Reviewerly/exHarmony huggingface dataset:
Deep Learning for Detection of Prostate Tumors by Microscopic Cells and MRI

The most common type of cancer in men is prostate cancer. Since it initially grows slowly at the level of the prostate, it does not cause any major damage. However, some types of prostate cancer are not dangerous because they grow slowly and require little or no treatment. In contrast, other types of prostate cancer are aggressive and can spread quickly. This paper studies the effectiveness of Local Binary Patterns (LBP) method, skewness coefficients for asymmetry detection and correction, morphological transformations, and image classification using Convolutional Neural networks (CNNs), to detect prostate cancer tumor from microscopic cells and MRI imaging. The experimental results carried out have shown the effectiveness of the proposed methods using deep learning architectures.
Multiple Blind Signature for e-Voting and e-Cash

Abstract In this paper, we propose a new cryptographic primitive, called multiple blind signature (MBS), which is designed based on the integration of both normal blind signature scheme and dual signature. The major difference between a normal blind signature and an MBS is that using a normal blind signature, only one message, $m$, can be verified, but using an MBS, any subset, ${M}^{\prime }$, of multiple messages in a set, $M$, where ${M}^{\prime}{\subseteq} M$, can be verified. With this additional property, we will show that MBS is especially suitable for e-voting and e-cash applications. In other words, we classify these processes in two applications into two phases, on-line and off-line phases. One unique property of this design is that most time-consuming computation and interaction can be performed in advance in off-line phase. There is no cost of computation and interaction in the online phase.
Uniform Initialization in Response Space for PSO and its Applications

Particle swarm optimization (PSO) is widely used in the parameter estimation for complex models, which is the key to establishing a mathematical model. However, convergence to the local optimal easily occurs in PSO. A substantial amount of researches have been conducted to improve the evolutionary process of PSO; nonetheless, the study on initialization method is relatively limited. Generally, initial particle positions are uniformly distributed in the parameter space but unevenly distributed in the response space for nonlinear models, which can hinder optimization. In this paper, a novel initialization method for PSO with an uninformative prior of parameters in the model (UPPSO) is proposed. The method initializes particle positions uniformly distributed in the response space and makes the effect of particle velocity uniform in the response space. The UPPSO and other five algorithms were applied to estimate parameters for three different polymer models, that is, viscosity and PVT (pressure-volume-temperature) models which are very important and must be estimated for each type of polymers. In comparison with other algorithms, the optimization capacity of UPPSO for each model was ranked second, and UPPSO was particularly outstanding in obtaining the optimal parameter. Moreover, UPPSO was also competitive in computation performance. In general, UPPSO is conducive to optimization, efficient and adaptable, and it can also be generalized to other swarm intelligence algorithms, such as the differential evolution (DE) and artificial bee colony (ABC), etc.
Agile Software Development Methods and Hybridization Possibilities Beyond Scrumban

Traditional software development methods could no longer cope with the fast-growing software development market, as well as the increasing complexity of the software being developed. Using outdated practices was no longer viable as it could lead to reduced competitiveness and losing possible revenue. Therefore, many companies have turned to implementing agile software development methods to keep up with the fast-paced market. Agile allows them to develop quickly, consistently, and in a flexible manner. However, different approaches are better suited for different projects, and, sometimes, an approach combining appropriate aspects of several other approaches is the best option. This paper describes five popular agile software development approaches of which four can be considered "pure", namely Scrum, Lean, Kanban, and Kaizen, and a hybrid approach called Scrumban. The paper then aims to assess other possible combinations of the approaches, both found in the literature and those not yet considered.
A Systematic Literature Review on Using Blockchain Technology in Public Administration

Blockchain technology promises that it can transform completely modern society and its applications in the near future. Its main characteristics could change activities that are related to public politics and services, such as administrative processes, security, privacy, transparency, integrity, and confidentiality. The citizens concern about the potential risks, that are mainly related to data security, respectively documents falsification in a corrupted and non-transparent government. Not only in terms of administrative efficiency, but also in terms of boosting confidence, transparency, and accessibility of services, blockchain technology has the potential to play a major role in the digitalization of the public sector. Blockchain-based solutions facilitate citizen-government collaboration by safely streamlining and automating procedures while maintaining privacy and secrecy. To better understand the adoption of blockchain in public administration, we conducted a Systematic Literature Review (SLR) including 32 primary papers in this study. We intend to address one of the Informatics Society's major concerns: the data security of thousands of individuals and the inviolability of citizen databases. Furthermore, we identified gaps and future trends to support researchers in this domain.
Assessing the Vulnerabilities of Internet Users to Cyber-Attacks using their Password Login Patterns

The purpose of this work is to assess the vulnerability of Internet users to cyber-attacks using their password login patterns. A vulnerability is a weak point or weakness through which an intruder or attacker can gain unauthorized access to Internet users’ accounts. A cyber-attack can maliciously steal data, disable computers, or use a breached computer as a launch point for other attacks. A login pattern is a string of characters that are used to verify the identity of an electronic user account during the authentication process. The data for the analysis was generated from the questionnaire designed using Google form. Statistical Package for Social Science (SPSS) was used to carry out the analysis and a total number of 175 responses were gathered. The results were presented in a table and chart.
Reducing the training time of deep learning models using synchronous SGD and large batch size

Recently, deep learning research has demonstrated that being able to train big models improves performance substantially. In this work, we consider the problem of training a deep neural network with millions of parameters using multiple CPU cores. On a single machine with a modern CPU platform, training a benchmark dataset of Dogs vs Cats can take up to hours; however, distributing training across numerous machines has been seen to dramatically reduce this time. The current state of the art for a modern distributed training framework is presented in this study, which covers the many methods and strategies utilized to distribute training. We concentrate on synchronous versions of distributed Stochastic Gradient Descent, different All Reduce gradient aggregation algorithms, and best practices for achieving higher throughput and reduced latency, such as gradient compression and large batch sizes. We show that using the same approaches, we can train a smaller deep network for an image classification problem in a shorter time. Although we focus on and report on the effectiveness of these approaches when used to train convolutional neural networks, the underlying methods may be used to train any gradient-based machine-learning algorithm.
Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system's high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human's emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human's health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.
Characterization of MPC-based Private Inference for Transformer-based Models

In this work, we provide an in-depth characterization study of the performance overhead for running Transformer models with secure multi-party computation (MPC). MPC is a cryptographic framework for protecting both the model and input data privacy in the presence of untrusted compute nodes. Our characterization study shows that Transformers introduce several performance challenges for MPC-based private machine learning inference. First, Transformers rely extensively on “softmax” functions. While softmax functions are relatively cheap in a non-private execution, softmax dominates the MPC inference runtime, consuming up to 50% of the total inference runtime. Further investigation shows that computing the maximum, needed for providing numerical stability to softmax, is a key culprit for the increase in latency. Second, MPC relies on approximating non-linear functions that are part of the softmax computations, and the narrow dynamic ranges make optimizing softmax while maintaining accuracy quite difficult. Finally, unlike CNNs, Transformer-based NLP models use large embedding tables to convert input words into embedding vectors. Accesses to these embedding tables can disclose inputs; hence, additional obfuscation for embedding access patterns is required for guaranteeing the input privacy. One approach to hide address accesses is to convert an embedding table lookup into a matrix multiplication. However, this naive approach increases MPC inference runtime significantly. We then apply tensor-train (TT) decomposition, a lossy compression technique for representing embedding tables, and evaluate its performance on embedding lookups. We show the trade-off between performance improvements and the corresponding impact on model accuracy using detailed experiments.
SAPCo Sort: Optimizing Degree-Ordering for Power-Law Graphs

We introduce the Structure-Aware Parallel Counting (SAPCo) Sort algorithm that optimizes performance of degree-ordering, a key operation in graph analytics. SAPCo leverages the skewed degree distribution to accelerate sorting. The evaluation for graphs of up to 3.6 billion vertices shows that SAPCo sort is, on average, 1.7-33.5 times faster than state-of-the-art sorting algorithms such as counting sort, radix sort, and sample sort.
Practical Verifiable Computation on Encrypted Data

We focus on the problem of how to boost greater confidence in computations outsourced to an untrusted cloud server. Naturally, we aim for solutions that cater for the following two security requirements: data confidentiality/privacy and verifiability of computations. This problem is addressed by the research community through an intriguing paradigm combining a verifiable computation (VC) and a fully homomorphic encryption (FHE). Despite continuing advances in achieving efficient VC and FHE, existing approaches that meet both requirements simultaneously are expensive and impractical for servers and clients. In this work, we first introduce an interesting primitive, called versatile message authenticators, to achieve a verifiable outsourced computation on encrypted data and then give a concrete construction. Without the involvement of FHE, our scheme mainly depends on a pseudo-random function and a adapted Homomorphic MAC scheme. Our solution is proven secure in a strong security model where the adversary is allowed to issue any verification queries. Furthermore, it allows for arbitrary composition and generates constant-size tags. However, our construction just supports linear functions. Finally, our security proof shows its semantic security and unforgeability.
Remote iVR for Nutrition Education: From Design to Evaluation

While different crowdsourcing platforms promote remote data collection, experiments in the immersive Virtual Reality (iVR) research community are predominantly performed in person. The COVID-19 pandemic, however, has forced researchers in different disciplines, including iVR, to seriously consider remote studies. In this paper, we present a remote study using the Immersive Virtual Alimentation and Nutrition (IVAN) application, designed to educate users about food-energy density and portion size control. We report on the results of a remote experiment with 45 users using the IVAN app. In IVAN, users actively construct knowledge about energy density by manipulating virtual food items, and explore the concept of portion size control through hypothesis testing and assembling virtual meals in iVR. To explore the feasibility of conducting remote iVR studies using an interactive health-related application for nutrition education, two conditions were devised (interactive vs. passive). The results demonstrate the feasibility of conducting remote iVR studies using health-related applications. Furthermore, the results also indicate that regardless of level of interactivity learners significantly improved their knowledge about portion size control after using the IVAN ( p &amp;lt; 0.0001). Adding interactivity, however, suggests that the perceived learning experience of users could be partially affected. Learners reported significantly higher scores for immediacy of control in the interactive condition compared to those in the passive condition ( p &amp;lt; 0.05). This study demonstrates the feasibility of conducting an unsupervised remote iVR experiment using a complex and interactive health-related iVR app.
Bifrost: End-to-End Evaluation and optimization of Reconfigurable DNN Accelerators

Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allows for the exploration of accelerator designs and configuration space. However, preparing models for evaluation and exploring configuration space in STONNE is a manual developer-time-consuming process, which is a barrier for research. This paper introduces Bifrost, an end-to-end framework for the evaluation and optimization of reconfigurable DNN inference accelerators. Bifrost operates as a frontend for STONNE and leverages the TVM deep learning compiler stack to parse models and automate offloading of accelerated computations. We discuss Bifrost’s advantages over STONNE and other tools, and evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost introduces a module leveraging AutoTVM to efficiently explore accelerator designs and datatlow mapping space to optimize performance. This is demonstrated by tuning the MAERI architecture and generating efficient datatlow mappings for AlexNet, obtaining an average speedup of $50\times$ for the convolutional layers and $11\times$ for the fully connected layers. Our code is available at www.github.com/gicLAB/bifrost.
Automatic Scoring for Translations Based on Language Models

With the development of English education, translation scoring has gradually become a time-consuming and energy-consuming task, and it is difficult to ensure objectivity because of the subjective factors in manual correcting. Due to the similarity between the quality evaluation of responses generated by the dialogue system and the translation results submitted by students, we selected two metrics of dialogue to automatically score the translations, which are applied in a case study. The experiments show that the hybrid scores of two metrics are close to human scores. In conclusion, the method is feasible to apply the evaluation metrics of dialogue systems to translation scoring, and it can provide an improvement idea for the automatic scoring of translations in the future.
Discretization of numerical meta-features into categorical: analysis of educational and business data sets

Meta-learning is learning from previous experience gained while applying learning algorithms to different data. Meta-learning consists of three steps: (i) establishing meta-features, (ii) performing learning, and (iii) prediction. This paper focuses on the first step, meta-features. Meta-features are a mix of numerical and categorical variables. We build upon the idea that learning from numerical meta-features is often less effective and less efficient than learning from categorical meta-features. Thus, the objective of this study is to discretize numerical meta-features into categorical values. An overview of meta-features is given in the paper, along with a taxonomy of discretization methods. In addition, a survey of significant discretization methods is provided. Then, discretization is performed on 58 datasets selected from two domains of social sciences: educational and business domains. Research results are discussed, and contributions for meta-learning process improvement are provided.
A general model for creating robust choropleth maps

Choropleth maps visualize areal geographical data by grouping data into a few map classes and assigning different colors, shades, or patterns. Recent studies show that data uncertainty, commonly observed in real-life applications, should also be accounted for when determining the best classification scheme. Due to data uncertainty, a few studies note that map units might be placed in a wrong class, and the concept of map robustness has been introduced to minimize such misplacement. Recently, an algorithm has been developed to integrate robustness into the design of the optimal map classification scheme. However, the existing algorithm has two limitations: first, it is only suitable for certain robustness metrics. Second, when identifying the optimal class breaks, the existing algorithm requires predefined candidate class break values, which might lead to sub-optimal solutions. This paper resolves these issues by proposing a new model, namely, the Continuous Robust Map Classification Problem (CRMCP), and the associated solution approach. The CRMCP allows mapmakers to customize robustness metrics according to their data and applications. In addition, a particle swarm optimization algorithm is developed to solve the CRMCP. The model and algorithm are tested using American Community Survey data. Test results suggest that the new approach can find better solutions than the existing algorithm. The study improves the usability of choropleth maps when uncertain geographical attributes are involved and allows spatial analysts and decision-makers to incorporate robustness into the mapmaking process more flexibly.
Deepfake video detection using InceptionResnetV2

Recently, deepfake face-swapping technology has become popular, making it easy to create ultra-realistic fake videos. Detecting the authenticity of videos is becoming increasingly important due to the potential negative impact videos can have on the world. This research is a method for developing a deep learning model, which can make a distinction between real and fake videos. Research papers on the subject of transfer learning in the domain of computer vision to take advantage of the earlier created neural network functions for image classification and to create new models based on them. Deep learning continues to evolve in both generating and detecting deepfakes. Models developed to detect deepfakes are designed using older datasets, may become outdated over time, and require new detection techniques all the time. Research results are promising with over 90% accuracy and areas for development and further development
Privacy and location-based services

Location-based services (LBSs) use geographic information to provide a variety of benefits to users—most ubiquitously, through users’ mobile devices (phones and smart wearables). While such information offers value—through navigation apps, for example—it contains troves of personal data that can be exploited against users.
Breast Cancer Prediction Using Machine Learning

Abstract: Breast cancer is one ofi the diseases which cause number ofi deaths ever year across the globe, early detection and diagnosis ofi such type ofi disease is a challenging task ini orderi to reduce the numberi ofi deaths. Now a days various techniques ofi machinei learning and data miningi are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like canceri which can save the life’s ofi the patients suffering from such type ofi disease. The major concern ofi this study is to findi the prediction accuracy ofi the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Randomi Forest and to suggest the best algorithm.
65‐2: A Light and Fast Branched Neural Network using Perceptual Optimization for High‐Quality Visual Restoration of UDC images

We propose a light and fast method to restore degraded images captured from under‐display cameras (UDCs) by modifying a branched deep neural network (BNUDC). The existing BNUDC is trained to minimize the per‐pixel difference of intensity between a ground truth and a restored image using L1 or L2 loss, and it has a large number of learnable parameters for achieving state‐of‐theart performance in terms of numerical distances such as PSNR and SSIM. Commercializing a deep‐learning‐based image restoration requires high perceptual image quality and real‐time inference speed. To this end, we downscale the original BNUDC to restore a 2k resolution image within 44ms which is the frame time for a 24Hz video, and train our network to reconstruct perceptually‐pleasing images using ‘perceptual optimization’. Our model has less than 1M learnable parameters, its inference time is less than 40ms, and it outperforms the original network in terms of perceptual image quality.