Reviewerly/exHarmony huggingface dataset:
Inverse Reinforcement Learning: A Control Lyapunov Approach

Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to solve this problem is the framework of inverse reinforcement learning (IRL), where the observed agent, e.g., a human demonstrator, is assumed to behave according to an intrinsic cost function that reflects its intent and informs its control actions. In this work, we reformulate the IRL inference problem to learning control Lyapunov functions (CLF) from demonstrations by exploiting the inverse optimality property, which states that every CLF is also a meaningful value function. Moreover, the derived CLF formulation directly guarantees stability of the system under the inferred control policies. We show the flexibility of our proposed method by learning from goal-directed movement demonstrations in a continuous environment.
Robust video summarization algorithm using supervised machine learning

The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don't address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe dataset is 56.13 and 45.06 respectively.
Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic

For decades, the Příbram district in the Czech Republic has been affected by industrial and mining activities, which are the main sources of heavy metal pollutants and negatively affect soil quality. A recent study examined visible–near-infrared (VNIR), shortwave-infrared (SWIR), and X-ray fluorescence (XRF) spectroscopy to model soil lead (Pb) content in a selected area located in Příbram. Following that study, and using the data, we examined the spatial distribution of Pb content in the soil, with a combination of traditional techniques (Moran's I, hotspot analysis, and Kriging). One of the novel points of this work is the use of the Getis–Ord hotspot analysis before the execution of Kriging interpolation to better emphasize clustering patterns. The results indicated that Pb was a spatially dependent soil property and through extensive in-situ sampling, it was possible to generate an accurate interpolation model. The high-Pb hotspots coincided with topographic obstacles that were modeled using topographic profiles extracted from Google Earth, indicating that Pb content does not always exhibit a direct relationship with topographic height as a result of runoff, due to the contribution of topographic steps. This observation provides a new perspective on the relationship between Pb content and topographic patterns.Highlights Different spatial analyses were executed to examine the spatial distribution of PbExtensive in-situ sampling provided an accurate Kriging interpolation model of PbPb hotspots with field soil data were mapped with a high degree of certaintyThe Pb hotspots coincided with topographic obstacles modeled using Google Earth
Deep Grouped Non-Negative Matrix Factorization Method for Image Data Representation

Non-negative matrix factorization (NMF) is an unsupervised learning method that can be exploited for parts-based image representation due to non-negativity constraints. However, singer-layer NMF cannot capture the latent hierarchical structure features from images, while deep features play more important roles in the image representation and recognition tasks. To overcome the limitation of NMF, this paper proposes a novel deep grouped NMF (DGNMF) approach to learn different level attributes of the data. It is interesting that DGNMF approach automatically makes the data from distinct classes share different basis images and the feature vectors among different classes are mutually orthogonal at the same layer. Meanwhile, to preserve the local information, our DGNMF model establishes the objective function with graph regularization, and its optimization problem is solved using gradient descent method. The developed DGNMF algorithm is proved to be convergent and is finally evaluated on face datasets for classification. Compared with some state-of-the-art deep NMF variants, the results demonstrate the proposed DGNMF algorithm achieves surpassing performances using different layer features.
Layered Controllable Video Generation

We introduce layered controllable video generation, where we, without any supervision, decompose the initial frame of a video into foreground and background layers, with which the user can control the video generation process by simply manipulating the foreground mask. The key challenges are the unsupervised foreground-background separation, which is ambiguous, and ability to anticipate user manipulations with access to only raw video sequences. We address these challenges by proposing a two-stage learning procedure. In the first stage, with the rich set of losses and dynamic foreground size prior, we learn how to separate the frame into foreground and background layers and, conditioned on these layers, how to generate the next frame using VQ-VAE generator. In the second stage, we fine-tune this network to anticipate edits to the mask, by fitting (parameterized) control to the mask from future frame. We demonstrate the effectiveness of this learning and the more granular control mechanism, while illustrating state-of-the-art performance on two benchmark datasets. Our project website/code can be found at gabriel-huang.github.io/layered_controllable_video_generation .
Automation of risk management processes

Information technology and computing, such as computers, printers, network devices, cloud storage, cloud services and, last but not least, application software, are an integral part of any organization. These information assets are key to the organization, and it is therefore essential that they adhere to three aspects of information security. Namely availability, integrity, and confidentiality. Security, as well as these three aspects, is addressed by information security management, which is defined in ISO 2700x standards. According to these standards, security is dedicated to the planning, implementation, control and subsequent monitoring and improvement of the information security management system. To successfully secure a system, it is necessary to know what and to what extent we want to secure. In order for this to be possible, it is necessary to take steps to identify and assess information assets, as well as to identify and assess risks. Based on the information obtained, it is then possible to create security policies and define countermeasures to reduce security risks. However, the information security risk management is a costly, demanding and complex activity. There are some possibilities how to automate and improve the process of identifying and assessing risks, but the first step in the whole process is always the identification of information assets. And this is still largely done manually and at length, based on available resources. In this article, we will provide a description of individual subprocesses of information security risk management and we identify the possibilities of applying automation to individual subprocesses and their interconnection to a complex information system.
Type-2 Fuzzy Logic Systems

In this chapter, a brief overview of type-2 fuzzy systemsFuzzy systemType-2 is presented. This overview is intended to provide the basic concepts needed to understand the methods and algorithms presented later in this book.
Temporal Attention for Language Models

Pretrained language models based on the transformer architecture have shown great success in NLP.Textual training data often comes from the web and is thus tagged with time-specific information, but most language models ignore this information.They are trained on the textual data alone, limiting their ability to generalize temporally.In this work, we extend the key component of the transformer architecture, i.e., the self-attention mechanism, and propose temporal attention-a time-aware selfattention mechanism.Temporal attention can be applied to any transformer model and requires the input texts to be accompanied with their relevant time points.It allows the transformer to capture this temporal information and create time-specific contextualized word representations.We leverage these representations for the task of semantic change detection; we apply our proposed mechanism to BERT and experiment on three datasets in different languages (English, German, and Latin) that also vary in time, size, and genre.Our proposed model achieves state-of-the-art results on all the datasets.
Near-infrared spectroscopy for prediction of potentially toxic elements in soil and sediments from a semiarid and coastal humid tropical transitional river basin

The input of potentially toxic elements (PTE) in river basins is a major environmental problem. PTE concentrations are determined using traditional analytical methods, which are mostly time consuming, expensive, and reliant on hazardous reagents. An alternative method to traditional chemical analysis is near-infrared (NIR) spectroscopy, which allows for the quantification of several PTEs through chemometric models. The aim of this study is to apply NIR spectroscopy for the prediction of PTE concentrations in the Ipojuca river basin, an area exposed to potential pollutant activities. We collected 145 soil samples and 33 bed sediments samples. All samples were ground and sieved at ≤ 100-μm, and then analyzed for Al, Ce, Co, Cr, Fe, La, Mn, Mo, Ni, Pr, Sc, Sm, Sn, Sr, Th, Ti, V, and Y concentrations by inductively coupled plasma optical emission spectroscopy. Spectral data were retrieved from all samples using an FT-IR/NIR spectrometer in the range of 1000 – 2500 nm. The samples were subdivided into two sets: (i) soil and (ii) soil and bed sediments. Prediction models were built using the random forest algorithm (RF) and partial least squares regression (PLS) combined with different spectral preprocessing methods. Satisfactory results were obtained for Al, Ti, Sc, and V, and reasonable results for Fe, La, Mn, Pr, Sm, Sr, and Th. Our findings indicate that RF models obtains generally better results than PLS, and also that NIR spectroscopy can be a viable alternative assessment tool even in large areas with geochemical and pedological heterogeneity.
DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual system.This paper presents an image scrambling method that is very useful for grayscale secret images.In this method, the secret image decomposes in three parts based on the pixel's threshold value.The division of the color image into three parts is very easy based on the color channel but in the grayscale image, it is difficult to implement.The proposed image scrambling method is implemented in image steganography using discrete wavelet transform (DWT), singular value decomposition (SVD), and sorting function.There is no visual difference between the stego image and the cover image.The extracted secret image is also similar to the original secret image.The proposed algorithm outcome is compared with the existed image steganography techniques.The comparative results show the strength of the proposed technique.
MACCIF-TDNN: Multi Aspect Aggregation of Channel and Context Interdependence Features in TDNN-Based Speaker Verification

Most of the recent state-of-the-art results for speaker verification are achieved by X-vector and its subsequent variants. In this paper, we propose a new network architecture which aggregates the channel and context interdependence features from multi aspects based on Time Delay Neural Network (TDNN). Firstly, we use the SE-Res2Blocks as in ECAPA-TDNN to explicitly model the channel interdependence to realize adaptive calibration of channel features, and process local context features in a multi-scale way at a more granular level compared with conventional TDNN-based methods. Secondly, we explore to use the encoder structure of Transformer to model the global context interdependence features at an utterance level which can capture better long term temporal characteristics. Before the pooling layer, we aggregate the outputs of SE-Res2Blocks and Transformer Encoders to leverage the complementary channel and context interdependence features learned by themself respectively. Finally, instead of performing a single attentive statistics pooling, we also find it beneficial to extend the pooling method in a multi-head way which can discriminate features from multiple aspects. The proposed MACCIF-TDNN architecture can outperform most of the state-of-the-art TDNN based systems on VoxCeleb1 test sets.
Real-Time Management: When AI Goes Fast and Flow

Real-time management of Artificial Intelligence (AI) becomes a central enabling function for coping with the rapid market changes and increasing demands of stakeholders. But diverse sensing of real time makes it tricky for enterprises to adjust business processes towards a real-time-based era and build the temporal conditions needed for deploying AI in a humanistic manner. This chapter, therefore, introduces the concept of Fast and Flow in an AI engagement context. Fast and Flow encompasses two ideas: one considers time as a monetary asset that helps to increase value; the second does not seek to control time and does not define it on the clock scale, rather, it describes the sense of presence. By introducing the components of Fast and Flow interaction, we provide a cognitive psychology dimension to the management of AI and explore the balancing of Fast and Flow in three possible AI scenarios. The first scenario is “business as usual,” but faster and more complex; the second scenario is more focused on consumers and is based on an ideal combination of AI and Fast and Flow management; in the third scenario, there is an overflow of technology—AI is too fast, and people are unable to control it. Finally, we are asking what Fast and Flow management can do for a humanistic deployment of AI in enterprises and societies.
Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment

This paper deals with the path planning problem of unmanned aerial vehicle (UAV) swarm in the dense-obstacle environment. A novel hierarchical path planning approach with two-level structure is proposed to obtain collision-free and smooth paths for UAV swarm. In the first level, an improved particle swarm optimization (PSO) method is proposed to generate a collision-free global optimal path to determine the overall movement orientation of UAV swarm. In the second level, the improved artificial potential field (APF) combined with consensus theory is used for local path planning of each UAV in the swarm with the turning points extracted from the global optimal path obtained previously as a series of destinations under the leader-follower formation control framework. Numerical simulations are implemented to prove the validity of our proposed algorithm.
Unsupervised multi-view stereo network based on multi-stage depth estimation

In current years, supervised learning multi-view stereo (MVS) methods have achieved impressive performance. However, these methods still suffer the limitation of hard to acquire large-scale depth supervision data, which hinders the generalization ability in never-seen-before scenarios. Recently, some unsupervised-learning methods have been proposed, which relieved the requirement of depth supervision data. However, the generated depth map with lower resolution since the memory consumption grows cubically. In this paper, we propose a novel unsupervised multi-view stereo network based on multi-stage depth estimation, which can increase depth map resolution and generate a dense 3D model with rich details without relying on depth supervision data. To reduce the 3D cost volume highly memory consumption, the progressive coarse-to-fine multiple stages are adopted. Besides, a multi-view group-wise correlation (MV-GwC) module is designed to introduce multi-view correlation prior, which can enhance network performance and further reduce memory consumption. Qualitative and quantitative experiment results show the effectiveness of our method. We outperform some previous supervised and unsupervised MVS methods on DTU and Tanks & Temples benchmarks.
Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions

Symbolic regression, as a regression analysis technique, can find the structure and coefficients of a regression model simultaneously. Genetic programming is an attractive and leading technique for symbolic regression, since it does not require any predefined model structure and has a flexible representation. However, genetic-programming-based symbolic regression (GPSR) often has a poor generalisation ability that hampers its applications to science or industry modelling. In recent years, many researchers have realised the issue and devoted much effort to enhance the generalisation ability of GPSR. This chapter first introduces the generalisation in GPSR and then reviews the state-of-the-art contributions. This chapter also analyses challenges in the area and highlights a number of future directions for interested researchers.
Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps &amp; CHiME-4 Corpora

In this study, we propose to investigate triplet loss for the purpose of an alternative feature representation for ASR. We consider a general non-semantic speech representation, which is trained with a self-supervised criteria based on triplet loss called TRILL, for acoustic modeling to represent the acoustic characteristics of each audio. This strategy is then applied to the CHiME-4 corpus and CRSS-UTDallas Fearless Steps Corpus, with emphasis on the 100-hour challenge corpus which consists of 5 selected NASA Apollo-11 channels. An analysis of the extracted embeddings provides the foundation needed to characterize training utterances into distinct groups based on acoustic distinguishing properties. Moreover, we also demonstrate that triplet-loss based embedding performs better than i-Vector in acoustic modeling, confirming that the triplet loss is more effective than a speaker feature. With additional techniques such as pronunciation and silence probability modeling, plus multi-style training, we achieve a +5.42% and +3.18% relative WER improvement for the development and evaluation sets of the Fearless Steps Corpus. To explore generalization, we further test the same technique on the 1 channel track of CHiME-4 and observe a +11.90% relative WER improvement for real test data.
The Consistency Measurement in Document Publications and Citation Using &amp;lt;i&amp;gt;t&amp;lt;/i&amp;gt;-Index

Recent time handling uncertainty and its measurement is considered as one of the major issues by data science and applied mathematics researchers.It becomes more complex when the dynamicity exists in data sets.One of the suitable examples is Scopus data sets which changes every time.In this case, precise measurement of consistency in document and citation publications is considered as one of the issues.It becomes more complex when the parameter like h-index and document count can be also manipulated over the period of time.To resolve this issue, a time-based index called as "t-index" is illustrated in this paper with an example.This method measures the randomness in document publication and citation using the average h-index and its entropy measurement.
A Novel Model-Based Defogging Method for Particle Images With Different Fog Distributions

Defogging of particle images is a common task in machine vision-based measurement of particle size distribution (PSD) for granular materials or products. Current defogging methods are challenged by particle images captured in industrial environment, where artificial light is dominant in the imaging process and the fog is often unevenly distributed in the image due to its rapid and random movement. The recovered images may contain serious overexposures and result in failures in PSD measurement. To solve this problem, we propose a novel model-based defogging method for particle images with different fog distributions. First, a physical model is established to describe the image formation in industrial foggy environment with artificial light. Then, an adaptive method is proposed to estimate the range of the key model parameter (light intensity coefficient) and to obtain candidate defogged images. Finally, the fog-free image is generated by fusing the candidate images to reduce overexposures while retaining the image brightness. The proposed method was validated experimentally using iron ore particle images and compared with ten current defogging methods. Results demonstrate that our proposed method is adaptive to different illuminations and different fog contributions. It outperforms other defogging methods in removing unevenly distributed fog from the image and thus greatly improves the measuring accuracy of particle size. The proposed image defogging method can be integrated into machine vision systems for particle-handling processes to facilitate PSD measurement in foggy environment. The sample implementation of the proposed method is available at “ <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhoushuyi/particle-image-defog</uri> .”
Tabular Data Anomaly Detection Based on Density Peak Clustering Algorithm

Anomaly detection, also known as outlier detection, is one of the basic tasks of data mining, which aims to eliminate noise and discovers potential knowledge, and has received wide attention in the fields of data mining, machine learning, etc. Although many anomaly detection methods have been proposed, there are still some problems that have not been well addressed, including 1) Sample imbalance. Abnormal data only occupies a small portion of the entire dataset, making modeling in difficulty; 2) Lack of available labeled data; 3) Deep learning model has higher computational complexity. In this paper, we proposed TADPC(tabular anomaly density peak clustering) method, focus on tabular data, studying anomaly detection method based on density peak clustering algorithm, and introduce a method of pruning anomalies to ensure that the detected abnormal values are not the result of conventional noise, as a way to reduce false positives. Compared with the widely used anomaly detection algorithms such as ABOD(angle-based outlier detection), LOF(Local Outlier Factor), IForest(Isolation Forest), KNN(K- Nearest Neighbor) on six different datasets, our proposed method TADPC effectively improves the accuracy and reduces the false rate.