Reviewerly/exHarmony huggingface dataset:
Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.
Parallel Waveform Synthesis Based on Generative Adversarial Networks with Voicing-Aware Conditional Discriminators

This paper proposes voicing-aware conditional discriminators for Parallel WaveGAN-based waveform synthesis systems. In this framework, we adopt a projection-based conditioning method that can significantly improve the discriminator’s performance. Furthermore, the conventional discriminator is separated into two waveform discriminators for modeling voiced and unvoiced speech. As each discriminator learns the distinctive characteristics of the harmonic and noise components, respectively, the adversarial training process becomes more efficient, allowing the generator to produce more realistic speech waveforms. Subjective test results demonstrate the superiority of the proposed method over the conventional Parallel WaveGAN and WaveNet systems. In particular, our speaker-independently trained model within a FastSpeech 2 based text-to-speech framework achieves the mean opinion scores of 4.20, 4.18, 4.21, and 4.31 for four Japanese speakers, respectively.
SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks

Cryptocurrency networks have evolved into multi-billion-dollar havens for a variety of disputable financial activities, including phishing, ponzi schemes, money-laundering, and ransomware. In this paper, we propose an efficient graph-based method, SigTran, for detecting illicit nodes on blockchain networks. SigTran first generates a graph based on the transaction records from blockchain. It then represents the nodes based on their structural and transactional characteristics. These node representations accurately differentiate nodes involved in illicit activities. SigTran is generic and can be applied to records extracted from different networks. SigTran achieves an \(F_1\) score of 0.92 on Bitcoin and 0.94 on Ethereum, which outperforms the state-of-the-art performance on these benchmarks obtained by much more complex, platform-dependent models.
Graph-Based Visual-Semantic Entanglement Network for Zero-Shot Image Recognition

Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the Graph-based Visual-Semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features; 2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations; 3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. Our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN by promoting the semantic linkage modelling of visual features.
Political stability and economic growth in developing economies: lessons from Morocco, Tunisia and Egypt ten years after the Arab Spring

In 2011, the Arab region has seen an unprecedented popular uprising commonly referred to as the "Arab Spring".The objective of this paper is to analyze the economic performance and institutional changes that have taken place in Egypt, Tunisia, and Morocco following the Arab Spring, and understand the interconnect between the socio-political context on the one side, and economic performance and growth on the other side, in a period marked by severe turbulences, especially in Tunisia and Egypt.The analysis covers the economic, institutional, competitiveness, business environment, infrastructural, and human capital aspects in the three countries.It is based on the author's own research and knowledge of the region, the recent emerging literature on the topic, newspaper archives, and the publicly available economic and business data and reports provided by international organizations.Our analysis shows that Tunisia, the country that has ignited the Arab Spring, was the one most hit by its aftermath on the economic, social, and institutional levels.We argue that, although pure authoritarian regimes were historically a failure in the region, "Western" democracy has so far shown several limits when applied into the Arab region context as it led to neither socio-political stability nor economic growth.
Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) are increasingly popular for civil applications due to their flight capabilities and mobility. This paper proposed an indoor patrol and surveillance system using unmanned aerial vehicles. The components were integrated successfully and the designed system was experimented in an indoor setup. Results has shown that the proposed system is suitable and feasible to be used as an autonomous patrol and surveillance agent for indoors use. An application is developed as proof of concept with detailed design that can take advantage of unmanned urban vehicle to be directly configured and controlled in real time. The advantages are numerous; it can be used for many purposes. For example, it can be used for observing critical and important area for intruder activities or to know the current state of any object of interest. Intrusion of terrorists and trespassers are adversely affecting the peace and harmony in the nation. The fatalities and disturbances caused by the latest Uri attack in Indian Army Camp show the necessity of an efficient border surveillance and intruder detection system for the effective monitoring and detecting the unauthorized movement of intruders across the national borders. Conventional border patrolling lacks an integrated multi-sensing system that coordinates various technologies for surveillance and detection of human intruder movement in the different border scenarios: flat surface movement, river/pond crossing and dry leaves movement.I.
Are we there yet? Exploring clinical domain knowledge of BERT models

We explore whether state-of-the-art BERT models encode sufficient domain knowledge to correctly perform domain-specific inference.Although BERT implementations such as BioBERT are better at domain-based reasoning than those trained on general-domain corpora, there is still a wide margin compared to human performance on these tasks.To bridge this gap, we explore whether supplementing textual domain knowledge in the medical NLI task: a) by further language model pretraining on the medical domain corpora, b) by means of lexical match algorithms such as the BM25 algorithm, c) by supplementing lexical retrieval with dependency relations, or d) by using a trained retriever module, can push this performance closer to that of humans.We do not find any significant difference between knowledge supplemented classification as opposed to the baseline BERT models, however.This is contrary to the results for evidence retrieval on other tasks such as open domain question answering (QA).By examining the retrieval output, we show that the methods fail due to unreliable knowledge retrieval for complex domain-specific reasoning.We conclude that the task of unsupervised text retrieval to bridge the gap in existing information to facilitate inference is more complex than what the state-of-the-art methods can solve, and warrants extensive research in the future.
D2S: Document-to-Slide Generation Via Query-Based Text Summarization

Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming.There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking.In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences (e.g., ACL).Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering.Our evaluation suggests that longform QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.
Learning to Recognize Dialect Features

Building NLP systems that serve everyone requires accounting for dialect differences.But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He ∅ running".In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers.For most dialects, largescale annotated corpora for these features are unavailable, making it difficult to train recognizers.We train our models on a small number of minimal pairs, building on how linguists typically define dialect features.Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples.We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.
Design and implementation of true random number generators based on semiconductor superlattice chaos

True random number generator (TRNG) is of the fundamental guarantees of information security. However, the commercially-available all-electronic TRNGs, no matter whether the thermal-noise-based or the oscillator-based ones, can merely reach the speed of a few Mbit/s due to the very limited entropy source, which is unable to meet the requirements of ultra-high-speed and ultra-large capacity of information propagation or storage. This paper proposes the design and implementation of semiconductor superlattice random number generator (SSL-RNG) by employing semiconductor superlattice chaos as entropy source. We introduce the entropy estimation method to evaluate the raw data from the entropy harvester quantitatively, thus optimized the parameter design of the LFSR-Toeplitz extractor for post-processing. We have also integrated the entropy acquisition circuit into an embedded FPGA-based platform as a standalone design to trade off the performance and cost. The resulting hardware's output passes the NIST standard statistical test with a full entropy throughput above 300 Mbit/s.
On Learning Text Style Transfer with Direct Rewards

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the styletransferred outputs.In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts.We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training.The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies. 1
Comparative effectiveness of medical concept embedding for feature engineering in phenotyping

Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks.We implemented 5 embedding methods including node2vec, singular value decomposition (SVD), LINE, skip-gram, and GloVe with 2 data sources: (1) knowledge graphs obtained from the observational medical outcomes partnership (OMOP) common data model; and (2) patient-level data obtained from the OMOP compatible electronic health records (EHR) from Columbia University Irving Medical Center (CUIMC). We used phenotypes with their relevant concepts developed and validated by the electronic medical records and genomics (eMERGE) network to evaluate the performance of learned MCEs in retrieving phenotype-relevant concepts. Hits@k% in retrieving phenotype-relevant concepts based on a single and multiple seed concept(s) was used to evaluate MCEs.Among all MCEs, MCEs learned by using node2vec with knowledge graphs showed the best performance. Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively.MCE enables scalable feature engineering tasks, thereby facilitating phenotyping. Based on current phenotyping practices, MCEs learned by using knowledge graphs constructed by hierarchical relationships among medical concepts outperformed MCEs learned by using EHR data.
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study

Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model.This holds promise for low web-resource languages (LRL) as multilingual models can enable transfer of supervision from high resource languages to LRLs.However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts.In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM.We focus on Indian languages, and exploit relatedness along two dimensions: (1) script (since many Indic scripts originated from the Brahmic script), and (2) sentence structure.RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case).While exploiting similar sentence structures, RelateLM utilizes readily available bilingual dictionaries to pseudo translate RPL text into LRL corpora.Experiments on multiple real-world benchmark datasets provide validation to our hypothesis that using a related language as pivot, along with transliteration and pseudo translation based data augmentation, can be an effective way to adapt LMs for LRLs, rather than direct training or pivoting through English.
Obtaining Better Static Word Embeddings Using Contextual Embedding Models

The advent of contextual word embeddingsrepresentations of words which incorporate semantic and syntactic information from their context-has led to tremendous improvements on a wide variety of NLP tasks.However, recent contextual models have prohibitively high computational cost in many use-cases and are often hard to interpret.In this work, we demonstrate that our proposed distillation method, which is a simple extension of CBOW-based training, allows to significantly improve computational efficiency of NLP applications, while outperforming the quality of existing static embeddings trained from scratch as well as those distilled from previously proposed methods.As a side-effect, our approach also allows a fair comparison of both contextual and static embeddings via standard lexical evaluation tasks.
What is the Vocabulary of Flaky Tests? An Extended Replication

Software systems have been continuously evolved and delivered with high quality due to the widespread adoption of automated tests. A recurring issue hurting this scenario is the presence of flaky tests, a test case that may pass or fail non-deterministically. A promising, but yet lacking more empirical evidence, approach is to collect static data of automated tests and use them to predict their flakiness. In this paper, we conducted an empirical study to assess the use of code identifiers to predict test flakiness. To do so, we first replicate most parts of the previous study of Pinto et al. (MSR 2020). This replication was extended by using a different ML Python platform (Scikit-learn) and adding different learning algorithms in the analyses. Then, we validated the performance of trained models using datasets with other flaky tests and from different projects. We successfully replicated the results of Pinto et al. (2020), with minor differences using Scikit-learn; different algorithms had performance similar to the ones used previously. Concerning the validation, we noticed that the recall of the trained models was smaller, and classifiers presented a varying range of decreases. This was observed in both intra-project and inter-projects test flakiness prediction.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge

Question: I have five fingers but I am not alive.What am I? Answer: a glove
Choice of Ethereum Clients for Private Blockchain: Assessment from Proof of Authority Perspective

Enterprises are realizing their distributed ledger use-cases with private blockchains as it is more secure, efficient, and reliable compared to the public networks. Due to this reason we evaluate the behavior and performance of Ethereum clients namely Geth, OpenEthereum (Parity), and Hyperledger Besu along with their Proof of Authority consensus algorithms from an enterprise perspective. We propose a testing methodology adapted to Microsoft Azure Cloud infrastructure overcoming the drawbacks of existing works. We analyze the behavior in each of the clients from different Ethereum focused parameters. We propose a novel load-balancer middleware specific to Ethereum and integrate in our test tool for uniform and reliable distribution of transactions. We analyze the bottlenecks and their root causes in each of the clients and report it to the community.
Promoting Graph Awareness in Linearized Graph-to-Text Generation

Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graphencoding neural networks.However, recent applications of pretrained transformers to linearizations of graph inputs have yielded stateof-the-art generation results on graph-to-text tasks.Here, we explore the ability of these linearized models to encode local graph structures, in particular their invariance to the graph linearization strategy and their ability to reconstruct corrupted inputs.Our findings motivate solutions to enrich the quality of models' implicit graph encodings via scaffolding.Namely, we use graph-denoising objectives implemented in a multi-task text-to-text framework.We find that these denoising scaffolds lead to substantial improvements in downstream generation in low-resource settings.
BlinkListener

Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.