Reviewerly/exHarmony huggingface dataset:
Interactive Imitation Learning in State-Space

Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing feedback while the agent executes its task. In this work, we propose a novel Interactive Learning technique that uses human feedback in state-space to train and improve agent behavior (as opposed to alternative methods that use feedback in action-space). Our method titled Teaching Imitative Policies in State-space~(TIPS) enables providing guidance to the agent in terms of `changing its state' which is often more intuitive for a human demonstrator. Through continuous improvement via corrective feedback, agents trained by non-expert demonstrators using TIPS outperformed the demonstrator and conventional Imitation Learning agents.
Comparative Analysis on Prediction of Software Effort Estimation Using Machine Learning Techniques

Effort Estimation (EE) is a technique for finding the entire effort required to predict the accuracy of a model. It’s a significant chore in software application development practice. To find accurate estimation, numerous predictive models have developed in recent times. The estimate prepared during the early stage of a model expansion is inaccurate since requirements at that time are not very clear, but as the model progresses, the accuracy of the estimation increases. Therefore, accurate estimation is essential to choose for each software application model development. Here, Linear Regression (LR), Multi-layer perceptron (MLP), Random Forest (RF) algorithms are implemented using WEKA toolkit, and results shows that Linear Regression shows better estimation accuracy than Multilayer Perceptron and Random Forest.
Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search

In location-based search, user's click behavior is naturally bonded with trilateral spatiotemporal information, i.e., the locations of historical user requests, the locations of corresponding clicked items and the occurring time of historical clicks. Appropriate modeling of the trilateral spatiotemporal user click behavior sequence is key to the success of any location-based search service. Though abundant and helpful, existing user behavior modeling methods are insufficient for modeling the rich patterns in trilateral spatiotemporal sequence in that they ignore the interplay among request's geo- graphic information, item's geographic information and the click time. In this work, we study the user behavior modeling problem in location-based search systematically. We propose TRISAN, short for Trilateral Spatiotemporal Attention Network, a novel attention- based neural model that incorporates temporal relatedness into both the modeling of item's geographic closeness and the modeling of request's geographic closeness through a fusion mechanism. In addition, we propose to model the geographic closeness both by distance and by semantic similarity. Extensive experiments demonstrate that the proposed method outperforms existing methods by a large margin and every part of our modeling strategy contributes to its final success.
Multi-Graph Fusion Networks for Urban Region Embedding

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. https://github.com/wushangbin/MGFN
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers.

Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically. Here we demonstrate that by revisiting model configurations as basic as scaling of embeddings, early stopping, relative positional embedding, and Universal Transformer variants, we can drastically improve the performance of Transformers on systematic generalization. We report improvements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematics dataset. Our models improve accuracy from 50% to 85% on the PCFG productivity split, and from 35% to 81% on COGS. On SCAN, relative positional embedding largely mitigates the EOS decision problem (Newman et al., 2020), yielding 100% accuracy on the length split with a cutoff at 26. Importantly, performance differences between these models are typically invisible on the IID data split. This calls for proper generalization validation sets for developing neural networks that generalize systematically. We publicly release the code to reproduce our results.
Big Data Analytics and Artificial intelligence in business and Marketing: Cloud security and encryption influencing business

Security is one of the most important aspects of any business. However, it is often the most neglected aspect as most businesses focus their resources on development and marketing. Security is the paramount concern of the user in the domain of cloud computing. Cloud based services offer a certain level of security from various attacks but a lot of the responsibility lies in the hands of the user. Furthermore, the current protocols and technologies employed in cloud services have a lot of potential for exploits that are ignored due to lower priority. In this paper the aspects of cloud security that are most important from a business ownersâ€™ standpoint are explored. Current security techniques have been discussed and state of art optimized framework has been proposed along with discussion about the complexity and usability of said framework.
Research on path planning based on improved RRT-Connect algorithm

Aiming at the low efficiency of path planning of RRT-Connect algorithm and random sampling, an improved algorithm based on RRT-Connect is proposed. The algorithm introduces a target bias strategy on the original RRT-Connect algorithm, guides the sampling point to expand in the direction of the target point, and changes the length of the step during the random tree expansion process, thereby increasing the speed of the random tree exploring the space, and finally adopts the greedy algorithm Prune the random tree to achieve path smoothing. The improved RRT-Connect algorithm is compared with the RRT and RRT-Connect algorithms respectively, and the results show that the algorithm proposed in this paper is significantly better than the compared algorithm in terms of path length and execution time.
A novel semi-supervised self-training method based on resampling for Twitter fake account identification

Purpose Twitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems. Design/methodology/approach In the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration. Findings The proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts. Originality/value This paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.
Orogenic gold ores in three-dimensions: A case study of distinct mineralization styles at the world-class Cuiabá deposit, Brazil, using high-resolution X-ray computed tomography on gold particles

Ore studies using conventional petrographic methods have an intrinsic limitation that spatial (three-dimensional, 3-D) distribution, orientation, shape and mineral associations have to be inferred by 2-D observations and measurements. Further, developing statistically significant data sets is particularly difficult for gold ores, given the fine grain size and low gold abundance even in high-grade ores. We use high-resolution X-ray computed tomography (HRXCT) and improved processing methods to provide 3-D data of ore textures and in-situ gold particles to discern mineralization styles and improve ore recovery. This is a case study of the Archean, world-class Cuiabá orogenic gold deposit in the Quadrilátero Ferrífero province, the largest underground mine in Brazil. Selected samples represent distinct mineralization styles at the Cuiabá mine: BIF-hosted ore from the Fonte Grande Sul (FGS) orebody, and gold-bearing quartz-carbonate veins hosted in a metamafic unit from the Veio de Quartzo (VQZ) orebody. This study characterizes shape, size, orientation and mineral associations of over 16,000 gold particles. Segmentation and quantitative measurements of gold particles are particularly challenging due to their small grain size. This issue is addressed using the recently formalized partial volume and blurring (PVB) method. Gold mineralization styles differ in 3-D textural and quantitative aspects including size and spatial distribution. Analysis of the VQZ sample shows that gold particles are mainly free in the quartz-carbonate matrix, with many particles in direct contact with a diverse sulfide mineral assemblage. Gold particles in the BIF-hosted FGS ore form bands in association with high pyrrhotite zones within iron carbonate-rich bands. PVB analysis show that the long axis of measured particles in both ore types ranges from ∼25 μm to 1.4 mm, with median values around 115 μm. Larger particles dominantly are sub-equant to prolate spheroids, with smaller particles appearing to be dominantly sub-equant to equant grains. The HRXCT data reveal that the kinematic processes affect gold shape and orientation and indicate the influence of deformation at the grain scale.
Explainable activity recognition in videos: Lessons learned

Abstract We consider the following activity recognition task: given a video, infer the set of activities being performed in the video and assign each frame to an activity. This task can be solved using modern deep learning architectures based on neural networks or conventional classifiers such as linear models and decision trees. While neural networks exhibit superior predictive performance as compared with decision trees and linear models, they are also uninterpretable and less explainable. We address this accuracy‐explanability gap using a novel framework that feeds the output of a deep neural network to an interpretable, tractable probabilistic model called dynamic cutset networks, and performs joint reasoning over the two to answer questions. The neural network helps achieve high accuracy while dynamic cutset networks because of their polytime probabilistic reasoning capabilities make the system more explainable. We demonstrate the efficacy of our approach by using it to build three prototype systems that solve human‐machine tasks having varying levels of difficulty using cooking videos as an accessible domain. We describe high‐level technical details and key lessons learned in our human subjects evaluations of these systems.
Radioactive mineralization detection using remote sensing and airborne gamma-ray spectrometry at Wadi Al-Miyah area, Central Eastern Desert, Egypt

Band rationing, principal component analysis (PCA) and decorrelation stretch (DS) techniques of Landsat-8 with Sentinel-2 data successfully used in the discrimination of the lithological units and the surface structure features of Wadi Al-Miyah area in the Central Eastern Desert of Egypt. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were processed to identify the hydrothermal mineralized alteration zones related to the radioactive minerals. Airborne gamma-ray spectrometry (AGRS) has been used to determine the naturally occurring radioactive anomalous zones such as potassium (K), equivalent uranium (eU) and equivalent thorium (eTh) and to follow their spatial distribution in Wadi Al-Miyah area. The analysis of AGRS data revealed that, the study area owns a wide range of radioelement concentrations ranging from 15.5 to 98.4 μR/h, 5.9–37.1 ppm, 17.2–95.4 ppm, and 0.53%-10.29% of total count (TC), (eU), (eTh), and (K), respectively. The interpretation of the Radioelements Composite Image (RCI) successfully delineated the enrichment zones with radioelement concentrations within the alkali feldspar granite, biotite-muscovite schist, granodiorite and the hydrothermal alteration areas within the diorite of Gabal Al- Bakriyah. The results concluded that the integrated remote sensing and airborne AGRS data are promising in detecting the potential areas enriched with K, eU and eTh.
An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs

Set reachability query in directed graphs has a plethora of graph-based applications such as dependency analysis and graph centrality calculation. Given two sets <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> of source and target vertices, set reachability query needs to acquire all pairs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s{\in }S$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$t{\in }T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> can reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> . The state-of-the-art approach distributed set reachability (DSR) investigates the set reachability query in a distributed environment and adopts a static graph-based index to enhance the query efficiency. Nevertheless, DSR needs to store the graph-based index in all partitions, which causes a huge space overhead. Furthermore, it cannot efficiently solve the negative query <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> cannot reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , since DSR needs to traverse the whole reachable paths and becomes unable to efficiently reduce the computations. To alleviate these issues, we propose a novel multi-level 2-hop (ML2hop) index for the set reachability query in a distributed environment. Based on ML2hop, we further present a bi-directional query algorithm, called MLQA, to achieve efficient support for both positive and negative queries in Pregel-like systems. Generally, MLQA is equipped with the following three significant properties: (1) Low computation costs. It reduces redundant local computations in each partition by controlling the rounds of path traversals. (2) Low communication costs. It restricts the message exchange among different partitions within one single round with guaranteed accuracy of query results. (3) High parallelism. It adopts a bi-directional query technique for message propagation, achieving the better query efficiency than the forward-traversal query strategy utilized in DSR. Experimental results over several real-world graphs demonstrate that MLQA significantly outperforms the state-of-the-art algorithm by up to two orders of magnitude speedup.
Visual storytelling of Song Ci and the poets in the social–cultural context of Song dynasty

Song Ci is treasured in traditional Chinese culture, which indicates social and cultural evolution in ancient times. Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci, it is still unclear how to effectively distribute and promote Song Ci in the public sphere. The complexity and abstraction of Song Ci hamper the general public from closely reading, analyzing, and appreciating these excellent works. By means of a set of visual analysis methods, e.g. the spatio-temporal visualization, we exploit visual storytelling to explicitly present the latent and abstractive features of Song Ci. We apply straightway visual charts and lighten the burden of understanding the stories, in order to achieve an effective public distribution. The effectiveness and aesthetics of our work are demonstrated by a user study of three participants with different backgrounds. The result reveals that our story is effective in the distribution, understanding, and promotion of Song Ci.
EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data

High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set of items with a utility higher or equivalent to a set benchmark. These itemsets are utilized to build association rules for data mining systems. However, in the age of big data, conventional (HUIM) strategies are least effective with limited processing capabilities. This work proposes an optimized technique, Enhanced Absolute High Utility Itemset Miner (EAHUIM) by incorporating various refinements into the Absolute High Utility Itemset Miner (AHUIM) Algorithm. EAHUIM discovers the itemsets from large datasets in near real-time and serves as the foundation for information management and decision-making systems by providing diverse insights. The experimental analysis reveals that EAHUIM outclasses other state-of-the-art algorithms for HUIM.
New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer

Universal quantum computers are far from achieving practical applications. The D-Wave quantum computer is initially designed for combinatorial optimizations. Therefore, exploring the potential applications of the D-Wave device in the field of cryptography is of great importance. First, although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table (factor up to 1 005 973), this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored. A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave device. Second, by using the quantum computing cryptography based on the D-Wave 2000Q system, this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing (QISA) framework. Good functions and a high-performance platform are introduced, and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be found. Further comparison between QISA and Quantum Annealing (QA) on six-variable bent functions not only shows the potential speedup of QA, but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.
Hand Gesture Based Virtual Blackboard Using Webcam

The hand gesture recognition technology brought a new era to the artificial intelligence branch of human-computer interaction. This initiative will nevertheless provide a more natural and dynamic form of communication by use of gestures. The gesture is made by the user who is detected by the machine via the image processing and the operation unique to the machine is carried out, thereby eliminating the requirement of any hardware input device. In the application is included an additional database of distinct gestures. The entire system is greatly dependent on the surrounding lighting condition. A well-lit room is a must as the major constraint system. The system utilizes nothing but a camera with good quality and can follow the hand of the user in 2 dimensions and identify up to four mouse-defined hand motions. The input picture from the camera is transformed first into the color space of HSV, which detects the skin and removes the backdrop. The edge detection is then done in order to get the contour of the hand utilized to track the hands and to recognize gesture. This application will detect the numbers written by hand gestures. This will work as a virtual blackboard where numbers and letters can be written by using gesture. The purpose of this post is to create a virtual blackboard which can be used to make online confrontation by moving fingers as the hand gesture. Everyone will help to reduce the use of a whiteboard or smart whiteboard. This application will add a new era of HCI implementation in educational purpose. Experiments will demonstrate that implementation is sufficiently dependable for practical application.
Developing Professional Identity of Future Specialists in the Digital Educational Space of the University

Developing professional identity of future specialists remains an important task of modern universities under different circumstances, either in traditional form of education or in digital educational space. Professional and learning communication acquire new forms under the influence of the rapid development of information and communication technologies. Online survey organized among teachers and students of Ryazan State University named after S.A. Yesenin (57 respondents) showed, that most of the respondents support blended learning, combining traditional and online learning. In the process of developing professional identity of future specialists, online learning theory is possible, but practical training needs traditional format. That is why medical, technical, military and some other universities avoid online learning. Nevertheless, some forms of online communication remain very popular, among them organizing online conferences and round table discussions, using resources of electronic library systems, organizing independent work of students in distance courses on the platform of LMS Moodle, in-service training, etc.