Reviewerly/exHarmony huggingface dataset:
Research on Key Technologies and System Construction of Smart Mine

Smart mines are not only the ultimate transformation model of the traditional coal industry but the best approach to drive the intelligence of classical mines as well. To realize the transformation of mines from tradition to wisdom, this paper proposes the key technologies for building smart mines and discusses the construction of its systems. Furthermore, through the design of technology and system platform, the application technologies such as the Internet of Things, big data, artificial intelligence, etc. are organically integrated into the system construction of the smart mine, to achieve the overall intelligence of mine development, excavation, transportation, selection, safety production management, and also realize the safe, efficient and green mine production and operation objectives.
The Application of BIM Technology in Landscape Garden Engineering Projects

The urban landscape design project is an important project for the construction of modern cities in China and an important part of urban ecological greening. Strengthening the planning and design of urban landscapes is not only a need for ecological construction, but also an objective requirement for improving the taste of residents’ lives. With the rapid development of modern science and technology, the R&D and implementation of BIM technology has gradually involved many industries, including architectural design, construction, and business operations, and the landscape industry is no exception. With the application of BIM technology, it can effectively reduce cost calculation errors and drawing calculation errors. Compared with other engineering construction industries, the development of BIM technology in the landscape gardening industry is relatively slow. At the same time, due to the characteristics of landscape gardening, the application of BIM technology has certain difficulties. This article will discuss the application of BIM technology in landscape engineering projects.
Fast Reduction of Algebraic Lattices over Cyclotomic Fields

We describe two very efficient polynomial-time algorithms for reducing module lattices defined over arbitrary cyclotomic fields that solve the $$\gamma $$ -Hermite Module-SVP problem. They both exploit the structure of tower fields and the second one also uses the symplectic geometry existing in these fields. We conjecture that a rank-2 module over a cyclotomic field of degree n with B-bit coefficients can be heuristically reduced within approximation factor $$2^{\widetilde{\text {O}}\left( n\right) }$$ in time $$\widetilde{\text {O}}\left( n^2B\right) $$ . In the symplectic algorithm, if the condition number C of the input matrix is large enough, this complexity shrinks to $$\widetilde{\text {O}}\left( n^{\log _2 3}C\right) $$ . In cryptography, matrices are well-conditioned and we can take $$C=B$$ , but in the worst case, C can be as large as nB. This last result is particularly striking as for some matrices, we can go below the $$n^2B$$ swaps lower bound given by the analysis of LLL based on the potential. These algorithms are parallel and we provide a full implementation. We apply them on multilinear cryptographic concrete parameters by reducing matrices of dimension 4096 with 6675-bit integers in 4 days. Finally, we give a quasicubic time for the Gentry-Szydlo algorithm and run it in dimension 1024. It requires efficient ideal multiplications which need fast lattice reductions.
Instagram User Behavior Identification Based on Multidimensional Features

The development of smartphones and social networks has brought great convenience to our lives. Due to the increasing requirements of user privacy, user data are protected by encryption protocol. But it also makes it difficult to regulate malicious behavior. The existing user behavior identification adopts the statistical features of encrypted traffic, which fluctuates greatly in different transmission environments. In this paper, we propose a method to obtain the stable features of encrypted traffic. Based on the principle of maximum entropy, we put forward an approach to divide the distribution ranges of these stable features, and map the feature space into SVM vector space. Our research focuses on multiple user behavior in the Instagram application. The evaluation results achieve 99.8% accuracy, 99.3% precision, 99.3% recall, and 0.09% false positive rate(FPR) on average.
Realistic Dynamic Projection Mapping Using Real-Time Ray Tracing

We propose the introduction of modern rendering techniques, such as path tracing, to realistic dynamic projection mapping. Path-tracing rendering requires significant time, but dynamic projection mapping requires high-frame-rate rendering, e.g., 500-1000 fps. To overcome this gap, our method leverages the concept of persistence of vision. We find that path-traced noisy images can be integrated perceptually, even in dynamic scenes, by being projected them at 947 fps. The reproduced images can be consistent with the physical motion and observer viewpoint at low latency while being highly realistic.
Catastrophic Interference in Predictive Neural Network Models of Distributional Semantics

The semantic memory literature has recently seen the emergence of predictive neural network models that use principles of reinforcement learning to create a “neural embedding” of word meaning when trained on a language corpus. These models have taken the field by storm, partially due to the resurgence of connectionist architectures, but also due to their remarkable success at fitting human data. However, predictive embedding models also inherit the weaknesses of their ancestors. In this paper, we explore the effect of catastrophic interference (CI), long known to be a flaw with neural network models, on a modern neural embedding model of semantic representation (word2vec). We use homonyms as an index of bias as a function of the order in which a corpus is learned. If the corpus is learned in random order, the final representation will tend towards the dominant sense of the word (bank ➔ money) as opposed to the subordinate sense (bank ➔ river). However, if the subordinate sense is presented to the network after learning the dominant sense, CI almost completely erases the dominant sense and the final representation strongly tends towards the more recent subordinate sense. We demonstrate the impact of CI and sequence of learning on the final neural embeddings learned by word2vec in both an artificial language and in an English corpus. Further, we evaluate the effectiveness of a recently proposed solution to CI from neuroscience, elastic weight consolidation, as well as a Hebbian learning architecture from the memory literature that does not produce an error signal on mitigating the effects of CI.
Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

Motion pattern segmentation for crowded video scenes is an open problem because of the inability of existing approaches to tackle unpredictable crowd behaviour across varied scenes. To address this problem, we propose a Spatio-Angular Density-based Clustering (SADC) approach, which performs motion pattern segmentation by clustering the spatial and angular information obtained from the input trajectories. The k-nearest neighbours of each trajectory and the angular deviation between trajectories constitute the spatial and angular information, respectively. Effective integration of the spatio-angular information with an improvised density-based clustering algorithm makes this approach scene-independent. The performance of most clustering algorithms in the literature is parameter-driven. Choosing a single parameter value for different types of scenes decreases the overall clustering performance. In this article, we have shown that our approach is robust to scene changes using a single threshold, and, through the analysis of parameters across eight commonly occurring crowded scenarios, we point out the range of thresholds that are suitable for each scene category. We evaluate the proposed approach on the benchmarked CUHK dataset. The experimental results show the superior clustering performance and execution speed of the proposed approach when compared to the state-of-the-art over different scene categories.
Diversity in Recommendation System: A Cluster Based Approach

The recommendation system is used to process a large amount of data to recommend new item to users, which are achieved using the many developed algorithms. Hence, it is a challenging task for lots of online applications to establish an efficient algorithm for a recommendation system that follows a good trade-off between accuracy and diversity. Diversity in recommendation systems is used to avoid the overfitting problem as well as excellent skill, which provides a recommendation based on increasing the quality of user experiences. In this paper, we proposed a methodology of recommendation to the user with diversity. The impact of diversity on the system leads to user experience for new items. The aim of this paper is to provide a brief overview of diversification with state of the art. A further similarity measure based on heuristic similarity measure “proximity impact popularity” is used to provide a new model with the better-personalized recommendation. The proposed approach gives profitability to many applications for better user experience and diverse item recommendations.
<i>AttentionBoost</i>: Learning What to Attend for Gland Segmentation in Histopathological Images by Boosting Fully Convolutional Networks

Fully convolutional networks (FCNs) are widely used for instance segmentation. One important challenge is to sufficiently train these networks to yield good generalizations for hard-to-learn pixels, correct prediction of which may greatly affect the success. A typical group of such hard-to-learn pixels are boundaries between instances. Many studies have developed strategies to pay more attention to learning these boundary pixels. They include designing multi-task networks with an additional task of boundary prediction and increasing the weights of boundary pixels' predictions in the loss function. Such strategies require defining what to attend beforehand and incorporating this defined attention to the learning model. However, there may exist other groups of hard-to-learn pixels and manually defining and incorporating the appropriate attention for each group may not be feasible. In order to provide an adaptable solution to learn different groups of hard-to-learn pixels, this article proposes AttentionBoost, which is a new multi-attention learning model based on adaptive boosting, for the task of gland instance segmentation in histopathological images. AttentionBoost designs a multi-stage network and introduces a new loss adjustment mechanism for an FCN to adaptively learn what to attend at each stage directly on image data without necessitating any prior definition. This mechanism modulates the attention of each stage to correct the mistakes of previous stages, by adjusting the loss weight of each pixel prediction separately with respect to how accurate the previous stages are on this pixel. Working on histopathological images of colon tissues, our experiments demonstrate that the proposed AttentionBoost model improves the results of gland segmentation compared to its counterparts.
Free-space quantum secure direct communication based on decoherence-free space

We propose a free-space quantum secure direct communication protocol by using hyper-encoded single photons. The communication parties can securely exchange private information in a deterministic way. In this protocol, single photons are encoded simultaneously by both the polarization and spatial-mode degrees of freedom that constitute a decoherence-free space. The detrimental effect introduced by either reference-frame misalignment or practical channel rotational noise is passively suppressed. Finally, the security of our protocol against classical individual attacks is proved by quantum mutual information.
DeepAalo: Auto-adjusting Demotion Thresholds for Information-agnostic Coflow Scheduling

The existing coflow scheduling schemes minimize coflow completion time (CCT) based on the information of previous coflows, which makes them hard to use in practice. Thus, new scheduling mechanisms without considering prior knowledge are proposed, such as Aalo. In general, these algorithms demote coflows from the highest priority queue into several lower priority queues when their sent-bytes exceed predefined thresholds. However, most of the information-agnostic algorithms use static thresholds, and these coflow scheduling mechanisms may suffer a performance penalty when threshold settings mismatch traffic. In this paper, an information-agnostic coflow scheduler DeepAalo is proposed to minimize CCT by automatically adjusting thresholds of queues. DeepAalo applies Deep Reinforcement Learning(DRL) techniques to translate the design of thresholds into a continuous learning process. Specifically, DeepAalo can collect network information, learn from past decisions, and automatically update the thresholds of queues every t time. Therefore, DeepAalo has better self-adaptability when traffic changes. A flow-level simulator using python is developed, and the simulation results show that DeepAalo improves the average CCT up to 1.37× over Aalo.
Large-Scale and Rapid Flow Size Estimation for Improving Flow Scheduling

To minimize Flow Completion Time (FCT), existing flow scheduling schemes assume prior knowledge of accurate per-flow information, eg, flow sizes or deadlines, to achieve superior performance. In practice, it is hard to get accurate per-flow information, especially in multi-tenant cloud environments. Rather than such unrealistic assumption (using accurate per-flow information), this paper proposes a flow size estimation mechanism (called LFE), which uses machine learning algorithms to learn and explore the flow characteristics or patterns from historical data. LFE can estimate the flow size rapidly without accurate per-flow information. To evaluate the impact of flow size estimation on flow scheduling performance, we implement LFE in a flow-level simulator and test its performance with KMeans and PageRank workload, respectively. Compared with FLUX, the average FCT reduces 13% at 90% load. The results show that LFE has a better flow size prediction accuracy and can improve the flow scheduling performance.
Delegation with Updatable Unambiguous Proofs and PPAD-Hardness

In this work, we construct an updatable and unambiguous delegation scheme based on the decisional assumption on bilinear groups introduced by Kalai, Paneth and Yang [STOC 2019]. Using this delegation scheme, we show PPAD-hardness (and hence the hardness of computing Nash equilibria) based on the quasi-polynomial hardness of this bilinear group assumption and any hard language that is decidable in quasi-polynomial time and polynomial space. The delegation scheme is for super-polynomial time deterministic computations and is publicly verifiable and non-interactive in the common reference string (CRS) model. It is updatable meaning that given a proof for the statement that a Turing machine reaches some configuration C in T steps, it is efficient to update it into a proof for the statement that the machine reaches the next configuration $$C'$$ in $$T+1$$ steps. It is unambiguous meaning that it is hard to find two different proofs for the same statement.
Covid-19: When Species and Data Meet

Abstract This article explores how species meet, in particular humans and the Covid-19 virus. It also draws attention to the digital world through the lens of contact-tracing apps. Here, I examine human-virus-data relations, with humans, Covid-19, and data meeting and intra-acting. This article examines what has led us to this situation with Covid-19 and the role data is currently playing. The article offers an answer to two questions. How do humans, Covid-19, and Covid-19 contact-tracing apps meet and intra-act? What are the social justice issues and problems associated with contact-tracing apps? This article examines how species meet and intra-act, as well as how the Anthropocene has contributed to the current situation. The article also discusses contact-tracing apps and what these apps mean for society. Finally, the article shows how entanglements are not only constrained to those which are multispecies but also stretch out to the digital. These postdigital hybrid assemblages enable the coming together of humans, biological-more-than-human-worlds, and the digital. Postdigital hybrid assemblages enable us to push beyond boundaries, helping us understand Covid-19 and its impacts on society. Hopefully, this discussion about the postdigital hybrid assemblage will contribute to discussions in the future, and long after Covid-19, about how we are living our lives, and who and what we are living our lives with.
A Novel Encryption Design for Wireless Body Area Network in Remote Healthcare System Using Enhanced RSA Algorithm

Most emerging field of WSN is Wireless Body Area Network (WBAN). WBAN is a group of sensors in body carried by the human to gather frequent health information transmit those health data to medical server via wireless medium of communication. The cloud-based WBAN will be helpful to save patient life in case of emergency because it providing anywhere/anytime access to the patient data. Since the patient's information is private and highly sensitive, it is vital to afford high-level security and guard to the patient's medical information over an insecure public communication medium. In this paper, a secure architecture for accessing and observing health data gathered by WBAN is proposed. For providing a strong security Enhanced RSA (E-RSA) authentication mechanism is designed. While generating secret key for the user in addition to the master key, some of the user attributes are also considered to improve the security of the health data transmission process, and also random value for secret key generation is generated based on the bilinear mapping concept. The simulation model demonstrates how efficiently the proposed system preserving the confidentiality and privacy to the patient health data in remote health monitoring system.
Cerberus: Privacy-Preserving Computation in Edge Computing

Edge computing reduces the overhead of data centers and improves the efficiency of data processing. However, traditional cloud data protection mechanisms are no longer applicable to edge devices. Data leakage and other privacy issues may occur when computation is outsourced to edge nodes. The decentralization raises new privacy challenge for data control, storage and computation. In this work, we present Cerberus, a brand-new framework that preserves data privacy in edge computing by combining blockchain, distributed data storage and trusted execution environment (TEE). Blockchain is used to maintain a global computation state, and also acts as a medium of information interaction. Distributed data storage provides a secure and large-capacity storage. TEE-based off-chain computation guarantees confidentiality and efficiency of data processing. We also implement a prototype of Cerberus using Hyperledger Fabric and Intel SGX. Our evaluation on a sample of data sorting application shows that Cerberus achieves significant speed ups over previous cryptographic schemes. Compared with non secure computation, Cerberus can preserve data privacy without incurring much performance loss.
Precise Measurement of Physical Activities and High-Impact Motion: Feasibility of Smart Activity Sensor System

Today, over 72% of the world-wide population lives beyond the age of 65. Senior care has become one of the single most relevant challenges globally. There are many relevant issues related to monitoring and managing the daily activities of senior citizens. In this study, we will examine the role of advanced activity sensor platform to monitor their daily activity levels. It will assist in understanding the lifestyle of the individual seniors to promote safety and improve the quality of life through measurement of physical strength and independence. We investigate the requirements of wearable technology for the seniors, employing Inertial Measurement Unit (IMU) sensor for senior care. The proposed system includes a wearable device for each senior, Internet of Thing (IoT) receiver environment, smart alert, and cloud-based machine learning algorithm with Application Processing Interface (API) enabled remote Internet access. The primary application of our proposed IMU sensor application includes precise measurement of individual physical activity level.
SketchDesc: Learning Local Sketch Descriptors for Multi-View Correspondence

In this article, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multi-scale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets.
Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection

Suffering from both high computational complexity and high memory bandwidth is the major challenge in realizing the deep neural network in low-power for real-time applications. Binarizing the feature maps as well as the filter coefficients in deep neural network is an efficient way to reduce the high power consumption in deep learning object detection, however, it greatly scarifies the detection accuracy when reducing the bit-width of a 32-bit word to a binary bit in a floating-point deep neural network. This paper proposes a hybrid fixed point/binary deep neural network design methodology for object detection to achieve low-power consumption by taking advantage of both the fixed-point and binary deep neural networks, which allocates enough bit-width to design the hardware datapath in different layers of deep neural network. The proposed methodology combines dynamic fixed-point quantization and binarization techniques together to extremely compress the object detection model to result in a compact hybrid fixed-point/binary detection neural network, which achieves lower bandwidth and lower computational complexity. An automation tool based on the proposed methodology is also developed to train a hybrid deep neural network under a specified quality loss range. Taking MobileNet-SSD as an example, using the proposed methodology, the resulted model achieves 91% model size reduction and 75.8% memory bandwidth reduction at the cost of less than 1% mAP quality degradation. The proposed design methodology for hybrid fixed-point/binary deep neural networks achieves a good balance on detection accuracy, model size compression ratio and feature map reduction for low-power deep learning object detection applications.