Reviewerly/exHarmony huggingface dataset:
Challenges in Combining Agile Development and CMMI: A Systematic Literature Review

Recently, Agile Development has emerged as an alternative approach in software engineering. The Agile Software Development (ASD) process provides the ability to cope with ever-changing requirements. On the other hand, the Capability Maturity Model Integration, one of the Software Process Improvement Framework, is widely used to provide software development organizations with structure and stability in their software process. It enables organizations to generate software processes with better quality software, the productivity increase of the development team, and minimal risk of failure. The idea behind combining the Capability Maturity Model Integration (CMMI) and Agile aims to create a software development procedure that is stable and continues to improve while also providing agility for dealing with dynamic business needs. This research compiles the experiences of combining both software and identifies challenges for the collaborative implementation process. This study also highlights the extent of the compatibility of CMMI and Agile Development. This research followed a standardized Systematic Literature Review procedure and used the approach known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses or PRISMA. The findings indicate that the main challenges with regard to combining CMMI and Agile are the lack of relevant knowledge and experience and the culture of CMMI, Agile, or sometimes both. The limited scope of Agile itself poses another considerable challenge. Agile focuses on software development projects, while CMMI has a broader range, from project-level development to organization-level process improvement. This review also finds that Agile Development is compatible with CMMI at a maturity level of 2 and 3. Future studies should determine the optimal method for overcoming the obstacles associated with combining CMMI and Agile.
Quantum attacks on Sum of Even-Mansour pseudorandom functions

At CRYPTO 2019, constructions of a pseudorandom function from public random permutations were presented. We consider one of the constructions called Sum of Even-Mansour (SoEM), and present quantum attacks against the construction. Our attacks are based on two quantum algorithms, Simon's algorithm and Grover's algorithm, and derive the secret key. We also present quantum attacks against natural variants of SoEM.
Unsupervised Anomaly Detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS) Using a Deep Residual Autoencoder

Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires a human expert monitoring the procedure from a console, whereas automated anomaly detection systems in this area typically rely on classical supervised learning. Anomalous surgical events, however, are rare, making it difficult to capture data to train a model in a supervised fashion. In this work we propose an unsupervised approach to anomaly detection for robotic MIS based on deep residual autoencoders. The idea is to make the autoencoder learn the `normal' distribution of the data and detect abnormal events deviating from this distribution by measuring a reconstruction error. The model is trained and validated upon both the publicly available Cholec80 dataset and a set of videos captured on procedures using artificial anatomies (`phantoms') as part of the Smart Autonomous Robotic Assistant Surgeon (SARAS) project. The system achieves recall and precision equal to 78.4%, 91.5%, respectively, on Cholec80 and of 95.6%, 88.1% on the SARAS phantom dataset. The system was developed and deployed as part of the SARAS platform for real-time anomaly detection with a processing time of 25 ms per frame.
Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

Abstract Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving denseness results in Lebesgue spaces, when inputs and outputs variables are both compactly supported. We further prove an almost uniform convergence result when the input is univariate. Auxiliary lemmas are proved regarding the richness of the soft-max gating function class, and their relationships to the class of Gaussian gating functions.
Orbit Estimation for Spacecraft Based on Intermittent Measurements: An Event-Triggered UKF Approach

This article addresses a problem of spacecraft orbit estimation with measurement transmission restrictions through an event-triggered state estimation approach. Specifically, we consider the scenario that the ground station communicates with the spacecraft through multiple channels and each channel decides whether or not to transmit the measurements to the spacecraft based on separate deterministic event-triggering conditions. Considering $J_2$ perturbations, an event-triggered unscented Kalman filter-based state estimation algorithm is designed on the basis of a model of orbit dynamics and a measurement model of the ground station. The performance of the estimator is evaluated based on real-time two-line element data. The obtained results indicate that the proposed algorithm can achieve satisfactory estimation performance at reduced measurement transmission rates.
Call Transcription Methodology for Contact Center Systems

Nowadays, one of the key areas of research on contact centre systems is their automation. The main element that influences the possibility of automation of contact centre processes is the call transcription methods implemented by automatic speech recognition (ASR) systems. Such systems enable developing intention recognition methods and, consequently voice bots. The current solutions used in ASR systems for many less popular languages do not guarantee a fully satisfactory transcription quality for hotline voice calls. This is due to the unique characteristics of the sound signal generated there, whose quality parameters differ significantly from those of studio recordings. The paper presents a comparative study of selected speech recognition systems that were additionally supplemented with elements of preprocessing of sound recordings and postprocessing of originally produced transcriptions. As for preprocessing, the following methods were tested: separation of the client and agent channels into two independent signals, training of ASR systems, and audio signal correction. With regards to postprocessing, on the other hand, tests were performed for inarticulate sounds, normalization of standard phrases (e.g. numbers, dates, times, etc.), and identification of close-sounding phrases and foreign language phrases, and lemmatization. Based on the research conducted and the analyses performed, a new method of call transcription intended specifically for contact center systems was proposed. The research conducted for this paper was based on the Polish language model, for which major problems are observed with the quality of automatic contact center call transcriptions.
Learn to Forget: Machine Unlearning via Neuron Masking

Nowadays, machine learning models, especially neural networks, have became prevalent in many real-world applications. These models are trained based on a one-way trip from user data: as long as users contribute their data, there is no way to withdraw. To this end, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine unlearning</i> becomes a popular research topic, which allows the model trainer to unlearn unexpected data from a trained machine learning model. In this article, we propose the first uniform metric called forgetting rate to measure the effectiveness of a machine unlearning method. It is based on the concept of membership inference and describes the transformation rate of the eliminated data from “memorized” to “unknown” after conducting unlearning. We also propose a novel unlearning method called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Forsaken</monospace> . It is superior to previous work in either utility or efficiency (when achieving the same forgetting rate). We benchmark <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Forsaken</monospace> with eight standard datasets to evaluate its performance. The experimental results show that it can achieve more than 90% forgetting rate on average and only causeless than 5% accuracy loss.
A dummy location generation algorithm based on the semantic quantification of location

With the widespread use of location-based services, the risk of location privacy leakage increases. Aiming at the problem that the traditional privacy protection scheme does not fully consider the location privacy leakage caused by the attacker's ability to infer the attack through location semantics, this paper proposes a dummy location generation algorithm based on semantic quantification of location (Virtual Location -based on Semantics, VLBS). The location semantics were quantified by the number of users visiting in different time periods, and a multi-objective optimized dummy location set was constructed from three aspects: historical query probability, semantics of location and physical dispersion uniformity. Experiments show that, compared with other algorithms, the proposed scheme improves the anonymity success rate by 45.6%, the anonymity time by 20.5%, and the physical dispersion uniformity by 12.7%.
CapablePtrs: Securely Compiling Partial Programs Using the Pointers-as-Capabilities Principle

Capability machines such as CHERI provide memory capabilities that can be used by compilers to provide security benefits for compiled code (e.g., memory safety). The existing C to CHERI compiler, for example, achieves memory safety by following a principle called “pointers as capabilities” (PAC). Informally, PAC says that a compiler should represent a source language pointer as a machine code capability. But the security properties of PAC compilers are not yet well understood. We show that memory safety is only one aspect, and that PAC compilers can provide significant additional security guarantees for partial programs: the compiler can provide security guarantees for a compilation unit, even if that compilation unit is later linked to attacker-provided machine code.As such, this paper is the first to study the security of PAC compilers for partial programs formally. We prove for a model of such a compiler that it is fully abstract. The proof uses a novel proof technique (dubbed TrICL, read trickle), which should be of broad interest because it reuses the whole-program compiler correctness relation for full abstraction, thus saving work. We also implement our scheme for C on CHERI, show that we can compile legacy C code with minimal changes, and show that the performance overhead of compiled code is roughly proportional to the number of cross-compilation-unit function calls.
Communicative Learning with Natural Gestures for Embodied Navigation Agents with Human-in-the-Scene

Human-robot collaboration is an essential re-search topic in artificial intelligence (AI), enabling researchers to devise cognitive AI systems and affords an intuitive means for users to interact with the robot. Of note, communication plays a central role. To date, prior studies in embodied agent navigation have only demonstrated that human languages facilitate communication by instructions in natural languages. Nevertheless, a plethora of other forms of communication is left unexplored. In fact, human communication originated in gestures and oftentimes is delivered through multimodal cues, e.g., “go there” with a pointing gesture. To bridge the gap and fill in the missing dimension of communication in embodied agent navigation, we propose investigating the effects of using gestures as the communicative interface instead of verbal cues. Specifically, we develop a VR-based 3D simulation environment, named Gesture-based THOR (Ges-THOR), based on AI2-THOR platform. In this virtual environment, a human player is placed in the same virtual scene and shepherds the artificial agent using only gestures. The agent is tasked to solve the navigation problem guided by natural gestures with unknown semantics; we do not use any predefined gestures due to the diversity and versatile nature of human gestures. We argue that learning the semantics of natural gestures is mutually beneficial to learning the navigation task—learn to communicate and communicate to learn. In a series of experiments, we demonstrate that human gesture cues, even without predefined semantics, improve the object-goal navigation for an embodied agent, outperforming various state-of-the-art methods.
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis

Vision-and-language (V&L) models take image and text as input and learn to capture the associations between them. These models can potentially deal with the tasks that involve understanding medical images along with their associated text. However, applying V&L models in the medical domain is challenging due to the expensiveness of data annotations and the requirements of domain knowledge. In this paper, we identify that the visual representation in general V&L models is not suitable for processing medical data. To overcome this limitation, we propose BERTHop, a transformer-based model based on PixelHop++ and VisualBERT for better capturing the associations between clinical notes and medical images. Experiments on the OpenI dataset, a commonly used thoracic disease diagnosis benchmark, show that BERTHop achieves an average Area Under the Curve (AUC) of 98.12% which is 1.62% higher than state-of-the-art while it is trained on a 9× smaller dataset.
On Attacking Kerberos Authentication Protocol in Windows Active Directory Services: A Practical Survey

Organizations use Active Directory Windows service to authenticate users in a network with the extended Kerberos Authentication protocol. Therefore, it is necessary to investigate its resistance to the different types of attacks it can suffer, the best way to detect them and to parameterize it to mitigate the effects of the attacks. This work analyzes the main Kerberos attacks in Active Directory Windows networks, inherent in the design of the protocol and not resolved. For each attack the objective is studied, implementation is developed in a virtual laboratory and detection is analyzed, proposing measures for mitigation and response. Subsequently, they are discussed in a general way and the results of the attacks are analyzed according to some parameters. As conclusions of the work carried out, it should be noted that although the attacks are mostly difficult to implement, their detection is even more complicated, and the damage is very severe so it’s necessary to continuously monitor the logs in these environments to detect them and taking into account strict recommendations for mitigation and response.
Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification

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TianheGraph: Customizing Graph Search for Graph500 on Tianhe Supercomputer

As the era of exascale supercomputing is coming, it is vital for next-generation supercomputers to find appropriate applications with high social and economic benefit. In recent years, it has been widely accepted that extremely-large graph computation is a promising killer application for supercomputing. Although Tianhe series supercomputers are leading in the world-wide competition of supercomputing (ranked No. 1 in the Top500 list for six times), previously they had been inefficient in graph computation according to the Graph500 list. This is mainly because the previous graph processing system cannot leverage the advanced hardware features of Tianhe supercomputers. To address the problem, in this paper we present our integrated optimizations for improving the graph computation performance on our next-generation Tianhe supercomputing system, mainly including sorting with buffering for heavy vertices, vectorized searching with SVE (Scalable Vector Extension) on matrix2000+ CPUs, and group communication on the proprietary interconnection network. Performance evaluation on a subset of the Tianhe supercomputer (with 512 nodes and 196,608 cores) shows that our customized graph processing system effectively improves the graph search performance and achieves the BFS performance of 2131.98 GTEPS.
Incremental Learning for Mobile Encrypted Traffic Classification

With the rising popularity of mobile networks and applications, network traffic classification has gradually become essential to mobile network management and cyberspace security. Existing state-of-the-art methods have achieved high accuracy in the closed-world mobile encrypted traffic classification, where the classifier only needs to process the classes seen in the training. When we update the dataset with new mobile applications, these methods must retrain a new classifier from scratch to learn the knowledge of all applications because directly fine-tuning the existing classifier would lead to the catastrophic forgetting problem. Thus, it is challenging to incrementally add new applications to the classification system while preserving the learned knowledge of the existing classifier. To tackle this issue, we propose an incremental learning framework based on the one vs rest (OvR) strategy and neural network classifiers. Moreover, we adopt a sample selection algorithm to balance the conflict between the growing training effort caused by new applications and the high classification accuracy. The experimental results demonstrate that our proposed framework achieves incremental learning with high classification accuracy like the closed-world method, and the selection algorithm significantly reduces training efforts to meet the dataset scale control and classification accuracy requirement in the lifetime incremental learning.
Long-term evolution of clear sky surface solar radiation and its driving factors over East Asia

This study evaluated the performance of clear sky surface solar radiation (SSR) and its long-term evolution mechanism in the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) in East Asia. The results revealed that although MERRA-2 overestimated the clearness index and clear sky SSR, the reanalysis performed well in northwestern, northern, northeastern, and central East Asia. From 1980 to 2019, clear sky SSRs decreased significantly in most areas. Aerosols play a dominant role in the annual variation of clear sky SSR rather than water vapor. In northwestern East Asia, owing to the influence of the increase in aerosol optical depth (AOD), the clear sky SSR showed a strong downward trend during 1993–2013. After 2007, the increase in water vapor played an important role in the weakening of the clear sky SSR. In northern, northeastern, and central East Asia, clear sky SSRs ushered in a brightening stage at the beginning of the 21st century because of the reduction in AOD. The magnitude of the change in Central East Asia is even more pronounced. In the Tarim Basin and northwestern India, dust plays an important role in the variation of clear sky SSR, whereas anthropogenic aerosols drive clear sky SSR in highly industrialized regions. • Clear-sky SSR and its impact factors was studied over East Asia in four decades. • The increase in aerosol dominated clear-sky SSR decrease in most areas. • The increase in water vapor played an important role after 2007.
LSTM-RNN Based Sentiment Analysis to Monitor COVID-19 Opinions using Social Media Data

Lately people are using social media for sharing their thoughts, insights about different topics and issues. The main aim of social media is to connect users and update their statuses/thoughts. One of the most used online social networking sites to share information is Twitter with roughly 330 million uses globally and 48.35 million US users where they share their opinions and thoughts. Recently, the world faced a serious pandemic, Corona virus disease (COVID-19) outbreak and the World Health Organization (WHO) declared the virus as a global health emergency. The COVID-19 started in late December of 2019 in Wuhan City, Hubei Province, China. Around the world during this time, individuals use social media to share their opinions about the pandemic. Because of the lack of information about the virus, people switched to micro-blogging platforms such as Twitter. In this study, we utilize natural language processing (NLP) techniques for opinion mining to extract negative and positive sentiments/tweets on COVID-19. We investigate NLP based sentiment analysis using Recurrent Neural Network (RNN) model with Long-Short Term Memory networks (LSTMs). Predicted sentiment using LSTM-RNN, which gives high accuracy, can be used to educate people about the virus.
A Comparative Analysis of Image Dehazing using Image Processing and Deep Learning Techniques

The haziness presence in atmosphere now poses to be a critical issue, especially in terms of visibility and visual quality. The major challenge faced in the present day scenario is to stitch a technique that detects the presence of haziness and to provide an efficient method to remove it as well. This process is known as image dehazing. Although different dehazing algorithms has been introduced, still better performances can be achieved which is addressed in this paper. This challenge is overcome by using a suitable classification algorithm to detect hazy images and then cleaning it to get corresponding clear image. In this method, two goals are presented and executed, Initial one be a comparative analysis between the machine learning and deep learning models is presented for the classification of hazy and clear images and then the hazy images are processed by a robust deep neural network for the removal process. In order to classify, two ML algorithms, namely, SVM and Logistic Regression are used and two CNN standard models Resnet, Vgg are also used and their performances are compared to obtain best classification algorithm for this problem. In classification model, standard CNN models outperforms Machine Learning models by a fair margin by proving to be more accurate. Similarly for dehazing also DCP Image processing algorithm is implemented with a proposed CNN de-hazing network and their performances are also compared to prove the efficiency of proposed CNN model. ITS, HSTS, Indoor SOTS Dataset are used as inputs. The performance of CNN network is compared with an image processing technique called Dark Channel Prior (DCP) algorithm. Comparatively the CNN model outperforms the DCP algorithm by attaining higher image quality metrics. To evaluate the image quality, two metrics are compared, they are Structural-similarity-index (SSIM) and peak-signal-to-noise-ratio (PSNR).
Resource theory of unextendibility and nonasymptotic quantum capacity

In this paper, we introduce the resource theory of unextendibility as a relaxation of the resource theory of entanglement. The free states in this resource theory are the k-extendible states, associated with the inability to extend quantum entanglement in a given quantum state to multiple parties. The free channels are k-extendible channels, which preserve the class of k-extendible states. We define several quantifiers of unextendibility by means of generalized divergences and establish their properties. By utilizing this resource theory, we obtain non-asymptotic upper bounds on the rate at which quantum communication or entanglement preservation is possible over a finite number of uses of an arbitrary quantum channel assisted by k-extendible channels at no cost. These bounds are significantly tighter than previously known bounds for both the depolarizing and erasure channels. Finally, we revisit the pretty strong converse for the quantum capacity of antidegradable channels and establish an upper bound on the non-asymptotic quantum capacity of these channels.