Reviewerly/exHarmony huggingface dataset:
A benchmark for evaluating Arabic contextualized word embedding models

Word embeddings, which represent words as numerical vectors in a high-dimensional space, are contextualized by generating a unique vector representation for each sense of a word based on the surrounding words and sentence structure. They are typically generated using such deep learning models as BERT and trained on large amounts of text data and using self-supervised learning techniques. Resulting embeddings are highly effective at capturing the nuances of language, and have been shown to significantly improve the performance of numerous NLP tasks. Word embeddings represent textual records of human thinking, with all the mental relations that we utilize to produce the succession of sentences that make up texts and discourses. Consequently, the distributed representation of words within embeddings ought to capture the reasoning relations that hold texts together. This paper makes its contribution to the field by proposing a benchmark for the assessment of contextualized word embeddings that probes into their capability for true contextualization by inspecting how well they capture resemblance, contrariety, comparability, identity, relations in time and space, causation, analogy, and sense disambiguation. The proposed metrics adopt a triangulation approach, so they use (1) Hume's reasoning relations, (2) standard analogy, and (3) sense disambiguation. The benchmark has been evaluated against 22 Arabic contextualized embeddings and has proven to be capable of quantifying their differential performance in terms of these reasoning relations. Results of evaluation of the target embeddings revealed that they do take context into account and that they do reasonably well in sense disambiguation but have weakness in their identification of converseness, synonymy, complementarity, and analogy. Results also show that size of an embedding has diminishing returns because the highly frequent language patterns swamp low frequency patterns. Furthermore, the suggest that future research endeavors should not be concerned with the quantity of data as much as its quality, and that it should focus more on the representativeness of data, and on model architecture, design, and training.
Incremental Few-Shot Object Detection with scale- and centerness-aware weight generation

Our work targets the task of Incremental Few-Shot Detection (iFSD), which requires quickly detecting objects of novel classes after seeing only a few training examples, while maintaining the ability to detect old classes. Different from re-training a new detector to incorporate few-shot novel categories, we tackle iFSD in a direct weight generation way. Specifically, we propose to reorganize base weights by leveraging responses of novel region features, hence transferring learned information from base weights to the novel but relevant ones. To obtain representative region features for the novel weight generation, two strategies are further presented. The scale-aware strategy respects the scale variation in object detection, and assigns each object to its corresponding pyramid to obtain the scale-specific region feature. The centerness-aware strategy reorganizes region features in the spatial range, since the localization quality of each pixel on the feature map varies. Extensive experiments are conducted on the challenging MS COCO and PASCAL VOC datasets, and our method achieves promising performances under various settings with a direct weight generation fashion. Furthermore, our generated weights can be used as good initialization for fine-tuning to induce faster convergence, thus mitigating the performance drop on old classes and improving overall results in the incremental setting.
High Throughput and Energy Efficient SHA-2 ASIC Design for Continuous Integrity Checking Applications

High throughput and energy efficient integrated cryptographic hash primitives are important for the continuous integrity checking and tampering detection in secure access management mechanisms of on-chip instrumentation, such as the IJTAG. However, previous SHA-256 cores focus only on throughput. In this paper, we synthesize with a 32 nm CMOS Technology SHA-256 cores that can be integrated in ASICs, and we present insights on their achieved throughput and energy efficiency. Moreover, we present a novel clock-gated design for reducing dynamic power dissipation of SHA-256 cores; and a novel Multi-Vt design for reducing static power dissipation of SHA-256 cores. The proposed designs can achieve upto 25.9% improvement of the energy efficiency of existing SHA-256 designs, without impacting their performed throughput. To the best of our knowledge, this is the first work that applies low power design techniques on SHA-256 cores.
Speaker Timbre Supervision Method Based on Perceptual Loss for Voice Cloning

Current voice cloning models are only trained by approximating the generated speech to match the reference speech, without directly supervising the speaker timbre information during training. This approach is prone to interference from other factors in speech, such as semantic and prosodic information, which hinders the voice cloning model's ability to learn speaker timbre information. To address this issue, this paper proposes a speaker timbre supervision training method based on perceptual loss. Specifically, we use a speaker recognition model highly correlated with speaker information to extract speaker perceptual features and calculate speaker perceptual loss. Furthermore, we input high-level perceptual features into a speaker discriminator for adversarial training, which guides the voice cloning model to learn speaker timbre information more effectively. The experimental results show that both speaker perceptual loss and speaker discriminators based on perceptual features improve the cloned timbre similarities of the voice cloning model, and combining both yields even better results.
CBDC Bridging between Hyperledger Fabric and Permissioned EVM-based Blockchains

The last few years have seen a steep increase in blockchain interoperability research. Most solutions connect public blockchains, where the main cross-chain use case is token transfer. By-design platform transparency, tamper resistance, and auditability make blockchains a candidate infrastructure for Central Bank Digital Currencies (CBDCs), but bridging CBDCs is an important missing piece in that scenario. In this paper, we leverage an asset transfer protocol, SATP, to define an extendable and dependable blockchain interoperability middleware that can bridge CBDC between Hyperledger Fabric and EVM-based permissioned blockchains. The key interoperation enabler in the solution is a shared asset definition enforced by both sides of the bridge, accompanied by a mapping between Fabric identities and Ethereum addresses for identity management. We implemented our design using Hyperledger Cacti. A preliminary evaluation shows that latency is more influenced by the ledgers than the bridging components.
Software Supply Chain Risk Assessment Framework

Sonatype has recorded an average 700% jump in software supply chain attacks [1], measured by the number of newly-published malicious packages in open-source repositories. The 2022 Synopsys report [2] assessed the reliance of the software industry on open-source software (OSS), and estimated that 97% of applications use OSS and 78% of the code comes from OSS. Practitioners did not anticipate how the software supply chain would become a deliberate attack vector and how the risk of the software supply chain would keep growing. Practitioners are more aware of the supply chain risks and want to know how to detect the implementation of package security practices and the security risk so they can make informed decisions to select dependencies for their projects. The goal of this research is to aid practitioners in producing more secure software products that are resistant to supply chain attacks through the identification and evaluation of actionable security metrics to detect risky components in the dependency graph. To achieve this goal, the thesis presents research on software security metrics evaluation in different ecosystems by leveraging software security frameworks, malicious attack vectors, and the OpenSSF Scorecard project to detect the implementation of secure practices and their significance to security outcomes.
What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers may encounter unique problems when employing open-source AI repositories.This paper presents an empirical study that investigates the issues in the repositories of open-source AI repositories to assist developers in understanding problems during the process of employing AI systems. We collect 576 repositories from the PapersWithCode platform. Among these repositories, we find 24,953 issues by utilizing GitHub REST APIs. Our empirical study includes three phases. First, we manually analyze these issues to categorize the problems that developers are likely to encounter in open-source AI repositories. Specifically, we provide a taxonomy of 13 categories related to AI systems. The two most common issues are runtime errors (23.18%) and unclear instructions (19.53%). Second, we see that 67.5% of issues are closed. We also find that half of these issues resolve within four days. Moreover, issue management features, e.g., label and assign, are not widely adopted in open-source AI repositories. In particular, only 7.81% and 5.9% of repositories label issues and assign these issues to assignees, respectively. Finally, we empirically show that employing GitHub issue management features and writing issues with detailed descriptions facilitate the resolution of issues. Based on our findings, we make recommendations for developers to help better manage the issues of open-source AI repositories and improve their quality.
CodeS: Towards Code Model Generalization Under Distribution Shift

Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation.Although DL has been becoming a driving force for large-scale source code analysis in the big code era, limited progress has been made on distribution shift analysis and benchmarking for source code tasks.To fill this gap, this paper initiates to propose CodeS, a distribution shift benchmark dataset, for source code learning.Specifically, CodeS supports two programming languages (Java and Python) and five shift types (task, programmer, time-stamp, token, and concrete syntax tree).Extensive experiments based on CodeS reveal that 1) out-of-distribution detectors from other domains (e.g., computer vision) do not generalize to source code, 2) all code classification models suffer from distribution shifts, 3) representation-based shifts have a higher impact on the model than others, and 4) pretrained bimodal models are relatively more resistant to distribution shifts.
DeFi Risk Transfer: Towards A Fully Decentralized Insurance Protocol

In this paper, we propose a fully decentralized and smart contract-based insurance protocol. We identify various issues in the Decentralized Finance (DeFi) insurance context and propose a solution to overcome these shortcomings. We introduce an economic model that allows for risk transfer without any external dependencies or centralized intermediaries. In particular, our proposal does not need any sort of subjective claim assessment, community voting or external data providers (oracles). Moreover, it solves the problem of over-insurance and proposes various ways to mitigate the capital inefficiencies usually seen with DeFi collateral. The work takes inspiration from peer-to-peer (P2P) insurance and collateralized debt obligations (CDO). We formally describe the protocol, assess its efficiency and key properties and present a reference implementation. Finally, we address limitations, extensions and ideas for further research.
AutoML from Software Engineering Perspective: Landscapes and Challenges

Machine learning (ML) has been widely adopted in modern software, but the manual configuration of ML (e.g., hyper-parameter configuration) poses a significant challenge to software developers. Therefore, automated ML (AutoML), which seeks the optimal configuration of ML automatically, has received increasing attention from the software engineering community. However, to date, there is no comprehensive understanding of how AutoML is used by developers and what challenges developers encounter in using AutoML for software development. To fill this knowledge gap, we conduct the first study on understanding the use and challenges of AutoML from software developers’ perspective. We collect and analyze 1,554 AutoML downstream repositories, 769 AutoML-related Stack Overflow questions, and 1,437 relevant GitHub issues. The results suggest the increasing popularity of AutoML in a wide range of topics, but also the lack of relevant expertise. We manually identify specific challenges faced by developers for AutoML-enabled software. Based on the results, we derive a series of implications for AutoML framework selection, framework development, and research.
A fine-grained vision and language representation framework with graph-based fashion semantic knowledge

Vision and language representation learning has been demonstrated to be an effective means of enhancing multimodal task performance. However, fashion-specific studies have predominantly focused on object-level features, which might neglect to capture region-level visual features and fail to represent the fine-grained correlations between words in fashion descriptions. To address these issues, we propose a novel framework to achieve a fine-grained vision and language representation in the fashion domain. Specifically, we construct a knowledge-dependency graph structure from fashion descriptions and then aggregate it with word-level embedding, which can strengthen the fashion semantic knowledge and obtain fine-grained textual representations. Moreover, we fine-tune a region-aware fashion segmentation network to capture region-level visual features, and then introduce local vision and language contrastive learning for pulling closer the fine-grained textual representations to the region-level visual features in the same garment. Extensive experiments on downstream tasks, including cross-modal retrieval, category/subcategory recognition, and text-guided image retrieval, demonstrate the superiority of our method over state-of-the-art methods.
Robust human motion estimation using bidirectional motion prior model and spatiotemporal progressive motion optimization

We propose a bidirectional conditional variational autoencoder (CVAE) motion prior model and a spatiotemporal progressive motion Optimization approach for robust human motion estimation. Priors have been playing an important role in handling pose estimation problems with occlusion and noise. The bidirectional motion prior model(BMP) we proposed can generate better motion priors by adjusting the initial states compared to the unidirectional generative model. To achieve plausible optimization results, we introduce a spatiotemporal progressive motion optimization (SPMO) strategy. The SPMO guides the optimization process towards a feasible and optimal solution. Qualitative and quantitative results on both 2D and 3D datasets have demonstrated the importance of the bidirectional motion model and the effectiveness of our optimization approach
4D facial analysis: A survey of datasets, algorithms and applications

Facial information plays an important role in human communication, e.g., rich and nuanced facial expressions effectively convey emotions. Traditional data such as 2D facial images and videos are susceptible to perturbation from lighting and occlusion, while 3D static data such as mesh models lack the temporal information which is necessary to describe facial dynamics. Therefore, researches on 4D facial data (3D facial models together with time as the fourth dimension) have received considerable attention in recent years. 4D data can simultaneously reflect the complex facial temporal and space information. To fully explore these characteristics, we present a systematic overview of the 4D facial research in this paper. We give a review in terms of historical development of 4D facial datasets, acquisition process of these datasets, related algorithms and applications, and discussion of outstanding issues. We also analyze the 4D facial research works by summarizing and comparing them; in particular, we present the results on three kinds of tasks conducted on the 4D facial datasets, i.e., expression-related tasks (AU detection, expression recognition and retrieval), generation tasks (3D facial reconstruction, facial expression and facial animation generation), and other tasks (facial registration, facial recognition and facial disease diagnosis). Finally, we summarize the future research directions.
DCDRO:A true random number generator based on dynamically configurable dual-output ring oscillator

As an essential component of modern encryption systems, a true random number generator (TRNG) has made significant contributions to information security. A configurable true random number generator with low hardware overhead and high throughput is presented in this paper. The structure utilizes an odd number of NAND gates and XOR gates to create a dynamically configurable dual-output ring oscillator. By increasing the phase noise in a short period, the time jitter range is expanded, thereby improving the randomness of the entropy source. Additionally, the XOR post-processing reduces the bias of the random sequence. The structure has undergone several tests and validations on the Xilinx Kintex-7, and the experimental results demonstrate that the proposed TRNG has strong robustness under varying environmental conditions, including different temperatures (0 °C–80 °C) and output voltages (0.8 V–1.2 V). It only consumes 28 LUTS and 5 DFFs in hardware resources and achieves a throughput of up to 200 Mb/s.
Autopsy of Ethereum's Post-Merge Reward System

Like most modern blockchain networks, Ethereum has relied on economic incentives to promote honest participation in the chain's consensus.The distributed character of the platform, together with the "randomness" or "luck" factor that both proof of work (PoW) and proof of stake (PoS) provide when electing the next block proposer, pushed the industry to model and improve the reward system of the system.With several improvements to predict PoW block proposal rewards and to maximize the extractable rewards of the same ones, the ultimate Ethereum's transition to PoS applied in the Paris Hard-Fork, more generally known as "The Merge", has meant a significant modification on the reward system in the platform.In this paper, we aim to break down both theoretically and empirically the new reward system in this post-merge era.We present a highly detailed description of the different rewards and their share among validators' rewards.Ultimately, we offer a study that uses the presented reward model to analyze the performance of the network during this transition.
Reducing the Gas Usage of Ethereum Smart Contracts without a Sidechain

To prevent DoS attacks, Ethereum assigns a fixed gas cost to every atomic operation in the EVM and the party who creates a transaction has to pay for its overall gas usage. While the gas model is successful in preventing DoS attacks, it causes significant costs in transaction fees. For example, in June-September 2022, the average daily gas usage of Ethereum was almost four million dollars. We propose a solution to minimize these fees by moving most of the execution of a contract off-chain and storing only the bare minimum on-chain. We then trigger an on-chain execution only if there is a disagreement between the parties to the contract, which is in turn only possible if at least one party is acting dishonestly. In such cases, our approach can identify and penalize the dishonest party by making them pay not only for the gas usage of their own function calls, but also calls made by other parties. Thus, it is game-theoretically irrational to behave dishonestly in this protocol. If all parties are rational, the total gas usage goes down significantly. Notably, our approach does not require a sidechain and works directly on the main Ethereum blockchain. We also provide extensive experiments over real-world Ethereum smart contracts, demonstrating that our protocol reduces their gas usage by 40.09%.
Evaluating Software Documentation Quality

The documentation of software libraries is an essential resource for learning how to use the library. Bad documentation may demotivate a developer from using the library or may result in incorrect usage of the library. Therefore, as developers select which libraries to use and learn, it would be beneficial to know the quality of the available documentation. In this paper, we follow a systematic process to create an automatic documentation quality evaluation tool. We identify several documentation quality aspects from the literature and design metrics that measure these aspects. We design a documentation quality overview visualization to visualize and present these metrics, and receive intermediate feedback through a focused interview study. Based on the received feedback, we implement a prototype for a web service that can evaluate a given documentation page for Java, JavaScript, and Python libraries. We use this web service to conduct a survey with 26 developers where we evaluate the usefulness of our metrics as well as whether they reflect developers’ experiences when using this library. Our results show that participants rated most of our metrics highly, with Text Readability, and Code Readability (of examples) receiving the highest ratings. We also found several libraries where our evaluation reflected developers’ experiences using the library, indicating the accuracy of our metrics.
PRoofster: Automated Formal Verification

Formal verification is an effective but extremely work-intensive method of improving software quality. Verifying the correctness of software systems often requires significantly more effort than implementing them in the first place, despite the existence of proof assistants, such as Coq, aiding the process. Recent work has aimed to fully automate the synthesis of formal verification proofs, but little tool support exists for practitioners. This paper presents oofster, a web-based tool aimed at assisting developers with the formal verification process via proof synthesis. oofster inputs a Coq theorem specifying a property of a software system and attempts to automatically synthesize a formal proof of the correctness of that property. When it is unable to produce a proof, oofster outputs the proof-space search tree its synthesis explored, which can guide the developer to provide a hint to enable oofster to synthesize the proof. oofster runs online at https://proofster.cs.umass.edu/ and a video demonstrating oofster is available at https://youtu.be/xQAi66IRfwI/.