Reviewerly/exHarmony huggingface dataset:
Leveraging Speculative Design to Re‐Imagine Product Roadmaps

Businesses often have strategic visions for the future of a product space; however, identifying and building toward preferable futures is a daunting task, especially when designing for complex systems, e.g., Digital advertising platforms that include multiple complex interfaces and internal organizational structures. Moreover, because businesses need to iterate on products quickly, often in a reactive manner, many businesses, and consequently researchers, struggle to go beyond short‐term needs to tackle long‐term solutions; that is, they mostly react to immediate needs and changes rather than taking a proactive strategic approach towards building a favorable future. Speculative design as a methodology to support proactive strategic thinking helps set a pathway to explore a variety of future states with participants, in our case business owners. It does so by designing immersive and impactful experiments for participants that draws insight from well‐researched forecast of the probable future, as viewed from today but also more open‐form future possibilities, that rely on speculative and fantastical prompts (e.g., science fiction, modern art, fantastical scenarios etc) to generate reflection and dialogue. In this paper, we will present different variations of speculative design implementations at Facebook that allowed us to explore different potential futures of digital advertising. In addition to discussing this research approach, which was novel to this space, we have included retrospective thoughts and best practices that emerged among our product design teams as they reflected on their participation in this work. We hope that our lessons learned can benefit the research community, by broadening the strategic impact of research to design products and services that better anticipate future user needs and identify potential risks early in the process.
A unidirectional certificateless proxy re‐signature scheme based on lattice

Abstract As a kind of special digital signature, proxy re‐signature is becoming more and more important in transparent certification and management of group signatures. But most existing proxy re‐signature schemes cannot resist collusion attack. So we construct a scheme by using dual modulus technology and lattice structure. Dual modulus technology can ensure that our solution can resist various collusion attacks. Lattice structure can ensure that our scheme is secure in the postquantum era. Compared with the previous scheme, our scheme is unidirectional and certificateless, is secure in the post‐quantum era, does not require key escrow, can prevent man‐in‐the‐middle attacks, and can resist collusion attacks of proxy and delegatee as well as proxy and delegator. Moreover, our scheme is unidirectional and has better privacy. Furthermore, under the random oracle model, we prove that our scheme is strong nonforgeability against external attackers and the internal key generation center.
Interpretable deep learning method for attack detection based on spatial domain attention

Deep learning methods can directly extract effective features from original data. However, this type of model is complex and considered to be a “black box”, which leads to low interpretability of the models. Since the results of attack detection are significant to cybersecurity, every decision should be supported with convincing reasons. Hence, the problem of interpretability has become a bottleneck for deep learning methods applied to attack detection. We propose an interpretable deep learning method based on spatial domain attention. The model can discover and locate the feature strings in the packets, thereby providing a meaningful semantic explanation for the detection results. We conducted qualitative and quantitative experiments on the DARPA1998, UNSW-NB15, and CIC-IDS-2017 datasets. Experimental results show that the interpretability of our method is superior to the state-of-the-art interpretable models in quantifiable criteria, while maintaining comparable classification accuracy.
A novel approach for visualization, monitoring, and control techniques for Scrum metric planning using the analytic hierarchy process

Abstract Although Scrum is one of the most preferred agile development frameworks that guide the development process, measuring sprint productivity is still challenging. In fact, it is hard to provide a continuous measurement during consecutive Scrum sprints, especially selecting the optimal metrics that fit better for real industrial applications. To bridge this gap, we conducted an industrial case study within the TÜBİTAK SAGE software development group to demonstrate the performance and applicability of a systematic selection process for fitting add‐on components using various scrum metrics tools. Next, we analyzed the combination of software developers' preferences of process metrics concerning their characteristics and their defense industry compatibility. Consequently, we assessed the metrics that might likely integrate into the Scrum development using the analytic hierarchy process. The results indicated that the Actionable Agile Add‐on scored the highest, followed by the Screenful Add‐on. Ultimately, this contemporary study presented a novel approach that has increased individuals' participation in metric planning, implementation, and monitoring, therefore moving towards more achievable software development goals.
Blind three-qubit exact Grover search on a nitrogen-vacancy-center platform

Here we put into practice the concept of blind client-server quantum computation, in which a client with limited quantum power controls the execution of a quantum computation on a powerful server, without revealing any details of the computation. In particular, it is a three-node setting in which an oracular quantum computation can be executed blindly. In this Blind Oracular Quantum Computation (BOQC), the oracle (Oscar) is another node, with limited power, who acts in cooperation with the client (Alice) to supply quantum information to the server so that the oracle part of the quantum computation can also be executed blindly. We develop tests of this protocol using two- and three-qubit versions of the exact Grover algorithm (i.e., with database sizes $4\ensuremath{\le}N\ensuremath{\le}8$), obtaining optimal implementations of these algorithms within a gate array scheme and the blinded cluster-state scheme. We discuss the feasibility of executing these protocols in state-of-the-art three-node experiments using nitrogen-vacancy diamond electronic and nuclear qubits.
Developing a Framework for Credit Card Fraud Detection

Credit card is one of the most popular online or manual payment methods, and credit card fraud is increasing that can cause enormous financial damage. Protective action must therefore be taken to stop the credit card. Several new technologies can be utilized to detect frauduleous transactions based on artificial intelligence, data mining, and machine learning. Several new techniques may be used to identify artificial intelligence, data mining, machine learning, sequence alignment, genetic programming, etc. This article provides a new paradigm for credit card fraud detection based on the characteristics of previous user credit card transactions. Detecting fraudulent purchases from past credit card transactions is a difficult challenge, as it depends on a number of factors, like timing, amount, etc. The data on credit card transactions is rising at a huge rate every day. This constant influx of new data is also challenging to handle and to construct new models to determine if a transaction is fraudulent or not. To do this, we use the PCA data type of user transformation since user data supply sensitive information about users and user transactions. We utilize a tweaked fraud detection model utilizing a RandomizedSearchCV hyperparameter tweaking for detecting fraudulent transactions. Our mechanism provided can determine whether a transaction is fraudulent based on the data patterns of prior transactions of the user.
Gaussian Process-based Interpretable Runtime Adaptation for Safe Autonomous Systems Operations in Unstructured Environments

Autonomous vehicles may not behave as expected when subject to environmental disturbances. For instance, control commands suitable for driving on dry, paved roads may lead to unsafe conditions and undesired deviations when on slippery dirt or icy roads. Furthermore, it becomes increasingly important to offer human-understandable explanations of autonomous robots’ actions – especially when they operate in a space shared with humans such as public roads. In this work, we present an interpretable, risk-aware online adaptation approach that leverages a combination of Gaussian process regression and decision tree theory. Our approach predicts the system state in unknown environments using pre-trained models, which are continuously refined during runtime using a fast lookup table-based procedure. Every time the originally intended behavior is predicted to cause an unsafe state, our method computes a set of safe behaviors under the current model uncertainty and risk. The most suitable behavior is subsequently selected depending on mission requirements and a human-understandable explanation about the system’s decision-making is delivered. The proposed framework is applied to unmanned ground vehicles and validated in simulations, in which we demonstrate a successful safety-critical cargo delivery through a priori unknown, rough and slippery terrain.
Research and application of the convolutional hierarchical neural network classifier

The paper presents an algorithm for constructing convolutional hierarchical neural network classifiers designed for image classification. It is a modification of the algorithm for constructing hierarchical neural network classifiers. The algorithm is tested on the CIFAR-10 problem.
Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.
Game to Dethrone: A Least Privilege CTF

Identity and Access Management (IAM) misconfiguration is one of the most critical threats to the security of cloud environments. As more infrastructure is being migrated to the cloud, the importance of following the principle of least privilege (PoLP) to mitigate security risks has significantly increased. Unfortunately, the mechanisms provided for doing so in the cloud are complex and substantially different than traditional legacy infrastructure. As a result, the number of practitioners that know how to secure cloud projects is insufficient compared to the number of cloud projects being deployed. To address the issue, this paper describes a Least Privilege CTF, a series of exercises that allows practitioners to practice applying least privileges on cloud deployments that are easily deployed with minimal cost.
Talk the talk and walk the walk: Dialogue-driven navigation in unknown indoor environments

Prior work in natural-language-driven navigation demonstrates success in systems deployed in synthetic environments or applied to large datasets, both real and synthetic. However, there is an absence of such frameworks being deployed and rigorously tested in real environments, unknown a priori. In this paper, we present a novel framework that uses spoken dialogue with a real person to interpret a set of navigational instructions into a plan and subsequently execute that plan in a novel, unknown, indoor environment. This framework is implemented on a real robot and its performance is evaluated in 39 trials across 3 novel test-building environments. We also demonstrate that our approach outperforms three prior vision-and-language navigation methods in this same environment.
The UI Design of the Picture Logo of the Terminal Human-Computer Interaction Interface

Abstract With the advancement of the information society and the development of China’s economy, enterprises to digital, information, intelligent production requirements continue to improve, the application of industrial software gradually popularized in the production of enterprises. However, in the Internet era, the research on man-machine interface design is in full rage, but the man-machine interface design of domestic industrial software has not been paid attention to. The level of industrial software interface is seriously backward, disjointed with The Times, and completely contrary to, high technology, high efficiency and other industrial execution concepts. On the one hand, this phenomenon will affect the working efficiency of the staff in the operation of the software and the user experience, reduce manufacturing enterprises related to attract talent, on the other hand can also lead to the software under the same technical level, domestic industry software products to attract customers’ ability is insufficient, in the competition with foreign brand products in bad situation.
Methodological case of scientometric factors study in individual discipline

The authors analyze scientometric factors and rates of the Russianlanguage scientific journals in archeology provided by RSCI. The list of journals under study was based on the list of peer-reviewed journals comprising findings of candidate’s and doctor’s theses in the domain 07.00.06 – Archeology (historical sciences). The key factors of these journals were specified and rates were made for Science Index (SI), public evaluation, and 5-year impact-factor. The journals with high impact factor and low SI rating were revealed; absolute discrepancy of 2019 SI and public evaluation rating. The authors suggest to make changes to the methods of calculating SI integrated index of scientific journals: to accumulate ratings and add special equalizing coefficients for monodisciplines: to calculate factors with improved rubricator.
Deep Features with Improved Extreme Learning Machine for Breast Cancer Classification

Breast cancer classification problem is receiving more attention among researchers due to its global impact on women's healthcare. There is always a demand for research analysis in the earlier diagnosis of breast cancer. The paper proposes a new computer-aided diagnosis (CAD) framework which integrates deep learning and Extreme Learning Machine (ELM) for feature extrication and classification of breast cancer. The proposed CAD tool is very much helpful for radiologists in the earlier diagnosis of breast cancer using digital mammograms. Herein, the research uses the Sine-Cosine Crow-Search Optimization Algorithm (SC-CSOA) for improving the ELM&#x2019;s classification performance. And to extricate the robust features from the input mammograms, the concept of transfer learning is applied. For that, the work adopts the three most efficient Residual Network (ResNet) families of CNN, namely ResNet18, ResNet50, and ResNet101 architectures. The input database used for evaluation is the INbreast dataset which comprises Full-Field Digital Mammogram (FFDM) images. At this point, the research compares the results obtained with the existing ELM and K-NN algorithms where it is found that the performance of the proposed framework provides the supreme classification (95.811% of accuracy) over others.
HPC Data Storage at a Glance: The Santos Dumont Experience

High-Performance Computing (HPC) platforms are used to solve the most diverse scientific problems in research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific software, which have different requirements. These requirements include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle a mixed workload scenario when storing data from the applications. Knowledge of the application set and its performance running in a supercomputer is needed to understand the storage system&#x0027;s usage, pinpoint possible bottlenecks, and guide optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer&#x0027;s data storage infrastructure&#x0027;s performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, where we were able to identify inefficient usage and problematic factors of performance.
Regenerating a Graphical User Interface Using Deep Learning

Transforming a conceptual drawing of a graphical user interface into computer code is a long and complex process, which is a time-consuming work and requires an expertise developer. To overcome this problem, we propose an automated system using computer vision and optical character recognition (OCR) techniques. This paper proposes a novel hybrid approach using convolutional neural networks (CNNs), which is composed of AlexNet, single-shot detectors (SSDs), and recurrent neural networks, in order to detect user interface elements such as images retrieval, texts, button, links, and lists in the input sketch. The output from the network is a file representing a list of UI elements detected along with their properties. We evaluate our model on two public datasets including sketched elements and IAM datasets. Our experimental results demonstrate that our hybrid approach achieves over 90% of accuracy.
AR-Based Gimmick Picture Book for Household Use by Projection Mapping

In this paper, an effective digital material for early childhood education in a general household is proposed. We realize a projection system of gimmick picture book by an augmented reality methodology that combines visual illusion effects to three-dimensionally visualize contents with a projection mapping technique. Especially, we also build a projector camera system with a calibration function of projection gap to facilitate installation at home. In experiments, we validated the performance of the proposed system, and three-dimensional images from the observer’s viewpoint could be shown.
Graph Neural Networks for Privacy-Preserving Recommendation with Secure Hardware

Local differential privacy (LDP) is widely used in graph neural networks (GNNs) for recommendation to protect users’ privacy. However, existing LDP-based GNNs usually introduce too much noise caused by the untrusted servers and result in poor model accuracy. The emergence of trusted execution environments such as intel SGX can guarantee code integrity and data confidentiality, and lead a new direction in differential privacy. In this paper, we propose a federated GNN recommendation system based on SGX and DP, which converts the LDP model into a central differential privacy (CDP) model without a trusted server. Specifically, in our scheme, the SGX runs differentially private computations on the data and reveals the results, which introduces less noise while achieving the same privacy protection level compared with LDP. And in order to address the privacy concerns caused by side channel attacks in SGX, we additionally use homomorphic encryption to encrypt the data before uploading, so that even if SGX is breached, the adversary could only access the ciphertext, not the plaintext. We prove that our algorithm satisfies (epsilon, delta) -CDP for data owners and conduct experiments on several real-world datasets. The result shows that our method is outperforming existing LDP-based GNN recommendation systems.
Implementing a Talent Management System in a Group of Companies—A Case Study from UAE

This case study aims to examine the status quo of an information system in the context of talent management (TM), in a leading group of companies that is under the ports and industrial lands sector in the United Arab Emirates. This study identified the major issues encountered by the target organization from the perspective of the employee. It also analyzed prominent state-of-the-art tools that can solve issues related to TM in the targeted organization, and proposed recommended solutions on a TM system implementation. The data obtained was mainly collected using open-ended interviews with the group employees, as well as qualitative information collection by the researcher through participant observation of mergers and acquisitions in the group. In addition, information from previous studies was also leveraged to formulate the case. The findings indicate that the major issues related to TM include career development plan, learning management system, succession plan, data island, and mobile-first. The study concluded that although it might be easier for the organization to extend their current solution to cover TM, a system such as “BambooHR” and “Ceridian Dayforce” may be worth considering to solve the current organizational TM issues. The outcomes of this case study shed light on the current TM challenges faced by a group of companies during mergers and acquisitions, and offer an essential case study for researchers in TM, HR, information systems and knowledge management.
Breast Cancerous Tumor Detection Using Supervised Machine Learning Techniques

Around the globe the leading cause of cancer mortality in women is Breast Cancer; BCs, have been under many studies and several analyses have shown that many abnormal conditions and risk of BCs can be diagnosed with the help of fast and perfect judgment of the clinical physicians of this domain. This kind of intelligent decision making is not the thing where every person is good at so here, we have kept Artificial intelligence to use with supporting steps like Machine Learning Classifiers from Wisconsin Breast Cancer Dataset; WBCD. In the Combination of Human and Computer Intelligence, we have worked our way around the hybrid Mechanism where we can make the use of both entities and detect as well as prevent the risks of Very Early Stages of BCs. Our Mechanism includes the use of Principle Component Analysis; PCA, at the first stage of pre-processing the decreasing features in the pre-processed data the second stage of pre-processing was more beneficial for accessing the multi pre-processed data for analysis and diagnosis. When the Multi Pre-processed data is accessed by the Support Vector Machine; SVM, the old Accuracy, Specificity and Significance level was ranging from 95 to 100% but the new levels after the multi pre-processing were decreased to 5% and the improvement was considerable and confirmed that the proposed Algorithm has achieved the optimized results then RF, KNN, DT. It means the Proposed algorithm can assist the human factor with faster decision making and can categorize data (High Risk) malignant and (Low Risk) Benign because to avoid Human errors AI is useful and can achieve optimal results.