Reviewerly/exHarmony huggingface dataset:
Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation

End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of reliability (e.g., domain-inconsistent responses, repetition problem, etc) and efficiency (e.g., long computation time, etc). In this paper, we propose a task-oriented dialogue system via action-level generation. Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions. Further, we train a Sequence-to-Sequence model which takes the dialogue history as the input and outputs a sequence of dialogue actions. The generated dialogue actions are transformed into verbal responses. Experimental results show that our light-weighted method achieves competitive performance, and has the advantage of reliability and efficiency.
Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search

Semantic retrieval, which retrieves semantically matched items given a textual query, has been an essential component to enhance system effectiveness in e-commerce search. In this paper, we study the multimodal retrieval problem, where the visual information (e.g, image) of item is leveraged as supplementary of textual information to enrich item representation and further improve retrieval performance. Though learning from cross-modality data has been studied extensively in tasks such as visual question answering or media summarization, multimodal retrieval remains a non-trivial and unsolved problem especially in the asymmetric scenario where the query is unimodal while the item is multimodal. In this paper, we propose a novel model named SMAR, which stands for Semantic-enhanced Modality-Asymmetric Retrieval, to tackle the problem of modality fusion and alignment in this kind of asymmetric scenario. Extensive experimental results on an industrial dataset show that the proposed model outperforms baseline models significantly in retrieval accuracy. We have open sourced our industrial dataset for the sake of reproducibility and future research works.
Learning Through Interpolative Augmentation of Dynamic Curvature Spaces

Mixup is an efficient data augmentation technique, which improves generalization by interpolating random examples. While numerous approaches have been developed for Mixup in the Euclidean and in the hyperbolic space, they do not fully use the intrinsic properties of the examples, i.e., they manually set the geometry (Euclidean or hyperbolic) based on the overall dataset, which may be sub-optimal since each example may require a different geometry. We propose DynaMix, a framework that automatically selects an example-specific geometry and performs Mixup between the different geometries to improve training dynamics and generalization. Through extensive experiments in image and text modalities we show that DynaMix outperforms state-of-the-art methods over six downstream applications. We find that DynaMix is more useful in low-resource and semi-supervised settings likely because it displays a probabilistic view of the geometry.
SimTDE: Simple Transformer Distillation for Sentence Embeddings

In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small transformers via a compact token embedding block and a shallow encoding block, connected with a projection layer, relaxing dimension match requirement. SimTDE simplifies distillation loss to focus only on token embedding and sentence embedding. We evaluate on standard semantic textual similarity (STS) tasks and entity resolution (ER) tasks. It achieves 99.94% of the state-of-the-art (SOTA) SimCSE-Bert-Base performance with 3 times size reduction and 96.99% SOTA performance with 12 times size reduction on STS tasks. It also achieves 99.57% of teacher's performance on multi-lingual ER data with a tiny transformer student model of 1.4M parameters and 5.7MB size. Moreover, compared to other distilled transformers SimTDE is 2 times faster at inference given similar size and still 1.17 times faster than a model 33% smaller (e.g. MiniLM). The easy-to-adopt framework, strong accuracy and low latency of SimTDE can widely enable runtime deployment of SOTA sentence embeddings.
Taming Misaligned Graph Traversals in Concurrent Graph Processing (Abstract)

This work introduces Glign, a runtime system that automatically aligns the graph traversals for concurrent queries. Glign introduces three levels of graph traversal alignment for iterative evaluation of concurrent queries. First, it synchronizes the accesses of different queries to the active parts of the graph within each iteration of the evaluation---intra-iteration alignment. On top of that, Glign leverages a key insight regarding the "heavy iterations" in query evaluation to achieveinter-iteration alignment andalignment-aware batching. The former aligns the iterations of different queries to increase the graph access sharing, while the latter tries to group queries of better graph access sharing into the same evaluation batch. Together, these alignment techniques can substantially boost the data locality of concurrent query evaluation. Based on our experiments, Glign outperforms the state-of-the-art concurrent graph processing systems Krill and GraphM by 3.6× and 4.7× on average, respectively.
Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law

In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed.The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles.The experimental results for Al 2 O 3 , CuO, ZnO, and TiO 2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature.Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods.ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.
Research and design of individual intelligent wearable information equipment

Aiming at the wearable equipment for individual combat, starting from the functional requirements of individual combat, this article aims to improve the efficiency of individual combat interaction and the cognitive performance of information flow. And it proposes a design idea for individual soldier intelligent wearable information equipment. Including the hardware composition, software composition and intelligent software service design of individual smart wearable equipment, and expounds the application methods of enhanced display, voice interaction, and eye tracking technology in individual wearable equipment. The research and development of smart wearable devices provide a reference for ideas.
Design of a Samier Cracker Dough Molding Machine Semi-Automatic with Roll Cutting Method

The main obstacle that UMKM SA has is limited production technology which is still manual so that workers take a long time and production results are not good because the thickness of the crackers is uneven and the sizes cannot match. So it is necessary to design a cracker-making machine by making a printing roll tool. The design method uses the manufacturing process method which is based on the cutting process and has a different perspective from the previous thing. Where the researcher analyzes the advantages and disadvantages of the product to be made, has an overview of the product to be made by taking samples of material sources and data from the analysis that has been carried out directly to the resource persons as well as by analyzing the processes that have been carried out. The results of the study are from data analysis of the cracker dough cutting machine with the selected concept, it can be said to be effective with a faster time record in each test, good efficiency and effectiveness of the first tool requires an average time of 03:80 to 01:12 with cracker dough size 46x46x1 totaling 121 pieces.
Analisis Blibiometrik: STEAM Dalam Pembelajaran Matematika Berbasis VOV Viewers

STEAM can be formed in several ways, one of which is through the process of learning mathematics. The purpose of the blibiometric analysis is to see the development of research trends related to STEAM in mathematics learning based on VOS Viewers. The data collection technique is by searching using Google Scholar using the keyword STEAM in learning mathematics based on 2010 to 2023, there are 1000 articles or journals. Visualization in analysis on vov viewers can be seen in the form of network visualization, overlay visualization and density visualization. Network visualization maps are used to see linkages and research theme clusters related to keywords. Ovelay Visualization is used to identify the year in which the related research theme was carried out. Meanwhile, density visualization is used to analyze research themes that are already saturated and are still rarely researched
A Multi Soft-sensor Approach for the Development of Emergencies Detection Units on the Edge

The adoption of multi-sensor hardware units to detect different types of emergencies in complex scenarios such as Industry 4.0 and Smart Cities is a recent development trend, which have demanded research efforts to optimize the way these units are assembled, configured, and deployed. However, although their expected advantages, usual multi-sensors emergency-oriented approaches may be constrained by the tight association between physical sensor devices and the emergency detection process. Therefore, this paper proposes an innovative approach to concurrently detect multiple emergencies exploiting the concept of soft-sensor modules, which are implemented on Emergency Detection Units (EDUs) as concurrent tasks along with a high-priority emergency alerting task. The soft-sensor concept defines physical sensors as ordinary input data streams, which will be processed according to the defined algorithms by each particular soft-sensor, allowing detection decisions to be performed on the edge. In order to support the practical exploitation of this concept, a formal specification based on Petri Nets is defined, modeling the concurrent operation of the tasks within an EDU. A proof of concept is also designed based on the affordable Raspberry Pi Pico microcontroller, demonstrating how the proposed approach would behave when two soft-sensors are implemented in a typical detection unit.
CORRELATION OF AUTOMATED HEMATOLOGY ANALYZER GENERATED RBC INDICES AND HISTOGRAM WITH MANUAL PERIPHERAL SMEAR EXAMINATION IN ANEMIA

Background: Anemia is a common medical condition, mostly diagnosed in developing nations. Peripheral smear examination is the gold standard for diagnosing most RBC disorders, which may fail to get diagnosed by automated cell counters. Few studies exist that compare automated analyzer based histogram, complete blood counts (CBC) parameters with peripheral smear examination. Present study was carried out to correlate these parameters. Objectives: The current study was conducted with the aim to correlate RBC indices and histograms obtained from automated analyzer with peripheral smear examination in cases of anemia. Materials And Methods: In the haematology laboratory, blood samples from 775 anemic patients were obtained during a six-month period. The haematology analyzer-based metrics (RBC indices &amp; histogram) and the peripheral smear examination were used to type each blood sample for anemia. The acquired diagnosis was examined to determine the signicance of the mean difference in various parameters. Results: 775 cases examined on peripheral blood smear showed the highest number of cases to have Normocytic normochromic anemia (57%), followed by Microcytic hypochromic anemia (35%). Dimorphic anemia (4.3%), macrocytic anemia (3.1%) and haemolytic anemia (0.6%). Analysis by erythrocyte indices and histogram showed similar frequency of various types of anemia, however the percentages varied in individual methods. The correlation between the diagnosis made by these methods was statistically signicant, p&lt;0.0001. Conclusion: Histogram based data provide early intimation of subtle changes in numerical parameters, subjective to evaluation. RBC histogram and indices should be interpreted in the light of peripheral smear
FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation.
AutoDPQ: Automated Differentiable Product Quantization for Embedding Compression

Deep recommender systems typically involve numerous feature fields for users and items, with a large number of low-frequency features. These low-frequency features would reduce the prediction accuracy with large storage space due to their vast quantity and inadequate training. Some pioneering studies have explored embedding compression techniques to address this issue of the trade-off between storage space and model predictability. However, these methods have difficulty compacting the embedding of low-frequency features in various feature fields due to the high demand for human experience and computing resources during hyper-parameter searching. In this paper, we propose the AutoDPQ framework, which automatically compacts low-frequency feature embeddings for each feature field to an adaptive magnitude. Experimental results indicate that AutoDPQ can significantly reduce the parameter space while improving recommendation accuracy. Moreover, AutoDPQ is compatible with various deep CTR models by improving their performance significantly with high efficiency.
MA-MRC: A Multi-answer Machine Reading Comprehension Dataset

Machine reading comprehension (MRC) is an essential task for many question-answering applications. However, existing MRC datasets mainly focus on data with single answer and overlook multiple answers, which are common in the real world. In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. To achieve this purpose, we design a novel pipeline method: data collection, data cleaning, question generation and test set annotation. Based on these procedures, we construct a high-quality multi-answer MRC dataset (MA-MRC) with 129K question-answer-context samples. We implement a sequence of baselines and carry out extensive experiments on MA-MRC. According to the experimental results, MA-MRC is a challenging dataset, which can facilitate the future research on the multi-answer MRC task.
Personalized Retrieval over Millions of Items

Personalized retrieval seeks to retrieve items relevant to a user event (e.g. a page visit or a query) that are adapted to the user's personal preferences. For example, two users who happen to perform the same event such as visiting the same product page or asking the same query should receive potentially distinct recommendations adapted to their individual tastes. Personalization is seldom attempted over catalogs of millions of items since the cost of existing personalization routines scale linearly in the number of candidate items. For example, performing two-sided personalized retrieval (with both event and item embeddings personalized to the user) incurs prohibitive storage and compute costs. Instead, it is common to use non-personalized retrieval to obtain a small shortlist of items over which personalized re-ranking can be done quickly. Despite being scalable, this strategy risks losing items uniquely relevant to a user that fail to get shortlisted during non-personalized retrieval. This paper bridges this gap by developing the XPERT algorithm that identifies a form of two-sided personalization that can be scalably implemented over millions of items and hundreds of millions of users. Key to overcoming the computational challenges of personalized retrieval is a novel concept of morph operators that can be used with arbitrary encoder architectures, completely avoids the steep memory overheads of two-sided personalization, provides millisecond-time inference and offers multi-intent retrieval. On multiple public and proprietary datasets, XPERT offered upto 5% superior recall and AUC than state-of-the-art techniques. Code for XPERT is available at https://github.com/personalizedretrieval/xpert.
Traceability in Fast-Moving Consumer Goods Industries by Application of Blockchain Technology and Smart Contracts

Blockchain and generally decentralized technologies will be used quickly in all industrial fields. Fast-Moving Consumer Goods (FMCG) is one of the areas that has attracted a lot of attention from different perspectives, both in terms of marketing and in terms of contributing to environmental sustainability through the reduction of CO2 production. This study attempts to propose a suitable and near-to-optimal supply chain network for FMCG by using the critical advantages of blockchain technology, especially traceability. With the help of blockchain technology, information can be transferred to this network with greater transparency, traceability, and security. Moreover, the smart contract will provide the possibility of autonomous contract execution among the stakeholders in the supply chain. The smart contract eliminates payment delays, error risks, and the complexity of a traditional contract while maintaining the authenticity and credibility of supply chain interactions.
Efficient Time-Series Data Delivery in IoT with Xender

Large amounts of time-series data need to be continually delivered from IoT devices to the cloud for real-time data analytics. The data delivery process is intrinsically slow and costly. Therefore, lots of work proposes various data reduction methods to accelerate it. Yet, they are either designed for the simple linear time-series data or computation-intensive, which is not suitable for the IoT devices with limited resources. In this paper, we propose Xender, a system to accelerate time-series data delivery. Xender consists of two key components: data sampler and data generator. Data sampler works on IoT devices to sample time-series data with low resource footprint, and data generator works on the cloud to efficiently generate data that significantly resembles the original. Besides, Xender can adapt to the dynamic characteristics of the time-series data with the content-aware mechanism, as well as the dynamic computation resources by supporting multiple data generation quality levels and using the anytime generation mechanism. We implement Xender and evaluate it with testbed experiments using six real-world datasets. The results show that it can significantly reduce data delivery time by 45.79% on average compared against existing schemes, and adapt to computation resources with up to 1014.40Mbps data generation throughput.
Adaptive Domain Generalization Via Online Disagreement Minimization

Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed (Gulrajani and Lopez-Paz, 2020), reveals that most of them do not beat simple empirical risk minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target-domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.
The Analysis of Impoliteness Strategies on Ice Cube's Songs

This study analyzes impoliteness strategies, specifically identifying the types and functions of impoliteness strategies contained in the lyrics of rap songs from Ice Cube. The total rap songs used in this study amounted to 10 rap songs. This research uses descriptive qualitative as a research method. There are 53 lines of stanza in lyrics taken and after being identified the data related to the type and function of Jonathan Culpeper's impolite strategy theory. The results of the analysis indicate that most used impoliteness strategy appears in the lyrics of rap songs from Ice Cube is Positive Impoliteness with 24 data. In the lyrics of the song, Ice Cube uses a lot of taboo words, unfamiliar words, and improper identity changes. In addition, the function Affective Impoliteness is the most used in the lyrics with 20 data. In addition, this study also describes the representation of the African-American race described by Ice Cube through the lyrics of the rap song. What can be found is the use of terms that refer to the African-American race, the way that the police treat the African-American race, and also the representation of the injustices that the African-American race gets.