Reviewerly/exHarmony huggingface dataset:
Discriminative Video Representation with Temporal Order for Micro-expression Recognition

Micro-expression recognition is a challenging task due to its low intensity and short duration and how to extract the subtle facial changes is a key issue in this field. Although there are many methods attempt to cope with this problem, they are difficult to encode the temporal order of all frames in the video clips. For these reasons, this paper employs rank pooling and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm to obtain the discriminative video representation with temporal order. In particular, we extract Local Two-Order Gradient Pattern (LTOGP) feature of each frame to describe the subtle information. Then, the video representation is generated by using rank pooling, which captures the temporal order among all frames. Furthermore, considering the sparsity of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm, we can select those discriminant features. Finally, micro-expression classification is accomplished using SVM. Experiments are conducted on two publicly available micro-expression databases i.e. CASME and CASME2. The results demonstrate that our method achieves better performance than the state-of-the-art algorithms.
The development of interactive mathematics learning media based on schoology and visual basic through industrial revolution 4.0

The application of technology in learning is very appropriate to against the industrial revolution 4.0. The use of technology is the development of online-based learning media. The aimed of this research is to develop interactive mathematics learning media based on schoology and visual basic which valid, practical and effective. This developed learning media can be accessed by online using computer devices and android smartphones. The type of this research is a development research that refers to the modified 4D development model. This research consists of three phases that is defining, designing and developing phase. The subjects of this research were 17 the 8th Grade students of SMPN 5 Jember, East Java, Indonesia. The data were collected by validation, interview, questionnaires and test. Based on the findings, showed the level of validity 95%, practicality level 98%, and the effectiveness of 88.25%.
Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data-driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.
Activation of Nonlocality in Bound Entanglement

We discuss the relation between entanglement and nonlocality in the hidden nonlocality scenario. Hidden nonlocality signifies nonlocality that can be activated by applying local filters to a particular state that admits a local hidden-variable model in the Bell scenario. We present a fully-biseparable three-qubit bound entangled state with a local model for the most general (non-sequential) measurements. This proves for the first time that bound entangled states can admit a local model for general measurements. We furthermore show that the local model breaks down when suitable local filters are applied. Our results demonstrate the first example of activation of nonlocality in bound entanglement. Hence, we show that genuine hidden nonlocality does not imply entanglement distillability.
Leveraging Image-to-image Translation Generative Adversarial Networks for Face Aging

Here, face images of a specific age class are translated to images of different age classes in an unsupervised manner that enables training on independent sets of images for each age class. In order to learn pairwise translations between age classes, we adopt the UNsupervised Image-to-image Translation framework that employs Variational AutoEncoders and Generative Adversarial Networks. By mapping face images of different age classes to shared latent representations, the most personalized and abstract facial characteristics are preserved. To effectively diffuse age class information, a pyramid of local, neighbour, and global encoders is employed so that the latent representations progressively cover an increased age range. The proposed framework is applied to the FGNET aging database and compared to state-of-the-art techniques and the ground truth. Appealing experimental results demonstrate the ability of the proposed method to efficiently capture both intense and subtle aging effects.
An Autoencoder-based Approach for Recognizing Null Class in Activities of Daily Living In-the-wild via Wearable Motion Sensors

Recognizing activities of daily living (ADL) in-the-wild, while users follow their daily routine, is challenging due to the presence of various activities that do not belong to the set of desired activities in which the system is interested (i.e., NULL class). In this paper, we propose a framework for ADL recognition via wearable motion sensors with the ability to detect NULL class. Existing ADL recognition systems either ignore the NULL class or use some training data to train a model for recognizing it. However, our framework uses only samples of the desired activities in the training phase and learns to detect the NULL samples based on a modified variational autoencoder model that outputs reconstruction probability. Experimental results show that in detecting six ADL with accelerometer data, our system achieves 14% higher F1-score compared to the models that use training samples of NULL activities.
Modelling Sample Informativeness for Deep Affective Computing

Using data with high quality annotation is crucial in emotion recognition applications, especially because the task is subjective and the raters may exhibit disagreement with respect to each sample. In this paper, we propose a meta-learning methodology that can reason about the training data and detect potentially less informative instances in order to reduce their impact in the training process. The way we inform the meta-learner on the importance of each sample is by utilising recent advances in uncertainty modelling with Bayesian neural networks that can decompose predictive uncertainty into: a) model uncertainty that is due to a lack of observations and b) label uncertainty that is due to inherent randomness in the data labelling, which we adapt for affective computing. Our proposed method for soft data selection exhibits a 6% absolute improvement in Concordance Correlation Coefficient with respect to the baseline in a two-dimensional continuous affect recognition task.
Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification

Short utterance-based spoken language identification (LID) is a challenging task due to the large variation of its feature representation. Improving feature representation of short utterances using a teacher-student method has been shown its effectiveness for LID tasks. However, conventional teacher-student methods use fixed pre-trained teacher models, that makes it difficult to optimize student models. In this paper, rather than using a fixed pre-trained teacher model, we investigate an interactive teacher-student learning by adjusting the teacher model with reference to the performance of the student model when the student model is stuck in a local minimum. Experiments on a 10-language LID task were carried out to test the algorithm. Our results showed its effectiveness of the proposed algorithm on short utterance LID tasks.
Evaluation of Natural Ventilation Characteristics on the Sultanate of Ternate Mosque

Thermal comfort is very important in mosque buildings, especially during prayer times in order to increase the comfort and solemn in prayer. The usage of air conditioning for the mosque in Ternate City is not recommended because of the energy resources is limited (rotating blackout) and also to reduce its operational cost every month. Therefore, the application of natural ventilation is essential to improve thermal comfort in the mosque. This study aims to identify the characteristics of the natural ventilation system of Sultan Ternate Mosque by analyzing the orientation of inlet and outlet, ratio and dimension of openings, type and position of ventilation through the literature studies and field observations. The results show that the natural ventilation system of Sultan Ternate Mosque uses clerestory ventilation and cross ventilation systems, therefore the indoor thermal conditions can reach a comfortable level. Ventilation types are semicircular and vertical jalousie on the walls, and roof ventilation of 4-levels. The total area of ventilation holes are 110.24 m2, with the ratio is about 1.04 (inlet): 1.00 (outlet).
Light Field Image Compression Using Depth-based CNN in Intra Prediction

Recently, light field images have received extensive attention due to their potential applications. Since they take up a huge memory because of its super-high resolution, efficient compression methods are fundamentally required. In this paper, we propose a novel intra prediction mode by using depth-adaptive convolutional neuro network (DCNN). Light field projection finds the imaging response distribution for each object point using the depth estimated from each macropixel in the light field image. The highly correlated imaging responses are used to select the neural network structure. The network structure also adapts to the to-be-encoded block size. Adding the proposed DCNN-based prediction mode into the rate-distortion optimization loop with other 35 intra prediction modes of HEVC, the proposed encoding scheme achieves a significant bit-rate saving compared to representative compression approaches with limited computational complexity increment. Statistical data are also provided and analyzed to demonstrate the efficiency of the proposed method.
Audio Feature Generation for Missing Modality Problem in Video Action Recognition

Despite the recent success of multi-modal action recognition in videos, in reality, we usually confront the situation that some data are not available beforehand, especially for multi-modal data. For example, while vision and audio data are required to address the multi-modal action recognition, audio tracks in videos are easily lost due to the broken files or the limitation of devices. To cope with this sound-missing problem, we present an approach to simulating deep audio feature from merely spatial-temporal vision data. We demonstrate that adding the simulating sound feature can significantly assist the multi-modal action recognition task. Evaluating our method on the Moments in Time (MIT) Dataset , we show that our proposed method performs favorably against the two-stream architecture, enabling a richer understanding of multi-modal action recognition in video.
Adversarially Trained Autoencoders for Parallel-data-free Voice Conversion

We present a method for converting the voices between a set of speakers. Our method is based on training multiple autoencoder paths, where there is a single speaker-independent encoder and multiple speaker-dependent decoders. The autoencoders are trained with an addition of an adversarial loss which is provided by an auxiliary classifier in order to guide the output of the encoder to be speaker independent. The training of the model is unsupervised in the sense that it does not require collecting the same utterances from the speakers nor does it require time aligning over phonemes. Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset. We present subjective tests corroborating the performance of our method.
Business Models and Blockchain: What Can Change?

Abstract The goal is to identify the characteristics of innovative business models that use blockchain technology. Through a qualitative and descriptive research, we sought, in the Crunshbase database, the companies that had in their activity description the word Blockchain, obtaining a total of 810 companies. The data obtained were tabulated in Excel spreadsheet and we carried out a collection of additional information on the websites of the organizations. The process of data analysis used the technique of document analysis and content analysis. The results allowed us to identify that most organizations' foundation date from 2014 and are located in North America. Regarding the area of expertise of these organizations, it can be observed that most of them belong to financial and technology areas. Moreover, 10 companies were already in operation before the creation of blockchain technology, pointing out the characteristic of a disruptive technology, which is the readjustment of established companies. Finally, we highlight the contributions related to the field, by identifying the main areas of business that use blockchain technology, as well as revealing important descriptions about these companies. In addition to contributions to the theory, by presenting empirical evidences of active business with this technology and its contributions to the various areas.
Supervised and unsupervised relevance sampling in handcrafted and deep learning features obtained from image collections

Image collections are currently widely available and are being generated in a fast pace due to mobile and accessible equipment. In principle, that is a good scenario taking into account the design of successful visual pattern recognition systems. However, in particular for classification tasks, one may need to choose which examples are more relevant in order to build a training set that well represents the data, since they often require representative and sufficient observations to be accurate. In this paper we investigated three methods for selecting relevant examples from image collections based on learning models from small portions of the available data. We considered supervised methods that need labels to allow selection, and an unsupervised method that is agnostic to labels. The image datasets studied were described using both handcrafted and deep learning features. A general purpose algorithm is proposed which uses learning methods as subroutines. We show that our relevance selection algorithm outperforms random selection, in particular when using unlabelled data in an unsupervised approach, significantly reducing the size of the training set with little decrease in the test accuracy.
A Learning Framework for Target Detection and Human Face Recognition in Real Time

Inspired by the function, mechanism and efficiency of the visual nerve system of human beings, a revolutionary detection and reorganization method named YOLO is present to provide an accurate, stable and fast arithmetic for a variety of targets, be it target detection for unmanned vehicle, car license recognition and optimization for surveillance. The traditional method for object detection is to reuse the classifier to implement detection, in contrast, the method named YOLOV2 process this problem by considering it in the mathematical area as a regression of spatially discrete bordered areas and relative class probability. However, as a cost of stable and fast response of this arithmetic, inaccurate detection maybe caused by YOLOV2 when the detected object is tiny (e.g., face recognition in surveillance). In this article, the authors provide a new method to further improve the performance of YOLOV2 by utilizing the accurate, stable and fast properties of YOLOV2 and editing the original code of YOLOV2 to eliminate the inaccuracy of tiny object detection, and implement this method on an embedded system.
Community Inference from Graph Signals with Hidden Nodes

Many recent works on inference of graph structure assume that the graph signals are fully observable. For large graphs with thousands or millions of nodes, this entails high complexity on the data collection and processing steps. Here, we study a community inference problem on partially observed (sub-sampled) graph signals which sidesteps topology inference, while revealing the coarse structure of the graph directly. Two variants of the inference task are studied: (i) a blind method that infers the communities that the observable nodes belong to; and (ii) a semi-blind method that infers the communities of all nodes using, in addition, side information about the sub-graph between observable and hidden nodes. These techniques for community inference are shown to be efficient and suitable for large graphs analytically and empirically.
An Enhanced Hierarchical Extreme Learning Machine with Random Sparse Matrix Based Autoencoder

Recently, by employing the stacked extreme learning machine (ELM) based autoencoders (ELM-AE) and sparse AEs (SAE), multilayer ELM (ML-ELM) and hierarchical ELM (H-ELM) has been developed. Compared to the conventional stacked AEs, the ML-ELM and H-ELM usually achieve better generalization performance with a significantly reduced training time. However, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm based SAE may suffer the overfitting problem and it is unable to provide analytical solution leading to long training time for big data. To alleviate these deficiencies, we propose an enhanced H-ELM (EH-ELM) with a novel random sparse matrix based AE (SMA) in this paper. The contributions are in two aspects, 1) utilizing the random sparse matrix, the sparse features can be obtained; 2) the proposed SMA can provide an analytical solution so that the high computational complexity issue in SAE can be addressed. Experimental results on benchmark datasets show that the proposed EH-ELM achieves a higher recognition rate and a faster training speed than H-ELM and ML-ELM.
Estimation of Gaze Region Using Two Dimensional Probabilistic Maps Constructed Using Convolutional Neural Networks

Predicting the gaze of a user can have important applications in human computer interactions (HCI). They find applications in areas such as social interaction, driver distraction, human robot interaction and education. Appearance based models for gaze estimation have significantly improved due to recent advances in convolutional neural network (CNN). This paper proposes a method to predict the gaze of a user with deep models purely based on CNNs. A key novelty of the proposed model is that it produces a probabilistic map describing the gaze distribution (as opposed to predicting a single gaze direction). This approach is achieved by converting the regression problem into a classification problem, predicting the probability at the output instead of a single direction. The framework relies in a sequence of downsampling followed by upsampling to obtain the probabilistic gaze map. We observe that our proposed approach works better than a regression model in terms of prediction accuracy. The average mean squared error between the predicted gaze and the true gaze is observed to be 6.89° in a model trained and tested on the MSP-Gaze database, without any calibration or adaptation to the target user.
Evolutionary Algorithms for the Design of Quantum Protocols

In this paper, we use evolutionary algorithm to evolve customized quantum key distribution (QKD) protocols designed to counter attacks against the system in order to optimize the speed of the secure communication. This is in contrast to most work in QKD protocols, where a fixed protocol is designed and then its security is analyzed to determine how strong an attack it can withstand. We show that our system is able to find protocols that can operate securely against attacks where ordinary QKD protocols would fail. Our algorithm evolves protocols as quantum circuits, thus making the end result potentially easier to implement in practice.
Speech Waveform Reconstruction Using Convolutional Neural Networks with Noise and Periodic Inputs

This paper presents a method for upsampling and transforming a compact representation of acoustics into a corresponding speech waveform. Similar to a conventional vocoder, the proposed system takes a pulse train derived from fundamental frequency and a noise sequence as inputs and shapes them to be consistent with the acoustic features. However, the filters that are used to shape the waveform in the proposed system are learned from data, and take the form of layers in a convolutional neural network. Because the network performs the transformation simultaneously for all waveform samples in a sentence, its synthesis speed is comparable with that of conventional vocoders on CPU, and many times faster on GPU. It is trained directly in a fast and straightforward manner, using a combined time- and frequency-domain objective function. We use publicly available data and provide code to allow our results to be reproduced.
Quantized Gaussian Embedding Steganography

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.
Factographic information retrieval for semiconductor physics, micro - and nanosystems

This paper proposes a factographic information retrieval for semiconductor physics, as well as micro - and nanosystems. This factographic information retrieval includes a pattern recognition algorithm and factographic database. The first factographic database includes special keywords from semiconductor physics and micro - and nanosystems. The second factographic database includes document descriptions in the form of Searching Documents Patterns for the fields of semiconductor physics and microelectronics. An analytical model of the factographic information retrieval system is developed. This model is presented by an effectiveness indicator: the average length of the recommendatory list used for semiconductor physics and micro - and nanosystems for space application.