Reviewerly/exHarmony huggingface dataset:
Centipede: Leveraging the Distributed Camera Crowd for Cooperative Video Data Storage

Surveillance cameras have been extensively used in smart cities and high security zones. However, with the exploding deployment of smart cameras, the rapid growth of cloud workloads from vision-based IoT applications are becoming a huge burden for all cloud service providers. Some researchers have proposed mechanisms, such as compression and deduplication to reduce the video traffic size, but these methods cannot offset the enormous growth of data volume. Most of the surveillance video data do not need to be proceeded in real time. By making use of the IoT camera’s onboard resources to store the data, the cloud workloads can be fundamentally reduced. However, recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. Existing simple onboard solutions cannot offer secure defense against such geo-range attacks. To tackle the problem, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Centipede</i> , a cooperative video data storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances data security by judiciously distributing erasure-coded video blocks across optimally-chosen surveillance cameras. In this article, we implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Centipede</i> and evaluate its performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Centipede</i> is the first solution that can fundamentally reduce the cloud workload and defend against geo-range attacks.
Generative Chat Bot Implementation Using Deep Recurrent Neural Networks and Natural Language Understanding

There has been not much development in the area of neural conversational models/dialogue systems till the recent times. Neural networks are gaining much more importance once again due to the exponentially decreasing cost of memory and cheap cloud services which has made it possible to do such huge computations with ease. In this paper, we present an architecture of recurrent neural network called as Sequence to Sequence model which is unlike traditional dialogue systems built until now. The architecture aims at building the neural network without using components like Named Entity Recognition (NER) and huge lines of code with conditional statements to be written to get decent performance. It actually consists of two neural networks, encoder-decoder. The encoder encodes input sequence of tokens into a neural machine readable form and decoder decodes the sequence output from encoder. The architecture is complemented with the attention mechanism which allows to pay attention to certain parts of the input sequence which are more important in generating output sequence. In this paper, we also show that using the Bidirectional Long Short Term Memory (LSTM) cells instead of regular RNN cells or GRU's, increases the performance in terms of model convergence and performance. Using this approach we aim to deliver a conversational model with performance same as the current one with very less overhead. We have selected an open domain as the target as it is necessary to get dialogues of a particular domain to get optimum performance from the model.
A new hardware architecture of the adaptive vector median filter and validation in a hardware/software environment

Summary Presented in this paper is a new hardware architecture of the adaptive vector median filter (AVMF). The suggested structure yielded important values in impulsive noise removal from color images while preserving their fine details. The software (SW) study of this filter demonstrated that its implementation is too complex. To overcome this limitation, some approximations using a ROM memory were proposed to perform the square root for a hardware (HW) implementation. Comparative results between the ideal and approximated SWs of the AVMF showed a relative error equal to 0.01 % . Then, sequential and parallel HW architectures were developed for this filter based on the approximated method. Finally, the validation of these architectures was conducted using an field‐programmable gate array (FPGA) platform on an HW/SW environment. The validation results demonstrated that the proposed HW/SW implementation of the AVMF can speed up the execution time 180 times in the worst case at 140 MHz compared to the SW solution as well as preserving a high data quality (same image quality).
A Deep Learning Approach for Human Face Sentiment Classification

This paper presents the development of a deep learning approach for human face sentiment classification. Bidirectional Long-Short Term Memory (Bi-LSTM) and recurrent neural networks (RNNs) are used for object-based segmentation in images, and CNN-RNN model is adopted for non-linear mapping. To test the applicability of this proposed approach, we have trained several deep neural networks to recognize facial expressions in 10,000 images. Our experiments show that the proposed approach can achieve an accuracy of 99.12% in classifying human face sentiment. Moreover, results indicate that the model not only boosts the classification model but also lessen the overhead.
Performance evaluation of web search engines in image retrieval: An experimental study

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.
Dummy Shuffling Against Algebraic Attacks in White-Box Implementations

At CHES 2016, Bos et al. showed that most of existing white-box implementations are easily broken by standard side-channel attacks. A natural idea to apply the well-developed side-channel countermeasure - linear masking schemes - leaves implementations vulnerable to linear algebraic attacks which exploit absence of noise in the white-box setting and are applicable for any order of linear masking. At ASIACRYPT 2018, Biryukov and Udovenko proposed a security model (BU-model for short) for protection against linear algebraic attacks and a new quadratic masking scheme which is provably secure in this model. However, countermeasures against higher-degree attacks were left as an open problem. In this work, we study the effectiveness of another well-known side-channel countermeasure - shuffling - against linear and higher-degree algebraic attacks in the white-box setting. First, we extend the classic shuffling to include dummy computation slots and show that this is a crucial component for protecting against the algebraic attacks. We quantify and prove the security of dummy shuffling against the linear algebraic attack in the BU-model. We introduce a refreshing technique for dummy shuffling and show that it allows to achieve close to optimal protection in the model for arbitrary degrees of the attack, thus solving the open problem of protection against the algebraic attack in the BU-model. Furthermore, we describe an interesting proof-of-concept construction that makes the slot function public (while keeping the shuffling indexes private).
A novel fast constructing neighborhood covering algorithm for efficient classification

Due to the nonparametric property and the robustness to complex data, neighborhood covering model has been widely used for data classification. However, there are two defects which limit its performance. One is that most existing methods based on neighborhood covering suffer high computational complexity to train the model. The other is that the significance of neighborhood is evaluated only by the number of samples in the neighborhood, which leads to some overlaps among each neighborhood and diminishes the classification ability of the model. Therefore, to resolve the above issues, a new fast constructing neighborhood covering algorithm for efficient classification (FNC-EC) is proposed in this paper. First, data samples are divided into k disjoint clusters by k-means algorithm. Next, an adaptive neighborhood radius is defined for constructing neighborhood. And the k-means algorithm is utilized to process the heterogeneous neighborhoods, so that all neighborhoods are homogeneous. Then, the local density and relative distance of neighborhood center are defined for evaluating the significance of neighborhood, and a neighborhood selection method based on local density and relative distance is proposed for classification task. Finally, the experimental results demonstrate that FNC-EC performs well in classification results and efficiency.
Global Context-Based Network with Transformer for Image2latex

Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. Many researchers use encoder-decoder based model to solve this task and achieved good results. However, these methods don't make full use of the structure and position information of the formula. To solve this problem, we propose a global context-based network with transformer that can (1) learn a more powerful and robust intermediate representation via aggregating global features and (2) encode position information explicitly and (3) learn latent dependencies between symbols by using self-attention mechanism. The experimental results on the dataset IM2LATEX-100K demonstrate the effectiveness of our method.
Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes

We propose a new deep learning framework to decompose monocular videos into 3D geometry (camera pose and depth), moving objects, and their motions, with no supervision. We build upon the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view to obtain supervisory signals, specified by a predicted relative 6-degree-of-freedom pose and depth map. However, the typical view synthesis equations rely on a strong assumption: that objects in scenes do not move. This rigid-world assumption limits the predictive power, and rules out learning about objects automatically. We propose a simple solution: minimize the synthesis error on small local regions of the image instead. While the scene as a whole may be non-rigid, it is always possible to find small regions that are approximately rigid, such as inside a moving object. Our network can learn a dense pose map describing poses for each local region. This represents a significantly richer model, including 6D object motions, with little additional complexity. We establish very competitive results on unsupervised odometry and depth prediction on KITTI. We also demonstrate new capabilities on EPIC-Kitchens, a challenging dataset of indoor videos, where there is no ground truth information for depth, odometry, object segmentation or motion - yet all are recovered automatically by our approach.
Towards Developing a Framework to Analyze the Qualities of the University Websites

The website of a university is considered to be a virtual gateway to provide primary resources to its stakeholders. It can play an indispensable role in disseminating information about a university to a variety of audience at a time. Thus, the quality of an academic website requires special attention to fulfil the users’ need. This paper presents a multi-method approach of quality assessment of the academic websites, in the context of universities of Bangladesh. We developed an automated web-based tool that can evaluate any academic website based on three criteria, which are as follows: content of information, loading time and overall performance. Content of information contains many sub criteria, such as university vision and mission, faculty information, notice board and so on. This tool can also perform comparative analysis among several academic websites and generate a ranked list of these. To the best of our knowledge, this is the very first initiative to develop an automated tool for accessing academic website quality in context of Bangladesh. Beside this, we have conducted a questionnaire-based statistical evaluation among several universities to obtain the respective users’ feedback about their academic websites. Then, a ranked list is generated based on the survey result that is almost similar to the ranked list got from the University ranking systems. This validates the effectiveness of our developed tool in accessing academic website.
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification

SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.
ATCC: Accurate tracking by criss-cross location attention

In recent years, discriminative correlation filters (DCF) and Siamese networks based trackers have significantly advanced the performance in tracking. However, the problem of accurate target state estimation is not fully solved yet. Therefore, in this paper, we propose a Criss-Cross Location Attention (CCLA) module, which pays more concerns to global and local contextual information and is used for the adaptation of IoU-Net based trackers. Besides, our CCLA module has capability of high computational efficiency with a slight increase of network parameters. Then, we present our tracker called ATCC, a Siamese architecture with CCLA. Finally, we evaluate our tracker on OTB100, VOT-2018, LaSOT, and TrackingNet benchmark datasets. Experimental results show that our tracker performs favorably against other state-of-the-art trackers, while operating at 30 FPS on single GPU. We will release the code and models at https://github.com/yongwuSHU/atcc.
A computational-graph partitioning method for training memory-constrained DNNs

Many state-of-the-art Deep Neural Networks (DNNs) have substantial memory requirements. Limited device memory becomes a bottleneck when training those models. We propose ParDNN, an automatic, generic, and non-intrusive partitioning strategy for DNNs that are represented as computational graphs. ParDNN decides a placement of DNN's underlying computational graph operations across multiple devices so that the devices' memory constraints are met and the training time is minimized. ParDNN is completely independent of the deep learning aspects of a DNN. It requires no modification neither at the model nor at the systems level implementation of its operation kernels. ParDNN partitions DNNs having billions of parameters and hundreds of thousands of operations in seconds to few minutes. Our experiments with TensorFlow on 16 GPUs demonstrate efficient training of 5 very large models while achieving superlinear scaling for both the batch size and training throughput. ParDNN either outperforms or qualitatively improves upon the related work.
Zero Directory Eviction Victim: Unbounded Coherence Directory and Core Cache Isolation

A directory structure is traditionally employed for tracking coherence information of the privately cached blocks in a cache-coherent chip-multiprocessor (CMP). The eviction of a directory entry necessarily invalidates the privately cached copies of the block that the evicted entry was tracking. These forced directory eviction victims pose two major challenges. First, with decreasing directory size, the volume of these victim blocks increases significantly causing performance degradation. As a result, sizing the directory remains an important challenge. Second, the tight coupling between the directory evictions and the private cache contents can be exploited to launch timing-based side-channel attacks, as has been demonstrated recently. The existing solutions to the first problem allow reducing the directory capacity only up to a certain extent before the performance starts degrading. The existing mitigation technique for the security vulnerability avoids generation of only a certain specific subset of directory victims. In this paper, we present the Zero Directory Eviction Victim (Ze-roDEV) coherence protocol and accompanying novel mechanisms that guarantee freedom from invalidations arising from directory victims, thereby completely isolating the private core caches from the coherence directory evictions. This is the first fully hardwired design proposal that enables a practically unbounded coherence directory which, to the core caches in a CMP, appears to never evict a live entry. Unlike the prior proposals that have completely eliminated the directory and the coherence information eviction victims in a multi-/many-core CMP, our proposal does not require any operating system or application software changes. Our proposal, instead, repurposes the on-die last-level cache (LLC) space for holding the evicted directory entries and engineers a novel mechanism to handle directory entry eviction from the LLC without generating any invalidation to the private core caches. The ZeroDEV protocol evaluated on multi-threaded and multi-programmed workloads for inclusive and two popular non-inclusive CMP cache hierarchy designs performs within 1-2% of a well-provisioned traditional baseline. Importantly, as an additional benefit of eliminating directory eviction victims and utilizing the large on-die LLC for caching directory entries, we show that our proposal does not need any dedicated directory structure at all for certain classes of CMP cache hierarchy designs while maintaining the performance level and continuing to guarantee complete isolation of the core caches from directory entry eviction.
Grammatical evolution for constraint synthesis for mixed-integer linear programming

The Mixed-Integer Linear Programming models are a common representation of real-world objects. They support simulation within the expressed bounds using constraints and optimization of an objective function. Unfortunately, handcrafting a model that aligns well with reality is time-consuming and error-prone. In this work, we propose a Grammatical Evolution for Constraint Synthesis (GECS) algorithm that helps human experts by synthesizing constraints for Mixed-Integer Linear Programming models. Given relatively easy-to-provide data of available variables and parameters, and examples of feasible solutions, GECS produces a well-formed Mixed-Integer Linear Programming model in the ZIMPL modeling language. GECS outperforms several previous algorithms, copes well with tens of variables, and seems to be resistant to the curse of dimensionality.
Forensically reconstructing biomedical maintenance labor: <scp>PDF</scp> metadata under the epistemic conditions of <scp>COVID</scp>‐19

This study examines the documents circulated among biomedical equipment repair technicians in order to build a conceptual model that accounts for multilayered temporality in technical healthcare professional communities. A metadata analysis informed by digital forensics and trace ethnography is employed to model the overlapping temporal, format-related, and annotation characteristics present in a corpus of repair manual files crowdsourced during collaborations between volunteer archivists and professional technicians. The corpus originates within iFixit.com's Medical Device Repair collection, a trove of more than 10,000 manuals contributed by working technicians in response to the strain placed on their colleagues and institutions due to the COVID-19 pandemic. The study focuses in particular on the Respiratory Analyzer subcategory of documents, which aid in the maintenance of equipment central to the care of COVID-19 patients experiencing respiratory symptoms. The 40 Respiratory Analyzer manuals in iFixit's collection are examined in terms of their original publication date, the apparent status of their original paper copies, the version of PDF used to encode them, and any additional metadata that is present. Based on these characteristics, the study advances a conceptual model accounting for circulation among multiple technicians, as well as alteration of documents during the course of their lifespans.
Hierarchical identification of solar radiation zones in China

Solar radiation zones serve as guidelines for the assessment, investment, and policymaking for solar energy utilization. Most zoning methods of cluster analysis depend on discrete measurements at solar and meteorological stations (usually sparsely and unevenly distributed); thus, the zone boundaries are empirically determined by artificial delineation, leading to misclassifications at the junction of adjacent zones. In this study, we propose a solar zoning algorithm that combines the station measurements of monthly global solar radiation (GSR) with spatially continuous satellite estimates to identify the definite solar radiation zones in China. To avoid the overfitting effect resulting from a fixed cluster number, this algorithm uses the Gaussian mixture model with the Dirichlet process to automatically infer the appropriate cluster number from the training data in a fully Bayesian manner. Unlike the traditional five-zone opinion, our experiments indicate that the number of reasonable solar radiation zones in China can range from five to ten, thus forming a hierarchical system. The five primary zones reflect the disparity of the annual total GSR caused by dominant climates, while the ten fine zones reveal the differences in the seasonal GSR variations associated with the local topography, altitude, and latitude. The comparative results demonstrate that the identified zone boundaries are accurate and reliable and are in agreement with the natural geographical dividing lines. The statistical analysis of the Chinese-installed photovoltaic capacity suggests that the proposed hierarchical system conforms to the multi-level needs of diverse applications.