Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets

Modern machine learning (ML) models are being used heavily in business domains to build effective decision support systems. As a primary requirement, supervised ML models need large labeled datasets. However, obtaining a high volume of labeled training data is both expensive and time-consuming. Researchers have proposed several labeling approaches to avoid manual labeling efforts. Active learning (AL) and Data Programming (DP) are two state-of-the-art techniques used to label datasets. Nevertheless, both approaches have their strengths and weaknesses. For example, AL is computationally expensive to apply on large industrial datasets; and labels generated by DP are often inaccurate and difficult to interpret. To address these challenges, in this paper, we propose a novel hybrid method that integrates the scalability of DP with the user engagement and accuracy of AL. The proposed approach aims at optimizing the labeling process by applying DP to generate initial noisy training data and then use AL to query the user to label only those points that maximize the accuracy of the final labels with a minimum annotation cost. To evaluate the proposed approach, we have used five open source datasets and a real-world business dataset of 1.5 million records. We use traditional active learning and data programming techniques as baselines to compare the performance and annotation cost of our proposed approach. The results show that the proposed method can achieve higher labeling accuracy than data programming. It also can minimize the labeling cost in real-world business scenarios, while delivering a comparable level of performance (accuracy) with active learning.
Blood levels of lead, cadmium and selenium in children from Bytom, Poland

A study was carried out on the concentration of cadmium, lead and selenium in blood from 140 school children, aged 8–9 years, living in Bytom in the Silesian Region, an industrialized area of Poland. Concentrations of lead in blood ranged from 0.17 to 1.67 μmol Pb 1‐1 with a median of 0.38 μmol Pb 1‐1. Levels of cadmium in blood ranged from 2 to 22 nmol Cd 1‐1 with a median of 4 nmol Cd 1‐1. The values obtained were relatively low and indicated only a moderate exposure to lead and cadmium in the majority of the study population. However, 20% of the children had blood lead levels exceeding 0.48 μmol Pb 1‐1, which is a level of concern issued by Centers for Disease Control (CDC), USA. Concentrations of selenium in blood were 0.63 to 1.40 μmol Se 1‐1 with a median of 0.91 μmol Se 1‐1. The activity of glutathione peroxidase (GSH‐px) was measured in whole blood in 70 of the children and correlated to selenium concentration.
Scene Classification With Recurrent Attention of VHR Remote Sensing Images

Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.
The relationship between malocclusion, lip competence and gingival health in a group of schoolchildren

Objectives: The aim of this study was to determine the relationship between malocclusion, lip competenceand gingival health among a sample of school children. Material and method: A multi stagesampling technique was used to randomly select 239 school children. Malocclusion was evaluated andgraded from the children’s dental casts using the Dental Aesthetics Index (DAI) and Littles’ IrregularityIndex (LII). The lips were examined for competence according to Jackson’s classifi cation. Gingival healthwas assessed using the Plaque Index and Index of gingival infl ammation. Results: A mean DAI scoreof 26.37 ± 6.67 was obtained while the mean score of the LII was 1.88 ± 1.68 mm. Majority of thechildren (97.1%) had competent lips. Results of correlation tests between DAI scores and the periodontalindices were low and not statistically signifi cant, while that of the LII score and periodontal indiceswere also low but statistically signifi cant (p < 0.05). Conclusion: A cause and effect relationship couldnot be established between malocclusion and gingivitis. A weak correlation exists between malocclusionassessed by the DAI and LII and periodontal indices used. This supports the opinion that prescribingorthodontic treatment solely for the purpose of maintaining periodontal health is not justified.
On the use of Probabilistic Parallel Bit-Flipping decoder for the storage systems

The soft-decision Low-Density Parity-Check (LDPC) decoders have been applied in several storage systems thanks to their powerful error correction capability. However, these systems may suffer a long read latency since the soft-decision decoders require an intensive computations as well as a long sensing time for the soft-information before decoding. In this paper, we modify the recent-introduced Probabilistic Parallel Bit-Flipping (PPBF) LDPC decoder, to use on the storage systems in replacing the soft decision decoders, to improve the memory reading speed. The modified decoder is named Non-Syndrome Probabilistic Parallel Bit-Flipping (NS-PPBF). A special flipping mechanism is introduced such that the decoder can stop flipping without requiring the syndrome check results, which helps significantly improve the decoding frequency. We provide also the hardware architecture to implement NS-PPBF on the LDPC code used on the memory systems, which are usually very long block length with very high rate. The advantages of using NS-PPBF decoder in terms of error correction and decoding throughput are confirmed by the simulating decoding performance and the hardware synthesis.
Unsupervised Domain Adaptation with Generative Adversarial Networks for Facial Emotion Recognition

Cross-dataset facial emotion recognition (FER) aims to reduce the discrepancy between the source and the target facial database. The topic is very challenging in FER, where facial features differ across different domains, such as ethnicity, age, gender and environmental condition. In practice, the labels of target facial expression database may be unavailable, making it impossible to fine-tune a pre-trained model via supervised transfer learning. To address this issue, we propose an unsupervised domain adaptation framework with adversarial learning for cross-dataset FER. We perform cross-dataset FER on three well-known publicly available facial expression databases, viz. CK+, Oulu-CASIA, and RAF-DB, showcasing the efficiency of our proposed approach.
Dominant Criteria and Its Factor Affecting Student Achievement Based on Rough-Regression Model

the ordinary least square model has been widely considered to estimate the significant factors which influence the student achievement. Some factor is qualitative type and measured using criteria or categories. However, the decisive criteria for each factor which affect to the cumulative grade point average of student cannot be determined by this model. In this paper, we are interested to build a new procedure using rough-regression model in determining the dominant criteria from each factor based on generalization of dependency attribute. Based on result, the proposed procedure is capable to investigate the dominant criteria and factors affecting student achievement, such as, language spoken with dominant criteria is “many-many”, FB friend with dominant criteria is “many” and fast food with dominant criteria is “never”. This proposed procedure is very appropriate to implement for handling categorical data.
A Robust Digital RRAM-Based Convolutional Block for Low-Power Image Processing and Learning Applications

Currently, there is a growing attention toward developing efficient hardware convolutional blocks for several applications such as computer vision or image processing. Recent works have shown that using binary values in convolutional blocks can considerably reduce the overall power consumption while achieving a high degree of accuracy. In parallel, some works employed resistive random-access memory (RRAM) as an in-memory accelerator to directly store the convolution kernels and perform analog dot product operations in the array, reducing the overall power consumption by limiting the number of memory accesses. However, such architecture is hampered by the limited resistance precision and large intrinsic variability of RRAMs. In this paper, we present a purely digital robust RRAM-based convolutional block using single-ended XNOR sensing capable of performing dot product operations in a single cycle. By carefully considering physical design and RRAM limitations at the 28-nm technology node, we show that at the circuit level, our architecture can tolerate a resistance window as low as 1.09, ensuring reliable operations even under a high RRAM variability ( $\sigma /\mu = 25\%$ for a resistance window between both states around 50). When integrated in ISAAC, a state-of-the-art learning accelerator, our block can reduce the power by $2.7\times $ while guaranteeing robust operations.
Massive Text Mining for Abnormal Market Trend Detection

The sentiment behind financial text has been observed to have correlations with stock market trend. Though widely discussed, the study on this topic faces the challenge coming from the lack of open dataset and labeled financial text. In this work, we collected a large amount of Chinese financial text from financial news, research report, stock BBS and corporate announcements. It contains 3 million articles about 128 stocks from 2010 to 2018. And then we proposed a model mapping from the text and latent sentiment to the abnormal market trend. It combines the posting amount, daily market index with the RBM-embedded document vector, and extracts the abnormal features via LSTM. After that a neural net is employed to identify the abnormal trend. The experimental results on our dataset show the effectiveness of our approach comparing to baseline methods.
User Feedback in Latent Space Robotic Skill Learning

In order to operate in everyday human environment, humanoids robots will need to autonomously learn and adapt their actions, using among other reinforcement learning methods (RL). A common challenge in robotic RL is also the generation of appropriate reward functions. A vast body of literature investigates how active human feedback can be introduced into an interactive learning loop, with recent publications showing that user feedback can be used for the RL reward. However, increased complexity of robotic skills in everyday environment also increases their dimensionality, which can practically prevent use of user feedback for the reward, because too many trials are needed. In the paper we present the results of using discretized, user-assigned reward for RL of robotic throwing, with an emphasis on learning in the feature space, i. e. , latent space of a deep autoencoder network. Statistical evaluation of a user study with 15 participants, who provided feedback for robotic throwing experiments, show that for certain tasks, RL with discrete user feedback can be effectively applied for robot learning.
An Encoding Technique for CNN-based Network Anomaly Detection

An important challenge in the cyber-space is the effective identification of network anomalies, often caused by malicious activities. With the remarkable advances, machine learning algorithms have widely been studied for network intrusion and anomaly detection. In particular, deep learning based on neural network structures has recently been given a greater attention to deal with the growing complexity of data with higher dimensions and non-linearity. Convolutional Neural Networks (CNNs) is one of the widely employed deep learning methods. In this work, we introduce a new encoding technique that enhances the performance for the identification of anomalous events using a CNN structure. To evaluate, we utilize three different datasets for the extensive analysis. The experimental results show that our method consistently outperforms the gray-scale encoding technique previously proposed over the datasets employed in the evaluation.
Scalable Ground-Truth Annotation for Video QoE Modeling in Enterprise WiFi

Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications – Skype, FaceTime and Google Hangouts.
Arabic Natural Language Processing and Machine Learning-Based Systems

Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts. ANLP makes an important contribution to many existing developed systems. It provides Arabic and non-Arabic speakers with helpful and convenient tools that can be used in different domains. Modern ANLP tools are developed using machine learning (ML) techniques. ML algorithms are widely used in NLP because of their high accuracy rate regardless of the robustness of the data that is used and because of the ease with which they can be implemented. On the other hand, the methodology of ANLP applications based on ML involves several distinct phases. It is, therefore, crucial to recognize and understand these phases in detail as well as the most widely used ML algorithms. This survey discusses this concept in detail, shows the involvement of ML techniques in developing such tools, and identifies well-known techniques used in ANLP. Moreover, this survey discusses the characteristics and complexity of the Arabic language in addition to the importance and needs of ANLP.
Reveal the Cognitive Process of Deep Learning during Identifying Nucleosome Occupancy and Histone Modification

Nucleosome occupancy and histone modifications are among the most significant factors that influence gene expression. Although Chromatin Immunoprecipitation-chip (ChIP-chip) and Chromatin Immunoprecipitation-sequencing (ChIP-seq) have significantly accelerated revealing the nucleosome regulation mechanism in the course of organism life, these experimental techniques require to take plenty of material resources and may produce noise. To this end, Computer-based information processing algorithms, such as Support Vector Machines (SVMs), have given different insights into the above deficiencies. Recent researches have demonstrated that deep learning can not only conquer the above unfavorable factors in many kinds of gene regulation tasks, but also exceed the prediction performance of SVM. However, there is no explicit understanding of why the so-called “Black box” performs so well. Here, we constructed deep learning model to identify the nucleosome occupancy and histone modifications, and evaluated the performance. Then from different perspectives (alignment-based and optimization-based approaches respectively), we reported the inside mechanism of deep learning model for revealing the distribution of nucleosome occupancy and histone modification states in Yeast. Eventually, our approaches not only achieved ascertain accuracy improvement on the published datasets compared with traditional machine learning, but also we found the sequence preference of nucleosome occupancy that is consistent with previous findings.
Enabling Privacy-Preserving Header Matching for Outsourced Middleboxes

Over the past few years, enterprises start adopting software middlebox services from cloud or NFV service providers. Although this new service model is recognized to be cost-effective and scalable for traffic processing, privacy concerns arise because of traffic redirection to outsourced middleboxes. To ease these concerns, recent efforts are made to design secure middlebox services that can directly function over encrypted traffic and middlebox rules. But prior designs only work for portions of frequently-used network functions. To push forward this area, in this work, we investigate header matching based functions like firewall filtering and packet classification. To enable privacy-preserving processing on encrypted packets, we start from the latest primitive “order-revealing encryption (ORE)” for encrypted range search. In particular, we devise a new practical ORE construction tailored for network functions. The advantages include: 1) guaranteed protection of packet headers and rule specified ranges; 2) reduced accessible information during comparisons; 3) rule-aware size reduction for ORE ciphertexts. We implement a fully functional system prototype and deploy it at Microsoft Azure Cloud. Evaluation results show that our system can achieve per packet matching latency 0.53 to 15.87 millisecond over 1.6K firewall rules.
Long-time Object Tracking Based on Hierarchical Convolution Features

Traditional object tracking methods, mainly based on manual features (e.g., histograms of oriented gradients, color names and color histograms), have limited ability to complex scenarios. As convolution neural network has achieved great progress on image classification, it has been proved to have strong feature extraction ability and superiority to traditional object tracking methods. In this paper, we propose an improved object tracking method, where the pre-trained convolution neural network is used for hierarchical feature extraction. Moreover, a robust model updating strategy and an object re-detection strategy are introduced to our method. Several experiments on public data sets are provided to demonstrate that our method indeed improves the performance in illumination changing, occlusion, low resolution, while enhancing the overall accuracy and success rates.
Pola Asuh Ibu Berhubungan dengan Tingkat Perkembangan Bahasa pada Anak Prasekolah di TK Al Farabi Yogyakarta

Setiap manusia mengalami proses perkembangan selama masa hidupnya. Proses perkembangan manusia meliputi beberapa aspek yakni perkembangan fisik, perkembangan psikososial dan perkembangan kognitif. Salah satu perubahan yang terjadi pada perkembangan kognitif adalah perubahan dalam bahasa disamping perubahan pada pemikiran dan intelegensi individu dan pola asuh ibu merupakan hal yang sangat mempengaruhi perkembangan pada anak, termasuk perkembangan bahasa. Tujuan penelitian untuk mengetahui hubungan antara pola asuh ibu dengan tingkat perkembangan bahasa pada anak prasekolah di TK AL FARABI Yogyakarta 2012. Penelitian deskriptif analitik dengan rancangan cross sectional. Subjek dalam penelitian ini adalah wali murid anak-anak prasekolah TKIT AL FARABI Yogyakarta yang berusia sekitar 3-6 tahun beserta anaknya. Analisis data dilakukan dengan analisis chi-squere. Hasil penelitian pola asuh ibu pada anak prasekolah di TKIT AL FARABI Yogyakarta.sebagian besar ibu memberikan pola asuh pada anaknya dengan pola asuh Authoritative (demokratis) sebanyak 34(78,1%). Tingkat Perkembangan Bahasa pada Anak Prasekolah di TKIT AL FARABI Yogyakarta sebagian besar memiliki perkembangan bahasa normal sebanyak 25 (58,1%). Hasil analisis uji chi-square, diketahui x hitung sebesar 6,033, nilai signifikansi 0,014 (p<0,05). Nilai koefisien kontingensi korelasi sebesar 0,351 nilai tersebut menunjukkan bahwa keeratan hubungan antara pola asuh ibu dengan tingkat perkembangan bahasa pada anak prasekolah kategori rendah. Kesimpulan terdapat hubungan pola asuh ibu dengan tingkat perkembangan bahasa pada anak prasekolah di TKIT AL FARABI Yogyakarta.
Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data

The advances in proteomic technologies have offered an unprecedented opportunity and valuable resources to reveal molecular targets for treatment. Although a number of approaches have been designed to develop mathematical models using the time series proteomic profiles, the recently published single-patient proteomic data raised substantial challenges for analysing these non-time series datasets. To address this issue, this work proposes the first attempt for designing mathematical models using the non-time series proteomic data. Using the triple-negative breast cancer (TNBC) as the test system, we first use the single-cell analysis algorithm to derive the pseudo-time trajectory of the protein activities. Our integrated approach includes both a top-down approach (namely the Gaussian graphical model) and a bottom-up approach (i.e. differential equation model) to reverse-engineer the regulatory network. Based on the information from GO-enrichment analysis and KEGG database, we select 16 proteins that are key components in the mitogen-activated protein (MAP) kinase pathways. We construct the structure of a network with 16 proteins and a dynamic model for a network of 12 proteins. The derived protein-protein relationships are partially supported by the established protein activation relationships, and our model also predicts potential protein relationships that may be confirmed by further experimental studies. In summary, our results suggest that the proposed integrated framework is an effective approach to reconstruct regulatory networks using non-time course proteomic data.