Reviewerly/exHarmony huggingface dataset:
Synthesizing Abstract Transformers

This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the way $\textit{yacc}$ automates the construction of parsers. Our method treats the problem as a program-synthesis problem. The user provides specifications of (i) the concrete semantics of a given operation $\textit{op}$, (ii) the abstract domain A to be used by the analyzer, and (iii) the semantics of a domain-specific language $L$ in which the abstract transformer is to be expressed. As output, our method creates an abstract transformer for $\textit{op}$ in abstract domain A, expressed in $L$ (an "$L$-transformer for $\textit{op}$ over A"). Moreover, the abstract transformer obtained is a most-precise $L$-transformer for $\textit{op}$ over A; that is, there is no other $L$-transformer for $\textit{op}$ over A that is strictly more precise. We implemented our method in a tool called AMURTH. We used AMURTH to create sets of replacement abstract transformers for those used in two existing analyzers, and obtained essentially identical performance. However, when we compared the existing transformers with the transformers obtained using AMURTH, we discovered that four of the existing transformers were unsound, which demonstrates the risk of using manually created transformers.
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query

Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword. This is a recommendation task with two queries, a.k.a. user ID and keyword. However, existing methods focus on recommendation according to one query, a.k.a. user ID, and are not applicable to solving this problem. In this paper, we propose a novel click-through rate (CTR) prediction model with heterogeneous graph neural network, called AMinerGNN, to recommend papers with two queries. Specifically, AMinerGNN constructs a heterogeneous graph to project user, paper, and keyword into the same embedding space by graph representation learning. To process two queries, a novel query attentive fusion layer is designed to recognize their importances dynamically and then fuse them as one query to build a unified and end-to-end recommender system. Experimental results on our proposed dataset and online A/B tests prove the superiority of AMinerGNN.
A Man-in-the-Middle Attack against Object Detection Systems

Object detection systems using deep learning models have become increasingly popular in robotics thanks to the rising power of CPUs and GPUs in embedded systems. However, these models are susceptible to adversarial attacks. While some attacks are limited by strict assumptions on access to the detection system, we propose a novel hardware attack inspired by Man-in-the-Middle attacks in cryptography. This attack generates an Universal Adversarial Perturbation (UAP) and then inject the perturbation between the USB camera and the detection system via a hardware attack. Besides, prior research is misled by an evaluation metric that measures the model accuracy rather than the attack performance. In combination with our proposed evaluation metrics, we significantly increases the strength of adversarial perturbations. These findings raise serious concerns for applications of deep learning models in safety-critical systems, such as autonomous driving.
"To Clean-Code or Not To Clean-Code" A Survey among Practitioners

Context: Writing Clean Code understandable by other collaborators has become crucial to enhancing collaboration and productivity. However, very little is known regarding whether developers agree with Clean Code Principles and how they apply them in practice.\\ Objectives: In this work, we investigated how developers perceive Clean Code principles, whether they believe that helps reading, understanding, reusing, and modifying Clean Code, and how they deal with Clean Code in practice. Methods: We conducted a Systematic Literature Review in which we considered 771 research papers to collect Clean Code principles and a survey among 39 practitioners, some of them with more than 20 years of development experience.\\ Results: So far, the results show a shared agreement with Clean Code principles and its potential benefits. They also show that developers tend to write messy code to be refactored later.
The reproducing kernel Hilbert spaces underlying linear SDE Estimation, Kalman filtering and their relation to optimal control

It is often said that control and estimation problems are in duality. Recently, in (Aubin-Frankowski,2021), we found new reproducing kernels in Linear-Quadratic optimal control by focusing on the Hilbert space of controlled trajectories, allowing for a convenient handling of state constraints and meeting points. We now extend this viewpoint to estimation problems where it is known that kernels are the covariances of stochastic processes. Here, the Markovian Gaussian processes stem from the linear stochastic differential equations describing the continuous-time dynamics and observations. Taking extensive care to require minimal invertibility requirements on the operators, we give novel explicit formulas for these covariances. We also determine their reproducing kernel Hilbert spaces, stressing the symmetries between a space of forward-time trajectories and a space of backward-time information vectors. The two spaces play an analogue role for filtering to Sobolev spaces in variational analysis, and allow to recover the Kalman estimate through a direct variational argument. For comparison, we then recover the Kalman filter and smoother formulas through more classical arguments based on the innovation process. Extension to discrete-time observations or infinite-dimensional state, tough technical, would be straightforward.
Robust quantum control with disorder-dressed evolution

The theory of optimal quantum control serves to identify time-dependent control Hamiltonians that efficiently produce desired target states. As such, it plays an essential role in the successful design and development of quantum technologies. However, often the delivered control pulses are exceedingly sensitive to small perturbations, which can make it hard if not impossible to reliably deploy these in experiments. Robust quantum control aims at mitigating this issue by finding control pulses that uphold their capacity to reproduce the target states even in the presence of pulse perturbations. However, finding such robust control pulses is generically hard, since the assessment of control pulses requires the inclusion of all possible distorted versions in the evaluation. Here we show that robust control pulses can be identified based on disorder-dressed evolution equations. The latter capture the effect of disorder, which here stands for the pulse perturbations, in terms of quantum master equations describing the evolution of the disorder-averaged density matrix. In this approach to robust control, the purities of the final states indicate the robustness of the underlying control pulses, and robust control pulses are singled out if the final states are pure (and coincide with the target states). We show that this principle can be successfully employed to find robust control pulses. To this end, we adapt Krotov's method for disorder-dressed evolution and demonstrate its application with several single-qubit control tasks.
A Hybrid Model of Classification and Generation for Spatial Relation Extraction

Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. HMCGR contains a generation and a classification model, while the former can generate those null-role relations and the latter can extract those non-null-role relations to complement each other. Moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity principle of spatial relation. Experimental results on SpaceEval show that HMCGR outperforms the SOTA baselines significantly.
Evaluating Dense Passage Retrieval using Transformers

Although representational retrieval models based on Transformers have been able to make major advances in the past few years, and despite the widely accepted conventions and best-practices for testing such models, a $\textit{standardized}$ evaluation framework for testing them has not been developed. In this work, we formalize the best practices and conventions followed by researchers in the literature, paving the path for more standardized evaluations - and therefore more fair comparisons between the models. Our framework (1) embeds the documents and queries; (2) for each query-document pair, computes the relevance score based on the dot product of the document and query embedding; (3) uses the $\texttt{dev}$ set of the MSMARCO dataset to evaluate the models; (4) uses the $\texttt{trec_eval}$ script to calculate MRR@100, which is the primary metric used to evaluate the models. Most importantly, we showcase the use of this framework by experimenting on some of the most well-known dense retrieval models.
Targeted Honeyword Generation with Language Models

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. The major difficulty is how to produce honeywords that are difficult to distinguish from real passwords. Although the generation of honeywords has been widely investigated in the past, the majority of existing research assumes attackers have no knowledge of the users. These honeyword generating techniques (HGTs) may utterly fail if attackers exploit users' personally identifiable information (PII) and the real passwords include users' PII. In this paper, we propose to build a more secure and trustworthy authentication system that employs off-the-shelf pre-trained language models which require no further training on real passwords to produce honeywords while retaining the PII of the associated real password, therefore significantly raising the bar for attackers. We conducted a pilot experiment in which individuals are asked to distinguish between authentic passwords and honeywords when the username is provided for GPT-3 and a tweaking technique. Results show that it is extremely difficult to distinguish the real passwords from the artifical ones for both techniques. We speculate that a larger sample size could reveal a significant difference between the two HGT techniques, favouring our proposed approach.
A Comparative Study on Reassembly of Image Fragments

The reassembly of image fragments has different application domains. The problem is very challenging due to the specific needs of different applications. This paper represents a detailed and comparative study on the fragment reassembly problem. The main findings of this study are (a) pre-processing is very important on the scanned fragments (b) boundary shape is mostly used features to address the reassembly problem (c) the dependency on the content of the image should be high if all the fragments are of the same shape (d) the major importance should be given on global matching optimality than the local matching (e) the machine learning-based solution can be a new direction to address this problem by balancing the dependency on all type of features.
Analytical Study on Feature Selection for Big Data Using Optimization Algorithms

With the innovation of analytical models, the amount of data produced has increased at an accelerating rate. Therefore, it is considered to be challenging to use such data in any area without appropriate preprocessing. This, in turn, leads to increased uncertainty and consistency issues, resulting in numerous problems such as storing, assessment, data protection, and stability. For most existing data mining and machine learning methods, learning from such big data is a major challenge. As a result, huge datasets are difficult to manage, as they necessitate a lengthy procedure owing to the complexity. One of the key difficulties for big data in selecting features and feature reduction approaches is overfitting. Although certain existing feature selection techniques for high-dimension data based on mutual information are computationally efficient, defining the connections between features or class characteristics, which impact the final selection outcomes, remains a significant issue. The goal of this study is to examine the feature selection problem utilizing bio-inspired optimization techniques in order to increase efficiency.
Revisiting a Privacy-Preserving Location-based Service Protocol using Edge Computing

Location-based services are getting more popular day by day. Finding nearby stores, proximity-based marketing, on-road service assistance, etc., are some of the services that use location-based services. In location-based services, user information like user identity, user query, and location must be protected. Ma et al. (INFOCOM-BigSecurity 2019) proposed a privacy-preserving location-based service using Somewhat Homomorphic Encryption (SHE). Their protocol uses edge nodes that compute on SHE encrypted location data and determines the k-nearest points of interest contained in the Location-based Server (LBS) without revealing the original user coordinates to LBS, hence, ensuring privacy of users locations.
The Arch-I-Scan Project: Artificial Intelligence and 3D Simulation for Developing New Approaches to Roman Foodways

This article presents the aims, technical processes, and initial results of the Arch-I-Scan Project, which is using artificial intelligence and machine learning to enhance the collection of Roman ceramic data so that these data can contribute more effectively to improved understandings of Roman foodways. The project is developing a system for the automated identification of ceramic types (fabrics, forms and sizes), and potentially the automated collation of the resulting datasets, to facilitate more holistic recording of these big archaeological data, and avoiding the current time-consuming and costly specialist process for classifying these artefacts. The particular focus of the project is to develop datasets that are suitable for inter- and intra-site analyses of eating and drinking behaviours in the Roman world which require more comprehensive recording of these remains than the current sampling practices used to date sites or to investigate production and trade practices. The article includes a brief overview of approaches to material culture, particularly ceramics, for improving understandings of cultural patterns in past food-consumption practices. We then outline the project’s rationale and planned approaches to harnessing the potential of artificial intelligence and machine learning for artefact recording, specifically of Roman <em>terra sigillata</em> tablewares, and the processes used to develop a sufficiently large dataset to develop and test the AI system. The important aspect of this article is the changes made to these processes to mitigate the impact of the Covid pandemic on our ability to record large datasets of real ceramics. These changes involved the development of simulated datasets that substantially enhance our original real dataset and the accuracy of identification. Here we present our results to date, contextualised within the overall aims of the project and briefly discuss the steps we are taking to improve these.
Pengembangan Perangkat Pembelajaran Berbasis Multiple Intelligences untuk Meningkatkan Motivasi dan Hasil Belajar Fisika Peserta Didik SMA

This research aims to develop a learning device based on the multiple intelligences that can be valid, practical, and effective to increase students' motivation and learning achievement in physics. In this research, the learning device was developed in form of a syllabus, teaching materials, lesson plans, student worksheets, and test instruments for learning achievement. This was a research and development (R&amp;D) with a procedural development model, named the 4D model (define, design, develop, and disseminate). The average percentage of learning device validation related to syllabus, teaching materials, lesson plans, student worksheets, and test instruments of learning achievement was 86.00%, 83.00%, 81.00%, 80.05%, and 82.50%. The practicality of the learning device was able to be seen in the percentage of learning implementation in every meeting, it was 79.60% and 75.65%, while the students’ responses toward the learning process and the student worksheets got an average percentage of 80.91% and 77.68%. The effectiveness of learning devices was proved by the increase in students' cognitive learning achievement calculated by the N-Gain equation, the results were obtained 0.63 for the medium category. The average percentage of students' learning motivation also increased from 67.56% to 75.17%. Based on these results, the learning device that was developed is valid, practical, and effective to use with some revisions.
Research on Voip-Over- Vpn s Classification And Mitigation

With the development of IP technology, VoIP(Voice over IP) service is the most popular voice communication mode in the world. At the same time, more and more individual and enterprise users use VPN s to encrypt transmitted content to protect communication privacy. However, the operators and CSPs(Carrier Service Provider) are interested in their subscribers who used VoIP-Over- Vpntechnology, since it will result in their huge revenue loss while the subscribers escaped the high voice charging. To solve this problem, this paper develops a mechanism to detect and mitigate the VOIP-over- VPN traffic to protect the revenue for the telecommunication industry. In this paper, an advanced packet matching engine is introduced to extract traffic packet features and flow features from encrypted VOIP traffic, and input them into an intelligent detection engine based on heuristic algorithm to identify and classify voIP-over- VPN traffic. Based on the identification and classification results, an Action engine is introduced to block, shape, charge or report the event records. Through plenty of experiments and practices, the VOIP-over-VPN detection and mitigation solution proposed in this paper can achieve more than 95&#x0025; detection accuracy, and can be alleviated according to different mitigation policies.
Polysemy Deciphering Network for Robust Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is important to human-centric scene understanding tasks. Existing works tend to assume that the same verb has similar visual characteristics in different HOI categories, an approach that ignores the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net) that decodes the visual polysemy of verbs for HOI detection in three distinct ways. First, we refine features for HOI detection to be polysemyaware through the use of two novel modules: namely, Language Prior-guided Channel Attention (LPCA) and Language Prior-based Feature Augmentation (LPFA). LPCA highlights important elements in human and object appearance features for each HOI category to be identified; moreover, LPFA augments human pose and spatial features for HOI detection using language priors, enabling the verb classifiers to receive language hints that reduce intra-class variation for the same verb. Second, we introduce a novel Polysemy-Aware Modal Fusion module (PAMF), which guides PD-Net to make decisions based on feature types deemed more important according to the language priors. Third, we propose to relieve the verb polysemy problem through sharing verb classifiers for semantically similar HOI categories. Furthermore, to expedite research on the verb polysemy problem, we build a new benchmark dataset named HOI-VerbPolysemy (HOIVP), which includes common verbs (predicates) that have diverse semantic meanings in the real world. Finally, through deciphering the visual polysemy of verbs, our approach is demonstrated to outperform state-of-the-art methods by significant margins on the HICO-DET, V-COCO, and HOI-VP databases. Code and data in this paper are available at https://github.com/MuchHair/PD-Net.
Assistência fisioterapêutica ofertada à pessoa idosa pelo SUS nas disfunções musculoesqueleticas no município de Ponte Nova

Introduction: With aging, some chronic non-communicable diseases may appear.Physiotherapy works from prevention to the reduction of signs and symptoms of these diseases.Objectives: To quantify the physical therapy services offered by the SUS to patients with musculoskeletal disorders, in the period between 2011 and 2021, carried out in the municipality of Ponte nova and to discuss whether the number of physical therapy services offered is sufficient to meet the demand of the municipality.Methodology: This is a descriptive and quantitative research.Data were obtained from the Information System on Health Care in Outpatient Production.Elderly people of both sexes were included.Results and Discussion: There is a great demand of patients for consultations, but with the number of physical therapists being lower than expected for the number of inhabitants in the municipality, it generates some difficulty to obtain consultations.Conclusion: As people get older, the number of physical therapy visits decreases and with the increase in life expectancy, the demand of elderly patients will be greater.
Statistic selection and MCMC for differentially private Bayesian estimation

This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a noisy statistic of a sample from that population is shared to provide differential privacy. This work mainly addresses two problems. (1) What statistics of the sample should be shared privately? For this question, we promote using the Fisher information. We find out that the statistic that is most informative in a non-privacy setting may not be the optimal choice under the privacy restrictions. We provide several examples to support that point. We consider several types of data sharing settings and propose several Monte Carlo-based numerical estimation methods for calculating the Fisher information for those settings. The second question concerns inference: (2) Based on the shared statistics, how could we perform effective Bayesian inference? We propose several Markov chain Monte Carlo (MCMC) algorithms for sampling from the posterior distribution of the parameter given the noisy statistic. The proposed MCMC algorithms can be preferred over one another depending on the problem. For example, when the shared statistic is additive and added Gaussian noise, a simple Metropolis-Hasting algorithm that utilises the central limit theorem is a decent choice. We propose more advanced MCMC algorithms for several other cases of practical relevance. Our numerical examples involve comparing several candidate statistics to be shared privately. For each statistic, we perform Bayesian estimation based on the posterior distribution conditional on the privatised version of that statistic. We demonstrate that the relative performance of a statistic, in terms of the mean squared error of the Bayesian estimator based on the corresponding privatised statistic, is adequately predicted by the Fisher information of the privatised statistic.