Reviewerly/exHarmony huggingface dataset:
Enhancing Database Courses Through the EDNA Project: A Preliminary Framework for the Extraction of Diverse Datasets and Analysis

To enhance and grow the database certificate program and enhance database courses offered by the Computer Science/Information Technology department (CSIT) at the Community College of Baltimore County (CCBC), we are in the process of developing the EDNA Project. The EDNA Project- Extraction of Diverse Datasets and Analysis will be an educational database and project set encompassing several large real-world datasets that will be accessible to students in the database courses. This project was created out of the need for more elaborate examples and hands-on experience with database concepts. This repository will help serve to teach database concepts, data analytics, and lead to topics related to big data to students in these courses and certificates programs. In addition, this paper describes collaboration with the University of Baltimore and the beginnings of Project RED. This paper discusses our current work in developing this project.
Comparison of Deep Feature Classification and Fine Tuning for Breast Cancer Histopathology Image Classification

Convolutional Neural Networks (ConvNets) are increasingly being used for medical image diagnostic applications. In this paper, we compare two transfer learning approaches - Deep Feature classification and Fine-tuning ConvNets for Diagnosing Breast Cancer malignancy. BreaKHis dataset is used to benchmark our results with ResNet-50, InceptionV2 and DenseNet-169 pre-trained models. Deep feature classification accuracy ranges from 81% to 95% using Logistic Regression, LightGBM and Random Forest classifiers. Fine-tuned DenseNet-169 model accuracy outperformed all other classification models with 99.25 ± 0.4%.
Comparison-space selection to achieve efficient tracklet-to-object association

A major challenge when maintaining a space object catalog is the proper association of new measurements to already cataloged objects. Optical observations are typically associated by comparing the modeled observation to the measured one. The modeled observation is generated from cataloged object states by propagating them to the epoch of observation and transforming them from state space, e.g. orbital elements, to the observation space, e.g. right ascension and declination angles. In addition to propagating the states, their propagated uncertainty distribution is transformed to observation space as well. Statistical distance metrics, such as the Mahalanobis distance, are then evaluated to test whether the observation originated from the cataloged object or not. These distance measures often assume that the uncertainty can be represented with a normal distribution. Assuming that the catalog state uncertainty is properly represented by a normal distribution, it can still loose this property during the propagation in time and the transformation to observation space. The uncertainty of the catalog state is typically much larger than the one from new measurements (only a few arc seconds for optical telescopes) and is therefore more affected by transformation distortions. It is therefore beneficial to perform the comparison in a space advantageous for the state representation. This study will present a projection-based transformation of tracklet information into a favorable frame around the cataloged object state. The effect of the comparison-space selection on cataloguing performance is assessed, i.e. it is systematically tested if it is beneficial to directly compare angles and angular rates, or to compare in the newly proposed projected frame.
Identifying solar panel defects with a CNN

With the development of green energy and its means of production, more and more companies chose to build solar panel farms. However, those technologies remain relatively expensive to maintain, and prone to damages (due to natural hazards, or internal defects). Since any kind of damage on a panel cell drastically reduce a panel's efficiency, solar panels must be kept under tight supervision. With more solar panel that must be checked for damage relatively often, a cheap, accurate and fast way to find those damages must be settled. Some processes have been developed to identify panels in a true color image [1], and various ways to identify defective panels exist through image processing [2], [3] or other ways [4]. On another hand, handmade features suggest the input data obeys to some specific conditions (color, illumination), and small changes can impact accuracy. CNN [5], however, can be trained to face such changes with the appropriate dataset, and therefore be more resilient . They represent a reliable solution for identification and classification of complex features [2], [6], and can be improved more easily than handmade feature detection. In this paper is detailed the pipeline of such process, combining the straightforward approach of handmade feature detection for preprocessing to reduce the input’s complexity, with the resilience of neural networks for the final identification. Detailed explanations for the different steps of the process are given: Dataset acquisition, preprocessing, and finally classification. The various leads that were followed to improve the quality of the results are also given, before comparing results with a previously used handmade detection process, and finally proposing a web user interface to exploit this process, and enrich its dataset.
An Appraisal on Assortment of Optimization Factors in Cloud Computing Environment

Computers play essential roles in variety of fields to make their timely work efficient, with the help of high-performance computing architecture like Distributed Computing, Grid architecture, Cluster and Cloud technology. Cloud Computing is experiencing rapid development in both Academic and Industry. A Cloud Environment is a computing model with diverse capabilities which enables users with appropriate and need based access to reciprocated pool of computing resources like networking facility, storage servers, application servers and other services. Essential part of Cloud environment is proficient allocation of resources to user with suitable task. The Cloud Computing Environment has anthology of uncountable nodes, variety of resources, with potential challenges in task scheduling and execution. This paper presents a systematic study on Cloud Computing Environment with an assortment of optimization factors and its functionality in Cloud Computing.
The English disease in Finnish compound processing: Backward transfer effects in Finnish–English bilinguals

Abstract Most English compounds are spaced compounds, whereas spelling regulations prescribe Finnish compounds to be written in a concatenated format. However, as in English, Finnish compounds are commonly spaced nowadays (e.g., piha juhla ‘garden party’), a phenomenon that we labeled the ‘English disease’. In this eye movement study with Finnish–English bilinguals we investigate whether the reading of a concatenated or illegally spaced Finnish compound is affected by the spelling of an English translation equivalent (ETE). We found that spaced Finnish compounds were read slower than their concatenated counterparts, but this effect was attenuated when ETEs were thought to be spaced. Similarly, concatenated Finnish compounds were read faster when their ETEs were also concatenated. These backward transfer effects are in line with studies that show that processing behavior in L1 is affected by a strong concurrent L2, even when the L1 is the native language as well as the dominant community language.
Webshell Detection Model Based on Deep Learning

Aiming at the problem that the existing Webshell detection method relies on manual extraction of features, low automation and easy to bypass, a Webshell detection algorithm based on deep learning is proposed. Some methods to escape the detection of deep learning model and the solution is proposed. Through the noise reduction and malicious payload reduction of Webshell and normal web pages, the features are automatically extracted in the deep learning model. The experimental results show that the recognition accuracy of the model is 99.56%. The detection range is wide and can cope with many kinds of bypass strategies.
3D Object Trajectory Reconstruction using Stereo Matching and Instance Flow based Multiple Object Tracking

This paper presents a method to reconstruct three-dimensional object motion trajectories in stereo video sequences. We apply stereo matching to each image pair of a stereo sequence to compute corresponding binocular disparities. By combining instance-aware semantic segmentation techniques and optical flow cues, we track two-dimensional object shapes on pixel level. This allows us to determine for each frame pair object specific disparities and corresponding object points. By applying Structure from Motion (SfM) we compute camera poses with respect to background structures. We embed the vehicle trajectories into the environment reconstruction by combining the object point cloud of each image pair with corresponding camera poses contained in the background SfM reconstruction. We show qualitative results on the KITTI and CityScapes dataset and compare our method quantitatively with previously published monocular approaches on synthetic data of vehicles in an urban environment. We achieve an average trajectory error of 0.11 meter.
Reflections on Elements of a Game Design Model Applied to Inclusive Digital Games

Cultural relevance of digital games characterizes them as social objects. In such a way digital games are created based on our culture and society, at the same time they contributed to shaping our world. Thus, digital games need to be accessible to all members of society, including people with disabilities. Based on those points, we discuss how a game development tool, such as game design models, can be articulated with social inclusion principles. This research proposes a series of critical reflections about how digital game elements can be used and thought also regarding people with disabilities. In this way, it is possible to expand game accessibility, and digital game development proceeds towards inclusive digital games.
Distributed Gaussian Process: New Paradigm and Application to Wireless Traffic Prediction

Distributed Gaussian Process (GP) is a scalable Bayesian method that is promising for handling big data. Our contribution in applying GP for traffic prediction is two-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for distributed hyper-parameter optimization in the training phase, where the ADMM training framework well balances local estimation and information consensus in a principled way. Second, in the prediction phase, we fuse local predictions obtained from distributed computing units via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, the cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP prediction properties. Experimental results show that our proposed distributed GP model can outperform the state-of-the-art distributed GP models considerably, in terms of wireless traffic prediction performance.
Service-Oriented Control-Command Components for Designing Complex Systems

An important task in designing control-command systems is defining components for two essential parts of the system: command and monitoring. Instead of developing a monolithic executable, designers use reusable blocs, named components, which are saved in a library. Services Oriented Architecture (SOA) was introduced in the design of control-command systems to improve flexibility and reusability [1]. However, this approach does not consider the composition of the services [2], disregarding this important characteristic in the design of control-command systems. In some industrial areas such as shipbuilding, the component-based approach is typically used since it enables better legibility for the applications; it uses a modular approach based on the system architecture recorded on the Piping and Instrumentation Diagram (P&ID) [3]. Each software component is associated with a unique type of equipment. This approach enables to produce components highly optimized to their functions. The counterpart is that services integrated in each component cannot be reused in designing another component. In fact, part of the component services can also be present in another component (Fig. 1). Including services in component does not optimize functions reusability. In this paper, we propose an approach that facilitate and improve the design of quality components whilst complying with specifications and timelines in a more efficient way; it also reduces efforts required to redesign services provided by these components.
Privacy-Preserving Social Media Forensic Analysis for Preventive Policing of Online Activities

Social media is extensively used nowadays and is gaining popularity among the users with the increasing growth in the network capacity, connectivity, and speed. Moreover, affordable prices of data plans, especially mobile data packages, have considerably increased the use of multimedia by different users. This includes terrorists who use social media platforms to promote their ideology and intimidate their adversaries. It is therefore very important to develop automated solutions to semantically analyse given multimedia contents to assist law enforcement agencies in the preventive policing of online activities. A major challenge for the social media forensic analysis is to preserve the privacy of citizens who use online social networking platforms. This paper presents results of European H2020 project RED-Alert that aims to enable secure and privacy preserving data processing; hence the malicious content and the corresponding personality can be tracked while the privacy of innocent citizens can be preserved. We have mined seven social media channels for content and providing support for ten languages for analysis. Our proposed solution is designed to ensure security and policing of online contents by detecting terrorist material. We have used speech recognition, face and object detection besides audio event detection to extract information from multimedia files. We have applied anonymization techniques to ensure the privacy of citizens using social media. We have discussed the challenges and prospects of this work especially the need of using digital forensic techniques while respecting European and national data protection laws notably GDPR.
Visual concept conjunction learning with recurrent neural networks

Learning the conjunction of multiple visual concepts shows practical significance in various real world applications (e.g. multi-attribute image retrieval and visual relationship detection). In this paper, we propose Concept Conjunction Recurrent Neural Network (C2RNN) to tackle this problem. With our model, visual concepts involved in a conjunction are mapped into the hidden units and combined in a recurrent way to generate the representation of the concept conjunction, which is then used to compute a concept conjunction classifier as the output. We also present an order invariant version of the proposed method based on attention mechanism to learn the tasks without pre-defined concept order. To tackle concept conjunction learning from multiple semantic domains, we introduce a multiplicative framework to learn the joint representation. Experimental results on multi-attribute image retrieval and visual relationship detection show that our method achieves significantly better performance than other related methods on various datasets.
GEC-HR: Gamification Exercise Companion for Home Robot with IoT

BACKGRUND: A growing number of home robots were developed by many robot industries (i.e., iRobot, HACHI) In modern society, more people are used to utilize wearable device to transmit amount of exercise to their APP on smart phone by Bluetooth transmission; nevertheless, the research tends to connect smart watch to home robot to provide a robotic system for exercise companion service and a game of progression. OBJECTIVE: The research is to propose and develop a ‘gamification exercise companion for home robot; GEC-HR’ that can accompany people exercise or physical fitness in front of the home robot at home anytime and anywhere. GEC-HR can synchronize to estimate attainment rate of exercise by robotic movement recognition technology when they exercise. Next, the users can play an interactive game afterwards, which can enhance interesting for the users. METHODOLOGY: GEC-HR will be implemented by Android APPs for ASUS Zenbo. It needs a built-in camera to detect and photograph user’s motion to detect by Zenbo SDK. In addition, a smart watch will transmit amount of exercise (i.e., heart rate, exercise time) to Google cloud platform for data acquisition of home robot. GEC-HR can detect, acquire, and analyze user’s exercise data by IoT and recognition technology. The users watch the exercise videos robot recommended and finish its exercise or physical fitness along with the video play, and then user obtains accomplishment points (i.e., flower diagram) based on movement recognition (Robotic API) and smart watch (Wear OS) (Fig. 1). CONCLUSION: This is a cross-disciplinary research (Sports science, AI) that is to propose and develop a robotic system integrating IoT (wearable device) A recommendation system will be implemented into the GEC-HR system to provide an on-demand exercise service and a reward game afterwards. For the verification, the amount of exercise can be further analyzed to verify the performance and benefits of GEC-HR.
Research on Construction Method of Chinese Domain Ontology Based on Relation Extraction

Abstract Domain ontology plays an increasingly important role in the information extraction process and has become a useful tool for knowledge acquisition, knowledge sharing, and modeling. However, the existing domain ontology construction methods are mainly based on manual mode, lacking systematic and engineering methods. This paper proposes a construction method of Chinese domain ontology based on relation extraction, which fully considers the automatic extraction of semantic hierarchical relations, and guarantees the semantic richness and accuracy of relational triples. The construction method of Chinese domain ontology based on relation extraction is applied to the field of food safety events. The constructed ontology has the advantages of reusability and scalability, which proves that the method is logical and operability.
Threshold-Driven Class Decomposition

Software evolves with modifications which lead to a deviation from the single responsibility principle. Continuous changes deteriorate the quality of software, thereby decreasing cohesion and increasing coupling. There has been much work on improving class cohesion by decomposing a large class of multiple responsibilities into smaller classes with a single responsibility. The decomposition process is iterative until it reaches to the point where classes have a single-responsibility. However, it is hard to determine when to terminate the process. Early termination results in premature decomposition, while late termination leads to over decomposition. In this work, we present a threshold-driven approach to determine a termination point in the decomposition process developed in our previous work. We demonstrate the approach using a case example.
A Fistful of Votes: Understanding the Voting System in Social Question and Answer Websites

Recently, there is an increasing trend that people start sharing their comments in online question and answer (QA) websites. For such websites, the ranking of answers is usually determined based on several factors like the received upvotes and downvotes, the publishing time, and the reputation of users, such that a highly ranked answer has much more chances to be propagated over the Internet. Due to the popularity of these QA websites, some public relationship companies try to cheat users by promoting or blocking certain answers. In this paper, we take Quora and Zhihu as two case studies to understand the impact of follower numbers, the publishing time, the upvotes and downvotes on the ranking of answers, and to investigate how these websites are vulnerable to voting spammers. Meanwhile, we propose a scheme to estimate the list of downvotes, which is usually hidden behind such websites.
Changes in Eye Movements and Body Sway While Viewing Stereoscopic Movies Under Controlled Consciousness

In our previous study, we found that it is possible to have an effect of change in the condition of consciousness (allocation of consciousness) on visually evoked postural responses (VEPRs) and subjective symptoms of visually induced motion sickness (VIMS). Thus, in this study, we verified the effect of controlling consciousness on body sway and eye movements while participants viewed stereoscopic movies. Participants watched a three-dimensional movie consisting of several colored balls that moved sideways sinusoidally at 0.25 Hz for 180 s after pre-instruction. Pre-instructions included "uncontrolled consciousness," "keep a static, upright posture," and "sway body in the same/opposite direction." This study recorded both center of pressure, as body sway, and electrooculography (EOG) data, as eye movements. Recorded EOG data were converted to eye movement velocity. The results clearly showed that (1) the influence of the pre-instruction appeared to be much stronger than that of the VEPRs, (2) the pre-instruction changed the frequency of the saccade, or interquartile range of eye movement, velocity histogram according to each task as measured by changes in retinal slip velocity, and (3) the condition of consciousness that controlled body motion changed the participants' postural instability and eye movement results. Thus, after considering many theories for the origin of VIMS, we concluded that the condition of consciousness controlled VIMS.
Matching a Trope Ontology to the Basic Formal Ontology

Applied ontology, at the foundational level, is as much philosophy as engineering and as such provides a different aspect of contemporary natural philosophy. A prominent foundational ontology in this field is the Basic Formal Ontology (BFO). It is important for lesser known ontologies, like the trope ontology of interest here, to match to BFO because BFO acts like the glue between many disparate ontologies. Moreover, such matchings provide philosophical insight into ontologies. As such, the core research question here is how we can match a trope ontology to BFO (which is based on universals) and what insights such a matching provides for foundational ontology. This article provides a logical matching, starting with BFO’s top entities (continuants and occurrences) and identifies key ontological issues that arise, such as whether universals and mereological sums are equivalent. This article concludes with general observations about the matching, including that matching to universals is generally straightforward, but not so much the matching between relations. In particular, the treatment of occurrences as causal chains is different in the trope ontology, compared to BFO’s use of time arguments.