Reviewerly/exHarmony huggingface dataset:
Modeling Context Pair Interaction for Pairwise Tasks on Graphs

Predicting pairwise relationships between nodes in graphs is a fundamental task in data mining with many real-world applications, such as link prediction on social networks, relation prediction on knowledge graphs, etc. A dominating methodology is to first use advanced graph representation methods to learn generic node representations and then build a pairwise prediction classifier with the target nodes' vectors concatenated as input. However, such methods suffer from low interpretability, as it is difficult to explain why certain relationships are predicted only based on their prediction scores. In this paper, we propose to model the pairwise interactions between neighboring nodes (i.e., contexts) of target pairs. The new formulation enables us to build more appropriate representations for node pairs and gain better model interpretability (by highlighting meaningful interactions). To this end, we introduce a unified framework with two general perspectives, node-centric and pair-centric, about how to model context pair interactions. We also propose a novel pair-centric context interaction model and a new pre-trained embedding, which represents the pair semantics and shows many attractive properties. We test our models on two common pairwise prediction tasks: link prediction task and relation prediction task, and compare them with graph feature-based, embedding-based, and Graph Neural Network (GNN)-based baselines. Our experimental results show the superior performance of the pre-trained pair embeddings and that the pair-centric interaction model outperforms all baselines by a large margin.
A Framework to Create Collaborative Games for Team Building using Procedural Content Generation

We present a framework to design collaborative games for team building that employs a search-based procedural content generation toolset to help designers creating levels. It comprises a multiplayer game with asymmetric interaction in which a player must reach the exit of a maze within a time limit, while wearing a head mounted display. The maze is complex and the player could not reach the exit on time without a team there to help her using a large number of printed maps describing candidate mazes. The player describes what she sees to the team who use such information to identify which one of the several available maps the player is currently navigating in virtual reality. Each game requires the generation of hundreds of very similar maps with several aliasing (confusing) situations but at the same time need to have a solution to guarantee that if the team members collaborate effectively they can guide the player to the exit. It would be infeasible for a human designer to provide such a massive number of maps for every game played. Accordingly, we developed automatic authoring tools to help designers generate such large sets of maps and also to optimize them based on design principles focusing on fun and pace. Our preliminary results show that the authoring pipeline we created can generate games (set of maps) adherent to such design principles.
Autonomous Target Allocation Recommendations

We consider the problem of land vehicles under attack from a number of unmanned aerial systems. As the number of unmanned aerial systems increase, it may become difficult for human operators to coordinate actions across vehicles in a timely manner. In this paper, we study a number of algorithms designed to recommend actions to operators that will maximise the survivability of the vehicle fleet. We present a comparison of several assignment approaches including evolutionary strategies, genetic algorithms, multi-armed bandits, probability trees and basic heuristics. The performance of these algorithms is analysed across six different simulated scenarios. Our findings indicate that while there was no single best approach, Evolution Strategies, Ensemble and Genetic Algorithms were the strongest performers. It was also seen that a number of heuristic algorithms and the multi-armed bandits approach offered reliable performance in a number of scenarios without the need for any training.
Trends and Advances in Neural Machine Translation

In the time of globalization, there is a requirement for conquering the language barrier and interfacing with an ever-increasing number of individuals. The value of speaking to customers in their language is tangible with retention and engagement rates up over 70% when there are effective localization workflows in place. To do this, integrating Neural Machine Translation into traditional localization workflows is an efficient way. In this paper, we break down different models, approaches, inception, principle development, and structures utilized in NMT to discover a productive strategy to make a translation system and identifying the advances and imperfections of the equivalent. It also talks about evolution of various NMT techniques leading to the current methodologies and numerous challenges which could be taken further for future research trends and improvements carried on them.
Semantic-Based Query Expansion for Academic Expert Finding

Expert finding in academic domain is useful for many purposes, such as: to find research collaborators, article reviewers, thesis advisors, thesis examiners, etc. This work examines the use of semantic information, i.e. word embedding and document embedding, for query expansion to enhance the effectiveness of expert finding system. This information is utilized to bridge the lexical gap between the query and the expertise evidence of the experts. This semantic-based query expansion approach is then combined with a BM25 retrieval method to find relevant experts to the given query. The results show that our methods consistently outperform the strong retrieval method BM25, the semantic-based retrieval, and query expansion using pseudo relevance feedback method according to all recall- and precision-based measures used in this work. This indicates the effectiveness of our methods in improving the number and the accuracy of relevant experts retrieved.
Covert Channels in Transport Layer Security

Network covert channels embedded within network conversations are becoming widely adopted to enforce privacy of users or bypass censorship attempts as well as by malware to remain unnoticed while exfiltrating data or coordinating an attack. As a consequence, being able to design a network covert channel or anticipate its exploitation is of paramount importance to fully assess the security of the Internet. Since prime requirements for a successful covert channel are its stealthiness and bandwidth, the popularity, availability and performances of the overt traffic flows used as the carrier play a major role. Therefore, in this paper we investigate the use of ubiquitous Transport Layer Security (TLS) to contain hidden information for implementing network covert channels. Specifically, we review seven methods targeting TLS traffic and investigate the performances of three covert channels through an experimental measurement campaign. Obtained results indicate the feasibility of using TLS traffic as the carrier and also allow to derive some general indications for the development of countermeasures.
STAR

Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form “If X is true at time t , then Y will likely be true at time ( t +τ).” As with ARs, solving the TAR mining problem can generate huge numbers of rules. We show that methods to summarize ARs cannot work directly with TARs, and we develop two notions— strong and weak summaries—to summarize a set of TARs. We show that the problems of finding strong/weak summaries are NP-hard, and we provide polynomial-time approximation algorithms. We show experimentally that the coverage provided by our summarization methods is very high. Both technical measures based on coverage and human experiments on six World Bank datasets using 100 subjects from Mechanical Turk and a separate experiment with terrorism experts on a terrorism dataset show that while both summarization methods perform well, weak summaries are preferred, despite their taking more time to compute than strong summaries.
Wavelet transformation on a finite interval

Integral transformations on a finite interval with a singular basis wavelet are considered. Using a sequence of such transformations, the problem of nonparametric approximation of a function is solved. Traditionally, it is assumed that the validity condition must be met for a basic wavelet (the average value of the wavelet must be zero). The paper develops the previously proposed method of singular wavelets when the tolerance condition is not met. In this case Delta-shaped functions that participate in Parzen – Rosenblatt and Nadaray – Watson estimations can be used as a basic wavelet. The set of wavelet transformations for a function defined on a numeric axis, defined locally, and on a finite interval were previously investigated. However, the study of the convergence of the decomposition on a finite interval was carried out only in one particular case. It was due to technical difficulties when trying to solve this problem directly. In the paper the idea of evaluating the periodic continuation of a function defined initially on a finite interval is implemented. It allowed to formulate sufficient convergence conditions for the expansion of the function in a series. An example of approximation of a function defined on a finite interval using the sum of discrete wavelet transformations is given.
We See You: Terrorist Prediction Framework through Psychological and Social Behaviors

Over the last decades, terroristic attacks have caused human tragedies and loss of human life and materials and had negative impact on the economy and the development of the world. There have been over 48,000 terrorist incidents from 2000 to 2014 claiming over 107,000 lives according to the Global Terrorism Index GTI. Therefore, there is an urgent need for a system to proactively detect and predict any potential threats that can be caused by terrorist attacks. Artificial Intelligence (AI), Internet of things (IoT), and cloud technology can be integrated to prevent terroristic attacks. In this paper, I propose a novel framework called “We See You” (WSY). WSY is a complete comprehensive system framework which proposed to prevent and reduce the terroristic attacks by predicting any instance of terrorist attack. The prediction is based on monitoring the ongoing psychological and social behaviors, some important physiological signs and other markers, using IoT devices. Physiological signs related to the terrorist attacks include the blood concentration in the face, the eyes spotting, the gait, the hair and hair shaving, the face expressions, and the weight. The other personal markers include the clothes, the speech, the speech stress & emotion, and the smell. The prediction is based on the analysis of previous individual terrorist's profile (built from News, law enforcement records, etc.), and previous recording cases of video and audio of terrorist incidents. To the best of my knowledge, this framework system is the first framework that proposed to predict and identify the terrorist timely before the implementation of the suicide incidents, through the psychological and social behaviors, speech, some important physiological signs and other markers.
Template Adaptation of 2D Quasi-Periodic Data Using a Soft-Assign Localized Correspondence Matrix

In this work we propose a framework for the adaptation of arbitrary quasi-periodic time series. We parameterize and adapt data using traditional free form deformations. The method follows an alternating approach, inspired by the robust point matching algorithm, under which a correspondence matrix is updated and the subsequent deformation is obtained; thus, the template data are adapted to the target data. We demonstrate the performance of the algorithm across several electrocardiogram (ECG) and photoplethysmogram (PPG) databases and compare it to previous works.
Semi-Autonomous Parking system for Automatic Transmission vehicles

Abstract This paper proposes an automated perpendicular parking strategy for a car which is to be designed. The design considers general cases of perpendicular parking for a rectangular body within a rectangular space. The system works in phases named: The Scanning phase, the parking environment is detected by ultrasonic sensors mounted on the extreme ends of body and a parking position and maneuvering path is produced if the space is enough. Then in the positioning phase, the model positions itself and get aligned with the parking space avoiding potential collisions. Finally, in maneuvering phase, the model moves to the parking position in the parking space in a specified path, which requires clutch, brake control, backward and forward maneuvers depending on the dimensions of the parking space. Based on the characteristics, a collision-free path is planned about the surroundings. The strategy is to be integrated into an automated parking system, and implemented in the car, showing capable of safe parallel parking in tight situations and as automated parking device to help vehicle drivers.
Device Placement Optimization for Deep Neural Networks via One-shot Model and Reinforcement Learning

With the development of deep learning that employs deep neural networks (DNN) as powerful tool, its computational requirement grows rapid together with the increasing size (e.g. depth and parameter) of DNN. Currently, model and data parallelism are employed for accelerating the training and inference process of DNN. However, the above techniques make placement decision on devices for DNN based on heuristics and intuitions by machine learning experts. In this paper, we propose an novel approach for designing device placement of DNN in an automatic way. For a DNN, we employ a sequence-to-sequence model as controller to sample device placement from a one-shot model, which contains all possible device placements with respect to a specific hardware environment (e.g. CPU and GPU). Then, reinforcement learning treats the execution time of sampled device placement as reward to guide the sequence-to-sequence model for finding better one. The proposed approach is employed to optimize the device placement for both model and data parallelism of Inception-V3 on ImageNet. Experimental results show that the optimal placements discovered by our method can outperform hand-crafted one.
Comparative Analysis of Machine Learning Techniques using Customer Feedback Reviews of Oil and Gas Companies

Sentiment analysis is the process of computationally identifying and categorizing opinions from a piece of text to determine whether the writer's attitude towards a practical topic, products or services is positive, negative or neutral. In this study, Machine Learning techniques are used to perform sentiment analysis on Oil and Gas customer feedback data. We present a comparison of different classification algorithms used for opinion mining, including Support Vector Machine (SVM), Naïve Bayes (NB), Instance Based Learning (IB3), Random Forest (RF), Partial Decision trees (PART), and Logit Boost (LB). Many studies have been performed on sentiment analysis in different sectors, but research into Oil and Gas customer feedback has been limited. Therefore, we have targeted a pathless sector, namely the Petroleum sector, where companies express their opinions towards specific products or services. Waikato Environment for Knowledge Analysis (WEKA) is used for experimental results. The WEKA environment is open source software entailing a collection of machine learning algorithms to solve data mining problems. The main aim of this study is to evaluate the efficiency of the above mentioned classifiers in terms of Precision, Recall, F-Measure and Accuracy. The findings of the comparison analysis indicate that the Naïve-Bayes classifier gives the best Accuracy of all classifiers. A small dataset could be considered as a limitation to our study due to the difficulty of gaining more datasets at the time of the research. However, this research will play a vital role for researchers in making decisions about the algorithm that they are going to use to solve their data mining problems.
Life Cycle Assessment of Producing Synthetic Fuel via the Fischer-Tropsch Power to Liquid Process

A life cycle assessment is a method for evaluating the total life impacts of a product on the environment. This paper conducts a comparative life cycle assessment that compares the environmental impacts for a traditional aviation fuel refining process to a novel power to liquid fuel manufacturing process. The paper covers the manufacturing methods for both types of aviation fuel mentioned above. It chronicles the boundaries and assumptions made; the technical data acquisition and interpretation on the resource inputs and outputs for the two manufacturing methods; and a comparison showing how each system compares to the other based on environmental consequences. The analysis concludes that both the power to liquid process and the traditional crude oil refining process are similar in resource depletion based on usage of the US National Power Grid’s current sourcing of electrical energy. However, we also see that a shift to greater reliance on renewable resources makes the power to liquid manufacturing process a more attractive alternative.
A Machine Learning Approach for the Classification of the Buddha Statues of Borobudur (Indonesia)

The displacement of artwork from one part of the world to another is very common in highly connected world. In this paper, our study is limited to the images of Buddha. The statues of Buddha in different part of the world are having some distinct features, which can be used to classify them. We have made a sincere attempt to recognize and classify the Buddha statues of Borobudur, Indonesia, owing to its remarkable and significant features. A machine learning-based approach has been applied to do the same. A face recognition system is also being used, which extracts the features of a statue using machine learning libraries. A two-step approach has been performed to identify the face: face encoding using deep neural networks and classification using support vector machines.
An Improved Adaptive Optimization Technique for Image Classification

In deep learning, the optimization techniques are the most part dependent on gradient descent methods, such as SGD, Adam; adopt the leading place in the area of optimization methods. Conventional methodologies which depend on stochastic gradients are non-adaptive because the prescribed parameter worth's usage should be tuned for every application. However, the generalization performance of the stochastic optimizers is far superior to the adaptive methods, whereas Adam and its variants cannot maintain this without a fast convergence rate in deep neural networks. To improve this generalization performance, we need to diminish the oscillation of the weights, the general problem of the accuracy fall. To stable the generalization performance, we have introduced Mean-ADAM, a variance of Adam, extends the updated weights by an external weight to diminish the oscillation and overcome a superior accuracy rate than all other adaptive gradient methods till the conclusion of the training. Therefore, we substantially improve the generalization performance, permit it to contend with the existing algorithms on different image classification datasets such as Mnist, Cifar 10, Cifar 100, ImageNet, etc. We have attained 82% at 150 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> epochs with Cifar 10 and 99.49% with Mnist where the Adam has indicated 76% and 99.43% individually. The graphical statistics and the quantitative results can indicate that the proposed method is more likely to perform in more diverse image datasets.
Measuring land subsidence of buildings in Semarang Chinatown

Abstract There are not many studies that discuss land subsidence and try to measure it in historic areas. This paper will take an inventory of historical areas that have existed since the early growth of cities such as Semarang Chinatown, which has existed since the 17 th century. This paper is part of the results of research on land subsidence in coastal cities in Java, which have left many historical areas. Semarang City is one of the case studies because it has a high land subsidence rate of around 20 cm/year in the North Semarang area. Indeed, this area in the past was an ocean. However, due to sedimentation, on the old 1700s map, this area has become a marsh. After becoming mainland, several historical areas stood on young alluvial land. The purpose of this paper is to make an inventory of old buildings in the historic area of Chinatown. On the map from 1695 to now, after 305 years, the area has experienced land subsidence, which can be seen visually in several buildings. The research method used old photos, old maps, and field surveys by taking samples of several old residences that still exist. To strengthen the data, the use of Google street view is very helpful in seeing conditions in the field. The recommendation of this research is to provide directions for an inventory of the list of buildings that need to be conserved due to the hazard of subsidence, which is currently not being felt by the local community.
An Improved Neural Network Based Load Invariant Electrical Harmonic Detector

Application of neural network (NN) for electrical harmonic detection in electrical power system is being carried on by researchers extensively after the rapid development of computational capability of computers. Approach is made by real time data and simulation data to train the neural networks to detect harmonics. But still the implication of neural network needs many improvements to reduce complexity of network and computational time with minimum training dataset possible. Only successful detection of harmonics should not be the concern, because for real time detection and filtering, the network should be worthy of application by dint of its simplicity and fast action. Here, in this research work, an improved NN based harmonic detector with faster and easier training scheme is proposed which includes simple preprocessing of training data and careful selection of training algorithm. The proposed model is also interfaced with real time experimental setup to justify its applicability against traditional approaches made for electrical harmonic detection by neural networking.
Rethinking Blockchain and Decentralized Learning: Position Paper

Blockchain technology and decentralized learning are attracting growing attention. Most existing methods of machine learning is in the centralized form which relies upon the third party in terms of the raw datasets and mining resources. Blockchain solves world centralization problems that keep the system secure through complex mathematical computations puzzle solved by blockchain miners. Concurrently, decentralized learning such as federated model allows the user to collaboratively access the updated prediction model without revealing the training data to the public. By doing so, it provides less power consumption, lower latency that respects the user’s privacy concern. Therefore, we study the extent to which these two technologies can be applied in the real world for faster convergence without compromising user’s security.