Reviewerly/exHarmony huggingface dataset:
Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy

This paper discusses the worldwide trend of aging as the lifespan of humans increases. Nonetheless, most people do not write wills, which results in many legal problems after their death. There are many reasons for this including the problem of the validity of their heritage possibly not being legally certified. Wills can be divided into two categories, i.e., testimony and documents. A lawyer in the middle should notarize them, however, instead of providing these notarized services, we propose more transparent algorithms, blockchain shading, and smart country functions. Architectures are designed based on a neural network, the blockchain deep neural network (DNN), and deep neural network-based units are built with a necessary artificial neural network (ANN) base. A heritage inherited blockchain architecture is designed to communicate between nodes based on the minimum distance algorithm and multichannel protocol. In addition, neurons refer to the nerve cells that make up the nervous system of an organism, and artificial neurons are an abstraction of the functions of dendrite, soma, and axon that constitute the neurons of an organism. Similar to the neurons in organisms, artificial neural algorithms such as the depth-first search (DFS) algorithm are expressed in pseudocode. In addition, all blockchain nodes are equipped with verified nodes. A research model is proposed for an artificial network blockchain that is needed for this purpose. The experimental environment builds the server and network environments based on deep neural networks that require verification. Weights are also set for the required verification and performance. This paper verifies the blockchain algorithm equipped with this non-fiction preprocessor function. We also study the blockchain neuron engine that can safely construct a block node for a suicide blockchain. After empirical testing of the will system with artificial intelligence and blockchain, the values are close to 2 and 10 and the distribution is good. The blockchain node also tested 50 nodes more than 150 times, and we concluded that it was suitable for actual testing by completing a demonstration test with 4500 TPS.
RackMem

High-performance computing (HPC) clusters suffer from an overall low memory utilization that is caused by the node-centric memory allocation combined with the variable memory requirements of HPC workloads. The recent provisioning of nodes with terabytes of memory to accommodate workloads with extreme peak memory requirements further exacerbates the problem. Memory disaggregation is viewed as a promising remedy to increase overall resource utilization and enable cost-effective up-scaling and efficient operation of HPC clusters, however, the overhead of demand paging in virtual memory management has so far hindered performant implementations. To overcome these limitations, this work presents RackMem, an efficient implementation of disaggregated memory for rack scale computing. RackMem addresses the shortcomings of Linux's demand paging algorithm and automatically adapts to the memory access patterns of individual processes to minimize the inherent overhead of remote memory accesses. Evaluated on a cluster with an InfiniBand interconnect, RackMem outperforms the state-of-the-art RDMA implementation and Linux's virtual memory paging by a significant margin. RackMem's custom demand paging implementation achieves a tail latency that is two orders of magnitude better than that of the Linux kernel. Compared to the state-of-the-art remote paging solution, RackMem achieves a 28% higher throughput and a 44% lower tail latency for a wide variety of real-world workloads.
EFFRBNet: A Deep Super Resolution Network using Edge-Assisted Feature Fusion Residual Blocks

Deep convolutional networks provide very high quality super resolution images through a learning process by a nonlinear end-to-end mapping between low and high resolution images. Many of the state-of-the-art super resolution networks employ residual blocks in their network architectures, where in each residual block the high frequency residual signals are added to the feature maps input to the block. In this paper, a new residual block is proposed for the problem of image super resolution. The proposed residual block consists of three modules, namely, feature transformation module, nonlinear edge extraction module and feature fusion module. The feature transformation module produces high frequency residual signals and the nonlinear edge extraction module extracts the edges of the features input to the block. These generated high frequency features are then fused using the feature fusion module in order to produce a very rich set of high frequency residual features. The performance of the super resolution network using the proposed residual block is compared with that of the state-of-the-art light-weight super resolution schemes on four benchmark datasets. It is shown that the proposed super resolution scheme outperforms the state-of-the-art light-weight super resolution networks, when both the performance and number of parameters of the network are simultaneously taken into consideration.
Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data

Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.
Computation-Affordable Recognition System for Activity Identification using a Smart Phone at Home

With consideration of the aging society, the homecare associated with seniors becomes a critical issue in which there is a need for activity recognition of subjects at home. In this work, we propose the computation-affordable recognition system which includes the pre-trained MobileNetV2, temporal bidirectional Gated Recurrent Unit (GRU), Finite State Machine (FSM), and Incremental Majority Voting (IMV), to identify four activities of standing, walking, sitting, and falling. To compromise the accuracy and complexity, the proposed recognition engine adopts the pre-trained MobileNetV2 on ImageNet accompanied with the temporal bidirectional GRU, named as MoBiG. The schemes of FSM and IMV are used to remove the infeasible activity state transitions and to preserve the reasonable continuous activity states, respectively. The application scenario is to adopt a portable camera locating at the living room to record daily activities which are identified by the proposed recognition system embedded on a smart phone. The experimental results reveal that the proposed recognition system yields an average accuracy of 93.1% where the MoBiG and FSM+IMV contributes 91.7% and 1.4%, respectively. Compared to conventional work, the recognition system proposed herein shows the well compromised performance between accuracy and complexity for homecare applications.
Quantitative Risk Modeling and Analysis for Large-Scale Cyber-Physical Systems

Threats of cyber attacks are very real today and greatly impact everything including the public health sector, economics, electric grids, internet of things (IoT), and national security. The number of new evolving threats and reported vulnerabilities has severely increased in the last few years [1]. Perpetually refined cyber-attacks have set data, organizational assets, organizations, and individuals at considerable risk. Protecting sophisticated networks and interdependent systems, or reducing the impact of cyber-attacks has become a major challenge, where today's effective countermeasures can be completely ineffective tomorrow. The various risk assessment frameworks and methodologies are either high-level, missing risk metrics values, not suitable for all kinds of networks, or publicly not available. To address this issue, we present a quantitative risk assessment model, that helps to model the organizational security posture, evaluates the security controls in place, and provides an understanding of the associated risks. We further provide a detailed explanation of the formulations and evaluate the proposed model on an industrial scenario.
Learning socio-organizational network structure in buildings with ambient sensing data

Abstract We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.
Algorithm for Target Detection in Smart City Combined with Depth Learning and Feature Extraction

The target detection algorithms have the problems of low detection accuracy and susceptibility to occlusion in existing smart cities. In response to this phenomenon, this paper presents an algorithm for target detection in a smart city combined with depth learning and feature extraction. It proposes an adaptive strategy is introduced to optimize the algorithm search windows based on the traditional SSD algorithm, which according to the target operating conditions change, strengthening the algorithm to enhance the accuracy of the objective function which is combined with the weighted correlation feature fusion method, and this method is a combination of appearance depth features and depth features. Experimental results show that this algorithm has a better antiblocking ability and detection accuracy compared with the conventional SSD algorithms. In addition, it has better stability in a changing environment.
Enumerating Optimal Quantum Circuits using Spectral Classification

This work targets fault-tolerant quantum computing and focuses on the problem of mapping reversible circuits into the Clifford+T quantum gate library.We present an automatically-generated database containing minimal-cost quantum circuits for Boolean functions up to 5 inputs.The database contains three circuits for each spectral-equivalent class representative, which are respectively optimized for the T -count, the T -depth, and the number of qubits.We show that any Boolean function can be derived from the implementation of its class representative without increasing any of the stated cost functions.
Locality Guided Neural Networks for Explainable Artificial Intelligence

In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly generate sequences to closely mimic the training data, without direct optimization of desired goals or properties specific to the task. We introduce OptiGAN, a generative model that incorporates both Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to optimize desired goal scores using policy gradients. We apply our model to text and real-valued sequence generation, where our model is able to achieve higher desired scores out-performing GAN and RL baselines, while not sacrificing output sample diversity.
Twitter sentiment analysis for the estimation of voting intention in the 2017 Chilean elections

In this paper, we apply sentiment analysis methods in the context of the first round of the 2017 Chilean elections. The purpose of this work is to estimate the voting intention associated with each candidate in order to contrast this with the results from classical methods (e.g., polls and surveys). The data are collected from Twitter, because of its high usage in Chile and in the sentiment analysis literature. We obtained tweets associated with the three main candidates: Sebastián Piñera (SP), Alejandro Guillier (AG) and Beatriz Sánchez (BS). For each candidate, we estimated the voting intention and compared it to the traditional methods. To do this, we first acquired the data and labeled the tweets as positive or negative. Afterward, we built a model using machine learning techniques. The classification model had an accuracy of 76.45% using support vector machines, which yielded the best model for our case. Finally, we use a formula to estimate the voting intention from the number of positive and negative tweets for each candidate. For the last period, we obtained a voting intention of 35.84% for SP, compared to a range of 34–44% according to traditional polls and 36% in the actual elections. For AG we obtained an estimate of 37%, compared with a range of 15.40% to 30.00% for traditional polls and 20.27% in the elections. For BS we obtained an estimate of 27.77%, compared with the range of 8.50% to 11.00% given by traditional polls and an actual result of 22.70% in the elections. These results are promising, in some cases providing an estimate closer to reality than traditional polls. Some differences can be explained due to the fact that some candidates have been omitted, even though they held a significant number of votes.
DispLagBox

The latency of a computing system affects users' performance. One important component of end-to-end latency is display lag - the time required to turn framebuffer contents into photons emitted by a computer screen. However, there is no well-documented and widely available method for measuring display lag. Thus, the effect of display lag is rarely considered in scientific studies and system development. We developed DispLagBox, a simple open-source device for measuring display lag. It supports the International Display Measurements Standard but also offers additional metrics for characterizing display lag with a resolution of 0.1 ms. The device, based on a Raspberry Pi computer, measures the time between VSYNC and a change in brightness on the connected display. Repeated measurements can be conducted automatically, so that not only average latency but also latency distributions for each device can be reported. For most displays we tested, DispLagBox reports latencies that are close to those reported by a commercial black-box measurement device. Typically, the difference is 1 -- 3 ms.
CS-RPCA: Clustered Sparse RPCA for Moving Object Detection

Moving object detection (MOD) is an important step for many computer vision applications. In the last decade, it is evident that RPCA has shown to be a potential solution for MOD and achieved a promising performance under various challenging background scenes. However, because of the lack of different types of features, RPCA still shows degraded performance in many complicated background scenes such as dynamic backgrounds, cluttered foreground objects, and camouflage. To address these problems, this paper presents a Clustered Sparse RPCA (CS-RPCA) for MOD under challenging environments. The proposed algorithm extracts multiple features from video sequences and then employs RPCA to get the low-rank and sparse component from each representation. The sparse subspaces are then emerged into a common sparse component using Grassmann manifold. We proposed a novel objective function which computes the composite sparse component from multiple representations and it is solved using non-negative matrix factorization method. The proposed algorithm is evaluated on two challenging datasets for MOD. Results demonstrate excellent performance of the proposed algorithm as compared to existing state-of-the-art methods.
The benefits of synthetic data for action categorization

In this paper, we study the value of using synthetically produced videos as training data for neural networks used for action categorization. Motivated by the fact that texture and background of a video play little to no significant roles in optical flow, we generated simplified textureless and background-less videos and utilized the synthetic data to train a Temporal Segment Network (TSN). The results demonstrated that augmenting TSN with simplified synthetic data improved the original network accuracy (68.5%), achieving 71.8% on HMDB-51 when adding 4,000 videos and 72.4% when adding 8,000 videos. Also, training using simplified synthetic videos alone on 25 classes of UCF-101 achieved 30.71% when trained on 2500 videos and 52.7% when trained on 5000 videos. Finally, results showed that when reducing the number of real videos of UCF-25 to 10% and combining them with synthetic videos, the accuracy drops to only 85.41%, compared to a drop to 77.4% when no synthetic data is added.
Active Image Sampling on Canonical Views for Novel Object Detection

To alleviate the costly data annotation problem in deep learning-based object detection, we leverage the canonical view model for active sample selection to improve the effectiveness of learning. Inspired by the view-approximation model, we hypothesize that visual features learned from canonical views denote better representations of objects, thus boosting the effectiveness of object learning. We validate the hypothesis empirically in the context of robot learning for novel object detection. Based on this, we propose a novel on-line viewpoint exploration (OLIVE) method that (1) defines goodness-of-view by combining informativeness of visual features and consistency of model-based object detection, and (2) systematically explores and selects viewpoints to boost learning efficiency. Furthermore, we train a legacy Faster R-CNN model with a data augmentation method while leveraging data samples generated by the OLIVE pipeline. We test our method on the T-LESS dataset and show that the proposed method outperforms competitive benchmarking methods, especially when the samples are few.
LSHWE: Improving Similarity-Based Word Embedding with Locality Sensitive Hashing for Cyberbullying Detection

Word embedding methods use low-dimensional vectors to represent words in the corpus. Such low-dimensional vectors can capture lexical semantics and greatly improve the cyberbullying detection performance. However, existing word embedding methods have a major limitation in cyberbullying detection task: they cannot represent well on "deliberately obfuscated words", which are used by users to replace bullying words in order to evade detection. These deliberately obfuscated words are often regarded as "rare words" with a little contextual information and are removed during preprocessing. In this paper, we propose a word embedding method called LSHWE to solve this limitation, which is based on an idea that deliberately obfuscated words have a high context similarity with their corresponding bullying words. LSHWE has two steps: firstly, it generates the nearest neighbor matrix according to the co-occurrence matrix and the nearest neighbor list obtained by Locality Sensitive Hashing (LSH); secondly, it uses an LSH-based autoencoder to learn word representations based on these two matrices. Especially, the reconstructed nearest neighbor matrix generated by the LSH-based autoencoder is used to make the representations of deliberately obfuscated words close to their corresponding bullying words. In order to improve the algorithm efficiency, LSHWE uses LSH to generate the nearest neighbor list and the reconstructed nearest neighbor list. Empirical experiments prove the effectiveness of LSHWE in cyberbullying detection, particularly on the "deliberately obfuscated words" problem. Moreover, LSHWE is highly efficient, it can represent tens of thousands of words in a few minutes on a typical single machine.
Design and Development of Voice Controllable Wheelchair

In this work a voice controlled wheelchair is made for physically challenged peoples or patients whose hind limbs are not in working condition. Hence, he or she can control the wheelchair by own voice commands or of the family members. To accomplish the task Arduino microcontroller board is used which receives the voice commands either from Bluetooth module via smartphone or from voice recognition module from microphone attached to it. Further matching it with preloaded voice commands and develop movement accordingly. In the subsequent sections of the paper give more concentration on further objectives to achieve reduction in motor speed smooth movement of vehicle by two motors and the wheels as per the command. The main objective of current work is to make it as simple as possible. Certain theoretical results are also obtained by formulation and design calculation.