Reviewerly/exHarmony huggingface dataset:
Use of TensorFlow and OpenCV to detect vehicles

Streets are becoming overcrowded, especially in larger cities. Traffic monitoring and control is a solution to mitigate this problem, but is essential to have actuated information about vehicles. In this project the aim is to build a traffic monitoring system that detect the movement of vehicles, to observe and to count the different categories. The real-time processing (15-30 fps) of video streams works mainly in daylight. The system consists of three subsystems: image processing, motion detector, control and display. To maximize the detection speed, each subsystem can be processed on a different thread. Image processing is partially performed via OpenCV with a data set previously trained with TensorFlow. The monitoring area will be marked with a polygon. It is possible to filter sidewalks and the opposite traffic direction, so the system will avoid processing in those areas.
Preliminary results of a proposed CNN framework for use in motorway applicable detection systems

This paper presents the preliminary results of the first stage of a proposed framework of existing image detection convolutional algorithms, and sets the image portable detector requirements, focusing on portable detection devices. Such devices perform image segmentation and transmit monitoring meta-data. Four pre-trained CNNs have been used to classify images of vehicles passing through toll posts, and the results are presented and compared. At subsequent stages of the presented framework certain types of vehicles will be detected and categorized, based on the type of cargo they carry. The proposed framework is wrapped by an appropriate system architecture which interacts with incident response or facility management systems, and tries to achieve precise speed/memory/accuracy balance for each targeted detection application accordingly. To this end, various ways are investigated and evaluated, to trade accuracy for speed and memory usage in modern mobile convolutional algorithm driven detection systems monitoring specific regions of interest. Such tradeoffs provide increased detection capabilities to incident response systems that interact with such detectors in real-time, or in post-processing with facility management systems for accurate and automated decisions adaptation.
3D Spherical Panoramic Epipolar Line Based on Essential Matrix

A 3D spherical panoramic epipolar line based on essential matrix is proposed in this study to solve the search range to improve calculation efficiency and reliability of the matching.First, this method uses the essential matrix of computer vision principles to establish the epipolar geometry model of spherical panoramic image.Second, it derives the epipolar line mathematical equations and characteristics of spherical panoramas.Third, it calculates statistics on the distribution law of epipolar images.Experimental results show that the feasibility of the method proposed in this study.The proposed method effectively verified the trajectory of the spherical panoramic epipolar line, finished distribution of epipolar line sequence trajectory and decreased the search range of the corresponding points.The findings help lay a solid foundation for the matching and measurement of the panoramic measurement model and the generation of depth maps.
GotFunding: A grant recommendation system based on scientific articles

Abstract Obtaining funding is an important part of becoming a successful scientist. Junior faculty spend a great deal of time finding the right agencies and programs that best match their research profile. But what are the factors that influence the best publication–grant matching? Some universities might employ pre‐award personnel to understand these factors, but not all institutions can afford to hire them. Historical records of publications funded by grants can help us understand the matching process and also help us develop recommendation systems to automate it. In this work, we present GotFunding (Grant recOmmendaTion based on past FUNDING), a recommendation system trained on National Institutes of Healthʼs (NIH) grant–publication records. Our system achieves a high performance (NDCG@1 = 0.945) by casting the problem as learning to rank. By analyzing the features that make predictions effective, our results show that the ranking considers most important (a) the year difference between publication and grant, (b) the amount of information provided in the publication, and (c) the relevance of the publication to the grant. We further discuss the implications and future improvements to this work.
AMADEUS

The proper configuration of systems has become a fundamental factor to avoid cybersecurity risks. Thereby, the analysis of cybersecurity vulnerabilities is a mandatory task, but the number of vulnerabilities and system configurations that can be threatened is extremely high. In this paper, we propose a method that uses software product line techniques to analyse the vulnerable configuration of the systems. We propose a solution, entitled AMADEUS, to enable and support the automatic analysis and testing of cybersecurity vulnerabilities of configuration systems based on feature models. AMADEUS is a holistic solution that is able to automate the analysis of the specific infrastructures in the organisations, the existing vulnerabilities, and the possible configurations extracted from the vulnerability repositories. By using this information, AMADEUS generates automatically the feature models, that are used for reasoning capabilities to extract knowledge, such as to determine attack vectors with certain features. AMADEUS has been validated by demonstrating the capacities of feature models to support the threat scenario, in which a wide variety of vulnerabilities extracted from a real repository are involved. Furthermore, we open the door to new applications where software product line engineering and cybersecurity can be empowered.
Attention Sensing through Multimodal User Modeling in an Augmented Reality Guessing Game

We developed an attention-sensitive system that is capable of playing the children's guessing game "I spy with my litte eye" with a human user. In this game, the user selects an object from a given scene and provides the system with a single-sentence clue about it. For each trial, the system tries to guess the target object. Our approach combines top-down and bottom-up machine learning for object and color detection, automatic speech recognition, natural language processing, a semantic database, eye tracking, and augmented reality. Our evaluation demonstrates performance significantly above chance level, and results for most of the individual machine learning components are encouraging. Participants reported very high levels of satisfaction and curiosity about the system. The collected data shows that our guessing game generates a complex and rich data set. We discuss the capabilities and challenges of our system and its components with respect to multimodal attention sensing.
JECI++: A Modified Joint Knowledge Graph Embedding Model for Concepts and Instances

Concepts and instances are important parts in knowledge graphs, but most knowledge graph embedding models treat them as entities equally, that leads to inaccurate embeddings of concepts and instances. Aiming to solve this problem, we propose a novel knowledge graph embedding model called JECI++ to jointly embed concepts and instances. First, JECI++ simplifies hierarchical concepts based on subClassOf relation and instanceOf relation, then re-links instances to the simplified concepts as new instanceOf triples. Consequently, an instance can be obtained by its neighbor instances and its belonging simplified concepts. Second, circular convolution is utilized to locate an instance in the embedding space, based on neighbor instances and simplified concepts. Finally, simplified concepts and instances are jointly embedded by the embeddings learner with CBOW (Continuous Bag-of-Words) or Skip-Gram strategies. Especially, JECI++ can alleviate the problem of complex relations by incorporating neighbor information of instances. JECI++ is evaluated by link prediction and triple classification on real world datasets. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.
LMFNet: Human Activity Recognition Using Attentive 3-D Residual Network and Multistage Fusion Strategy

Human activity recognition plays a fundamental role in smart home systems and contributes to remote health monitoring for the elderly or disabled. However, the shallow architecture and heavy parameters of the current 3-D convolutional networks (3-D ConvNets) still restrict the recognition efficiency and spatiotemporal representations. For these issues, this article proposes a framework called LMFNet, which is mainly composed of a deep attentive 3-D residual network (A3D ResNet) and a multistage fusion strategy. Specifically, LMFNet changes the information flow of transmission in the C3D network and implements a residual learning method for efficient training. Besides, the two-stream-fused spatiotemporal attention 3-D ConvNets (2S-F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ST</sub> A3DCN) are built based on the A3D ResNet. The experimental results show that the proposed LMFNet can achieve a higher recognition accuracy and satisfactory training efficiency compared with the existing methods.
Privacy-Preserving Publicly Verifiable Databases

Verifiable databases (VDB) enables the data owner to outsource a huge unencrypted database to the powerful but untrusted cloud such that any client could later retrieve the database and check whether the cloud returns valid records or not. To the best of our knowledge, there is no prior work considering privacy. Besides, they assume that the data owner and the client are fully trusted while they may be semi-honest in the real world. To address these problems, we propose a new primitive called privacy-preserving publicly verifiable database (PPVDB), which not only guarantees the integrity of the queried result but also leaks no information. At the end of this protocol, the client can check whether the cloud returns a valid result and learns the queried result but nothing else about the database. Besides, the cloud learns nothing about the database and the query, and the data owner does not know which item that the client has queried. Motivated by the comparison among some strawman solutions, we incorporate verifiable computation for the polynomial with oblivious pseudorandom function to construct a PPVDB scheme, which is the first VDB scheme providing stronger security against the malicious cloud and the semi-honest client and data owner.
Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique (MLRBT)

Genetic algorithms (GAs) are powerful heuristic search techniques that are used successfully to solve problems for many different applications.Seeding the initial population is considered as the first step of the GAs.In this work, a new method is proposed, for the initial population seeding called the Multi Linear Regression Based Technique (MLRBT).That method divides a given large scale TSP problem into smaller sub-problems and the technique works frequently until the sub-problem size is very small, four cities or less.Experiments were carried out using the well-known Travelling Salesman Problem (TSP) instances and they showed promising results in improving the GAs' performance to solve the TSP.
QML Based Community Detection in the realm of Social Network Analysis

Social network analysis (SNA) has been the topic of interest for researchers and analysts for quite a while as social networks have become the indispensable part of the semantic web. SNA has now forked itself into community detection to cater to the needs of business analytics, health monitoring, military, academia, recommendation systems, etc.; thus utilizing and processing huge amount of data. In this regard, quantum computing (QC) has has come in time to augment the classical systems with quantum characteristics ensuring unprecedented data storage and manipulation capabilities. It thus becomes necessary to facilitate effective data utility in SNA by incorporating emerging technologies like QML (Quantum machine learning) in the domain of community detection. The paper aims to present a brief insight into social network analysis and community detection highlighting the limitations prevalent in the current technologies. A systematic analysis of evolution of QML from QC has been presented whilst reviewing and discussing the relevance of QML based community detection algorithms in resolving the issues associated with the current approaches. Lastly, QML has been proposed as a novel approach to harness the power of ML (Machine learning) in quantum empowered systems by illustrating its supremacy to classical ML based community detection algorithms.
What Industry Wants from Requirements Engineers in China?

[Background] Publications on the professional occupation of Requirements Engineering (RE) reported on market demands for both RE and non-RE qualifications and indicated the state-of-the-practice of RE roles in industry. However, prior research was not from the perspective of the RE area in the Software Engineering Body of Knowledge (SWEBOK). Nor, they shed light on the industry needs of RE professionals in China. [Aims] This paper focused on RE-specific tasks and skills sought after in China, from the perspective of RE activities elaborated in SWEBOK. [Method] Using an empirical qualitative research method, we selected and analyzed 535 job ads from China's two largest job portals. Job titles and descriptions of these ads were analyzed to uncover RErelevant responsibilities in the categories of RE activities in SWEBOK as well as RE skills. [Results] We identified the qualifications, experience and skills demanded by Chinese employers. Specifically, we reported 23 RE tasks demanded in the 535 job ads, from the perspective of SWEBOK RE activities. [Conclusion] Our findings reveal that in China's job market, 'requirements engineer' is explicitly used as a title of job ads. Plus, around 78% of the selected job positions want the employees to perform tasks in requirements elicitation. Editing requirements specification is the most in-demand task in RE activities. In addition, employers placed more emphasis on both RE-specific and broad industry experience.
Dynamic Strategy Planning of Humanoid Robots Using Glowworm-Based Optimization

SUMMARY In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions.
Active metric learning for supervised classification

Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. To this end, learning optimal distance functions from data, known as metric learning, aims to facilitate supervised classification, particularly in high-dimensional spaces where visualization is challenging or infeasible. In particular, the Mahalanobis metric is the default choice due to simplicity and interpretability as a transformation of the simple Euclidean metric using a combination of rotation and scaling. In this work, we present several novel contributions to metric learning, both by way of formulation as well as solution methods. Our approach is motivated by agglomerative clustering with certain novel modifications that enable natural interpretation of the user-defined classes as clusters with the optimal metric. Our approach generalizes and improves upon leading methods by removing reliance on pre-designated “target neighbors,” “triplets,” and “similarity pairs.” Starting with the definition of a generalized metric that has the Mahalanobis metric as the second order term, we propose an objective function for metric selection that does not aim to isolate classes from each other like most previous work, but tries to distort the space minimally by aggregating co-class members into local clusters. Further, we formulate the problem as a mixed-integer optimization that can be solved efficiently for small/medium datasets and approximated for larger datasets. Another salient feature of our method is that it facilitates active learning by recommending precise regions to sample using the optimal metric to improve classification performance. These regions are indicated by boundary and outlier points of the dataset as defined by the metric. This targeted acquisition can significantly reduce computation and data acquisition by ensuring training data completeness, representativeness, and economy, which could also provide advantages in training data selection for other established methods like Deep Learning and Random Forests. We demonstrate classification and computational performance of our approach through several simple and intuitive examples, followed by results on real image and benchmark datasets.
Multi-source Data Multi-task Learning for Profiling Players in Online Games

Profiling game players, especially potential churn and payment prediction, is of paramount importance for online games to improve the product design and the revenue. However, current solutions view either churn or payment prediction as an independent task and most of the previous attempts only depend on the single data source, i.e., the tabular portrait data. Based on the data of two real-world online games, we conduct extensive data analysis. On the one hand, there exists a significant correlation between the player churn and payment. On the other hand, heterogeneous multi-source data, including player portrait, behavior sequence, and social network, can complement each other for a better understanding of each player. To this end, we propose a novel Multi-source Data Multi-task Learning approach, named MSDMT, to capture the multi-source implicit information and predict the churn and payment of each player simultaneously in a multi-task learning fashion. Comprehensive experiments on two real-world datasets validate the effectiveness and rationality of our proposed method, which yields significant improvements against other baseline approaches.
Using Neural Networks Application for the Font Recognition Task Solution

The purpose of the paper is research and development of font visual classification system, that will allow users to work with fonts more effectively, specifically with the use of the required font fetching on pre-screening, employment of the wide range of filters and font management tools application.There were reviewed basic fonts classification approaches with the definition of pivot advantages and disadvantages. After assessing these approaches, it was decided to deploy neural network with the font symbols images as an input.As a result, neural networks driven computer fonts classification system, that allows to classify fonts by slope, basic style and weight according to their graphical representation, was developed and integrated into the font organizer. High classification accuracy of the developed neural network model was confirmed by control tests. Percentage of correctly classified fonts during the system testing reaches 91% in determining weight, 92% in determining basic style, 96% in determining slope. The developed system can be applied to tasks of fonts classification solution as supplement for digital documents structure determination, and as system of font selection for design creation.
Adversarial Attacks on Hierarchical Composite Classifiers VIA Convex Programming

Adversarial perturbation attacks were shown to inflict severe damage on simple one-stage classifiers. In this paper, we examine the vulnerability of Hierarchical Composite Classifiers to such attacks. We formulate a maximin program to generate perturbations attacking these models, and obtain an approximate solution based on a convex relaxation of the proposed program. With the proposed approach, the relative loss in classification accuracy for the super-labels increases drastically in comparison to perturbations generated for One Stage Composite Classifiers. Additionally, we show that fooling a classifier about the `big picture' is generally more perceptible.
Force9: Force-assisted Miniature Keyboard on Smart Wearables

Smartwatches and other wearables are characterized by small-scale touchscreens that complicate the interaction with content. In this paper, we present Force9, the first optimized miniature keyboard leveraging force-sensitive touchscreens on wrist-worn computers. Force9 enables character selection in an ambiguous layout by analyzing the trade-off between interaction space and the easiness of force-assisted interaction. We argue that dividing the screen's pressure range into three contiguous force levels is sufficient to differentiate characters for fast and accurate text input. Our pilot study captures and calibrates the ability of users to perform force-assisted touches on miniature-sized keys on touchscreen devices. We then optimize the keyboard layout considering the goodness of character pairs (with regards to the selected English corpus) under the force-based configuration and the users? familiarity with the QWERTY layout. We finally evaluate the performance of the trimetric optimized Force9 layout, and achieve an average of 10.18 WPM by the end of the final session. Compared to the other state-of-the-art approaches, Force9 allows for single-gesture character selection without addendum sensors.
Vision: Why Johnny Can’t Configure Smart Home? A Behavioural Framework for Smart Home Privacy Configuration

Despite the advancements in smart technologies, the lack of user awareness of the capabilities of IoT smart home devices can expose them to privacy breaches. For example, a user may be unaware that their smart home devices are recording and storing their conversations (e.g. personal, perhaps sensitive or confidential communications). It could be argued that this is due to the lack of users' threat perceptions and the challenges of altering existing (smart home) configuration behaviours. Therefore, we propose a behavioural framework to enhance users' smart home configuration behaviours through their motivation. To develop the framework, we derived and extended the elements from the Technology Threat Avoidance Theory (TTAT) and Fogg's Behavioural Motivation (FBM) model. The elements of the proposed framework were incorporated into an application scenario, i.e. a game design. The designed gaming application aims to educate users about smart home device capabilities and enhance their configuration behaviour through motivation. More research is required to evaluate the proposed framework and its gaming application scenario through empirical investigations.