Reviewerly/exHarmony huggingface dataset:
Inferring Contextual Data from Real-World Photography

The ability to collect complementary information when acquiring photographs, for instance, data and time, has gained much attention since the introduction of digital photography, especially due to the potential impact in the organization of photographic catalogues and archives. Much effort has been put to understand the types of metadata that would be useful for people when using digital images. However, scarce attention has been paid to the use of metadata to enable creative editing work with the photograph. With the definitive transition to digital in image acquisition and editing workflows, it became viable to introduce metadata throughout the complete content value chain with the potential to automate or assist the creative work. In this paper, we present eCAT, a solution developed in the framework of the FotoInMotion project that offers two contributions towards the fulfillment of such aim. Firstly, it defines tools that seamlessly acquire multiple types of contextual data from a smartphone’s built-in sensors at the time the photograph is captured, and process such data to infer physical conditions at that moment and place. Secondly, it devises methods to fuse that meta-information with tags associated to the photograph and to obtain additional knowledge from external sources that can be used in the creative process. Together, these contributions pave the way for the development of metadata-assisted creative workflows, offering context acquisitioning mechanisms to photographers to enrich their work and build engaging multimedia stories based on a still image.
Methods for Detecting Fraud in Civil and Military Service Examinations: A Systematic Mapping

Civil and military service examinations are carried out in several countries for the recruitment and admission of public servants in various spheres/levels of government. This is considered an effective and rational method for selection based on merit. Due to the constant economic variations and the stability provided by public offices, the interest in some offered positions can be huge. Criminals specialized in defrauding public examinations offer candidates the possibility of facilitated and illegal admission. Various types of information could be submitted to methods and techniques (e.g., application data, test performance, geodata, etc.) to detect fraud. We present a systematic mapping of the literature on fraud detection methods in several domains, which can be adapted and improved to detect fraud in public examinations. 31 articles were identified, and after analysis, 19 selected works were analyzed and classified. The usages of machine learning and data mining techniques were uppermost methods adopted in the analyzed papers. This work is aimed at researchers who seek to develop fraud detection techniques in admission exams.
Usage of Homomorphic Encryption Algorithms in Process Control

Safe and secure control is one of the most important parts of the industry in terms of process control. Basically, it represents two main aspects. First one is safe control, which means using the right control algorithm, thus using a properly designed controller, which ensures the closed-loop stability, therefore the stable operation of the process. The secure part is used to insure the secured communication between the process and the control unit. This paper is mainly focused on the secure part. We describe two types of cryptosystems, namely Paillier and Benaloh cryptosystem. For each we present the scheme describing how the cryptosystem works, mainly the encryption and decryption algorithms. These algorithms will be compared from the implementation point of view and their usage in real device control. The real device will be the Flexy 2.0, which is a simple device with quite fast dynamics. All the gained results are discussed in conclusion.
Multi-Scale Enhanced Active Learning for Skeleton-Based Action Recognition

Skeleton-based models have been widely used, because of their robustness to complex backgrounds and high computational efficiency. However, annotating skeleton sequences is labor-intensive. It is appealing to reduce the cost of acquiring data with accurate labels for skeleton-based models. This paper presents an active learning method for the skeleton-based action recognition model, which boosts the performance of the model with less labeled data by instructing humans to annotate the most valuable samples. The key issue in active learning is to train a model that precisely predicts the values of samples. To achieve this, we propose to enhance the ability of our model to evaluate samples by modeling actions from different granularities from multi-scale representations of skeletons. The multi-scale method is simple and easy to use, which can be treated as a plug-and-play extension to strengthen the skeleton-based models. We conduct experiments on the SHREC, NTU-60, and Kinetics-Skeleton. Extensive experimental results demonstrate the effectiveness of the proposed method.
Data-Driven Deep Learning Emulators for Geophysical Forecasting

We perform a comparative study of different supervised machine learning time-series methods for short-term and long-term temperature forecasts on a real world dataset for the daily maximum temperature over North America given by DayMET. DayMET showcases a stochastic and high-dimensional spatio-temporal structure and is available at exceptionally fine resolution (a 1 km grid). We apply projection-based reduced order modeling to compress this high dimensional data, while preserving its spatio-temporal structure. We use variants of time-series specific neural network models on this reduced representation to perform multi-step weather predictions. We also use a Gaussian-process based error correction model to improve the forecasts from the neural network models. From our study, we learn that the recurrent neural network based techniques can accurately perform both short-term as well as long-term forecasts, with minimal computational cost as compared to the convolution based techniques. We see that the simple kernel based Gaussian-processes can also predict the neural network model errors, which can then be used to improve the long term forecasts.
Teaching Design and Practice of Software Engineering Series Courses Based on OBE

Taking the software engineering series courses of Shenyang Institute of technology as an example, this paper designs and practices the teaching process design, course teaching methods, curriculum evaluation methods and other elements. It emphasizes that the good teaching design, which is close to the needs of enterprises and centered on the development of students, can improve the classroom teaching and classroom quality from the perspective of training objectives, close to the needs of enterprises. This mechanism can be extended to other curriculum construction, cultivate high-quality and high-capacity applied talents, and make more contributions to regional economic development.
A development framework of granular prototypes with an allocation of information granularity

In this paper, a hybrid model with both clustering mechanism and regression mechanism is built with aid of an allocation of information granularity. Some numeric prototypes constructed by clustering methods are expanded into granular prototypes. The regression module accepts granular prototypes and outcomes information granules. Some advantages of this idea can be concluded: 1). The constructed granular prototypes are best able to represent original data with comparable smaller quantity and high quality. 2). The granular output could be used to predict a new sample’s real number output by giving a rational range. 3). It can be used to solve clustering and regression problems simultaneously. Two different granularity allocation strategies are proposed and experimented while constructing granular prototypes: non-uniformly allocation of information granularity to each cluster and non-uniformly allocation to each feature. A comprehensive objective function is defined considering specificity and generality of the output. The allocation of granularity itself is in fact a multiple-parameters optimization problem which invokes the usage of an evolutionary method. Two popular methods (GA and PSO) are tried and compared with a real data set’s experiment. Three data sets are collected from UCI website to testify the effectiveness of our approach in the experimental part.
Exploring the “Presence” of Prospective Teachers in the Virtual Reality Environment: A Qualitative Study

This study aimed to investigate the perception of “presence” of 43 prospective teachers participating in a virtual reality (VR) environment with an immersion function and to describe their opinions and experiences regarding the implementation process. The case study model, a qualitative research approach, was used. Participants experienced the VR environment using an HTC Vive device with an immersive feature. Qualitative data were collected through a structured interview form at the end of the application. Findings indicated that most participants provided positive perceptions regarding their VR experience. Additionally, the perceptions of participants were categorized into three main areas: technology, design, and individual characteristics. These categories and the participants’ experiences may contribute to the process of integrating VR systems into educational environments and developing novel VR applications.
Latent Clues Investigation in Discipline Inspection and Supervision Based on Knowledge Graph Analysis

The big data technology is reshaping the ecology of national governance, and also brought new opportunities for the development of discipline inspection and supervision system. Especially, in initial reviewing stage of the disciplinary inspection and supervision case investigation, the current processing methods are inefficient and invisible clues are difficult to find, and it takes a lot of manpower to conduct comparative investigations. In order to improve such latent clues discovering work, this paper well utilize the knowledge graph technology to propose a latent clues investigation method to promote the investigation efficiency for the anti-corruption cases in discipline inspection and supervision. Throughout the actual situation of such clues investigation, we proves that our proposed method could uncover meaningful latent clues during the initial reviewing stage of the investigation of corruption cases, thereby achieving the purpose of assisting the investigation of the discipline inspection and supervision.
Superconductor qubits hamiltonian approximations effect on quantum state evolution and control

Abstract Microwave IQ-mixer controllers are designed for the three approximated Hamiltonians of charge, phase and flux qubits and the controllers are exerted both on approximate and precise quantum system models. The controlled qubits are for the implementation of the two quantum-gates with these three fundamental types of qubits, Quantum NOT-gate and Hadamard-gate. In the charge-qubit, for implementation of both gates, in the approximated and precise model, we observed different controlled trajectories. But fortunately, applying the controller designed for the approximated system over the precise system leads to the passing of the quantum state from the desired state sooner that the expected time. Phase-qubit and flux qubit have similar behaviour under the control system action. In both of them, the implementation of NOT-gate operation led to same trajectories which arrive at final goal state at different times. But in both of those two qubits for implementation of Hadamard-gate, desired trajectory and precise trajectory have some angle of deviation, then by exerting the approximated design controller to precise system, it caused the quantum state to approach the goal state for Hadamard gate implementation, and since the quantum state does not completely reach the goal state, we can not obtain very high gate fidelity.
Decentralised Verification Technologies and the Web

The Internet, the Web and social media have radically transformed a number of core pillars of our social fabric. The way billions of citizens work, interact and socialise is underpinned by our global network infrastructure. Unfortunately, we have also seen a number of negative effects from this transformation. As has been widely publicised, undesirable impacts include the spread of disinformation and fake news; attacks on democratic elections and the ‘weaponisation’ of personal data. This article describes some of the technological approaches that are being taken to address some of the above issues. At the core of these technologies are notions around decentralisation. With blockchains it is possible that citizens can create their own ‘self-sovereign’ identity – the digital equivalent of writing one's name onto a piece of paper – and acquiring verification through blockchain-based techniques. An approach to alleviating the ‘weaponisation’ of personal and sensitive data is to give citizens their own data store. Initiatives such as Sir Tim Berners-Lee's Solid allow users to store, manage and control their own data according to any personal preferences or constraints. We believe that a combination of personal data stores and blockchains will lead to a new type of resilient communication and collaboration mechanism, whereby personal rights and empowerment are enhanced and transparency at the community level is integral.
Efficient and Accurate Hypergraph Matching

Feature matching is used to match features in the model image to their correspondences in the test image. Hypergraph matching makes use of the relationship among multiple features to improve matching accuracy, and existing algorithms usually achieve this aim by maximizing the matching score between features. In this paper we transform hypergraph matching of features to hypergraph clustering of candidate matches, which is then solved in the scenario of a multi-player clustering game. Noticing that the number of matches generated by this algorithm is usually small, we discuss the reason and present a density based group expansion method to increase the number of matches. Furthermore, we enforce the one-to-one constraint to maintain a high matching accuracy. Experiments on three real datasets show that our algorithm is able to generate a large number of matches efficiently without degrading the matching accuracy evidently.
Early Ductal Carcinoma Revealing in Mammography Images Using Machine Learning

Everywhere in the world, the Ductal Carcinoma is broke down in about 11.76% of ladies during their lifetime and is the driving intention after the passing of ladies. Since early discoveries can improve treatment results and possess energy for patients with delayed perseverance infection, it is imperative to develop chest threatening development recognition strategies. The Conversational Neural Network (CNN) can normally eliminate features from pictures and request them later. Gigantic checked pictures might be needed to prepare CNN without any planning, which is workable for specific sorts of clinical picture data, for instance, mammographic tumor pictures. A promising system is to actualize move learning on CNN. In this article, we applied the MIAS dataset on three preparing procedures: CNN to feature recently arranged VGG-16 models with input mammograms, and to make a Neural Network (NN) - classifier. Utilized these features for and invigorated the heap. By back-spreading (tweaking) to distinguish irregular regions in pre-arranged VGG-16 model layers. The examinations are identified with Normal versus Risky and Normal versus Sporadic versus Explicit from the DDSM (Digital Database for Screening Mammography) information base with 10-overlay cross endorsement. Contrast proposed models and boundaries related to execution measurements.
Towards Putting Deep Learning on the Wrist for Accurate Human Activity Recognition

The use of smart wearables enables regular monitoring of human activities, which can lower risks of health complications due to cardiovascular diseases, diabetes, etc. The signal data generated by the accelerometer and gyroscope of the inertial measurement unit (IMU) within wearables, aid in recognizing motion. In the past, researchers have used Signal Processing techniques for human activity recognition (HAR). However, these techniques are not adaptive to variations of the same activities in different people. Machine Learning and Deep Learning are comparatively adaptive to variations but require more computational power, especially Deep Learning. To circumvent high computational requirements, state-of-the-art solutions typically deploy the learning algorithms on a platform with more compute power such as a smartphone, or even Cloud. In this paper, we propose a classification model for HAR that outperforms state-of-the-art prediction accuracy on raw signal data and is suitable for deployment in resource-constrained environments of smart wearables and IoT devices.
An Approach for Detecting and Identifying Suspected Weapons Using YOLO Algorithm

The prominence of brilliant cell phones and the minimal effort of reconnaissance frameworks, visual information are progressively being utilized in computerized legal examination. Advanced recordings have been generally utilized as key proof sources in proof identification, investigation, introduction, and report. The principal objective is to create progressed measurable video examination methods to help the scientific examination. A measurable video examination structure is proposed that utilizes an efficient video/picture upgrading calculation for the low nature of film investigation. A versatile video upgrade calculation dependent differentiation restricted versatile histogram levelling (CLAHE) is acquainted which will improve the circuit TV (CCTV) film quality for the utilization of computerized measurable examination, to help the video-based scientific examination, a deep learning-based article location and calculation are recommended and proposed that can distinguish and recognize potential suspects and devices from recordings.
Implementation of Controller for Self-balancing Robot

A two-wheeled self-balancing robot that works on the principle of inverted pendulum theory is most commonly used in control systems as an exemplar for comparing various control and optimization algorithms. This nonlinear dynamic system has gained much interest among researchers and engineers and the main aim is to stabilize this system with minimum deviation about any desired axis. As this system is affected by external disturbances and measured parametric errors, the need for the implementation of controllers has become crucial. Hardware and software implementation form the basis of the system design. The proposed work aims at the implementation of a PID controller for the successful working of the two-wheeled self-balancing robot driven by stepper motors. The closed-loop controller’s parameters have been optimized by using pole placement method. Besides, the response of the system under the fuzzy controller, created using Mamdani fuzzy interface engine in Simulink, has been illustrated.
Securing Connected &amp; Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward

Abstract Associated and self-ruling automobiles (CAVs) will shape a foundation for the future cutting edge canny transport frameworks (ITS) giving comfortable travelling, street wellbeing, alongside various worth added administrations. Such a change—which would be fuelled by attendant advances on advances for AI (Machine Learning) and remote interchanges—will empower a future vehicular biological system that is better included and more effective. Notwithstanding and are sneaking security issues identified with the utilization of Machine Learning in a particularly basic setting where a mistaken Machine Learning choice may not exclusively be an aggravation yet can prompt loss of valuable lives. Here it’s present a top to bottom outline of the different difficulties related with the utilization of Machine Learning In vehicular organizations. Moreover, we form the Machine Learning Pipeline of CAVs and present different potential security issues related with the selection of Machine Learning Strategies. Specifically, it’s center around the point of view of antagonistic Machine Learning Assaults on CAVs and framework an answer for protecting against ill-disposed assaults in different settings.
Improving 3D Hand Pose Estimation with Synthetic RGB Image Enhancement Using RetinexNet and Dehazing

Hand pose estimation has recently attracted increasing research interest, especially with the advance of deep learning. Albeit many successes, the current state of research does present some opportunities for improvement of estimation accuracy. This paper presents several image enhancement techniques to embed with existing deep learning architectures to improve the performance of hand pose estimation. In particular, we propose a preprocessing approach for image data using a low light illuminance model or a dehazing algorithm before passing the image data to a hand pose estimation model. Both the preprocessing methods are evaluated on a rendered hand-pose dataset using different evaluation metrics. The experimental results shows success in boosting the performance of hand pose estimation for both 2D and 3D image data.
A Novel Sentiment Classification Architecture Based on Self-attention Mechanism

Due to the huge amount of network data and low efficiency of artificial analysis, one branch of natural language processing, sentiment classification, has become the focus of current research. Attention mechanism has been widely applied in the field of natural language processing, and the accuracy is higher, but due to the different aspects in a sentence to express sentiments tend to be different and attention mechanism is difficult to focus on more text sequences of internal relations, this paper studies the part-of-speech embedded into the attention mechanism, based on fusion embedded speech sentiment classification new architecture since the attention mechanism, including the two kinds of algorithms, respectively is based on the Pos-IdSA sentiment classification algorithm and based on the Pos-ItSA sentiment classification algorithm; The parameters of the data set are optimized and the performance of the algorithm is analyzed. The research in this paper shows that the new algorithm of sentiment classification based on fused part-of-speech embedding has a higher accuracy rate, and part-of speech embedding is conducive to learning the grammatical relationship between different parts of speech on the network, and effectively reduces the noise caused by the complex context.
Existential Ontology and Thinging Modeling in Software Engineering

This study is a sequel to a previous study entitled “Thinging for Software Engineers”, which showed that the notion of thing, in contrast to objectification, has some beneficial orientations in modeling. The incorporation of thinging in conceptual modeling is required to explain the roots of Heidegger’s conception of things. This requires an understanding of Heidegger’s existential ontology to identify any relationship to thinging. This paper is an exploration of existential ontology in search of further clarification of the concept of thinging. We start by reviewing the thinging machine (TM) introduced in “Thinging for Software Engineers” and provide a full example of its utilization in modeling an “ordering system”. We follow this with a discussion of the being (existence) of things in the word and Heidegger’s interpretation of time as a possible horizon for any understanding whatsoever of being. We emphasize that the TM is not related directly to the Heideggerian notion of existence and its elaborate analysis of Dasein. However, there may be some benefit to studying non-Dasein things to provide a philosophical foundation to thinging, as utilized in TM modeling. Interestingly, the TM can be utilized to model existential ontology, thus increasing the level of understanding about them.