Reviewerly/exHarmony huggingface dataset:
Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

With the rapid development of Internet of Things technology, the image data on the Internet are growing at an amazing speed. How to describe the semantic content of massive image data is facing great challenges. Attentional mechanisms originate from the study of human vision. In cognitive science, due to bottlenecks in information processing, humans selectively attend to a portion of all information while ignoring the rest of the visible information. This study mainly discusses the natural language description generation method of Internet of Things intelligent image based on attention mechanism. In this study, a CMOS sensor based on Internet of Things technology is used for image data acquisition and display. FPGA samples cis16bit parallel port data, writes FIFO, stores image data, and then transmits it to host computer for display through network interface. In order to minimize the value of cross-entropy loss function, maximum-likelihood estimation is used to maximize the joint probability of word sequences in the language model when sentence descriptions are generated using the encoder-decoder framework. At each moment, in addition to image features, additional text features are input. Image feature vector and text feature vector are weighted and summed by attention mechanism at each time. In decoding, the attention mechanism gives each image region feature weight, and the long-term and short-term memory network decodes in turn, but the long-term and short-term memory network has limited decoding ability. We use bidirectional long-term and short-term memory network instead of long-term memory network, and dynamically focus on context information through forward LSTM and reverse LSTM. The specificity of the proposed network is 5% higher than that of the 3D convolution residual link network. The results show that the performance of image description model is improved by inputting image context and text context into long-term memory network decoder.
Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study

The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users.To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works.We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network.With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values.To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.
Toward fast theta‐join: A prefiltering and amalgamated partitioning approach

Abstract As one of the most useful online processing techniques, the theta‐join operation has been utilized by many applications to fully excavate the relationships between data streams in various scenarios. As such, constant research efforts have been put to optimize its performance in the distributed environment, which is typically characterized by reducing the number of Cartesian products as much as possible. In this article, we design and implement a novel fast theta‐join algorithm, called Prefap , by developing two distinct techniques— prefiltering and amalgamated partitioning —based on the state‐of‐the‐art FastThetaJoin algorithm to optimize the efficiency of the theta‐join operation. Firstly, we develop a prefiltering strategy before data streams are partitioned to reduce the amount of data to be involved and benefit a more fine‐grained partitioning. Secondly, to avoid the data streams being partitioned in a coarse‐grained isolated manner and improve the quality of the partition‐level filtering, we introduce an amalgamated partitioning mechanism that can amalgamate the partitioning boundaries of two data streams to assist a fine‐grained partitioning. With the integration of these two techniques into the existing FastThetaJoin algorithm, we design and implement a new framework to achieve a decreased number of Cartesian products and a higher theta‐join efficiency. By comparing with existing algorithms, FastThetaJoin in particular, we evaluate the performance of Prefap on both synthetic and real data streams from two‐way to multiway theta‐join to demonstrate its superiority.
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification

Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined.In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings.We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.
Nonlinear Dynamics of the Financial–Growth Nexus in African Emerging Economies: The Case of a Macroprudential Policy Regime

A panel data analysis of nonlinear financial growth dynamics in a macroprudential policy regime was conducted in a panel of 10 African emerging countries from 1983–2020, where it had been a non-prudential regime from 1983–1999 and a prudential regime from 2000–2020. The paper explored the validity of invented U-shape hypothesis in the prudential policy regime as well as the threshold level at which excessive finance boosts growth using the panel smooth transition regression (PSTR) model. The PSTR model was adopted due to its ability to address the problems of endogeneity and heterogeneity in a nonlinear framework. The results reveal evidence of a nonlinear effect between financial development and economic growth, where the minimum level of financial development is found to be 60.5% of GDP, above which financial development increases growth in African emerging countries. The findings confirmed a U-shaped relationship, contradicting the invented U-curve hypothesis. The focal policy recommendation is that the financial sector should be given adequate consideration and recognition by, for example, implementing appropriate financial reforms, developing a suitable investment portfolio, and keeping spending on technological investment in Africa’s emerging countries below the threshold. Again, caution is needed when introducing macroprudential policies at a low level of the financial system.
CLEF Workshop JOKER: Automatic Wordplay and Humour Translation

Humour remains one of the most difficult aspects of intercultural communication: understanding humour often requires understanding implicit cultural references and/or double meanings, and this raises the question of its (un)translatability. Wordplay is a common source of humour in due to its attention-getting and subversive character. The translation of humour and wordplay is therefore in high demand. Modern translation depends heavily on technological aids, yet few works have treated the automation of humour and wordplay translation, or the creation of humour corpora. The goal of the JOKER workshop is to bring together translators and computer scientists to work on an evaluation framework for wordplay, including data and metric development, and to foster work on automatic methods for wordplay translation. We propose three pilot tasks: (1) classify and explain instances of wordplay, (2) translate single words containing wordplay, and (3) translate entire phrases containing wordplay.
Drone-Aided Path Planning for Unmanned Ground Vehicle Rapid Traversing Obstacle Area

Even with visual contact equipment such as cameras, an unmanned ground vehicle (UGV) alone usually takes a lot of time to navigate an unfamiliar area with obstacles. Therefore, this study proposes a fast drone-aided path planning approach to help UGVs traverse an unfamiliar area with obstacles. In this scenario called UAV/UGV mobile collaboration (abbreviated UAGVMC), a UGV initially invokes an unmanned aerial vehicle (UAV) at the scene to take a ground image and send it back to the cloud to proceed with object detection, image recognition, and path planning (abbreviated odirpp). The cloud then sends the UGV a well-planned path map to help traverse an unfamiliar area. This approach uses the one-stage object detection and image recognition algorithm YOLOv4-CSP to quickly and accurately identify obstacles and the New Bidirectional A* (NBA*) algorithm to plan an optimal route avoiding ground objects. Experiments show that the execution time of path planning for each scene is less than 10 s on average. It does not affect the image quality of the path map. It ensures that the user can correctly interpret the path map and remotely drive the UGV rapidly, passing through that unfamiliar area with obstacles. As a result, the selected model can outperform the other alternatives significantly by average performance ratio up to 3.87 times on average.
Learning to Express in Knowledge-Grounded Conversation

Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context.In this work, we mainly consider two aspects of knowledge expression, namely the structure of the response and style of the content in each part.We therefore introduce two sequential latent variables to represent the structure and the content style respectively.We propose a segmentation-based generation model and optimize the model by a variational approach to discover the underlying pattern of knowledge expression in a response.Evaluation results on two benchmarks indicate that our model can learn the structure style defined by a few examples and generate responses in desired content style.
Multi-Sensor Approach Combined with Pedological Investigations to Understand Site-Specific Variability of Soil Properties and Potentially Toxic Elements (PTEs) Content of an Industrial Contaminated Area

A combination of indirect soil investigation by proximal soil sensors (PSS), based on geophysical (ARP, EMI), physical (Cone Index –CI– by ultrasound penetrometry) and spectrometric (γ-rays) techniques, as well as pedological surveys, was applied in the field to assess the spatial variability of soil pollution and physical degradation in an automobile-battery recycling plant in southern Italy. Five homogeneous zones (HZs) were identified by the PSS and characterized by soil profiles. CI measurements and field analysis showed clear features of physical (i.e., soil compaction, massive structure) degradation. XRF in situ (on profiles) analysis using portable equipment (pXRF) showed Pb, Cd and As concentrations exceeding the contamination thresholds provided by the Italian regulation for industrial land use up to 20 or 100 cm of depth. Hence, a validation procedure, based on pXRF field survey, was applied to the PSS approach used for the HZs identification. High consistency was found between the HZs and the PTEs in the most contaminated areas. Significant negative Pearson correlation coefficients were found between γ-rays dose rate and Pb, Cu, Zn, As and Ni; positive ones were found between γ-rays and autochthonous lithogenic elements (V, Ti, Mn, K, Sr, Nb, Zr, Rb, Th), confirming that higher radionuclide activity correlated with lower pollution levels.
Evolution of Agents in the Case of a Balanced Diet

Agents are always in an interactive environment. With time, the intelligence of agents will be affected by the interactive environment. Agents need to coordinate the interaction with different environmental factors to achieve the optimal intelligence state. We consider an agent&#x0027;s interaction with the environment as an action-reward process. An agent balances the reward it receives by acting with various environmental factors. This paper refers to the concept of interaction between an agent and the environment in reinforcement learning and calculates the optimal mode of interaction between an agent and the environment. It aims to help agents maintain the best intelligence state as far as possible. For specific interaction scenarios, this paper takes food collocation as an example, the evolution process between an agent and the environment is constructed, and the advantages and disadvantages of the evolutionary environment are reflected by the evolution status of the agent. Our practical case study using dietary combinations demonstrates the feasibility of this interactive balance.
An SHA-3 Hardware Architecture against Failures Based on Hamming Codes and Triple Modular Redundancy

Cryptography has become one of the vital disciplines for information technology such as IoT (Internet Of Things), IIoT (Industrial Internet Of Things), I4.0 (Industry 4.0), and automotive applications. Some fundamental characteristics required for these applications are confidentiality, authentication, integrity, and nonrepudiation, which can be achieved using hash functions. A cryptographic hash function that provides a higher level of security is SHA-3. However, in real and modern applications, hardware implementations based on FPGA for hash functions are prone to errors due to noise and radiation since a change in the state of a bit can trigger a completely different hash output than the expected one, due to the avalanche effect or diffusion, meaning that modifying a single bit changes most of the desired bits of the hash; thus, it is vital to detect and correct any error during the algorithm execution. Current hardware solutions mainly seek to detect errors but not correct them (e.g., using parity checking or scrambling). To the best of our knowledge, there are no solutions that detect and correct errors for SHA-3 hardware implementations. This article presents the design and a comparative analysis of four FPGA architectures: two without fault tolerance and two with fault tolerance, which employ Hamming Codes to detect and correct faults for SHA-3 using an Encoder and a Decoder at the step-mapping functions level. Results show that the two hardware architectures with fault tolerance can detect up to a maximum of 120 and 240 errors, respectively, for every run of KECCAK-p, which is considered the worst case. Additionally, the paper provides a comparative analysis of these architectures with other works in the literature in terms of experimental results such as frequency, resources, throughput, and efficiency.
SNPD: Semi-Supervised Neural Process Dehazing Network with Asymmetry Pseudo Labels

Haze can cause a significant reduction in the contrast and brightness of images. CNN-based methods have achieved benign performance on synthetic data. However, they show weak generalization performance on real data because they are only trained on fully labeled data, ignoring the role of natural data in the network. That is, there exists distribution shift. In addition to using little real data for training image dehazing networks in the literature, few studies have designed losses to constrain the intermediate latent space and the output simultaneously. This paper presents a semi-supervised neural process dehazing network with asymmetry pseudo labels. First, we use labeled data to train a backbone network and save intermediate latent features and parameters. Then, in the latent space, the neural process maps the latent features of real data to the latent space of synthetic data to generate one pseudo label. One neural process loss is proposed here. For situations where the image may be darker after dehazing, another pseudo label is created, and one new loss is used to guide the dehazing result at the output end. We combine the two pseudo labels with designed losses to suppress the distribution shift and guide better dehazing results. Finally, the artificial and hazy natural images are tested experimentally to demonstrate the method’s effectiveness.
A Deep Learning-Based Sentiment Classification Model for Real Online Consumption

Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers' product experience and contain a lot of important information, but inevitably there are malicious or irrelevant reviews. It is especially important to discover and identify the real sentiment tendency in online reviews in a timely manner. Therefore, a deep learning-based real online consumer sentiment classification model is proposed. First, the mapping relationship between online reviews of goods and sentiment features is established based on expert knowledge and using fuzzy mathematics, thus mapping the high-dimensional original text data into a continuous low-dimensional space. Secondly, after obtaining local contextual features using convolutional operations, the long-term dependencies between features are fully considered by a bidirectional long- and short-term memory network. Then, the degree of contribution of different words to the text is considered by introducing an attention mechanism, and a regular term constraint is introduced in the objective function. The experimental results show that the proposed convolutional attention-long and short-term memory network (CA-LSTM) model has a higher test accuracy of 83.3% compared with other models, indicating that the model has better classification performance.
Multi-Sensor Data Fusion Approach for Kinematic Quantities

A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All methods defined through the framework allow the combination of signals obtained from position, velocity and acceleration sensors addressing the same target, and improvement in the observation of the kinematics of the target. Differently from several alternative methods, the considered ones need no dynamic and/or error models to operate and can be implemented with low computational burden. In fact, they gain measurements by summing filtered versions of the heterogeneous kinematic quantities. In particular, in the case of position measurement, the use of filters with finite impulse responses, all characterized by finite gain throughout the bandwidth, in place of straightforward time-integrative operators, prevents the drift that is typically produced by the offset and low-frequency noise affecting velocity and acceleration data. A simulated scenario shows that the adopted method keeps the error in a position measurement, obtained indirectly from an accelerometer affected by an offset equal to 1 ppm on the full scale, within a few ppm of the full-scale position. If the digital output of the accelerometer undergoes a second-order time integration, instead, the measurement error would theoretically rise up to 12n(n+1) ppm in the full scale at the n-th discrete time instant. The class of methods offered by the proposed framework is therefore interesting in those applications in which the direct position measurements are characterized by poor accuracy and one has also to look at the velocity and acceleration data to improve the tracking of a target.
Same Author or Just Same Topic? Towards Content-Independent Style Representations

Linguistic style is an integral component of language.Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author?The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training.However, a good performance on the AV task does not ensure good "generalpurpose" style representations.For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone.We introduce a variation of the AV training task that controls for content using conversation or domain labels.We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework.We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.
Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm

Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is the task of specifying and tagging each word of a sentence with nouns, verbs, adjectives, adverbs, and more. Sentiment Analysis may well be a procedure accustomed to determining if a given sentence's emotional tone is neutral, positive or negative. To assign polarity scores to the thesis or entities within phrase, in-text analysis and analytics, machine learning and natural language processing, approaches are incorporated. This Sentiment Analysis using POS tagger helps us urge a summary of the broader public over a specific topic. For this, we are using the Viterbi algorithm, Hidden Markov Model, Constraint based Viterbi algorithm for POS tagging. By comparing the accuracies, we select the foremost accurate result of the model for Sentiment Analysis for determining the character of the sentence.