Reviewerly/exHarmony huggingface dataset:
Analysis of Software Development Methodologies

The researcher focuses on the analysis of most common diverse methodologies of software development to choose the best one on the basis of different factors such as project type, size, development environment, and available resources.Software projects provided are positive and negative impacts and provide the stages of software development methodology.Subsequently, the author gives brief details about the common stages of software development in this paper.These stages are mostly used in every software development methodologies (SDMs).The main motive of this research is to provide the details of figures of steps and stages about currently available most common twenty-one (21) SDMs.Software projects are on the functions or stages of the methodology, the project owner's feedbacks in each methodology and suitability of methodology on the small, medium and large size of projects.The Result conducted based on an analysis between them by applying different strategies, development environments, and common practices and based on available resources, which can easily be understood to choose the best methodology, which can be feasible for Small, and Medium Enterprises (SMEs).
Cohort selection for clinical trials using deep learning models

Abstract Objective The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task. Materials and Methods Cohort selection can be formulated as a multilabeling problem whose goal is to determine which criteria are met for each patient record. We explore several deep learning architectures such as a simple convolutional neural network (CNN), a deep CNN, a recurrent neural network (RNN), and CNN-RNN hybrid architecture. Although our architectures are similar to those proposed in existing deep learning systems for text classification, our research also studies the impact of using a fully connected feedforward layer on the performance of these architectures. Results The RNN and hybrid models provide the best results, though without statistical significance. The use of the fully connected feedforward layer improves the results for all the architectures, except for the hybrid architecture. Conclusions Despite the limited size of the dataset, deep learning methods show promising results in learning useful features for the task of cohort selection. Therefore, they can be used as a previous filter for cohort selection for any clinical trial with a minimum of human intervention, thus reducing the cost and time of clinical trials significantly.
Investigating Task-Driven Latent Feasibility for Nonconvex Image Modeling

Properly modeling latent image distributions plays an important role in a variety of image-related vision problems. Most exiting approaches aim to formulate this problem as optimization models (e.g., Maximum A Posterior, MAP) with handcrafted priors. In recent years, different CNN modules are also considered as deep priors to regularize the image modeling process. However, these explicit regularization techniques require deep understandings on the problem and elaborately mathematical skills. In this work, we provide a new perspective, named Task-driven Latent Feasibility (TLF), to incorporate specific task information to narrow down the solution space for the optimization-based image modeling problem. Thanks to the flexibility of TLF, both designed and trained constraints can be embedded into the optimization process. By introducing control mechanisms based on the monotonicity and boundedness conditions, we can also strictly prove the convergence of our proposed inference process. We demonstrate that different types of image modeling problems, such as image deblurring and rain streaks removals, can all be appropriately addressed within our TLF framework. Extensive experiments also verify the theoretical results and show the advantages of our method against existing state-of-the-art approaches.
Multi-Label-Based Similarity Learning for Vehicle Re-Identification

The massive attention to the surveillance video-based analysis makes the vehicle reidentification one of the current hot areas of interest to study.Extracting discriminative visual representations for vehicle re-identification is a challenging task due to the low-variance among the vehicles that share same model, brand, type, and color.Recently, several methods have been proposed for vehicle re-identification, that either use feature learning or metric learning approach.However, designing an efficient and costeffective model is significantly demanded.In this paper, we propose multi-label-based similarity learning (MLSL) for vehicle re-identification obtaining an efficient deep-learning-based model that derives robust vehicle representations.Overall, our model features two main parts.First, a multi-label-based similarity learner that employs Siamese network on three different attributes of the vehicles: vehicle ID, color, and type.The second part is a regular CNN-based feature learner that employed to learn feature representations with vehicle ID attribute.The model is trained jointly with both parts.In order to validate the effectiveness of our model, a set of extensive experiments has been conducted on three of the largest well-known datasets VeRi-776, VehicleID, and VERI-Wild datasets.Furthermore, the parts of the proposed model are validated by exploring the influence of each part on the entire model performance.The results prove the superiority of our model over multiple state-of-the-art methods on the three mentioned datasets.
The future of breast cancer screening: what do participants in a breast cancer screening program think about automation using artificial intelligence?

BackgroundIf screening participants do not trust computerized decision-making, screening participation may be affected by the introduction of such methods. PurposeTo survey breast cancer screening participants’ attitudes towards potential future uses of computerization. Material and MethodsA survey was constructed. Women in a breast cancer screening program were invited via the final report letter to participate. Data were collected from February 2018 to March 2019 and 2196 surveys were completed. Questions asked participants to rate propositions using Likert scales. Data analysis was done using χ2 and logistic regression tests. ResultsThe mean age of participants was 61 years. Response rate was 1.3%. Of the submitted surveys, 97.5% were complete; 38% of respondents reported a preference for a computer-only examination. The highest level of confidence was given a computer-only reading followed by a physician reading. Participants with > 12 years of education were more likely to prefer a computer-only reading (odds ratio [OR] 1.655, 95% confidence interval [CI] 1.168–2.344), had a greater trust in letting a computer determine screening intervals and the need for a supplemental MRI (OR 1.606, 95% CI 1.171–2.202 and OR 1.577, 95% CI 1.107–2.247, respectively). Age was not found to be a significant predictor. ConclusionA high level of trust in computerized decision-making was expressed. Higher age was associated with a lower understanding of technology but did not affect attitudes to computerized decision-making. A lower level of education was associated with a lower trust in computerization. This may be valuable knowledge for future studies.
Securing MQTT by Blockchain-Based OTP Authentication

The Internet of Things is constantly capturing interest from modern applications, changing our everyday life and empowering industrial applications. Interaction and the collaboration among smart devices offer new challenges to security since they conflict with economic and energy consumption requirement constraints. On the other hand, the lack of security measures could negatively impact the concrete adoption of this paradigm. This paper focuses on the Message Queuing Telemetry Transport (MQTT) protocol, widely adopted in the Internet of Things. This protocol does not implement natively secure authentication mechanisms, which are demanded to developers. Hence, this paper proposes a novel OTP (one-time password)-authentication schema for MQTT, which uses the Ethereum blockchain to implement a second-factor out-of-band channel. The proposal enables the authentication of both local and remote devices preserving user privacy and guaranteeing trust and accountability via Ethereum smart contracts.
Towards storage rings as quantum computers

We explore the possible use of particle beam storage rings as quantum computers. More precisely, we consider creating an ion trap system, in which the same computational basis states can be defined as in a modern ion trap system, but in which the ions have a constant velocity and are rotating in a circular trap. The basic structures that we explore are classical and ultracold crystalline beams. What we propose is a novel method that uses the ion trap quantum computer concept, but puts the ions into a rotating frame of reference. The benefits of this approach are discussed.Received 28 February 2020Accepted 4 May 2020DOI:https://doi.org/10.1103/PhysRevAccelBeams.23.054701Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information with trapped ionsAccelerators & Beams
Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks

In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is a well known ensemble learning method, which trains multiple base models on random subsamples of a training dataset using a base learning algorithm and uses majority vote to predict labels of testing examples. We prove the intrinsic certified robustness of bagging against data poisoning attacks. Specifically, we show that bagging with an arbitrary base learning algorithm provably predicts the same label for a testing example when the number of modified, deleted, and/or inserted training examples is bounded by a threshold. Moreover, we show that our derived threshold is tight if no assumptions on the base learning algorithm are made. We evaluate our method on MNIST and CIFAR10. For instance, our method achieves a certified accuracy of 91.1% on MNIST when arbitrarily modifying, deleting, and/or inserting 100 training examples. Code is available at: https://github.com/jjy1994/BaggingCertifyDataPoisoning.
Chest X-Ray Report Generation Through Fine-Grained Label Learning

Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established metrics.
Attentron: Few-Shot Text-to-Speech Utilizing Attention-Based Variable-Length Embedding

On account of growing demands for personalization, the need for a so-called few-shot TTS system that clones speakers with only a few data is emerging. To address this issue, we propose Attentron, a few-shot TTS model that clones voices of speakers unseen during training. It introduces two special encoders, each serving different purposes. A fine-grained encoder extracts variable-length style information via an attention mechanism, and a coarse-grained encoder greatly stabilizes the speech synthesis, circumventing unintelligible gibberish even for synthesizing speech of unseen speakers. In addition, the model can scale out to an arbitrary number of reference audios to improve the quality of the synthesized speech. According to our experiments, including a human evaluation, the proposed model significantly outperforms state-of-the-art models when generating speech for unseen speakers in terms of speaker similarity and quality.
Cost-effective, Energy-efficient, and Scalable Storage Computing for Large-scale AI Applications

The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and energy-efficient computational storage developed for realizing the full potential of in-storage processing. To the best of our knowledge, Newport is the first commodity SSD that can be configured to run a server-like operating system, greatly minimizing the effort for creating and maintaining applications running inside the storage. We analyze the benefits of using Newport by running complex AI applications such as image similarity search and object tracking on a large visual dataset. The results demonstrate that data-intensive AI workloads can be efficiently parallelized and offloaded, even to a small set of Newport drives with significant performance gains and energy savings. In addition, we introduce a comprehensive taxonomy of existing computational storage solutions together with a realistic cost analysis for high-volume production, giving a good big picture of the economic feasibility of the computational storage technology.
Conditional Causal Relationships between Emotions and Causes in Texts

The causal relationships between emotions and causes in text have recently received a lot of attention.Most of the existing works focus on the extraction of the causally related clauses from documents.However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related.To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset.Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses.Experiments demonstrate the effectiveness and generality of our aggregation module.
Spammer Group Detection Using Machine Learning Technology for Observation of New Spammer Behavioral Features

Recently, the rapid growth in the number of customer reviews on e-commence platforms and in the amount of user-generated content has begun to have a profound impact on customer purchasing decisions. To counter the negative impact of social media marketing, some firms have begun hiring people to generate fake reviews which either promote their own products or damage their competitor's reputation. This study proposes a framework, which takes advantage of both supervised and unsupervised learning techniques, for the observation of behaviors among spammers. Then, based on the behavior of participants on web forums, the authors build up a post-reply network. The main focus is on the behavior-related features of the reviews, their propagation, and their popularity. The primary objective of this study is to build an effective online spammer detection model and the method detailed in this work can be used to improve the performance of spammer detection models. An experiment is carried out with a real dataset, the results of which indicate that these new features are important for identifying spammers. Finally, random walk clustering is applied to investigate the post-reply network. Some interesting and important features are observed in the interactions between a group of spammers which could be subjected to further research.
Detecting malicious accounts in permissionless blockchains using temporal graph properties

Abstract Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover

The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensive language models, we propose a simple feature-based classifier for the detection problem, using carefully crafted features that attempt to model intrinsic differences between human and machine text. Our research contributes to the field in producing a detection method that achieves performance competitive with far more expensive methods, offering an accessible “first line-of-defense” against the abuse of language models. Furthermore, our experiments show that different sampling methods lead to different types of flaws in generated text.
Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI

Multiparty Private Set Intersection (mPSI), enables n parties, each holding private sets (each of size m) to securely compute the intersection of these private sets. While several protocols are known for this task, the only concretely efficient protocol is due to the work of Kolesnikov et al. (KMPRT, CCS 2017), who gave a semi-honest secure protocol with communication complexity O(nmtƛ), where t < n is the number of corrupt parties and ƛ is the security parameter. In this work, we make the following contributions:
Increasing Robustness to Spurious Correlations using Forgettable Examples

Neural NLP models tend to rely on spurious correlations between labels and input features to perform their tasks.Minority examples, i.e., examples that contradict the spurious correlations present in the majority of data points, have been shown to increase the out-ofdistribution generalization of pre-trained language models.In this paper, we first propose using example forgetting to find minority examples without prior knowledge of the spurious correlations present in the dataset.Forgettable examples are instances either learned and then forgotten during training or never learned.We empirically show how these examples are related to minorities in our training sets.Then, we introduce a new approach to robustify models by fine-tuning our models twice, first on the full training data and second on the minorities only.We obtain substantial improvements in out-of-distribution generalization when applying our approach to the MNLI, QQP, and FEVER datasets.