Reviewerly/exHarmony huggingface dataset:
Petr4: formal foundations for p4 data planes

P4 is a domain-specific language for programming and specifying packet-processing systems. It is based on an elegant design with high-level abstractions like parsers and match-action pipelines that can be compiled to efficient implementations in software or hardware. Unfortunately, like many industrial languages, P4 has developed without a formal foundation. The P4 Language Specification is a 160-page document with a mixture of informal prose, graphical diagrams, and pseudocode, leaving many aspects of the language semantics up to individual compilation targets. The P4 reference implementation is a complex system, running to over 40KLoC of C++ code, with support for only a few targets. Clearly neither of these artifacts is suitable for formal reasoning about P4 in general. This paper presents a new framework, called Petr4, that puts P4 on a solid foundation. Petr4 consists of a clean-slate definitional interpreter and a core calculus that models a fragment of P4. Petr4 is not tied to any particular target: the interpreter is parameterized over an interface that collects features delegated to targets in one place, while the core calculus overapproximates target-specific behaviors using non-determinism. We have validated the interpreter against a suite of over 750 tests from the P4 reference implementation, exercising our target interface with tests for different targets. We validated the core calculus with a proof of type-preserving termination. While developing Petr4, we reported dozens of bugs in the language specification and the reference implementation, many of which have been fixed.
Evaluation of Different Visualization Techniques for Perception-Based Alignment in Medical AR

Many Augmented Reality (AR) applications require the alignment of virtual objects to the real world; this is particularly important in medical AR scenarios where medical imaging information may be displayed directly on a patient and is used to identify the exact locations of specific anatomical structures within the body. For optical see-through AR, alignment accuracy depends both on the optical parameters of the AR display as well as the visualization parameters of the virtual model. In this paper, we explore how different static visualization techniques influence users' ability to perform perception-based alignment in AR for breast reconstruction surgery, where surgeons must accurately identify the locations of several perforator blood vessels while planning the procedure. We conducted a pilot study in which four subjects used four different visualization techniques with varying degrees of opaqueness and brightness as well as outline contrast to align virtual replicas of the relevant anatomy to their 3D-printed counterparts. We collected quantitative scores on spatial alignment accuracy using an external tracking system and qualitative scores on user preference and perceived performance. Results indicate that the highest source of alignment error was along the depth dimension, with users consistently overestimating depth when aligning the virtual renderings. The majority of subjects preferred visualization techniques rendered with lower levels of opaqueness and brightness as well as higher outline contrast, which were also found to support more accurate alignment.
QR-3S: A High Payload QR Code Secret Sharing System for Industrial Internet of Things in 6G Networks

The communication in a 6G-enabled network in a box (NIB) needs to meet the characteristics of fast, convenient, and safe. Secret sharing scheme has become a hot topic nowadays due to unconditional security and simple decryption. At the same time, a quick response (QR) code as a popular carrier has been widely applied in various industrial applications because of the data payload and convenience. Thus, the combination of secret sharing and QR code provides a solution by satisfying the requirements of 6G-enabled NIB. In this article, we design a QR code secret sharing scheme with authentication to protect private data and prevent cheater. In this scheme, a secret image is first divided into a series of shadows based on a polynomial, and authentication bits are generated based on the generated shadows. Then shadows and the authentication bits are embedded into the cover QR codes according to the error correction redundancy and the homomorphism of the Reed-Solomon code in the QR code. In addition, the secret can be restored with the qualified shares and the authentication bits could verify the authenticity of the embedded shadows. Compared with existing schemes, the proposed scheme not only guarantees a high capacity but also embeds more authentication bits to improve the authentication ability. In addition, experimental results have demonstrated that the proposed scheme is both robust and secure.
Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization

Corona Virus 19 (COVID-19) is a contagious viral infection that has now spread to various countries, one of which is Indonesia.Monitoring of the spread of COVID-19 in Indonesia is handled directly by the Government of Indonesia, especially by the Ministry of Communication and Information (KOMINFO) with the creation of the Protected application found on Google Play.Users provide reviews or comments about the application, of course, users will choose applications that have good reviews.However, monitoring reviews from the general public is not easy, because there are so many of them to process.So that the researcher wants to know the extent of the analysis of user reviews of the PeduliLindungi application based on reviews of user comments by using classification techniques, namely the Support Vector Machine (SVM) Algorithm and Naive Bayes Based on Particle Swarm Optimization (PSO).The test results with the accuracy value and AUC value of each, namely for the PSO-based Naive Bayes algorithm the accuracy value = 69.00%,and AUC value = 0.659, while for the PSO-based SVM algorithm the accuracy value = 93.0%and the AUC value = 0.977.For this reason, the application of Particle Swarm Optimization (PSO) -based Support Vector Machine in this study has higher accuracy so that it can be used to provide solutions to sentiment analysis problems in review comments of users of the PeduliLindungi application.
Constituency Lattice Encoding for Aspect Term Extraction

One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. In this paper, we aim to address this issue by incorporating the span annotations of constituents of a sentence to leverage the syntactic information in neural network models. To this end, we first construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the base models, respectively. We experimented on two benchmark datasets to evaluate the two models, and the results confirm their superiority with respective 3.17 and 1.35 points gained in F1-Measure over the current state of the art. The improvements justify the effectiveness of the constituency lattice for aspect term extraction.
Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms

This paper aims to design and optimize a sports training system through wireless sensor network technology, which mainly includes a hardware system and software system. In the hardware system, the design of sensor nodes and the base station is realized, which can realize the real-time collection of movement parameters by the movement collectors. In the software system, the design of base station control, node control, and motion database software is realized, which can effectively collect, store, and analyze the motion parameters. Finally, the wireless sensor network-based sports training system is tested, and the test results show that the system designed in this paper can meet the needs of sports training use. The inertial measurement unit in the wearable device is used to collect the data generated by the human body during exercise, and low-power Bluetooth is used as the data transmission protocol between the wearable device and the smartphone. The server side provides computing resources in the form of cloud computing to achieve client-server interaction. In combination with the idea of service containerization, a deployment scheme for cloud services was proposed using container choreography, and compared to the pre-training period, the subjects’ trunk sway in the front and back directions decreased by 60.5% and 54.0%, and the trunk sway in the left and right directions decreased by 67.1% and 50.3% during and after training, respectively, and the ratio of time occupied by the equilibrium state increased by 63.5% and 47.4%, subjects’ balance was effectively improved.
Does size matter? The effects of public sector organizational size’ on knowledge management processes and operational efficiency

Purpose Firm size is an important contingency variable in macro-organizational studies. Several questions arise in relation to knowledge management and organizational size that is critical to both public and private organizations. Unfortunately, despite its significance, all or most of the studies that examined the effects of organizational size’ on knowledge management have been in the private sector. This paper aims to empirically study the effects of organizational size on the key knowledge management processes and subsequent operational efficiency derived from its implementation in the public sector. Design/methodology/approach A structured country-wide survey of United Arab Emirates public sector organizations was conducted. The 383 completed responses obtained were then analysed to assess the hypothesized differences in the implementation of knowledge management processes (knowledge acquisition, knowledge creation, knowledge capture, knowledge storage and retrieval, knowledge sharing, knowledge utilization) and its impact on the operational efficiency across small and medium, large and very large public sector organizations. Findings The results revealed that the extent of implementation of all six knowledge management processes and operational efficiency followed an inverted “V” pattern, in which, both knowledge management processes and operational efficiency was found to increase while transitioning from small and medium entities to large entities, but was found to decrease while transitioning from large to very large entities. In terms of relationships, while all knowledge management processes had a significant positive impact on the operational efficiency of the public sector, the ability to derive operational efficiency from knowledge management processes was found to be the highest for very large public sector organizations. Practical implications The novel findings are useful for practitioners and policymakers, especially those overseeing a country’s knowledge management initiatives to devise strategies, policies and support mechanisms to ensure public sector organizations, regardless of their size, can implement efficient and effective knowledge management processes to improve their operational efficiency. Originality/value The study is arguably the first comprehensive attempt to understand the impact of organizational size on knowledge management in the public sector.
GraphCodeBERT: Pre-training Code Representations with Data Flow

Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of where-the-value-comes-from between variables. Such a semantic-level structure is neat and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search.
A statistical theory of cold posteriors in deep neural networks

To get Bayesian neural networks to perform comparably to standard neural networks it is usually necessary to artificially reduce uncertainty using a tempered or cold posterior. This is extremely concerning: if the prior is accurate, Bayes inference/decision theory is optimal, and any artificial changes to the posterior should harm performance. While this suggests that the prior may be at fault, here we argue that in fact, BNNs for image classification use the wrong likelihood. In particular, standard image benchmark datasets such as CIFAR-10 are carefully curated. We develop a generative model describing curation which gives a principled Bayesian account of cold posteriors, because the likelihood under this new generative model closely matches the tempered likelihoods used in past work.
Good Explanation for Algorithmic Transparency

Machine learning algorithms have gained widespread usage across a variety of domains, both in providing predictions to expert users and recommending decisions to everyday users. However, these AI systems are often black boxes, and end-users are rarely provided with an explanation. The critical need for explanation by AI systems has led to calls for algorithmic transparency, including the "right to explanation'' in the EU General Data Protection Regulation (GDPR). These initiatives presuppose that we know what constitutes a meaningful or good explanation, but there has actually been surprisingly little research on this question in the context of AI systems. In this paper, we (1) develop a generalizable framework grounded in philosophy, psychology, and interpretable machine learning to investigate and define characteristics of good explanation, and (2) conduct a large-scale lab experiment to measure the impact of different factors on people's perceptions of understanding, usage intention, and trust of AI systems. The framework and study together provide a concrete guide for managers on how to present algorithmic prediction rationales to end-users to foster trust and adoption, and elements of explanation and transparency to be considered by AI researchers and engineers in designing, developing, and deploying transparent or explainable algorithms.
A Framework for Generating Extractive Summary from Multiple Malayalam Documents

Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduce the redundant news from the different newspapers. A multi-document summary is more challenging than a single-document summary since it has to solve the problem of overlapping information among sentences from different documents. Extractive text summarization yields the sensitive part of the document by neglecting the irrelevant and redundant sentences. In this paper, we propose a framework for extracting a summary from multiple documents in the Malayalam Language. Also, since the multi-document summarization data set is sparse, methods based on deep learning are difficult to apply. The proposed work discusses the performance of existing standard algorithms in multi-document summarization of the Malayalam Language. We propose a sentence extraction algorithm that selects the top ranked sentences with maximum diversity. The system is found to perform well in terms of precision, recall, and F-measure on multiple input documents.
Recognition of Hand Gesture Image Using Deep Convolutional Neural Network

In recent technology, there is tremendous growth in computer applications that highlight human–computer interaction (HCI), such as augmented reality (AR), and Internet of Things (IoT). As a consequence, hand gesture recognition was highlighted as a very up-to-date research area in computer vision. The body language is a vital method to communicate between people, as well as emphasis on voice messages, or as a complete message on its own. Thus, automatic hand gestures recognition systems can be used to increase human–computer interaction. Therefore, many approaches for hand gesture recognition systems have been designed. However, most of these methods include hybrid processes such as image pre-processing, segmentation, and classification. This paper describes how to create hand gesture model easily and quickly with a well-tuned deep convolutional neural network. Experiments were performed using the Cambridge Hand Gesture data set for illustration of success and efficiency of the convolutional neural network. The accuracy was achieved as 96.66%, where sensitivity and specificity were found to be 85% and 98.12%, respectively, according to the average values obtained at the end of 20 times of operation. These results were compared with the existing works using the same dataset and it was found to have higher values than the hybrid methods.
Generalized Many-Way Few-Shot Video Classification

Few-shot learning methods operate in low data regimes. The aim is to learn with few training examples per class. Although significant progress has been made in few-shot image classification, few-shot video recognition is relatively unexplored and methods based on 2D CNNs are unable to learn temporal information. In this work we thus develop a simple 3D CNN baseline, surpassing existing methods by a large margin. To circumvent the need of labeled examples, we propose to leverage weakly-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities, yielding further improvement. Our results saturate current 5-way benchmarks for few-shot video classification and therefore we propose a new challenging benchmark involving more classes and a mixture of classes with varying supervision.
Deep Multi-Instance Learning with Induced Self-Attention for Medical Image Classification

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw

This paper proposes a novel pretext task for self-supervised video representation learning by exploiting spatiotemporal continuity in videos. It is motivated by the fact that videos are spatiotemporal by nature and a representation learned by detecting spatiotemporal continuity/discontinuity is thus beneficial for downstream video content analysis tasks. A natural choice of such a pretext task is to construct spatiotemporal (3D) jigsaw puzzles and learn to solve them. However, as we demonstrate in the experiments, this task turns out to be intractable. We thus propose Constrained Spatiotemporal Jigsaw (CSJ) whereby the 3D jigsaws are formed in a constrained manner to ensure that large continuous spatiotemporal cuboids exist. This provides sufficient cues for the model to reason about the continuity. Instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable. The four tasks aim to learn representations sensitive to spatiotemporal continuity at both the local and global levels. Extensive experiments show that our CSJ achieves state-of-the-art on various benchmarks.
A Reference Framework to Combine Model-Based Design and AR to Improve Social Sustainability

Product and process digitalization is pervading numerous areas in the industry to improve quality and reduce costs. In particular, digital models enable virtual simulations to predict product and process performances, as well as to generate digital contents to improve the general workflow. Digital models can also contain additional contents (e.g., model-based design (MBD)) to provide online and on-time information about process operations and management, as well as to support operator activities. The recent developments in augmented reality (AR) offer new specific interfaces to promote the great diffusion of digital contents into industrial processes, thanks to flexible and robust applications, as well as cost-effective devices. However, the impact of AR applications on sustainability is still poorly explored in research. In this direction, this paper proposed an innovative approach to exploit MBD and introduce AR interfaces in the industry to support human intensive processes. Indeed, in those processes, the human contribution is still crucial to guaranteeing the expected product quality (e.g., quality inspection). The paper also analyzed how this new concept can benefit sustainability and define a set of metrics to assess the positive impact on sustainability, focusing on social aspects.