Reviewerly/exHarmony huggingface dataset:
Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems

Recently, neural networks based models have been widely used for recommender systems (RS). Unfortunately, the existing neural network based RS solutions are often treated as black-boxes, which gain little trust and confidence from users. Thus, there is an increasing demand of explainability. Several explainable recommendation methods have been introduced to RS. However, there is a trade-off between explainability and performance among these methods. In this paper, we propose a novel framework, the Tower Bridge Net (TB-Net), using the proposed bidirectional embedding propagation approach to achieve both superior recommendation and explainability performances. Extensive validation on three public datasets shows that the performance of TB-Net dominates the state-of-the-art models. We quantitatively evaluate the explainability by using numerical metrics and experimentally prove that TB-Net achieves a significant improvement on explainability compared with existing methods. More importantly, TB-Net has been deployed and offers explainable recommendation service for the largest bank in China, Industrial and Commercial Bank of China Limited (ICBC). Results on a billion-scale dataset (1.2 billion nodes and edges) from ICBC show that TB-Net can provide both accurate recommendations and semantic explanations, and is very effective and deployable in practice.
A Journey from Commit Processing in Distributed Databases to Consensus in Blockchain

Blockchain Technology (BT) is a futuristic data management technology that eliminates the intermediary/ third-party/central authority as it is operated over a peer-to-peer network running cryptographic protocols. It is tamper-proof as it utilizes a cryptographic hash feature. Some other features, it offers, are transparency, security, inalterability, immutability, and decentralization; these features of the BTs have acquired the wide attention of the computing research community. While a transaction is occurring between two parties, it ensures that the trust is a consensus-driven built-in technology component instead of relying on a third party for the same. With the help of a consensus algorithm, the newly generated block is appended at the end of the already existing chain. In this paper, a thorough analysis of the various blockchain consensus algorithms is conducted including their benefits and drawbacks. Finally, it enlists the challenges and opportunities in designing blockchain consensus algorithms. Further, it proposes an idea for designing a blockchain consensus algorithm and concludes the discussion.
ELDA: Learning Explicit Dual-Interactions for Healthcare Analytics

Interaction learning plays an essential role in learning patients' comprehensive representations that contribute to improved performance in many analytical tasks. In healthcare, interactions among medical features (i.e., feature-level interactions) can exhibit different abnormal patterns in detail, while interactions among time steps (i.e., time-level interactions) can indicate the dynamic changes in patients' health conditions. Therefore, it is necessary to capture and analyze both types of interactions when conducting healthcare analytics, In this paper, we propose a general framework ELDA that is supported by the novel model ELDA-Net to learn dual-interactions for healthcare analytics in an explicit manner. Specifically, we devise a Feature-level Interaction Learning Module that can enrich a separately processed medical feature by learned interactions among medical features, and a Time-level Interaction Learning Module that can enhance the representations of the patients' health conditions by learned interactions among time steps. In both levels, ELDA can provide explicit and intuitive interpretations via explaining through the designed attention mechanism. Further, to facilitate the feature-level interaction learning, we propose a novel Bi-directional Embedding Module in ELDA-Net which can efficiently embed the medical features recorded in numerical values. We evaluate the effectiveness and interpretability of ELDA over two public real-world clinical datasets. The experimental results confirm that ELDA consistently outperforms existing state-of-the-art methods with a significant margin, and supports fine-grained interpretability in both the feature level and the time level with medical insights.
Verifying Fairness in Quantum Machine Learning

Abstract Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure—Tensor Networks—and implemented on Google’s TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ( $$2^{27}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mn>27</mml:mn></mml:msup></mml:math> -dimensional state space) tripling ( $$2^{18}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mn>18</mml:mn></mml:msup></mml:math> times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
Assessing the employees’ efficiency and adaptive performance for sustainable human resource management practices and transactional leadership: HR-centric policies for post COVID-19 era

The utilization of sustainable organizational human resource management (HRM) practices as predictors of adaptive performance has received little attention, despite the importance of adaptive performance in improving organizational efficiency. This study explores the relationship between HRM activities (training, performance appraisal, job enlargement, employee involvement, and work enrichment) and employees’ adaptive performance using the resource-based view (RBV) theory. It also looked into the role of transactional leadership in regulating these correlations to draw up the policies for the post-pandemic era. A quantitative study is conducted and a questionnaire was distributed randomly among the Ministry of Health of the Sultanate of Oman and 233 people have participated in the questionnaire survey. PLS-SEM was utilized to analyze the data. In this study, performance appraisal, job enlargement training, and work enrichment all have a considerable influence on employees’ adaptive performance. Furthermore, transactional leadership moderates the linkage between job enrichment and employee adaptive performance. As a result, the higher the organization’s attention on certain HR policies, the better their adaptive performance. HR practices are important predictors of adaptive performance; thus, managers and practitioners should take into account workers’ adaptability to encourage more adaptive behavior in the workplace. This article further discusses the study’s weaknesses as well as future research directives on HRM practices and adaptive performance.
Using the Comparative Hybrid Approach to Disentangle the Role of Substrate Choice on the Evolution of Cognition

Understanding the structure and evolution of natural cognition is a topic of broad scientific interest, as is the development of an engineering toolkit to construct artificial cognitive systems. One open question is determining which components and techniques to use in such a toolkit. To investigate this question, we employ agent-based AI, using simple computational substrates (i.e., digital brains) undergoing rapid evolution. Such systems are an ideal choice as they are fast to process, easy to manipulate, and transparent for analysis. Even in this limited domain, however, hundreds of different computational substrates are used. While benchmarks exist to compare the quality of different substrates, little work has been done to build broader theory on how substrate features interact. We propose a technique called the Comparative Hybrid Approach and develop a proof-of-concept by systematically analyzing components from three evolvable substrates: recurrent artificial neural networks, Markov brains, and Cartesian genetic programming. We study the role and interaction of individual elements of these substrates by recombining them in a piecewise manner to form new hybrid substrates that can be empirically tested. Here, we focus on network sparsity, memory discretization, and logic operators of each substrate. We test the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observe many trends, we see that discreteness of memory and the Markov brain logic gates correlate with high performance across our test conditions. Our results demonstrate that the Comparative Hybrid Approach can identify structural subcomponents that predict task performance across multiple computational substrates.
Towards Blockchain-Based Document Authentication

Nowadays, with the massive development of technologies, documents are usually made, modified and published digitally. Unlike paper documents, digital documents are easily falsified, and it is difficult to verify the authenticity of these advanced documents in effective and fast ways. Mushaf Al-Quran is one of the most sensitive digital documents that deserves to be preserved due to the divine messages it carries. There is no way to maintain an immutable document, as today's blockchain technology has proven. Therefore, to take advantage of blockchain technology, this article presents a decentralized web application (dApp) to authenticate and verify the digital Mushaf based on smart contracts and the Ethereum network. The results show the efficiency of the blockchain technology in preserving immutable documents and its effectiveness in protecting and proving the authenticity of the digital Mushaf Al-Quran.
Integrated navigation system (INS/auxiliary sensor) based on adaptive robust Kalman filter with partial measurements

Inertial navigation system (INS) is the most common positioning system in the underwater vehicle due to the limitations of penetration of the global navigation system. Amendment of INS cumulative error is made possible using data of auxiliary sensors such as Doppler velocity log (DVL). Due to the environmental conditions of the seabed, access to full DVL beams is impractical. Moreover, the statistical characteristics of measuring noise are unknown. These dramatically reduce the performance of the navigation system. Therefore, a loosely coupled (LC) approach is used to fuse partial DVL measurements in this study. This approach benefits from a virtual beam containing the velocity obtained in the last INS step instead of inaccessible DVL beams. To deal with the unknown statistical characteristics of the measurement and the limitations of access to DVL beams, an adaptive robust Kalman filter (ARKF) based on the Huber cost function is proposed. The performance of the proposed integrated navigation system for a remotely operated vehicle is investigated in the presence of observation noise and outliers. Results show that the proposed ARKF is robust against vigorous maneuvers, improves the estimation accuracy effectively, and can effectively reject measurement outliers.
Niyama: Node scheduling for cloud workloads with resource isolation

Summary Cloud providers place tasks from multiple applications on the same resource pool to improve the resource utilization of the infrastructure. The consequent resource contention has an undesirable effect on latency‐sensitive tasks. In this article, we present Niyama—a resource isolation approach that uses a modified version of deadline scheduling to protect latency‐sensitive tasks from CPU bandwidth contention. Conventionally, deadline scheduling has been used to schedule real‐time tasks with well‐defined deadlines. Therefore, it cannot be used directly when the deadlines are unspecified. In Niyama, we estimate deadlines in intervals and secure bandwidth required for the interval, thereby ensuring optimal job response times. We compare our approach with cgroups: Linux's default resource isolation mechanism used in containers today. Our experiments show that Niyama reduces the average delay in tasks by 3–20 when compared to cgroups. Since Linux's deadline scheduling policy is work‐conserving in nature, there is a small drop in the server‐level CPU utilization when Niyama is used naively. We demonstrate how the use of core reservation and oversubscription in the inter‐node scheduler can be used to offset this drop; our experiments show a 1.3–2.24 decrease in delay in job response time over cgroups while achieving high CPU utilization.
Improving transaction succeed ratio in payment channel networks via enhanced node connectivity and balanced channel capacity

Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain-based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in processing transactions. In this paper, we demonstrate the feasibility of tremendously increasing the success ratio of transactions and improving their execution efficiency by enhancing network nodes' connectivity and enforcing a balanced network channel capacity. To implement such ideas, multiple designs have been made. First, to extent nodes connectivity, we transform the nearly-linear ordered nodes into a star payment structured typology, and design an incentive financing mechanism to restructure a new landmark routing typology design. Especially, for the marginalized or dissociative nodes, we utilize specific financial loan strategies to encourage them to (re)join the system. Besides, we propose the Power Atomic Multi-Path Payments (Power AMP) traffic distribution method, which hierarchically allocates the bottleneck's currently-available capacity (to replace the random or equal division used in traditional AMP), and thus archives a balanced traffic usage. With such efforts, we improve the transaction success ratio and efficiency of transaction exertion by order of magnitude—compared with traditional PCN using the benchmark of landmark route, our method improves the success ratio by 11.06%.
Surface sediment composition and depositional environments in tropical Lake Sentani, Papua Province, Indonesia

Abstract Tropical Lake Sentani in the Indonesian Province Papua consists of four separate basins and is surrounded by a catchment with a very diverse geology. We characterized the surface sediment (upper 5 cm) of the lake’s four sub-basins based on multivariate statistical analyses (principal component analysis, hierarchical clustering) of major element compositions obtained by X-ray fluorescence scanning. Three types of sediment are identified based on distinct compositional differences between rivers, shallow/proximal and deep/distal lake sediments. The different sediment types are mainly characterized by the correlation of elements associated with redox processes (S, Mn, Fe), carbonates (Ca), and detrital input (Ti, Al, Si, K) derived by river discharge. The relatively coarse-grained river sediments mainly derive form the mafic catchment geology and contribution of the limestone catchment geology is only limited. Correlation of redox sensitive and detrital elements are used to reveal oxidation conditions, and indicate oxic conditions in river samples and reducing conditions for lake sediments. Organic carbon (TOC) generally correlates with redox sensitive elements, although a correlation between TOC and individual elements change strongly between the three sediment types. Pyrite is the quantitatively dominant reduced sulfur mineral, monosulfides only reach appreciable concentrations in samples from rivers draining mafic and ultramafic catchments. Our study shows large spatial heterogeneity within the lake’s sub-basins that is mainly caused by catchment geology and topography, river runoff as well as the bathymetry and the depth of the oxycline. We show that knowledge about lateral heterogeneity is crucial for understanding the geochemical and sedimentological variations recorded by these sediments. The highly variable conditions make Lake Sentani a natural laboratory, with its different sub-basins representing different depositional environments under identical tropical climate conditions.
Environmental Geology of Barite Deposits in Nevada

Abstract Barite is an important industrial mineral used as a weighing agent in fluids during the drilling of oil and natural gas wells, with the majority of United States production originating from Nevada. This paper considers three barite properties at varying stages of development—Rossi, Heavy Spar, and Ann—all located within the northern Nevada barite belt. At all three sites, barite is hosted in the Roberts Mountains allochthon consisting of siliceous sedimentary rocks. Geochemical characterization of waste rock, ore, and process products has been undertaken as part of project development at each site, including 212 acid-base accounting (ABA) analysis and 19 humidity-cell tests operated for up to 114 weeks. This study demonstrates that the presence of barite greatly limits the application of using traditional Sobek-style ABA methods. Barite will undergo incomplete dissolution and extraction in the ABA tests that will result in an overprediction of acid generation for materials that contain barite (i.e., ore and jig by-products). Results indicate use of the net acid generation test to inform waste rock management represents a more reliable indicator of acid generation for these three deposits and potentially also other barite deposits. Potential barite mine sites should continue to be evaluated using the same geochemical testing tools as base metal mines but using those tools in a selective way to achieve balance between regulatory requirements and costs.
Preparing for the Quantum Future: Perspectives of an Entrepreneurial Innovator

Commercial use of quantum computing technology is in our future, and the CEO of one quantum technology company is positioning his company to deliver a first-mover advantage for enterprises. However, the focus is on satisfying customers today, so he is—without pause—preparing customers to leverage the most advanced quantum technology today and into the future. Christopher Savoie, a serial entrepreneur and a founder of Zapata Computing, recently discussed his views on innovation processes, forming companies around emergent technologies, and how he is building a business within the nascent quantum computing industry.
Do Data Breach Notification Laws Work?

Over 1.5 million Americans have reported identify theft in recent years, costing the U.S. economy at least $13 billion in 2020. In response to this growing problem, all 50 states have enacted some form of data breach notification law in the past 20 years. Despite their prevalence, evaluating the efficacy of these laws remains elusive. This Article fills this gap, while further creating a new taxonomy to understand these laws.Legal scholars have generally treated these laws as doing just one thing—disclosing information to consumers. But this approach ignores rich variation: differences in disclosure requirements to regulators and credit monitoring agencies; varied mechanisms for public and private enforcement; and a range of thresholds that define how firms should assess the likelihood that a data breach will ultimately harm consumers.This Article leverages the Federal Trade Commission’s Consumer Sentinel database to build a comprehensive dataset measuring identity theft rates since 2000. Using staggered synthetic control – a popular method for policy evaluation that has yet to be widely applied in empirical legal studies – this Article finds that whether identify theft laws work depends on which of these different strands of legal provisions are employed. In particular, while baseline disclosure requirements and private rights of action have negligible effects, requiring firms to notify state regulators reduces identity theft rates by approximately 10%. And surprisingly, laws that fail to exclude low-risk breaches from reporting requirements are counterproductive, increasing identify theft rates by 5%.The Article ties together these results within a functional typology: namely, whether legal provisions (1) increase information flows to interested third-parties and regulators, (2) enable mechanisms for public or private enforcement, or (3) balance the costs of consumer confusion with the benefits of disclosure in determining when and how firms must give notice. It explains how these results and typology provide lessons for current federal and state proposals to expand or amend the scope of breach notification laws. A new federal law that simply mimics existing baseline requirements is unlikely to have an additional deterrence effect and may preempt further innovations. At the state level, introducing private rights of action may help at the margins, but likely suffers from well-identified issues of adequately establishing standing and damages. States that close loopholes surrounding breach requirements for encrypted data see lower identity-theft rates, which suggests that other states may be wise to tighten these requirements as well.
Design of a Path Planning Method for a Robotized Optimal Trimming

This paper presents a method for planning the cutting path of a trimming robot. The proposed approach, differently with respect to other works, considers a non-omnidirectional end-effector. After the evaluation and definition of the constraints that such an end-effector imposes for generating an effective motion, a method that foresees a first clustering phase followed by a path planning one is presented, implemented and numerically evaluated .
Application of an Improved Clustering Algorithm of Neural Networks in Performance Appraisal Systems

With the development of economic globalization, human resource competition has long become the key core of enterprise development and peer competition. Reasonably Formulating an enterprise’s employee performance appraisal management system and conducting standardized, fair, and just appraisal management are the basic requirements for the survival and development of an enterprise. This paper studies the application of an improved clustering algorithm based on neural network in an employee performance appraisal management system and explores its application value in the employee performance appraisal management system by using the improved ART2 clustering method that draws on leakage competition and Hebb rules. The experimental results of this paper show that the satisfaction of this system in the four aspects of integrated data management, system stability and convenience, and transparency in performance appraisal are all above 66%. This shows that this system has superior performance and good reference value.