Reviewerly/exHarmony huggingface dataset:
Retracted: A Quality Representation of Tumor in Breast Using Hybrid Model Watershed Transform and Markov Random Fields

Mammography is the best available procedure for the non invasive detection of signs of breast cancer by way of identifying masses, calcifications, bilateral asymmetry and architectural distortion. The work is divided into four parts, they are pre-processing, detection of lesions (or masses), architectural distortion and micro calcifications in mammograms. An attempt has also been made for segmenting the diseased regions by applying Markov random fields along with watershed transform, together with mathematical morphological operators, The proposed algorithm is experimented with 10 mammogram image sets. The results are evaluated with ground truth values
A Quality Framework for Evaluating Grammatical Structure of User Stories to Improve External Quality

The quality assessment is an essential step in the software development process; it takes place previous and after the development. Internal quality is a set of characteristics from an internal approach in the software such as requirements, UML diagrams, and the code. These characteristics are constant during the software life cycle. The external quality metrics are the ones that require the end product and, in many cases, the users involvement. Several authors stated that the external quality of the product is related to the assurance of the internal quality of the project. For that reason, this study proposes a framework for assessing the internal quality of user stories to improve the external quality of the project deliveries. The internal quality assessment is based on the grammatical structure of the user stories, and the external quality considers the functionality and usability of the product deliveries. The experiment for validating the framework was conducted with 19 developers considering three different projects which gathered 46 user stories. The findings suggest that the presence of adjectives in user stories improves the usability and the correctness of the product is related to the developer's experience.
Blockchain for EU electricity market

Electricity systems around the world are currently undergoing fundamental changes on two fronts, decarbonization and digitalization. The transition towards a low carbon economy means increased penetration and utilization of Renewable Energy Sources (RES) and the electrification of transport and other sectors. The expansion of Distributed Energy Resources (DERs), electricity grid storage, Electric Vehicles (EV's), the associated charging infrastructure, smart buildings and the upgraded ICT infrastructure, also provides opportunities for end-customers to play a more active role in the electricity markets. Blockchain technology can help avail these opportunities through innovative business models which are not just centered around the end-users but also in improving the overall market operation. Blockchain is a forward-looking technology that can significantly disrupt the way in which the electricity markets and its participants operate. The consensus based decision making, decentralized and distributed characteristics of blockchain technology render it well suited for a future energy system which is increasingly decentralized, distributed and with more active end-customers participating in the market. In this paper, we discuss in brief the blockchain technology outlook and its suitability for the future energy grid and market operation. We will also discuss how blockchain can help disrupt the energy sector and the associated challenges. Finally, we also provide our recommendation on what applications of blockchain in the energy sector we predict the biggest potential.
A Quantum Computer Architecture Based on Silicon Donor Qubits Coupled by Photons

Abstract A comprehensive circuit architecture and a protocol for realizing 3D cluster states through photonic‐measurement‐based donor‐spin‐qubit entanglement and readout are described. The basic building blocks and protocol are chosen to be compatible with a fully integrated photonic circuit implementation, using Se + as the photonically coupled matter qubit. The basic operational building blocks of this universal quantum computing machine are local measurements and unitaries, plus an entangling measurement of non‐local Pauli operators. By analyzing several sources of error, a theoretical fault‐tolerant threshold value is estimated on the order of 1%. Considering the literature where required components have already been realized in integrated silicon circuits, albeit not all on the same chip, this suggests the threshold is within reach.
Making Sense of Scientific Simulation Ensembles With Semantic Interaction

Abstract In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as ‘ensembles’. Ensembles are then a number of runs that are each multi‐dimensional and multi‐variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically‐Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.
Modeling Access Control on Streaming Data in Apache Storm

Streaming analytics on real-time data is very important in the IOT world since analytics, decisions and operations have to be done in real-time for practical applicability. In such scenarios, one key consideration is the security of the real-time stream. Stream security as in other network security can be modeled using the CIA (Confidentiality/Integrity/Availability) model [1]. However, most practical implementations just take care of the first two aspects viz Confidentiality and Integrity using standard techniques such as encryption and signatures. In this paper, we introduce an access control mechanism on the stream that annotates the stream with additional security metadata. This metadata can be used to allow/deny access to elements in the stream and also protect the privacy of the data. We demonstrate a work in progress implementation of the access control mechanism on the popular streaming engine Apache Storm [2] and demonstrate how access control can be modeled and implemented on streaming data.
EfficientFAN: Deep Knowledge Transfer for Face Alignment

Face alignment plays an important role in many applications that process facial images. At present, deep learning-based methods have achieved excellent results in face alignment. However, these models usually have a large number of parameters, resulting in high computational complexity and execution time. In this paper, a lightweight, efficient, and effective model is proposed and named Efficient Face Alignment Network (EfficientFAN). EfficientFAN adopts the encoder-decoder structure, using a simple backbone Efficient-Net-B0 as the encoder and three deconvolutional layers as the decoder. Compared with state-of-the-art models, it achieves equivalent performance with fewer model parameters, lower computation cost, and higher speed. Moreover, the accuracy of EfficientFAN is further improved by transferring deep knowledge of a complex teacher network through feature-aligned distillation and patch similarity distillation. Extensive experimental results on public data sets demonstrate the superiority of EfficientFAN over state-of-the-art methods.
A Study on the Factors Influencing the Participation of Face-to-Face Discussion and Online Synchronous Discussion in Class

Discussion is an effective way of improving teaching quality. Face-to-face discussion and online discussion have their own advantages and disadvantages respectively, but they both face the challenge that how to enhance students' participation. There are many related studies on factors affecting students' participation, most of which are around external factors, while studies on internal factors are fewer. Spiral of Silence (SOS) theory can be applied for explaining the factors affecting people engage in public discussion, and this study apply extended SOS framework for analyzing psychological factors that affect students' participation willingness and outspoken willingness in face-to-face discussion and online synchronous discussion. The result shows that communication apprehension can significantly hinder participation, and fear of isolation can significantly reduce outspokenness willingness. Online discussion will cause less communication apprehension and more participation willingness and outspoken willingness. Contemporary, Chinese university students have the courage to face the competition and are not afraid of expressing minority opinions, but they may fear the authority of the government and their teachers.
Variational model for low-resource natural language generation in spoken dialogue systems

Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown improving results irrespective of providing sufficient annotated data. Nevertheless, how to build a generator that can effectively utilize as much of knowledge from a low-resource setting data is a crucial issue for NLG in SDSs. This paper presents a variational-based NLG framework to tackle the NLG problem of having limited annotated data in two scenarios, domain adaptation and low-resource in-domain training data. Based on this framework, we propose a novel adversarial domain adaptation NLG taclking the former issue, while the latter issue is also handled by a second proposed dual variational model. We extensively conducted the experiments on four different domains in a variety of training scenarios, in which the experimental results show that the proposed methods not only outperform previous methods when having sufficient training dataset but also show its ability to work acceptably well when there is a small amount of in-domain data or adapt quickly to a new domain with only a low-resource target domain data.
Efficient Quantum Circuits for Square-Root and Inverse Square-Root

In recent years, rapid advancements in quantum technologies have resulted in heightened research activity, particularly in the domain of efficient implementation of computationally intensive kernels from prominent quantum algorithms. This is partly addressed by increasingly sophisticated synthesis techniques for quantum circuits. In this paper, we study the efficient Clifford +T circuit realization of two kernels, namely square root and inverse square root for different number formats. Quantum algorithms for solving Toeplitz system of linear equations, numerical methods for solving Poisson equation and in general for scientific computing require such kernels with customizable trade-off points for accuracy and performance. We present an efficient circuit design, obtained via detailed exploration of design choices in terms of accuracy and quantum costs. The manual circuits designed provide upto $50 \times $ reduction in qubit count for high precision circuits. Furthermore, a detailed benchmarking of state-of-the-art synthesis tools is undertaken for these kernels along with detailed comparison against manually-implemented designs.
Cooperative Memory Expansion via OS Kernel Support for Networked Computing Systems

The growing popularity of in-memory computing for bigdata analytics often causes performance bottlenecks to memory subsystem resided in operating systems (OS). This article purposes cooperative memory expansion (COMEX), an OS kernel extension. COMEX establishes a stable pool of memory collectively across nodes in a cluster and enhances OS's memory subsystem for memory aggregation from connected machines by allowing process's page table to track remote memory page frames without programmer effort or modifications to application codes. COMEX employs Remote Direct Memory Access (RDMA) for low-latency data transfer with destination kernel bypassed and does not rely on an old design of the I/O block subsystem usually adopted by all known remote paging. COMEX fits soundly in the emerging system design approach of resource disaggregation which breaks hard walls between server-centric machines into a new design paradigm of separated resource pools. The new architecture facilitates both system scaling-up and scaling-out, also eliminates imbalance resources existing in datacenters. We have implemented COMEX based on Linux kernel 3.10.87 and deployed on our 32 networked servers. Performance evaluation results under ten applications from two benchmark suites reveal the speedup of up to 170 times when application execution footprints are 10 times larger than available system memory.
A survey on Deep learning techniques for human action recognition

Throughout recent years, deep learning with human action recognition has become one of the most popular research studies. It has a variety of applications such as automation, surveillance, health care tracking and study of consumer behaviour. Human behavior detection includes many difficulties in videos, including occlusions, camera movements, cluttered backgrounds, speed of execution, etc. In analyzes such as single point of view, multiple point of view, and RGB-Depth images, often three types of datasets are used. This paper presents a report on the three types of data sets for the identification of human action based on deep learning techniques.
On Designing a Forensic Toolkit for Rapid Detection of Factors that Impact Face Recognition Performance When Processing Large Scale Face Datasets

Due to the overlap between the fields of forensic investigation and biometric recognition, including face recognition, there have been several interesting applications that bridge the gap between the two sciences and better connect the associated communities. These applications have been developed with the intent to assist law enforcement officers with computer assisted and biometrics related capabilities. Thus, utilizing biometric algorithms within the forensics field can support law enforcement investigations in a wide array of applications, including fingerprint comparisons, sketch-to-photo face comparisons, and even find persons of interest via soft biometrics such as scars, marks, and tattoos. In this book chapter, we focus on facial recognition, which can help provide clues when other forensic evidence is not present or available and, most importantly, help investigators eliminate the time consuming processes of interviewing potential witnesses or manually searching through thousands of mugshots to determine a suspect's identity. To aid in this mission, we propose a software toolkit to automatically and hierarchically categorize face images with a set of binary classifiers using three different attributes, which depending on their true label/condition can affect facial recognition performance. These attributes are: based on facial photo, (1) determining whether a subject's eyes are open or closed, (2) whether the subject is wearing glasses or not, and (3) whether the facial pose of the subject is either frontal or non-frontal. Our toolkit offers batch processing and therefore can aid forensic operators with a capability to rapidly categorize large scale face datasets in terms of the aforementioned attributes, and thus, determine, which individuals have a higher chance to be identified based on their face information. The proposed forensic toolkit will allow the operators to analyze, enhance, group, or exclude face data before being used for face matching.
Scene image retrieval with siamese spatial attention pooling

Content-based image retrieval (CBIR) aims to retrieve images from a given image collection according to similarities of image contents. In this paper, we focus on retrieval of scene images. We propose a siamese spatial attentive model which bases on siamese architecture and incorporates attention mechanism to generate compatible image embeddings. It extracts local features with a convolutional neural network (CNN), which starts with pre-trained parameters and is well fine-tuned for retrieval. Spatial attention pooling is proposed to take feature maps as input and generate weights for local features, which are then used to refine local features via weighted sum-pooling. Such pooling alleviates impacts from disturbance and concentrates on meaningful parts of images. Therefore, the model is able to output robust representations for noisy images. We also propose a multi-stage training scheme for the model, which leads to better performance than normal one-pass training scheme. Extensive experimental results on benchmark image retrieval datasets show that our model is competitive in retrieval performance.
Unsupervised Bilingual Sentiment Word Embeddings for Cross-lingual Sentiment Classification

In recent years, bilingual word embeddings have been used to promote sentiment classification task in low-resource languages. However, existing bilingual word embedding methods either require annotated cross-lingual data or fail to capture enough sentiment information. In this paper, we propose Unsupervised Bilingual Sentiment word Embeddings (UBSE), which only need source-language annotated corpora and a monolingual sentimental lexicon. This method is constructed in an unsupervised way, we pre-train a projection matrix between the source and target languages using Generative Adversarial Nets (GAN) without using any parallel corpora. Further, we incorporate a monolingual sentiment lexicon from the source language to fine-tune the model, making it more sensitive to sentiment implication. Experiments on Spanish, Catalan and Basque demonstrate that the proposed approach on sentence-level cross-lingual sentiment classification significantly outperforms competitive baseline models which use cross-lingual dictionaries, even comparable with translation based methods.
Data managers' perspectives on OAIS designated communities and the FAIR principles: mediation, tools and conceptual models

Purpose This paper presents results of a study on data managers' perspectives on the evolution of Designated Communities and the FAIR Principles using an example of a geological repository. Design/methodology/approach The study employed 10 semi-structured interviews with data managers at a state geological survey and qualitative analysis of the interview transcripts. Findings The Designated Community for a collection in this data repository has evolved from petroleum industry users to include academic researchers and the public. This change was accompanied by significant user interaction changes from in-person, reference interview-style conversations to anonymous digital, automated interactions. The main factors driving these changes were developments in technology which allowed the data managers to shift data discovery and access into the online environment. The online data portal has seen increasing non-expert use, driving the data team to develop additional services for these new communities. Repository data team participants varied in their familiarity with the FAIR Principles and their perceptions of the FAIRness of the data in the repository. Research limitations/implications The study was limited to one organization in the United States. However, the results are applicable to other data environments working through the tensions between high-level global frameworks such as FAIR, and continuing to serve the day-to-day needs of their designated communities. Continued work on how to assess success in this complex space is needed. Originality/value This paper lies at the nexus of two digital preservation frameworks and contributes to a limited extant literature providing guidance on implementing the concept of a Designated Community in practice.
Towards a Set of Factors to Identify the Success in Scrum Project Delivery: A Systematic Literature Review

Agile-based software development is increasingly being adopted by software professionals, as it guarantees the early development of high-quality software and software products. One of the most popular agile methods is Scrum, which is a methodology that involves an iterative, incremental and empirical process. In addition, it is designed to add value, focus, clarity, and transparency to the activities and products of a project. Agile methods have been criticized and defended, and research has shown that accommodating change can be a factor in both success and failure. Although Scrum is a light and easy to understand process, its adoption is sometimes difficult. To gather a set of factors to identify success during a Sprint implementation, a systematic review of the literature is carried out in which we find several factors that help to achieve the success of a Sprint; these are classified into four groups: personnel, project, product, and organization. The results represent a basis for companies seeking to improve the implementation of Scrum.
Face Recognition Technology Analysis Based on Deep Learning Algorithm

Abstract with the Explosive Development of Deep Learning Technology Face Recognition and Other Recognition Technologies Mostly Adopt Deep Learning Algorithm for Recognition. Although the Deep Learning Algorithm Has High Recognition Accuracy, It Has a Huge Demand for Computing. in the Mobile Terminal, We Can Use Artificial Intelligence Chips That Can Accelerate Deep Learning Operations to Complete Relevant Operations. Deep Learning Has Fixed Modes, Like Convolution. Ai Chips Can Significantly Improve the Efficiency of Deep Learning Operations by Optimizing the Corresponding Operation Modes. in This Way, Mobile Terminals Can Quickly Implement Complex Deep Learning Operations, Such as Face Recognition Based on Deep Learning. One Representative of Ai Chips is the Tensor Processing Unit of Google, Which is Able to Accelerate the Tensor Flow of the Deep Learning System, Which is Far More Efficient Than Gnus. the Tpu Provides 1,530 Times the Performance Improvement and 3,080 Times the Efficiency (Performance/Watt) Improvement over the Same Cpu and Cpu. Traditional Face Recognition Algorithms Include Face Recognition Technology Based on Pca(Principal Components Analysis) and Face Location Technology Based on Ad Boost. Although the Traditional Face Recognition Technology is Fast, the Detection Effect is Much Different from the Deep Learning Technology. on the One Hand, the Accuracy of the Traditional Face Recognition Methods Represented by Pca is Far Lower Than That of the Deep Learning Algorithm. on the Other Hand, for the Recognition of Massive Users, the Traditional Pca Face Recognition Technology is Not Competent.