Reviewerly/exHarmony huggingface dataset:
Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Anomaly detection in complex data structures is one of the most challenging problems in computer vision. In many real-world problems, for example in the quality control in modern manufacturing, the anomalous samples are usually rare, resulting in (highly) imbalanced datasets. However, in current research practice, these scenarios are rarely modeled, and as a consequence, evaluation of anomaly detection algorithms often do not reproduce results that are useful for practical applications. First, even in case of highly unbalanced input data, anomaly detection algorithms are expected to significantly reduce the proportion of anomalous samples, detecting “almost all” anomalous samples (with exact specifications depending on the target customer). This places high importance on only the small part of the ROC curve, possibly rendering the standard metrics such as AUC (Area Under Curve) and AP (Average Precision) useless. Second, the target of automatic anomaly detection in practical applications is significant reduction in manual work required, and standard metrics are poor predictor of this feature. Finally, the evaluation may produce erratic results for different randomly initialized training runs of the neural network, producing evaluation results that may not reproduce well in practice. In this paper, we present an evaluation methodology that avoids these pitfalls.
Revisiting the Training of Very Deep Neural Networks without Skip Connections

Deep neural networks (DNNs) with many layers of feature representations yield state-of-the-art results on several difficult learning tasks.However, optimizing very deep DNNs without shortcut connections known as PlainNets, is a notoriously hard problem.Considering the growing interest in this area, this paper investigates holistically two scenarios that plague the training of very deep PlainNets: (1) the relatively popular challenge of 'vanishing and exploding units' activations', and (2) the less investigated 'singularity' problem, which is studied in details in this paper.In contrast to earlier works that study only the saturation and explosion of units' activations in isolation, this paper harmonizes the inconspicuous coexistence of the aforementioned problems for very deep PlainNets.Particularly, we argue that the aforementioned problems would have to be tackled simultaneously for the successful training of very deep PlainNets.Finally, different techniques that can be employed for tackling the optimization problem are discussed, and a specific combination of simple techniques that allows the successful training of PlainNets having up to 100 layers is demonstrated.
Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data

Mining anomalous objects from multi-view data is a challenging issue as data collected from diverse sources have more complicated distributions and exhibit inconsistently heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on transduction, which becomes very costly when new data points are introduced by an input stream. Besides, the existing detection methods use either the pairwise multiplication of cross-view data vectors to quantify outlier scores or the predicted joint probability to measure anomalousness, which are less extensible to support more sources. To resolve these challenges, we propose in this paper a Bayesian probabilistic model for finding multi-view outliers in an inductive learning manner. Specifically, we first follow the probabilistic projection method of latent variable for exploring the structural correlation and consistency of different views. Then, we seek for a promising Bayesian treatment for the generative model to approach the issue of selecting the optimal dimensionality. Next, we explore a variational approximation approach to estimate model parameters and achieve model selection. The outlier score for every sample can then be predicted by analyzing mutual distances of its representations across different views in the latent subspace. Finally, we benchmark the efficacy of the proposed method by conducting comprehensive experiments.
Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy.
Applying matrix factorization to consistency-based direct diagnosis

Abstract Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering . We show that our approach improves runtime performance and diagnosis quality at the same time.
Fractional chaotic cryptovirology in blockchain - analysis and control

The manuscript analyzes fractional order chaotic cryptovirology in blockchain using various methods in dynamics such as Lyapunov exponents, stagnation points, symmetry, bifurcation diagrams etc. The internet is the most powerful tool that has changed every aspect of living. But this internet has also become primary medium of cyber-crime. The dynamics of the modeled system for variable fractional order between 0.8 to 1 is also observed. The chaos in the studied dynamical system is controlled about its two stagnation points using SMC controllers assuming uncertainties and disturbances.
Modurec: Recommender Systems with Feature and Time Modulation

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time). To address these problems, we propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism, which has demonstrated its effectiveness in several fields. While time information helps mitigate the effects of concept shift, the combination of user and item features improve prediction performance when little data is available. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings compared with standard autoencoder-based methods and other collaborative filtering approaches.
A Novel Region of Interest for Selective Color Image Encryption Technique based on New Combination between GLCM Texture Features

Selective image encryption is an efficient way to reduce the amount of encrypted data that can achieve an acceptable level of security. Determining and choosing the region of interest in digital color images is a challenging task in selective image encryption due to their complex structure and distinct regions of varying importance based on instinctive feelings and opinions. Gray Level Co-occurrence Matrix (GLCM) is the core primitive method for texture analysis. To develop a novel selective encryption strategy, new features in acquiring and selecting Region of Interest (ROI) for the color images based on GLCM and Faster R-CNN for object detections is proposed. The Faster R-CNN is implemented for determining the boundary of an object in an image and the roughness criteria that representing the busy area are proposed for each object. The roughness state is based on developing new combinational relation between features for determining image regions that are developed upon the object texture analyses map blocks. The roughness block for each region is encrypted using AES Algorithm with a dynamic secret key generation based on sine and tan chaotic map methods and permutation of other blocks in each region. The security performance of selective image encryption is found to enhance considerably based on the rates of selective encrypted area. Thus, the proposed strategy achieves good alternatives, fulfills the desired confidentiality, and safe the privacy of the image.
RSINet: Rotation-Scale Invariant Network for Online Visual Tracking

Most Siamese network-based trackers perform the tracking process without model update, and cannot learn target-specific variation adaptively. Moreover, Siamese-based trackers infer the new state of tracked objects by generating axis-aligned bounding boxes, which contain extra background noise, and are unable to accurately estimate the rotation and scale transformation of moving objects, thus potentially reducing tracking performance. In this paper, we propose a novel Rotation-Scale Invariant Network (RSINet) to address the above problem. Our RSINet tracker consists of a target-distractor discrimination branch and a rotation-scale estimation branch, the rotation and scale knowledge can be explicitly learned by a multi-task learning method in an end-to-end manner. In addtion, the tracking model is adaptively optimized and updated under spatio-temporal energy control, which ensures model stability and reliability, as well as high tracking efficiency. Comprehensive experiments on OTB-100, VOT2018, and LaSOT benchmarks demonstrate that our proposed RSINet tracker yields new state-of-the-art performance compared with recent trackers, while running at real-time speed about 45 FPS.
Learning with Delayed Feedback

We propose a novel supervised machine learning strategy, inspired by human learning, that enables an Agent to learn continually over its lifetime. A natural consequence is that the Agent must be able to handle an input whose label is delayed until a later time, or may not arrive at all. Our Agent learns in two steps: a short Seeding phase, in which the Agent's model is initialized with labelled inputs, and an indefinitely long Growing phase, in which the Agent refines and assesses its model if the label is given for an input, but stores the input in a finite-length queue if the label is missing. Queued items are matched against future input-label pairs that arrive, and the model is then updated. Our strategy also allows for the delayed feedback to take a different form. For example, in an image captioning task, the feedback could be a semantic segmentation rather than a textual caption. We show with many experiments that our strategy enables an Agent to learn flexibly and efficiently.
Learning Image Inpainting from Incomplete Images using Self-Supervision

Current approaches for semantic image inpainting rely on deep neural networks (DNNs) that learn under full supervision, i.e., using a training set comprising pairs of (i) corrupted images with holes and (ii) corresponding uncorrupted images. However, for several real-world applications, obtaining large sets of uncorrupted images is challenging or infeasible. Current methods also rely on adversarial training involving min-max optimization that is prone to instability during learning. We propose a novel self-supervised image-inpainting DNN framework that can learn in both completely unsupervised and semi-supervised modes. Moreover, our DNN learning formulation bypasses adversarial training and, thereby, lends itself to more stable training. Results on the publicly available CelebA dataset show that our method, even when learning unsupervisedly, outperforms the state of the art that learns with full supervision.
Secure and Differentiated Fog-Assisted Data Access for Internet of Things

Abstract The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional $q$-parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended $q$-parallel bilinear Diffie–Hellman exponent (E$q$-PBDHE) assumption and prove that the proposed approach provides ‘indistinguishably chosen-plaintext attacks secure’ data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art .
Preprocessing Phase of Text Sequence Generation for Gujarati Language

Within India, many languages are spoken as well as used as a communication platform in day-to-day life which represents the basic requirements to be able to generate NLP based functionalities in different Indian languages. Text processing of digital content has experienced a great expansion process in recent years in the development of innovative technologies that develop new content of information in a very quick and understandable form. This paper presents a survey on various text sequence processing challenges like Word Sense Disambiguation, Part of Speech tagging, Named Entity Recognition in the Gujarati Language. It is an Indo-Aryan Linguistic originated from Sanskrit. Text Process generating each word in the corpus corresponding to a specific part of speech to text conversion like noun, verb, adjective, pronoun, adverb, conjunction, etc. It is a very critical task and preprocessing moves for entire natural language processing behaviors. There are many challenges part in text processing like ambiguities reduction, foreign text detection, stop words and annotation task finding, etc.
Modeling of Vehicle Driving Behavior in Tunnel Entrance Zone Based on Artificial Potential Field Theory

The special traffic rules and environmental factors in the entrance area of the tunnel increase the difficulty of modeling vehicle driving behavior. In response to this problem, this paper incorporates the speed limit, lane changing restrictions, and other traffic rules and tunnel environment on vehicle driving into the potential field model based on the traditional potential field theory. According to the characteristics of the tunnel entrance area, the vehicle potential field and the lane line potential field were improved respectively, and a new tunnel potential field was established. Besides, the relative velocity is introduced to establish an improved potential field force model. On this basis, the vehicle driving behavior model in the tunnel entrance zone is obtained by analyzing the improved artificial potential field(APF) model established. Besides, the driving simulator is used to collect the driving data of five drivers in the tunnel entrance zone, and the least square method and genetic algorithm are used to calibrate and verify the parameters of the improved artificial potential field. And the driving behavior model was verified through experiments. The results of driver-in-the-loop experiments show that the established vehicle driving behavior model can reveal the dynamic evolution of the driving behavior of vehicles in the tunnel entrance zone. It can provide a basis for the study of cooperative driving behavior of vehicles in the tunnel entrance zone and the driving decision-making research of driving assistance systems.
Immersive Authoring of Virtual Reality Training

The use of VR in industrial training contributes to reduce costs and risks, supporting more frequent and diversified use of experiential learning activities, an approach with proven results. In this work, we present an innovative immersive authoring tool for experiential learning in VR-based training. It enables a trainer to structure an entire VR training course in an immersive environment, defining its sub-components, models, tools, and settings, as well as specifying by demonstration the actions to be performed by trainees. The trainees performing the immersive training course have their actions recorded and matched to the ones specified by the trainer.
Raising Awareness of SMEs for Real-Time Locating Systems (RTLS) in Intralogistics

Digital transformation in small and medium-sized enterprises (SMEs) is progressing only slowly. In addition to scarce resources, there is often a lack of the necessary IT or technology knowledge and acceptance among employees. In this paper, we present a possible approach for introducing a specific technology, real-time location, in an intralogistics scenario. Together with a pilot company, load carriers are equipped with tags for tracking in an exemplary scenario. The potential of the technology and its concrete application are demonstrated in a teaching and learning concept specially developed for the employees of SMEs. The selection of the technology, the procedure for setting up the scenario and the conception of the workplace learning approach are described in detail.
Lightweight Non-Local Network for Image Super-Resolution

The popular deep convolutional networks used for image super-resolution (SR) reconstruction often increase the network depth and employ attention mechanism to improve image reconstruction effect. However, these networks suffer from two problems. The first is the deeper network easily causes higher computational cost and more GPU memory usage. The second is traditional attention mechanism often misses the spatial information of images leading the loss of image detail information. To address these issues, we propose a lightweight non-local network (LNLN) for image super resolution in this paper. The proposed network makes two contributions. First, we use non-local module instead of normal attention module to obtain larger receptive field and extract more comprehensive feature information, which is helpful for improving image SR reconstruction results. Secondly, we use the depthwise separable convolution (DSC) instead of the vanilla convolution to reconstruct the residual block, which greatly reduces the number of parameters and computational cost. The proposed LNLN and comparative networks are evaluated on five commonly public datasets, and experiments demonstrate that the proposed LNLN is superior to state-of-the-art networks in terms of reconstruction performance, the number of parameters and storage space.
Instructional Design and Application of Hydraulic Turbine Based on MR (Mixed Reality) HoloLens 2

Introducing HoloLens 2 based MR (mixed reality) technology into hydraulic turbine course can solve the problems that students cannot see the internal structure of hydraulic turbine and cannot disassemble the parts of the hydraulic turbine and can improve the instructional result of the course. MR will transform traditional instructional method of hydraulic turbine. MR instructional designs of hydraulic turbine were discussed in detail, such as some questions for MR instructional design, instructional design model and MR instructional design of hydraulic turbine. The development of hydraulic turbine MR course was also presented and four modules of bulb tubular turbine, such as structure module, principle module, equipment awareness module and assemble test module, were introduced in detail.
Hybrid Line-Based and Region-Based Interactive Set Data Visualization

A generic step in data analysis is to group data items into multiple sets based on specific attribute values. In this paper, we propose an interactive set-data exploration tool named BalloonVis, to make nested balloons as a visual metaphor to group set data over a timeline and visualize the set overlapping information while preserving the original layout. We employ a hybrid region-based and line-based scheme, which allows placing a representative image of each set data item at its region position and helps reduce visual clutter by line connection design. Energy optimization is exploited to compute the layouts of region-based set data items (balloons) and connected lines. The case study and the user study suggest the BalloonVis can visualize set data with more information. Besides, the proposed hierarchical scheme is more scalable on set data item size.
Syntactic Representation Learning For Neural Network Based TTS with Syntactic Parse Tree Traversal

Syntactic structure of a sentence text is correlated with the prosodic structure of the speech that is crucial for improving the prosody and naturalness of a text-to-speech (TTS) system. Nowadays TTS systems usually try to incorporate syntactic structure information with manually designed features based on expert knowledge. In this paper, we propose a syntactic representation learning method based on syntactic parse tree traversal to automatically utilize the syntactic structure information. Two constituent label sequences are linearized through left-first and right-first traversals from constituent parse tree. Syntactic representations are then extracted at word level from each constituent label sequence by a corresponding uni-directional gated recurrent unit (GRU) network. Meanwhile, nuclear-norm maximization loss is introduced to enhance the discriminability and diversity of the embeddings of constituent labels. Upsampled syntactic representations and phoneme embeddings are concatenated to serve as the encoder input of Tacotron2. Experimental results demonstrate the effectiveness of our proposed approach, with mean opinion score (MOS) increasing from 3.70 to 3.82 and ABX preference exceeding by 17% compared with the baseline. In addition, for sentences with multiple syntactic parse trees, prosodic differences can be clearly perceived from the synthesized speeches.