Reviewerly/exHarmony huggingface dataset:
Image Inpainting Research Based on Deep Learning

Abstract With the rapid development of computer technology, image inpainting has become a research hotspot in the field of deep learning. Image inpainting belongs to the intersection of computer vision and computer graphics, and is an image processing technology between image editing and image generation. The proposal of generative adversarial network effectively improves the problems of poor image inpainting effect and large difference between the inpainting image and the target image, and promotes the development of image inpainting technology. In this paper, the image inpainting is based on the generation of confrontation networks. Its network structure establishes two repair paths, namely the reconstruction path and the generation path, and the two paths correspond to two groups of networks. The encoder and generator in the network respectively complete the encoding and decoding tasks based on the residual network. The discriminator also uses the patch block discriminator on the basis of the residual network to discriminate the authenticity of the image. This paper uses Places2 data set to verify the algorithm, and uses PSNR and SSIM two objective evaluation methods to evaluate the quality of the repaired image. Experiments show that the algorithm inpainting effect is better.
Ant_ViBe: Improved ViBe Algorithm Based on Ant Colony Clustering under Dynamic Background

Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. A universal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample attenuation FTDA based on background modeling. However, the traditional ViBe has three limitations: (1) the noise problem under dynamic background; (2) the ghost problem; and (3) the target adhesion problem. In order to solve the three problems above, ant colony clustering is introduced and Ant_ViBe is proposed in this paper to improve the background modeling mechanism of the traditional ViBe, from the aspects of initial sample modeling, pheromone and ant colony update mechanism, and foreground segmentation criterion. Experimental results show that the Ant_ViBe greatly improved the noise resistance under dynamic background, eased the ghost and targets adhesion problem, and surpassed the typical algorithms and their fusion algorithms in most evaluation indexes.
Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine

Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE.&#x0D; The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin.&#x0D; The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data.
Energy-Saving Cloud Workflow Scheduling Based on Optimistic Cost Table

In recent years, intelligent flow sensors have been applied to many fields.Cloud operation is a design method to further improve the intelligence of such sensors.However, the cloud workflows of intelligent flow sensors consume too much energy, making it imperative to schedule cloud workflows.With the growing awareness of energy conservation, it is a hot topic to design an energy-efficient workflow scheduling algorithm.Therefore, this paper puts forward the predict minimum energy consumption (PMEC) algorithm, a cloud workflow scheduling algorithm that strikes a balance between energy consumption and execution time.Firstly, the optimistic cost table (OCT) was adopted to rank the tasks by priority.Then, the resources, i.e. virtual machines, were assigned statically to the tasks, in the light of task priority and energy consumption.After that, the workflow was scheduled according to the assignments.Simulation results show that the PMEC is much more energy efficient than traditional listbased scheduling algorithms.
Big Data Audit of Banks Based on Fuzzy Set Theory to Evaluate Risk Level

The arrival of big data era has brought new opportunities and challenges to the development of various industries in China. The explosive growth of commercial bank data has brought great pressure on internal audit. The key audit of key products limited to key business areas can no longer meet the needs. It is difficult to find abnormal and exceptional risks only by sampling analysis and static analysis. Exploring the organic integration and business processing methods between big data and bank internal audit, Internal audit work can protect the stable and sustainable development of banks under the new situation. Therefore, based on fuzzy set theory, this paper determines the membership degree of audit data through membership function, and judges the risk level of audit data, and builds a risk level evaluation system. The main features of this paper are as follows. First, it analyzes the necessity of transformation of the bank auditing in the big data environment. The second is to combine the determination of the membership function in the fuzzy set theory with the bank audit analysis, and use the model to calculate the corresponding parameters, thus establishing a risk level assessment system. The third is to propose audit risk assessment recommendations, hoping to help bank audit risk management in the big data environment. There are some shortcomings in this paper. First, the amount of data acquired is not large enough. Second, due to the lack of author’ knowledge, there are still some deficiencies in the analysis of audit risk of commercial banks.
ALBERT-Based Chinese Named Entity Recognition

Chinese named entity recognition (NER) has been an important problem in natural language processing (NLP) field. Most existing methods mainly use traditional deep learning models which cannot fully leverage contextual dependencies that are very important for capturing the relations between words or characters for modeling. To address this problem, various language representation methods such as BERT have been proposed to learn the global context information. Although these methods can achieve good results, the large number of parameters limited the efficiency and application in real-world scenarios. To improve both of the performance and efficiency, this paper proposes an ALBERT-based Chinese NER method which uses ALBERT, a Lite version of BERT, as the pre-trained model to reduce model parameters and to improve the performance through sharing cross-layer parameters. Besides, it uses conditional random field (CRF) to capture the sentence-level correlation information between words or characters to alleviate the tagging inconsistency problems. Experimental results demonstrate that our method outperforms the comparison methods over 4.23–11.17% in terms of relative F1-measure with only 4% of BERT’s parameters.
Dataset regarding access to information for persons with disabilities in Serbia

Access to information is key for improving the position of persons with disabilities in society. Familiarity with state regulations regarding access to information could be influenced by communication with state authorities concerning the rights of persons with disabilities, especially access to information. Familiarity with these regulations and the specified communication with state authorities might be affected by a number of background variables, such as age and education completed. To clarify relations among these variables, which would enable state authorities and other relevant institutions to define and implement policies that might improve matters, there is a need to prepare and analyze appropriate datasets concerning them. This paper describes such a dataset, preliminary in nature, obtained from answers to part of a questionnaire administered to persons with disabilities living in Serbia. Persons with innate or acquired physical and/or sensory disability were included in the research. This dataset contains raw data of nine variables, as well as analyzed data of ten variables derived from most of the raw data. Besides correlative analyses, the dataset was previously analyzed using PLS (partial least squares) path modeling. To reuse the dataset, a path model with Bayesian estimations may be applied, whose outcomes for different model priors (prior distributions) may be compared to those of the PLS path modeling. The dataset also contains data of two variables that may be included in further research.
DE-auth of the Blue! Transparent De-authentication Using Bluetooth Low Energy Beacon

While user authentication (e.g., via passwords and/or biometrics) is considered important, the need for de-authentication is often underestimated. The so-called “lunchtime attack”, whereby a nearby attacker gains access to the casually departed user’s active log-in session, is a serious security risk that stems from lack of proper de-authentication. Although there have been several proposals for automatic de-authentication, all of them have certain drawbacks, ranging from user burden to deployment costs and high rate of false positives. In this paper we propose DE-auth of the Blue (DEB) – a cheap, unobtrusive, fast and reliable system based on the impact of the human body on wireless signal propagation. In DEB, the wireless signal emanates from a Bluetooth Low Energy Beacon, the only additional equipment needed. The user is not required to wear or to be continuously interacting with any device. DEB can be easily deployed at a very low cost. It uses physical properties of wireless signals that cannot be trivially manipulated by an attacker. DEB recognizes when the user physically steps away from the workstation, and transparently de-authenticates her in less than three seconds. We implemented DEB and conducted extensive experiments, showing a very high success rate, with a low risk of false positives when two beacons are used.
Registered Report: Transcriptional Analysis of Savings Memory Suggests Forgetting is Due to Retrieval Failure

Abstract There is fundamental debate about the nature of forgetting: some have argued that it represents the decay of the memory trace, others that the memory trace persists but becomes inaccessible because of retrieval failure. These different accounts of forgetting lead to different predictions about savings memory, the rapid re-learning of seemingly forgotten information. If forgetting is because of decay, then savings requires re-encoding and should thus involve the same mechanisms as initial learning. If forgetting is because of retrieval failure, then savings should be mechanistically distinct from encoding. In this registered report, we conducted a preregistered and rigorous test between these accounts of forgetting. Specifically, we used microarray to characterize the transcriptional correlates of a new memory (1 d after training), a forgotten memory (8 d after training), and a savings memory (8 d after training but with a reminder on day 7 to evoke a long-term savings memory) for sensitization in Aplysia californica ( n = 8 samples/group). We found that the reactivation of sensitization during savings does not involve a substantial transcriptional response. Thus, savings is transcriptionally distinct relative to a newer (1-d-old) memory, with no coregulated transcripts, negligible similarity in regulation-ranked ordering of transcripts, and a negligible correlation in training-induced changes in gene expression ( r = 0.04 95% confidence interval (CI) [–0.12, 0.20]). Overall, our results suggest that forgetting of sensitization memory represents retrieval failure.
Interestingness Improvement of Face Images by Learning Visual Saliency

Connecting features of face images with the interestingness of a face may assist in a range of applications such as intelligent visual human-machine communication. To enable the connection, we use interestingness and image features in combination with machine learning techniques. In this paper, we use visual saliency of face images as learning features to classify the interestingness of the images. Applying multiple saliency detection techniques specifically to objects in the images allows us to create a database of saliency-based features. Consistent estimation of facial interestingness and using multiple saliency methods contribute to estimate, and exclusively, to modify the interestingness of the image. To investigate interestingness – one of the personal characteristics in a face image, a large benchmark face database is tested using our method. Taken together, the method may advance prospects for further research incorporating other personal characteristics and visual attention related to face images.
Deep adversarial transition learning using cross-grafted generative stacks

As a common approach of deep domain adaptation in computer vision, current works have mainly focused on learning domain-invariant features from different domains, achieving limited success in transfer learning. In this paper, we present a novel "deep adversarial transition learning" (DATL) framework that bridges the domain gap by generating some intermediate, transitional spaces between the source and target domains through the employment of adjustable, cross-grafted generative network stacks and effective adversarial learning between transitions. Specifically, variational auto-encoders (VAEs) are constructed for the domains, and bidirectional transitions are formed by cross-grafting the VAEs' decoder stacks. Generative adversarial networks are then employed to map the target domain data to the label space of the source domain, which is achieved by aligning the transitions initiated by different domains. This results in a new, effective learning paradigm, where training and testing are carried out in the associated transitional spaces instead of the original domains. Experimental results demonstrate that our method outperforms the state-of-the-art on a number of unsupervised domain adaptation benchmarks.
ISCG

Artificial intelligence has paved the way for different areas of computing such as speech recognition and translation, object detection, machine translation, and others. One of the goals of artificial general intelligence is to simulate human thinking and rationality within machines such that they are able to perceive their environment and then perform reasonable actions based on their perception. Creating a single model that performs every single task from visual perception to actuation is currently impossible. The system must be divided into several models each of which functions independently as well also contribute to the operation of the whole intelligent machine. In this paper, an intelligent sensing and caption generation (ISCG) system is proposed which is capable of detecting living/non-living objects and states of motion in images. The system consists of two separate modules of caption generator and intelligence engine with a Convolutional Neural Network (CNN) for determining the different objects in the images. Our model yields state-of-the-art performance on benchmarked dataset.
Multilingual Neural Machine Translation involving Indian Languages

Neural Machine Translations (NMT) models are capable of translating a single bilingual pair and require a new model for each new language pair. Multilingual Neural Machine Translation models are capable of translating multiple language pairs, even pairs which it hasn’t seen before in training. Availability of parallel sentences is a known problem in machine translation. Multilingual NMT model leverages information from all the languages to improve itself and performs better. We propose a data augmentation technique that further improves this model profoundly. The technique helps achieve a jump of more than 15 points in BLEU score from the multilingual NMT model. A BLEU score of 36.2 was achieved for Sindhi–English translation, which is higher than any score on the leaderboard of the LoResMT SharedTask at MT Summit 2019, which provided the data for the experiments.
A System for Mitigating the Problem of Deepfake News Videos Using Watermarking

This paper describes how watermarking technology can be used to prevent the proliferation of Deepfake news. In the proposed system, digital watermarks are embedded in the audio and video tracks of video clips of trusted news agencies at the time the videos are captured or before they are distributed. The watermarks are detected at the social media network&#x2019;s portals, nodes, and back ends. The embedded watermark imparts a unique identifier to the video, that links it to a blockchain. The watermarks also allow video source tracking, integrity verification, and alteration localization. The watermark detectors can be standalone software applications, or they can be integrated with other applications. They are used to perform three main tasks: (1) they alert the internet user when he watches an inauthentic news video, so that he may discard it, (2) they prevent a Deepfake news video from propagating through the network (3) they perform forensic analysis to help track and remove Deepfake news video postings. The paper includes Proof-of- Concept simulation results.
Increasing TPS rate of state‐based blockchains by parallel mining

Currently, the transaction per second (TPS) rate of a block‐chain is quite low ( 5 − 10 TPS), which limits its acceptability in the mainstream ICT technology. This letter proposes a parallel mining approach to enhance the efficiency of the state‐based block‐chains, in terms of TPS rate. The proposed methodology, partitions the transaction pool into disjoint sets, allowing parallel mining of the blocks by multiple miners. Furthermore, we show that the proposed methodology maintains the block‐chain fundamentals. Our experimental results show that parallel mining can significantly increase the TPS rate of a blockchain.
Identifying the Mood of a Software Development Team by Analyzing Text-Based Communication in Chats with Machine Learning

Software development encompasses many collaborative tasks in which usually several persons are involved. Close collaboration and the synchronization of different members of the development team require effective communication. One established communication channel are meetings which are, however, often not as effective as expected. Several approaches already focused on the analysis of meetings to determine the reasons for inefficiency and dissatisfying meeting outcomes. In addition to meetings, text-based communication channels such as chats and e-mails are frequently used in development teams. Communication via these channels requires a similar appropriate behavior as in meetings to achieve a satisfying and expedient collaboration. However, these channels have not yet been extensively examined in research. In this paper, we present an approach for analyzing interpersonal behavior in text-based communication concerning the conversational tone, the familiarity of sender and receiver, the sender’s emotionality, and the appropriateness of the used language. We evaluate our approach in an industrial case study based on 1947 messages sent in a group chat in Zulip over 5.5 months. Using our approach, it was possible to automatically classify written sentences as positive, neutral, or negative with an average accuracy of 62.97% compared to human ratings. Despite this coarse-grained classification, it is possible to gain an overall picture of the adequacy of the textual communication and tendencies in the group mood.
Integrating Reinforcement Learning into Behavior Trees by Hierarchical Composition

This thesis investigates ways to extend the use of Reinforcement Learning (RL) to Behavior Trees (BTs). BTs are used in the field of Artificial Intelligence (AI) in order to create modular and reac ...
Quantum Zermelo problem for general energy resource bounds

A solution to the quantum Zermelo problem for control Hamiltonians with general energy resource bounds is provided. Interestingly, the energy resource of the control Hamiltonian and the control time define a pair of conjugate variables that minimize the energy-time uncertainty relation. The resulting control protocol is applied to a single qubit as well as to a two-interacting qubit system represented by a Heisenberg spin dimer. For these low-dimensional systems, it is found that physically realizable control Hamiltonians exist only for certain, quantized, energy resources.
Using Systematized Nomenclature of Medicine clinical term codes to assign histological findings for prostate biopsies in the Gauteng province, South Africa: Lessons learnt

Prostate cancer (PCa) is a leading male neoplasm in South Africa.The aim of our study was to describe PCa using Systemized Nomenclature of Medicine (SNOMED) clinical terms codes, which have the potential to generate more timely data.The retrospective study design was used to analyse prostate biopsy data from our laboratories using SNOMED morphology (M) and topography (T) codes where the term 'prostate' was captured in the narrative report. Using M code descriptions, the diagnosis, sub-diagnosis, sub-result and International Classification of Diseases for Oncology (ICD-O-3) codes were assigned using a lookup table. Topography code descriptions identified biopsies of prostatic origin. Lookup tables were prepared using Microsoft Excel and combined with the data extracts using Access. Contingency tables reported M and T codes, diagnosis and sub-diagnosis frequencies.An M and T code was reported for 88% (n = 22 009) of biopsies. Of these, 20 551 (93.37%) were of prostatic origin. A benign diagnosis (ICD-O-3:8000/0) was reported for 10 441 biopsies (50.81%) and 45.26% had a malignant diagnosis (n = 9302). An adenocarcinoma (8140/3) sub-diagnosis was reported for 88.16% of malignant biopsies (n = 8201). An atypia diagnosis was reported for 760 biopsies (3.7%). Inflammation (39.03%) and hyperplasia (20.82%) were the predominant benign sub-diagnoses.Our study demonstrated the feasibility of generating PCa data using SNOMED codes from national laboratory data. This highlights the need for extending the results of our study to a national level to deliver timeous monitoring of PCa trends.