Reviewerly/exHarmony huggingface dataset:
Integrating Sustainability into Strategic Management: a Path Towards Long-Term Business Success

Purpose: This study aims to examine the integration of sustainability into the strategic management practices of banks in Jordan, identifying both challenges and opportunities. Theoretical framework: The study's conceptual framework outlines the independent variable as strategic management, including formulation, implementation, monitoring and evaluating, and leadership, while the dependent variable is sustainability, focusing on both social and environmental goals. Existing models and frameworks for sustainability-oriented strategic management are reviewed and a customized model for Jordanian banks is developed. Design/methodology/approach: The research employs a qualitative research design, using semi-structured interviews with senior managers from ten Jordanian banks. Data is analyzed using content analysis and thematic analysis. Findings: The study finds that implementing strategic management processes can have a positive impact on sustainability, particularly with regards to environmental sustainability goals. The study recommends that Jordanian banks prioritize sustainability in their strategic management practices, establish sustainability targets, evaluate risks and opportunities, and adopt sustainable practices. Research, Practical &amp; Social implications: The study has implications for both research and practice. It highlights the importance of incorporating sustainability into strategic management practices for banks in Jordan and identifies the challenges and opportunities for doing so. The study also has social implications, as incorporating sustainability into strategic management practices can enhance the reputation of banks, decrease costs, and generate positive societal and environmental outcomes. Originality/value: This study contributes to the literature on sustainability-oriented strategic management by developing a customized model for Jordanian banks. The study also provides insights into the challenges and opportunities for banks in Jordan in integrating sustainability into their strategic management practices.
Out-of-distribution generalization for learning quantum dynamics

Abstract Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution. Here, we prove out-of-distribution generalization for the task of learning an unknown unitary. In particular, we show that one can learn the action of a unitary on entangled states having trained only product states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics on near term quantum hardware, and further opens up new methods for both the classical and quantum compilation of quantum circuits.
QoE-Aware Analysis and Management of Multimedia Services in 5G and Beyond Heterogeneous Networks

The explosion of mobile applications and phenomenal adoption of mobile connectivity by endusers has generated an increasing amount of mobile data traffic.Application posing stringent network requirements of high bandwidth and low latency (e.g., immersive videos) and the substantial amount of data traffic has put tremendous pressure on existing network infrastructures.Cognizant of the need of increasing network capacity, the simultaneous use of heterogeneous network technologies (HetNet) has been proposed to address this imperative problem.5G is expected to further drive the concept of HetNet by allowing the use of huge available bandwidth at milli-meter wave frequencies.While HetNet concentrates on improving network capacity from the data transmission perspective, it overlooks the importance of enhancing the support of user's Quality of Experience (QoE) for the evolving new services.There is a wide range of factors influencing user's QoE, such as network performance including delay, jitter and throughput, contextual influence such as personalized content delivery, mobility aware content caching and dissemination, and human impact such as human roles and demographic attributes.This paper initially presents a QoE-centric analysis, and evaluation of multimedia services over 5G networks.Then, to address the shortcomings of existing mobile networks, we propose a framework to enhance the support of QoE, to enable smooth delivery of personalized immersive video environment and personalized interaction with an immersive video, anywhere, anytime and on any device.Finally, we propose new solutions to achieve practically feasible spectrum allocation and personalized content caching and dissemination, to provide uninterrupted multimedia services to end-users.
Revisiting Relation Extraction in the era of Large Language Models

Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text.Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them.Recent work has instead treated the problem as a sequence-tosequence task, linearizing relations between entities as target strings to be generated conditioned on the input.Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision.We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching.Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the fewshot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results.We release this model as a new baseline for RE tasks 1 .
PAED: Zero-Shot Persona Attribute Extraction in Dialogues

Persona attribute extraction is critical for personalized human-computer interaction.Dialogue is an important medium that communicates and delivers persona information.Although there is a public dataset for triplet-based persona attribute extraction from conversations, its automatically generated labels present many issues, including unspecific relations and inconsistent annotations.We fix such issues by leveraging more reliable text-label matching criteria to generate high-quality data for persona attribute extraction.We also propose a contrastive learning-and generation-based model with a novel hard negative sampling strategy for generalized zero-shot persona attribute extraction.We benchmark our model with stateof-the-art baselines on our dataset and a public dataset, showing outstanding accuracy gains.Our sampling strategy also exceeds others by a large margin in persona attribute extraction.
Language Models that Seek for Knowledge: Modular Search &amp; Generation for Dialogue and Prompt Completion

Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2022) in combination with retrieval (Adolphs et al.).We extend the recent approach of Adolphs et al.to include internet search as a module.Our SeeKeR (Search-engine→Knowledge→Response) method thus applies a single LM to three modular tasks in succession: search, generating knowledge, and generating a final response.We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model Blender-Bot 2 (Chen et al., 2021) on open-domain knowledge-grounded conversations for the same number of parameters, in terms of consistency, knowledge and engagingness.SeeKeR applied to topical prompt completions as a standard language model outperforms GPT2 (Radford et al., 2019) and GPT3 (Brown et al., 2020) in terms of factuality and topicality, despite GPT3 being a vastly larger model.Our code and models are made publicly available 1 .
GLR-SEI: Green and Low Resource Specific Emitter Identification Based on Complex Networks and Fisher Pruning

Better neural networks, more powerful computer hardware and signal Big Data make deep learning increasingly important in Specific Emitter Identification (SEI). However, its implementation uses large amounts of resources and releases CO2. With the requirement of green and low-carbon resource sustainability, deep learning-based SEI faces the great challenge of compressing the model while ensuring its performance. In this article, we propose a novel green and low resource specific emitter identification method using complex networks and Fisher pruning (GLR-SEI). Specifically, we divide the learning process into two stages: knowledge distillation and fisher pruning, which involves transferring knowledge from complex number networks to real number networks. The proposed approach is evaluated using aircraft Automatic Dependent Surveillance Broadcast (ADS-B) data collected in the real world and many pruning experiments are performed. In addition, we add descriptive experiments on Raspberry Pi to provide a more effective data presentation in order to better demonstrate our research results. The experimental results show that the proposed lightweight network in this article can reduce the recognition rate by only 0.7% and the inference time by 10.1% compared to the complex neural network.
Teaching Communication And Teamwork In Engineering And Computer Science

This paper describes a project in which Communication Studies pedagogy was incorporated into Software Engineering courses at California State University, Northridge.Responding to the needs of potential employers as well as the ABET 2000 criteria, faculty from the Departments of Communication Studies and Computer Science developed a variety of instructional strategies to enhance students' ability to work in teams and maximize group effectiveness.The project involved specific instruction in group communication skills as part of the regular course of instruction, the administration of the Kolbe A TM Index, and assessment by students and instructors of the value of the team process and projects.
SquadSmart — Hierarchical Planning and Coordinated Plan Execution for Squads of Characters

This paper presents an application of Hierarchical Task Network (HTN) planning to a squad-based military simulation. The hierarchical planner produces collaborative plans for the whole squad in real time, generating the type of highly coordinated behaviours typical for armed combat situations involving trained professionals. Here, we detail the extensions to HTN planning necessary to provide real-time planning and subsequent collaborative plan execution. To make full hierarchical planning feasible in a game context we employ a planner compilation technique that saves memory allocations and speeds up symbol access. Additionally, our planner can be paused and resumed, making it possible to impose a hard limit on its computation time during any single frame. For collaborative plan execution we describe several synchronization extensions to the HTN framework, allowing agents to participate in several plans at once and to act in parallel or in sequence during single plans. Overall, we demonstrate that HTN planning can be used as an expressive and powerful real-time planning framework for tightly coupled groups of in-game characters.
Find Me the Right Content! Diversity-Based Sampling of Social Media Spaces for Topic-Centric Search

Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is: how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large-scale information spaces are inherently diverse, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can significantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase breadth in the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the "compressive sensing" literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users find samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15--45%.
Monte-Carlo Tree Search for the Multiple Sequence Alignment Problem

The paper considers solving the multiple sequence alignment, a combinatorial challenge in computational biology, where several DNA RNA, or protein sequences are to be arranged for high similarity. The proposal applies randomized Monte-Carlo tree search with nested rollouts and is able to improve the solution quality over time. Instead of learning the position of the letters, the approach learns a policy for the position of the gaps. The Monte-Carlo beam search algorithm we have implemented has a low memory overhead and can be invoked with constructed or known initial solutions. Experiments in the BAliBASE benchmark show promising results in improving state-of-the-art alignments.
Player Skill Modeling in Starcraft II

Starcraft II is a popular real-time strategy (RTS) game, in which players compete with each other online. Based on their performance, the players are ranked in one of seven leagues. In our research, we aim at constructing a player model that is capable of predicting the league in which a player competes, using observations of their in-game behavior. Based on cognitive research and our knowledge of the game, we extracted from 1297 game replays a number of features that describe skill. After a preliminary test, we selected the SMO classifier to construct a player model, which achieved a weighted accuracy of 47.3% (SD = 2.2). This constitutes a significant improvement over the weighted baseline of 25.5% (SD = 1.1). We tested from what moment in the game it is possible to predict a player’s skill, which we found is after about 2.5 minutes of gameplay, i.e., even before the players have confronted each other within the game. We conclude that our model can predict a player’s skill early in the game.
When Will You Answer This? Estimating Response Time in Twitter

We present a study analyzing the response times of users to questions on Twitter. We investigate estimating these response times using an exponential distribution-based wait time model learned from users’ previous responses. Our analysis considers several different model building approaches, including personalized models for each user, general models built for all users, and time-sensitive models specific to a day of the week or hour of the day. Our evaluation using a real world question-answer dataset shows the effectiveness of our approach.
Regular families of kernels for nonlinear approximation

This article studies sufficient conditions on families of approximating kernels which provide N-term approximation errors from an associated nonlinear approximation space which match the best known orders of N-term wavelet expansion. These conditions provide a framework which encompasses some notable approximation kernels including splines, cardinal functions, and many radial basis functions such as the Gaussians and general multiquadrics. Examples of such kernels are given to justify the criteria. Additionally, the techniques involved allow for some new results on N-term Greedy interpolation of Sobolev functions via radial basis functions.
Design of Stochastic Machines Dedicated to Approximate Bayesian Inferences

We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture the prior knowledge on which the inference is built. The output of the machine is devised to continuously sample the joint probability distribution of interest. While the method is explained on a simple example, we show that our machine computes a good approximation of the solution to a problem intractable in exact inference.
Review of Wichmann, Fligelstone, McEnery &amp; Knowles (1997): Teaching and language corpora

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Quantum Contract Signing with Entangled Pairs

We present a quantum scheme for signing contracts between two clients (Alice and Bob) using entangled states and the services of a third trusted party (Trent). The trusted party is only contacted for the initialization of the protocol, and possibly at the end, to verify clients’ honesty and deliver signed certificates. The protocol is fair, i.e., the probability that a client, say Bob, can obtain a signed copy of the contract, while Alice cannot, can be made arbitrarily small, and scales as N − 1 / 2 , where 4 N is the total number of rounds (communications between the two clients) of the protocol. Thus, the protocol is optimistic, as cheating is not successful, and the clients rarely have to contact Trent to confirm their honesty by delivering the actual signed certificates of the contract. Unlike the previous protocol (Paunković et al., Phys. Rev. A 84, 062331 (2011)), in the present proposal, a single client can obtain the signed contract alone, without the need for the other client’s presence. When first contacting Trent, the clients do not have to agree upon a definitive contract. Moreover, even upon terminating the protocol, the clients do not reveal the actual contract to Trent. Finally, the protocol is based on the laws of physics, rather than on mathematical conjectures and the exchange of a large number of signed authenticated messages during the actual contract signing process. Therefore, it is abuse-free, as Alice and Bob cannot prove they are involved in the contract signing process.
Performance analysis of an evolutionary recurrent Legendre Polynomial Neural Network in application to FOREX prediction

In this paper, a hybrid FOREX predictor model is developed by using a recurrent Legendre polynomial neural network (RLPNN) with an improved shuffled frog leaping (ISFL) based learning strategy. The recurrent network used in this study is a high order single layer neural network, structured using Legendre polynomials with feedback paths. The new recurrent network assembled integrating a functional expansion block with a delay block helps to map the internal nonlinearity associated with the input and output samples. Further a nature inspired learning strategy based on the memetic evolution of a team of frogs in search of their food locations is set forth to estimate the unrevealed parameters of the network. Empirically the model validation is realized over three currency exchange data sets accumulated within same period of time. Result investigation clearly illustrates the higher predictability of the proposed model compared to other models included in the study.
QSWalk.jl: Julia package for quantum stochastic walks analysis

The paper describes QSWalk.jl package for Julia programming language, developed for the purpose of simulating the evolution of open quantum systems. The package enables the study of quantum procedures developed using stochastic quantum walks on arbitrary directed graphs. We provide a detailed description of the implemented functions, along with a number of usage examples. The package is compared with the existing software offering a similar functionality. Program Title: QSWalk.jl Program Files doi: http://dx.doi.org/10.17632/6x37kcvvrp.1 Licensing provisions: MIT Programming language: Julia Nature of problem: The package implements functions for simulating quantum stochastic walks, including local regime, global regime, and nonmoralizing global regime (Julia documentation, 2018). It can be used for arbitrary quantum continuous evolution based on GKSL master equation on arbitrary graphs. Solution method: We utilize Expokit routines for fast sparse matrix exponentials on vectors. For dense matrices, exponentiation is computed separately, which is faster for small matrices. Restrictions: Currently package requires Julia v0.6 or higher. [1] K. Domino, A. Glos, M. Ostaszewski, Superdiffusive quantum stochastic walk definable of arbitrary directed graph, Quantum Inform. Comput 17 (11-12) (2017) 973–986.
Polynomial functions in the residue class rings of Dedekind domains

In this paper, as an extension of the integer case, we define polynomial functions over the residue class rings of Dedekind domains, and then we give canonical representations and counting formulas for such polynomial functions. In particular, we give an explicit formula for the number of polynomial functions over the residue class rings of polynomials over finite fields.
FAS: Forward secure sequential aggregate signatures for secure logging

Audit logs are an elemental part of computer systems due to their forensic value. Safeguarding audit logs on a physically unprotected machine are a challenging work, especially in the presence of active adversaries. Forward security is necessary for such a logging system. In this paper, we propose a new Forward secure sequential Aggregate Signature (FAS) scheme with optimal storage and communication for keys and signatures. To the best of our knowledge, our FAS scheme is the only scheme that has constant-sized public and secret keys as well as constant-sized aggregate signatures. Our proposed scheme supports aggregation of consecutive signatures by a third party. We prove the security of our proposed scheme under the hardness of factoring assumption, in the random oracle model.