Reviewerly/exHarmony huggingface dataset:
Intriguing generalization and simplicity of adversarially trained neural networks

Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks. Yet, it remains unknown (a) how adversarially-trained classifiers (a.k.a classifiers) generalize to new types of out-of-distribution examples; and (b) what hidden representations were learned by robust networks. In this paper, we perform a thorough, systematic study to answer these two questions on AlexNet, GoogLeNet, and ResNet-50 trained on ImageNet. While robust models often perform on-par or worse than standard models on unseen distorted, texture-preserving images (e.g. blurred), they are consistently more accurate on texture-less images (i.e. silhouettes and stylized). That is, robust models rely heavily on shapes, in stark contrast to the strong texture bias in standard ImageNet classifiers (Geirhos et al. 2018). Remarkably, adversarial training causes three significant shifts in the functions of hidden neurons. That is, each convolutional neuron often changes to (1) detect pixel-wise smoother patterns; (2) detect more lower-level features i.e. textures and colors (instead of objects); and (3) be simpler in terms of complexity i.e. detecting more limited sets of concepts.
Characteristics and Histopathological Grading of Malignant Spiculated Mass in regards to Histopathological Grading of Breast Cancer Based on The Nottingham Grading System

Introduction: Spiculation in mammography is a typical finding for invading breast cancer and is an important criterion in diagnosis and in predicting prognostic and plays an important role in management. The purpose of this research is to determine the characteristics of malignant spiculating mass in mammography in regards to histopathological grading using The Nottingham Grading System.Methods: Patients whom had spiculation in mammography was reviewed using medical record data by two breast imaging consultants and then combined with The Nottingham Grading System criteria obtained from histopathological examination results of core biopsy and surgery specimen. There was 29 cases that met the inclusion criteria.Results: Of the 29 patients that met the inclusion, the spiculated masses grade I, II has mitotic index of 0-12, grade III has mitotic index of 13-25. Histopathologic grade II, III has the most tubular formation of &lt;10%, grade I has 10-75%. Grade I, II has moderate nuclear pleomorphism, grade III has severe nuclear pleomorphism. Most were grade III (44.8%), followed by grade II (37.9%), and minimally grade I (17.2%). Most patients are in stage 3 breast cancer.Conclusion: Malignant spiculated mass with grade I, II has low proliferation index (mitotic index 0-12), histopathological grade II, III had worse cellular differentiation (tubular formation &lt;10%), histopathological grade II, III has moderate to severe nuclear pleomorphism.
Correction to: Thirty years of small business economics: a bibliometric overview

The original version of this article unfortunately contained a mistake in some of the data in the Table 3.
Trimodal Attention Module for Multimodal Sentiment Analysis (Student Abstract)

In our research, we propose a new multimodal fusion architecture for the task of sentiment analysis. The 3 modalities used in this paper are text, audio and video. Most of the current methods deal with either a feature level or a decision level fusion. In contrast, we propose an attention-based deep neural network and a training approach to facilitate both feature and decision level fusion. Our network effectively leverages information across all three modalities using a 2 stage fusion process. We test our network on the individual utterance based contextual information extracted from the CMU-MOSI Dataset. A comparison is drawn between the state-of-the-art and our network.
The Impact of “Scramble for Talent” Campaign on College Graduates’ Employment and Entrepreneurship in the Context of Indigenous Innovation and Industrial Upgrading

Indigenous innovation and industrial upgrading are the defining trends of economic development in the new era. With the formation of city clusters and the more scientific industry plant layout, the demand for talents in various cities is increasingly pressing. College students have become the major groups in the context of talent introduction policy. Major cities have initiated preferential policies to attract talents. The Scramble for Talent Campaign occurs among cities, making a considerable difference on the college graduates’ employment and entrepreneurship. Since the initiation of “Scramble for Talent” campaign, the talent flow in relevant cities has accelerated measurably. The research shows that Hukou (the household registration) is the main element that the college graduates factor into consideration when selecting places of employment, and the fund and policy support they are able to receive are the key elements to promote innovation. In the “Scramble for Talent”, such measures as relaxing restrictions on household registration policy, launching preferential loans policy and other support policies have considerably influenced on the college graduates’ employment and entrepreneurship, which helps to optimize the distribution of talent resources and improve the willingness and success rate of entrepreneurship. In order to illustrate the effect, a series of policies and the transformation and upgrading of pillar industries are required in the introduction of talents. The blind scramble for talents will not only unbalance the allocation of human resources but will also cause social problems. It will spread the “City Disease” from the first-tier cities to the second and third-tier cities. The college students and all relative administration in all regions should pay high attention to this problem during the course. Generally speaking, the “Scramble for Talent” Campaign is a special phenomenon in a special period, which positively impact the college graduates’ employment and entrepreneurship.
Learning Transferable Cooperative Behavior in Multi-Agent Teams

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. To better utilize the inherent structure of our environment, we propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other, allowing agents to selectively attend to different parts of the environment, while also introducing invariance to the number of agents or entities present in the system as well as permutation invariance. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized execution framework.
Robust Markerless 3D Head Tracking of a Vehicle Occupant Using OpenPose

This study presents an improved version of three-dimensional (3D) marker-less head-tracking method published previously. The new method improves the reliability of the technique and also minimizing the required manual procedure. A time-of-flight depth camera, Microsoft Kinect V2, was utilized to obtain infrared images with depth information at the frame rate of about 30 Hz. Open-Pose, an image-processing toolkit that estimates two-dimensional facial and joint locations from monocular images using a convolutional neural network, was employed to obtain a few facial landmarks from each captured infrared image. A 3D face scan was aligned to reconstructed depth points based on the estimated landmarks and fitted to the points to obtain an accurate 3D head orientation and location of the frame. The newly proposed method significantly improved the robustness of head tracking compared to the previous method. In testing, 93% of 1330 movement trials between 2 and 5 s in duration were automatically tracked without tracking loss, while the previous method had a success rate of 82%.
Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum

Physical agents that can autonomously generate engaging, life-like behaviour will lead to more responsive and interesting robots and other autonomous systems. Although many advances have been made for one-to-one interactions in well controlled settings, future physical agents should be capable of interacting with humans in natural settings, including group interaction. In order to generate engaging behaviours, the autonomous system must first be able to estimate its human partners' engagement level. In this paper, we propose an approach for estimating engagement during group interaction by simultaneously taking into account active and passive interaction, i.e. occupancy, and use the measure as the reward signal within a reinforcement learning framework to learn engaging interactive behaviours. The proposed approach is implemented in an interactive sculptural system in a museum setting. We compare the learning system to a baseline using pre-scripted interactive behaviours. Analysis based on sensory data and survey data shows that adaptable behaviours within an expert-designed action space can achieve higher engagement and likeability.
Neural Networks with Small Weights and Depth-Separation Barriers

In studying the expressiveness of neural networks, an important question is whether there are functions which can only be approximated by sufficiently deep networks, assuming their size is bounded. However, for constant depths, existing results are limited to depths $2$ and $3$, and achieving results for higher depths has been an important open question. In this paper, we focus on feedforward ReLU networks, and prove fundamental barriers to proving such results beyond depth $4$, by reduction to open problems and natural-proof barriers in circuit complexity. To show this, we study a seemingly unrelated problem of independent interest: Namely, whether there are polynomially-bounded functions which require super-polynomial weights in order to approximate with constant-depth neural networks. We provide a negative and constructive answer to that question, by showing that if a function can be approximated by a polynomially-sized, constant depth $k$ network with arbitrarily large weights, it can also be approximated by a polynomially-sized, depth $3k+3$ network, whose weights are polynomially bounded.
3D Segmentation of Brain Tumor Imaging

A brain tumor is a collection of anomalous cells that grow in or around the brain.Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening.In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain.Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
Learning Object Affordances from Sensory-Motor Interaction via Bayesian Networks with Auto-Encoder Features

In this paper, we study learning relationships between objects, actions, and effects."Affordance" is an ecological psychology concept, which addresses how humans learn these relationships and which is also studied in cognitive robotics to transfer the same ability to robots.Our model is built on top of two existing models in this field and uses their strengths to introduce a novel system, where an anthropomorphic robot observes its environment and changes in that environment after executing pre-learned actions.Robot transfers these observations to object and effect properties in the same space and object affordances are learned using Bayesian Networks.The dimensions of features are decreased through autoencoders to achieve a compact network.Usage of a probabilistic model helps our system to deal with missing information or to make predictions for object properties and actions along with effect properties.We illustrate the advantages of our model by comparing it with the two aforementioned models.
Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition

The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents.However, modern neural network models such as long shortterm memory network process expressions in a linear fashion and do not seem to incorporate more complex compositional patterns.In this work, we show that we can explicitly induce grammar by tracing the computational process of a long short-term memory network.We show: (i) the multiplicative nature of long short-term memory network allows complex interaction beyond sequential linear combination; (ii) we can generate compositional trees from the network without external linguistic knowledge; (iii) we evaluate the syntactic difference between the generated trees, randomly generated trees and gold reference trees produced by constituency parsers; (iv) we evaluate whether the generated trees contain the rich semantic information. 1
Rule Based Approach for Word Normalization in Transliterated Search Queries

SMS based Information Systems is the need of the age.Most of the present SMS based information systems send one way SMS based informative text messages generated from respective knowledge systems.By applying information retrieval methodology using models like Vector Space Mode, the systems can allow its users to send queries as per their requirement of information.This makes the system more fruitful from the user's point of view.This paper is about such initiatives for accessing relevant literature like poems, phrases, Rhymes, stories, abhang and much more.The mobile based quick library access system MQuickLib allows users to access such literature by formulating transliterated queries.The Vector Space Model is used to create the systems knowledge base by processing.The document terms and matched with the query terms by allowing variation in spelling due to transliteration style of the users.The matching score is assigned by devising a set of rules that identify the distance between two terms d k the term from document and q j the query term.The original Levenshtein's minimum edit distance algorithm is modified by applying this rule based approach.These rules are identified by collecting SMS queries from users for a given set of known queries in Marathi (Devnagari).Experiments were carried out for the collection of Marathi and Hindi literature that mainly include songs, gazals, powadas, bharud and other types.These documents are available in a standard transliteration form like ITRANS (an Indic Transliteration System).This paper elaborated a rule based approach and analyses the results to select appropriate rule based model that is further applied for the development of MQuickLib system.
Modeling of composite stock price index (CSPI) using semiparametric regression truncated spline based on GUI R

Abstract The Composite Stock Price Index (CSPI) is widely used as the beginning of consideration for investors to invest, because from the CSPI it can be known the general situation of market conditions is whether stock prices are experiencing an increase or decrease. This condition is characterized by a decrease or increase in the price of the CSPI. The Amount of Money Supply and the NASDAQ Index is thought to affect the price movements of the CSPI. The Amount of Money Supply has a nonparametric relationship pattern to the CSPI and the NASDAQ Index has a parametric relationship pattern to the CSPI. The correct method for conducting modeling is use the semiparametric spline truncated regression. The semiparametric regression spline truncated coefficient is estimated using the Ordinary Least Square (OLS) method which is determined based on the polynomial degree, much and the location of the optimum knot point is seen from the criteria of Mean Square Error (MSE). This study uses ( Graphical User Interface ) GUI R with the intention of facilitating the analysis process. The data used are monthly data from June 2014 to March 2019. Based on the results of the analysis that has been done, the best semiparametric spline truncated regression model with order 3 with the optimal three knots is 4246.361, 4443.078, and 4730.38. Evaluation results the in-sample data model produces a coefficient of determination of 90.25%. The results of the performance evaluation of the out sample data model resulted in a MAPE value of 3770204% indicating the performance of the model was very good.
Pengembangan Aplikasi Sistem Informasi Smart Register Online Berbasis Android Menggunakan Algoritma BruteForce

The process of registering new students (PMB) for sharing information displayed on the website based PMB information system. However, constraints in the usage of the website are prospective students must monitor the website frequently to obtain information. If not accordingly a student is at risk of lagging behind the latest information. The research aims to expand a registration information system on the enrollment of student-based websites and developed into an Android-based application. So it is very easy for students to access PMB information, and registration through the Android application. This study designed an Android-based registration information system application using the waterfall method, brute force algorithm, integrated realtime firebase database and firebase storage cloud. The results of testing the brute force algorithm on the word search "S1 Informatics Engineering" with the input string "Informatics" carried out 8 steps of searching. These results by calculating the parameters of cyclomate complexity, region, and independent path from whitebox tests obtained the same results, the amount of 16. The results of testing blackbox method declared successful and as expected. So it can be concluded the development of an online smart register information apply the brute force algorithm is said to be feasible to implementation in the process of registering new students in accordance with the testing that has been done.
Obfuscation via Information Density Estimation

Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined threshold are deemed information-leaking features. Once these features are identified, we sequentially pass them through a targeted obfuscation mechanism with a provable leakage guarantee in terms of $\mathsf{E}_\gamma$-divergence. The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE). We then use TIDE to implement our mechanism on three real-world datasets. Our approach can be used as a data-driven pipeline for designing obfuscation mechanisms targeting specific features.
Variable Shift SDD: A More Succinct Sentential Decision Diagram.

The Sentential Decision Diagram (SDD) is a tractable representation of Boolean functions that subsumes the famous Ordered Binary Decision Diagram (OBDD) as a strict subset. SDDs are attracting much attention because they are more succinct than OBDDs, as well as having canonical forms and supporting many useful queries and transformations such as model counting and Apply operation. In this paper, we propose a more succinct variant of SDD named Variable Shift SDD (VS-SDD). The key idea is to create a unique representation for Boolean functions that are equivalent under a specific variable substitution. We show that VS-SDDs are never larger than SDDs and there are cases in which the size of a VS-SDD is exponentially smaller than that of an SDD. Moreover, despite such succinctness, we show that numerous basic operations that are supported in polytime with SDD are also supported in polytime with VS-SDD. Experiments confirm that VS-SDDs are significantly more succinct than SDDs when applied to classical planning instances, where inherent symmetry exists.
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration

Off-policy reinforcement learning (RL) is concerned with learning a rewarding policy by executing another policy that gathers samples of experience. While the former policy (i.e. target policy) is rewarding but in-expressive (in most cases, deterministic), doing well in the latter task, in contrast, requires an expressive policy (i.e. behavior policy) that offers guided and effective exploration. Contrary to most methods that make a trade-off between optimality and expressiveness, disentangled frameworks explicitly decouple the two objectives, which each is dealt with by a distinct separate policy. Although being able to freely design and optimize the two policies with respect to their own objectives, naively disentangling them can lead to inefficient learning or stability issues. To mitigate this problem, our proposed method Analogous Disentangled Actor-Critic (ADAC) designs analogous pairs of actors and critics. Specifically, ADAC leverages a key property about Stein variational gradient descent (SVGD) to constraint the expressive energy-based behavior policy with respect to the target one for effective exploration. Additionally, an analogous critic pair is introduced to incorporate intrinsic rewards in a principled manner, with theoretical guarantees on the overall learning stability and effectiveness. We empirically evaluate environment-reward-only ADAC on 14 continuous-control tasks and report the state-of-the-art on 10 of them. We further demonstrate ADAC, when paired with intrinsic rewards, outperform alternatives in exploration-challenging tasks.
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm. We develop a context-aware mixture of deep models termed the {\alpha}-\b{eta} network coupled with uncertainty quantification (UQ) based upon maximum entropy to enhance human activity recognition performance. We improve accuracy and F score by 10% by identifying high-level contexts in a data-driven way to guide model development. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets, demonstrating improved accuracy through unknown context discovery.
Deep Ranking for Style-Aware Room Recommendations (Student Abstract)

We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.