Reviewerly/exHarmony huggingface dataset:
A novel recommendation algorithm with knowledge graph

Abstract Personalized recommendation is an important topic in recommendation algorithm research. Traditional collaborative filtering recommendation algorithms suffer the sparseness and cold start problems. Existing knowledge graph based recommendation algorithms miss the high-order similarity at the subgraph level. This paper introduces a RNN-based distributed representation model of knowledge graph called KG-GRU, which uses a sequence containing nodes and relationships to model the subgraph similarity in the same embedded vector space. Then, a personalized recommendation algorithm based on knowledge graph called KG-GRU4Rec, is proposed. KG-GRU4Rec implements an end-to-end predictive user rating model. Experimental results demonstrate that KG-GRU learns more accurate knowledge about entities and relationships in the graph and KG-GRU4Rec outperforms the comparison algorithm in both hit ratio and average reciprocal ranking.
Hindsight Curriculum Generation Based Multi-Goal Experience Replay

In multi-goal tasks, an agent learns to achieve diverse goals from past experiences. Hindsight Experience Replay (HER)—which replays experiences with pseudo goals—has shown the potential to learn from failed experiences. However, not all the pseudo goals are well-explored to provide reliable value estimates. In view of value estimation, the agent should learn from achievable goals towards desired goals distribution progressively. To tackle the problem, we propose to generate a hindsight curriculum, which maintains a sequence of balancing distributions of achieved goals to replay. Based on the hindsight curriculum, the agent evaluates hindsight experiences with a batch of similar well-explored experiences, and strikes a dynamic balance between function approximation and task solving. We implement Hindsight Curriculum Generation (HCG) with the vanilla Deep Deterministic Policy Gradient (DDPG), and experiments on several multi-goal tasks with sparse binary rewards demonstrate that HCG improves sample efficiency of the state-of-the-art.
Solving the problem of autonomous navigation using underlying surface models tolerant to survey conditions

The problem of autonomous navigation becomes more relevant in the aerospace industry. To solve it successfully, it is necessary to build underlying surface models tolerant to survey conditions. Such models can serve as a basis for solving wide variety of tasks, such as building topographic map of the terrain, navigating the underlying surface, automatic landing of the aircraft. The main problem with building underlying surface models according to monitoring data in the visible spectrum are changes in the survey conditions, such as time of year, time of day, various weather events. The problem can be solved by using algorithms of integration of data collected in various frequency bands and image preprocessing. The paper looks at the task of using data obtained through remote sensing of the underlying surface in optical and radar ranges with the purpose of determining navigational parameters and binding objects to space axes. The paper also considers different approaches to image preprocessing. Comparative studies of existing methods in this sphere were carried out. The algorithm for conversion of digital images into a tolerant to survey conditions form was suggested and realized. We developed the structure and software implementation of the system for determining geographical coordinates using preprocessed underlying surface image. Effectiveness of the suggested algorithms and software was validated by solving the problem of determining positions on images obtained with the help of satellite, radar, and aerial photography under different environmental and lighting conditions.
The Hybrid Navigation Method in Face of Dynamic Obstacles

With the development of society and lifestyle changes. Robo-tic navigation based on traditional navigation and positioning techniques can hardly cope with navigation in complex dynamic scenarios. Traditional navigation and positioning algorithms, such as Dijkstra and A*, cannot perform path planning in dynamic scenarios. In this paper, we present an improved reinforcement learning-based algorithm for local path planning that allows it to still perform well when there are more dynamic obstacles. The algorithm has the advantage of low cost to transform into others situation and fast training speed over the navigation algorithm based entirely on reinforcement learning.
Smart Contract-based Protocol for Efficient Project Scheduling in Industrial Internet

Multi-robot services are widely used to improve the efficiency of industry Internet applications, especially in smart factories. Under the situation that the tasks are becoming more and more intensive, how can smart factories use limited robot resources to complete tasks more efficiently? In order to solve this problem, we transform it into a resource-constrained multiproject scheduling problem, and consider using a combinatorial auction method to get the solution. In order to ensure the security of the system and solve the transaction cost of the robot system, we adopt Blockchain technology and smart contracts to organize the work of the robots. We finally conducted performance analysis of our proposed method, and the results show that smart contracts and combined auction algorithms are safe and effective.
Using Unsupervised Clustering for Analyzing Airborne Gamma-Ray Spectrometry Data

Unsupervised clustering analysis is becoming a useful tool in several fields, as can group similar entities together. Airborne gamma-ray spectrometry (AGRS) is widely used for geological mapping, mineral exploration and environmental studies, as it measures the natural radiation of potassium (K), uranium (eU) and thorium (eTh) in rocks and weathered materials. Ternary maps derived from K, eU and eTh provides an image of the three elements together, however, it's interpretation relies on the interpreter. In this work we apply unsupervised clustering on principal component analysis images derived from primary variables (potassium, uranium and thorium) to automatically map geological units in Mara Rosa- Goiás, Brazil. We applied Gaussian Mixture Models (GMMs) as an unsupervised clustering method that automatically estimates the number of clusters in a dataset and was poorly explored in the context of geological mapping.
Accelerating Poisoning Attack Through Momentum and Adam Algorithms

Machine learning has demonstrated promising application prospects in the field of vehicular technology during the past decade, for instance, it effectively propelled the development of autonomous vehicles and intelligent transportation systems. However, machine learning is still vulnerable to numerous malicious attacks. Amongst them, poisoning attack is one of the most severe security threats to the training process of machine learning, where the attacker injects some poisoned samples to the training dataset to make the learned model unavailable. As the crucial part of poisoning attack is generating poisoned samples, most proposals for poisoning attack have employed traditional gradient-based optimization algorithms to optimize the poisoned samples. Nevertheless, conventional gradient-based optimization algorithms are liable to get trapped in local optimums or saddle points and have a slow rate of convergence. As a result, these problems may lead to a reduction of the poisoned samples' effect. To address these issues, we propose two improved gradient-based poisoning attack algorithms. Specifically, in order to accelerate the convergence speed, we propose the first poisoning attack algorithm by employing momentum algorithm. Also, we propose the second poisoning attack algorithm by utilizing adam algorithm, which can get rid of some local optimums and has a faster convergence speed simultaneously. After that, support vector machines (SVM), linear regression and logistics regression are chosen as exemplary algorithms to conduct our attack algorithms and the effectiveness and computational overhead of the two attack algorithms are evaluated. Finally, we propose a countermeasure algorithm, which can detect suspicious samples using mahalanobis distance.
Basic Block Encoding Based Run-time CFI Check for Embedded Software

Modern control flow attacks circumvent existing defense mechanisms to transfer the program control to attacker chosen malicious code in the program, leaving application vulnerable to attack. Advanced attacks such as Return-Oriented Programming (ROP) attack and its variants, transfer program execution to gadgets (code-snippet that ends with return instruction). The code space to generate gadgets is large and attacks using these gadgets are Turing-complete. One big challenge to harden the program against ROP attack is to confine gadget selection to a limited locations, thus leaving the attacker to search entire code space according to payload criteria. In this paper, we present a novel approach to label the nodes of the Control-Flow Graph (CFG) of a program such that labels of the nodes on a valid control flow edge satisfy a Hamming distance property. The newly encoded CFG enables detection of illegal control flow transitions during the runtime in the processor pipeline. Experimentally, we have demonstrated that the proposed Control Flow Integrity (CFI) implementation is effective against control-flow hijacking and the technique can reduce the search space of the ROP gadgets upto 99.28%. We have also validated our technique on seven applications from MiBench and the proposed labeling mechanism incurs no instruction count overhead while, on average, it increases instruction width to a maximum of 12.13%.
Multi-node Training for StyleGAN2

StyleGAN2 is a Tensorflow-based Generative Adversarial Network (GAN) framework that represents the state-of-the-art in generative image modelling. The current release of StyleGAN2 implements multi-GPU training via Tensorflow’s device contexts which limits data parallelism to a single node. In this work, a data-parallel multi-node training capability is implemented in StyleGAN2 via Horovod which enables harnessing the compute capability of larger cluster architectures. We demonstrate that the new Horovod-based communication outperforms the previous context approach on a single node. Furthermore, we demonstrate that the multi-node training does not compromise the accuracy of StyleGAN2 for a constant effective batch size. Finally, we report strong and weak scaling of the new implementation up to 64 NVIDIA Tesla A100 GPUs distributed across eight NVIDIA DGX A100 nodes, demonstrating the utility of the approach at scale.
Evolution of Face Recognition Technologies

We, humans, identify each other by recognizing faces and we are doing this since ages. Now, we want our computers to do the same. Geometric models were used primarily in old algorithms used for face detection, but now the methodologies have changed. We have various efficient scientific approaches and mathematical representation for the process of matching. All the researches in the previous decade have brought the face recognition technology into the limelight. It is an emerging technology which can be successfully used for identification, verification and various other new domains like sentiment analysis.
A Cloud-Migration Feasibility Advisor

Many organizations, driven by the need for greater productivity and lower costs, are moving their portfolio of applications to the cloud. However, there are many challenges in migrating an application to the cloud. That is, there are different aspects of an application that affect its cloud-migration feasibility. That is why assessing the feasibility of migrating an application to the cloud is not an easy task. Also, it is non-trivial to map which cloud solution - whether infrastructure as a service, platform as a service, or software as a service, should be used for hosting the application, if it is shown to be cloud-migration feasible. In this paper, we present a novel cloud-migration feasibility advisor that analyzes the meta data received about an application and evaluates whether it is feasible to move it to the cloud or not. In addition, the advisor uses dependency graphs and the aforementioned meta data to classify which cloud solution would suit best for the application, if it is deemed migration feasible.
Sufficient Context for Real-Time Semantic Segmentation

Abstract In recent years, the research on semantic segmentation has progressed rapidly, and many segmentation models with good effects have emerged, but these models often require a large amount of parameters. In actual application scenarios, the computing resources and memory of the device are limited, so the research on lightweight real-time semantic segmentation has very important practical significance. In this paper, we propose SDSNet, which is mainly composed of SDS module that includes some SDS-units. SDS unit is based on split-shuffle and dilated convolution, which can get sufficient context by increasing the interaction capability between channels and expanding the receptive field without increasing the amount of calculation.Our network combines the high feature generated by the SDS module with low-level spatial information, yielding a perfect segmentation results. Experiments on CityScapes prove that the proposed SDSNet achieved a comparable result to the state-of-the-art models.
Design of Environmental Quality Management System in Ecotourism Area Based on RBF Network

In order to improve the effect of eco-tourism area environmental quality management, this paper designs a new eco-tourism area environmental quality management system from hardware, database and software using RBF network. In the hardware environment, construct the network environment and connection circuits of the system, and install RFID equipment. Store all data and information about environmental quality in the eco-tourism area in the same way in the database, and combine the logical relationship between the data to get the database link. With the support of hardware environment and database, collect real-time environmental quality information data, and use RBF network to evaluate current environmental quality. Finally, the environmental quality management of the ecotourism area will be realized from the three aspects of air, greening and water environment. The experimental results show that: the application of this system to the daily management of eco-tourism area can effectively improve the environmental quality of air, green and water environment, which proves that the management effect of this system is better.
Towards scalable zero-shot modulation recognition

With the advent of Dynamic Spectrum Access networks, practical modulation recognition (ModRec) has become an important problem with critical applications to spectrum enforcement and resource allocation. Existing ModRec frameworks hinge on exhaustive classifier training and require labeled observations for all modulations before recognition can be performed. As a result, a general ModRec system based on fully supervised classification quickly becomes intractable due to the emergence of new proprietary waveforms and the need to collect labeled training data for them and retrain the system. This underpins the need to perform modulation classification of previously unobserved modulations. Thus, the question emerges can learned classifiers be transferred to "unobserved" modulations? In this paper, we investigate the utility of zero-shot learning to address the above challenge in the context of ModRec. We design ModRec-0, which employs unseen modulations' constellation diagrams within a zero-shot framework to transfer learned classifiers from observed modulations. We propose several important properties to enable this transfer, such as the number of constellation points, phase and amplitude levels. Our system is flexible in that additional such properties as well as a variety of classification features can be incorporated. We evaluate our framework on synthetic and real over-the-air traces and investigate the necessary conditions such as SNR levels and observed training classes for successful transfer. We demonstrate that ModRec-0 achieves over 85% average accuracy across multiple modulation classes both in synthetic and real-world traces.
QUANTITATIVE PREDICTIONS OF REE ABUNDANCES IN CARBONATITES USING REFLECTANCE SPECTROSCOPY

Rare earth elements (REE) became a strategic raw material in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century and carbonatite-related deposits frequently carry potentially economic levels of these critical metals. To determine major and trace elements (including REE content) conventional analysis of bulk rock geochemical samples are conducted by ICP-OES and ICP-MS. However, these analytic methods are costly and time-consuming. In contrast, modern Remote Sensing (RS) - e.g. proximal remote sensing, mineral or imaging spectroscopy - has become a novel tool for detecting and quantifying geological materials as they offer an efficient, nondestructive and cost-effective way not only to identify different minerals and/or chemical elements but also model their abundances. In this study, we investigated whether multivariate regression modelling (PLSR) allows quantitative prediction of bulk REE concentrations in natural carbonatite samples collected in India (Samalpatti, Sevattur, Amba Dongar), USA (Iron Hill, Lemitar Hills) and East Africa (Tororo, Sukulu, Oldoinyo Dili, Panda Hill).
The Speaker Verification System Based on GMM Adaption Clustering and i-vector

The speaker verification system based on i-vector confirms the poor robustness and the discriminability of LDA is not strong. To solve this problem, the GMM adaptive clustering algorithm is used in this paper which is contributed to reduce the size of GMM, then the cluster center model is used to generate GMM supervector adopted to extract i-vector. Meanwhile, the MFD algorithm is improved which obtain the median value after removing the maximum and minimum value of i-vectors. Combined with GPLDA model, the simulation results show that the improved algorithm is an effective method.
Comparative Analysis of Artificial Neural Network Training Algorithms

This study compares training algorithms for artificial neural networks such as genetic, adaptive and hybrid. The “Fisher's Irises” were used as a data for the classification problem and KNIME Analytics Platform was chosen as the experimental environment. Given results comparison analysis of three methods training for neural networks and their parameters are presented. The choice of the optimal architecture was carried out on the basis of the classification algorithm, based on the graph of classification accuracy and error.
Analysis of Feed Forward Neural Network Autonomous Driving Agents in Games

Artificial neural networks (ANN) have proven to be a good solution for many problems. ANNs are able to create models which can handle the complexity and dynamic of many problems. The conventional methods used for creating autonomous driving agents in games tend to feed a waypoint to the control system of the agent and make the agent move accordingly. This process will limit the performance of the agents due to the fact that these agents will only be able to drive on tracks with available waypoint. On the other hand, by considering the capabilities of learning systems, it is possible to train agents capable of driving on tracks which do not necessarily have a waypoint to follow. In this paper the process of training an ANN agent will be examined. The training dataset is gathered from a privileged hard-coded agent's controller which follows a waypoint. Additionally in order to enhance the performance of the ANN agent, a function used for measuring Risk values from the race track will be introduced in the pre-processing stage. All of these factors are considered by having the playability of the game in mind, since non-player characters (NPCs) are one of the key features which determine the entertainment quality of a game. Unity as an industry tool which provides the necessary assets that are required for a driving game is used as a testbed for the proposed ANN agents.
A New 10-Variable Cubic Bent Function Outside the Completed Maiorana-McFarland Class

In this letter, we present a construction of bent functions which generalizes a work of Zhang et al. in 2016. Based on that, we obtain a cubic bent function in 10 variables and prove that, it has no affine derivative and does not belong to the completed Maiorana-McFarland class, which is opposite to all 6/8-variable cubic bent functions as they are inside the completed Maiorana-McFarland class. This is the first time a theoretical proof is given to show that the cubic bent functions in 10 variables can be outside the completed Maiorana-McFarland class. Before that, only a sporadic example with such properties was known by computer search. We also show that our function is EA-inequivalent to that sporadic one.
Automatic Speech Processing of Marathi Speaker İdentification for Isolated Words System

In the prevailing period of innovation the automatic identification of speaker assumes a significant role. The application of speaker recognition is spin towards Biometric security. This paper depicted a speaker recognition framework for isolated word dataset. The feature extraction has been finished utilizing Mel Frequency Cepstral Coefficient (MFCC) techniques. The database of the research is structure and creates utilizing 25 Male and 25 Female speakers. The size of dataset is 2500 isolated words. The content for the dataset recording is chosen based on vowel letters in order. The execution of the framework is determined utilizing False Rejection Rate (FRR), False Acceptance Rate (FAR). The precision of the Speaker Recognition rate for Male is better as compare with the exactness of female. This structure is utilized for speaker recognition framework for the confined word distinguishing proof framework by applying highlight extraction methods as MFCC and arrangement is finished with Euclidian Distance. We got a normal exactness for Male rate is 85% and 81% for female. The exhibition of the haphazardly chosen subject gathering was 79%. This is the general precision pace of Speaker Recognition framework for Marathi Isolated Words.
Dynamic Web Service Selection Based on Score Voting

The QoS aware service selection is one of the major issues of the service-oriented computing paradigm. Most of the existing works handle the QoS attributes as static variables. In real scenarios, the QoS criteria are dynamic and change according to time and space. Consequently, the solutions provided by the existing approaches are error prone and less appropriate to the real context. In this paper, we tackle the above mentioned issue by proposing a two-stage approach. The first stage reduces the space by adopting the score voting heuristic; the second one finds the TopK service compositions that ensure the global constraints. The experiments show that the proposed heuristic is very promising.