Reviewerly/exHarmony huggingface dataset:
ASSD: Arabic Semantic Similarity Dataset

Finding semantic similarity between sentences is very useful in many NLP applications, such as information retrieval, plagiarism detection, information extraction, and machine translation. Limitations in Arabic language resources have led to a poor level of research in Arabic sentence similarity. This challenge makes identifying semantically similar sentences in Arabic even more difficult. This paper presents a new Arabic dataset for the sentence similarity task. This dataset can be used to help develop sentence similarity approaches. In addition, the main purpose of the created dataset is to evaluate the sentence similarity approach. The dataset has been collected from Wikipedia, an intermediate lexicon, and other WWW resources. This paper gives more details about the processes of collecting data, filtering, preprocessing the pairs of sentences and some statistics about the dataset, for building a benchmark for semantic textual similarity. The dataset is available for future research in this field. The experiment shows that the created dataset is an efficient tool for evaluating semantic similarity approaches for the Arabic language.
Comparative Study of Different Tokenization Strategies for Streaming End-to-End ASR

Most End-to-End Automatic Speech Recognition (ASR) models use character-based vocabularies: characters, sub-words (BPE), or words. While these work well for training a monolingual model, there are certain limitations when ap-plying these to a multilingual model. Rare characters from character-rich languages like Korean can easily result in large vocabulary size, limiting the model's compactness. Repre-senting text at the level of bytes has also been proposed. However, a byte sequence representation of text is often much longer, which increases the decoding time and makes it computationally expensive for on-device use. Byte-based sub-words (BBPE) are proposed in neural machine translation for word representation but are still unexplored in the ASR domain. In this work, we conduct an empirical study comparing the above three tokenization strategies across three metrics: Word Error Rate (WER), model size, and the decoding time, which are critical for an on-device ASR. We did extensive experiments for both monolingual and bilingual, with languages belonging to same (English and Spanish) and different (English and Korean) language families. Our exper-iments show that BBPE and BPE models yield a similar WER for English and Spanish. While for a character-rich language like Korean, we get 26% and 14% relative WER improvement with BBPE monolingual and bilingual models, respectively. In contrast, the byte models trade-off small model size and a fixed vocabulary at the cost of high xRT. Among all three, we found the BBPE strategy to be the most flexible and optimal for most cases.
Application of Bidirectional Gated Recurrent Unit (BiGRU) in Sentiment Analysis of Tokopedia Application Users

Customer satisfaction is an important thing in business. The aim is to develop future business strategies. Tokopedia is one of the largest marketplaces in Indonesia and has become the fastest growing online company since its official launch. This study conducted a sentiment analysis of the Tokopedia application on the Google Play Store by classifying reviews into three groups, namely Positive, Negative and Neutral. The method chosen is Bidirectional gated recurrent unit (BiGRU), because BiGRU has the best accuracy rate compared to other methods. BiGRU is able to predict customer reviews with an accuracy 74.79%, recall 74.80%, and F1-Score 70.88%, which is considered good enough and can be used to help the development of Tokopedia.
Passage Based Answer-Set Graph Approach for Query Performance Prediction

Approaches involving the use of post-retrieval information for a given query have been adopted in a variety of ways in the past for query performance prediction (QPP) tasks. Researchers have utilized information via document retrieval as well as passage retrieval approaches for QPP. We present a novel approach of representing the top returned passages (answer-set) as a graph where each node represents a passage and an edge weight indicates the similarity score between these passages. By examining the answer-set graph we developed new predictors that utilizes graph features such as cohesion and minimum spanning tree. Based on the empirical evaluation, we show that our answer-set graph predictors are very effective and perform even better (for Cranfield and Ohsumed Collection) than the current state-of-the-art QPP approaches.
Perspective on efficiency enhancements in processing streaming data in industrial IoT networks

Both data compression and anomaly detection are very deeply studied areas for the last decades and gain significance for the Internet of Things (IoT), especially industrial IoT (IIoT). Due to the advantages like fewer latency and security aspects, edge computing is often preferred to cloud solutions. While the amount of data as well as the demand for edge data processing increases, resources like bandwidth, computational performance, memory and, in case of Wireless Sensor Networks (WSN), also energy are still limited. This leads primarily to a trade-off between maximum data reduction, information extraction and minimal computational effort. Often, both data compression and anomaly detection are required. This paper demonstrates additional benefits if already one is implemented. Although in many cases the algorithms for both are based on the same models, there are almost no studies on their combined use. In this work, a perspective on the efficiency of combined model usage with only different interpretations for anomaly detection and data compression is proposed. Concrete examples for selected models and the detection of different kinds of anomalies are given. Finally, an outlook on the planned future work is given.
Analysis of Poetry Style Based on Text Classification Algorithm

In order to realize the effect of intelligent poetry style analysis, this paper applies the text classification algorithm to the poetry style analysis, combines the knowledge representation algorithm to perform text classification and recognition, improves the algorithm, and applies it to the poetry style analysis model. Moreover, this paper combines intelligent algorithms to construct a poetry style analysis system, constructs the system’s functional modules, preprocesses the poetry documents in the corpus, and maps them to the vector space that can be directly processed by the computer. In addition, after constructing the system model, this paper verifies the poetry style analysis system based on the text classification algorithm through simulation experiments. From the research results, the effect of the poetry style analysis method based on the text classification algorithm proposed in this paper is very good, which meets the actual needs of poetry style analysis.
Solving the Lunar Lander Problem using Reinforcement Learning

Reinforcement Learning is an area of machine learning concerned with enabling an agent to solve a problem with feedback with the end goal to maximize some form of cumulative long-term reward. In this paper, two different Reinforcement Learning techniques from the value-based technique and policy gradient based method headers are implemented and analyzed. The algorithms chosen under these headers are Deep Q Learning and Policy Gradient respectively. The environment in which the comparison is done is OpenAI Gym’s LunarLander environment. A comparative analysis of the two techniques is then performed in order to understand the differences in a deterministic episodic state space. Both of these algorithms are model free, that is, they can be applied irrespective of the environment and do not need to have any knowledge about the exact details of the environment itself, hence the comparison can be extended to any other environment that shares these characteristics.
Acquisition and Development of Code-Mixed Corpus for Sentiment Analysis of Resource-Scarce Languages

Code-mixing on social media sites has emerged as the new way of writing in a multilingual country. The researchers are already facing challenges on pre-processing (spell correction, POS tagging, stemming, language identification, etc. of code-mixed multilingual text. However, in this article some code-mixed bilingual phonetic text has been developed for sentiment analysis. The bilingual English-Punj abi phonetic word dictionary is developed using Gurmukhi to Roman transliteration (GRT) system. The training and testing code-mixed dataset has been developed using a bilingual word dictionary and GRT system. The English and bilingual code-mixed sentiment-bearing word dictionary has been developed manually using existing English SentiWordNet 3.0. This article focused on the development of code-mixed corpus and datasets for resource-scarce languages.
Local Feature Enhancement Network for Set-based Face Recognition

Set-based Face Recognition is widely applied in scenarios like law enforcement and online media data management. Compared with face recognition using a single image, the faces in the set often contain abundant appearance changes. Therefore, how to make full use of the rich information from the set and integrate them into a unified set representation become the key to set-based face recognition. Inspired by the fact that humans usually complete this fine-grained task through integrating the information from the congruent local regions (e.g. an eye to an eye) of multiple faces in a set, we propose a novel method called Local Feature Enhancement Network (LFENet), which can automatically enhance the local feature through transferring the local information across the images. Specifically, we retain the spatial semantic information of the feature maps and apply different relational functions to establish the correlation among the local features. The contained local information will be transferred to the relevant local features to enhance their discriminability. By doing so, the valuable local information carried in some local features can complement those with incomplete information. Besides, the various local information is aligned across faces under different conditions to help the model learn intra-set-compact face representations. Our method achieves state-of-the-art performances on two mainstream set-based face recognition benchmarks: IJB-A and IJB-C, which fully reflects the rationality and effectiveness of our local feature enhancement mechanism.
Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

Tracking ground moving target with sensors proves to be a challenge due to the uncertainty of target motion area, the existence of Doppler blind zone (DBZ), and the complex terrain. In this paper, a multisensor management method based on DBZ information is presented, in which the available sensors are selected to obtain the best operational revenues for ground moving target tracking. First, the ground target motion model is established considering the off-road/on-road state based on road topology information. Second, the sensor measurement model is given combined with the DBZ information, and a decorrelation method of measurement noise is proposed. Third, a target state estimation algorithm is derived using particle filter, in which the DBZ information is regarded as prior information. Then, combined with the variable structure interacting multiple model method, an estimation algorithm for tracking maneuvering target is proposed. Furthermore, an optimization model of nonmyopic sensor management is constructed to obtain the best sensor management scheme. Finally, the advancement and effectiveness of the proposed management method are verified in the simulations.
Fine-grained attribute-aware analysis for person re-identification

How and where to learn the discriminative feature has always been a critical issue of person re-identification (re-ID). Most of the previous methods focus on extracting global representation from overall identity image and lack of high-level semantic attribute information. Based on the considerations above, we focus on looking for a way to make people's attribute play a role in person re-identification task. In this paper, a novel multitask-like network, namely, Attribute-Identity Complementary Network (AICNet), is designed. It contains two branches to learn the identity and attribute feature separately, and a reciprocal interaction process is added to enrich the discrimination of the resulting feature. In addition, in order to better extract high-level semantic attribute information, multi-scale features are combined in the process of attribute extraction. With the help of this attribute-identity complementary strategy, the generated feature can be guided to learn most distinctive attribute feature that is most relevant to identity feature. The present paper carries out extensive experiments on two large-scale datasets, including Market-1501, and DukeMTMC-reID, after which it is found that our attribute-identity complementary framework significantly outperforms the baseline model and achieves competitive performance compared with the state-of-the-art person re-ID methods.
EXPERIENTIAL EDUCATION EXPERIENCES AND DISCOVERIES IN A PRIMARY SCHOOL

Going deeper into the subtleties of experiential education, the aim was – to analyse teachers’ experience in organising experiential teaching/learning for primary school students and to present the discoveries of a novice teacher in conducting experiential educational activities. Five primary school teachers-experts participated in the study. Teachers define experiential learning as the application of knowledge in practice, thus creating stronger links between knowledge and skills. Experiential education is especially recommended by primary school teachers in grades 3-4, and particularly for teaching the world cognition, as students already have sufficient experience working in pairs and groups, are able to work collaboratively, and world cognition offers very diverse and interesting, complex contexts for activities. Teachers notice that preparing for experiential learning activities, it is very important for the teacher himself to have a good knowledge of the topic discussed, to have analysed the curriculum, textbooks, in order to open as many activities as possible. Educational activities involving 17 third graders enriched the beginning teacher with the new experiences and discoveries: for example: if students use all their senses while learning and seriously reflect on their experiences, it is easier for them to create individual knowledge. Keywords: interview, student’s experience, experiential education, primary education, primary school teachers, educational activities.
STRENUOUS: Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery

COVID-19 is not the last virus; there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject’s cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.
Fair Data Generation and Machine Learning Through Generative Adversarial Networks

In this chapter, we present a fair generative adversarial networks framework (named FairGAN) for fair data generation and fair predictive learning. The FairGAN framework can accommodate various fairness notions by changing the network architecture and objective functions of generators and discriminators. Under the FairGAN framework, we present three previously published model designs, Simplified-FairGAN [1], Causal-FairGAN [2], and FairGAN $$^+$$ [3], discuss their designs, fairness, utility, convergence, and implementation. We then present a short literature review of closely related works of using GANs for structural data generation and achieving differential privacy in GAN. At the end, we introduce several future research directions including architecture design, achieving long-term fairness in dynamic decision making, achieving fairness in regression and recommendation, and open source development. This paper expects to advance understanding and applicability of GAN from image data generation to fair data generation and fair predictive learning.
Using BAR Switch to Prevent Black Bird Embedded Double Spending Attack

Blockchain technology has made significant success, but it is also vulnerable for cyberattacks. A particular form of attack is black bird embedded double spending attack. In our previous research we have proposed block access restriction (BAR) switch to deal with the threat of double spending attack (DSA) in blockchain systems. In this paper we continue our work and present the research of extending the basic BAR switch mechanism to prevent DSA in which black birds are embedded. We will refer this kind of attacks as black bird embedded double spending attack (BBEDSA). We discuss main considerations of how to extend basic BAR mechanism to handle BBEDSA and describe detailed steps of using this mechanism.
Quantum walk based quantum secret sharing in a verifiable framework

Secret sharing is a cryptographic primitive which plays a central role in various tasks including secure multi-party computation and key management. Quantum schemes for secret sharing commonly rely on multi-particle entangled quantum states, which remains challenging with current experimental technologies. Firstly, in this paper we present a (t,n) quantum secret sharing scheme based on quantum walks on a circle, which only involves two-particle product states as the initial quantum resource. When recovering the secrets, it requires at least t participants sequentially completing their operations on the communicated state, while less than t participants can not acquire any information about the secrets. Secondly, a verification mechanism is designed, which can detect cheats with an asymptotic probability 100%. Finally, we show that the scheme is perfectly correct and is secure against common attacks including collusion attack. In conclusion, the proposed scheme will be useful for quantum communication networks due to its inherent scalability, flexibility and information-theoretical security.
Towards improving coherence and diversity of slogan generation

Abstract Previouswork in slogan generation focused on utilising slogan skeletons mined from existing slogans. While some generated slogans can be catchy, they are often not coherent with the company’s focus or style across their marketing communications because the skeletons are mined from other companies’ slogans. We propose a sequence-to-sequence (seq2seq) Transformer model to generate slogans from a brief company description. A naïve seq2seq model fine-tuned for slogan generation is prone to introducing false information. We use company name delexicalisation and entity masking to alleviate this problem and improve the generated slogans’ quality and truthfulness. Furthermore, we apply conditional training based on the first words’ part-of-speech tag to generate syntactically diverse slogans. Our best model achieved a ROUGE-1/-2/-L $\mathrm{F}_1$ score of 35.58/18.47/33.32. Besides, automatic and human evaluations indicate that our method generates significantly more factual, diverse and catchy slogans than strong long short-term memory and Transformer seq2seq baselines.
Probabilistic Study of Corrosion Pit-Induced Fatigue

Abstract Corrosion fatigue is the major damage mechanism responsible for the premature failure of the aircrafts and turbine parts, especially in the marine environment. It has been seen that even relatively mild corrosive atmospheres can reduce the fatigue strength of many structures considerably as compared to their fatigue strength in dry air. The process starts with the surface degradation due to the corrosion pits, that become fatigue initiation sites and the initial damage when the part is loaded. Microstructure and morphology of the surface pits is critical for the crack initiation. It is assumed that the cracks are initiated at the sharp corners and bottoms of the narrow “micropits”. The paper provides a statistical characterization of the crack initiation process based on these pits’ density and microstructure distribution. Based on the surface damage initiation analysis, new morphological characteristics combining the pit size and highest curvature are introduced and have shown to be the efficient metrics for pitting fatigue with the distribution of introduced morphological characteristics. The distribution of these characteristics is estimated from the measured data. Effects of pitting morphology are evaluated for various heat-treated Aluminum 2024 specimens, with varying distribution of pit shapes and curvatures. The statistical distribution of specimen life is estimated using the “weakest link” approach, that is, by computing the probability that at least one crack is initiated anywhere on the surface. The paper contains a detailed description of crack initiation’s statistical model, methodology of corrosion parameter estimation and representative numerical examples of statistical modeling. The surface pit characterization has been nondestructively measured on a Zygo ZeGage 3D profiler. The ZeGage uses a Coherence Scanning Interferometry (CSI) technique which uses the wavelength of light to define its high precision. A carefully calibrated CSI profiler can measure a larger range of surface qualities with repeatable results in sub-nanometer precision. High magnification (20x, 50x, 100x) lenses increase the versatility in measuring even smoother surfaces. Large area scanning was done using a segmentation approach. A large field of view of the selected lens and automated rapid scanning allowed for reasonably smaller data files obtained after stitching these individual segments.