Reviewerly/exHarmony huggingface dataset:
Students’ Learning Performance Evaluation Using a New Fuzzy Inference System

The research of computer technology in the field of education makes Intelligent Tutoring System (ITS) birth and develop rapidly. The main advantage of an ITS is it can provide appropriate teaching content according to students’ knowledge state, level and ability. Reasonable evaluation of students’ learning performance is the key and difficult point in ITS design. Using fuzzy logic to evaluate students’ learning performance is not a very new method. However, most approaches rely extremely on student’s answer time for each question, which are seldom collected in common exams. Given this insight, we present a new fuzzy inference mechanism in this paper to calculate students’ ranking order without dependence of the answer times. According to the importance, complexity and difficulty of the problem, new reasoning rules are set up to accurately evaluate the comprehensive level of problem. The results of the case study show that each student’s grades are distinguished, even for those with the same grades. The proposed method in this paper enriches the present evaluation approach of students’ performance, and is utilizable to analyze the direct relationship of students’ scores with the attributes of each question.
BLInG: Beads-Laden Interactive Garment

This design work introduces on-body beaded electronics for enhanced aesthetics in wearable technology by reflecting fashion trends. The seamless integration of visually unappealing wires and electronic components into the body surface has always been an issue in the development of on-body interfaces. In this paper, we develop methods for incorporating digital electronics into fashion design and show aesthetic and functional on-body interfaces based on traditional beading techniques. Conductive threads/elastomers, copper wires, and a shape memory alloy work as supporting components to create beaded net fashion items. Also, we explore boundaries of the integration of small electronic components such as an LED, a metal bead, and an NFC chip, as one of the beads in the system. Finally, we discuss diverse challenges and opportunities of multimodal interactions in beaded electronics in the context of wearable computing.
D2Defend: Dual-Domain based Defense against Adversarial Examples

Convolutional neural networks (CNNs) have recently been widely applied in computer vision tasks, yet they are seriously vulnerable to imperceptible adversarial perturbations. Such phenomena have caused great attention on the adversary topic. Existing adversarial defense methods mainly focus on improving the robustness of models (e.g., adversarial training) or removing adversarial perturbations (e.g., input-transformation based methods) directly, while rarely considering the accurate recovery of image structures of the input, which also play a vital role in making predictions for CNNs. To this end, we propose a Dual-Domain based Defense (D2Defend) method by recovering low-frequency and high-frequency image structures in both spatial and transform domains, while removing adversarial perturbations simultaneously. Unlike the existing input-transformation based methods, our method can decompose the input image into edge feature and texture feature layers, accompanied with bilateral filtering and short-time fourier transform (STFT) filtering. Experimental results demonstrate the effectiveness of our method against various adversarial attacks, and show the superiority of our method over other adversarial defense methods especially at strong adversarial strength.
User Preference Prediction in Visual Data on Mobile Devices

The article considers the possibility to predict preferences of a user given a set of his or her photos. The novel algorithm is introduced with detection of private photos based on facial clustering. Such photos are analyzed on the mobile device with the lightweight neural networks. The remaining photos may be sent to the remote server to be processed by the state-of-the-art models. In both cases each photo is associated with the outputs of neural networks trained on scene classification and object detection tasks. Qualitative and quantitative results demonstrate the potential of the proposed approach in event recognition and understanding of preferences of celebrities given their real photos from social networks.
Development of an Application for Tourism Route Recommendations with the Dijkstra Algorithm

The trend of tourism 4.0 has become one of the issues in tourism and implemented in many ways. Malang city, Kota Batu and Kabupaten Malang areas such as Kota Batu and Kabupaten Malang are well-known as Malang Raya. Tourist attraction can be found in Malang raya. Since there are so many tourist attraction places and scattered across Malang Raya, tourist needs to find the best path in order to visit as many places as possible. This research project is conducted to find and apply the algorithm of Dijkstra to give recommendation of shortest path to tourist interesting places. There are four considerations in this research including first attraction place that will be visited by the tourist, distance between attraction places, types of the attraction places and the nearest attraction places from the first attraction place. Methodology in this research consist of several steps, including identification of required parameters, analysis of recommendation systems, data gathering, data validation and analysis, system development based on Dijkstra Algorithm, implementation and evaluation of the recommendation system. The proposed recommendation system able to recommend shortest path in three to four seconds. It is hoped that this project able to provide a ready – to – apply recommendation system for tourist attraction places in Indonesian’s cities, especially in Malang City.
Deep variational clustering framework for self-labeling large-scale medical images

One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Recent studies propose to simultaneously learn representation using deep neural networks and perform clustering by defining a clustering loss on top of embedded features. Unsupervised image clustering naturally requires good feature representations to capture the distribution of the data and subsequently differentiate data points from one another. Among existing deep learning models, the generative variational autoencoder explicitly learns data generating distribution in a latent space. We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multivariate Gaussian posterior through the probabilistic convolutional encoder, and the likelihood distribution with the probabilistic convolutional decoder; and optimizes cluster labels assignment. Here, the learned multivariate Gaussian posterior captures the latent distribution of a large set of unlabeled images. Then, we perform unsupervised clustering on top of the variational latent space using a clustering loss. In this approach, the probabilistic decoder helps to prevent the distortion of data points in the latent space, and to preserve local structure of data generating distribution. The training process can be considered as a self-training process to refine the latent space and simultaneously optimizing cluster assignments iteratively. We evaluated our proposed framework on three public datasets that represented different medical imaging modalities. Our experimental results show that our proposed framework generalizes better across different datasets. It achieves compelling results on several medical imaging benchmarks. Thus, our approach offers potential advantages over conventional deep unsupervised learning in real-world applications. The source code of the method and of all the experiments are available publicly at: https://github.com/csfarzin/DVC
Video Captioning in Compressed Video

Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information already encoded in the compressed videos is generally neglected. We propose a video captioning method which operates directly on the stored compressed videos. To learn a discriminative visual representation for video captioning, we design a residuals-assisted encoder (RAE), which spots regions of interest in I-frames under the assistance of the residuals frames. First, we obtain the spatial attention weights by extracting features of residuals as the saliency value of each location in I-frame and design a spatial attention module to refine the attention weights. We further propose a temporal gate module to determine how much the attended features contribute to the caption generation, which enables the model to resist the disturbance of some noisy signals in the compressed videos. Finally, Long Short-Term Memory is utilized to decode the visual representations into descriptions. We evaluate our method on MSRVTT dataset and demonstrate the effectiveness of our approach.
A GLCM Algorithm for Optimal Features of Mammographic Images for Detection of Breast Cancer

Mammography is the golden standard for imaging and diagnosis of breast cancer in early stages. However, it is difficult for radiologists to interpret results from mammograms, particularly mammogram images have low contrast and the image quality related to technician and devices. In this paper, optimal features from a Gray Level Co-occurrence Matrix (GLCM) algorithm are applied to the mammography images for increseasing the accuracy of breast cancer detection. In particular, two datasets of the mammography images are filtered and segmented to produce Glandular Tissue Region (GTR) which contain significant features. Therefore, we just choose 4 optimal features of 10 ones using the GLCM algorithm through statistic evaluation. The results show that the optimal selected features have significant impact to produce breast cancer detection with high performance using a SVM classifier.
Polarity Classification of Social Media Feeds Using Incremental Learning — A Deep Learning Approach

Online feeds are streamed continuously in batches with varied polarities at varying times. The system handling the online feeds must be trained to classify all the varying polarities occurring dynamically. The polarity classification system designed for the online feeds must address two significant challenges: i) stability-plasticity, ii) category-proliferation. The challenges faced in the polarity classification of online feeds can be addressed using the technique of incremental learning, which serves to learn new classes dynamically and also retains the previously learned knowledge. This paper proposes a new incremental learning methodology, ILOF (Incremental Learning of Online Feeds) to classify the feeds by adopting Deep Learning Techniques such as RNN (Recurrent Neural Networks) and LSTM (Long Short Term Memory) and also ELM (Extreme Learning Machine) for addressing the above stated problems. The proposed method creates a separate model for each batch using ELM and incrementally learns from the trained batches. The training of each batch avoids the retraining of old feeds, thus saving training time and memory space. The trained feeds can be discarded when new batch of feeds arrives. Experiments are carried out using the standard datasets comprising of long feeds (IMDB, Sentiment140) and short feeds (Twitter, WhatsApp, and Twitter airline sentiment) and the proposed method showed positive results in terms of better performance and accuracy.
Resolution improvement in real-time and video mosaicing for fiber bundle imaging

A fiber bundle allows easy access to a wide range of human tissue. It has smaller diameters, which limits its effective field of view (FOV), and consists of a large number of cores surrounded by a cladding layer, which reduces its spatial resolution. In this paper, we develop an algorithm that processes successively captured raw fiber bundle images in an online fashion. Our algorithm tackles the tasks of super-resolution (SR) and video mosaicing jointly. The natural movement of the fiber tip in successive frames produces offsets that are random in the pixel domain to apply multi-frame SR imaging. Meanwhile, the associated FOV can be extended by mosaicing reconstructed SR images with obtained shifted information. Our approach has low computational complexity that allows for processing in real-time. The performance of resolution improvement in real-time and video mosaicing is demonstrated on the resolution target and biological samples.
Teachers’ competencies for working in an intercultural environment

BACKGROUND: Today, multiculturalism is present in all spheres of life. Teachers are at the forefront to first formally encounter children from different cultures, and as the first ones, children from different cultures will face it in their social life. Teachers are supposed to prepare students to meet and live with people from cultures different from their own. Hence, it is necessary to identify the competencies teachers need to work in an intercultural environment, given the globalising world where different cultures meet more often, coexist, and exchange values and ways of living. OBJECTIVE: This paper discusses the teachers’ competencies to work in an intercultural environment and presents the results from data collected within the RoMigSc project. These data are analysed to identify the competencies teachers need to work in a multicultural environment. METHODS: In the study, data from the RoMigSc survey were used, where information on the inclusion of migrant students was collected using the survey questionnaire method. All analyses in this paper are on a descriptive level with exploratory purpose. RESULTS: The results show that teachers don’t feel well prepared to support learning or teach human rights, emigration and immigration, shared values and discrimination. They also need more support in teaching in an intercultural and multicultural environment, especially out-of-school support. CONCLUSIONS: In general, teachers find their school and own practices inclusive for migrant and Roma students, but not in all aspects. None of the teachers has participated in a project to raise awareness of Roma issues.
DRDF: A Deceptive Review Detection Framework of Combining Word-Level, Chunk-Level, And Sentence-Level Topic-Sentiment Models

With the rapid development of the Internet, online reviews have touched on all aspects of our lives and have provided us with much convenience. However, they may also mislead our behavior, especially the deceptive online reviews. In order to filter out deceptive online reviews more precisely, mainly based on sentence joint topic sentiment model (SJTSM), and also integrating the good ideas of sentence latent dirichlet allocation (SenLDA) and CopulaLDA, in this paper we propose a deceptive review detection framework (DRDF) that combines the word-level, chunk-level, and sentence-level topic sentiment models to extract the reviews different features, which is regarded as the vector input of a multiple-classifier to detect deceptive reviews. It not only utilizes the correlation of different level topic-sentiment model representations of a text, but also incorporates the structure and the sentiment label information of reviews. Our compared experimental result on different public datasets has shown that its performance is superior to the existing methods of deceptive review detection.
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models

Continual (or “incremental”) learning approaches are employed when additional knowledge or tasks need to be learned from subsequent batches or from streaming data. However these approaches are typically adversary agnostic, i.e., they do not consider the possibility of a malicious attack. In our prior work, we explored the vulnerabilities of Elastic Weight Consolidation (EWC) to the perceptible misinformation. We now explore the vulnerabilities of other regularization-based as well as generative replay-based continual learning algorithms, and also extend the attack to imperceptible misinformation. We show that an intelligent adversary can take advantage of a continual learning algorithm's capabilities of retaining existing knowledge over time, and force it to learn and retain deliberately introduced misinformation. To demonstrate this vulnerability, we inject backdoor attack samples into the training data. These attack samples constitute the misinformation, allowing the attacker to capture control of the model at test time. We evaluate the extent of this vulnerability on both rotated and split benchmark variants of the MNIST dataset under two important domain and class incremental learning scenarios. We show that the adversary can create a “false memory” about any task by inserting carefully-designed backdoor samples to the test instances of that task thereby controlling the amount of forgetting of any task of its choosing. Perhaps most importantly, we show this vulnerability to be very acute and damaging: the model memory can be easily compromised with the addition of backdoor samples into as little as 1% of the training data, even when the misinformation is imperceptible to human eye.
Supporting the Onboarding of 3D Printers through Conversational Agents

In view of its capacity to create physical objects for a wide range of different potential applications, 3D printing has become increasingly popular over the years. However, given its scope of application, 3D printing can be challenging. Novice users often need assistance from experts, who are not always available. Recent interest in the development of conversational agents opens up the possibility of assisting novice users in their interactions with 3D printers, thus improving their experience. In this paper, we illustrate a potential concept of a conversational agent and present a prototype of a Telegram chatbot to improve the user experience of 3D printing.
Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets

The computational challenges arising from increasingly large search spaces in hyperparameter optimization necessitate the use of performance prediction methods. Previous works have shown that approximated performances at various levels of fidelities can efficiently early terminate sub-optimal model configurations. In this paper, we design a Sequence-to-sequence learning curve forecasting method paired with a novel objective formulation that takes into account earliness, multi-horizon and multi-target aspects. This formulation explicitly optimizes for forecasting shorter learning curves to distant horizons and regularizes the predictions with auxiliary forecasting of multiple targets like gradient statistics that are additionally collected over time. Furthermore, via embedding meta-knowledge, the model exploits latent correlations among source dataset representations and configuration trajectories which generalizes to accurately forecasting partially observed learning curves from unseen target datasets and configurations. We experimentally validate the superiority of the method to learning curve forecasting baselines and several ablations to the objective function formulation. Additional experiments showcase accelerated hyperparameter optimization culminating in near-optimal model performance.
Subjective acoustic survey of Korean traditional wind instruments, piri and daegeum, in a concert hall using auralisation techniques

In this study, acoustics of piri and daegeum, which are representative traditional Korean wind instruments, was investigated objectively and subjectively. The acoustic directivity patterns of piri and daegeum were measured in a full anechoic chamber. A subjective acoustic survey of piri and daegeum solo performances was conducted with 60 music students using an auralised concert hall with a volume of 18,838 m3. The sound produced by piri had extensive high-frequency harmonics and strong forward and downward directivity. In addition to a strong lateral and upward directivity, the sound produced by daegeum exhibited more explicit fundamental notes than those produced by piri. The reverberance, loudness, and envelopment of piri and daegeum were affected by early decay time (EDT), sound pressure level (SPL), and lateral energy fraction (LF80), respectively. The clarity of piri showed no explicit relationship with the clarity index (C80), but the clarity of daegeum was affected by the C80. Room acoustical conditions around a reverberation time of 2.6 s were preferred for the piri solo. However, for the daegeum solo, room acoustical conditions around a reverberation time of 1.7 s were preferred. The overall impression of piri was highly correlated with loudness and mid-treble balance. However, the overall impression of daegum did not show a strong linear correlation with other subjective attributes, such as piri (r > 0.6).
Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.
Support Vector Regression Method for Regional Economic Mid- and Long-Term Predictions Based on Wireless Network Communication

In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.