Reviewerly/exHarmony huggingface dataset:
Enhancing Image Coding for Machines with Compressed Feature Residuals

As computer vision technologies have tremendously improved over the last decade, videos and images are often consumed by machines instead of humans which are the main target for traditional video codecs. In many use cases, although machines are the main consumers, human involvement is also required, or even mandatory. In this paper, we propose a novel image coding technique targeted for machines, while maintaining the capability for human consumption. Our proposed codec generates two bitstreams: one bitstream from a traditional codec, referred to as human bitstream, optimized for human consumption; the other bitstream, referred to as machine bitstream, generated from an end-to-end learned neural network-based codec and optimized for machine tasks. Instead of working on the image domain, the proposed machine bitstream is derived from feature residuals – the difference between the features extracted from the input image and the features extracted from the reconstructed image generated by the traditional codec. With the help of the machine bitstream, we can significantly improve machine task performance in the low bitrate range. Our system beats the state-of-the-art traditional codec, the Versatile Video Coding (VVC/H.266), achieving −40.5% in Bjontegaard delta bitrate reduction on average for bitrates up to 0.07 BPP.
Research on Search Ranking Technology of Chinese Electronic Medical Record Based on Adarank

Electronic Medical Records (EMR) run through every link in the whole medical activities. How to find the required EMR quickly and accurately has become a difficult point in the process of EMR retrieval. This paper introduces a method to optimize the sorting of search results in Chinese electronic medical record search engine system by using the sorting learning AdaRank retrieval algorithm. In 248 Chinese electronic medical records manually annotated, five sorting learning methods were used for training and testing. Our AdaRank sorting algorithm achieved NDCG@10 of 0.28178, ERR@10 of 0.38038 and MAP@10 of 0.59368. Compared with RankNet, the sorting learning model, LambdaRank, ListNet and LambdaMART, AdaRank methods have better sorting effect.
Edge Detection of SAR Images Based on Shearlet

Synthetic aperture radar (SAR) has the characteristics of all-weather imaging and is widely used in various fields. However, owe to coherent imaging mechanism, speckle appears in SAR images, which seriously affects the interpretation of images. Due to merits of the strong directional sensitivity and the optimal sparsity, shearlet is used to construct an edge detector for SAR images. The detector firstly converts the constructed even symmetric shearlet into odd symmetry by Hilbert transform, then determines the main direction of the edge according to the odd symmetric shearlet coefficient of the SAR image, and calculates the possibility of edge existence according to both odd and even symmetric shearlet coefficient. Experiments show that, in comparison with the traditional difference-based edge detectors, the proposed edge detector has stronger ability of anti-speckle interference, and can better detect the edge information in SAR images.
Can you trust what you hear: Effects of audio‐attacks on voice‐to‐face generation system

Owing to the widespread deployment of face and speaker recognition systems, research on attacks on neural-network-based biometric systems, which involves face or voice signal classification problems with a low-dimensional output vector, has drawn increasing attention. Recently, cross-modal voice-to-face (VTF) systems have learned to generate faces from voices by matching several biometric characteristics of the generated faces to those of speakers. However, attacks focusing on VTF systems with high-dimensional face image outputs have not yet been conducted. In this paper, we introduce various adversarial attack methods for the VTF system under different attack conditions. These methods can generate a fake face close to the target face or far from the original face, by adding subtle perturbations to the original voice. Under the white-box setting, we formulate a multiobjective optimization to generate target faces and improve the imperceptibility of the adversarial sample. Further a stepwise iterative optimization strategy is proposed to achieve faster and more effective attacks. Finally, the results of comparative experiments with various methods are demonstrated. Under the black-box setting, the adversarial samples generated from surrogate models are able to generate the fake face far from the original one. Qualitative and quantitative experimental results show the high target face-matching rate and irrelevance to the original face, as well as the imperceptibility of the adversarial audio. This study provides useful insights for privacy protection and improving generation robustness for information security.
Intruder Detection System Through Walking Pattern Analysis for Home Security

Modern home automation systems have home security-enhancing features such as face detecting camera systems, and fire alarm systems. In this paper, a novel home security system is proposed which can detect intruders by analyzing walking patterns and updating the owner immediately. The system architecture adopts the concept of the Internet of Things (IoT), providing a network and user-friendly system, which supports simple expansion through plug-and-play devices. The paper first presents a simple hardware prototype of an IoT device to detect the walking patterns of home dwellers. Then it presents a Machine Learning based solution to analyze the walking patterns to detect intruders. The proposed prototype has shown promising performance in five machine learning algorithms tested with 96% of average performance accuracy in detecting intruders
Chapter 7. Syntactic changes in verbal clauses and noun phrases from 1500 onwards

Can the promise of data-driven methods hold in historical linguistics? Can they detect salient syntactic changes and open new research avenues? I first use data-driven measures to detect patterns in the ARCHER corpus. Secondly, I qualitatively interpret the differences and build hypotheses. Thirdly, I validate these, with the Penn Corpora, investigating frequency and creativity. We observe several trends – among them: verbal (‘Doric’) style is decreasing, nominal (‘Attic’) style is increasing. A cascade from full to non-finite clause, and from paratactic to hypotactic style unfolds. Furthermore, constituent order is increasingly becoming fixed, strengthening the principle of dependency length minimisation. While data-driven approaches entail a complex interpretation step, their holistic perspective goes beyond well-trodden envelopes of variation to more global language models.
DAP-BERT: Differentiable Architecture Pruning of BERT

The recent development of pre-trained language models (PLMs) like BERT suffers from increasing computational and memory overhead. In this paper, we focus on automatic pruning for efficient BERT architectures on natural language understanding tasks. Specifically, we propose differentiable architecture pruning (DAP) to prune redundant attention heads and hidden dimensions in BERT, which benefits both from network pruning and neural architecture search. Meanwhile, DAP can adjust itself to deploy the pruned BERT on various edge devices with different resource constraints. Empirical results show that the \(\text {BERT}_\text {BASE}\) architecture pruned by DAP achieves \(5\times \) speed-up with only a minor performance drop. The code is available at https://github.com/OscarYau525/DAP-BERT.
Efficient Data Sharing using Multi-authority Attribute Based Encryption in Blockchain

The process of data exchange relies heavily on attribute-based encryption. The secret key can be shared with other users for transactions and it does not contain any user-related data. The attribute authority creates the secret key from a collection of attributes. It is difficult to tell whether the authority or the other nodes are providing the altered private key. When the structure of access control in a dispersed network is flawed, sensitive information is exposed. The efficiency of attribute-based encryption is a hindrance in its applications. Data is more reliable and approved because to blockchain technology. In the suggested system a multi-authority attribute based encryption is been implemented. The attribute bloom filter is used in access control and to hide attributes. The performance and security analysis reveals a safe and efficient privacy system.
Comment Text Grading for Chinese Graduate Academic Dissertation Using Attention Convolutional Neural Networks

Educational big data connects learning science with data science, where various educational problems are formulating into data mining tasks towards new solutions and new discoveries. This paper provides a path of automatically grading graduate academic dissertations according to the expert-given comment texts. The proposed method fed comment texts to an attention convolutional neural network consisted of an embedding layer, an attention mechanism layer, a convolutional layer, and a fully connected neural network, where the data imbalance issue was handled by data augmentations. The used comment texts were collected from 943 students spreading at 145 universities in China, where these review comments were yielded by experts to grade the academic dissertations. The results from the proposed method achieve a classification accuracy of 77% that gains 12% and 15% implementations compared to the classical convolutional neural network and the linear support vector machine. However, the result analyses show that there are many conflicts between expert-given comments and their given grades in the collected data. This study provides an automatic tool that could remove these conflicts in the dissertation review, leading to more objective dissertation grades.
Learning sub-patterns in piecewise continuous functions

Most stochastic gradient descent algorithms can optimize neural networks that are sub-differentiable in their parameters; however, this implies that the neural network’s activation function must exhibit a degree of continuity which limits the neural network model’s uniform approximation capacity to continuous functions. This paper focuses on the case where the discontinuities arise from distinct sub-patterns, each defined on different parts of the input space. We propose a new discontinuous deep neural network model trainable via a decoupled two-step procedure that avoids passing gradient updates through the network’s only and strategically placed, discontinuous unit. We provide approximation guarantees for our architecture in the space of bounded continuous functions and universal approximation guarantees in the space of piecewise continuous functions which we introduced herein. We present a novel semi-supervised two-step training procedure for our discontinuous deep learning model, tailored to its structure, and we provide theoretical support for its effectiveness. The performance of our model and trained with the propose procedure is evaluated experimentally on both real-world financial datasets and synthetic datasets.
Intelligent Site Detection Based on Improved YOLO Algorithm

An improved YOLOv4 algorithm is proposed to improve the detection accuracy of helmets and reflective clothing for safety problems on construction sites that require simultaneous automatic detection of helmets and reflective clothing. Firstly, the clustering algorithm in YOLOv4 network is optimized, and weighted kernel k-means clustering algorithm is used to analyze the dataset, so as to select the anchor box more suitable for small targets and improve the average accuracy and speed of detection; secondly, the Darknet feature network layer inside the YOLO network is optimized, and the feature maps extracted by quadruple downsampling are doubly upsampled, then convolved and fused with the doubled downsampling, and transported to the subsequent network together with quadruple downsampling, eightfold downsampling, and sixteenfold downsampling to achieve the reduction of the probability of missing detection of small targets. Experimental results show that, the average detection accuracy of the improved algorithm is improved by 8.4% when the helmets and reflective clothing are worn at the same time.
Wearable Fatigue Detection Based on Blink-Saccade Synchronisation

Automatic detection of fatigue based on remote cameras and computer vision has been investigated for many years, and is a viable solution in many contexts, but not for mobile contexts. This paper investigates eye activity extraction methods for wearable fatigue detection using low-cost hardware that is becoming ubiquitous in glasses form-factors, and proposes a new measure for fatigue based on the synchronization between blink and saccade. Evaluation on a novel dataset shows that the novel blink-saccade synchronization measure achieves statistically significant separation of control and fatigued participants, and provides automatic fatigue detection accuracy improvements of 10% relative to existing saccade measures based on velocity and amplitude.
Sentiment Analysis Using Part-of-Speech-Based Feature Extraction and Game-Theoretic Rough Sets

Sentiment analysis, one of the most trending natural language processing tasks, is used to mine opinions or sentiments from a given text. Two significant challenges of sentiment analysis are 1) complexity in data pre-processing caused by the high dimensionality of textual data; 2) uncertainty in classifying sentiment polarities due to the ambiguity of natural languages. To address these issues, we propose a model using part-of-speech-based feature extraction to reduce dimensionality and game-theoretic rough sets (GTRS) to establish a balance between the accuracy and coverage trade-off. We evaluate this model with three different sizes of datasets (Yelp reviews, IMDB movie reviews, and Amazon product reviews). The experiment results show that the proposed model outperforms Pawlak’s rough set model and 0.5-probabilistic rough set model. In comparison with four traditional binary classification models (i.e., SVM, naïve Bayes, decision tree, and KNN), the proposed model also achieves higher accuracy rates. This research suggests that the proposed model is promising to deal with the complexity and uncertainty in sentiment analysis tasks.
Question-formed Query Suggestion

Traditional Query Suggestion (TQS) aims to retrieve or generate completed queries given input keywords and query logs, which plays a vital role in information retrieval. Nearly all existing TQS methods obtain suggested queries, which are usually in the form of keywords or phrases. However, queries like keywords or phrases suffer from incomplete or ambiguous se-mantics. Ideally, question-formed queries are more intuitive and closer to the information needs of users, which can improve their satisfaction during a search. Motivated by this idea, thus, this paper defines a novel question-formed query suggestion task that generates question-formed queries given input keywords and web page texts. Moreover, we also propose a novel pipeline method for this novel task. Specifically, a query generation module is first employed to generate related question-formed queries given keywords and web page texts. Then, a selection module selects the most representative tops among all generated queries as the final suggestion. Extensive experiments demonstrate that our method outperforms the state-of-the-art baselines in human evaluation.
HYBRID GOVERNANCE SYSTEM VALUE PERCEPTION MODEL

This paper aims to analyze the negative perception about the ability to generate value from a Governance System (GS). A model that would explain the reason for the less positive perception regarding the ability to generate value from the GS of the organizations, from the analysis of the relationship between the constructs Hybridism, GS, Accountability and Perception of Value based on IR, was proposed and validated with structural equation modeling (SEM), based on 658responses from professionals of Brazilian organizations from the public, private and non-profit sectors.It is suggested that conflicts related to organizational hybridism negatively influence the results orientation of the GS, which in turn influences the imbalance of its Accountability.As a result of the GS's loss of results orientation, and considering the IR capitals in the disclosure of results, there is a negative perception of the GS's ability to add value to the results.
Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes

Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions, allow general neural networks (NNs) to perform near-optimal point estimation. However, the mechanism of sampling-based probabilistic inference has not been clarified. In this study, we trained two types of artificial NNs, feedforward NN (FFNN) and recurrent NN (RNN), to perform sampling-based probabilistic inference. Then we analyzed and compared their mechanisms of sampling. We found that sampling in RNN was performed by a mechanism that efficiently uses the properties of dynamical systems, unlike FFNN. In addition, we found that sampling in RNNs acted as an inductive bias, enabling a more accurate estimation than in maximum a posteriori estimation. These results provide important arguments for discussing the relationship between dynamical systems and information processing in NNs.
MEABRS: A Multi-objective Evolutionary Framework for Software Bug Report Summarization

Software developers frequently use existing bug text reports to help them in understanding the different aspects of the specific defects and changes made to resolve that bug. But, bug reports are usually lengthy in nature and require considerable effort in understanding. In this direction, summarization of bug reports seems to be useful, covering relevant and diversified information. In the current article, we investigate the use of Multi-objective Evolutionary Algorithm (MEA) for BRS and thus, we name our approach as MEABRS. For MEA, we utilize the search capability of multi-objective bi-nary differential evolution (MOBDE) where we simultaneously optimize different aspects of BRS including diversity among sentences, sentence relevance using term weighting scheme, and length of the sentence. A keyword-based objective function is also incorporated in our optimization process to improve the quality of a bug report’s summary and in order to do so, rapid automatic keyword extraction (RAKE) toolkit is utilized. The generated summaries are evaluated with the available gold summaries corresponding to the two benchmark datasets (ADS and SDS) in terms of precision, recall, F-measure, and ROUGE measure. Results obtained demonstrate the efficacy of MEABRS with an average improvement (over both datasets) of 4.5% in terms of F1-Measure. Further, results are also validated using a statistical significance t-test.
Dark Channel Prior based Image Dehazing with Contrast Enhancement

The visibility of images and videos captured by a camera is affected due to the haze particles in the atmosphere. The haze particles cause absorption and scattering of light which leads to poor image quality in outdoor images. The process of removal of haze from the images or videos is called dehazing, which is typically performed under the physical degradation model. In this work, image dehazing is performed by estimating two parameters namely transmission map and atmospheric light. The proposed work combines a histogram equalization approach with the technique of dehazing called dark channel prior. The results of the proposed method is validated using parameters SSIM, FADE and PSNR on a dataset called D-Hazy dataset and is found to be better when compared with other existing techniques.
Effect of Neural Decay Factors on Prediction Performance in Chaotic Echo State Networks

An echo state network (ESN) is a reservoir computing framework consisting of an input layer, a reservoir, and a readout layer. A reservoir is a recursive network comprising neuron models. In the reservoir, various models use analog neurons with a logistic output function. Time-series learning for ESNs requires a high memory capacity for storing the historical information of past inputs. However, analog neurons are incapable of storing time history by themselves. Therefore, historical information gained by introducing internal neural dynamics can enhance the memory capacity of ESNs. In this context, we hypothesized that the evaluation of the functions of internal-neural decay factors and optimal balances between the decay factors of chaotic neurons can provide useful results for improving the performance of ESNs comprising chaotic neural networks. Therefore, to validate this hypothesis, we investigated the performance of an ESN using a reservoir comprising a chaotic neural network (ChESN). The ChESN significantly outperformed a conventional ESN owing to its high memory capacity at a large temporal scale, even when the spectral radius of the reservoir synaptic weight was small. The proposed approach is expected to have wide applications in reservoir computing.
MinorNet: A Lightweight neural network for battlefield scene classification

Computer vision has a wide range of applications, and the current demand for intelligent battlefields is increasing. However, most research on CNN (Convolutional Neural Network) models did not consider both accuracy and lightweight. To achieve an efficient neural network model for battlefield scene classification, we propose a lightweight and efficient neural network named the MinorNet. The design of MinorN et refers to the advantages and avoids the shortcomings of the previous models. Min bottleneck is an essential part of Minor Net. The Min bottleneck contains Depthwise Separable Convolution, channel shuffle, linear transformation, and residual part. They can further improve model capabilities and efficiency without increasing inference computation. This paper establishes a novel dataset named battlefield scene dataset of 7 different kinds of a real battlefield scenes. As a result, our MinorNet achieves 88.3% with only 2.4M parameters in the battlefield scene dataset, being about 0.5x smaller yet still more accurate 3.7% than the previously most commonly used lightweight place classification model.
Mouse Assistance for Motor-Disabled People Using Computer Vision

Technological innovations draw the attention of all people, but not for those with motor disabilities as the contact with devices and such users draw on a line of frustration. One of the major problems faced by them is that they are unable to have full mouse access, the device which plays a major role in human–computer interaction. Several solutions have been made to address this issue, but they have limitations like using external devices like sensors which may not be affordable to all and require high-end computing because of the processing of data generated by external devices. To overcome this, a system is proposed for hands-free mouse control using facial gesture recognition techniques that can benefit people with motor disabilities. The proposed system intends to eliminate the use of external equipment and also simplify the interface for the disabled which was the major drawback in most of the existing systems. It makes use of face recognition and eye gestures by using only a webcam and uses this for mouse operations which makes the system much more cost effective and simpler. The proposed system uses a face detection algorithm using Histogram of Oriented Gradients (HOG) and was trained using the iBUG 300-W dataset which made the system a suitable solution for all lighting and environmental conditions. The evaluation of experimental performance indicates that the proposed system has extensive performance and never compromises in terms of stability and sensitivity.