subsequently resulting in information loss. In this paper, a truncation method is explored to preserve the sensitive information of the network traffic database (i.e. IP addresses). The truncated database is then tested with 10 machine learning classifiers from Weka. We tested four different options of IP address truncation against the 6 percent of GureKDDCup dataset.
Architectural Runtime Verification

Analyzing runtime behavior is an important part of developing and verifying software systems. This is especially true for complex component-based systems used in the vehicle industry. Here, locating the actual cause of (mis-)behavior can be time-consuming, because the analysis is usually not performed on the architecture level, where the system has initially been designed. Instead, it often relies on source code debugging or visualizing signals and events. The results must then be correlated to what is expected regarding the architecture. With an ever-growing complexity of the systems, the advent of model-based development, code generators and the distributed nature of the development process, this becomes increasingly difficult. This paper therefore presents Architectural Runtime Verification (ARV), a generic approach to analyze system behavior on architecture level using the principles of Runtime Verification. It relies on the architecture description and on the runtime information that is collected in simulation-based tests. This allows an analyst to easily verify or refute hypotheses about system behavior regarding the interaction of components, without the need to inspect the source code. We have instantiated ARV as a framework that allows a client to make queries about architectural elements using a timed LTL-based constraint language. From this, ARV generates a Runtime Verification monitor and applies it to runtime data stored in a database. We demonstrate the applicability of this approach with a running example from the automotive industry.
Lossless compression codec of aurora spectral data using hybrid spatial-spectral decorrelation with outlier recognition

Aurora spectral data, the particular hyperspectral data of auroral spectra, have an irreplaceable research value in bridging the gap between solar activity and terrestrial evolution. Their requirement for high-volume storage and real-time transmission had been a great challenge until we proposed a CPU-paralleled and online-biprediction-based method. As it is no longer applicable because of the recent spectrograph reassembling, this paper presents a replacement strategy combines the unidirectional predictor with the entropy coder of the other dimension, as well as with using smoothing and outlier recognition. The hybrid encoders of Spat-SPCC and Spec-SPCC are developed distinguished by their respective prediction direction. Spat-SPCC tuned on one-day trial data is used for its better capability for compression, in the further comparison with various classical algorithms, it achieves the top-ranking compression performance and has the average processing time of 1 s per file so that its availability for the practical applications is validated.
Image Classification Using Deep Neural Networks: Transfer Learning and the Handling of Unknown Images

Deep learning is a subset of machine learning that is powerful at recognizing patterns and extensively used for image classification. However, it typically requires a large amount of data and it is computationally expensive for training an application from scratch. ImageNet database has millions of images pertaining to different categories that are acquired by years of hard work. Getting such a database for every application is tough and time consuming. Transfer learning is an alternative to conventional training. Transfer learning results in much faster and easier training of a network. This research set out to evaluate the effect of transfer learning on the performance of a Deep Neural Network (DNN). Pre-trained AlexNet was selected, modified and retrained for 3 image classification applications (gears, connectors and coins) with a modest database. This approach gave 99% classification accuracy using transfer learning. To test the robustness of the network, unknown images were added to one of the classes and the accuracy was reinforced using a probability threshold. This approach succeeded in compensating for the effect of unknowns in the accuracy.
Adapting to environmental dynamics with an artificial circadian system

One of the core challenges of long-term autonomy is the environmental dynamics that agents must interact with. Some of these dynamics are driven by reliable cyclic processes, and thus are predictable. The most dominant of these is the daily solar cycle, which drives both natural phenomena like weather, as well as the activity of animals and humans. Circadian clocks are a widespread solution in nature to help organisms adapt to these dynamics, and demonstrate that many organisms benefit from maintaining simple models of their environments and how they change. Drawing inspiration from circadian systems, this work models relevant environmental states as times series, allowing for forecasts of the state to be generated without any knowledge of the underlying physics. These forecasts are treated as special percepts in a behavior-based architecture; providing estimates of the future state rather than measurements of the current state. They are incorporated into an ethologically based action-selection mechanism, where they influence the activation levels of behaviors. The approach was validated on a simulated agricultural task: a solar-powered agent monitoring pest populations. By using the artificial circadian system to leverage the forecasted state, the agent was able to improve performance and energy management.
Identification of scope creep factors and their impact on software project success

Scope creep refers to the variation or growth of project scope or pressure to deliver more than what was approved. It is considered as a significant risk in software development projects. The incapability to manage scope creep is the main cause for 80% of failing software projects. The motivation behind this research is to identify the various factors of scope creep that can threat the software project's success. Due to exploratory nature of this study the Systematic Literature Review was conducted to identify the scope creep factors and their impact on project success. A total of 13 factors were identified based on our criteria of most commonly used and critical factors in terms of our study.
United We Stand

This work proposes a scheme to detect, isolate and mitigate malicious disruption of electro-mechanical processes in legacy PLCs where each PLC works as a finite state machine (FSM) and goes through predefined states depending on the control flow of the programs and input-output mechanism. The scheme generates a group-signature for a particular state combining the signature shares from each of these PLCs using (k,l)-threshold signature scheme. If some of them are affected by the malicious code, signature can be verified by k out of l uncorrupted PLCs and can be used to detect the corrupted PLCs and the compromised state. We use OpenPLC software to simulate Legacy PLC system on Raspberry Pi and show I/O pin configuration attack on digital and pulse width modulation (PWM) pins. We describe the protocol using a small prototype of five instances of legacy PLCs simultaneously running on OpenPLC software. We show that when our proposed protocol is deployed, the aforementioned attacks get successfully detected and the controller takes corrective measures. This work has been developed as a part of the problem statement given in the Cyber Security Awareness Week-2017 competition.
Blind Quality Assessment for Slice of Microtomographic Image

The paper considers a new algorithm for blind quality assessment of a slice of X-ray microtomographic image. We selected the following factors impacting on micro-CT image quality with respect to Digital Rock technology: smoothness, sharpness, contrast, absence of high-density regions and ring artifacts. We propose algorithms for estimation of partial quality measures for named factors inside Region-of-Interest, that is in area associated with a sample of rock or granular material. Total quality metrics is calculated as a product of these partial measures. Our method for quality assessment provides reasonable outcomes for synthetic and real slices of micro-CT images. We collected experts' judgments about quality of slices. Proposed solution has a high correlation with scores of experts and outperforms existing blind quality metrics. An application of developed method to all slices allows to obtain quality estimation for 3D micro-CT image.
A Technique About Neural Network for Passageway Detection

This article brings a proposal to implement a passageway detector for a drone, like windows, doors, holes etc. In this case, it was applied on the model Tello of Ryze Tech. The modeling technique uses a Neural Convolutional Deep Learning Network, with a pre-supervised training. This training is done with three image classes: unobstructed path, obstructed path, and the passageway inside of the path, to a specific environment, such as a school or a hospital. After the detection of a way defined by the user, the technique uses image filters to find a polygon through the window and compare the returned data with the values stored in the neural network dataset. To find the best parameters to identify the passage in the processing, the algorithm makes an adjustment through parameters interpolation that allows the drone to perceive its crossing for many cases of environmental variations. The Network model used is the SSD implemented on Google’s TensorFlow framework and for the image processing, it uses filters and functions from OpenCV library, where both codes are implemented in Python programming language.