Reviewerly/exHarmony huggingface dataset:
Visualization and Analysis Tools for Explainable Choquet Integral Regression

The Choquet integral (ChI) is an aggregation operator defined with respect to a fuzzy measure (FM). The FM of a ChI encodes the worth of individual subsets of sources of information, and is an excellent tool for nonlinear aggregation. The monotonicity and boundary conditions of the FM limit the ChI to applications such as decision-level fusion. In a recent work, we removed the boundary and monotonicity constraints of the FM to propose a ChI-based regression (CIR) approach that enables capability beyond previously proposed ChI regression methods. However, the number of values in an FM scales as 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , where d is the number of input sources. Thus, with such a large number of trained parameters, we tend to lose the explainability (or interpretability) of the learned solution-which comes readily with simpler methods like ordinary linear regression. In this paper, we enhance the explainability of CIR by extending our previously-proposed ChI visualization techniques to CIR. We also present a set of evaluation indices that quantitatively evaluate the importance of individual sources and interactions between groups of sources. We train CIR on real-world regression data sets, and the learned models are visualized and analyzed with the proposed methods. The demonstrations of the proposed visualizations and analyses are shown to significantly enhance the explainability of the learned CIR models.
A Burmese Dependency Parsing Method Based on Transfer Learning

Dependency parsing is a fundmental task in natural language processing(NLP). Burmese belongs to a low resource language with a special language structure, therefore, it exists the problems with extremely lacking of high quality data for Burmese dependency parsing and the inaccurate representation of semantic. We propose a Burmese dependency parsing model based on transfer learning, our method generate partially accurate Burmese dependency parsing data by constructing the relationship of English-Burmese. The embedding of Burmese represented by syllables and words to obtain accurate bilingual word vectors representation of English-Burmese. To verify the effectiveness of our method, during the training process, we fuse the dependency parsing data of Burmese and English, which transfer the dependency arc and POS tagging of English to Burmese. The experimental results show that our proposed method has a UAS value of 44.10% and a LAS value of 30.01% on Burmese datadset.
Attention Transformer Model for Translation of Similar Languages.

This paper illustrates our approach to the shared task on similar language translation in the fifth conference on machine translation (WMT-20). Our motivation comes from the latest state of the art neural machine translation in which Transformers and Recurrent Attention models are effectively used. A typical sequence-sequence architecture consists of an encoder and a decoder Recurrent Neural Network (RNN). The encoder recursively processes a source sequence and reduces it into a fixed-length vector (context), and the decoder generates a target sequence, token by token, conditioned on the same context. In contrast, the advantage of transformers is to reduce the training time by offering a higher degree of parallelism at the cost of freedom for sequential order. With the introduction of Recurrent Attention, it allows the decoder to focus effectively on order of the source sequence at different decoding steps. In our approach, we have combined the recurrence based layered encoder-decoder model with the Transformer model. Our Attention Transformer model enjoys the benefits of both Recurrent Attention and Transformer to quickly learn the most probable sequence for decoding in the target language. The architecture is especially suited for similar languages (languages coming from the same family). We have submitted our system for both Indo-Aryan Language forward (Hindi to Marathi) and reverse (Marathi to Hindi) pair. Our system trains on the parallel corpus of the training dataset provided by the organizers and achieved an average BLEU point of 3.68 with 97.64 TER score for the Hindi-Marathi, along with 9.02 BLEU point and 88.6 TER score for Marathi-Hindi testing set.
Gravity Inspired Clustering Algorithm

This paper presents a new clustering algorithm inspired by Newtonian gravity that iteratively groups data and eliminates outliers. In particular, we impose a grid over the region of interest and define a particle with data-dependent mass for each grid square. We then calculate a Newtonian inspired force on each of the particles and move them in the direction of the force. We repeat the process until there is no further movement. We compare performance with existing algorithms and show that in cases of medium to high clutter, our algorithm has an order of magnitude lower estimation error.
Game Studies on Accounting Entity Behavior Based on Blockchain Technology

Blockchain technology has been favored by various industries since its emergence because of the concept of data sharing, and the accounting industry is no exception. Integrating blockchain technology into the accounting information system can not only break the development restrictions of the accounting information system, but also realize the sharing of resource information among enterprises in the industry, including existing customers and suppliers. However, due to the openness and transparency of blockchain technology, there have been speculations similar to the free-rider problem among corporate accounting entities. The author uses game theory to analyze speculative behavior among accounting entities based on blockchain technology. Through analyzing the prisoner's dilemma, the selection tendency of the interests represented by each node on the blockchain is obtained after the game. Based on the boxed pig game analysis, it is concluded that under the influence of factors such as optimal prices, sales prices, negotiation costs, and production costs, larger companies in the same industry will take the initiative to negotiate prices with customers. By contrast, under the influence of factors such as optimal prices, purchase prices, and negotiation costs, larger companies in the same industry will first negotiate prices with suppliers. Given this analysis, in order to avoid speculative behaviors disrupting economic development, this paper will propose countermeasures including legal constraints, institutional punishments, privacy protection, policy support, etc., to achieve a smooth transition of integrating blockchain technology into the accounting information system.
Multi-scale Hierarchy Feature Fusion Generative Adversarial Network for Low-Dose CT Denoising

Image noise is an inherent issue in low-dose CT (LDCT). Increasing radiation dose can alleviate this problem to some extent, but it also brings potential risks to the patients. Thus, LDCT denoising has raised increasing attention from researchers. Currently, many deep learning based LDCT denoising methods have been proposed with success, such as encoder-decoder. In this paper, we propose a novel multi-scale hierarchy feature fusion based encoder-decoder network within the GAN framework for LDCT denoising. Specifically, a four-stage multi-scale dilated blocks is introduced to integrate low-level features with high-level features. Comparing with the conventional skip connection, which ignores the semantic gap between low-level features and high-level features, the advantage of our method is the effective use of low-level information. In addition, residual learning is also adopted to boost the training of the network. Experimental results on public dataset have demonstrated the superiority of our method over the state-of-the-art methods under comparison in both visual quality and quantitative evaluation.
Using Semantic Analysis and Graph Mining Approaches to Support Software Fault Fixation

Software requirement specification (SRS) documents are written in natural language (NL) and are prone to contain faults due to the inherently ambiguous nature of NL. Inspections are employed to find and fix these faults during the early phases of development, where these are the most cost-effective to fix. Inspections being too manual are very tedious and time consuming to perform. After fixing a fault, the SRS author has to manually re-inspect the document to make sure if there are other similar requirements that need a fix, and also if fixing a fault does not reintroduce another fault in the document (i.e., change impact analysis). The proposed approach in this paper employs NL processing, machine learning, semantic analysis, and graph mining approaches to generate a graph of inter-related requirements (IRR) based on semantic similarity score. The IRR graph is next mined using graph mining approaches to analyze the impact of a change. Our approach when applied using a real SRS generated IRR and yielded promising results. Graph mining approaches resulted in a G-mean of more than 90% to accurately identify the highly similar requirements to support the CIA.
Fixed-MAML for Few-shot Classification in Multilingual Speech Emotion Recognition

In this paper, we analyze the feasibility of applying few-shot learning to speech emotion recognition task (SER). The current speech emotion recognition models work exceptionally well but fail when then input is multilingual. Moreover, when training such models, the models' performance is suitable only when the training corpus is vast. This availability of a big training corpus is a significant problem when choosing a language that is not much popular or obscure. We attempt to solve this challenge of multilingualism and lack of available data by turning this problem into a few-shot learning problem. We suggest relaxing the assumption that all N classes in an N-way, K-shot problem be new and define an N + F-way problem where N and F are the number of emotion classes and predefined fixed emotion classes, respectively. We propose this modification to the Model-Agnostic Meta-Learning (MAML) algorithm to solve the problem and call this new model F-MAML. This modification performs better than the original MAML and outperforms on EmoFilm dataset.
World Coordinate Virtual Traffic Cameras: Edge-Based Transformation and Merging of Multiple Surveillance Video Sources

Video cameras are an important high-fidelity source of surveillance information. They are especially useful in traffic monitoring scenarios in smart cities to reduce congestion, regulate traffic and enforce regulations. Unfortunately, increasing the number of cameras at a specific location makes it harder to keep quality attention on the scene due to high amount of unstructured data mixed with privacy breaching non-essential data, like faces of pedestrians etc. In this paper, we propose a method for real-time merging of multiple surveillance video sources to extract the required information in a single, world coordinate-based, virtual traffic camera view. Such a composite view allows for future improvements in quality of critical details using techniques as super resolution, while at the same time removing unnecessary private information. The implementation is validated on real world traffic data using NVIDIA Jetson TX2 as an edge device and consists of perspective transformation, image merging and object detection/tracking using YOLOv4 machine learning model for the extraction of significant objects only.
Recommendation Systems Based on Collaborative Filtering Using Autoencoders: Issues and Opportunities

With the advancement of deep-learning-based approaches, complex problems under Artificial Intelligence can now be addressed in a comparatively easier way. One such application domain is Recommendation Systems. Recommendation systems are powerful tools in this age of data explosion for providing meaningful insights from data. Collaborative Filtering is one of the popular approaches for building recommendation systems and extensive literary works suggest that it is very effective. In recent years deep-learning-based models have been bounteously applied for the development of recommendation systems using collaborative filtering. Autoencoders are a deep-learning based neural architecture which can be used for implementing collaborative filtering. This paper presents a survey of different autoencoder based models which employ collaborative filtering methodology for making recommendation systems. The paper initially provides an understanding of models and thereafter summarizes various works reported in the literature in the light of the methodology used, taxonomy, datasets used for experimentation, limitations and results reported.
MSLp: Deep Superresolution for Meteorological Satellite Image

High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp (Meteorological Satellite Loss phase). Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32 (the full score is 10). These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.
Autoencoder latent space: an empirical study

Feature extraction is essential to many machine learning tasks. By extracting features, it is possible to reduce the dimensionality of datasets, focusing on the most relevant features and minimizing redundancy. Autoencoders (AE) are neural network architectures commonly used for feature extraction. A usual metric used to evaluate AEs is the reconstruction error, which compares the AE output data with the original one. However, many applications depend on how the input representations in intermediate layers of AEs, i.e. the latent variables, are distributed. Therefore, additionally to the reconstruction error, an interesting measure - to study the latent variables - is the Kullback-Leibler divergence (KLD). This work analyzes how some variations on the AE training process impact the aforementioned measures. Those variations are: 1. the AE depth, 2. the AE middle layer architecture, and 3. the data setup used for training. Results have shown a possible relation between the KLD and the reconstruction error. In fact, lower errors have happened for higher KLDs and less compressed latent variables, i.e. more neurons on the AE middle layers.
YOLOFKP

With the rapid development of convolutional neural networks, the performance of face detection algorithms has been improved. However, the recall rate of the lightweight face detection algorithm is low when it is applied to the small-scale face and dense face. And high-speed and high-accuracy should be considered at the same time. To solve the problems, we propose a lightweight face detection algorithm based on the YOLOv3 key point. The contributions include: (1) A lightweight face detection algorithm, YOLOFKP, face detector, with scale invariance and enlarged receptive field, is proposed based on YOLOv3. (2) Use mosaic for data preprocessing. (3) The loss function Wing loss and activation function Mish for face detection are used in YOLOv3. (4) The classroom face dataset (CLASSROOMFACE) is made. The experimental results show that YOLOFKP has a fast detection speed and a small number of model parameters. It has robustness for small scale faces and dense faces and effectively improves the recall rate of the lightweight face detection algorithm. The test results on the WIDER FACE dataset show that the face recall rate is 91.19%, and the detection speed of 49 FPS is achieved on a single GTX 1080Ti GPU.
Properties of Multiple-Valued Partition Functions

We focus on a set of r-valued n-variable functions that are defined by a partition P on the set of r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> input vectors. Specifically, each block of P specifies input vectors, all of which map to the same function value. For example, a symmetric function is defined by a partition where input vectors in the same block are permutations of each other. Given the partition P and the set S of functions associated with P, we analyze the set S' of functions that are a maximal distance from S. Such functions hold promise for use in crypto-systems. In this paper, we characterize functions in S'. From this, we compute the distance to their corresponding partition functions. We show that, when r and n increase without bound, this distance approaches the maximum possible, r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . Bent functions achieve only half the maximum possible distance when n is large. We show that functions a maximal distance from partition functions tend to have a uniform distribution across the r possible function values. Such functions tend to be immune to statistics-based attacks. Finally, we show that, if the set S' of functions is maximally distant from a set S of partition functions, then the converse is true; that is, S is maximally distant from S'.
Multiple Radar Targets Tracking and Trajectories Fitting

This paper proposes a data association and tracking algorithm followed by polynomial fitting, in the case of multiple radar targets to improve the accuracy of targets' trajectories estimated by an automotive radar sensor.Starting from the conjecture that any real trajectory is polynomial in cartesian coordinates the proposed method supposes to fit the data obtained at the output of Kalman filter to a polynomial. We used the polynomial fitting method for the trajectories of three targets, which we generated in two different scenarios. In both scenarios the targets could be tracked properly using Kalman filters after data association, and the trajectories were improved by applying the polynomial fitting.
A Fast Hybrid Image Encryption Method for Networked Inverted Pendulum Visual Servo Control Systems

This paper proposes a fast hybrid image encryption method to address the cyber security problem of image transmission in networked inverted pendulum visual servo control systems (NIPVSCSs). Firstly, the original image is encrypted by ranks crossing to resist image content leakage. Secondly, the randomly spaced watermark embedding operation are interspersed in each round of ranks cross encryption to locate the tampered image region. Furthermore, by analyzing the extracted watermark, the attack intensity can be pre-judged, which are then used to determine whether to decrypt. The tamper detection ability of digital watermark technology is combined with the security and robustness of image encryption algorithm effectively. Finally, the experimental results and analysis confirm the performance of the proposed algorithm in precisely locating regional tampering whilst maintaining high security and efficiency.
WITHDRAWN: Hybrid neural synchronization blowfish algorithm for secret key exchange over public channels

A secured Exchange of information between authorized or dedicated users over the public channels without revealing information to anyone of unauthorized users is the main purpose of a cryptographic system. Since a large computation power is required in cryptographic systems and is involves with complex process, commonly shared secret keys are generated by using the neural synchronization based algorithms which in turn use a large number of learning rules for the exchanging of these secret keys on public channels. In this paper, the secret key is formed as input bits of a neural network on a public channel using a hybrid Blowfish (BF) algorithm for the securing of the files. The information files or messages sent through a public channel can be encrypted and decrypted by using this generated secret key. In this, first he two communication networks in this neural cryptography system produce mutual output bits by receiving the identical input vectors and are trained on their mutual output bits. Identical time independent vectors are calculated to synchronize these communication networks upto a state. Then the generated common secret key can be exchanged on public channels. This secret key can be utilized for the implementation of any encryption and decryption algorithms. According to the throughput and runtimes the proposed algorithm is analyzed by using the medical report files.
Tracker Terrain Losses

This paper reports on a study to quantify the scale of energy yield losses due to variable terrain and to determine how much of the loss can be reduced by advanced tracker technologies. The study uses a single-axis array in Hopewell, NC, which has and average 4% southwest slope. The site was simulated with trackers in two models to predict the theoretical best and worst case scenarios. In the first model the trackers are in a virtual mono-plane and in the second model the trackers follow the terrain but track as if in the same plane. The results were inconclusive indicating the models did not have the required accuracy to differentiate between the two simulations. The models bin tracker rotations into coarse 10° increments before calculating the shade used to determine the irradiance input and energy output. Future work has already begun to geometrically model the shade for each tracker position so irradiance input and energy output are sufficiently accurate to evaluate the terrain loss. Then if sufficient samples can be studied, we hope to present a simplified correlation that can be applied to 2-D models.
High-precision visual localization based on an improved shape-based matching algorithm

To deal with the requirement of high-precision localization of large-size workpieces in an industrial environment, an improved shape-based matching algorithm is proposed based on the phase stretching transformation and the iterative closest point algorithms. Basler industrial cameras are used to collect images of large-size workpieces, such as glass. The experimental results show that the average localization error is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0.05</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> </mml:mrow> <mml:mspace width="thinmathspace" /> <mml:mn>0.013</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mspace width="thinmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> , which can meet the requirements of practical applications. This algorithm can effectively and accurately achieve high-precision localization of different positions of multi-directionally transformed objects in industrial environments.