Reviewerly/exHarmony huggingface dataset:
Scrum Language Use in a Software Engineering Firm: An Exploratory Study

Background: Although agile and Scrum have been important frameworks in software engineering for over a decade, little research has explored how teams use Scrum language within their sprints. Literature review: Most explorations of Scrum communication have been collected through self-reported means. These studies are inherently unable to explore how Scrum teams use Scrum-centric language in their meetings in ways that adhere or run counter to standard Scrum practice. Research questions: 1. In what ways is Scrum reflected in the language used by team members in various sprint meetings? 2. What associations exist between the job title of team members and their use of Scrum language? 3. What does a discourse analysis reveal about the ways in which this team uses language to value and discount Scrum? Research methodology: For three sprints over 10 weeks, I recorded meetings of 27 Scrum team members. I transcribed these meetings, developed a codebook for assigning Scrum language categories, conducted an interrater reliability agreement on the data, completed a correspondence analysis on how Scrum language associates with meeting types and job titles, and conducted a discourse analysis to determine in what ways these teams value and discount Scrum. Results/discussion: Scrum language was found in all recorded meetings across all three sprints, with much language found in the planning meetings. Few associations existed between Scrum language and job title, suggesting that Scrum at this engineering firm is an egalitarian process. In addition, the discourse analysis revealed that this engineering firm valued User Story and Sprint Execution language while discounting Capacity and Story Pointing language. Conclusions: Although this group broadly adheres to Scrum practices about 68% of the time, this study finds that several current standard components of Scrum are routinely discounted. This exploratory study suggests that more research into the in-situ use of Scrum language in engineering workplaces is necessary to better inform engineering professionals about the communicative expectations of Scrum and to better enable engineering communication educators to prepare future engineers for Scrum realities.
A Text Feature Selection Technique based on Binary Multi-Verse Optimizer for Text Clustering

Feature selection is regarded as an important task in data mining. The applications of machine learning eliminate irrelevantly, redundant features so that the learning performance is improved. A novel feature selection method for unsupervised text clustering, that is, binary multi-verse optimizer algorithm (BMVO) is proposed in this paper. A new application of the MVO algorithm is introduced via this method, which selects important text features. Then, these important features are tested using a k-means clustering algorithm to enhance performance and lessen the cost of the proposed algorithm computational time. The BMVO performance is examined on 6 datasets that are published including Classic4, Wap, tr41, tr12, 20Newsgroups, and CSTR. Based on the measures of the evaluation, the obtained results showed that the BMVO performance has outperformed the rest of the comparative algorithms.
Bug Off Pain: An Educational Virtual Reality Game on Spider Venoms and Chronic Pain for Public Engagement

Virtual reality (VR) technology has been capturing the public imagination for decades. VR software applications that allow for interactive immersion are emerging as a renowned medium in many areas, including educating the public in biochemistry-related subjects via public engagement events. This report provides information about an immersive, interactive and educational virtual reality (VR) game named Bug Off Pain that increases scientific literacy about chronic pain and spider venoms among the public and high school students. Here, VR was shown to be an innovative and fun approach to learning and public engagement in biochemistry. Bug Off Pain places the viewer inside the brain and shows the molecular system that allows people to sense pain. After securing three (learning) points via the multimedia-based clips, this experience translates to the interactive game. Here, a player has to choose a venom that shuts down the pain until that results in “pain over”. Bug Off Pain can be played (free of charge) on two different VR platforms: Oculus Rift and Android devices.
Pressure Vessel Design Simulation

The new era knowledge of optimization algorithm is massively boosted recently. Among several optimization models, multi-swarm approach has been proposed most recently for balancing the exploration and exploitation capability through the Particle Swarm Optimization (PSO) algorithm. The proposed multi-swarm model which is called Meeting Room Approach (MRA), is tested and evaluated based on solving normal and large-scale problems. In the current research, the feasibility of the proposed Multi-Swarm Particle Swarm Optimization (MPSO) is adopted to simulate mechanical engineering problem namely pressure vessel design (PVD). The results indicated the potential of the proposed MPSO model on simulating the PVD problem with optimum solution over the standalone PSO. Further, the current study results authenticated against other famous meta-heuristics models. Overall, MPSO reported an excellent optimization solution with fast convergence learning process.
Discrete Semantic Alignment Hashing for Cross-Media Retrieval

Cross-media hashing, which maps data from different modalities to a low-dimensional sharing Hamming space, has attracted considerable attention due to the rapid increase of multimodal data, for example, images and texts. Recent cross-media hashing works mainly aim at learning compact hash codes to preserve the class label-based or feature-based similarities among samples. However, these methods ignore the unbalanced semantic gaps between different modalities and high-level semantic concepts, which generally results in less effective hash functions and unsatisfying retrieval performance. Specifically, the key words of texts contain semantic meanings, while the low-level features of images lack of semantic meanings. That means the semantic gap in image modality is larger than that in text modality. In this paper, we propose a simple yet effective hashing method for cross-media retrieval to address this problem, dubbed discrete semantic alignment hashing (DSAH). First, DSAH formulates to exploit collaborative filtering to mine the relations between class labels and hash codes, which can reduce memory consumption and computational cost compared to pairwise similarity. Then, the attribute of image modality is employed to align the semantic information with text modality. Finally, to further improve the quality of hash codes, we propose a discrete optimization algorithm to learn discrete hash codes directly, and each bit has a closed-form solution. Extensive experiments on multiple public databases show that our model can seamlessly incorporate attributes and achieve promising performance.
Gold endowment in the evolution of the Allaqi-Heiani suture, Egypt: A synthesis of geological, structural, and space-borne imagery data

Field and structural studies supported by multi-sensor satellite-based imagery data are attempted to decipher the setting and controls of gold-bearing quartz veins in the central part of the greater Wadi Allaqi district in the South Eastern Desert of Egypt. Here, we assess the evolution of a complex part of the Neoproterozoic Allaqi-Heiani suture (∼730–700 Ma) with ample structures demonstrating the final Gondwanan collision. Abundant extensive N-trending folds, sinistral shear zones and oblate fold patterns and strain fabrics exemplifying an oblique convergence and torsion related to a regional ∼E-W compressional regime. In the Wadi Seiga area, the first order upright and overturned folds and thrusts (D1 structures) are mostly obliterated by intense NW-ward transpression, N-ward folding and transcurrent shearing assigned to as D2 and D3 structures. The domal crescentic and mushroom superimposed fold patterns suggest that Type 2 and Type 3 fold interference patterns were synchronous at some time during D2 or D3. Shear zones with abundant signs of plastic deformation and associated with quartz veins, within the axial planes of NNW- and NNE-trending F3 folds may imply gold mineralization during the third deformation stage (D3). The latter was supplemented or terminated by an episode of northward tectonic escape and slip reactivation of the pre-existing NW-trending faults. Integrated with field measurements, satellite-based radar and radiometer data abetted untangling the superimposed structures. Gold occurrences in the area are confined to domains of superimposed foliations, foliation deflection/steepening, foliation refraction and substantial strain partitioning, or at oppositely interfered plunging folds. The scattered small scale occurrences of ≤10 s g/t Au-bearing gold-quartz veins in such high strain zones, together with the lack of spatial association with a particular intrusive phase weigh with the metamorphic model for gold-only endowment in the area. The peak metamorphic conditions (amphibolite facies) in the central Allaqi-Heiani suture were attained not earlier than 630 Ma, during the collision of the Arabian-Nubian Shield with the Nile Craton to the west (provoked at 670–610 Ma). Gold-only endowment was complementing to the latest stages of deformation and terrane cooling between 610 and 585 Ma. Listvenitization of tectonized ophiolites and widespread sericite alteration of the island arc metavolcaniclastic rocks could have been promoted by the late-orogenic felsic intrusions (∼603 Ma) that filled oblate dilation spaces developed by structural transport to the north. The late orogenic timing of mineralization is one of many features making the Allaqi-Heiani suture comparable to well-documented global examples of accretionary metallogenic belts.
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning

Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes an end-to-end encrypted neural network for gradient updates transmission. This network first encodes the input gradient updates to a lower-dimension space in each client, which significantly mitigates the pressure of data communication in federated learning. The encoded gradient updates are directly recovered as a whole, i.e. the aggregated gradient updates of the trained model, in the decoding layers of the network on the server. In this way, gradient updates encrypted in each client are not only prevented from interception during communication, but also unknown to the server. Based on the encrypted neural network, a novel federated learning framework is designed in real applications. Experimental results show that the proposed network can effectively achieve two goals, privacy protection and data compression, under a little sacrifice of the model accuracy in federated learning.
Accuracy and Compactness in Decision Diagrams for Quantum Computation

Quantum computation is a promising research field since it allows to conduct certain tasks exponentially faster than on conventional machines. As in the conventional domain, decision diagrams are heavily used in different design tasks for quantum computation like synthesis, verification, or simulation. However, unlike decision diagrams for the conventional domain, decision diagrams for quantum computation as of now suffer from a trade-off between accuracy and compactness that requires parameter fine-tuning on a case-by-case basis. In this work, we-for the first time-describe and evaluate the effects of this trade-off. Moreover, we propose an alternative approach that utilizes an algebraic representation of the occurring irrational numbers and outline how this can be incorporated in a decision diagram in order to overcome this trade-off.
Faster Attend-Infer-Repeat with Tractable Probabilistic Models.

The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured probabilistic modeling, as it tackles the challenging problem of unsupcrviscd scene understanding via Baycsian inference. AIR expresses the composition of visual scenes from individual objects, and uses vari-ational autoencoders to model the appearance of those objects. However, inference in the overall model is highly intractable, which hampers its learning speed and makes it prone to suboptimal solutions. In this paper, we show that the speed and robustness of learning in AIR can be considerably improved by replacing the intractable object representations with tractable probabilistic models. In particular, we opt for sum-product networks (SPNs), expressive deep probabilistic models with a rich set of tractable inference routines. The resulting model, called SuPAIR, learns an order of magnitude faster than AIR, treats object occlusions in a consistent manner, and allows for the inclusion of a background noise model, improving the robustness of Bayesian scene understanding.
Misuse Attacks on Post-quantum Cryptosystems

Many post-quantum cryptosystems which have been proposed in the National Institute of Standards and Technology (NIST) standardization process follow the same meta-algorithm, but in different algebras or different encoding methods. They usually propose two constructions, one being weaker and the other requiring a random oracle. We focus on the weak version of nine submissions to NIST. Submitters claim no security when the secret key is used several times. In this paper, we analyze how easy it is to run a key recovery under multiple key reuse. We mount a classical key recovery under plaintext checking attacks (i.e., with a plaintext checking oracle saying if a given ciphertext decrypts well to a given plaintext) and a quantum key recovery under chosen ciphertext attacks. In the latter case, we assume quantum access to the decryption oracle.
Online Authentication Methods Used in Banks and Attacks Against These Methods

Growing threats and attacks to online banking security (e.g. phishing, identity theft) motivates most banks to look for and use stronger authentication methods instead of using a normal username and password authentication. The main objective of the research is to identify the most common online authentication methods used widely in international banks and compare it with the methods used in six banks operating in UAE. In addition, this research will cover the current authentication threats and attacks against these methods. Two well-defined comparison matrices [15], one based on characteristics and second one on attack vectors, will be used to examine and assess the authentication methods of those six banks. This paper is different than other studies and works since it will help to identify the common authentication methods used in banks operating in UAE. Moreover, the comparison matrices will help to examine those authentication methods, define their weaknesses, and evaluate them.
Advanced survey of Blockchain for the Internet of Things Smart Home

Internet of things have many applications, one of them is smart homes. The main drawback of IoT that depends on a centralized cloud. The Blockchain approach with a decentralized technique will solve the centralized cloud approach. Smart homes have many security issues, threats, attacks, user privacy, integrity, etc. Blockchain is playing role in many IoT applications also the solution of these security problems. This paper explains the Blockchain in smart home with the main three tiers, how Blockchain can help to secure the data, transaction, also describe the security analysis in IoT smart homes.
Three camps, one destination: the intersections of research data management, FAIR and Open

Open data, FAIR (findable, accessible, interoperable and reusable) and research data management (RDM) are three overlapping but distinct concepts, each emphasizing different aspects of handling and sharing research data. They have different strengths in terms of informing and influencing how research data is treated, and there is much scope for enrichment of data if they are applied collectively. This paper explores the boundaries of each concept and where they intersect and overlap. As well as providing greater definitional clarity, this will help researchers to manage and share their data, and those supporting researchers, such as librarians and data stewards, to understand how these concepts can best be used in an advocacy setting. FAIR and open both focus on data sharing, ensuring content is made available in ways that promote access and reuse. Data management by contrast is about the stewardship of data from the point of conception onwards. It makes no assumptions about access, but is essential if data are to be meaningful to others. The concepts of FAIR and open are more noble aspirations and are, this paper argues, a useful way to engage researchers and encourage good data practices from the outset.
A Network-Layer QoE Model for YouTube Live in Wireless Networks

YouTube Live is one of the most popular services on the Internet, enabling easy streaming of a live video with the acceptable video quality.Thus, understanding user perception of this service is of the utmost importance for network operators.As in other video streaming services, YouTube Live traffic is affected by delays and interruptions due to unfavorable network conditions, which translate into unacceptable initial reproduction times, image freezes, or abrupt changes in image quality.Detecting these events is key to ensure an adequate quality of experience (QoE).Unfortunately, data encryption makes it very difficult for operators to monitor the QoE from packet-level data collected in network interfaces.In this paper, an analytical model to estimate the QoE for encrypted YouTube Live service from packet-level data collected in the interfaces of a wireless network is presented.The inputs to the model are TCP/IP metrics, from which four service key performance indicators (S-KPIs) are estimated: initial video play start time, video interruption duration, video interruption frequency, and image quality.The model is developed with an experimental platform consisting of a live streaming server, a terminal agent, a radio access network (e.g., Wi-Fi access point), a network-level emulator, a probe software, and a man-in-the-middle proxy.Model assessment is carried out by comparing the S-KPI estimates with measurements from the terminal agent under different network conditions introduced by the network emulator.
Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal algorithm as well as a novel weighting scheme for cold-start content based news recommendation utilizing these named entities. Seeking to create a higher degree of user-specific relevance, our algorithm computes the shortest distance between named entities, across news articles, over a large knowledge graph. Moreover, we have created a new human annotated data set for evaluating content based news recommendation systems. Experimental results show our method is suitable to tackle the hard cold-start problem and it produces stronger Pearson correlation to human similarity scores than other cold-start methods. Our method is also complementary and a combination with the conventional cold-start recommendation methods may yield significant performance gains. The dataset, CNRec, is available at: https://github.com/kevinj22/CNRec
SGX-PySpark: Secure Distributed Data Analytics

Data analytics is central to modern online services, particularly those data-driven. Often this entails the processing of large-scale datasets which may contain private, personal and sensitive information relating to individuals and organisations. Particular challenges arise where cloud is used to store and process the sensitive data. In such settings, security and privacy concerns become paramount, as the cloud provider is trusted to guarantee the security of the services they offer, including data confidentiality. Therefore, the issue this work tackles is "How to securely perform data analytics in a public cloud?"
Using the Event Analysis of Systemic Teamwork (EAST) broken-links approach to understand vulnerabilities to disruption in a darknet market

Darknet markets provide an anonymous, online platform for users to trade illicit drugs, fraudulent identity data, and other commodities. Although law enforcement agencies have been successful in seising many markets, the Darknet is an agile and dynamic environment and market activities often persist and emerge in a new form. Given this constantly changing environment, new ways of disrupting darknet markets are required. This study used Event Analysis of Systemic Teamwork (EAST) to analyse market activity and understand vulnerabilities to disruption. This involved using the EAST broken-links approach to assess the effects of compromising the transmission of information between tasks and between agents. The analysis identified critical vulnerabilities in the system, which included information involved in registering, depositing funds, communicating listing details to buyers, and communicating dispute resolution messages. This study indicates that systems ergonomics methods-in particular, EAST-can provide insight into system vulnerabilities that might be targeted for disruption. Practitioner summary This study provides a conceptualisation of the processes, people, structures, and information involved in the buying and selling of goods on a darknet market. Law enforcement agencies may use broken-links analyses to systematically consider the effects of their interventions.
DeePattern

VLSI layout patterns provide critic resources in various design for manufacturability researches, from early technology node development to back-end design and sign-off flows. However, a diverse layout pattern library is not always available due to long logic-to-chip design cycle, which slows down the technology node development procedure. To address this issue, in this paper, we explore the capability of generative machine learning models to synthesize layout patterns. A transforming convolutional auto-encoder is developed to learn vector-based instantiations of squish pattern topologies. We show our framework can capture simple design rules and contributes to enlarging the existing squish topology space under certain transformations. Geometry information of each squish topology is obtained from an associated linear system derived from design rule constraints. Experiments on 7nm EUV designs show that our framework can more effectively generate diverse pattern libraries with DRC-clean patterns compared to a state-of-the-art industrial layout pattern generator.