Reviewerly/exHarmony huggingface dataset:
D2F: Description to Face Synthesis Using GAN

All the recent breakthroughs in the field of deep learning have enabled us to extract lower dimension knowledge from higher dimension data, this has led to state of the art utilization of tremendous data. At the same time, the generative aspect of deep learning models are rapidly emerging, out of all the generative models Generative Adversarial Networks (GANs) are immensely popular due to its ease of implementation and enhanced performance. Tasks like mapping textual data to visual data (i.e. lower dimension to higher dimension) is a feat which is still not easily achievable even by experts. The challenge here is extracting every possible information from lower dimension data to generate comparatively higher dimension data. This work collaborates GANs and Natural Language Processing (NLP) to come up with a model which will effectively translate human facial features from characters to pixels. In other words the model will generate realistic human faces given the description of the faces. This paper compares different architectures of GANs trained on different datasets. It also specifies dataset collection method using crowd intelligence and also proposes a deep learning model for description to face synthesis.
A Novel Non-maximum Suppression Algorithm Based on Affinity Propagation Clustering

Non-maximum suppression(NMS) is an common post-processing procedure indispensable of the existing object detectors, responsible for merging the excess detections. The standard NMS is a greedy algorithm, in which detections overlapping the selected one will be deleted rudely. Although simple and fast, this algorithm will fail completely in crowded scenarios. In this paper, faced with a crowded scenario of robot sorting, we analyze the inherent defects of standard NMS in depth, and then design a NMS based on Affinity Propagation Clustering(APC) and aware of location confidence and objects density. Our NMS leads to a satisfactory result and shows the effectiveness of using global information in NMS.
Coding Schemes and Resource Allocations for The Multi-Task Coded Distributed Computation

Resource allocation for the multi-task coded distributed computation with limited computing and storage re-sources is considered. We first propose an optimal coding scheme and resource allocation strategy that achieve the minimum execution time for the single-task case. We then extend the coding scheme and allocation strategy to the multi-task case and present two scheduling strategies: first-input-first-output (FIFO) strategy and a linear programming (LP) based strategy. The FIFO strategy allocates the resources to the tasks in the order of arrival, and is easy to implement in many practical distributed computing systems such as Apache Spark. The LP-based strategy jointly designs resource allocation among all tasks, and can achieve shorter schedule makespan (the amount of time elapses from the start of the schedule to its end) than the FIFO strategy. Moreover, we prove that the LP-based strategy is robust that achieves the optimal makespan within a constant multiplicative gap, regardless of the system parameters.
Sequential Learning-based IaaS Composition

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.
Situational Decision Making Using Situation Modeling and Deep Learning

This paper addresses the problem of situation modeling and machine learning-based decision making in open and non-predictive environments. Situational decision making incorporates the determination of an action based on the current situation, represented by the situation model and trained system behavior using deep neural networks. Commonly, the situation modeling is not considered an intermediate step for decision making in situational action selection. This contribution introduces a novel approach for decision making using situation modeling and deep neural networks. It uses an information structuring and representation technique for the generation of situation spectra used as input to deep learning-based decision making. Simulation-based experimental results show the proposed approach's effectiveness and importance.
Creditworthiness Assessment Using Natural Language Processing

NLP is a wide and quickly developing segment of today's new digital technology, which falls under the domain of artificial intelligence. Alternative approaches for qualifying and quantifying an individual's creditworthiness have emerged in recent years as a result of recent advancements in AI. Banks and creditors may use AI to rate potential borrowers' creditworthiness based on alternative data, such as social media messages and internet usage, such as which websites people visit and what they buy from e-commerce stores. These digital footprints may show whether or not an individual is able to repay their debts. In this chapter, how the approaches of NLP could offer financial solutions to unbanked communities is explored. This chapter includes the use of various machine learning algorithms and deep learning to find the most accurate credit score of a user. Since NLP is less intrusive than providing direct access to a person's entire contact list or a social media site, it is a more accessible way to measure risk while still having the potential to target a larger audience.
Adaptive Structural Deep Learning to Recognize Kinship Using Families in Wild Multimedia

Ichimura, Takumi Kamada, ShinDeep learning has a hierarchical network architecture to represent the complicated feature of input patterns. We have developed the adaptive structure learning method of deep belief network (adaptive DBN) that can discover an optimal number of hidden neurons for given input data in a restricted Boltzmann machine (RBM) by neuron generation–annihilation algorithm and can obtain appropriate number of hidden layers in DBN. In this paper, our model is applied to Families in Wild Multimedia (FIW): A multi-modal database for recognizing kinship. The kinship verification is a problem whether two facial images have the blood relatives or not. In this paper, the two facial images are composed into one image to recognize kinship. The classification accuracy for the developed system became higher than the traditional method.
Software Integrity and Validation Using Cryptographic Composability and Computer Vision

A significant aspect of software integrity is the ability to determine whether a system is correctly showing the information it is intended to show. This work combines abstract cryptographic composability principles with automated machine learning techniques to support a new methodology for software integrity checking, particularly in the intractable domain of interface validation. A subtle but often perilously overlooked cryptographic principle is the requirement that a sender be aware of the meaning of inputs, specifically of the cleartext behind encrypted messages. We propose using computer vision to evaluate whether interfaces exhibit technical awareness of information content, enabling automated development-time and real-time integrity checking that is far more efficient and extensive than manual analysis.
Hacked by Bits and Pieces: What Can We Learn from an Example of Corporate Espionage?

Information security often involves the development and application of sophisticated software to protect sensitive information stored in corporate computers.Yet, in this example of corporate espionage, a clever person, a cellphone and some readily available software were all it took to crack through one company's advanced security barriers.By reading this article it is hoped that employees at all levels of an organization's hierarchy will become more aware of-and recognize-how: 1) bits and pieces of seemingly harmless and easy-to-acquire information can be used for sinister purposes; 2) building rapport and trust with a person can make them more likely to become unknowing co-conspirators in a devious undertaking; and 3) how one must be constantly alert not to give out information without carefully considering the authenticity and justification of the source requesting it.
분산 표현을 통한 서법 조동사 유사도 분석

This paper attempts to measure the similarity of frequently occurring modal auxiliaries in both L1 and L2 writings. The modal auxiliaries are known to be difficult areas of study for both L1 and L2, due to the overlapping in their use. A subset of TOEFL11 corpus and a subset of PTB(Penn Treebank) corpus were used to capture the similarities among modal auxiliaries within L1 and L2, respectively, and also across L1 and L2. Based on the hypothesis of distributional representation, similarities of modal auxiliaries were computed by applying cosine similarity and mutual information to every pair of words in the corpus, and then finally reducing dimensions with Singular Value Decomposition. The results show that the modals are found to be similar to other modals, and the distribution of modals in the reduced dimensions show that the modals in L1 and those in L2 exhibits different patters of similarities, while the distance among the modals in L1 is further away than the distance among those L2 modals.
Simulation of Expert’s Assessments Adjustment for Cognitive Maps Building

The main chapters of the cognitive simulation and its usage in solving poorly structured problems are investigated. Proven that algorithm of building cognitive map for complicated unstructured problems and poorly structured problems require expert assessment. Algorithm of building cognitive map and adjusting expert assessment is described. Adjusting model list of concepts described by group of experts based on theory of fuzzy sets, which expands set of opportunities to build cognitive maps for cognitive model analysis in case of poorly structured or unstructured data. Group decision support system could be built by using described method of cognitive analysis of poorly structured systems.
Detection and positioning system for foreign body between subway doors and PSD

In view of the hidden danger of the operation of the gap between the subway doors and the platform screen doors (PSD) in the subway operation. And on the basis of comparing the advantages and disadvantages of existing detection technology and the development of micro webcam. An automatic foreign object detection and positioning system based on binocular vision is proposed. The 5-hour video is collected from a real subway platform and made into a data set, which is tested by the proposed system, and the results showed that the detection accuracy rate was 98%, and the positioning accuracy rate was 93.2%. Therefore, the system improves the safety of subway operation, and has very important social benefits.
CONDITIONAL LOGIC IS COMPLETE FOR CONVEXITY IN THE PLANE

We prove completeness of preferential conditional logic with respect to convexity over finite sets of points in the Euclidean plane. A conditional is defined to be true in a finite set of points if all extreme points of the set interpreting the antecedent satisfy the consequent. Equivalently, a conditional is true if the antecedent is contained in the convex hull of the points that satisfy both the antecedent and consequent. Our result is then that every consistent formula without nested conditionals is satisfiable in a model based on a finite set of points in the plane. The proof relies on a result by Richter and Rogers showing that every finite abstract convex geometry can be represented by convex polygons in the plane.
Recognizing the Faces from Surveillance Video

The face recognition (FR) is getting extremely well known in the field of biometric due to its unique nature. In spite of the fact that the progression of FR advancement has accomplished a moderate level of improvement with deep learning strategies, still there exists some disadvantage, for example, illumination variation, change in pose angle, occlusion, masked face, and so on. The deep learning strategies will be productive only if the learning database contains a large number of images for training purpose. It is a known fact that, a large number of images are very difficult to get always, especially from village people. In order to overcome all the discussed problems, this paper proposes two different FR approaches which functions admirably with the database even with few training images. The two methods proposed are (i) face recognition with local linear regression (LLR) using a trained artificial neural network (ANN), (ii) FR with support vector machine (SVM) by utilizing the well-known particle swarm optimization (PSO) technique for optimizing the SVM kernel parameters. The performance of both the techniques is measured in terms of statistical measures and the computation speed. The performance of the proposed methods is also compared with a very recent and similar active appearance model (AAM)-based FR. From the experiment and result analysis, it is clear that the proposed PSO-SVM-based FR yields a maximum accuracy of 95%, an average accuracy of 91.5% with a minimum time requirement of 0.03 s.
INTERACTIVE METHODS AND THEIR ROLE IN EDUCATION IN THE UNIVERSITY

This is an article about the use of interactive methods because in the era of information technologies a specialist must be a creative person who has mastered the basics of computer technology, who is free to use the possibilities of new technologies, who is able to carry out research work and carry out diagnostic analysis of his work independently. For this reason, it is especially important for the modern education system to use pedagogical innovations and to use interactive methods effectively and correctly in the educational process. Pedagogical innovations, interactive methods in order to ensure the expressiveness of the development of a student as a personality are taken as a basis for advantages within the framework of standards
An Real Time E Commerce application to Find Credit Card Fraud

Credit card fraud detection is one of the prominent problem in today's world. It is due to the extensive rise in both online and e-commerce transactions. The fraud happens when the users’ accessible card gets stolen from any unauthorized source or the use of credit card for fraudulent purposes. The present scenario is facing this kind of problem. So to detect the unethical activity, the credit card detection system was introduced. The main aim of this research is to focus on machine learning methods. So the algorithms used are unsupervised learning algorithms.
Усовершенствование алгоритма сегментации изображений с нечеткой кластеризацией на основе улучшения оптимизационного алгоритма китов

The Whale Optimization Algorithm (WOA) has fast convergence speed, high accuracy, and high efficiency in the process of finding the optimal solution.This article has made some improvements to it, and then uses the improved whale optimization algorithm to optimize the kernel fuzzy clustering In the process of generating cluster centers in the algorithm, a fuzzy clustering image segmentation algorithm (IWOA_KFCM) based on the optimization of the improved whale algorithm is proposed.This algorithm has high clustering accuracy and time efficiency.Applied to the problem of image segmentation, more accurate segmentation threshold and higher segmentation efficiency can be obtained, and it has strong engineering practicability.
Application of Computer Vision and Image Processing for Micro Deformation

Abstract In this study, a micro deformation monitoring system was designed for metal plate based on computer vision. A rhombic mark on the aluminum plate was made to measure the micro deformation inside the aluminum sheet. For achieving high accuracy, the sub-pixel-level corner detection was applied to achieve the corner coordinates information of the mark. The micro deformation would be calculated in real deformation situation by using the proposed method. This monitoring system could be used in industrial production to detect whether samples slightly deformed.
One Improved OpponentSIFT and HOPDFG Descriptor Based Image Classification Algorithm

There are abundance of image classification and recognition algorithms, which Bag of Features (BoF) model based ones are the most classic and effective methods. In this article, I use an original, high-performance characteristic elicitation strategy for picture classification in the methodological framework of the BoF approach. Taking into account the overall theoretical and experimental results, I use the OpponentSIFT which is invariant to photometric transformations such as ray intensity changes and ray colour changes. Also, I enhance the Histogram of Oriented Gradients (HOG) descriptor by subjecting the image to the simple yet powerful probability density function (p.d.f) and then calculating HOPDFG from the outcome in a manner that abounds the form information encoded in the descriptor. In the experiments, I compare the picture classification performance of the method used and related fusion techniques with other existing methods on various datasets and the method used exhibits superior performances on object recognition and scene categorization.
Third-Eye: Practical and Context-Aware Inference of Causal Relationship Violations in Commodity Kernels

A causal relationship implies that a function call should follow another function call. However, causal relationships can be implicit in practice and therefore often missed and violated by developers, causing many serious risks such as memory leaks and crashes. Although a set of works are proposed to mitigate the issue, they fall short in solving two main challenges: the contradiction between bugs in specific paths and intra-function path-explosion, and missing contextual constraints of causal relationships, which leads to high performance cost or failing to detect context-related bugs. This paper proposes Third-Eye, a practical static analysis tool that infers causal relationship violations for commodity kernels like Linux. Third-Eye leverages the intersection-based call sequence building algorithm to cope with intra-function path-explosion, which can reduce the number of paths while collecting callee information as much as possible. Besides, Third-Eye detects causal relationship violations context-sensitively based on a statistical method. Our experiments show Third-Eye is effective and efficient—successfully identified 60 bugs in Linux 5.3. Of them, 41 have been confirmed and fixed by Linux developers (The accepted patches are in https://ipads.se.sjtu.edu.cn:1312/opensource/third-eye .).
TextureMask: A merged architecture for low-resolution instance segmentation

Instance segmentation has a wide range of applications, including video surveillance, autonomous driving, and behavior analysis. Nevertheless, as a type of pixel-level segmentation, its prediction performance in practice is substantially affected by low-resolution (LR) images resulting from the limitations of image acquisition equipment and poor acquisition conditions. Moreover, because their immense computational costs prevent the implementation of existing segmentation models on embedded devices, the development of a lightweight segmentation model has become an urgent necessity. However, it is challenging to achieve sound results with high efficiency and portability. From another perspective, to improve understanding of detailed objects, an architecture is needed that promotes an advanced interpretation of the segmentation, that is, a refined mask with texture. Our main contribution, called TextureMask, consists of the MobileNet-FPN for Mask R-CNN methods, segmentation with cropping, and a gradient sensitivity map, which are then merged into a unified map to refine and enrich the mask with texture information. Furthermore, preprocessing and post-processing algorithms are incorporated. Experiments demonstrated that our technique exhibits good pixel-level segmentation performance in terms of both accuracy and computational efficiency for a given LR input, and it can be easily implemented in embedded platforms.
Towards a Generic Approach of Quantifying Evidence Volatility in Resource Constrained Devices

Forensic investigations of the Internet of Things (IoT) is often assumed to be a combination of existing cloud, network, and device forensics. Resource constraints in many of the peripheral things, however, are affecting the volatility of the potential forensic evidence, and evidence dynamics. This represents a major challenge for forensic investigations. In this chapter, we study the dynamics of volatile and non-volatile memory in IoT devices, with the Contiki operating system as an example. We present a way forward to quantifying volatility during the evidence identification phase of a forensic investigation. Volatility is expressed as the expected time before potential evidence disappears. This chapter aims to raise awareness and give a deeper understanding of the impact of IoT resource constraints on volatility and the dynamics of forensic evidence. We exemplify in which way volatility can be quantified for a popular operating system and provide a path forward to generalize this approach. The quantification of the volatility of potential evidence helps investigators to prioritize acquisition and examination tasks to maximize the likelihood of collecting relevant evidence from resource-constrained devices. Our work contributes to establishing a scientific base for evidence volatility and evidence dynamics in IoT devices. It strengthens methods for on-scene triage, event reconstruction, and for assessing the reliability of evidence findings.
Connecting Quaternion-Based Rotationally Invariant LCS to Pedestrian Path Projection

Pedestrian path projection is a crucial problem in computer vision, with a handful of applications ranging from surveillance to automotive systems. Out of several ways to approach this problem, one can consider trajectory matching with quaternion-based rotationally invariant LCS (QR-LCS) metric on motion features. In fact, this paper discusses the strategy to interpret this problem by means of trajectory matching and utilizes an invariant of LCS to resolve the same. It is necessary (for the systems implementing such strategies) to classify pedestrian action (walking/stopping) as well as precise path projection from a vehicle on motion (at momentary intervals). Hence, more sophisticated systems can utilize a probabilistic framework to efficiently reduce trajectory search space alongside the insights from the prominent trajectory matching approaches with the restoration of the underlying features that capture motion cues. In a nutshell, this paper investigates the process to link QR-LCS metrics to pedestrian path projection.
Mineralogical, alteration and fluid inclusion studies of the mineralization index at Yeylaghe Gharachi, northwest of Ahar, NW Iran

Mineralogical, alteration and fluid inclusion studies of the mineralization index at Yeylaghe Gharachi, northwest of Ahar, NW Iran