Reviewerly/exHarmony huggingface dataset:
A Decentralized Web 3.0 Platform for Manufacturing Customized Products

Web 3.0 promises a more decentralized and valuable Internet by allowing users to truly own and control their data. This is in contrast to Web 2.0, where users and their generated data and traffic have been centralized by several major Internet platforms. In this article, we investigate the design of Web 3.0 platforms through a case study. Specifically, we propose a platform targeting the long-tail market comprising customized products. To handle the decentralized user demands, we realize the concept of virtual enterprise (VE) on the platform, such that different users can cooperate through running specific VEs with predefined workflows, thus greatly reducing the transaction costs. Furthermore, we develop the micro-blockchain protocol, allowing users to store consistent copies of transaction data on their own devices. Unlike existing coin-based blockchains, micro-blockchain does not require global consensus, hence it can naturally scale with more concurrent transactions. Moreover, a data-driven trust model is utilized, so that users can evaluate their trust relationship with others solely based on the data of their past activities, instead of requiring the help of a centralized trust authority, which is usually acted by the platform. At last, we point out the key issues on designing general Web 3.0 applications.
slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed on encrypted data. However, developing HE-based solutions for encrypted PaaS is a tedious task which requires a careful design that predominantly depends on the deployment scenario and on leveraging the characteristics of modern HE schemes. Prior works on privacy-preserving PaaS focus solely on protecting the confidentiality of the client data uploaded to a remote model provider, e.g., a cloud offering a prediction API, and assume (or take advantage of the fact) that the model is held in plaintext. Furthermore, their aim is to either minimize the latency of the service by processing one sample at a time, or to maximize the number of samples processed per second, while processing a fixed (large) number of samples. In this work, we present slytHErin, an agile framework that enables privacy-preserving PaaS beyond the application scenarios considered in prior works. Thanks to its hybrid design leveraging HE and its multiparty variant (MHE), slytHErin enables novel PaaS scenarios by encrypting the data, the model or both. Moreover, slytHErin features a flexible input data packing approach that allows processing a batch of an arbitrary number of samples, and several computation optimizations that are model-and-setting-agnostic. slytHErin is implemented in Go and it allows end-users to perform encrypted PaaS on custom deep learning models comprising fully-connected, convolutional, and pooling layers, in a few lines of code and without having to worry about the cumbersome implementation and optimization concerns inherent to HE.
Privacy and Data Security

This chapter covers the development and administration of policies and procedures to comply with laws, regulations and industry standards relating to privacy, data security and overall collection and use of nonpublic personal information. Every company collects or otherwise gains access to nonpublic personal information of customers, other business partners and consumers who have yet to enter into a business relationship with the company and the need for the company to protect and avoid unauthorized use of this personal information has become a major compliance issue that cannot be ignored by senior management. Reports of privacy issues continue to increase and many companies, including major multinational businesses, have experienced serious security threats and breaches that have compromised the privacy rights of their customers and resulted in significant damage to their reputations. Laws and regulations relating to privacy and data security have multiplied at the federal and state levels and companies engaged in cross-border business activities must establish procedures and systems that comply with legal regimes in foreign countries that often much more restrictive than those that are applicable in the United States. The costs of complying with privacy and data security laws is increasing and the financial risk of failing to adapt and comply is also material. At the same time, the range of potential cyberattackers has expanded and their tools of the trade have become more sophisticated, a trend which has pushed companies to invest more financial resources in data security as a basic item of business continuation.
Aligning DevOps Concepts with Agile Models of the Software Development Life Cycle (SLDC) in Pursuit of Continuous Regulatory Compliance

In the historical landscape of software development, regulatory compliance considerations may have been considered of low-priority to teams outside of highly regulated industries, such as medical devices, finance, avionics, and cyber-physical systems. As mission and safety critical domains have traditionally favored requirement and verification driven linear forms of development, utilization of DevOps concepts to support regulatory compliance goals is still a relatively novel concept. With software development delivery and integration requirements increasing in highly regulated domains, and implementation of more ubiquitous standards for developers to show evidence of data handling (as seen in now ubiquitous standards, such as General Data Protection Regulation (GDPR)), research and practice in this area becomes increasingly valuable. Further, examples of customizing DevOps approaches for specific challenges, such as the example presented by DevSecOps for engineering security, present a plausible template that could be applied to engineering compliance in a rapidly iterative model. Through exploring learnings from domains comfortable with an Agile/DevOps approach to SDLC as they implement emerging requirements, confidence in exploring compatibility of such practices with delivery and integration of highly regulated software products is growing, laying the foundation for a movement towards continuous delivery and iteration of regulatory compliant software across the industry. This paper will review and discuss available literature in five key areas: Agile/DevOps alignment, alignment of regulation with requirement engineering, compatibility of regulations with DevOps concepts, emerging forms of DevOps, and the movement towards continuous compliance.
Contactless Student Attendance System Using BLE Technology, QR-Code, and Android

In an academic institution, taking attendance of the students is done to make sure if the students attend the class. Since COVID-19, safer, hygienic and precise taking-attendance system is needed to minimize the spreading of virus. The proposed system is contactless student attendance system based on BLE Devices, QR-Code and Android for high-rise building academic institution. To take attendance in this system, the instructor must display the QR-Code in-front of the class and students must connect to BLE device, scan the QR-Code and take selfie photo using their smartphone to taking-attendance. To detect student’s location, RSSI value between BLE device and the smartphone is used. −93.25 dBm is used as the predefined RSSI value in the system. With the predefined RSSI value, the system can successfully specify if the student is inside or outside the classroom. QR-Code can be scanned by student inside the classroom from each spot.
Digital Supply Chain Management Transformation in Industry: A Bibliometric Study

Technology is becoming today’s most promising approach to transform the company, specifically its supply chain system. This research is conducted through bibliometric analysis based on published data regarding digital transformation in supply chain systems from Scopus Database. The publication search is based on the keyword “Digital Supply Chain Transformation” and several criteria to better filter the obtained publication data. Overall, the bibliometric analysis from VOSViewer uncovers two research gaps and beneficial information. The first research gap shows that the digital supply chain transformation of other industrial sectors besides agriculture and food can be further explored. The second gap is AI identified as a promising technology that is still rarely used in digital supply chain transformation research. Furthermore, we find out that blockchain is a phenomenal technology being used in the research of digital supply chain transformation nowadays. We envision this study to encourage more research about digital supply chain transformation, which hopefully could accelerate the development of a more sustainable, efficient, and data-oriented supply chain system. This study is divided into seven sections, including Introduction, Literature Review, Methods, Data Collection, Result and Discussion, Conclusion, Recommendations.
Market-Sensing Capability Identifying Digital Banking Created

Market-sensing capability is effective in influencing Digital Banking performance. This theme has recently attracted the attention of marketing strategic academics, but it lacks guidance for researchers to navigate the dense, complex literature. Hence, this paper aims to provide a theoretically based review of dimensions of market sensing capability. The systematic quantitative literature review method is applied to select and analyze 18 relevant papers published in the period from 2002 to 2021. In doing so, this review market sensing capability related concepts such as behavioral. Moreover, a general framework of market sensing capability is developed to explain how size and type of industry. This paper suggests different ways to apply dimension of market-sensing capability in Digital Banking.
Students’ Preferences for the use of Padlet in Synchronous Learning

Two years of experience in synchronous learning due to the Covid-19 epidemic has challenged educators in Indonesia to choose an engaging and motivating method, strategies, or media in assisting teaching and learning. The assistance has to be the common trend that promotes students&amp;rsquo; interest and learning process. Utilizing Padlet was subsequently chosen as the solution to this particular study. Padlet is utilized as a virtual wall to captivate the motivation of the students in learning English especially writing. In this study, the researchers evaluated the students&amp;rsquo; preferences for using Padlet as a virtual wall by carrying out a quantitative study design. A questionnaire-based survey approach was used to determine numerous factors such as utility, knowledge, engagement, enjoyment, motivation, and ease of use. The results indicated that the students believe improving their knowledge is one of the most important factors influencing their preference for utilizing a Padlet in a synchronous writing environment.
A sublinear time quantum algorithm for longest common substring problem between run-length encoded strings

We give a sublinear quantum algorithm for the longest common substring (LCS) problem on the run-length encoded (RLE) inputs, under the assumption that the prefix-sums of the runs are given. Our algorithm costs $\tilde{O}(n^{5/6})\cdot O(\mathrm{polylog}(\tilde{n}))$ time, where $n$ and $\tilde{n}$ are the encoded and decoded length of the inputs, respectively. We justify the use of the prefix-sum oracles by showing that, without the oracles, there is a $\Omega(n/\log^2n)$ lower-bound on the quantum query complexity of finding LCS given two RLE strings due to a reduction of $\mathsf{PARITY}$ to the problem.
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval

Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conversational Information Retrieval (CIR). Previous work, including approaches and datasets, has not always been successful or sufficiently robust in taking human norms into consideration. To this end, we introduce a workflow that integrates ethical alignment, with an initial ethical judgment stage for efficient data screening. To address the need for ethical judgment in CIR, we present the QA-ETHICS dataset, adapted from the ETHICS benchmark, which serves as an evaluation tool by unifying scenarios and label meanings. However, each scenario only considers one ethical concept. Therefore, we introduce the MP-ETHICS dataset to evaluate a scenario under multiple ethical concepts, such as justice and Deontology. In addition, we suggest a new approach that achieves top performance in both binary and multi-label ethical judgment tasks. Our research provides a practical method for introducing ethical alignment into the CIR workflow. The data and code are available at https://github.com/wanng-ide/ealm .
ARN: A Comprehensive Framework and Benchmark for Analogical Reasoning on Narratives

Analogical reasoning is one of the prime abilities of humans and is linked to creativity and scientific discoveries. This ability has been studied extensively in natural language processing (NLP) and in cognitive psychology. NLP benchmarks often focus on proportional analogies, while the ones in cognitive psychology investigate longer pieces of text too. Yet, although studies that focus on analogical reasoning in an involved setting utilize narratives as their evaluation medium, analogical reasoning on narratives has not been studied extensively. We create an extensive evaluation framework for analogical reasoning on narratives that utilizes narrative elements to create lower-order and higher-order mappings that subsequently lead to the development of the Analogical Reasoning on Narratives (ARN) benchmark that covers four categories of far(cross-domain)/near(within-domain) analogies and far/near disanalogies, allowing us to study analogical reasoning in LLMs in distinct scenarios. Our results demonstrate that LLMs struggle to recognize higher-order mappings when they are not accompanied by lower-order mappings (far analogies) and show better performance when all mappings are formed simultaneously (near analogies). We observe that in all the scenarios, the analogical reasoning abilities of LLMs can be easily impaired by lower-order mappings in near disanalogies.
ViPlanner: Visual Semantic Imperative Learning for Local Navigation

Real-time path planning in outdoor environments still challenges modern robotic systems due to differences in terrain traversability, diverse obstacles, and the necessity for fast decision-making. Established approaches have primarily focused on geometric navigation solutions, which work well for structured geometric obstacles but have limitations regarding the semantic interpretation of different terrain types and their affordances. Moreover, these methods fail to identify traversable geometric occurrences, such as stairs. To overcome these issues, we introduce ViPlanner, a learned local path planning approach that generates local plans based on geometric and semantic information. The system is trained using the Imperative Learning paradigm, for which the network weights are optimized end-to-end based on the planning task objective. This optimization uses a differentiable formulation of a semantic costmap, which enables the planner to distinguish between the traversability of different terrains and accurately identify obstacles. The semantic information is represented in 30 classes using an RGB colorspace that can effectively encode the multiple levels of traversability. We show that the planner can adapt to diverse real-world environments without requiring any real-world training. In fact, the planner is trained purely in simulation, enabling a highly scalable training data generation. Experimental results demonstrate resistance to noise, zero-shot sim-to-real transfer, and a decrease of 38.02% in terms of traversability cost compared to purely geometric-based approaches. Code and models are made publicly available: https://github.com/leggedrobotics/viplanner.
Language Model Decoding as Direct Metrics Optimization

Despite the remarkable advances in language modeling, current mainstream decoding methods still struggle to generate texts that align with human texts across different aspects. In particular, sampling-based methods produce less-repetitive texts which are often disjunctive in discourse, while search-based methods maintain topic coherence at the cost of increased repetition. Overall, these methods fall short in achieving holistic alignment across a broad range of aspects. In this work, we frame decoding from a language model as an optimization problem with the goal of strictly matching the expected performance with human texts measured by multiple metrics of desired aspects simultaneously. The resulting decoding distribution enjoys an analytical solution that scales the input language model distribution via a sequence-level energy function defined by these metrics. And most importantly, we prove that this induced distribution is guaranteed to improve the perplexity on human texts, which suggests a better approximation to the underlying distribution of human texts. To facilitate tractable sampling from this globally normalized distribution, we adopt the Sampling-Importance-Resampling technique. Experiments on various domains and model scales demonstrate the superiority of our method in metrics alignment with human texts and human evaluation over strong baselines.
SeisT: A foundational deep learning model for earthquake monitoring tasks

Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake monitoring capabilities. This paper introduces a foundational deep learning model, the Seismogram Transformer (SeisT), designed for a variety of earthquake monitoring tasks. SeisT combines multiple modules tailored to different tasks and exhibits impressive out-of-distribution generalization performance, outperforming or matching state-of-the-art models in tasks like earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, back-azimuth estimation, and epicentral distance estimation. The performance scores on the tasks are 0.96, 0.96, 0.68, 0.95, 0.86, 0.55, and 0.81, respectively. The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1.7%, 9.5%, and 8.0%, respectively. Our study, through rigorous experiments and evaluations, suggests that SeisT has the potential to contribute to the advancement of seismic signal processing and earthquake research.
Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models

Temporal reasoning is a crucial NLP task, providing a nuanced understanding of time-sensitive contexts within textual data. Although recent advancements in LLMs have demonstrated their potential in temporal reasoning, the predominant focus has been on tasks such as temporal expression and temporal relation extraction. These tasks are primarily designed for the extraction of direct and past temporal cues and to engage in simple reasoning processes. A significant gap remains when considering complex reasoning tasks such as event forecasting, which requires multi-step temporal reasoning on events and prediction on the future timestamp. Another notable limitation of existing methods is their incapability to provide an illustration of their reasoning process, hindering explainability. In this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction. Our task offers a comprehensive evaluation of both the LLMs' complex temporal reasoning ability, the future event prediction ability, and explainability-a critical attribute for AI applications. To support this task, we present the first multi-source instruction-tuning dataset of explainable temporal reasoning (ExpTime) with 26k derived from the temporal knowledge graph datasets and their temporal reasoning paths, using a novel knowledge-graph-instructed-generation strategy. Based on the dataset, we propose the first open-source LLM series TimeLlaMA based on the foundation LlaMA2, with the ability of instruction following for explainable temporal reasoning. We compare the performance of our method and a variety of LLMs, where our method achieves the state-of-the-art performance of temporal prediction and explanation.
Non-negative isomorphic neural networks for photonic neuromorphic accelerators

Neuromorphic photonic accelerators are becoming increasingly popular, since they can significantly improve computation speed and energy efficiency, leading to femtojoule per MAC efficiency. However, deploying existing DL models on such platforms is not trivial, since a great range of photonic neural network architectures relies on incoherent setups and power addition operational schemes that cannot natively represent negative quantities. This results in additional hardware complexity that increases cost and reduces energy efficiency. To overcome this, we can train non-negative neural networks and potentially exploit the full range of incoherent neuromorphic photonic capabilities. However, existing approaches cannot achieve the same level of accuracy as their regular counterparts, due to training difficulties, as also recent evidence suggests. To this end, we introduce a methodology to obtain the non-negative isomorphic equivalents of regular neural networks that meet requirements of neuromorphic hardware, overcoming the aforementioned limitations. Furthermore, we also introduce a sign-preserving optimization approach that enables training of such isomorphic networks in a non-negative manner.
Leveraging Cutting Edge Deep Learning Based Image Matching for Reconstructing a Large Scene from Sparse Images

We present the top ranked solution for the AISG-SLA Visual Localisation Challenge benchmark (IJCAI 2023), where the task is to estimate relative motion between images taken in sequence by a camera mounted on a car driving through an urban scene. For matching images we use our recent deep learning based matcher RoMa. Matching image pairs sequentially and estimating relative motion from point correspondences sampled by RoMa already gives very competitive results -- third rank on the challenge benchmark. To improve the estimations we extract keypoints in the images, match them using RoMa, and perform structure from motion reconstruction using COLMAP. We choose our recent DeDoDe keypoints for their high repeatability. Further, we address time jumps in the image sequence by matching specific non-consecutive image pairs based on image retrieval with DINOv2. These improvements yield a solution beating all competitors. We further present a loose upper bound on the accuracy obtainable by the image retrieval approach by also matching hand-picked non-consecutive pairs.