Reviewerly/exHarmony huggingface dataset:
Revisiting Binary Code Similarity Analysis Using Interpretable Feature Engineering and Lessons Learned

Binary code similarity analysis (BCSA) is widely used for diverse security applications such as plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.
Analysis of a two-layer neural network via displacement convexity

Fitting a function by using linear combinations of a large number $N$ of “simple” components is one of the most fruitful ideas in statistical learning. This idea lies at the core of a variety of methods, from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is nonconvex and is solved by gradient descent or its variants. Unfortunately, little is known about global convergence properties of these approaches. Here, we consider the problem of learning a concave function $f$ on a compact convex domain $\Omega \subset {\mathbb{R}}^{d}$, using linear combinations of “bump-like” components (neurons). The parameters to be fitted are the centers of $N$ bumps, and the resulting empirical risk minimization problem is highly nonconvex. We prove that, in the limit in which the number of neurons diverges, the evolution of gradient descent converges to a Wasserstein gradient flow in the space of probability distributions over $\Omega $. Further, when the bump width $\delta $ tends to $0$, this gradient flow has a limit which is a viscous porous medium equation. Remarkably, the cost function optimized by this gradient flow exhibits a special property known as displacement convexity, which implies exponential convergence rates for $N\to \infty $, $\delta \to 0$. Surprisingly, this asymptotic theory appears to capture well the behavior for moderate values of $\delta $, $N$. Explaining this phenomenon, and understanding the dependence on $\delta $, $N$ in a quantitative manner remains an outstanding challenge.
Glomerular Classification Using Convolutional Neural Networks Based on Defined Annotation Criteria and Concordance Evaluation Among Clinicians

IntroductionDiagnosing renal pathologies is important for performing treatments. However, classifying every glomerulus is difficult for clinicians; thus, a support system, such as a computer, is required. This paper describes the automatic classification of glomerular images using a convolutional neural network (CNN).MethodTo generate appropriate labeled data, annotation criteria including 12 features (e.g., “fibrous crescent”) were defined. The concordance among 5 clinicians was evaluated for 100 images using the kappa (κ) coefficient for each feature. Using the annotation criteria, 1 clinician annotated 10,102 images. We trained the CNNs to classify the features with an average κ ≥0.4 and evaluated their performance using the receiver operating characteristic–area under the curve (ROC–AUC). An error analysis was conducted and the gradient-weighted class activation mapping (Grad-CAM) was also applied; it expresses the CNN’s focusing point with a heat map when the CNN classifies the glomerular image for a feature.ResultsThe average κ coefficient of the features ranged from 0.28 to 0.50. The ROC–AUC of the CNNs for test data varied from 0.65 to 0.98. Among the features, “capillary collapse” and “fibrous crescent” had high ROC–AUC values of 0.98 and 0.91, respectively. The error analysis and the Grad-CAM visually showed that the CNN could not distinguish between 2 different features that had similar visual structures or that occurred simultaneously.ConclusionThe differences in the texture or frequency of the co-occurrence between the different features affected the CNN performance; thus, to improve the classification accuracy, methods such as segmentation are required.
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation

Tweets are specific text data when compared to general text.Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages.The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain.We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages.Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.
A Critical Reassessment of the Saerens-Latinne-Decaestecker Algorithm for Posterior Probability Adjustment

We critically re-examine the Saerens-Latinne-Decaestecker (SLD) algorithm, a well-known method for estimating class prior probabilities (“priors”) and adjusting posterior probabilities (“posteriors”) in scenarios characterized by distribution shift, i.e., difference in the distribution of the priors between the training and the unlabelled documents. Given a machine learned classifier and a set of unlabelled documents for which the classifier has returned posterior probabilities and estimates of the prior probabilities, SLD updates them both in an iterative, mutually recursive way, with the goal of making both more accurate; this is of key importance in downstream tasks such as single-label multiclass classification and cost-sensitive text classification. Since its publication, SLD has become the standard algorithm for improving the quality of the posteriors in the presence of distribution shift, and SLD is still considered a top contender when we need to estimate the priors (a task that has become known as “quantification”). However, its real effectiveness in improving the quality of the posteriors has been questioned. We here present the results of systematic experiments conducted on a large, publicly available dataset, across multiple amounts of distribution shift and multiple learners. Our experiments show that SLD improves the quality of the posterior probabilities and of the estimates of the prior probabilities, but only when the number of classes in the classification scheme is very small and the classifier is calibrated. As the number of classes grows, or as we use non-calibrated classifiers, SLD converges more slowly (and often does not converge at all), performance degrades rapidly, and the impact of SLD on the quality of the prior estimates and of the posteriors becomes negative rather than positive.
How to protect reader lending privacy under a cloud environment: a technical method

Purpose In this paper, the authors propose an effective mechanism for the protection of digital library readers' lending privacy under a cloud environment. Design/methodology/approach The basic idea of the method is that for each literature circulation record, before being submitted to the untrusted cloud database of a digital library for storage, its reader number has to be encrypted strictly at a client, so as to make it unable for an attacker at the cloud to know the specific reader associated with each circulation record and thus protect readers' lending privacy. Moreover, the authors design an effective method for querying the encrypted literature circulation records, so as to ensure the accuracy and efficiency of each kind of database queries related to the encrypted reader number field of literature circulation records. Findings Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed methods. Originality/value This paper presents the first study attempt to the privacy protection of readers' literature circulations, which can improve the security of readers' lending privacy in the untrusted cloud, without compromising the accuracy and efficiency of each kind of database queries in the digital library. It is of positive significance to construct a privacy-preserving digital library platform.
Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Recently, Internet finance is increasingly popular. However, bad debt has become a serious threat to Internet financial companies. The fraud detection models commonly used in conventional financial companies is logistic regression. Although it is interpretable, the accuracy of the logistic regression still remains to be improved. This paper takes a large public loan dataset, e.g. Lending club, for example, to explore the potential of applying deep neural network for fraud detection. We first fill the missing values by a random forest. Then, an XGBoost algorithm is employed to select the most discriminate features. After that, we propose to use a synthetic minority oversampling technique to deal with the sample imbalance. With the preprocessed data, we design a deep neural network for Internet loan fraud detection. Extensive experiments have been conducted to demonstrate the outperformance of the deep neural network compared with the commonly-used models. Such a simple yet effective model may brighten the application of deep learning in anti-fraud for Internet loans, which would benefit the financial engineers in small and medium Internet financial companies.
Internet of Things and Blockchain-Based Framework for Coronavirus (COVID-19) Disease

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Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.
Fast Decoding of Codes in the Rank, Subspace, and Sum-Rank Metric

We speed up existing decoding algorithms for three code classes in different metrics: interleaved Gabidulin codes in the rank metric, lifted interleaved Gabidulin codes in the subspace metric, and linearized Reed-Solomon codes in the sumrank metric.The speed-ups are achieved by new algorithms that reduce the cores of the underlying computational problems of the decoders to one common tool: computing left and right approximant bases of matrices over skew polynomial rings.To accomplish this, we describe a skew-analogue of the existing PM-Basis algorithm for matrices over ordinary polynomials.This captures the bulk of the work in multiplication of skew polynomials, and the complexity benefit comes from existing algorithms performing this faster than in classical quadratic complexity.The new algorithms for the various decoding-related computational problems are interesting in their own and have further applications, in particular parts of decoders of several other codes and foundational problems related to the remainderevaluation of skew polynomials.
Is YouTube promoting the exotic pet trade? Analysis of the global public perception of popular YouTube videos featuring threatened exotic animals

The exploitation of threatened exotic species via social media challenges efforts to regulate the exotic pet trade and consequently threatens species conservation. To investigate how such content is perceived by the global community, mixed model sentiment analysis techniques were employed to explore variations in attitudes expressed through text and emoji usage in public comments associated with 346 popular YouTube ® videos starring exotic wild cats or primates in ‘free handling’ situations. Negative interactions between wild cats and primates with other species were found to be associated with both text and emoji median sentiment reduction, however were still accompanied by a median emoji sentiment above zero. Additionally, although a negative trend in median text sentiment was observed in 2015 for primates, an otherwise consistent positive median text and emoji sentiment score through time across all IUCN Red List categories was revealed in response to both exotic wild cat and primate videos, further implying the societal normalisation and acceptance of exotic pets. These findings highlight the urgency for effective YouTube ® policy changes and content management to promote public education and conservation awareness, whilst extinguishing false legitimisation and demand for the exotic pet trade.
COVID-19 compliant and cost effective teaching model for King Abdulaziz University

The global pandemic of COVID-19 has been going on for over sixteen months. During this period, we have witnessed a colossal loss of life, property, business, and a degradation of social life. Several different variants or strains of SARS-CoV-2, which causes COVID-19, have been found in different parts of the world. This pandemic has so far infected more than one hundred and thirty five million people, which has caused significant damage to the education sector. The majority of students around the world have lost access to face-to-face classes. While dealing with the crisis, some higher education institutions are still finding it difficult to adapt to alternative ways of imparting education. Many of them are using learning management systems and other online technologies and tools to facilitate online learning. The aim of this manuscript is to propose a cost-effective hybrid teaching model (CeHTM) for the King Abdulaziz University. The proposed model is designed after analyzing two anonymous online feedback surveys in which nearly four thousand students and more than four hundred instructors have participated. The CeHTM is novel as it is the first framework of its kind for imparting education during pandemic. Given the uniformity of educational system in Saudi Arabian universities, the proposed model can be used by other Saudi Arabian institutions, and adapted elsewhere, especially in the Middle East and North Africa.
Utility Mining Across Multi-Dimensional Sequences

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data are commonly seen in real life. Sequence data have been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, and profit), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this article, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract &lt;underline&gt;M&lt;/underline&gt;ulti-&lt;underline&gt;D&lt;/underline&gt;imensional &lt;underline&gt;U&lt;/underline&gt;tility-oriented &lt;underline&gt;S&lt;/underline&gt;equential useful patterns. To the best of our knowledge, this is the first study that incorporates the time-dependent sequence-order, quantitative information, utility factor, and auxiliary dimension. Two algorithms respectively named MDUS EM and MDUS SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on six real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.
Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation

Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.
Machine learning and quantum devices

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforcement learning (for discovering control strategies), recurrent neural networks (for analyzing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.
Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation.
Common media client data (CMCD)

In September 2020, the Consumer Technology Association (CTA) published the CTA-5004: Common Media Client Data (CMCD) specification. Using this specification, a media client can convey certain information to the content delivery network servers with object requests. This information is useful in log association/analysis, quality of service/experience monitoring and delivery enhancements. This paper is the first step toward investigating the feasibility of CMCD in addressing one of the most common problems in the streaming domain: efficient use of shared bandwidth by multiple clients. To that effect, we implemented CMCD functions on an HTTP server and built a proof-of-concept system with CMCD-aware dash.js clients. We show that even a basic bandwidth allocation scheme enabled by CMCD reduces rebuffering rate and duration without noticeably sacrificing the video quality.