Reviewerly/exHarmony huggingface dataset:
FEWER

In federated learning, the local devices train the model with their local data, independently; and the server gathers the locally trained model to aggregate them into a shared global model. Therefore, federated learning is an approach to decouple the model training from directly assessing the local data. However, the requirement of periodic communications on model parameters results in a primary bottleneck for the efficiency of federated learning. This work proposes a novel federated learning algorithm, Federated Weight Recovery(FEWER), which enables a sparsely pruned model in the training phase. FEWER starts with the initial model training with an extremely sparse state, and FEWER gradually grows the model capacity until the model reaches a dense model at the end of the training. The level of sparsity becomes the leverage to either increasing the accuracy or decreasing the communication cost, and this sparsification can be beneficial to practitioners. Our experimental results show that FEWER achieves higher test accuracies with less communication costs for most of the test cases.
Application and Thinking of Library Books Inventory Intelligent Robot: Take China Agricultural University Library as an Example

This article reviews the development path of books inventory from traditional manual inventory, handheld devices scanning inventory, to emerging of smart bookshelves, and then to the development of books intelligent robot inventory in Chinese libraries. Practical application effects of Nanjing University's books inventory robot products which is adopted by China Agricultural University new library on east campus are introduced and summarized. It has been found that books positioning accuracy are effectively improved, the efficiency and accuracy of books inventory are promoted, and books inventory normalization are achieved by applying the books inventory robot. It is finally pointed out that books inventory intelligent robot can be optimized in terms of operability, functional characteristics, and cost reduction for widely application. And front-line librarians better be trained so that books inventory intelligent robot can play a greater role in future library management services.
Partial Sums on the Ultra-Wide Word RAM

We consider the classic partial sums problem on the ultra-wide word RAM model of computation. This model extends the classic w-bit word RAM model with special ultrawords of length \(w^2\) bits that support standard arithmetic and boolean operation and scattered memory access operations that can access w (non-contiguous) locations in memory. The ultra-wide word RAM model captures (and idealizes) modern vector processor architectures.
Human Motion Deblurring Using Localized Body Prior

In recent decades, the skinned multi-person linear model (SMPL) is widely exploited in the image-based 3D body reconstruction. This model, however, depends fully on the quality of the input image. Degraded image case, such as the motion-blurred issue, downgrades the quality of the reconstructed 3D body. This issue becomes severe as recent motion deblurring methods mainly focused on solving the camera motion case while ignoring the blur caused by human-articulated motion. In this work, we construct a localized adversarial framework that solves both human-articulated and camera motion blurs. To achieve this, we utilize the result of the restored image in a 3D body reconstruction module and produces a localized map. The map is employed to guide the adversarial modules on learning both the human body and scene regions. Nevertheless, training these modules straight-away is impractical since the recent blurry dataset is not supported by the 3D body predictor module. To settle this issue, we generate a novel dataset that simulates realistic blurry human motion while maintaining the presence of camera motion. By engaging this dataset and the proposed framework, we show that our deblurring results are superior among the state-of-the-art algorithms in both quantitative and qualitative performances.
Toward Efficient Cloud Services: An Energy-Aware Hybrid Load Balancing Approach

Load balancing of tasks on virtual machines based on Nature-inspired algorithm has turned into a field of greater research interest. Algorithm for load balancing based on honey bee behavior (LBA_HB) was presented to decrease the processing time and overall response time. However, it considers only the number of tasks handled by VMs for balancing the load, which might not be well enough effective. This research proposes an Improved Algorithm Based on Honey Bee behavior (ILBA_HB) by considering the various QoS parameters including service response time, reliability, cost, and estimate the power consumption of VMs on a host to enhance load balancing. As the situation remains uncertain, fuzzy logic can be applied at various levels of input to achieve the desired tradeoff between power consumption and QoS. The main goal of the proposed study calculates the power consumption of VMs and considers the important QoS factors to choose the appropriate host and virtual machine to receive the task. The ILBA_HB algorithm has been simulated with the use of CloudSim. The performance of ILBA_HB algorithm is compared with that of LBA_HB in terms of makespan, response time, degree of imbalance.
Estimation of Linear Space-Invariant Dynamics

We propose a computationally efficient estimator for multi-dimensional linear space-invariant system dynamics with periodic boundary conditions that attains low mean squared error from very few temporal steps. By exploiting the inherent redundancy found in many real-world spatiotemporal systems, the estimator performance improves with the dimensionality of the system. This paper provides a detailed analysis of maximum likelihood estimation of the state transition operator in linear space-invariant systems driven by Gaussian noise. The key result of this work is that, by incorporating the space-invariance prior, the mean squared error of a estimator normalized to the number of parameters is upper bounded by N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> + O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ), where N is the number of spatial points, and M is the number of observed timesteps after the initial value.
An Appearance and Viewpoint Invariant Visual Place Recognition for Seasonal Changes

Place recognition has typically been addressed as a problem of recognizing the location of a given query image as a previously visited place while comparing it with the geotagged database images. Despite a lot of research in this area, vision-based place recognition is still an open challenge because of the changing environmental conditions which cause drastic appearance changes, making it difficult for a robot to recognize the place. This research addresses the above-mentioned problem and proposes the solution for place recognition at a low memory footprint. The proposed place recognition system focuses on identifying the combination of different feature detectors and descriptors that are invariant to the viewpoint and seasonal changes and can efficiently recognize a place at high accuracy. Through experimental results, it is shown that combination of CenSure based STAR detector and BRISK achieves high detection accuracy.
RationalGRL: A framework for argumentation and goal modeling

Goal-oriented requirements modeling approaches aim to capture the intentions of the stakeholders involved in the development of an information system as goals and tasks. The process of constructing such goal models usually involves discussions between a requirements engineer and a group of stakeholders. Not all the arguments in such discussions can be captured as goals or tasks: e.g., the discussion whether to accept or reject a certain goal and the rationale for acceptance or rejection cannot be captured in goal models. In this paper, we apply techniques from computational argumentation to a goal modeling approach by using a coding analysis in which stakeholders discuss requirements for a Traffic Simulator. We combine a simplified version of a traditional goal model, the Goal-oriented Requirements Language (GRL), with ideas from argumentation on schemes for practical reasoning into a new framework (RationalGRL). RationalGRL provides a formal semantics and tool support to capture the discussions and outcomes of the argumentation process that leads to a goal model. We also define the RationalGRL development process to create a RationalGRL model.
Guiding graph exploration by combining layouts and reorderings

Visualizing graphs is a challenging task due to the various properties of the underlying relational data. For sparse and small graphs the perceptually most efficient way are node-link diagrams whereas for dense graphs with attached data, adjacency matrices might be the better choice. Since graphs can contain both properties, being globally sparse and locally dense, a combination of several visualizations is beneficial. In this paper we describe a visually and algorithmically scalable approach to provide views and perspectives about graphs as interactively linked node-link as well as adjacency matrix visualizations. The novelty of the technique is that insights like clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the others. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by taking into account several layout and reordering properties in combination as well as different edge properties for the same set of nodes. We illustrate the usefulness of our tool by applying it to graph datasets like co-authorships, co-citations, and a CPAN distribution.
Few-Shot Classification with Transductive Data Clustering Transformation

Few-shot classification aims to recognize unlabeled samples from unseen classes given only a small number of labeled examples. Most methods addressing few-shot problem through meta-learning. They focus on learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task. However, the low-data problem in the novel classification task still remains. In this paper, we propose Transductive Data Clustering Transformation (TDCT), a novel and simple method which can potentially be applied to any metric-based few-shot classification approaches. TDCT exploits the task-specific knowledge and enhances the data representations by using a transformation that incorporates data clustering. This transformation implicitly does transductive inference by leveraging the relationships between all samples within a task, alleviating the low data problem. Extensive experiments show that TDCT is an effective and computationally efficient method which can improve few-shot learning performance by a large margin on two benchmarks.
Research on Optimization of Block chain Security Testing

With the increasing maturity of Block chain technology, more and more attention, more and more fields of application, application prospects are good However, in recent years, the security issues of the Block chain have become increasingly obvious. In the application of Block chain, security incidents have emerged one after another, causing a series of problems and causing hundreds of millions of economic losses. At present, there is some research on Block chain security testing, but there is still room for optimization based on Block chain security testing. This article analyzes the security problems of Block chain technology from various aspects, and proposes an optimization plan for existing security tests.
Text Analytics: Present, Past and Future

Text analytics is a large umbrella under which it is possibleIezzi, Domenica Fioredistella to report countless techniques, models, methods for automatic and quantitative analysis of textual data.Celardo, Livia Its development can be traced back the introduction of the computer, but the prodromes date back, the importance of text analysis has grown over time and has been greatly enriched with the spread of the Internet and social media, which constitute an important flow of information also in support of official statistics. This paper aims to describe, through a timeline the past, the present and the possible future scenario of text analysis. Moreover, the main macro-steps for a practical study are illustrated.
Weakly Supervised Person Search

While existing person search methods have achieved good performance, they require the images used for training contain labels about the identity and bounding box location of each person. However, it is expensive and difficult to manually annotate these labels in the large scale scenario. To overcome this issue, we consider weakly supervised person search. The weakly supervised setting means during training we only know which identities appear in the image set and how many individuals present in each image, without any identity or location information on the image. Facing this challenge, we propose a clustering and patch based weakly supervised learning (CPBWSL) framework, which separately addresses two sub-tasks including pedestrian detection and person re-identification. Particularly, we introduce multiple detectors to provide more detection results as well as fuzzy c-means clustering algorithm to cluster these results and remove low membership ones. Moreover, a patch based learning network is designed to generate different patches and learn discriminative patch features. Extensive experiments on two benchmarks indicate that the proposed weakly supervised setting is feasible and our method can achieve performance comparable to some fully supervised person search methods.
Does scaling player size skew one’s ability to correctly evaluate object sizes in a virtual environment?

This study attempts to evaluate whether a navigation technique based on scaling the user’s avatar impacts the user’s ability to correctly assess the size of virtual objects in a virtual environment. This study was realized during the CERN Open Days with data from 177 participants over eighteen years old. We were able to observe well-established phenomena such as the effect of inter-pupillary distance (IPD) on perception of scale, as well as original results associated with scaling factor and avatar embodiment. We observed that the user is more prone to overestimate object sizes from the Virtual Environment (VE) when provided with an avatar, while scaling the IPD according to the scale of the user’s avatar contributes to a reduction in the overestimation of object sizes within the VE.
The Improved Hu Moment and Its Application in Gesture Recognition

Hu moment is a classic gesture recognition algorithm. Although its calculation is simple, the accuracy is low. In order to solve the problem of low accuracy of traditional Hu moment recognition image features, this paper improves the algorithm by changing the characteristic value of Hu algorithm and calculating the similarity between the image to be recognized and the template image. This paper use Kinect sensors to collect gesture information. After gesture recognition processing, extract hand contour information in order to track important palm nodes. Experimental results show that the improved algorithm's gesture recognition accuracy is higher than 95%, which can effectively improve the gesture recognition accuracy.
Design and Implementation of Intelligent Chinese Chess System Device

As a representative chess game in China, Chinese chess is a kind of intellectual game that has been precipitated after thousands of years. With the maturity of cloud-based speech recognition technology and the development of artificial intelligence technology, the intelligent chess system has risen to a new level, expanding a more intelligent human-computer interaction experience. This paper proposes a chess piece traction and movement method based on electromagnet dot matrix, which gets rid of the complicated mechanical devices, and completes the game decision based on deep learning algorithm, realizes powerful chess force, and realizes voice control based on natural language processing algorithm. Eventually, when the system is started, you can play against the machine on the real chessboard.
Efficient Signature Scheme Using Extended Chaotic Maps for Medical Imaging Records

Digital security issues such as medical records, diagnostic certificates, medical images, etc. in the medical community have gradually been paid attention to. Nowadays, encrypting and signing electronic medical records including medical imaging records still have to rely on time-consuming exponential computations. Generally, medical image files are often larger and quite different from electronic medical text record file in characteristic, size and format. Recently, the chaotic map operation has been discovered to be superior to modular exponential or scalar multiplications on an elliptic curve. The extended chaotic maps also provide the characteristics of semigroup, commutative property, and the discrete logarithm problem, and thus are suitable for the development of asymmetric cryptosystems. This study develops a signature scheme based on extended chaotic maps that is suitable for medical imaging records. The proposed signature scheme not only provides the properties of digital signatures, including authentication, unforgeability and non-repudiation, but also is more efficient than the related signature mechanism used in medical institutions today.
MUKG: Unifying Multi-Task and Knowledge Graph Method for Recommender System

Recommendation systems (RS) are automated mechanisms designed exclusively to prescribe desired products scientifically based on the preferences of the consumer. The users' interest would be either implied or precisely defined by the processes. Implicit meta-data collections merely provide all the possible detection of usage profiles. Recommendations may produce predominantly based on consumer anticipations, item features, user-interactions among other contextual factors. In popular RS techniques, collaborative filtering stoically endures from sparsity as well as cold start problems. To adequately address the key issues, many academic studies subtly manipulate feature learning to positively enhance the exceptional performance of RS. This paper ponders the Knowledge Graph to serve as the source of heterogeneous information. We introduce the MUKG a unifying framework based on multi-task feature learning and Knowledge graph to improve tremendously the recommendation. The MUKG represents a deep succeeding framework that appropriates the knowledge graph representation task to support the recommendation task. We develop a Mapping Layer Network and two inter-link tasks that learn precisely higher-order interactions between specific items and entities. We convincingly demonstrate MUKG represents a comprehensive framework incorporating many representative methods of RS along with multi-task learning. Through extensive experiments on four pragmatic datasets, we profusely illustrate that the potency of the MUKG over different newfangled touchstone. Precisely, our developed model properly obtains average Accuracy accumulations of 11.6%, 11.5%, 12.7%, and 8.7% in movies, books, music and news recommendation, individually.
Research on Key Energy-Saving Technologies in Green Data Centers

With the rapid development of the data-intensive industries, the demands for large-scale data centers become tremendous. Accordingly, the problem of saving energy in the operation & maintenance of the data centers has become increasingly important. In this paper, we summarize the key technologies for networking energy saving and environment energy saving in large-scale data center networks. The key technologies include network topology optimization, network routing optimization, flow scheduling, and intelligent electric management and optimization of data centers. The network energy consumption with different network topology structures and routing algorithms are analyzed and compared, and important technical directions for several energy saving mechanisms such as energy-efficient routing and network device sleeping technology on network energy saving of the entire data center are studied. Besides, other research directions of relevant energy saving and cost reduction technologies are studied in the paper.
ScagnosticsViewer

Visual analytics studies the integration of human perceptional ability and the data exploration process. Besides, high dimensional time series has become more and more popular data in daily applications and various domains. It is essential to study visual techniques for this type of data set. This paper is an applied work that extends successes of the scatterplot matrix (SPLOM) for cross-sectional data to the high dimensional temporal data sets. We have built a web-based application that contains various visual techniques to display the temporal data sets by SPLOM at each snapshot and animate the SPLOM to illustrate the evolution of the data sets. Two real data sets in different domains are utilized to demonstrate the advances of this approach.
Design and implementation of a gradual verifier

Static program verification requires a large number of user-provided specifications, resulting in a significant upfront verification cost. One method for reducing this burden is gradual verification, a novel approach that enables developers to deal with the cost of verification incrementally — statically where possible and dynamically where necessary. In this paper, we discuss our work designing and implementing the static portion of a gradual verification tool.
WS-SM: Web Services - Secured Messaging Framework with Pluggable APIs

Dynamic composition of web services is important in B2B applications where user requirements and business policies change and new services get added to the service registry frequently. In a dynamic composition environment, ensuring the security of messages communicated among the web services becomes challenging since, several attacks are possible on SOAP messages in the public network due to their standardized interfaces. Most of the existing works on web services security provide solutions to ensure basic security features such as confidentiality, integrity, authentication, authorization, and non-repudiation. Few existing works that provide solutions such as schema validation and schema hardening for attacks on web services do not provide attack-specific solutions. The web services security standard and all the existing works have addressed only the security of messages between a client and a single web service but not the security for messages between two services which is quite challenging. Hence, a security framework for secured messaging among web services has been proposed to provide attack-specific solutions. Since new types of web service attacks are evolving over time, the proposed security solutions are implemented as APIs that are pluggable in any server where the web service is deployed. The proposed framework has been tested for compliance with WSI-BP to demonstrate its interoperability and subjected to vulnerability testing which proved its immunity to attacks. The stress testing results revealed that the throughput decreased only by 35% achieving a good trade-off between performance and security.