Reviewerly/exHarmony huggingface dataset:
Optimized autonomous UAV design with obstacle avoidance capability

Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. This article introduces a novel OAN algorithm – dubbed the Closest Obstacle Avoidance and A* Algorithm (COAA*) – that bridges the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits, and is very easy to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA* has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation.
Normal Science in the Time of Corona

This issue of Engineering Studies comes out during the COVID-19 pandemic, and this editorial is being written from home under ‘lockdown’ conditions. Right now, it is too early to know what the long...
Application of Coverage Path Planning Algorithm for Milling Operations

In this paper, we present an algorithm for automatic tool path generation for milling operations, where, the ‘cutter’ needs to pass through all the region that is required to be removed, without any gaps. We demonstrate the possibility of using mobile robot coverage path planning (CPP) algorithms for such applications. In the place of the robot footprint size that is used in a mobile robot CPP algorithm, here we use the size (diameter) of the tool as basis for the tool path generation. Here, we use a spanning tree-based competitive and truly complete robot coverage path planning algorithm, which is based on the approximate cellular decomposition. The proposed algorithm is first tested in V-Rep simulation environment with an arbitrary work piece and then real-time experiments were carried out on a CNC machine to demonstrate the proposed algorithm.
Risk Management for Software Projects in Banking

The management techniques for projects are critical to the success of large financial software projects. Project management is systematically monitored and controlled by establishing a baseline based on scope, schedule, and cost. In addition, risk management must be managed to identify and respond to risks in advance for the entire project. The study of risk management for most financial software projects is either a theoretical proposal with no empirical assessment or a case study of a similar project. Therefore, in this paper, authors would like to conduct research in real core banking project site in Vietnam which helps to know the method of process improvement, risk management, and results in various project situations as well.
Experience

The Internet Para Todos program is working to provide sustainable mobile broadband to 100 M unconnected people in Latin America. In this paper we present our commercial deployment in thousands remote small communities and describe the unique experience of maintaining this infrastructure. We describe the challenges related to managing operations containing the cost in these extreme geographical conditions. We also analyze operational data to understand outage patterns and present typical operational issues in this unique remote community environment. Finally, we present an extension of the operations support system (OSS) leveraging advanced analytics and machine learning with the goal of optimizing network maintenance while reducing costs.
An End-to-End Tool for News Processing and Semantic Search

In this demonstration, we present an intelligent system for business analysis and market research that can produce actionable competitive and strategic insight. It has two sub systems, a news content processing pipeline and a semantic search engine. The pipeline collects global business news and processes them to establish context, themes, topics, entities, sentiment and relationships. The semantic search system integrates all the information produced by the pipeline and provides users the semantic search and information exploration ability. This paper introduces all the components of this system.
Secure Data Management Using IPFS and Ethereum

Blockchain technology is receiving increased attention over the past few years. Blockchain is essentially a decentralized, distributed digital ledger in which data is organized into blocks. The data stored in Blockchain is secured using cryptographic properties. Storage is the main concern when the volume of transactions increases. Handling data in huge amount may lead to increased overheads as data can only be appended to it, and the previous data cannot be deleted. So, it led to the development of the InterPlanetary File System (IPFS). IPFS is a distributed database with peer-to-peer file system and uses content-addressable hash value which is used to verify the data. Using IPFS, one can upload the data in the form of text, images as well as videos. While uploading the data in IPFS, the protocol returns a unique hash value for the data via gateway. This hash value is securely stored in Blockchain. Based on the hash value, the data can be retrieved. In IPFS, data is distributed among the network participants locally. The proposed work combines both IPFS and Blockchain technologies. This scheme uses IPFS for storing large volume of data, removes duplication, share files, and hosts them. The proposed work minimizes the central server storage space, improves data security by using unique content-addressable hashes for data verification and also reduces computing power.
Improved K-Means Algorithm on Home Industry Data Clustering in the Province of Bangka Belitung

The Government of Bangka Belitung Islands Province has not classified the home industry until now. Based on these problems, we propose a k-means algorithm for clustering home industry data. The k-means algorithm is widely used because it is straightforward and very suitable for grouping data. However, in its application, the k-means algorithm has a weakness in determining the starting point of the cluster center and, in its selection, is still carried out randomly. As a result, if the random value for initializing the initial centroid value is not right, then the grouping is less than optimal. Internal cluster validation is one way to determine the optimal cluster without knowing prior information from the data. This study aims to identify the optimal group by making improvements to the k-means algorithm and then to test it by applying an internal cluster, namely the Davies-Bouldin Index (DBI) and the Silhouette Index (SI) on the data of home industry in Bangka Belitung Island Province. The optimal cluster calculation results based on internal cluster validation both show that the Silhouette index and the DBI index with k = 3 on improved k-means algorithm. While the traditional k-means algorithm of internal cluster validation both show that the Silhouette index and the Davies-Bouldin Index with k = 2. The conclusion is k = 3 on the Davies-Bouldin Index of this research data gives good results for clustering home industry data in Bangka Belitung Islands Province.
Tracking an Object Using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter

Nowadays object tracking is a challenge in the fields of computer vision, many algorithms are put forward to overcome the issues such as noisy, colored background, and occlusion of object and also illusion which is low and sudden changes in real-time videos, trackers cannot perform well in real-time environment. An efficient algorithm is needed for effective tracing of an object because of variation in position under various conditions. Here, the MS algorithm is used due to its efficiency and performance. MS tracking algorithm is used to obtain the position of an object by using a Kalman filter, therefore, the performance of tracking an object for different videos is evaluated but it does not improve the target localization. The advanced version is developed for tracking and representing the object in different video sequences are MS and CBWH MS using Kalman filter. Therefore, the proposed method is better than the traditional MS track, which results in the constant tracking of an object throughout the video. It does not get influenced by occlusions and less subjected to background cluttering.
Coherent combination of probabilistic outputs for group decision making: an algebraic approach

Current decision support systems address domains that are heterogeneous in nature and becoming progressively larger. Such systems often require the input of expert judgement about a variety of different fields and an intensive computational power to produce the scores necessary to rank the available policies. Recently, integrating decision support systems have been introduced to enable a formal Bayesian multi-agent decision analysis to be distributed and consequently efficient. In such systems, where different panels of experts independently oversee disjoint but correlated vectors of variables, each expert group needs to deliver only certain summaries of the variables under their jurisdiction, derived from a conditional independence structure common to all panels, to properly derive an overall score for the available policies. Here we present an algebraic approach that makes this methodology feasible for a wide range of modelling contexts and that enables us to identify the summaries needed for such a combination of judgements. We are also able to demonstrate that coherence, in a sense we formalize here, is still guaranteed when panels only share a partial specification of their model with other panel members. We illustrate this algebraic approach by applying it to a specific class of Bayesian networks and demonstrate how we can use it to derive closed form formulae for the computations of the joint moments of variables that determine the score of different policies.
Investigating topics, audio representations and attention for multimodal scene-aware dialog

With the recent advancements in Artificial Intelligence(AI), Intelligent Virtual Assistants (IVA) such as Alexa and Google Home have become a ubiquitous part of every home. Currently, such IVAs are mostly audio-based, but going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances. This will enable agents to have conversations with users about the objects, activities and events surrounding them. As part of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual Scene-Aware Dialog (AVSD) track, we explore three main techniques for multimodal dialog: 1) exploring ‘topics’ of the dialog as an important contextual feature for scene-aware conversations, 2) investigating several multimodal attention mechanisms during response generation and 3) incorporating an end-to-end audio classification sub network(AclNet) into our architecture. We present detailed analysis of our experiments and show that our model variations outperform the baseline system presented for this task.
The Effect of Room Complexity on Physical Object Selection Performance in 3-D Mobile User Interfaces

An important challenge in smart environments is how to manipulate the smart objects. Although mobile applications are typically used for controlling a smart environment, no previous study has evaluated the users performance in manipulating smart objects under different environmental complexities. This article presents an experimental comparison between three different selection techniques 3-D, 2-D, and physical user interfaces (UIs). We evaluate these techniques across two levels of environment complexity measuring 51 participants timing data and errors. Our results indicate that the 3-D UI is superior for task completion time and error, and the 2-D UI is not a better solution than the physical UI when the environment is not complex. The results also show the importance of considering the environment complexity in choosing the proper UI.
Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics

Human eye gaze patterns are highly individually characteristic. Gaze patterns observed during the routine access of a user to a device or document can therefore be used to identify subjects unobtrusively, that is, without the need to perform an explicit verification such as entering a password. Existing approaches to biometric identification from gaze patterns segment raw gaze data into short, local patterns called saccades and fixations. Subjects are then identified by characterizing the distribution of these patterns or deriving hand-crafted features for them. In this paper, we follow a different approach by training deep neural networks directly on the raw gaze data. As the distribution of short, local patterns has been shown to be particularly informative for distinguishing subjects, we introduce a parameterized and end-to-end learnable statistical aggregation layer called the quantile layer that enables the network to explicitly fit the distribution of filter activations in preceding layers. We empirically show that deep neural networks with quantile layers outperform existing probabilistic and feature-based methods for identifying subjects based on eye movements by a large margin.
CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums

Community discussion forums are increasingly used to seek advice; however, they often contain conflicting and unreliable information. Truth discovery models estimate source reliability and infer information trustworthiness simultaneously in a mutual reinforcement manner, and can be used to distinguish trustworthy comments with no supervision. However, they do not capture the diversity of word expressions and learn a single reliability score for the user. CrowdQM addresses these limitations by modeling the fine-grained aspect-level reliability of users and incorporate semantic similarity between words to learn a latent trustworthy comment embedding. We apply our latent trustworthy comment for comment ranking for three diverse communities in Reddit and show consistent improvement over non-aspect based approaches. We also show qualitative results on learned reliability scores and word embeddings by our model.
A Survey of Machine Translation Approaches for Konkani to English

Machine Translation is a popular field in computer science. It’s also a key part in the field of Natural Language Processing. Since India is a country where multiple languages are spoken there is a need for Language Translation. In this paper, we present a survey of several research works and the machine translation approaches mentioned in them. We begin with some well-established methodologies and further delve into more recent and hybrid techniques which are still in the state of improvement. We also compare these papers using several parameters such as the tools & technologies, datasets, advantages, disadvantages, and accuracy. We propose a machine translation model based on Statistical Machine Translation that will help us to translate from the Indian regional language Konkani to English and vice versa.
Semantically find similar binary codes with mixed key instruction sequence

Software similarity comparison has always been a common technique for software reuse detection, plagiarism detection, and defect detection. Considering the role of API calls and arithmetic operations in software execution, a semantic-based dynamic software analysis method–mixed key instruction sequence (MKIS) is proposed. MKIS embeds key value sets into a vector and constructs a novel software execution sequence that contains API calls and arithmetic operations during software execution. To determine the location of key values, a key-value equivalent matching algorithm is proposed, combined with the longest common subsequence algorithm to optimize the software execution sequence. Experiments show that MKIS can accurately compare the similarity of binary programs without obtaining the software source code, and has better resiliency and credibility. Moreover, in the case when the software source code is changed with some main function-independent modification and code obfuscator, software reuse can be successfully detected.
Feature Extraction Based on Improved Feature Weighting Algorithm

Text feature is an important category attribute of text. Feature extraction directly affects the accuracy of text classification. An improved feature weighting algorithm is proposed in this paper. The chi-square statistical method is applied to calculate feature weight, which improves the accuracy of extracting feature words of categories. The IDF calculation method is improved from the category concentration of keywords. This paper uses the TF-IDF before and after the improvement to extract feature and classify the same text data individually. The results show that the classification effect of the improved method is better than the traditional method.
Steganalysis of Color Images for Low Payload Detection

This paper introduces a steganalysis model that uses statistical texture features and the machine learning approach to detect the presence of hidden data in a benchmark dataset of RGB color images. The work analyzes features of an RGB image in PPM format as a composite unit. The feature set used in this study consists of 120 features per color channel, which includes the basic and extended Gray Level Co-Occurrence Matrix (GLCM). The machine learning binary classifier that is selected for this work is the Support Vector Machine (SVM) algorithm. A public dataset of 10,000 uncompressed PPM clean images is used, and seven stego image datasets of 10,000 images each were created from the clean images, which were embedded with random secret data at payload ratios from 0.01 to 0.5 bit per channel, using 1LSB steganography technique The steganalysis results, showed detection accuracy values ranging from 56.18% for 0.01 bit per channel to 91.00% for 0.5 bit per channel.
Training RNN and it’s Variants Using Sliding Window Technique

Recurrent neural networks are a type of neural network which was developed for handling sequential data more efficiently. Unlike feedforward neural networks, RNNs can use their internal state to process input sequences. A recurrent network can be implemented in many ways like Long Short Term Memory cell (LSTM), Gated Recurrent Unit (GRU), multidimensional LSTM, bidirectional LSTM, etc. In this paper, we have implemented variants of RNN. We have trained the models using conventional training technique as well as using a sliding window training technique. Later on in this paper, we have compared these techniques based on their performances and concluded which technique and model produce the best result for different datasets.
Security: Human Nature and Behaviour

The human factor has always been considered the weakest link in the security chain. In this chapter we use a social psychology approach to discuss people’s behaviour in relation to cybersecurity, by considering human errors, personality traits, the relationship between attitude and behaviour and the influence of social and situational factors. Human error has been widely studied in literature, especially in aviation and health care fields. Regardless of the area involved, analysing human factors is fundamental to understand the causes of accidents. With respect to cybersecurity, in fact, human errors—deriving from, e.g., work pressure, distraction, lack of awareness, organizational factors—can be considered one of the most important causes of security breaches. Moreover, looking at the relationship between attitude and behaviour, we can see how individuals do not always behave in coherence with their beliefs. For instance, regarding the protection of personal data, while many people are convinced that this is absolutely needed, in practice they continue to share data and information on social media, underestimating the related risks and consequences. A global perspective in investigating human factors within organizations requires considering the work environment. Furthermore, an increasingly technological context exposes individuals to different stimuli and stressors, generating critical conditions—such as information overload and technostress—affecting individuals’ behaviour. Therefore, for effective adoption of secure behaviour, the management of employees’ well-being has to be included in cybersecurity awareness programmes.
A Blockchain-based Security Approach Architecture for the Internet of Things

With the rapid development of Internet of things technology, IoT security problems are increasingly prominent. Blockchain is a new application model of distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and other computer technologies, which provides a new solution to solve the security problems of IoT. This paper envisages a blockchain-based security approach architecture for IoT, which aims to construct a space of mutual trust between devices for reliable data transmission.
Mixed-Signal Circuits and Architectures for Energy-Efficient In-Memory and In-Sensor Computation of Artificial Neural Networks

Von Neumann architecture is recently facing a critical challenge with the high demands of energy-efficient computing hardware for a variety of machine learning tasks such as image classification. In particular, battery-operated mobile devices with limited power budget cannot process artificial neural networks (ANNs) with relatively low complexity by using traditional digital circuits and architectures. The key challenge with the traditional Von Neumann architecture is its energy-inefficient data access between memory and processor. Recently, in-memory computing architecture has gained significant attention as an alternative, especially for running mobile artificial intelligence applications. The memory access energy has been drastically reduced by using local in-memory processing elements which directly use the data stored in local memory. To further improve the efficiency, the processor based on mixed-signal circuits instead of conventional digital circuits have recently been actively researched. However, mixed-signal circuits have several critical drawbacks, including nonlinearity, PVT variation, and the overhead of ADC/DAC for interfacing with the external digital domain. In this work, we first review the recent mixed-signal circuits and architectures for in-memory computing using different embedded memories. In addition, we introduce the concept of in-sensor computation for integrating partial computing units in an image-sensor array using low-power mixed-signal circuit techniques.