Reviewerly/exHarmony huggingface dataset:
Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

In this paper, we focus on protecting the facial privacy for people under the surveillance scenarios, by changing some visual appearances of the faces while keeping them recognizable by the current face recognition systems. This is a challenging problem because we need to retain the most important structures of the captured facial images, while modify the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture which can separately disentangle the facial feature representation into an appearance code and an identification code. Specifically, we first randomly divide the input face image into two groups, the source and target sets, where the identity and appearance codes can be correspondingly extracted. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity as the source subject. Finally, the synthesized faces are employed to replace the original face to protect the individual privacy. Note that our model is end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training process. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.
Providing assurance and scrutability on shared data and machine learning models with verifiable credentials

Abstract Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This article introduces a software architecture and implementation of a system based on design patterns from the field of self‐sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data contributions to ML models are recorded in a bill of materials (BOM), which is stored with the model as a verifiable credential. The BOM provides a traceable record of the supply chain for an AI system, which facilitates on‐going scrutiny of the qualities of the contributing components. The verified BOM, and its linkage to certified data qualities, is used in the AI scrutineer, a web‐based tool designed to offer practitioners insight into ML model constituents and highlight any problems with adopted datasets, should they be found to have biased data or be otherwise discredited.
Improving Pronunciation Assessment Via Ordinal Regression with Anchored Reference Samples

Sentence level pronunciation assessment is important for Computer Assisted Language Learning (CALL). Traditional speech pronunciation assessment, based on the Goodness of Pronunciation (GOP) algorithm, has some weakness in assessing a speech utterance: 1) Phoneme GOP scores cannot be easily translated into a sentence score with a simple average for effective assessment; 2) The rank ordering information has not been well exploited in GOP scoring for delivering a robust assessment and correlate well with a human rater’s evaluations. In this paper, we propose two new statistical features, average GOP (aGOP) and confusion GOP (cGOP) and use them to train a binary classifier in Ordinal Regression with Anchored Reference Samples (ORARS). When the proposed approach is tested on Microsoft mTutor ESL Dataset, a relative improvement of Pearson correlation coefficient of 26.9% is obtained over the conventional GOP-based one. The performance is at a human-parity level or better than human raters.
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning

Observing a set of images and their corresponding paragraph-captions, a challenging task is to learn how to produce a semantically coherent paragraph to describe the visual content of an image. Inspired by recent successes in integrating semantic topics into this task, this paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework, which couples a visual extractor with a deep topic model to guide the learning of a language model. To capture the correlations between the image and text at multiple levels of abstraction and learn the semantic topics from images, we design a variational inference network to build the mapping from image features to textual captions. To guide the paragraph generation, the learned hierarchical topics and visual features are integrated into the language model, including Long Short-Term Memory and Transformer, and jointly optimized. Experiments on public datasets demonstrate that the proposed models, which are competitive with many state-of-the-art approaches in terms of standard evaluation metrics, can be used to both distill interpretable multi-layer semantic topics and generate diverse and coherent captions.
A Cloud Data Access Authorization Update Scheme Based on Blockchain

In order to better realize the storage and sharing of data in the cloud and ensure the validity of data privacy and access authorization, this paper proposes a cloud data access authorization update scheme based on Blockchain, which supports keyword retrieval. Firstly, the risk of collusion attack is effectively reduced by using ciphertext storage separately. Secondly, the method of splitting and managing the proxy re-encryption key parameters can achieve the purpose of deterministic authority update only when the access authority changes and only needs to update the key parameter management. In addition, the scheme alleviates the amount of computation of users during data sharing and the pressure of high-frequency access of cloud storage. Through security analysis, the scheme is safe under the ciphertext attack. Finally, a numerical simulation is given to verify the efficiency of the scheme algorithm.
An Exploit Kits Detection Approach Based on HTTP Message Graph

The exploit kits (EKs) are used by attackers to distribute malware automatically and silently. Existing approaches to EKs detection usually need to perform dynamic analysis on the content contained in the network traffic, which requires dumping all the network traffic and thus causes high detection overhead. Although some approaches detect EKs based on static analysis, they usually fail to restore the complete attack path because of the obstruction set by the attackers. In this paper, we propose an approach that can detect EKs based on only information extracted by static analysis. Our method builds a graph for web sessions and extracts features from the graph to perform EKs detection. The built graph catches important structural characteristics of the interaction during EK attacks that were not revealed in existing methods, with which EKs can be detected with high accuracy. The experiments show that our method works well in both the ground-truth datasets and the latest practical cases. Our method can also identify the malicious websites concealed in EKs, which can further improve the efficiency of analysis.
Lightweight and Accurate Single Image Super-Resolution with Channel Segregation Network

Deep neural networks have witnessed great success in Single Image Super-Resolution (SISR). However, current improvements are mainly contributed by much deeper networks, which leads to huge computation cost and limited application for mobile devices. Moreover, most existing methods propagate the basic content of low-resolution images forward to deeper layers iteratively. Such duplicate computations inevitably result in inefficient reconstruction. To address this issue, we propose an efficient channel segregation block containing multiple branches with different depths, enabling the model to preserve basic content, and focusing on optimizing the detail content with fewer parameters. By merging the output of segregated branches, the block covers a large range of receptive fields. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods even with fewer parameters and lower computational complexity, which is more applicable to lightweight scenarios.
Camera Calibration Using Parallel Line Segments

In this paper, we propose a camera calibration method for surveillance cameras that uses the image projection of parallel 3D line segments of the same length. We assume that vertical line segments are perpendicular to the ground plane and their bottom end points are on the ground plane. Under this assumption, the camera parameters can be directly determined from at least two line segments without estimating vanishing points. By extending the minimal solution, we devise a closed-form solution to the least squares case with more than two line segments. Lens distortion is jointly optimized in bundle adjustment. Evaluation of synthetic data showed that the optimal depression angle of a camera is around 50 degrees. In real data evaluation, we used the joints of pedestrians as vertical line segments. The experimental results on public datasets showed that the proposed method used with a human pose detector can accurately calibrate wide-angle cameras that have radial distortion.
Hypothesis Stitcher for End-to-End Speaker-Attributed ASR on Long-Form Multi-Talker Recordings

An end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed recently to jointly perform speaker counting, speech recognition and speaker identification. The model achieved a low speaker-attributed word error rate (SA-WER) for monaural overlapped speech comprising an unknown number of speakers. However, the E2E modeling approach is susceptible to the mismatch between the training and testing conditions. It has yet to be investigated whether the E2E SA-ASR model works well for recordings that are much longer than samples seen during training. In this work, we first apply a known decoding technique that was developed to perform single-speaker ASR for long-form audio to our E2E SA-ASR task. Then, we propose a novel method using a sequence-to-sequence model, called hypothesis stitcher. The model takes multiple hypotheses obtained from short audio segments that are extracted from the original long-form input, and it then outputs a fused single hypothesis. We propose several architectural variations of the hypothesis stitcher model and compare them with the conventional decoding methods. Experiments using LibriSpeech and LibriCSS corpora show that the proposed method significantly improves SA-WER especially for long-form multi-talker recordings.
High spectral and spatial resolved encryption and decryption of 3D color object based on holographic imaging spectroscopy

We propose a new method to secure 3D color object using the holographic imaging spectroscopy technique. First, the original 3D color object is illuminated with a super-continuum white laser source with no necessity to decompose 3D color object into RGB components in advance which is commonly a preprocessing operation in traditional monochromatic illuminated optical cryptosystem. The output spectrum of white laser can be used as an additional key. Then, we design an optical cryptosystem based on hyperspectral holography in order to encrypt both the spectral and spatial information simultaneously with a high spectral-spatial resolution. Finally, the decrypted images at multiple-wavelength are numerically averaged to significantly reduce the speckle noise which is hard to be eliminated in optical cryptosystems with monochromatic illumination. The feasibility of our proposal is validated with experimental hyperspectral images and numerical simulation.
WITHDRAWN: Robust geophone string sensors fault detection and isolation using pattern recognition techniques based on Raspberry Pi4

In the early detection of process faults, safety and the reliability of technical plants are two key drivers for continuous improvement. Detecting faults in Geophone string sensors (SG-10) is necessary in oil exploration to prevent economic losses. Methods are being developed to allow for earlier detection of process faults than conventional limit and trend checking based on a single process variable, and the development of these methods is a key issue. This paper analyses and compares the efficiency of pattern recognition techniques using a low-computational power device for drift fault detection in sensors, such as the Raspberry Pi 4. Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and K-Nearest Neighbor (KNN) classifiers are among the pattern recognition algorithms being studied. The data for this study were from (SG-10). The four features (resistance, noise, leakage and tilt) were extracted from normal and faulty sensors in training data and testing data. Trained models have been offline tested, using models for sensor performance to detect drift faults. Specificity, sensitivity, and accuracy were used to compare algorithm efficiency, with 98% accuracy in the KNN, 98.3% accuracy in the ANN and 62% accuracy in the SVM. The results show that from the given classifiers the (ANN) and (KNN) outperform to the (SVM).
Graphcomm: A Graph Neural Network Based Method for Multi-Agent Reinforcement Learning

The communication among agents is important for Multi-Agent Reinforcement Learning (MARL). In this work, we propose GraphComm, a method makes use of the relation-ships among agents for MARL communication. GraphComm takes the explicit relations (e.g., agent types), which can be provided through some knowledge background, into account to better model the relationships among agents. Besides explicit relations, GraphComm considers implicit relations, which are formed by agent interactions. GraphComm use Graph Neural Networks (GNNs) to model the relational information, and use GNNs to assist the learning of agent communication. We show that GraphComm can obtain better results than state-of-the-art methods on the challenging StarCraft II unit micromanagement tasks through extensive experimental evaluation.
Attention-Based Convolutional Neural Network and Bidirectional Gated Recurrent Unit for Human Activity Recognition

Using the data collected by the sensors embedded in smartphones for human activity recognition has become an important research direction. In order to overcome the problem of handcrafted feature extraction in traditional machine learning, we propose an attention-based model which combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) for human activity recognition. This model can extract local features of the original data by CNN, and construct the extracted features into a time series as the input of BiGRU, the BiGRU can capture the temporal dependencies and extract the features from the input, by introducing the attention mechanism, the influence of key input can be strengthened. The features are classified by the Softmax function. The recognition accuracy rate on the Wireless Sensor Data Mining (WISDM) dataset reaches 98.5%, which is improved compared with the state-of-the-art methods.
Improved SIFT algorithm based on image filtering

Abstract In this paper, SIFT feature extraction algorithm was optimized through image filtering, so as to highlight the role of stable edge corner and improve the efficiency of stable edge corner collection. Afterwards, the experimental results were compared and verified by the FLANN feature matching method. Experimental results show that the improved SIFT feature extraction algorithm using image filtering can improve the extraction effect of feature points with stable edge response, while suppressing the extraction of feature points with unstable edge response, thus improving the accuracy of matching.
CIBench: A Dataset and Collection of Techniques for Build and Test Selection and Prioritization in Continuous Integration

Continuous integration (CI) is a widely used practice in modern software engineering. Unfortunately, it is also an expensive practice - Google and Mozilla estimate their CI systems in millions of dollars. There are a number of techniques and tools designed to or having the potential to save the cost of CI or expand its benefit - reducing time to feedback. However, their benefits in some dimensions may also result in drawbacks in others. They may also be beneficial in other scenarios where they are not designed to help. Therefore, we built CIBench, a dataset and collection of techniques for build and test selection and prioritization in Continuous Integration. CIBench is based on a popular existing dataset for CI - TravisTorrent [2] and extends it in multiple ways including mining additional Travis logs, Github commits, and building dependency graphs for studied projects. This dataset allows us to replicate and evaluate existing techniques to improve CI under the same settings, to better understand the impact of applying different strategies in a more comprehensive way.
Detecting Data Falsification by Front-line Development Workers: A Case Study of Vaccination in Pakistan

Front-line workers in global development are often responsible for data collection and record-keeping about their own work. The authenticity of such data and the role of mid-level supervisors, however, remains understudied. We report on the case of immunization in Pakistan, where, through interviews with 30 mid-level vaccination managers in Punjab district, we find that data falsification by vaccinators is common, though not necessarily rampant. Because of an intricate protocol for record-keeping, supervisors can detect data falsification, and we find they have devised an array of methods, broadly classifiable into four types: triangulation, supplementary data collection, anomaly detection, and interrogation. We also find that the strategies that supervisors use to detect falsification seem linked to their style of management, with authoritarian supervisors preferring supplementary data collection and spot checks, while supportive supervisors use triangulation. Our findings lead to recommendations for designing technologies intended to monitor and manage front-line data.
Variable subset DIC algorithm for measuring discontinuous displacement based on pixel-level ZNCC value distribution map

Digital image correlation (DIC) is one of the most used techniques for measuring deformation. It is widely used in mechanical performance testing, structural health monitoring and many other fields. The standard subset based DIC technology assumes that the deformation is continuous. However, discontinuous deformation such as cracks and dislocations are quite common in practical applications. The measurement accuracy of DIC in these areas will be significantly reduced, or even measurement failure. This is a limitation for the application of DIC. To overcome this problem, this paper proposes a variable subset DIC algorithm. Taking the zero-mean normalized cross-correlation (ZNCC) function as the correlation criterion, the concept of pixel-level ZNCC value distribution map is proposed. The proposed algorithm can automatically select the most suitable subset for matching under the guidance of the pixel-level ZNCC value distribution map. This method has the advantages of good flexibility, strong applicability, and high precision.
Overview of the Eighth Dialog System Technology Challenge: DSTC8

This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a pragmatic way for multi-domain task-completion, noetic response selection, audio visual scene-aware dialog, and schema-guided dialog state tracking tasks. This paper describes the task definition, provided datasets, baselines and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks.
Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy

Feature selection has become a crucial part of many classification problems in which high-dimensional datasets may contain tens of thousands of features. In this paper, we propose a novel feature selection method scoring the features through estimating the Bayes error rate based on kernel density estimation. Additionally, we update the scores of features dynamically by quantitatively interpreting the effects of feature relevance and redundancy in a new way. Distinguishing from the common heuristic applied by many feature selection methods, which prefers choosing features that are not relevant to each other, our approach penalizes only monotonically correlated features and rewards any other kind of relevance among features based on Spearman's rank correlation coefficient and normalized mutual information. We conduct extensive experiments on seventeen diverse classification benchmarks and the results show that our approach overperforms other seventeen popular state-of-the-art feature selection methods in most cases.
Network Classifiers Based on Social Learning

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results.
On Measurement Scales: Neither Ordinal Nor Interval?

There is a received view on measurement scales. It includes both a classification of scales (nominal, ordinal, interval, and ratio) and a set of prescriptions regarding measurement inferences. This article casts doubt on the adequacy of this received view. To do this, I propose an epistemic characterization of the ordinal/interval distinction, that is, one in terms of researchers’ beliefs. This novel characterization reveals the ordinal/interval distinction as too coarse grained and thus the received view as too restrictive of a framework for measurement research.
Data mining in Cloud Computing

Data mining is the way to perceive irregularities, designs, and interactions within large data sets. People will be able to use technology to increase sales, reduce costs, improve user relations, reduce dangers, and many other things. Cloud computing signifies the modern drift in Web administrations which going to rely on clouds of servers to tackle assignments. Data mining in cloud computing is the method of extricating organized data from unstructured or semi-structured web information sources. Data mining empowers a retailer to utilize point-of-sale records of client purchases to create items and advancements that offer assistance to the organization to draw in the client. Instead of owning their computing framework or information centers, companies can rent access to anything from applications to storage from a cloud benefit supplier. Firms can maintain a strategic distance from the forthright fetched and complexity of owning and possess IT foundation, and in-step essentially pay for what they have utilized, when to be utilized.