Reviewerly/exHarmony huggingface dataset:
Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems

In this paper, we introduce stochastic simulated quantum annealing (SSQA) for large-scale combinatorial optimization problems. SSQA is designed based on stochastic computing and quantum Monte Carlo, which can simulate quantum annealing (QA) by using multiple replicas of spins (probabilistic bits) in classical computing. The use of stochastic computing leads to an efficient parallel spin-state update algorithm, enabling quick search for a solution around the global minimum energy. Therefore, SSQA realizes quantum-like annealing for large-scale problems and can handle fully connected models in combinatorial optimization, unlike QA. The proposed method is evaluated in MATLAB on graph isomorphism problems, which are typical combinatorial optimization problems. The proposed method achieves a convergence speed an order of magnitude faster than a conventional stochastic simulaated annealing method. Additionally, it can handle a 100-times larger problem size compared to QA and a 25-times larger problem size compared to a traditional SA method, respectively, for similar convergence probabilities.
The Role of Adaptive E-Learning Co-Design as Main Solution to Higher Education’s Marketing Performance

This study investigated the role and influence of adaptive e-learning co-design on marketing performance of higher education institutions and provides recommendations for the academic community for improving learning and marketing performance. This paper employed a mixed method approach to better understand the role of adaptive e-learning co-design. The qualitative approach reviewed the relevant literature and used unstructured interviews. The quantitative method applied a questionnaire based on the instruction of each variable. It was shared with 257 participants, and then the data were processed with the structural equation modelling technique. In collecting information, a sharing session class was created with lecturers and students at universities in Toba, Indonesia. It was found that adaptive e-learning co-design mediates the influence of quality information on successful business intelligence. Adaptive e-learning co-design increases continuous innovation, whilst successful business intelligence improves higher education marketing performance. Adaptive e-learning co-design invites students and lecturers to be part of the design team of co-production knowledge and experience. Additionally, it demands higher education be a co-creator of values and implies a strategic orientation towards collaboration between stakeholders to integrate e-learning processes. Adaptive e-learning describes the role of stakeholders in the design and bidding creation process. This study assessed active students in two consecutive semesters participating in e-learning. adaptive e-learning co-design can improve higher education marketing performance through successful business intelligence.
The Benefits of Removing Dirty Blood with Traditional Cupping Treatment

In daily life, Muslims are ordered to behave by the teachings in the Al-Quran and the sunnah of the Prophet Muhammad, including regarding health. In the world of health, it is known that alternative medicine in the style of Rasulullah SAW is known as At-Thibb Al-Nabawi; one example is cupping therapy. The purpose of preparing this paper is to master the meaning of cupping, the procedures for carrying out cupping, and knowing the urgency and virtues of cupping. The research method used is a literature study. Cupping is the oldest traditional medicine method in the world, believed to have existed since 3000 BC. It aims to remove blood contaminated with toxins resulting from metabolism through the skin's surface by using a tube or glass placed on the skin's surface as a place to collect the dirty blood. There are five types of cupping: dry cupping, wet cupping, sliding cupping, fire cupping, and synergy cupping. Cupping features include pain relief, increasing blood circulation, lowering blood pressure, overcoming cardiovascular disease, lowering cholesterol, treating diabetes mellitus, and many more.
Sentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecture

Sentiment analysis is of great importance to parties who are interested is analyzing the public opinion in social networks.In recent years, deep learning, and particularly, the attention-based architecture, has taken over the field, to the point where most research in Natural Language Processing (NLP) has been shifted towards the development of bigger and bigger attention-based transformer models.However, those models are developed to be all-purpose NLP models, so for a concrete smaller problem, a reduced and specifically studied model can perform better.We propose a simpler attention-based model that makes use of the transformer architecture to predict the sentiment expressed in tweets about hotels in Las Vegas.With their relative predicted performance, we compare the similarity of our ranking to the actual ranking in TripAdvisor to those obtained by more rudimentary sentiment analysis approaches, outperforming them with a 0.64121 Spearman correlation coefficient.We also compare our performance to DistilBERT, obtaining faster and more accurate results and proving that a model designed for a particular problem can perform better than models with several millions of trainable parameters.
IDENTIFICATIONS OF DIFFICULTIES FACED BY MAHAKARYA ASIA UNIVERSITY STUDENTS IN SPEAKING ENGLISH

The aims of this research are to describe (1) linguistic factors that affect students’ difficulty in speaking and (2) internal and external factors that are contributive to the difficulty in speaking English faced by second semester students who took speaking class. The type of the study was descriptive qualitative. The object of the research was second semester students who took speaking class. In this study, the researcher took 31 students as the respondents. The instruments of the research were questionnaire and interview. Technique and data analysis were identifying the difficulties encountered by the students in the speaking class, classifying those difficulties, presenting and analyzing the data in, and discussing. The study reported that difficulties in speaking English were classifiable as linguistic, internal, and external factors. The linguistic factors affecting speaking difficulties were the lack of sufficient masteries of required vocabulary, grammar, and pronunciation and intonation. The internal factors hindering the learning of speaking were the detrimental over influence of the first language and the lack of either or both self-motivation and practice. The external factors were insufficient encouragement and driving force from lecturer, inadequate support from other fellow students, and problems
EVALUATING AN INTERACTIVE UNIVERSITY CURRICULUM DELIVERED DURING RUSSIA'S INVASION OF UKRAINE

The purpose of this article is the evaluation of earlier work by Sikra (2022), which supplied preliminary data about the author's rationale for designing and delivering online university education. The current evaluation was achieved by collecting psychology students' views via different sources with the use of trivial questions that corresponded to the Six core values (Sikra, 2022). In total, 22 students' responses were evaluated (17 females, 5 males, Mean Age 20.09). These responses were thematically analysed to support the author's evaluation of the interactive curriculum after its completion. The results of the thematic analysis evidence that the author delivered the online education in accordance with the Six core values. A short discussion of the findings' implications in connection to previous research is supplied. The conclusion brings the material to a finish and contextualises this approach within the global crises of COVID-19 and the War in Ukraine.
The DEVELOPMENT OF HYPERMEDIA LEARNING MEDIA IN AN EFFORT TO IMPROVE HIGH ER ORDER THINKING SKILLSS STUDENTS IN HIGH SCHOOL ECONOMICS CLASS X SUBJECTS

This study aims to develop Hypermedia Learning Media in an Effort to Improve students' higher order thinking skills in class X high school economics subjects. In this research, it uses the step of model development studies. The stage stage of the development studies research model according to akker such as preliminary research, prototyping stage, summative evaluation, systematic reflection and documentation. The hypermedia learning media developed was declared feasible This feasibility was assessed by two experts consisting of media experts and material experts. Media experts stated that hypermedia learning media is excellent and feasible to use (91.1%). Material experts stated that hypermedia learning media is good and feasible to use (92.2%). The hypermedia learning media developed is very effective in improving higher-order thinking skills. The student posttest results also stated that hypermedia learning media is very effective (75%) in improving students' high-level thinking skills. The N-gain score obtained by students in this study was 0.5 (medium category). Hypermedia learning media is stated based on the results of teacher interviews to improve higher-order thinking skills. Meanwhile, students stated that hypermedia learning media is practically used (82%). Learning media requires an android 6.0 version smartphone which not all students have this version, and if using the ios version or website, it requires a stable connection to run hypermedia learning media online.
“An Adapt-or-Die Type of Situation”: Perception, Adoption, and Use of Text-to-Image-Generation AI by Game Industry Professionals

Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 39 sub-themes on professionals' perception, adoption and use of TTIG in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG, and foster games as cultural artefacts.
Evaluating one-loop string amplitudes

We evaluate one-loop open-string amplitudes at finite $\alpha'$ for the first time. Our method involves a deformation of the integration contour over the modular parameter $\tau$ to a fractal contour introduced by Rademacher in the context of analytic number theory. This procedure leads to explicit and practical formulas for the one-loop four-point amplitudes in type-I superstring theory, amenable to numerical evaluation. We plot the amplitudes as a function of the Mandelstam invariants $s$ and $t$ and directly verify long-standing conjectures about their behaviour at high energies.
Testing AI performance on less frequent aspects of language reveals insensitivity to underlying meaning

Advances in computational methods and big data availability have recently translated into breakthroughs in AI applications. With successes in bottom-up challenges partially overshadowing shortcomings, the 'human-like' performance of Large Language Models has raised the question of how linguistic performance is achieved by algorithms. Given systematic shortcomings in generalization across many AI systems, in this work we ask whether linguistic performance is indeed guided by language knowledge in Large Language Models. To this end, we prompt GPT-3 with a grammaticality judgement task and comprehension questions on less frequent constructions that are thus unlikely to form part of Large Language Models' training data. These included grammatical 'illusions', semantic anomalies, complex nested hierarchies and self-embeddings. GPT-3 failed for every prompt but one, often offering answers that show a critical lack of understanding even of high-frequency words used in these less frequent grammatical constructions. The present work sheds light on the boundaries of the alleged AI human-like linguistic competence and argues that, far from human-like, the next-word prediction abilities of LLMs may face issues of robustness, when pushed beyond training data.
Can we avoid Double Descent in Deep Neural Networks?

Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the ``double descent'', has caught the attention of the deep learning community. As the model's size grows, the performance gets first worse, and then goes back to improving. It raises serious questions about the optimal model's size to maintain high generalization: the model needs to be sufficiently over-parametrized, but adding too many parameters wastes training resources. Is it possible to find, in an efficient way, the best trade-off? Our work shows that the double descent phenomenon is potentially avoidable with proper conditioning of the learning problem, but a final answer is yet to be found. We empirically observe that there is hope to dodge the double descent in complex scenarios with proper regularization, as a simple $\ell_2$ regularization is already positively contributing to such a perspective.
Training neural networks with structured noise improves classification and generalization

The beneficial role of noise-injection in learning is a consolidated concept in the field of artificial neural networks, suggesting that even biological systems might take advantage of similar mechanisms to optimize their performance. The training-with-noise algorithm proposed by Gardner and collaborators is an emblematic example of a noise-injection procedure in recurrent networks, which can be used to model biological neural systems. We show how adding structure to noisy training data can substantially improve the algorithm performance, allowing the network to approach perfect retrieval of the memories and wide basins of attraction, even in the scenario of maximal injected noise. We also prove that the so-called Hebbian Unlearning rule coincides with the training-with-noise algorithm when noise is maximal and data are stable fixed points of the network dynamics.
Pauli-based model of quantum computation with higher-dimensional systems

Pauli-based computation (PBC) is a universal model for quantum computation with qubits where the input state is a magic (resource) state and the computation is driven by a sequence of adaptively chosen and compatible multiqubit Pauli measurements. Here we generalize PBC for odd-prime-dimensional systems and demonstrate its universality. Additionally, we discuss how any qudit-based PBC can be implemented on actual, circuit-based quantum hardware. Our results show that we can translate a PBC on $n$ $p$-dimensional qudits to adaptive circuits on $n+1$ qudits with $O\left( pn^2/2 \right)$ $\mathrm{SUM}$ gates and depth. Alternatively, we can carry out the same computation with $O\left( pn/2\right)$ depth at the expense of an increased circuit width. Finally, we show that the sampling complexity associated with simulating a number $k$ of virtual qudits is related to the robustness of magic of the input states. Computation of this magic monotone for qutrit and ququint states leads to sampling complexity upper bounds of, respectively, $O\left( 3^{ 1.0848 k} \epsilon^{-2}\right)$ and $O\left( 5^{ 1.4022 k} \epsilon^{-2}\right)$, for a desired precision $\epsilon$. We further establish lower bounds to this sampling complexity for qubits, qutrits, and ququints: $\Omega \left( 2^{0.5431 k} \epsilon^{-2} \right)$, $\Omega \left( 3^{0.7236 k} \epsilon^{-2} \right)$, and $\Omega \left( 5^{0.8544 k} \epsilon^{-2} \right)$, respectively.
Forward LTLf Synthesis: DPLL At Work

This paper proposes a new AND-OR graph search framework for synthesis of Linear Temporal Logic on finite traces (\LTLf), that overcomes some limitations of previous approaches. Within such framework, we devise a procedure inspired by the Davis-Putnam-Logemann-Loveland (DPLL) algorithm to generate the next available agent-environment moves in a truly depth-first fashion, possibly avoiding exhaustive enumeration or costly compilations. We also propose a novel equivalence check for search nodes based on syntactic equivalence of state formulas. Since the resulting procedure is not guaranteed to terminate, we identify a stopping condition to abort execution and restart the search with state-equivalence checking based on Binary Decision Diagrams (BDD), which we show to be correct. The experimental results show that in many cases the proposed techniques outperform other state-of-the-art approaches. Our implementation Nike competed in the LTLf Realizability Track in the 2023 edition of SYNTCOMP, and won the competition.
Inseq: An Interpretability Toolkit for Sequence Generation Models

Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to democratize access to interpretability analyses of sequence generation models. Inseq enables intuitive and optimized extraction of models' internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. We showcase its potential by adopting it to highlight gender biases in machine translation models and locate factual knowledge inside GPT-2. Thanks to its extensible interface supporting cutting-edge techniques such as contrastive feature attribution, Inseq can drive future advances in explainable natural language generation, centralizing good practices and enabling fair and reproducible model evaluations.
Techniques and protocols for enhancing data privacy in cloud computing: A review

The emergence of cloud computing has enabled millions of devices across the globe to communicate and exchange massive amounts of data that is classified as private with each other. Given that these devices and platforms are scattered in different geographical areas, communicating with IoTs all the time, the issue of data security, in particular, assurance of data privacy has arisen. Several attempts in mitigating this issue has been by both academic and industry researchers in terms of technology, practices and protocols but it still remains a serious challenge even as cloud computing forges into the future. As a result, both individuals and organizations whose private data are held in the cloud are getting jittery due to the frequent data compromise or exposure in the cloud servers. This paper reviews the various challenges in data privacy and the mechanisms and techniques proposed by various researchers in bid to mitigate them and create a trustworthy cloud environment. It undertakes a comparative research analysis of the existing research work on data privacy protection techniques that have been proposed and implemented in the cloud computing environment.
Desain Multimedia Interaktif Berbantuan Software Adobe Flash CS6 untuk Siswa Kelas V SD

The study aims to design and determine the feasibility level of an interactive multimedia software-assisted Adobe Flash CS6 for elementary School students grade V. This type of research is research and development with the Multimedia Development Life Cycle (MDLC) method Luther-Sutopo version model through six stages, namely concept, design, material collection, assembly, testing, and distribution. Based on the stage of the development method, the result is an interactive CD in the theme of 9 subtheme 3 Learning 1, 2, and 5 with a percentage of 84.44% that meets valid criteria or deserves to be used as an interactive multimedia for students of the V grade Elementary School.
Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

The purpose of this study is to model electronic money in Indonesia using a hybrid model and compare its accuracy with the non-hybrid model. The hybrid model used is Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network. The data used is the amount of electronic money circulation for the monthly period January 2009 to October 2021. The ARIMA model formed from research data is ARIMA (1,1,0) with additive outliers and level shift outliers. For Artificial Neural Network modeling is limited by using one hidden layer with three neurons. In the modeling process, 20 repetitions were carried out. The smallest repetition value was obtained, namely the 13th repetition with an error value of 2.569. In this study, it was found that the ARIMA- Artificial Neural Network hybrid model had a smaller Root Mean Squared Error (RMSE) in sample and out sample than the non-hybrid model. Based on the results of the study, it can be concluded that by combining the ARIMA model with Artificial Neural Network, it can increase the accuracy of the data fit results and forecast results.
Invers Moore-Penrose pada Matriks Turiyam Simbolik Real

The symbolic Turiyam matrix is a matrix whose entries contain symbolic Turiyam. Inverse matrices can generally be determined if the matrix is a non-singular square matrix. Currently the inverse of the symbolic Turiyam matrix of size m × n with m 6= n can be determined by the Moore-Penrose inverse. The purpose of this research is to determine the inverse Moore-Penrose algorithm on a real symbolic Turiyam matrix of size m × n with m 6= n. Algebraic operations on symbolic Turiyam is a method used to obtain the Moore-Penrose inverse on real symbolic Turiyam matrices by applying symbolic Turiyam algebraic operations on the concept of Moore-Penrose inverses. The main result obtained is the inverse Moore-Penrose algorithm on the real symbolic Turiyam matrix. The demonstration example given shows that the Moore-Penrose inverse on a real symbolic Turiyam matrix always exists even though the matrix is not a square matrix.