Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammar Proficiency of Colegio de San Juan de Letran Calamba College Students

T his undertaking quantitatively assessed the grammar proficiency of Colegio de San Juan de Letran Calamba college students to identify weaknesses and eventually improve instruction and the quality of learning of students. Using the descriptive method of research, the researcher assessed the college students’ grammar proficiency by using a teacher-made questionnaire to identify the specific areas of grammar difficulties. A 50-item multiple choice test, which evaluated knowledge of the 10 areas of grammar, was specifically used. Grammar areas were based on 30 common grammatical errors of second language users resulting from the study done by Andrea Lungsford and Robert Corner of the University of California in San Diego. Demographic profile of students, as well as their perceptions on the importance of grammar to the use of correct English, was considered. Using proportional stratified random sampling, this study tested 359 students enrolled in the 2nd semester of AY 2005-2006 across all departments and in all year levels. The study likewise included a total enumeration of all the nine full-time and part-time college faculty of English. In general, Letran Calamba college students received fair ratings in parts of speech, possessive nouns, and verb mood and tense. Areas of plural nouns, pronoun usage, adjective and adverb usage, and pronoun-antecedent agreement needed improvement. The students failed in the areas of sentence structure, subject- verb agreement, and sentence construction. Respondents perceived grammar as extremely important to the correct use of English. No significant relationship was established between the overall perceived importance of the study of grammar to the correct use of English and student’s overall grammar proficiency. There was also no significant difference between the faculty and students’ perception regarding the importance of grammar to the correct use of English. Moreover, grammar was found to be extremely important in writing and speaking as perceived both by faculty and students. Research findings showed that grammar was relatively important in the development of identified English competencies among Letran Calamba college students. Hence, the school should consider making all freshmen students enroll in English 101 or Remedial English.
Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Pancreas Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach (Zhou et al. 2016), which used prediction from the coarse stage to shrink the input region provided to the fine stage. Although this strategy achieves high accuracy, we note that the coarse-scaled and fine-scaled networks were trained and tested individually, which limited the use of multi-stage visual cues for segmentation. This paper presents a Saliency Transformation Network, which contains a trainable saliency transformation module. This module computes spatial weights from the coarse-scaled segmentation score map, and applies them to the fine-scaled input image. In training, the coarse-scaled and fine-scaled segmentation networks are optimized in a joint manner, so that both networks become more powerful when they are evaluated individually. In testing, this strategy makes full use of the segmentation results at the coarse stage, so that we can deliver complementary information to the fine stage rather than merely providing a bounding box. We perform experiments on the NIH pancreas segmentation dataset with 82 CT volumes. Following the same testing process which involves a coarse-to-fine iteration, our approach outperforms the state-of-the-art approach (trained in a stage-wise manner) by an average of over 2%. In addition, our approach enjoys better convergence properties, making it more reliable in practice.
Two Birds with One Stone: Iteratively Learn Facial Attributes with GANs

Generating high fidelity identity-preserving faces has a wide range of applications. Although a number of generative models have been developed to tackle this problem, it is still far from satisfying. Recently, Generative adversarial network (GAN) has shown great potential for generating or transforming images of exceptional visual fidelity. In this paper, we propose to train GAN iteratively via regularizing the minmax process with an integrated loss, which includes not only the per-pixel loss but also the perceptual loss. We argue that the perceptual information benefits the output of a high-quality image, while preserving the identity information. In contrast to the existing methods only deal with either image generation or transformation, our proposed iterative architecture can achieve both of them. Experiments on the multi-label facial dataset CelebA demonstrate that the proposed model has excellent performance on recognizing multiple attributes, generating a high-quality image, and transforming image with controllable attributes.
Intelligible Artificial Intelligence

Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwhelmingly complex decisions by local approximation, vocabulary alignment, and interactive dialog.
Human-level CMR image analysis with deep fully convolutional networks

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing a wealth of information for sensitive and specific diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a dataset of unprecedented size, consisting of 4,875 subjects with 93,500 pixelwise annotated images, which is by far the largest annotated CMR dataset. By combining FCN with a large-scale annotated dataset, we show for the first time that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinical measures. We anticipate this to be a starting point for automated and comprehensive CMR analysis with human-level performance, facilitated by machine learning. It is an important advance on the pathway towards computer-assisted CVD assessment.
D-PCN: Parallel Convolutional Neural Networks for Image Recognition in Reverse Adversarial Style

In this paper, a recognition framework named D-PCN using a discriminator is proposed, which can intensify the feature extracting ability of convolutional neural networks. The framework contains two parallel convolutional neural networks, and a discriminator, which is introduced from the Generative Adversarial Nets and can improve the performance of parallel networks. The two nets are devised side by side, and the discriminator takes in the features from parallel networks as input, aiming to guide the two nets to learn features of different details in a reverse adversarial style. After that, the feature maps from two nets get aggregated, then an extra overall classifier is added and will output the final prediction employing the fused features. The training strategy of the D-PCN is also introduced which ensures the utilization of the discriminator. We experiment the D-PCN with several CNN models including NIN, ResNet, ResNeXt and DenseNet using single NVIDIA TITAN Xp, on the two benchmark datasets: CIFAR-100 and downsampled ImageNet-1k, the D-PCN enhances all models on CIFAR-100 and also reinforces the performance of ResNet on downsampled ImageNet-1k explicitly. In particular, it yields state-of-the-art classification performance on CIFAR-100 with compared to relative works.
ClusterNet : Semi-Supervised Clustering using Neural Networks

Clustering using neural networks has recently demon- strated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learn- ing or their dependence on large set of labeled data sam- ples. In this paper, we propose ClusterNet that uses pair- wise semantic constraints from very few labeled data sam- ples (< 5% of total data) and exploits the abundant un- labeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clus- tering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses con- volution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and com- pare the performance of ClusterNet on several datasets and state of the art deep clustering approaches.
Towards an Understanding of Our World by GANing Videos in the Wild

Existing generative video models work well only for videos with a static background. For dynamic scenes, applications of these models demand an extra pre-processing step of background stabilization. In fact, the task of background stabilization may very often prove impossible for videos in the wild. To the best of our knowledge, we present the first video generation framework that works in the wild, without making any assumption on the videos' content. This allows us to avoid the background stabilization step, completely. The proposed method also outperforms the state-of-the-art methods even when the static background assumption is valid. This is achieved by designing a robust one-stream video generation architecture by exploiting Wasserstein GAN frameworks for better convergence. Since the proposed architecture is one-stream, which does not formally distinguish between fore- and background, it can generate - and learn from - videos with dynamic backgrounds. The superiority of our model is demonstrated by successfully applying it to three challenging problems: video colorization, video inpainting, and future prediction.
Confined spatial networks with wireless applications

Many networks have nodes located in physical space, with links more common between closely spaced pairs of nodes. For example, the nodes could be wireless devices and links communication channels in a wireless mesh network. We describe recent work involving such networks confined to a finite region of space, considering effects due to the geometry (convex, non-convex, and fractal), node distribution, distance-dependent link probability, mobility, directivity and interference.
Inference Attacks Against Collaborative Learning

Collaborative machine learning and related techniques such as distributed and federated learning allow multiple participants, each with his own training dataset, to build a joint model. Participants train local models and periodically exchange model parameters or gradient updates computed during the training. We demonstrate that the training data used by participants in collaborative learning is vulnerable to inference attacks. First, we show that an adversarial participant can infer the presence of exact data points in others' training data (i.e., membership inference). Then, we demonstrate that the adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. We evaluate the efficacy of our attacks on a variety of tasks, datasets, and learning configurations, and conclude with a discussion of possible defenses.
Object Detection in Videos by Short and Long Range Object Linking

We address the problem of detecting objects in videos with the interest in exploring temporal contexts. Our core idea is to link objects in the short and long ranges for improving the classification quality. Our approach first proposes a set of candidate spatio-temporal cuboids, each of which serves as a container associating the object across short range frames, for a short video segment. It then regresses the precise box locations in each frame over each cuboid proposal, yielding a tubelet with a single classification score which is aggregated from the scores of the boxes in the tubelet. Third, we extend the non-maximum suppression algorithm to remove spatially-overlapping tubelets in the short segment, avoiding tubelets broken by the frame-wise NMS. Finally, we link the tubelets across temporally-overlapping short segments over the whole video, in order to boost the classification scores for positive detections by aggregating the scores in the linked tubelets. Experiments on the ImageNet VID dataset shows that our approach achieves the state-of-the-art performance.
Branched Generative Adversarial Networks for Multi-Scale Image Manifold Learning

We introduce BranchGAN, a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. What is unique about BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to and trained to learn image representations at a particular scale. During training, we progressively "de-freeze" the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes that are associated with specific feature scales. Experiments demonstrate the effectiveness of our training method in multi-scale, disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate two new applications enabled by BranchGAN.
The physical structure of grammatical correlations: equivalences, formalizations and consequences

In this paper we consider some well-known facts in syntax from a physics perspective, which allows us to establish some remarkable equivalences. Specifically, we observe that the operation MERGE put forward by N. Chomsky in 1995 can be interpreted as a physical information coarse-graining. Thus, MERGE in linguistics entails information renormalization in physics, according to different time scales. We make this point mathematically formal in terms of language models, i.e., probability distributions over word sequences, widely used in natural language processing as well as other ambits. In this setting, MERGE corresponds to a 3-index probability tensor implementing a coarse-graining, akin to a probabilistic context-free grammar. The probability vectors of meaningful sentences are naturally given by tensor networks (TN) that are mostly loop-free, such as Tree Tensor Networks and Matrix Product States. These structures have short-ranged correlations in the syntactic distance by construction and, because of the peculiarities of human language, they are extremely efficient to manipulate computationally. We also propose how to obtain such language models from probability distributions of certain TN quantum states, which we show to be efficiently preparable by a quantum computer. Moreover, using tools from quantum information and entanglement theory, we use these quantum states to prove classical lower bounds on the perplexity of the probability distribution for a set of words in a sentence. Implications of these results are discussed in the ambits of theoretical and computational linguistics, artificial intelligence, programming languages, RNA and protein sequencing, quantum many-body systems, and beyond.
Statistical Recurrent Models on Manifold valued Data

In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep neural network models to such non-Euclidean spaces. Several recent papers have shown how convolutional neural networks (CNNs) can be extended to learn with graph-based data. In this work, we study the setting where the data (or measurements) are ordered, longitudinal or temporal in nature and live on a Riemannian manifold -- this setting is common in a variety of problems in statistical machine learning, vision and medical imaging. We show how statistical recurrent network models can be defined in such spaces. We give an efficient algorithm and conduct a rigorous analysis of its statistical properties. We perform extensive numerical experiments showing competitive performance with state of the art methods but with far fewer parameters. We also show applications to a statistical analysis task in brain imaging, a regime where deep neural network models have only been utilized in limited ways.
Intelligent Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. However, traditional human-objects detection and tracking approaches are still computationally too expensive to edge devices. Aiming at intelligent surveillance as an edge network service, this work explored the feasibility of two popular humanobjects detection schemes, Harr-Cascade and SVM, at the edge. Understanding the existing constraints of the algorithms, a lightweight Convolutional Neural Network (L-CNN) is proposed using the depthwise separable convolution. The proposed L-CNN considerably reduces the number of parameters without affecting the quality of the output, thus it is ideal for an edge device usage. Being trained with Single Shot Multi-box Detector (SSD) to pinpoint each human-object location, it gives coordination of bounding box around the object. We implemented and tested L-CNN on an edge device using Raspberry PI 3. The intensive experimental comparison study has validated that the proposed L-CNN is a feasible design for real-time human-object detection as an edge service. Keywords—Edge Computing, Smart Surveillance, Lightweight Convolutional Neural Network (L-CNN), Human Detection.
A Classification-Based Perspective on GAN Distributions

A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ classification-based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. They also indicate that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, the diversity of such synthetic data is orders of magnitude smaller than that of the original data.
Towards Diverse Text Generation with Inverse Reinforcement Learning

Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation. Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately. Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by "entropy regularized" policy gradient, encourages to generate more diversified texts. Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples

Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. Until now, black-box attacks against neural networks have relied on transferability of adversarial examples. White-box attacks are used to generate adversarial examples on a substitute model and then transferred to the black-box target model. In this paper, we introduce a direct attack against black-box neural networks, that uses another attacker neural network to learn to craft adversarial examples. We show that our attack is capable of crafting adversarial examples that are indistinguishable from the source input and are misclassified with overwhelming probability - reducing accuracy of the black-box neural network from 99.4% to 0.77% on the MNIST dataset, and from 91.4% to 6.8% on the CIFAR-10 dataset. Our attack can adapt and reduce the effectiveness of proposed defenses against adversarial examples, requires very little training data, and produces adversarial examples that can transfer to different machine learning models such as Random Forest, SVM, and K-Nearest Neighbor. To demonstrate the practicality of our attack, we launch a live attack against a target black-box model hosted online by Amazon: the crafted adversarial examples reduce its accuracy from 91.8% to 61.3%. Additionally, we show attacks proposed in the literature have unique, identifiable distributions. We use this information to train a classifier that is robust against such attacks.
An improved competitive algorithm for reordering buffer management

We design and analyze an on-line reordering buffer management algorithm with improved O (log k/log log k) competitive ratio for non-uniform costs, where k is the buffer size. This improves on the best previous result (even for uniform costs) of Englert and Westermann (ICALP 2005) giving O(log k) competitive ratio, which was also the best (off-line) polynomial time approximation guarantee for this problem. Our analysis is based on an intricate dual fitting argument using a linear programming relaxation for the problem that we introduce in this paper.