Reviewerly/exHarmony huggingface dataset:
On the Implementation of a Semantic Model for Intelligent Vehicle Navigation

This paper considers the problem related to the navigation of multi-actuated intelligent vehicles in interaction with its occupants and its environment, by modeling and describing the semantics of the contexts associated to the movement. That is, formally defining the semantic model of the navigation context, as obstacles, pedestrians, roundabouts, information on the vehicle itself (load, power, etc.). This semantic modeling generates an additional degree of abstraction by “reasoning” about the information collected by the various sensors of an autonomous vehicle in order to build a navigation context for the latter. This navigation context is able to provide decision support for the autonomous or semi-autonomous vehicle (interaction to the driver). In this paper, we show initial modeling results by defining and selecting the relevant information in the creation of a context for the vehicle, structuring the contextual information so as to be able to reasoning on it and thus dynamically update the context in which the vehicle operates; maintaining information by choosing suitable data persistence models in order to be able to query previous simulations efficiently.
A Comprehensive Review on the Significance and Impact of Deep Learning in Medical Image Analysis

Healthcare sectors have evolved over the years to remain as one of the most demanding and important aspects of human lives requiring immediate services and attention in difficult times but the entire process is quite tedious and time-consuming when performed by medical experts. However, with the advent of Artificial Intelligence based machine learning or deep learning techniques, the medical image analysis task became quite smoother, faster, and efficient delivering more optimized performances. This manuscript briefs us about the various deep learning techniques and methodologies being applied till now in the domain of medical image processing besides emphasizing the overview of recent advances and overall contributions being made in this field along with its associated challenges. It also throws light on the future perspective to overcome those challenges specifically using better and innovative approaches.
A Deep Learning Approach to Count people Using Facenet Architecture

This paper proposes a unique methodology to count people using a vision based approach. Facenet, a deep learning architecture is the backbone of this system. We have designed a simple yet unique algorithm to count people and store the pictures of individuals in a database for later investigation if needed. Our vision based people counter rely on face detection or pedestrian detection. These types of algorithms do not serve the need while using it for security. Keeping that in mind we have designed our system in a manner which can be used for security purposes. Our system is also able to identify the number of people present in a particular room and how many have left that room in a particular time. The system not only counts people but also tracks each individual, when he/she enters and leaves. For this paper we have tested four different face detection methodologies and chose the best one. The whole system can be implemented in any computer. We have tested the system with two cameras. One is for entrance, another is for exit. SQLite is used here for storing and managing data throughout the system. In this paper we able to achieve about 88% accuracy with the ability of storing each individual in the database.
SQLi-Fuzzer: A SQL Injection Vulnerability Discovery Framework Based on Machine Learning

With the development of information technology, the function of Web application becomes more consummates, but its security problems are more prominent, among which SQL injection has become one of the most serious threat to Web application today. With the increase of the complexity of Web application, the difficulty of SQL injection vulnerability discovery increases gradually, and the traditional vulnerability discovery technology is difficult to deal with it. Fuzzing is more suitable for discovering SQL injection vulnerability because of its unique advantages. However, Fuzzing application in the field of Web application vulnerability discovery is limited by the difficulty of initial testcase construction and the complexity of mutation strategy selection. In this paper, we research the application of machine learning in Fuzzing field and solve the weakness of Fuzzing by machine learning algorithm. We design and implement a SQL injection vulnerability discovery framework based on machine learning--SQLi-Fuzzer, which realizes the Fuzzing function based on Genetic Algorithm, and applies machine learning algorithm to the generation and mutation of testcase. We evaluate the performance of the SQLi-Fuzzer from two aspects: payload ratio and vulnerability discovery ability. The result shows that SQLi-Fuzzer can not only generate higher quality testcases, but also discover SQL injection vulnerability of Web application more effectively.
Distribution Preserving Deep Semi-Nonnegative Matrix Factorization

Deep semi-nonnegative matrix factorization can obtain the hidden hierarchical representations according to the unknown attributes of the given data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intra-class data. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. Here we propose a novel distribution preserving deep semi-nonnegative matrix factorization method (DPNMF) to achieve this goal. As a result, the manifold structures in the raw data are well preserved in the feature space being from the top layer. The experimental results on the real-world datasets show that the proposed algorithm has good performance in terms of cluster accuracy and normalized mutual information (NMI).
Online/Offline Attribute-Based Encryptioin with Multi-Authority Access Control

The CP-ABE access control scheme can effectively implement fine-grained access control. As mobile devices are widely used, plenty of exponential calculations in ciphertexts calculation bring a huge challenge to users who use resource-limited mobile devices. To solve the problem, a decentralized CP-ABE access control scheme with offline/online encryption and outsourced decryption is proposed in this paper. The offline encryption will be accomplished when the device is not in use, which makes encryption faster and more efficient. In the decryption stage, the user will generate the transformed key and hand it over to the honest but curious cloud service provider for decryption preprocessing. The cloud service provider will complete most of the complex exponential calculations, so that the ciphertext can be decrypted quickly and safely by mobile devices. In addition, the scheme supports multi-attribute authority so that the conspiracy attacks can be effectively resisted. It is shown that the scheme can greatly reduce the computing time especially for mobile device users.
Hand Sign Language Detection Using Deep Learning

Hand gesture recognition is an important aspect of human computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google’s open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
Gesture Recognition-Based Interaction with Smartwatch and Electric Wheelchair for Assistive Mobility and Navigation

Smartwatches are becoming increasingly accessible to end users, thereby placing the power of mobile technology at their fingertips. On the other hand, electric powered wheelchairs are designed to assist people with mobility impairments to move with ease. However, people with cognitive difficulties may not be able to navigate the wheelchair as required and may require additional support from wearable devices. This paper presents a gesture recognition-based navigation for the wheelchair using a smartwatch. Users are able to navigate the wheelchair using gestures that would be easily interpreted by the smartwatch. This capability opens up new mobility possibilities never before envisaged.
Visual feature synthesis with semantic reconstructor for traditional and generalized zero‐shot object classification

Zero-shot learning (ZSL) addresses the novel object recognition problem by leveraging semantic embedding to transfer knowledge from seen categories to unseen categories. Generative ZSL models synthesize the visual features of unseen classes and convert ZSL task into a classical supervised learning problem. These generative ZSL models are trained by using the seen classes. Although promising progress has been achieved in the ZSL and generalized zero-shot learning (GZSL) tasks. The existing approaches still suffer from a strong bias problem between unseen and seen classes, where unseen objects in the target domain tend to be recognized as seen classes in the source domain. To deal with the problem, we propose a novel named semantic consistent Wasserstein generative adversarial network (scWGAN), which uses a semantic reconstructor to reconstruct semantic embeddings from generated visual features by incorporating a novel Semantic Consistent Loss noted L rec . The Semantic Consistent Loss guides our proposed scWGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. We also introduce a visual classifier to constrain visual feature generator. Extensive experiments show that the proposed approach is superior to previous state-of-the-art works under both traditional ZSL and challenging GZSL settings on six popular data sets AWA1, AWA2, CUB, APY, and SUN.
Custom Binary Cross Entropy Based Anomaly Detection in Bank Transactions using Deep Convolutional Neural Network

Credit cards have become popular, and their usage has gone up in recent times, due to expansion in e-business and electronic payment systems. The corresponding data is enormous in size and subsequently can cause difficulty in analysis, which has made some organizations to misuse this facility in inappropriate ways, resulting in increase of fraudulent transactions. These transactions are termed as anomalies, whose detection is difficult due to their small number. Anomaly detection with deep learning is a challenging task as the data is highly imbalanced. Vanilla Loss functions usually cannot be used for anomaly detection as certain scenarios require the loss function to assign more loss to false negatives in comparison to false positives. To implement this, we have introduced a hyper parameter, as a modification to the famous loss function Binary Cross Entropy, which can be tuned based on the applications and conditions to get the best results.
Inferring Sensitive Information in Cryptocurrency Off-Chain Networks Using Probing and Timing Attacks

Off-chain networks have recently emerged as a scalable solution for blockchains, allowing to increase the overall transaction throughput by reducing the number of transactions on the blockchain. However, off-chain networks typically require additional bootstrapping and route discovery functionality to determine viable routes. For example, the Lightning Network (LN) uses two mechanisms in conjunction: gossiping and probing. This paper shows that these mechanisms introduce novel vulnerabilities. In particular, we present two attacks. The first one, which we shall call a probing attack, enables an adversary to determine the (hidden) balance of a channel or route through active probing and differentiating the response messages from the route participants. The second one, which we shall call a timing attack, enables the adversary to determine the logical distance to the target in hops, given that geographical data of LN nodes is often publicly listed, or can be inferred from allocated IP addresses. We explore the setup and implementation of these attacks and address both the theoretical and practical limitations these attacks are subject to. Finally, we propose possible remediations and offer directions for further research on this topic.
Influential Study and Development of Global Solar Radiation Prediction Model Using ANN

An increase in global air pollution level diminishes the surface solar radiation due to prolonged usage of fossil fuel combustion. Hence, solar energy is used as an alternate and eco-friendly resource to improve the human health and other environmental issues prevailing around the world. In this paper, influential study of meteorological parameters and air pollutions data was analysed using ANOVA and their impact on solar radiation was identified using Response surface methodology (RSM). From the study, 8 parameters were considered as influential parameters. The influential parameters were considered in predicting global solar radiation using feedforward back-propagation neural network model in Alandur, Chennai. The accuracy and performance of the model showed 0.48 as Mean Square Error (MSE) and a correlation coefficient (R) of 0.89 was observed. From the results, it was observed that the air pollution concentration had a greater effect on solar radiation than meteorological parameters.
MLSR: Missing information-based fidelity and learned regularization for single-image super-resolution

For single-image super-resolution, constructing an appropriate observation model is a significant but unnoticed attempt to super-resolve the low-resolution image. Observation model describes the process that how the low-resolution image degrades from a high-resolution image. The traditional observation model takes the assumption that the low-resolution image is suffered from noising, blurring, and down-sampling, and it is widely used in the super-resolution field. In fact, these factors usually result to some information lost during the degradation process, however, the missing information goes beyond the widely used observation model, which makes single-image super-resolution highly ill-posed. To solve this issue, we have to consider the question, "what is being ignored in the degradation process?" In this paper, we propose a novel framework by extending typical degradation-based single-image super-resolution with a plug-and-play method to handle the low-resolution image obtained in more complex real scenes. Specifically, a new observation model is designed to describe the degradation process more reasonably. To optimize the corresponding induced energy function, a plug-and-play super-resolution algorithm is derived based on the half splitting quadratic technique, which allows us to insert a learned denoising model as a modular part. The quantitative and qualitative evaluations illustrate the superiority of the proposed method for detail preservation and noise removal over state-of-the-art algorithms.
Communication-Efficient Quantized SGD for Learning Polynomial Neural Network

This paper establishes the convergence rates for fitting a polynomial neural network with quadratic activation function via the mini-batch Stochastic Gradient Descent (SGD) algorithm. Specifically, we focus on the parallel implementation of calculating mini-batch gradients on a distributed computing platform. We first illustrate that the SGD converges at a linear rate to the optimal solution, and the convergence rate can be characterized as a function of mini-batch sizes. Next, we deploy the SGD with a distributed approach across multiple processors, where the partial mini-batch gradient is calculated and quantized to send to a master processor in each iteration, yielding a Quantized Stochastic Gradient Descent (QSGD) algorithm. This scheme can effectively reduce the communication overhead by the quantization strategy. Furthermore, we reveal that QSGD provably maintains a similar convergence rate of SGD to a globally optimal solution while significantly reduces the communication cost. In particular, the number of bits required for quantization and the mini-batch size affect the convergence rate of QSGD.
Forensic Analysis of Tampered Digital Photos

Deepfake in multimedia content is being increasingly used in a plethora of cybercrimes, namely those related to digital kidnap, and ransomware. Criminal investigation has been challenged in detecting manipulated multimedia material, by applying machine learning techniques to distinguish between fake and genuine photos and videos. This paper aims to present a Support Vector Machines (SVM) based method to detect tampered photos. The method was implemented in Python and integrated as a new module in the widely used digital forensics application Autopsy. The method processes a set of features resulting from the application of a Discrete Fourier Transform (DFT) in each photo. The experiments were made in a new and large dataset of classified photos containing both legitimate and manipulated photos, and composed of objects and faces. The results obtained were promising and reveal the appropriateness of using this method embedded in Autopsy, to help in criminal investigation activities and digital forensics.
The role of value added across economic sectors in modulating the effects of FDI on TFP and economic growth dynamics

This study investigates: (i) the effect of foreign direct investment (FDI) on total factor productivity (TFP) and economic growth dynamics, and (ii) the relevance of value added from three economic sectors in modulating the established effect of FDI on TFP and economic growth dynamics. The geographical and temporal scopes are respectively 25 Sub-Saharan African countries and the period 1980–2014. The empirical evidence is based on non-interactive and interactive Generalised Method of Moments. The following main findings are established. First, FDI has a positive effect on GDP growth, GDP per capita and welfare real TFP. Second, the effect of FDI is negative on real GDP and TFP, while the impact is insignificant on real TFP growth and welfare TFP. Third, values added to the three economic sectors largely modulate FDI to produce negative net effects on TFP and growth dynamics. Policy implications are discussed with particular emphasis on the need to complement added value across various economic sectors in order to leverage on the benefits of FDI in TFP and economic growth. To the best of knowledge, this is the first study to assess how value added from various economic sectors affect the relevance of FDI on macroeconomic outcomes.
Emotion Recognition with Conversational Generation Transfer

Emotion recognition in conversation is one of the essential tasks of natural language processing. However, this task’s annotation data is insufficient since such data is hard to collect and annotate. Meanwhile, there is large-scale data for conversational generation, and this data does not need annotation manually. But, whether the vector space between different datasets is similar will be a problem. Therefore, we utilize a same dataset to train the conversational generator and the classifier, and transfer knowledge between them. In particular, we propose an Emotion Recognition with Conversational Generation Transfer (ERCGT) framework to model the interaction among utterances by transfer learning. First, we train a conversational generator. In the second step, a transfer learning model is used to transfer the knowledge of generator to the emotion recognition model. Empirical studies illustrate the effectiveness of the proposed framework over several strong baselines on three benchmark emotion classification datasets.
The Natural Disaster Prone Index Map Model in Indonesia Using the Thiessen Polygon Method

Natural Disasters are natural phenomena that occur at any moment that can cause loss. Indonesia is an archipelagic country located at the meeting of four tectonic plates and volcanic belts. This condition causes Indonesia to be prone to natural disasters. Therefore, it is necessary to make a natural disaster-prone index map model minimize the impact of natural disasters. In this research, the researchers used a Polygon Thiessen method for it was one of the mapping methods to determine a natural disaster based on Indonesia's vast surface and many disasters. The BNPB and Polygon Thiessen data comparison shows that BNPB data has a low level of vulnerability of 302, a moderate level of vulnerability of 148, and a high level of vulnerability of 58. In contrast, the Thiessen polygon has a low level of vulnerability of 297, a moderate vulnerability of 158, and a high vulnerability of 59. Comparing BNPB data and the Thiessen Polygon method found five differences from 40 data in the Papua region. Suggestions for further research to create an application-based information system so that it can be accessed in real-time.
Skeleton-based multi-agent opponent search

In many games, players may run away and hide from NPC enemies that have previously observed them, either to avoid combat or as part of pursuing a stealth-based solution. Rational NPC response then requires searching for the hidden player, which for maximal realism should build on the last known location, and consider the relative likelihood of a player hiding or reaching each searched location. Unfortunately, search behavior is not usually systematic, and in practice is either limited to randomized goals within a small region, or exploits global information on the player position that should be unknown. In this work, we introduce a real-time method for directing a multi-agent search utilizing the environment's topology. This approach allows for more natural and wider-scoped search behavior. Experimental results show that this method scales to relatively large game maps, and performs better than or close to a naïve team of agents fully aware of the player's position.
Extraction of Poetic and Non-Poetic Relations From of-Prepositions Using WordNet

The main goal of this paper is to extract the semantic relations underpinning the concepts of English prepositional of-constructions derived from poetic and non-poetic datasets, using Princeton WordNet.The problem is addressed by two different algorithms, which are evaluated for their ability to model the different types of resources from which the relations are derived, and for their ability to predict unseen relations.The first algorithm introduces the concept of subsumption hierarchy between relations in order to derive the most general relations associated to each type of data source and identify a set of relations specific to each dataset.The second algorithm investigates the use of a weighting scheme in order to establish the importance of each association extracted.Of particular importance are the notions of subsumption hierarchies between relations (expressed as synset pairs) and the Inverse Relation Frequency (IRF) measure, which is inspired by the Inverse Document Frequency measure used in Information Retrieval.The ontological prospects of using Princeton WordNet and the above algorithms for the creation of ontologies are also briefly discussed.Although the main interest of the proposed methods lies to the identification of conceptual relations particular to poetic resources, the methods followed can be applied and are evaluated on other domains too.