Reviewerly/exHarmony huggingface dataset:
An Approach in Auto Valuing for Optimal Threshold of Viola Jones

The biometric features for people identification on the face include eyes, nose, and mouth. In this study, the three features are detected by using Viola Jones method. The affecting parameter on the accuracy of face feature detection by using Viola Jones is the threshold value because it is the input criterion for Mergethreshold parameter on Matlab. The appropriate threshold value will decrease the detection error so that yielding appropriate ROI. Therefore, this study employs a process of finding out the automatic threshold value by using Viola Jones feature detection. The input data of the system is in the form of frame video from 4 respondents which yield totally 938 face frames as the result of automatic detection that is extracted with matrix 130x110. The result of the testing yields accuracy as much as 79.17% for eyes feature, 94.81% for nose feature, and 99.49 for mouth feature. The whole features yield approximate accuracy as much as 91.15% while the testing result of the time needed by 4 respondents with total 20 video frames is 0.368 seconds for eyes feature detection, 1.065 seconds for nose feature, and 4.665 seconds for mouth feature.
Repairing Non-monotonic Knowledge Bases

Minimal inconsistent subsets of knowledge bases in monotonic logics play an important role when investigating the reasons for conflicts and trying to handle them. In the context of non-monotonic reasoning this notion is not as meaningful due to the possibility of resolving conflicts by adding information. In this paper we investigate inconsistency in non-monotonic logics while taking this issue into account. In particular, we show that the well-known classical duality between hitting sets of minimal inconsistent subsets and maximal consistent subsets generalizes to arbitrary logics even if we allow adding novel information to a given knowledge base. We illustrate the versatility of the main theorems by covering more sophisticated situations and demonstrate how to utilize our results to analyze inconsistency in abstract argumentation.
A Survey of Cloud Computing Access Control Technology

Cloud computing access control technology originated in the 1970s. The initial goal of this technology is to meet the requirements of the primary server for data access rights, identify the identity of the visitor through relevant procedures, and then set access rights based on the authentication result. And it also committed to protect important data and prevent the main server from being illegally invaded. With the development of technology, cloud computing access control technology has been widely used in computer systems, which has played a good role. This article will briefly discuss cloud computing access control technology research and present personal insights.
Confocal Microscopy for Surface Profilometry

The application of automated optical inspection (AOI) to advanced manufacturing processes with tight takt time and specifications is critical in winning today's global competition. In the past decades, great effort had been devoted to developing novel solutions for in-line optical inspection of surfaces and the dynamic characteristics of tested components or devices. Conventional approaches to microscale 3D surface profilometry have adopted novel optics or concepts in confocal microscopy for measuring 3D surface characteristics with high speed and precision. One-shot measurement capability is demanded to minimize measured uncertainty from environmental vibration or system instability. Nevertheless, extremely high-speed microscopic 3D confocal profilometric methodologies for 100% full-field inspection are yet to be developed. This chapter intends to provide the measurement principle and development of confocal optical profilometry in overcoming bottlenecks and developing feasible solutions for novel manufacturing technologies, such as roll-to-roll nanoimprinting or semiconductor processes. It contains the basic optics, the measuring algorithms and industrial applications in optical confocal microscopy required for surface profilometry.
Advancements in Resource-Driven Substructural Defeasible Logic

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources and reasoning with exceptions. We propose a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, and we discuss the design choices.
An Evaluation of Three Representative 360-Degree Image Projection Formats in Terms of JPEG2000 Coding Efficiency

360-degree contents are becoming more popular nowadays since they create new and promising segments in entertainment, education, professional training, and other markets. These contents are spherical in nature and are mapped to a two-dimensional representative plane using various projection formats. Due to the increased resolution to support the wide field of view, efficient compression of 360-degree content become crucial. The Joint Video Exploration Team (JVET) is investigating various projection formats, adapting to the input of existing video coding systems. Where it is of significant interest to see how different projection formats impact the compression efficiency of the 360-degree images. In this paper, we evaluate the performance of three well-known 360-degree image projection formats. Objective performance is evaluated by coding the 360-degree images projected on the two-dimensional representative plane using various projection formats such as equi-rectangular, cylindrical and cube-map projection format, with JPEG2000, a well-known still image coding standard. Our experimental results have proved that cubemap projection format is the appropriate solution considering the coding efficiency of JPEG2000.
Satellite-based Quantum key distribution using a compact terminal on Tiangong-2 Space lab

We report a quantum key distribution experiment using a compact terminal on Tiangong-2 spacecraft two years after launch. This robust platform provides an effective solution for realizing a practical satellite-constellation-based global quantum secure network.
Memory-Saving Evaluation Plans for Datalog

Ontology-based query answering (OBQA), without any doubt, represents one of the fundamental reasoning services in Semantic Web applications. Specifically, OBQA is the task of evaluating a (conjunctive) query over a knowledge base (KB) consisting of an extensional dataset paired with an ontology. A number of effective practical approaches proposed in the literature rewrite the query and the ontology into an equivalent Datalog program. In case of very large datasets, however, classical approaches for evaluating such programs tend to be memory consuming, and may even slow down the computation. In this paper, we explain how to compute a memory-saving evaluation plan consisting of an optimal indexing schema for the dataset together with a suitable body-ordering for each Datalog rule. To evaluate the quality of our approach, we compare our plans with the classical approach used by DLV over widely used ontological benchmarks. The results confirm the memory usage can be significantly reduced without paying any cost in efficiency.
Fast Brain Volumetric Segmentation from T1 MRI Scans

In this paper, we train a state-of-the-art deep neural network segmentation model to do fast brain volumetric segmentation from T1 MRI scans. We use image data from the ADNI and OASIS image collections and corresponding FreeSurfer automated segmentations to train our segmentation model. The model is able to do whole brain segmentation across 13 anatomical classes in seconds; in contrast, FreeSurfer takes several hours per volume. We show that this trained model can be used as a prior for other segmentation tasks, and that pre-training the model in this manner leads to better brain structure segmentation performance on a small dataset of expert-given manual segmentations.
A Deep Learning Approach for Vehicle and Driver Detection on Highway

The technology of the detection for vehicle and driver is a popular spot in these ten years. In particular, the driver detection is still a troubled question in the study of public security. In our paper, an algorithm based on YOLOv3 and support vector machine (SVM) is proposed for realizing the detection of vehicles on highway, as well as the detection and binary classification of people in the vehicles, so as to achieve the purpose of distinguishing drivers and passengers and form a one-to-one correspondence between vehicles and drivers. The effectiveness of the algorithm is verified under various complicated highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes and extreme lighting.
Multi-CNNs Bootstrap Against Label Noise with Applications in Age Estimation

The age estimation has achieved great success in recent years. For learning an age estimation model, the age label of each facial image is required. If the age labels depend on subjective perception and are supplied by a diversity of amateur users, there inevitably exists improper labels. We name the improper labels as label noise. It is undoubted that the performance of learned model will decrease due to the label noise. Current typical works first identify the mislabeled examples and then remove them before training. Obviously, the discarding strategy does not fully use the valuable training data. In this paper, we explore how to use all training data in spite of the existing label noise. First, a part of the training set is separated into several subsets. Then, multiple Convolutional Neural Networks (multi-CNNs) share low and middle layers and are fine-tuned to the last two full-connected layers respectively using different subsets. In particular, we choose the VGG-Face as the base network. Next, the multi-CNNs determine the label of the remaining training data respectively. If one of the CNNs outputs the same result as the given label and also obtains the highest probability, the corresponding data is absorbed and the corresponding sub-CNN is retrained from the enlarged training data. Such a bootstrap procedure is repeated till no consistent labels are found. Once the bootstrap ends, the inconsistent labels are modified in terms of the consequence given by multi-CNNs decisions with statistical average and combined with previous learned data to compose a label noise reduced training set. Finally, a CNN model is learned from the label noise reduced training set. The empirical results have demonstrated the effectiveness of our proposed method on two age estimation datasets including our new Yitong dataset and WIKI dataset.
Learning what we don’t care about: Anti-training with sacrificial functions

The traditional machine learning paradigm focuses on optimizing an objective. This task of optimization is carried out by adjusting the free parameters of a model; however, many times in classification we do not assess the performance of the model using the optimized objective. Furthermore, choosing a poor set of free parameters for the objective function could lead to unintentional overfitting of the data. In this work, we present a novel approach based on the theory of anti-training for generating sacrificial functions that transform data. These sacrificial functions incorporate new classifier-independent objectives into the task of choosing a models’ free parameters. Our approach builds upon recent work in anti-training to develop a new class of functions that a classifier should not perform well on. We use multi-objective evolutionary algorithms to solve the task of model selection by minimizing a sacrificial function(s) and functions the model should always perform well on (i.e., error, sensitivity, specificity, etc.). Our experiments found that the proposed method provides statistically significant improvements in the generalization error over the state-of-the-art optimizers.
Image smoothing which is invariant under general coordinate transformation

This paper proposes a method for image smoothing which is invariant under general coordinate transformation. This method is based on a position dependent metric tensor which transforms appropriately against the general coordinate transformation. Using this metric tensor, a method for invariant image smoothing against the general coordinate transformation is constructed. Effectiveness of the proposed method is confirmed by computer experiments.
TRANSFORMATION OF CONSULTING VALUABLE ORIENTATIONS OF MODERN UKRAINIAN STUDENT

The article deals with the problem of the dynamics of the manifestation of value orientations of modern Ukrainian students, the patterns of their occurrence and factors influencing their formation in the context of philosophical, sociological, ethical and cultural dimensions and cross-cultural comparative studies. The article focuses on the problem of the existence of modern youth, the formation of new ideological and humanistic values, and the search for basic principles of world attitude in the conditions of transformational changes in society as a whole. The process of integrative and social development of modern youth, which is accompanied the changes of hierarchical correlations of values and appearance of new, and also socially psychological features which show up on the different levels of valued-normative sphere of personality in a socio-cultural context under act of gender-age factors, is analysed. It is rotined that principles of organization, logician of functioning and processes which generate the individual and superindividual systems of values at all of likeness of common concepts have different nature and require different interpretations accordingly. Development of method of practical research of the individual and / either superindividual systems of values requires the constructions of operational determination of key categories which are utillized in research. From the functional point of view instrumental values activate as criteria, as standards of estimation during a choice only to modus (to the method) of conduct or actions, and a terminal is utillized an individual during the estimation of both aims of activity and possible methods of their achievement.
Automotive SPICE: 0-60 in No Time Flat

My company is very good at executing the Automotive Software Process Improvement and Capability Determination (ASPICE) process model for software development. We have delivered Automotive SPICE-grade software for many years to top original equipment manufacturers (OEMs) and have a long history of successful implementations. That being said, sometimes automotive solutions need to be delivered from an organization outside of the teams that are entrenched in the processes required to meet ASPICE requirements. We were challenged to bring up one of these teams in late 2018 to ensure compliance, adoption and rollout of the ASPICE process for multiple teams spread around the world. This took a bit of coordination and travel, but by mid-December the teams passed the audit and continue to drive improvements to attain even higher-levels of compliance and maturity.
An Adaptive Algorithm of Showing Intuitively the Structure of a Binary Tree

The adaptive software development(ASD) method is an agile development method based on complex adaptive system theory. It develops adaptive abilities through methods such as candidate program library, dynamic display selection, semantic self-description and self-test, and establishes expert system through the study of business rules, achieves intelligent adaptable software by decision theory, diagnosis, recovery and other methods. This paper presents an adaptive algorithm of showing intuitively the structure of a binary tree, the algorithm module is plug and play, and can be inserted directly into any program source code related to the binary tree without any modification. This technology can improve the efficiency of system design, reduce the workload of program maintenance and has some innovation and practicability.
Poincaré Metric in Algorithms for Data Mining Tools

Today we cannot imagine life without computers. The massive use of the information communication technologies has produced large amounts of data that are difficult to interpret and use. With data mining tools and machine learning methods, large data sets can be processed and used for prediction and classification. This paper employees the well known classification algorithm the k nearest neighbour and it modified use the Poincaré measurment distance instead of traditional Euclidean distance. The reason is that in different industries (economy, health, military ...) it increasingly uses and stores databases of various images or photographs. When recognizing the similarity between two photographs, it is important that the algorithm recognizes certain patterns. Recognition is based on metrics. For this purposes an algorithm based on Poincaré metric is tested on a data set of photos. A comparison was made on algorithm based on Euclidean metric.
Towards a Better Compromise Between Shallow and Deep CNN for Binary Classification Problems of Unstructured Data

Deep Neural Network is a large scale neural network. Deep Learning, refers to training very large Neural Networks in order to discover good representations, at multiple levels, with higher-level learned features. The rise of deep learning is especially due to the technological evolution and huge amounts of data. Since that, it becomes a powerful tool that everyone can use specifically on supervised learning, because it’s by far the dominant form of deep learning today. Many works based on Deep learning have already been proposed. However, these works have not given any explanation on the choice of the number of the network layers. This makes it difficult to decide on the appropriate deep of the network and its performances for a specific classification problem. In this paper the objective is threefold. The first objective was to study the effect of facial expressions on facial features deformations and its consequences on gender recognition. The second objective is to evaluate the use of Deep learning in the form of transfer learning for binary classification on small datasets (containing images with different Facial expressions). Our third goal is then to find a compromise between too much capacity and not enough capacity of the used deep Neural Network in order to don’t over fit nor under fit. Three different architectures were tested: a shallow convolutional neural network (CNN) with 6 layers, a deep CNN VGG16 (16 layers) and very deep CNN RESNET50 (50 Layers). Many conclusions have been drawn.
Human Activity Identification Using Novel Feature Extraction and Ensemble-Based Learning for Accuracy

The area of human activity recognition is gaining momentum with the rise of smart appliances towards tracking and monitoring human behavior system. Till last decade, there have been various works being carried out towards building such a robust system that has led its way to commercial products too. However, after an in-depth investigation, it was found there is a far way to go in order to build up a true and dependable recognition system. Therefore, the proposed system introduces a novel framework meant for human activity recognition system with the sole target to enhance the precision factor in the identification process. A simplified feature extraction process has been introduced in this work that after being subjected to ensemble-training approach is found to improve the identification performance significantly. The study outcome shows better accuracy as well as good system performance.
Using Machine Learning to Convey Emotions During Requirements Elicitation Interviews

In an effort to assist in an ongoing research project where stakeholders have been interviewed using voice recording platforms and the Empatica E4 wristband to gather biofeedback data, the purpose of this research project in relation to the aforementioned research is to be able to determine the stakeholder's emotional range during a requirements elicitation interview. In doing so, the requirements analyst is supported during the requirements elicitation interview because conveying the emotional range of the stakeholder can help eliminate any miscommunication or misunderstandings between the requirements analyst and the stakeholder due to ambiguity in questions, statements, and responses. Therefore, knowing the range of the emotional state of the stakeholder, in real-time, can allow the requirements analyst to recover or make adjustments to questions (i.e. sensitive topics) during the requirements elicitation interview. The main questions are: (1) What machine learning technique(s) would be most efficient in conveying the emotional range of the stakeholder through the voice recording data and biofeedback data? (2) What features would result in optimal performance from the chosen machine learning technique(s)? The objective is to use supervised machine learning techniques in order to convey an emotional range from the retrieved dataset. To accomplish this, exploring the most efficient machine learning technique for emotion detection for voice recordings and biofeedback data and finding a way to construct or utilize the techniques in an effective manner will be necessary. In conclusion, the machine learning technique(s) chosen will convey a range of emotion from the stakeholder based on the retrieved data. The machine learning technique(s) will be used to support a requirements analyst during requirements elicitation interviews and will help the analyst identify problems or concerns in the communication to better assess and engage the stakeholder.
Problems with information fusion under extreme multiple-counting conditions

This document concerns three information fusion methods: Information Matrix Fusion (IMF), Covariance Intersection (CI), and Sampling Covariance Intersection (SCI). These methods are compared for performance under extreme multiple-counting conditions, that is, when an estimate is improperly fused to a track multiple times as if the estimate was repeatedly found by independent measurements. This situation can possibly occur in networked fusion systems where data pedigree is less than properly maintained, especially when an information relay is implemented to handle diminished communication environments. Extreme multiple-counting behavior in particular is examined for the purposes of this document. This research demonstrates that the normally preferable methods, IMF and SCI, are prone to falsely optimistic covariance values in such situations. All three fusion methods result in the state estimate approaching the estimate being repeatedly fused; the more conservative CI method also results in the covariance approaching that of the repeated estimate. We obtain these results through inference from the governing equations and examination of behavior under Monte Carlo simulations.
Limit reachability for model-free reinforcement learning of ω-regular objectives

We have recently solved the model-free reinforcement learning of ω-regular objectives for Markov decision processes. We outline our constructive reduction from the almost-sure satisfaction of ω-regular objectives to an almost-sure reachability problem, and extend this technique to learning how to control an unknown model so that the chance of satisfying the objective is maximized. A key feature of our technique is the compilation of ω-regular properties into limit-deterministic Büchi automata, which sidesteps difficulties associated with the traditional Rabin automata. Our approach allows us to apply model-free, off-the-shelf reinforcement learning algorithms to compute optimal strategies from the observations of the Markov decision process.