Reviewerly/exHarmony huggingface dataset:
A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam

Feedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage.
The impact of traditional and interactive post-editing on Machine Translation User Experience, quality, and productivity

Abstract This paper presents a user study with 15 professional translators in the English-Spanish combination. We present the concept of Machine Translation User Experience (MTUX) and compare the effects of traditional post-editing (TPE) and interactive post-editing (IPE) on MTUX, translation quality and productivity. Results suggest that translators prefer IPE to TPE because they are in control of the interaction in this new form of translator-computer interaction and feel more empowered in their interaction with Machine Translation. Productivity results also suggest that IPE may be an interesting alternative to TPE, given the fact that translators worked faster in IPE even though they had no experience in this new machine translation post-editing modality, but were already used to TPE.
Expanding Domain-Specific Knowledge Graphs with Unknown Facts

Many knowledge graphs have been created to support intelligent applications, such as search engines and recommendation systems. Some domain-specific knowledge graphs contain similar contents in nature (e.g., the FreeBase contains information about actors and movies which are the core of the IMDB). Adding relevant facts or triples from one knowledge graph into another domain-specific knowledge graph is key to expanding the coverage of the knowledge graph. The facts from one knowledge graph may contain unknown entities or relations that do not occur in the existing knowledge graphs, but it doesn’t mean that these facts are not relevant and hence can not be added to an existing domain-specific knowledge graph. However, adding irrelevant facts will violate the inherent nature of the existing knowledge graph. In other words, the facts that conform to the subject matter of the existing domain-specific knowledge graph only can be added. Therefore, it is vital to filter out irrelevant facts in order to avoid such violations. This paper presents an embedding method called UFD to compute the relevance of the unknown facts to an existing domain-specific knowledge graph so that the relevant new facts from another knowledge graph can be added to the existing domain-specific knowledge graph. A new dataset, called UFD-303K, is created for evaluating unknown fact detection. The experiments show that our embedding method is very effective at distinguishing and adding relevant unknown facts to the existing knowledge graph. The code and datasets of this paper can be obtained from GitHub ( https://github.com/MiaoHu-Pro/UFD ).
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion

Denoising Diffusion Probabilistic Models have shown extraordinary ability on various generative tasks. However, their slow inference speed renders them impractical in speech synthesis. This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inference and high sample quality. Firstly, we employ linear interpolation between the target and noise to design a diffusion sequence for training, while previously the diffusion path that links the noise and target is a curved segment. When decreasing the number of sampling steps (i.e., the number of line segments used to fit the path), the ease of fitting straight lines compared to curves allows us to generate higher quality samples from a random noise with fewer iterations. Secondly, to reduce computational complexity and achieve effective global modeling of noisy speech, LinDiff employs a patch-based processing approach that partitions the input signal into small patches. The patch-wise token leverages Transformer architecture for effective modeling of global information. Adversarial training is used to further improve the sample quality with decreased sampling steps. We test proposed method with speech synthesis conditioned on acoustic feature (Mel-spectrograms). Experimental results verify that our model can synthesize high-quality speech even with only one diffusion step. Both subjective and objective evaluations demonstrate that our model can synthesize speech of a quality comparable to that of autoregressive models with faster synthesis speed (3 diffusion steps).
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) has the potential to solve complex long horizon tasks using temporal abstraction and increased exploration. However, hierarchical agents are difficult to train due to inherent non-stationarity. We present primitive enabled adaptive relabeling (PEAR), a two-phase approach where we first perform adaptive relabeling on a few expert demonstrations to generate efficient subgoal supervision, and then jointly optimize HRL agents by employing reinforcement learning (RL) and imitation learning (IL). We perform theoretical analysis to $(i)$ bound the sub-optimality of our approach, and $(ii)$ derive a generalized plug-and-play framework for joint optimization using RL and IL. PEAR uses a handful of expert demonstrations and makes minimal limiting assumptions on the task structure. Additionally, it can be easily integrated with typical model free RL algorithms to produce a practical HRL algorithm. We perform experiments on challenging robotic environments and show that PEAR is able to solve tasks that require long term decision making. We empirically show that PEAR exhibits improved performance and sample efficiency over previous hierarchical and non-hierarchical approaches. We also perform real world robotic experiments on complex tasks and demonstrate that PEAR consistently outperforms the baselines.
Generic Attacks against Cryptographic Hardware through Long-Range Deep Learning

Hardware-based cryptographic implementations utilize countermeasures to resist side-channel attacks. In this paper, we propose a novel deep-learning architecture for side-channel analysis called SCANET that generalizes across multiple implementations and algorithms without manual tuning or trace pre-processing. We achieve this by combining a novel input processing technique with several advanced deep learning techniques including transformer blocks and multi-task learning. We demonstrate the generality of our approach by successfully attacking four hardware-accelerated countermeasures for elliptic curve digital signatures in an end-to-end manner without human tuning. Additionally, we showcase SCANET's ability to generalize across multiple algorithms by successfully replicating state-of-the-art attacks against protected AES without the need for trace preprocessing, hand-tuning, or model architectural changes. These results offer promising prospects for generic and automated side-channel leakage evaluation without manual effort.
Investigating the Relation between Requirements Framing and Confirmation Bias in Testing

Requirements Engineering (RE) and Software Testing (ST) phases are essential to software development. Acknowledging the emergence of cognitive biases, systematic divergences from optimum decision-making, is crucial in software development because of the extensive human participation in various roles. How information is presented can impact decision-making, known as the framing effect—a cognitive bias. The framing effect hinders creativity in software design during the requirements phase. This cognitive effect may further lead to another bias —confirmation bias— in ST. Confirmation bias in ST is testing requirements-consistent behaviors instead of requirements-inconsistent ones. This research explores the effects of framing software requirements on the manifestation of confirmation bias during testing. Additionally, the work aims to propose mitigation strategies, referred to as debiasing, for framing and confirmation bias in the context of ST. This work employs both quantitative and qualitative empirical methods to address its aims. Hence, this research will explore the relation between the framing of requirements and the confirmation bias during ST. Furthermore, this research will also develop debiasing techniques to mitigate the negative effects of these biases to improve software quality.
Manage the Problem

With the core problem understood, establishing and managing a cyber risk program becomes infinitely more focused and effective. Bring management into focus with a known framework (as a guide, not a solution), structure the management approach in accordance with the program, and set a review cadence to ensure your management approach remains relevant.
A Comparative Study of the Educational Practice of Industrial Design Majors for New Engineering Disciplines

The implementation of the national innovation-driven development strategy has promoted the education of industrial design in China in the new era. With the promotion of "new engineering" education construction, domestic universities have carried out a series of educational reforms of industrial design. The relevant literature research is of great significance. Therefore, based on the bibliometric method and content analysis method, this paper uses citespace as a tool to visualize and analyze the literature data of industrial design education research in the context of new engineering in cnki, and conducts comparative analysis on the curriculum system, teaching mode and talent cultivation in domestic industrial design education practice through the measurement analysis and cluster study of key words, in order to continuously strengthen the connection between education theory and education practice.
Preserving the Privacy of Wearable IoT Device Data Using Blockchain

Personal healthcare has become the most important part of present human life due to the COVID-19 pandemic. The awareness of one’s health is made easily available by adopting IoT wearable devices like Fitbit, smart watches, oximeters, etc. All these devices store sensitive information like a heartbeat, BP, distance traveled, calories burnt, stress levels, location details, etc., and are stored in third-party data servers. These servers are vulnerable to illegitimate users and also have a single point of failure. The personal data of the individuals should be protected along with its integrity. At the same time, the data stored in the servers should be decentralized to overcome the problem of a single point of attack. The objective of this article is to provide a framework that uses SHA-56 for generating the hash code and Blockchain technology to store the data. Thus, providing privacy, security, and integrity for the data.
KENDALA PENINGKATAN KEMAMPUAN LITERASI NUMERASI BERBASIS PERMAINAN TRADISIONAL DI SDN PLOSOKEREP 2 BLITAR

The existence of the Covid-19 pandemic has become a threat to the education of the nation's future successors which is also exacerbated by the swift currents of globalization. Numerical literacy is one of the important literacy skills to be mastered by elementary school students. Especially if numeracy literacy learning is supported by traditional game media. This study aims to determine the obstacles in increasing traditional game-based numeracy literacy skills at SDN Plosokerep 2 Blitar. as for the research method used is qualitative research with the type of case study. This research was carried out at SDN Plosokerep 2, especially for grade 2 students
Penggunaan Metode STAD untuk Meningkatkan Konsentrasi Siswa Materi Bangun Datar melalui Aplikasi Canva pada Kelas I SDN 64/I Muara Bulian

The reduced level of student concentration and too much teacher dominance in learning results in low student learning outcomes. Analysis of the use of the STAD (Student Temas Achiefment Division) learning model through the canva application is to find out the extent to which student’s learning concentration in class I SDN 64/I Muara Bulian has increased in flat shape learning. In this study using the Classroom Action Research (PTK) type. The research was carried out in 2 cycles with 2 meetings in each cycle. Then the cubjects of this study were 27 students, consisting of 13 boys and 14 girls of class 1 SDN 64/I Muara Bulian. The data collection used is observation sheets, interviews, and document studies. Learning concentration and learning outcomes were analyzed descriptively quantitatively using the percentage technique. The results of student concentration increased from cycle I to cycle II, namely from the percentage of student completeness by 55% to 88%. It can be seen that the STAD learning model through the Canva application can increase student learning concentration in learning flat shapes.
A New Secure Encryption Algorithm Based on RC4 Cipher and 4D Hyperchaotic Sprott-S System

The widespread exchange of data in various formulas such as text and images via multimedia has created a great need for encryption techniques development, that can protect sharing of important secure data from untrusted or unauthorized access. In this paper, a two-level encryption method is proposed, the first level involves the use of an RC4 algorithm with several different length keys. The number of used keys depends on the length of the input original text. The keys are extracted from a 4D hyperchaotic Sprott_S system. The first encryption process is followed by the second encryption using the XOR gate for the first encryption output, with another key chain extracted from the same hyperchaotic system, that is pre-processed differently. Several measurements were used to measure the efficiency of the algorithm, including the time of application of the algorithm, histogram, bit error rate, and correlation. The experiments that were conducted on texts of different sizes showed the efficiency of the algorithm's performance, its high level of security, and the lack of time it needs, which confirms its quality.
Abstract 11005: The Impact of Time Censoring on Machine Learning Models Which Identify Patients With Undiagnosed Cardiac Amyloidosis

Introduction: Cardiac amyloidosis (CA) is a common cause of progressive heart failure. New therapies can improve outcomes but most CA patients remain undiagnosed and untreated. Machine learning models deployed on electronic health record (EHR) data may be able to find patients with undiagnosed CA. To date, most models have focused on identification of undiagnosed amyloid from uncensored data modalities (Fig 1). Hypothesis: We hypothesized that lack of post-diagnosis censoring when training CA models leads to poor performance in predicting patients with undiagnosed CA whereas training with appropriate time censoring improves performance. Methods: We used 41 EHR features (demographics, labs, electrocardiogram/echocardiography measurements, vitals) to train a boosted decision tree model with and without time censoring. This was applied to 112 patients with confirmed CA and 22,400 controls matched on age, sex, encounter frequency and timespan of EHR. We also compared our findings to a web-based CA algorithm that was publicly available in 2020. Results: The EHR algorithm had modestly higher performance on at-risk, time-censored patients when trained with and without time censoring (area under the receiver operating characteristic curve (AUROC) 0.84±0.09 vs 0.79±0.07). Testing on temporally uncensored data showed higher performance (AUROC: 0.91±0.05) which may be unrepresentative of deployment scenarios where post-diagnostic features are unavailable for model use. The publicly available algorithm demonstrated a similar trend when tested on uncensored data (AUROC: 0.67±0.03) as compared to an appropriately censored feature set (AUROC: 0.54±0.04). Conclusions: EHR algorithms can be trained to find patients with high risk of undiagnosed cardiac amyloidosis. These models should be evaluated on temporally censored data so that post-diagnostic features do not artificially inflate performance estimates and negatively impact real-world deployment.
Excel-oriented calculator for calculating results of entropy analysis of data distributed by categories

The goal of the work. Development of EXCEL-oriented calculator for calculating the results of entropy analysis of data, which are distributed by categories. The subject of research is histograms of arbitrary distribution laws and conjugation tables 2×2. Research methods: Entropy and information analysis of histograms of arbitrary distribution laws and conjugation tables. The obtained results. It is proposed to use methods of entropy analysis for the analysis of data distributed by categories; information on the structure of the EXCEL-oriented calculator designed for this purpose is given. The calculator makes it possible to calculate entropy characteristics of histograms, namely: histogram entropy, histogram dispersion, histogram confidence intervals, diversity information index. The calculator performs a pairwise comparison of entropies of histograms using the Hutcheson method, determines Hellinger and Kullback-Leibler distances between histograms of arbitrary distribution laws and thus complements the chi-square criterion, determines the informational correlation coefficient. The correspondence between the Pearson correlation coefficient and the information correlation coefficient is established by the method of statistical modeling. For 2×2 conjugation tables, the calculator makes it possible to estimate the significance of the interaction between the row factor and the column factor. The calculator determines the values of conditional entropies for 2×2 conjugation tables. The proposed calculator fills the gaps in existing software products and can be used to process data distributed by categories using entropy analysis methods. It is shown that entropy methods of analysis are appropriate to use in cases where histograms determine arbitrary distribution laws.
Towards masked autoencoding pre-training for wide area motion imagery

Transformer models are demonstrating remarkable and emergent capabilities in the natural language processing domain. These models are bounded only by the availability of large training datasets. These datasets can be tractably obtained since natural language models are pre-trained using self-supervision in the form of token masking. Papers like He et al. and Cao et al. have recently shown the power of this token masking technique by utilizing masked autoencoders as scalable vision learners in combination with a self-supervised pre-training technique for vision transformer models. Feichtenhofer et al. extended these techniques to video, proving that masked autoencoders are scalable spatiotemporal learners as well. To our best knowledge, these techniques have only been experimented on ground-level, object-centric style imagery and video. Extending these techniques to remote or overhead imagery presents two significant problems. First, the size of objects of interest are small compared to the typical mask patch size. Second, the frames are not object centered. In this study, we explore if modern self-supervised pre-training techniques like masked auto encoding extend well to overhead wide area motion imagery (WAMI) data. We argue that modern pre-training techniques like MAE are well suited to WAMI data given the typical object size in this domain as well as the ability to leverage strong global spatial contextual information. To this end, we conduct a comprehensive exploration of different patch sizes and masking ratios on the popular WAMI dataset, WPAFB 2009. We find that domain-specific adjustments to these pre-training techniques result in downstream performance improvements on computer vision tasks including object detection.
Applications of projection fringes and structured light surface metrology for semiconductor packaging and their challenges

In our paper, we discuss applications of projection fringes and structured light surface metrology for measurements of the topography of specular and diffuse surfaces in semiconductor manufacturing. We report recent progress in the projection fringes metrology applied to the packaging of crystalline solar cells, and heat sinks for power devices. In addition, we also discuss the progress of metrology of highly reflective surfaces encountered in thin film solar cell manufacturing and cell phone production. We discuss factors limiting the accuracy and speed of these methods.
Cognitive tip and cue: a novel framework for intelligent automated collection planning

We introduce a novel collection planning and orchestration framework called Cognitive Tip and Cue (CTC). CTC consists of three components: a natural language processing module that extracts entities and collection intent from free-text, a sensor recommendation module that scores sensors modalities based on collection parameters and environmental context, and an optimization module that uses a multi-objective genetic algorithm to evolve optimal data provider selections. CTC learns an association between intelligence, surveillance and reconnaissance needs and the historical performance of similar requests. We demonstrate that CTC generates collection plans that appropriately account for collection constraints while selecting data providers that align with tradecraft intuition.
Unpacking the Exploration–Exploitation Tradeoff on Snapchat: The Relationships between Users' Trace Data and Exploration-Exploitation Interests

Social media users continuously decide between looking for content they are already familiar with and exploring novel content. They choose between watching their favorite channels, scrolling their feeds to find something different, or doing both. Although prior research has extensively studied the exploration/exploitation (E/E) trade-off, little is known about how it operates on social media platforms. In this work, we paired a survey of 1,073 Snapchat users measuring their exploration and exploitation interests (EEI) with server logs of aggregated behavioral data. We found that users interested in exploring content were more likely to spend additional time on the app and had more impressions while using the app than users not interested in exploring content. Moreover, users reporting low interest in exploring content were more likely to use communication features. Contrary to previous E/E conceptualizations, users interested in exploring content were more likely to search for content from familiar sources rather than from novel sources. These results show that prior E/E definitions contrast with what users do on a social media platform. This study provides new perspectives on users' exploration and exploitation interests on social media platforms.