Reviewerly/exHarmony huggingface dataset:
Uncovering Library Features from API Usage on Stack Overflow

Selecting an appropriate library for reuse within a vast software ecosystem can be a daunting task. A list of features for each library, i.e., a short description of the functionality that can be reused with code examples that illustrate its usage, may alleviate this problem. In this paper, we propose a data-driven approach that uses both the code snippets and the accompanying natural language descriptions from Stack Overflow posts to produce a list of features of a given library. Each extracted feature corresponds to a cluster of API classes and methods considered related based on attributes of the Stack Overflow posts in which they appear. We evaluated the approach considering seven Maven libraries and compared the resulting features against library descriptions from cookbook-like tutorials. The approach achieves an average accuracy of 67&#x0025; across the seven libraries for the tutorial-like features. For at least 73&#x0025; of the features extracted by the approach but missing from the documentation, we found a matching library usage in a corpus of GitHub projects. These results suggest that our clusters represent library features, which paves the way to better tool support for documenting software libraries and for selecting a library in an ecosystem.
Toward Low-Latency and High-Quality Adaptive 360$^\circ$ Streaming

Advanced 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video streaming is essential for high-quality communication and industrial video applications, supporting novel and interactive visual experiences that promote consumption. However, due to the inherent fluctuations in communication networks, hardware resources, and power costs, a tradeoff needs to be achieved between occupying network bandwidth and guaranteeing 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> visual quality. To address this issue, this article proposes a quality-aware global optimization solution that combines different types of sensory characteristics to further enhance the 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> streaming efficiency in cellular networks. First, considering the characteristics of 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video, each frame is divided into three different regions based on a proposed field-of-view prediction method. Second, a new region-based rate-distortion model reflecting the bitrate-quality relationship is proposed. The divided regions are represented by different rate-distortion models. In addition, a rate-distortion model parameter update strategy with robustness to region changes is proposed to further guarantee transmission performance. Finally, we propose a globally optimized adaptive bitrate allocation algorithm to optimize 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> mobile streaming, which uses both the rate-distortion models and viewpoint prediction results. Evaluation results indicate that the proposed method outperforms state-of-the-art approaches in terms of several quality of experience objectives under various network conditions.
Lessons Learned in Piloting a Digital Personalized COVID-19 “Radar” on a University Campus

Health authorities encouraged the use of digital contact tracing mobile applications (apps) during the COVID-19 pandemic, but the level of adoption was low because apps offered few direct benefits to counterbalance risks to personal privacy. Adoption of such apps could improve if they provided benefits to users. NOVID (COVID-19 Radar), a smartphone app, provided users with personalized data on social proximity of COVID-19 cases and exposed contacts. We analyzed uptake of NOVID at the Georgia Institute of Technology (Georgia Tech) during the 2020-2021 academic year. Data included anonymous NOVID users who self-identified with Georgia Tech and their first- and second-degree network contacts. NOVID achieved 13%-30% adoption at Georgia Tech. Because of technical challenges, adoption waned after an initial peak. The largest increases in adoption (from 41 to 3704) followed administrative promotion of NOVID. Adoption increased modestly (from 2512 to 2661) after faculty- and student-led promotion, such as distribution of door hangers and a public seminar. Two-thirds of on-campus NOVID users were connected to a large network of other users, enabling them to receive data on social proximity of COVID-19 cases and exposed contacts. Network cohesion was observed to emerge rapidly when adoption rates passed just 10%, consistent with estimates from network theory. The key lesson learned in this case study is that top-down administrative promotion outperforms bottom-up grassroots promotion. Relatively high levels of adoption and network cohesion, despite technical challenges during the Georgia Tech pilot of NOVID, illustrate the promise of digital contact tracing when apps provide privacy and inherently beneficial personalized data to their users, especially in regions where Google Apple Exposure Notification is not available.
MATEM

The paradigms of Cloud Computing have risen at a very rapid rate. The Cloud Computing demands that a trustworthy or reliable service should be availed by its potential user. However, it is always a challenge for any cloud user to look for service that is suitable as well as reliable in every aspect. For a trustworthy discovery and delivery of cloud services, a multi-layered model i.e., Multi Agent based Trust Evaluation Model (MATEM) has been proposed. It is a multi-agent trust model that will make use of multiple agents to perform and evaluate the credibility of trust through trust evaluation system. Also, the performance validation has proven that the final calculated values of trust will be helpful in providing reliable cloud services to its users.
Blockchain Empowered Smart Home: A Scalable Architecture for Sustainable Smart Cities

Emerging growth in technology has rapidly changed our homes and cities. Present homes and cities will be upgraded to smart homes and smart cities in the near future. Various solutions used to build the smart-city network demand a scalable and decentralized solution. This study proposes a blockchain-empowered decentralized and scalable solution for a sustainable smart-city network. The Internet of Things (IoT), fog nodes, permissioned trust chain, smart contract, blockchain, and InterPlanetary file system (IPFS) are deployed to construct a scalable and decentralized solution for a sustainable smart city. Three main public sector departments, i.e., electricity, water supply, and health care, are studied over the proposed solution. The proposed solution is implemented over constrained application protocol (CoAP) and Ethereum blockchain. The performance of the proposed model is evaluated for 1500 devices and over 10,000 records. A total 77.44% improvement is registered during performance evaluation over a scalable environment. The performance evaluation of each case study and collaborative performance evaluation concludes the improvised performance of the proposed solution for scalable and distributed applications. Better performance, scalability, and the distributed nature of the presented model make it suitable for the sustainable smart-city network.
A Markov decision process-based secure consensus framework for leveraging blockchain technology in IoT applications

In this manuscript, we propose a secure consensus framework that implements Markov decision process (MDP) for blockchain technology in IoT applications. The proposed framework presents projection for the sojourn time and stationary efficiency measures for any transaction or block. Implementing MDP provides a group of states and a collection of actions to choose from. MDP also offers instantaneous reward purpose and a probabilistic transition matrix. The proposed framework that suggests implementation of MDP in blockchain technology further escalates the security of the network. The paper demonstrates the architecture of the proposed framework.
MTG: A Benchmark Suite for Multilingual Text Generation

We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation.It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k).It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese).The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks.Using MTG, we train and analyze several popular multilingual generation models from different aspects.Our benchmark suite fosters model performance enhancement with more human-annotated parallel data.It provides comprehensive evaluations with diverse generation scenarios.Code and
Sort by Structure: Language Model Ranking as Dependency Probing

Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored.The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natural Language Processing, however they lack coverage of linguistic tasks such as structured prediction.We propose probing to rank LMs, specifically for parsing dependencies in a given language, by measuring the degree to which labeled trees are recoverable from an LM's contextualized embeddings.Across 46 typologically and architecturally diverse LM-language pairs, our probing approach predicts the best LM choice 79% of the time using orders of magnitude less compute than training a full parser.Within this study, we identify and analyze one recently proposed decoupled LM-RemBERT-and find it strikingly contains less inherent dependency information, but often yields the best parser after full fine-tuning.Without this outlier our approach identifies the best LM in 89% of cases.
Detection of DeepFakes Using Local Features and Convolutional Neural Network

Fake photos and films are nothing unique. Humans have already been manufacturing counterfeit to fool or amuse. This trend has only grown with the broad acceptance on the web. But today, instead of photographs getting modified by modifying programs such as Photoshop or films being skillfully altered, there’s a new generation of machine-made fakes, so they may make it more difficult for humans to differentiate reality from imagination in the future. DeepFakes seem to be the most common type of “synthetic media,” which consists of pictures, audio, and film that look to be being made using traditional methods but were produced using advanced software. In this chapter, we have examined some articles to understand what DF is and its applications. Also explained are the generation techniques (autoencoder and GANs (GANs)) and the detection techniques (CNN and local features).
Finding and Exploiting CPU Features using MSR Templating

To ensure backward compatibility while adding new features to CPUs, CPU vendors enable a limited CPU configuration via so-called model-specific registers (MSRs). These MSRs have been introduced for various features, such as debugging, performance monitoring, or security. While many MSRs are documented, there is still a plethora of undocumented or sparsely documented MSRs in modern CPUs. Furthermore, with multiple hundred MSRs, each providing up to 64 configuration bits, it is tedious to find specific configuration options. In this paper, we show that MSRs and their configuration bits can be detected automatically on Intel and AMD CPUs. We introduce MSRevelio, a framework to automatically detect bits that influence the behavior of instructions and semi-automatically find bits controlled by BIOS settings. We show that previously overlooked bits can harden systems against microarchitectural attacks such as Medusa, CrossTalk, and software-prefetch attacks. Additionally, we show that an undocumented lock bit allows disabling AES-NI at runtime, forcing mbedTLS to fall back to an AES implementation vulnerable to cache attacks. Exploiting this fallback inside an SGX enclave, we fully recover the AES key used by the enclave. With our detection approach, we show that security features retrofitted with microcode updates can be easily detected, even before the public documentation of the underlying vulnerability. In our analysis of the Xen hypervisor, we show that Xen’s handling of MSRs was flawed for a long time, allowing guests to access undocumented and unhandled MSRs and fingerprint specific Xen versions. Using automated correlation analysis between documented and undocumented MSRs, we discover a previously undocumented MSR correlating with the CPU’s timestamp counter. This MSR is also accessible from Xen guests, and we demonstrate a Foreshadow attack when all other timers are unavailable or artificially deteriorated. Our results highlight that transparency is crucial for features interacting closely with CPU internals.
Fully Synthetic Training for Image Restoration Tasks

In this work, we show that neural networks aimed at solving various image restoration tasks can be successfully trained on fully synthetic data. In order to do so, we rely on a generative model of images, the scaling dead leaves model, which is obtained by superimposing disks whose size distribution is scale-invariant. Pairs of clean and corrupted synthetic images can then be obtained by a careful simulation of the degradation process. We show on various restoration tasks that such a synthetic training yields results that are only slightly inferior to those obtained when the training is performed on large natural image databases. This implies that, for restoration tasks, the geometric contents of natural images can be nailed down to only a simple generative model and a few parameters. This prior can then be used to train neural networks for specific modality, without having to rely on demanding campaigns of natural images acquisition. We demonstrate the feasibility of this approach on difficult restoration tasks, including the denoising of smartphone RAW images and the full development of low-light images.
Efficient Knowledge Compilation Beyond Weighted Model Counting

Abstract Quantitative extensions of logic programming often require the solution of so called second level inference tasks, that is, problems that involve a third operation, such as maximization or normalization, on top of addition and multiplication, and thus go beyond the well-known weighted or algebraic model counting setting of probabilistic logic programming under the distribution semantics. We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems. As 2AMC is to (algebraic) model counting what forall-exists-SAT is to propositional satisfiability, it is notoriously hard to solve. First level techniques based on Knowledge Compilation (KC) have been adapted for specific 2AMC instances by imposing variable order constraints on the resulting circuit. However, those constraints can severely increase the circuit size and thus decrease the efficiency of such approaches. We show that we can exploit the logical structure of a 2AMC problem to omit parts of these constraints, thus limiting the negative effect. Furthermore, we introduce and implement a strategy to generate a sufficient set of constraints statically, with a priori guarantees for the performance of KC. Our empirical evaluation on several benchmarks and tasks confirms that our theoretical results can translate into more efficient solving in practice.
A human learning optimization algorithm with competitive and cooperative learning

Abstract Human learning optimization (HLO) is a simple yet powerful metaheuristic developed based on a simplified human learning model. Competition and cooperation, as two basic modes of social cognition, can motivate individuals to learn more efficiently and improve their efficiency in solving problems by stimulating their competitive instincts and increasing interaction with each other. Inspired by this fact, this paper presents a novel human learning optimization algorithm with competitive and cooperative learning (HLOCC), in which a competitive and cooperative learning operator (CCLO) is developed to mimic competition and cooperation in social interaction for enhancing learning efficiency. The HLOCC can efficiently maintain the diversity of the algorithm as well as achieve the optimal values, demonstrating that the proposed CCLO can effectively improve algorithm performance. HLOCC has been compared with other heuristic algorithms on CEC2017 functions. In the second study, the uncapacitated facility location problems (UFLPs) which are one of the pure binary optimization problems are solved with HLOCC. The experimental results show that the developed HLOCC is superior to previous HLO variants and other metaheuristics with its improved exploitation and exploration abilities.
Visual Analytics: A New Paradigm for Process Monitoring

As a result of recent breakthroughs in computer vision technologies, significant research interest has emerged to encode process data into visual clues and treat process monitoring problems as computer vision tasks. Imaging time-series signals as a feature engineering step forms a new branch of data analytics called ”visual analytics”. In the context of process monitoring, we define visual analytics as the integration of visual representation of the data combined with the use of computer vision tools and analytical reasoning to support decision-making and knowledge extraction from the data. In this work, a novel end-to-end visual analytics pipeline for industrial process fault detection using 1D and 2D convolution operations is proposed. The proposed approach presents a visual representation of data that captures temporal and local features from historical time-series signals. Next, the learned features in a 2D format are visually recognized and classified using 2D convolution operations. Our experimental results demonstrate that this approach achieves better performance on an industrial multivariate dataset compared to other state-of-art signals imaging tools such as Gramian Angular Field (GAF) and Recurrence Plots (RP).
SPECTRAL: AWESOME SPECTRAL INDICES DEPLOYED VIA THE GOOGLE EARTH ENGINE JAVASCRIPT API

Abstract. Spectral Indices derived from Remote Sensing (RS) data are widely used for characterizing Earth System dynamics. The increasing amount of spectral indices led to the creation of spectral indices catalogues, such as the Awesome Spectral Indices (ASI) ecosystem. Google Earth Engine (GEE) is a cloud-based geospatial processing service with an Application Programming Interface (API) that is accessible through JavaScript (Code Editor) and Python. Tools for computing indices, including raster operations, normalized differences, and expression evaluation methods have been developed in the API. However, users still have to hard-code spectral indices for the JavaScript library since there are no implementations that link catalogues of spectral indices to the Code Editor. Here we present spectral, a module that links the Awesome Spectral Indices (ASI) catalogue to GEE for querying and computing spectral indices inside the Code Editor. The module allows accessing and computing spectral indices from the catalogue for multiple remote sensing products in GEE. All indices can be queried by using a key-value model and computed by using a single method. The module demonstrates that spectral indices can be easily computed inside the Code Editor. Image and Image Collection objects can be used for the calculation of all spectral indices in the catalogue if the specific dataset counts with the required bands. We anticipate that spectral will be used by most GEE users for Earth System research. Analyses conducted by the community will be sped up by avoiding hard-coding and RS investigations will be boosted.
SenseInput: An Image-Based Sensitive Input Detection Scheme for Phishing Website Detection

Phishing has persistently posed threats to the World Wide Web as phishing websites evolve over these years. Many previous works were devoted to extracting useful features and focused on the essential components of phishing websites. One of the essential components is sensitive inputs which require sensitive information. Yet, due to a large variety of web designs, detecting the existence of sensitive inputs is not trivial. Some previous works have provided rule-based approaches to detect login forms, which contain sensitive inputs, using HTML codes. However, the novel phishing websites modify HTML codes against the detection rules, which causes less accurate detection.To overcome the limitation of previous works, we proposed SenseInput using hybrid deep learning models to detect the existence of sensitive inputs and sensitive information because phishing websites eventually present sensitive inputs in their visual content. SenseInput achieved 96.94% f1-score for sensitive input detection on our dataset and 96.73% f1-score on a public dataset, Phishpedia Phish30K. Next, we used 22 features involving the proposed seven statistical features and two sensitive input features for phishing detection. The experiment shows that our approach achieves 98.48% and 95.87% f1-score on our validation and Phishpedia datasets, outperforming previous approaches. Finally, we investigated the influence of sensitive input features. The result shows that our sensitive input features are more effective than the rule-based login form. Besides, the experiment also indicates that proposed sensitive input features can reduce the impact of bias between different datasets.
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design

Cross-modal fashion image synthesis has emerged as one of the most promising directions in the generation domain due to the vast untapped potential of incorporating multiple modalities and the wide range of fashion image applications. To facilitate accurate generation, cross-modal synthesis methods typically rely on Contrastive Language-Image Pre-training (CLIP) to align textual and garment information. In this work, we argue that simply aligning texture and garment information is not sufficient to capture the semantics of the visual information and therefore propose MaskCLIP. MaskCLIP decomposes the garments into semantic parts, ensuring fine-grained and semantically accurate alignment between the visual and text information. Building on MaskCLIP, we propose ARMANI, a unified cross-modal fashion designer with part-level garment-text alignment. ARMANI discretizes an image into uniform tokens based on a learned cross-modal codebook in its first stage and uses a Transformer to model the distribution of image tokens for a real image given the tokens of the control signals in its second stage. Contrary to prior approaches that also rely on two-stage paradigms, ARMANI introduces textual tokens into the codebook, making it possible for the model to utilize fine-grain semantic information to generate more realistic images. Further, by introducing a cross-modal Transformer, ARMANI is versatile and can accomplish image synthesis from various control signals, such as pure text, sketch images, and partial images. Extensive experiments conducted on our newly collected cross-modal fashion dataset demonstrate that ARMANI generates photo-realistic images in diverse synthesis tasks and outperforms existing state-of-the-art cross-modal image synthesis approaches. Our code is available at https://github.com/Harvey594/ARMANI.