Reviewerly/exHarmony huggingface dataset:
Mining Purchase Intent in Twitter

Most social media platforms allow users to freely express their beliefs, opinions, thoughts, and intents. Twitter is one of the most popular social media platforms where users’ post their intent to purchase. A purchase intent can be defined as measurement of the probability that a consumer will purchase a product or service in future. Identification of purchase intent in Twitter sphere is of utmost interest as it is one of the most long-standing and widely used measures in marketing research. In this paper, we present a supervised learning strategy to identify users’ purchase intent from the language they use in Twitter. Recurrent Neural Networks (RNNs), in particular with Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for text classification. They effectively encode sequences with varying length and capture long range dependencies. We present the first study to apply LSTM for purchase intent identification task. We train the LSTM network on semi-automatically created dataset. Our model achieves competent classification accuracy (F1 = 83%) over a gold-standard dataset. Further, we demonstrate the efficacy of the LSTM network by comparing its performance with different classical classification algorithms taking this purchase intent identification task into account.
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation from Multimodal Unpaired Images

In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (UAGAN), which can perform any-to-any image modality translation and segment the target objects simultaneously in the case where two or more modalities are available. The translation stream is used to capture modality-invariant features of the target anatomical structures. In addition, to focus on segmentation-related features, we add attentional blocks to extract valuable features from the translation stream. Experiments on three-modality brain tumor segmentation indicate that UAGAN outperforms the existing methods in most cases.
PyMaxEnt: A Python software for maximum entropy moment reconstruction

PyMaxEnt is a software that implements the principle of maximum entropy to reconstruct functional distributions given a finite number of known moments. The software supports both continuous and discrete reconstructions, and is very easy to use through a single function call. In this article, we set out to verify and validate the software against several tests ranging from the reconstruction of discrete probability distributions for biased dice all the way to multimodal Gaussian and beta distributions. Written in Python, PyMaxEnt provides a robust and easy-to-use implementation for the community.
Procedural Reasoning Networks for Understanding Multimodal Procedures

This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong inductive bias and explore the question of how multimodality can be exploited to provide a complementary semantic signal. Towards this end, we introduce a new entity-aware neural comprehension model augmented with external relational memory units. Our model learns to dynamically update entity states in relation to each other while reading the text instructions. Our experimental analysis on the visual reasoning tasks in the recently proposed RecipeQA dataset reveals that our approach improves the accuracy of the previously reported models by a large margin. Moreover, we find that our model learns effective dynamic representations of entities even though we do not use any supervision at the level of entity states.
Decoupling texture blending and shape warping in face morphing

Automatic face recognition systems (FRS) are quite sensitive to the wellknown face morphing attack, as pointed out by several researchers. Considering that, in the perspective of a fraudulent document usage, a criminal would certainly do its best to fool both humans and FRSs, the design of effective countermeasures should consider the trickiest and challenging conditions. Cognitive studies show that facial texture and shape (the two main components modified by face morphing) play a different role in face recognition by humans. This paper aims at understanding the behavior of FRSs with respect to these two factors and to identify the morphing parameters that maximize the probability of a successful attack.
Neural network trajectory tracking of tracked mobile robot

In this paper, an online neural network trajectory tracking controller for a tracked mobile robot is proposed. The mobile robot used in this study has only three binary sensors to obtain its position/error from the target trajectory. Due to poor binary sensors which return only 0 or 1, it is impossible to get accurate position/error. Therefore it is not easy to apply ordinary linear control technique directly. For the mobile robot with such poor binary sensors, we propose the online neural network controller. The proposed online neural network controller has the following properties; 1. can train itself and control the mobile robot simultaneously; 2. has feedback structure which can play a role of derivative control like PD controller. Furthermore, we propose using the rough direction of the mobile robot in the input layer. Even though we only have inaccurate position/error, we consider using its direction. The direction is calculated by applying the least square to the inaccurate positions/errors. We expect that the direction by the least square works as a sort of derivatives, even though the positions/errors are inaccurate. Simulation and experimental results are given in order to present the effectiveness of the proposed online neural network controllers for the mobile robot. The comparison of the control performances among three different controllers is also given to show the importance of the derivatives to control the mobile robot and the efficacy of the two redundant derivatives we proposed.
Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which significantly affects the absorption and reflection of light, the spectral feature is proved to be important for leukocytes classification and identification. This paper proposes an accurate identification method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classification of five types of leukocytes and abnormal leukocytes. Compared with different classification methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classification and identification of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.
Deductive verification with ghost monitors

We present a new approach to deductive program verification based on auxiliary programs called ghost monitors . This technique is useful when the syntactic structure of the target program is not well suited for verification, for example, when an essentially recursive algorithm is implemented in an iterative fashion. Our approach consists in implementing, specifying, and verifying an auxiliary program that monitors the execution of the target program, in such a way that the correctness of the monitor entails the correctness of the target. The ghost monitor maintains the necessary data and invariants to facilitate the proof. It can be implemented and verified in any suitable framework, which does not have to be related to the language of the target programs. This technique is also applicable when we want to establish relational properties between two target programs written in different languages and having different syntactic structure. We then show how ghost monitors can be used to specify and prove fine-grained properties about the infinite behaviors of target programs. Since this cannot be easily done using existing verification frameworks, we introduce a dedicated language for ghost monitors, with an original construction to catch and handle divergent executions. The soundness of the underlying program logic is established using a particular flavor of transfinite games. This language and its soundness are formalized and mechanically checked.
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping

In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation metrics used thus far fail to properly account for the latter, relying instead on insufficient similarity comparisons. We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents. For such, we define the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efficiently calculated. Further, we define SDTW, which constrains nDTW to only successful paths. We collect human similarity judgments for simulated paths and find nDTW correlates better with human rankings than all other metrics. We also demonstrate that using nDTW as a reward signal for Reinforcement Learning navigation agents improves their performance on both the Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in particular highlight the superiority of SDTW over previous success-constrained metrics.
An Automated Grader for Chinese Essay Combining Shallow and Deep Semantic Attributes

Writing is a pivotal part of the language exam, which is considered as a useful tool to accurately reflect students' language competence. As Chinese language tests become popular, manual grading becomes a heavy and expensive task for language test organizers. In the past years, there is a large volume of research about the automated English evaluation systems. Nevertheless, since the Chinese text has more complex grammar and structure, much fewer studies have been investigated on automated Chinese evaluation systems. In this paper, we propose an automated Chinese essay evaluation system called AGCE (Automated Grader for Chinese Essay), which combines shallow and deep semantic attributes of essays. We implement and train our AGCE system on a Chinese essay dataset, which is created by ourselves based on more than 1000 student essays from a Chinese primary school. Experimental results indicate that our AGCE system achieves the quadratic weighted Kappa of 0.7590 on a small dataset, which is of higher grading accuracy compared with other four popular neural network methods trained on large-scale datasets. In addition, our AGCE system can provide constructive feedback about Chinese writing, such as misspelling feedback and grammatical feedback about writers' essays, which is helpful to improve their writing capability.
Learning from Rules Generalizing Labeled Exemplars

In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar model for collecting human supervision to combine the scalability of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.
Program Guided Agent

Developing agents that can learn to follow natural language instructions has been an emerging research direction. While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans. To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks. We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task. Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy. We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.
Adaptive Chunklets and AQM for Higher-Performance Content Streaming

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.
Neural tangent kernels, transportation mappings, and universal approximation

This paper establishes rates of universal approximation for the neural tangent kernel (NTK) in the standard setting of microscopic changes to initial weights. Concretely, given a target function f, a target width m, and a target approximation error eps>0, then with high probability, moving the initial weight vectors a distance B/(eps * sqrt{m}) will give a linearized finite-width NTK which is (sqrt(eps) + B/sqrt(eps * m))^2-close to both the target function f, and also the shallow network which this NTK linearized. The constant B can be independent of eps --- particular cases studied here include f having good Fourier transform or RKHS norm --- though in the worse case it scales roughly as 1/eps^d for general continuous functions. The method of proof is to rewrite f with equality as an infinite-width linearized network whose weights are a transport mapping applied to random initialization, and to then sample from this transport mapping. This proof therefore provides another perspective on the scaling behavior of the NTK: redundancy in the weights due to resampling allows weights to be scaled down. Since the approximation rates match those in the literature for shallow networks, this work implies that universal approximation is not reliant upon any behavior outside the NTK regime.
Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze our algorithm from a novel geometric perspective and we show that for {\it any monotone transform} of a smooth and strongly convex objective with latent dimension $k \ge n$, we present a novel analysis that shows convergence within an $\epsilon$-ball of the optimum in $O(kQ\log(n)\log(R/\epsilon))$ evaluations, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number. Our rates are the first of its kind to be both 1) poly-logarithmically dependent on dimensionality and 2) invariant under monotone transformations. We further leverage our geometric perspective to show that our analysis is optimal. Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on synthetic and MuJoCo benchmarks.
Episodic Reinforcement Learning with Associative Memory

Sample efficiency has been one of the major challenges for deep reinforcement learning. Non-parametric episodic control has been proposed to speed up parametric reinforcement learning by rapidly latching on previously successful policies. However, previous work on episodic reinforcement learning neglects the relationship between states and only stored the experiences as unrelated items. To improve sample efficiency of reinforcement learning, we propose a novel framework, called Episodic Reinforcement Learning with Associative Memory (ERLAM), which associates related experience trajectories to enable reasoning effective strategies. We build a graph on top of states in memory based on state transitions and develop an efficient reverse-trajectory propagation strategy to allow rapid value propagation through the graph. We use the non-parametric associative memory as early guidance for a parametric reinforcement learning model. Results on Atari games show that our framework has significantly higher sample efficiency and outperforms state-of-the-art episodic reinforcement learning models.
QSST: A Quranic Semantic Search Tool based on word embedding

Retrieving information from the Quran is an important field for Quran scholars and Arabic researchers. There are two types of Quran searching techniques: semantic or concept-based and keyword-based. Concept-based search is a challenging task, especially in a complex corpus such as Quran. This paper presents a concept-based searching tool (QSST) for the Holy Quran. It consists of four phases. In the first phase, the Quran dataset is built by manually annotating Quran verses based on the ontology of Mushaf Al-Tajweed. The second phase is word Embedding, this phase generates features’ vectors for words by training a Continuous Bag of Words (CBOW) architecture on large Quranic and Classic Arabic corpus. The third phase includes calculating the features’ vectors of both input query and Quranic topics. Finally, retrieving the most relevant verses by computing the cosine similarity between both topic and query vectors. The performance of the proposed QSST is measured by comparing results against Mushaf Al-Tajweed. Then, precision, recall, and F-score are computed and their percentages were 76.91%, 72.23% 69.28% respectively. In addition, the results are evaluated by three Islamic experts and the average precision was 91.95%. Finally, QSST results are compared with the recent existing tools; QSST outperformed them.
Dual differential confocal method for surface profile measurement with a large sensing measurement range

To meet the requirements of the large sensing measurement range and high axial depth resolution for profile measurement, a dual differential confocal method (DDCM) is proposed in this paper. The DDCM uses the confocal signal to process separately the signal of two pinholes with axial offset, and it adds the two processed signals to obtain an axial response curve with a large slope and linear response range, thereby achieving a high-precision surface profile measurement with no axial scanning. Preliminary experiments show that the DDCM has a sensing measurement range of 0.54 µm and an axial resolution of 1 nm at the numerical aperture of 0.9. Furthermore, the sensing measurement range of the DDCM is approximately 2.9 times that of the differential confocal microscopy.
Bit Allocation based on Visual Saliency in HEVC

As one of the important part in the HEVC reference software, R-lambda model adopts mean absolute difference (MAD) for the coding unit tree (CTU) level bit allocation. However, this optimum method may neglect some important characteristics of human visual system (HVS). In this paper, we propose a novel bit allocation algorithm to process some salient visual information priority. Firstly, an improved video saliency detection algorithm is proposed, which induces temporal correlation into a 2D visual attention model. Secondly, the visual saliency based CTU level bit allocation algorithm is presented by allocating bits for CTUs with their saliency weights. What's more, with considerations of the temporal quality consistence among Saliency Areas (SAs), a window based weight smoothing model is proposed to achieve better subjective quality. Finally, several experiments are performed on the HEVC reference software, HM16.9, under the low delay P configuration, and the experimental results show that the average BD-Rate of the entire test sequences and of the SAs reduce 1.7% and 6.2%, respectively. The proposed algorithm can also improve subjective quality remarkably.