Reviewerly/exHarmony huggingface dataset:
Topic Models with Sentiment Priors Based on Distributed Representations

In recent works, topic models for aspect-based opinion mining have been extended to automatically train sentiment priors for topic-word distributions, leading to automated discovery of sentiment words and improved sentiment classification. In this work, we propose an approach where sentiment priors are trained in the space of word embeddings; this allows us to both discover more aspect-related sentiment words and further improve classification. We also present an experimental study that validates our results.
Predicting Insider Breaches Using Employee Reviews

Data breaches committed by current and former employees are of growing concern to organizations. This paper draws on instrumental and expressive motives to predict the likelihood of insider breaches. Our research uses employee reviews on glassdoor.com to infer employees’ instrumental and expressive motives. Specifically, review ratings were used to obtain instrumental motives, while the tone of the reviews revealed expressive motives. Logistic regression on a matched-sample comparison of 71 public firms (41 breached and 30 non-breached) showed that employees’ perceptions of the overall rating of the firm as well as the ratings of compensation and benefits are significantly correlated with insider breaches. Emotional tones of joy, fear, and anxiety were also found to be significant predictors of an insider breach. The study contributes to theory and practice by deepening our understanding of factors affecting insider breaches.
Virtual Reality in Realistic Mathematics Education to Develop Mathematical Literacy Skills

AbstractAbstractAlthough mathematical literacy skills have been emphasized in education, standardized test results show that students still struggle in this regard. Hypothesizing that both virtual and realistic mathematics education approaches may be useful, this article combines two approaches as Virtual Realistic Mathematics Education (VRME) to enhance mathematical literacy. A one-group experimental research design was adopted with the participation of 20 6th-grade middle school students. The qualitative data were also used to explain the improvements in mathematical literacy. The results revealed that all of the dimensions of mathematical literacy skills; employ, interpret and formulate were improved while the improvements in interpret dimension was the lowest. The results also indicated the importance of knowledge co-construction through discussion for the development of mathematical literacy, especially for the interpret dimension. Finally, the study demonstrated that virtual tools can be used to improve mathematical literacy by expanding RME.Keywords: Virtual realitymathematical literacymathematization Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsÜnal ÇakıroğluÜnal Çakıroğlu is a professor at Trabzon University. He received the bachelor's degree from Ege University and the doctoral degree from Karadeniz Technical University, Trabzon. His research interests include virtual reality in education, online learning, learning analytics and approaches of teaching computer science.Mustafa GülerMustafa Güler is at Trabzon University. He graduated from Mathematics Education department of Karadeniz Technical University in 2011 and completed his master and doctoral program at the same university in 2014 and 2019, respectively. His research areas are teacher training, mathematics learning and teaching for elementary school students.Merve DündarMerve Dündar is a mathematics teacher and a doctorate student in the department of Mathematics Education at Trabzon University. She received her undergraduate education at Boğaziçi University and her master's degree at Ondokuz Mayıs University, Department of Mathematics Education. Her research interests include geometry education and technology assisted mathematics teaching.Filiz CoşkunFiliz Coşkun is a mathematics teacher in İzmir and a doctorate student in the department of Mathematics Education at Balıkesir University. She graduated from Mathematics Education Department of Hacettepe University in 2002. Her research interest includes technology assisted mathematics teaching.
Novel scene understanding, from gist to elaboration

ABSTRACTABSTRACTWe examined the cognitive experience of novel pictorial scenes, using observers' words. On each of two critical trials, a single novel scene (a photo) was presented briefly, after which observers described what they saw. The reports were highly valid, assessed against details of the stimuli. The most frequent concepts used by the observers defined scene gist empirically – there was wide agreement on the basic scene interpretation. The average extent of the reports, or cognitive bandwidth, was fairly high overall: a median of 14.4 correct meaningful words per scene. However, there was a wide range of report lengths across observers. With length, the reports became increasingly elaborative and unique (and still correct). The longer reports document the richer and more unique side of a gist-to-elaboration continuum. In addition, a manipulation of immediately prior experience (priming with other scenes) caused an increase in the reporting of prime-related information, conveyed by a wide range of concepts, including relational concepts. The results are consistent with cognitive theories of perception and the claim of substantial cognitive bandwidth. Furthermore, the highly elaborative reports document a measured diversity of powerful scene understanding.KEYWORDS: Scene perceptionscene understandingcontext effectsprimingfull report Disclosure statementNo potential conflict of interest was reported by the author(s).
AED: An black-box NLP classifier model attacker

Deep Neural Networks (DNNs) have been successful in solving real-world tasks in domains such as connected and automated vehicles, disease, and job hiring. However, their implications are far-reaching in critical application areas. Hence, there is a growing concern regarding the potential bias and robustness of these DNN models. A transparency and robust model is always demanded in high-stakes domains where reliability and safety are enforced, such as healthcare and finance. While most studies have focused on adversarial image attack scenarios, fewer studies have investigated the robustness of DNN models in natural language processing (NLP) due to their adversarial samples are difficult to generate. To address this gap, we propose a word-level NLP classifier attack model called "AED," which stands for Attention mechanism enabled post-model Explanation with Density peaks clustering algorithm for synonyms search and substitution. AED aims to test the robustness of NLP DNN models by interpretability their weaknesses and exploring alternative ways to optimize them. By identifying vulnerabilities and providing explanations, AED can help improve the reliability and safety of DNN models in critical application areas such as healthcare and automated transportation. Our experiment results demonstrate that compared with other existing models, AED can effectively generate adversarial examples that can fool the victim model while maintaining the original meaning of the input.
The Meanings of AI

Abstract This chapter investigates and compares the different meanings and connotations of the term ‘artificial intelligence’ (AI) across a range of languages. It starts with a brief account of the coining of the term by John McCarthy in 1956 and examines the connotations and cultural histories of ‘intelligence’ and ‘artificial’ in English. It then investigates what happens to those connotations when the term is translated and popularized in non-Anglophone and non-Western cultures. It examines how in some cases, such as many European languages, other cultural-linguistic groups have reproduced these associations; while in others, the English term is widely used, but with wholly different connotations, as in Japan. This chapter investigates the terms used to denote intelligent machines in five language groups—Germanic, Slavonic, Romance, Chinese, Japanese—to elucidate the value-laden character of the terminology and explore its influence on the perceptions of contemporary AI technologies around the globe.
Cache Bank-Aware Denial-of-Service Attacks on Multicore ARM Processors

In this paper, we identify that bank contention in the shared last-level cache (LLC) of multicore processors can cause significant execution time slowdown to cross-core victims even when the cache is partitioned. We propose a Cache Bank-Aware Denial-of-Service (DoS) attack, which is specially engineered to induce bank contention. This is accomplished by leveraging publicly available cache bank mapping information on commercial off-the-shelf (COTS) ARM multicore processors to generate a large number of concurrent memory accesses to a particular cache bank. Importantly, this attack can be mounted from the user-space to delay cross-core victims without requiring any special privilege or OS level support. We implement and evaluate the proposed DoS attack on two ARM multicore platforms using both synthetic and real-world workloads as victim tasks. The results show that the proposed Cache Bank-Aware DoS attack causes up to 9. 7X slowdown in synthetic workloads and 2. 3X slowdown in real-world workloads even when stateof-the-art isolation techniques, namely LLC partitioning and DRAM bandwidth throttling, are used to protect the victim. In other words, the proposed attack can effectively bypass existing DoS attack mitigation techniques. We identify LLC bandwidth throttling and LLC bank partitioning as potential mitigation solutions and discuss their limitations.
Kleene Algebra With Tests for Weighted Programs

Weighted programs generalize probabilistic programs and offer a framework for specifying and encoding mathematical models by means of an algorithmic representation. Kleene algebra with tests is an algebraic formalism based on regular expressions with applications in proving program equivalence. We extend the language of Kleene algebra with tests so that it is sufficient to formalize reasoning about a simplified version weighted programs. We introduce relational semantics for the extended language, and we generalize the relational semantics to an appropriate extension of Kleene algebra with tests, called Kleene algebra with weights and tests. We demonstrate by means of an example that Kleene algebra with weights and tests offers a simple algebraic framework for reasoning about equivalence and optimal runs of weighted programs.
MQL: ML-Assisted Queuing Latency Analysis for Data Center Networks

Data center network (DCN) performance analysis is becoming increasingly critical due to the growing data center scale and proliferation of latency-critical applications. Packetlevel simulators, the de-facto performance evaluation tools, allow accurate modeling of the network and protocols, but they are extremely slow. Simulation of large-scale DCNs with thousands of nodes can take days, making meaningful design space exploration impractical. Analytical techniques, such as queuing theory, can mitigate the scalability problem and offer high accuracy when specific workload assumptions are satisfied. However, their accuracy may decline as these assumptions break, and execution times explode unless designed carefully. To address these challenges, we propose a novel and scalable performance analysis methodology that combines two powerful techniques. First, it uses queuing theory and the maximum entropy (ME) principle to approximate the waiting time in each queue in a DCN. It then finds the end-to-end latency of each flow using traffic input, routing algorithm, and network parameters. This ME-based queuing model can approximate the latency under generalized exponential input traffic and general service distributions. Since its accuracy can degrade as traffic diverges from input and service time assumptions, the second step of the proposed methodology learns and corrects the systematic errors using a regression tree. The resulting ML-assisted technique achieves less than 3% modeling error on average compared to ns-3 simulations. Moreover, the speedup over ns-3 ranges from 100× to 9000× on DCNs with 128 to 1024 nodes.
MergePath-SpMM: Parallel Sparse Matrix-Matrix Algorithm for Graph Neural Network Acceleration

Graph neural networks have seen tremendous adoption to perform complex predictive analytics on massive and unstructured real-world graphs. The trend in hardware accelerator designs has identified significant challenges with harnessing graph locality and workload imbalance due to ultra-sparse and irregular matrix computations at a massively parallel scale. This paper addresses the load imbalance challenge and identifies that state-of-the-art either introduces complex specialized hardware to auto-tune for load-balanced execution at runtime or relies on software-only approaches that exploit parallelism. We propose a novel software-only load-balancing sparse matrix-matrix (SpMM) algorithm that unlocks fine-grain parallelism while maintaining controlled need-based targeted synchronizations to achieve robust performance scaling. The MergePath-SpMM algorithm achieves superior performance using commercial offthe-shelf GPU processors when compared to state-of-the-art hardware accelerators and software-only implementations.
Analyzing Energy Efficiency of a Server with a SmartNIC under SLO Constraints

Network processing has become a fast-increasing portion of the datacenter tax. To tackle this problem, the industry introduced SmartNICs comprising energy-efficient CPU cores, FPGA and/or accelerators. With architectures optimized for network-intensive applications, they promise to notably reduce the total cost of ownership (TCO) of datacenters primarily through improving energy efficiency. As such, this paper sets out to investigate the overall energy efficiency of an Intel Xeon CPU based server with an NVIDIA BlueField-2 SmartNIC, especially under service level objective (SLO) constraints which matter for many datacenter services.
An Adaptive Divergence-Based Non-Negative Latent Factor Model

A High-dimensional and incomplete (HDI) matrix is regularly adopted to portray the inherent non-negativity of interactions among numerous nodes, which is involved in countless industrial applications driven by big data. An inherently non-negative latent factor (LF) model can take out the intrinsical features from such data conveniently and effectually due to its unimpeded training process. However, it constructs the learning objective relying on a standard Euclidean distance, thereby seriously restricting its representative ability to HDI data generated by different domains. To address this issue, this work proposes an adaptive divergence-based non-negative LF (ADNLF) model following: 1) constructing a generalized objective function based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha - \beta $ </tex-math></inline-formula> -divergence to inflate its ability to represent various HDI data; 2) connecting the optimization variables with output LFs by a smooth and single LF-dependent bridging function to satisfy the non-negativity constraints constantly; and 3) facilitating adaptive divergence in the learning objective through particle swarm optimization for high scalability. Empirical studies on eight HDI matrices validate that an ADNLF model evidently outstrips state-of-the-art models in terms of estimation accuracy as well as computational efficiency for missing data of an HDI dataset.
Adversarial Defensive Framework for State-of-Health Prediction of Lithium Batteries

Neural networks are subject to malicious data poisoning attacks affecting the ability of the model to make accurate predictions. The attacks are generated using adversarial techniques imperceptible to the human eye since they use minimal noise to alter features, which end up affecting boundary decisions of the prediction model. Predicting the state of health (SOH) of lithium-ion batteries in an adversarial context becomes a challenging task, especially if the model is expected to always predict at a very high accuracy level. Our article presents three novel contributions. The first contribution is an SOH prediction model that shows one of the best accuracy rates in the literature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R2 = {99.82\%}$</tex-math></inline-formula> ) and yet uses the simplest long short-term memory model configuration compared to literature. The second contribution is the implementation of three state-of-the-art adversarial data poisoning attacks at decision time, namely fast gradient method, momentum iterative method, and basic iterative method, and the assessment of their impact on the original prediction accuracy. Most of the literature uses the attacks in a classification context while we are applying it to a time-series prediction context. The third and most important contribution of this article is presenting a generic defense strategy combined with a feature engineering method that can be generalized to prevent any potential adversarial attack attempt on any prediction model in a time-series prediction context using the same suggested feature engineering approach as suggested in this article. The accuracy of the model is assessed using error estimators: root-mean-square error, mean absolute error, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> . The results show that adversarial data poisoning attacks are lethal to a time-series prediction model, and our proposed defense strategy is able to detect and flag the existence of malicious data using a support vector machine classifier with a very high confidence rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{area under curve}=0.996$</tex-math></inline-formula> ), which allow our model to defend against any potential unseen adversarial attack.
On-Chain and Off-Chain Scalability Techniques

In the future of sharing economy, billions of underutilized IoT devices will be deployed to enable a powerful and large-scale sharing market that produces economic, environmental, and social benefits. Given the fact that communications in numerous IoT devices through wireless links are unreliable, blockchain technology, as a promising solution, has emerged to achieve reliable and secure sharing services in a decentralized manner. However, applying blockchain in large-scale wireless networks confronts scalability challenges. This motivates us to propose a real-time, trusted data interactive, and fine-grained transaction supportable sharing framework, the core of which is a novel two-layer scaling blockchain design. In the on-chain layer, sharing-oriented sharding is employed to enable secure and efficient processing of macro-transactions on the chain. In the off-chain layer, cross-zone off-chain channels are set up to provide real-time sharing transactions with high-frequency micro-trading scenarios. Finally, a proof-of-concept case study about electric vehicle sharing data is implemented with experimental results to demonstrate the feasibility of our framework.
Hypercubes and Isometric Words Based on Swap and Mismatch Distance

The hypercube of dimension n is the graph whose vertices are the $$2^n$$ binary words of length n, and there is an edge between two of them if they have Hamming distance 1. We consider an edit distance based on swaps and mismatches, to which we refer as tilde-distance, and define the tilde-hypercube with edges linking words at tilde-distance 1. Then, we introduce and study some isometric subgraphs of the tilde-hypercube obtained by using special words called tilde-isometric words. The subgraphs keep only the vertices that avoid a given tilde-isometric word as a factor. An infinite family of tilde-isometric words is described; they are isometric with respect to the tilde-distance, but not to the Hamming distance. In the case of word 11, the subgraph is called tilde-Fibonacci cube, as a generalization of the classical Fibonacci cube. The tilde-hypercube and the tilde-Fibonacci cube can be recursively defined; the same holds for the number of their edges. This allows an asymptotic estimation of the number of edges in the tilde-Fibonacci cube, in comparison to the total number in the tilde-hypercube.
Segmentation of Large Historical Manuscript Bundles into Multi-page Deeds

Archives around the world have vast uncatalogued series of image bundles of digitized historical manuscripts containing, among others, notarial records also known as “deeds” or “acts”. One of the first steps to provide metadata which describe the contents of those bundles is to segment these bundles into their individual deeds. Even if deeds are page-aligned, as in the bundles considered in the present work, this is a time-consuming task, often prohibitive given the huge scale of the manuscript series involved. Unlike traditional Layout Analysis methods for page-level segmentation, our approach goes beyond the realm of a single-page image, providing consistent deed detection results on full bundles. This is achieved in two tightly integrated steps: first, the probabilities that each bundle image is an “initial”, “middle” or “final” page of a deed are estimated, and then an optimal sequence of page labels is computed at the whole bundle level. Empirical results are reported which show that this approach achieves almost perfect segmentation of bundles of a massive Spanish series of historical notarial records.
How KLFCM Works—Convergence and Parameter Analysis for KLFCM Clustering Algorithm

KLFCM is a clustering algorithm proposed by introducing K-L divergence into FCM, which has been widely used in the field of fuzzy clustering. Although many studies have focused on improving its accuracy and efficiency, little attention has been paid to its convergence properties and parameter selection. Like other fuzzy clustering algorithms, the output of the KLFCM algorithm is also affected by fuzzy parameters. Furthermore, some researchers have noted that the KLFCM algorithm is equivalent to the EM algorithm for Gaussian mixture models when the fuzzifier λ is equal to 2. In practical applications, the KLFCM algorithm may also exhibit self-annealing properties similar to the EM algorithm. To address these issues, this paper uses Jacobian matrix analysis to investigate the KLFCM algorithm’s parameter selection and convergence properties. We first derive a formula for calculating the Jacobian matrix of the KLFCM with respect to the membership function. Then, we demonstrate the self-annealing behavior of this algorithm through theoretical analysis based on the Jacobian matrix. We also provide a reference strategy for determining the appropriate values of fuzzy parameters in the KLFCM algorithm. Finally, we use Jacobian matrix analysis to investigate the relationships between the convergence rate and different parameter values of the KLFCM algorithm. Our experimental results validate our theoretical findings, demonstrating that when selecting appropriate lambda parameter values, the KLFCM clustering algorithm exhibits self-annealing properties that reduce the impact of initial clustering centers on clustering results. Moreover, using our proposed strategy for selecting the fuzzy parameter lambda of the KLFCM algorithm effectively prevents coincident clustering results from being produced by the algorithm.