Reviewerly/exHarmony huggingface dataset:
The Impact of Digital Marketing on Consumer Buying Behavior in Saudi Arabia: Brand Popularity as a Mediator

The purpose of this research was to analyze the impact of digital marketing on consumer buying behavior, consumer purchasing decisions, and brand popularity. The aim of the results is to provide the importance of digital marketing in the current business environment. The current research adopted a quantitative research approach with primary data collected from 200 respondents. The research used data from a questionnaire circulated among the research respondents who were surveyed online, and data were analyzed through SPSS software. The outcomes of the research reflect that digital marketing has a positive and significant impact on brand popularity, consumer buying behavior, and consumer purchasing decisions in Saudi Arabia. The current research outcomes can be applied to improve the digital marketing strategy of any organization. Managers can modify their digital marketing business strategy to improve brand popularity. Only one independent variable was used in the current study: digital marketing through social media (Snapchat, TikTok, and Twitter) in Saudi Arabia. Future studies can be conducted via integrating different digital marketing variables.
Food safety news events classification via a hierarchical transformer model

<h2>Abstract</h2> In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance.
A RTL Implementation of Heterogeneous Machine Learning Network for French Computer Assisted Pronunciation Training

Computer-assisted pronunciation training (CAPT) is a helpful method for self-directed or long-distance foreign language learning. It greatly benefits from the progress, and of acoustic signal processing and artificial intelligence techniques. However, in real-life applications, embedded solutions are usually desired. This paper conceives a register-transfer level (RTL) core to facilitate the pronunciation diagnostic tasks by suppressing the mulitcollinearity of the speech waveforms. A recently proposed heterogeneous machine learning framework is selected as the French phoneme pronunciation diagnostic algorithm. This RTL core is implemented and optimized within a very-high-level synthesis method for fast prototyping. An original French phoneme data set containing 4830 samples is used for the evaluation experiments. The experiment results demonstrate that the proposed implementation reduces the diagnostic error rate by 0.79–1.33% compared to the state-of-the-art and achieves a speedup of 10.89× relative to its CPU implementation at the same abstract level of programming languages.
Understanding the implementation of an at-home language test: A case of an online version of TOEFL-PBT

An at-home test is a unique mode of language test delivery as a result of mass-gathering prohibition during the COVID-19 pandemic. Despite the uniqueness, little is known about how to effectively implement an at-home test. This study aims to provide a deeper understanding of the test by exploring the execution of the online version of TOEFL-PBT in the Language Center of Syiah Kuala University. Four test administrators were interviewed to share their experiences and opinions related to considerations for implementing an at-home proficiency test, which includes technological resources, security, and validity concerns. The data were then analyzed descriptively. The results of this study revealed that the Language Center used Safe Exam Browser to deliver the test and Zoom to supervise the test-takers in real time. The proctors could stop the test and privately investigate the test takers using the Zoom Breakout feature. The validity of the test was claimed not to be a concern since the test provider used the same form of questions as the offline version. In addition, the Language Center expressed exhaustion in carrying out the online test, thus suggesting the development of a less complicated procedure of an at-home test.
The Effectiveness of the Use of Academic Information Systems

Use information technology is used as a means that can facilitate a person or an agency in carrying out their work effectively and efficiently. This research is a qualitative descriptive study that aims to determine how effective the use of academic information systems is in the Office Administration Education Study Program, Faculty of Social Sciences, Universitas Negeri Makassar. The informants of this research are all parties who are directly involved in the use of academic information systems, namely students, study program operators, lecturers, and SIA managers at the UNM ICT Center. Data collection techniques used are observation, interviews, and documentation. The results of data analysis are presented in the form of a description that describes the procedures, constraints, and solutions in the use of the Academic Information System. The results showed that the effectiveness of the use of academic information systems in the Office Administration Education Study Program, Faculty of Social Sciences, Universitas Negeri Makassarwas classified as quite effective in terms of information quality indicators, namely accurate, timely, and relevant.
Indonesian Stand-Up Comedy: A New Developing Industry of Youth Culture

The research analyzed the growth of stand-up comedy in Indonesia, which had developed into a new trend or popular culture and a new creative business that produced skilled and well-known comics. Furthermore, it discussed how stand-up comedy might develop into a new creative business by evaluating the growth of the community, Instagram, and YouTube, as well as the growth of successful comics. In order to help the researchers doing the analysis, the researchers applied a qualitative method. They used two theories named Self-presentation concept by Goffman and Creative Industry theory by Richard Florida. The findings indicate that the creative industry does not always progress forward; interest in stand-up comedy decreased in various places, but it is still attempting to develop. Indonesian people are becoming more receptive to stand-up comedy as it is home to some of the world's most talented comics. Comics must be innovative in their approach to content creation to increase their popularity and viability. Apart from that, when comics are popular, they can inspire others to pursue careers as comics. As a result, comics members continue to grow in number, the community grows in size, and the creative business continues to flourish. Stand-up comedy has developed into popular culture and a new creative sector geared toward youth.
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference.
Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages

Vision-Language Pre-training (VLP) has advanced the performance of many vision-language tasks, such as image-text retrieval, visual entailment, and visual reasoning. The pre-training mostly utilizes lexical databases and image queries in English. Previous work has demonstrated that the pre-training in English does not transfer well to other languages in a zero-shot setting. However, multilingual pre-trained language models (MPLM) have excelled at a variety of single-modal language tasks. In this paper, we propose a simple yet efficient approach to adapt VLP to unseen languages using MPLM. We utilize a cross-lingual contextualized token embeddings alignment approach to train text encoders for non-English languages. Our approach does not require image input and primarily uses machine translation, eliminating the need for target language data. Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora. Our code is available at https://github.com/Yasminekaroui/CliCoTea.
SOSIALISASI MITIGASI BENCANA KEBAKARAN DIKAMPUNG HARENDONG DESA SINDANGGALIH KARANGPAWITAN KABUPATEN GARUT

Indonesia is a disaster-prone country, both natural and non-natural disasters as well as social disasters. The purpose of this service is to educate the public to be prepared for the arrival and impacts arising from disasters experienced by themselves or the community. The technique used in this community service is to socialize fire disaster mitigation and explore community knowledge about fire disaster mitigation so that a Community-Based Fire Disaster Mitigation Model poster is formed using a cooperative-participatory method in collaboration between the government and the community. The results obtained from the implementation of this activity were community knowledge from 40 people who took part in the socialization on disaster mitigation before participating in this activity 45% or 18 people who knew about disaster mitigation education, and after participating in the socialization as much as 90% or 36 people understood information on disaster mitigation. The overview obtained is that it is important for the community to know, understand and be able to apply the knowledge of fire disaster mitigation which is socialized to the Sindanggalih village community. Considering that the location of Sindanggalih Village is quite close to the city which is full of hills and mountains and there are still many trees, so that if a fire occurs, the community will certainly experience enormous difficulties because Sindanggalih Village does not yet have fire extinguisher facilities. So it becomes very important to socialize fire disaster mitigation for Sindanggalih villagers, in order to prevent the causes of fires at an early stage, including when a fire disaster occurs, how can residents already know and understand what actions must be taken in order to survive and successfully extinguish the fire.
DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference

Quantization is commonly used in Deep Neural Networks (DNNs) to reduce the storage and computational complexity by decreasing the arithmetical precision of activations and weights, a.k.a. tensors. Efficient hardware architectures employ linear quantization to enable the deployment of recent DNNs onto embedded systems and mobile devices. However, linear uniform quantization cannot usually reduce the numerical precision to less than 8 bits without sacrificing high performance in terms of model accuracy. The performance loss is due to the fact that tensors do not follow uniform distributions. In this paper, we show that a significant amount of tensors fit into an exponential distribution. Then, we propose DNA-TEQ to exponentially quantize DNN tensors with an adaptive scheme that achieves the best trade-off between numerical precision and accuracy loss. The experimental results show that DNA-TEQ provides a much lower quantization bit-width compared to previous proposals, resulting in an average compression ratio of 40% over the linear INT8 baseline, with negligible accuracy loss and without retraining the DNNs. Besides, DNA-TEQ leads the way in performing dot-product operations in the exponential domain, which saves 66% of energy consumption on average for a set of widely used DNNs.
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions

Model-agnostic feature attributions can provide local insights in complex ML models. If the explanation is correct, a domain expert can validate and trust the model's decision. However, if it contradicts the expert's knowledge, related work only corrects irrelevant features to improve the model. To allow for unlimited interaction, in this paper we provide model-agnostic implementations for two popular explanation methods (Occlusion and Shapley values) to enforce entirely different attributions in the complex model. For a particular set of samples, we use the corrected feature attributions to generate extra local data, which is used to retrain the model to have the right explanation for the samples. Through simulated and real data experiments on a variety of models we show how our proposed approach can significantly improve the model's performance only by augmenting its training dataset based on corrected explanations. Adding our interactive explanations to active learning settings increases the sample efficiency significantly and outperforms existing explanatory interactive strategies. Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model.
Chaotic Roots of the Modular Multiplication Dynamical System in Shor's Algorithm

Shor's factoring algorithm, believed to provide an exponential speedup over classical computation, relies on finding the period of an exactly periodic quantum modular multiplication operator. This exact periodicity is the hallmark of an integrable system, which is paradoxical from the viewpoint of quantum chaos, given that the classical limit of the modular multiplication operator is a highly chaotic system that occupies the "maximally random" Bernoulli level of the classical ergodic hierarchy. In this work, we approach this apparent paradox from a quantum dynamical systems viewpoint, and consider whether signatures of ergodicity and chaos may indeed be encoded in such an "integrable" quantization of a chaotic system. We show that Shor's modular multiplication operator, in specific cases, can be written as a superposition of quantized A-baker's maps exhibiting more typical signatures of quantum chaos and ergodicity. This work suggests that the integrability of Shor's modular multiplication operator may stem from the interference of other "chaotic" quantizations of the same family of maps, and paves the way for deeper studies on the interplay of integrability, ergodicity and chaos in and via quantum algorithms.
For Kernel Range Spaces a Constant Number of Queries Are Sufficient

We introduce the notion of an $\varepsilon$-cover for a kernel range space. A kernel range space concerns a set of points $X \subset \mathbb{R}^d$ and the space of all queries by a fixed kernel (e.g., a Gaussian kernel $K(p,\cdot) = \exp(-\|p-\cdot\|^2)$). For a point set $X$ of size $n$, a query returns a vector of values $R_p \in \mathbb{R}^n$, where the $i$th coordinate $(R_p)_i = K(p,x_i)$ for $x_i \in X$. An $\varepsilon$-cover is a subset of points $Q \subset \mathbb{R}^d$ so for any $p \in \mathbb{R}^d$ that $\frac{1}{n} \|R_p - R_q\|_1\leq \varepsilon$ for some $q \in Q$. This is a smooth analog of Haussler's notion of $\varepsilon$-covers for combinatorial range spaces (e.g., defined by subsets of points within a ball query) where the resulting vectors $R_p$ are in $\{0,1\}^n$ instead of $[0,1]^n$. The kernel versions of these range spaces show up in data analysis tasks where the coordinates may be uncertain or imprecise, and hence one wishes to add some flexibility in the notion of inside and outside of a query range. Our main result is that, unlike combinatorial range spaces, the size of kernel $\varepsilon$-covers is independent of the input size $n$ and dimension $d$. We obtain a bound of $(1/\varepsilon)^{\tilde O(1/\varepsilon^2)}$, where $\tilde{O}(f(1/\varepsilon))$ hides log factors in $(1/\varepsilon)$ that can depend on the kernel. This implies that by relaxing the notion of boundaries in range queries, eventually the curse of dimensionality disappears, and may help explain the success of machine learning in very high-dimensions. We also complement this result with a lower bound of almost $(1/\varepsilon)^{\Omega(1/\varepsilon)}$, showing the exponential dependence on $1/\varepsilon$ is necessary.
UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation

Uncertainty quantification for prediction is an intriguing problem with significant applications in various fields, such as biomedical science, economic studies, and weather forecasts. Numerous methods are available for constructing prediction intervals, such as quantile regression and conformal predictions, among others. Nevertheless, model misspecification (especially in high-dimension) or sub-optimal constructions can frequently result in biased or unnecessarily-wide prediction intervals. In this paper, we propose a novel and widely applicable technique for aggregating multiple prediction intervals to minimize the average width of the prediction band along with coverage guarantee, called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). The method also allows us to directly construct predictive bands based on elementary basis functions. Our approach is based on linear or convex programming which is easy to implement. All of our proposed methodologies are supported by theoretical guarantees on the coverage probability and optimal average length, which are detailed in this paper. The effectiveness of our approach is convincingly demonstrated by applying it to synthetic data and two real datasets on finance and macroeconomics.
Semi-definite programming and quantum information

This paper presents a comprehensive exploration of semi-definite programming (SDP) techniques within the context of quantum information. It examines the mathematical foundations of convex optimization, duality, and SDP formulations, providing a solid theoretical framework for addressing optimization challenges in quantum systems. By leveraging these tools, researchers and practitioners can characterize classical and quantum correlations, optimize quantum states, and design efficient quantum algorithms and protocols. The paper also discusses implementational aspects, such as solvers for SDP and modeling tools, enabling the effective employment of optimization techniques in quantum information processing. The insights and methodologies presented in this paper have proven instrumental in advancing the field of quantum information, facilitating the development of novel communication protocols, self-testing methods, and a deeper understanding of quantum entanglement. Overall, this study offers a resource for researchers interested in the intersection of optimization and quantum information, opening up new avenues for exploration and breakthroughs in this rapidly evolving field.
Automatic Calibration and Error Correction for Generative Large Language Models via Pareto Optimal Self-Supervision

Generative Large language models (LLMs) have demonstrated remarkable capabilities for a wide range of applications, but reducing ungrounded or erroneous responses remains a major growth area. Unlike task-specific models, there lack an effective method to calibrate the confidence level of LLM responses to indicate potential errors and facilitate human-in-the-loop verification. An important source of calibration stems from expert-stipulated programmatic supervision, which is often available at low cost but has its own limitations such as noise and coverage. In this paper, we introduce a Pareto optimal self-supervision framework that can leverage available programmatic supervision to systematically calibrate LLM responses by producing a risk score for every LLM response, without any additional manual efforts. This is accomplished by learning a harmonizer model to align with LLM output as well as other weak supervision sources. The model assigns higher risk scores to more uncertain LLM responses and facilitate error correction. Experiments on standard relation extraction and classification tasks in biomedical and general domains demonstrate that the proposed risk score is highly correlated with the actual LLM error rate. By using a dynamic prompting strategy based on the risk score, we observed significant accuracy improvement for off-the-shelf LLMs, boosting GPT-3.5 results past state-of-the-art (SOTA) weak supervision model and GPT-4 results past SOTA supervised results on challenging evaluation datasets.
Early warning signals for predicting cryptomarket vendor success using dark net forum networks

In this work we focus on identifying key players in dark net cryptomarkets that facilitate online trade of illegal goods. Law enforcement aims to disrupt criminal activity conducted through these markets by targeting key players vital to the market's existence and success. We particularly focus on detecting successful vendors responsible for the majority of illegal trade. Our methodology aims to uncover whether the task of key player identification should center around plainly measuring user and forum activity, or that it requires leveraging specific patterns of user communication. We focus on a large-scale dataset from the Evolution cryptomarket, which we model as an evolving communication network. While user and forum activity measures are useful for identifying the most successful vendors, we find that betweenness centrality additionally identifies those with lesser activity in the network. But more importantly, analyzing the forum data over time, we find evidence that attaining a high betweenness score comes before vendor success. This suggests that the proposed network-driven approach of modelling user communication might prove useful as an early warning signal for key player identification.