Reviewerly/exHarmony huggingface dataset:
Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC.
The Discriminativeness of Internal Syntactic Representations in Automatic Genre Classification

Genre characterizes a document differently from a subject that has been the focus of most document retrieval and classification applications. This work hypothesizes a close interaction between syntactic variation and genre differentiation by introspecting stylistic cues in functional and structural aspects beyond word level. It has engineered 14 syntactic feature sets of internal representations for genre classification through Machine Learning devices. Experiment results show significant superiority of fusing structural and lexical features for genre classification (F∆max. = 9.2%, sig. = 0.001), suggesting the effectiveness of incorporating syntactic cues for genre discrimination. In addition, the PCA analysis reports the noun phrases (NP) as the most principle component (66%) for genre variation and prepositional phrases (PP) the second. Particularly, noun phrases with dominant structures of prepositional complements and pronouns functioning as a subject are most effective for identifying printed texts of high formality, while prepositional phrases are useful for identifying speeches of low formality. Error analysis suggests that the phrasal features are particularly useful for classifying four groups of genre classes, i.e. unscripted speech, fiction, news reports, and academic writing, all distributed with distinct structural characteristics, and they demonstrate an incremental degree of formality in the continuum of language complexity.
Project-Based Learning Improves Critical Thinking for Software Development Students

Here we share our experiences in applying project-based learning and its impact on critical thinking skills. In the software development (SD) track of information technology majors, upper-level SD courses are designed to combine students' accumulated programming knowledge for learning to develop software applications, reaching their ultimate goal. Usually, SD courses aim to maximize student success and achievement by training students with instructor-defined projects having known solutions. On one hand, students apply past knowledge and skills and improve problem-solving while realizing the project. On the other, students can become disconnected from, or bored with, the problems since students do not own the project. In addition, the projects do not present unknown challenges requiring to exercise critical thinking and creativity. As an alternative, we assign students novel real-world projects, proposed by external clients who direct the development of the respective software product. In-class simulation of real-world scenarios is known to be more effective for learning than lecturing in software development and engineering education [1]. We propose that project-based learning using a previously unsolved problem will improve student critical thinking and problem solving skills, two key STEM competencies important for software engineering. Furthermore, we expect a positive synergy between student success, as project goals are attained, and motivation to complete the project. We measured a significant increase of critical thinking skills in a senior-level undergraduate Software Development course. We assessed these skills using the CAT (Critical Thinking Assessment Test), a standard instrument developed by the Tennessee Tech University [2]. We administered pre- and post- CAT in two sections of the course in Fall 2018, at the beginning and end of the semester, respectively. We observed a statistically significant (one-tailed, paired t-test, p<0.05) increase of 11% between pre- and post CAT scores from 16 students. Our preliminary results suggest that the project-based approach to teaching software development increases general critical thinking skills. Student projects can be accessed at https://soft-eng-practicum.github.io/
Flow Based Botnet Traffic Detection Using Machine Learning

A botnet is a network of infected bots that works independently under the control of a Botmaster, which issues commands to bots using command and control server. Over time botnet architectures have been become advanced to evade the detection and disruption. Botnets are used by cybercriminal attackers for performing an unwanted activity for personal advantages like performing DDOS attack, theft of bank related information like id, password, and other harmful activities through the network, which might be a great loss for the society. In this paper, a supervised machine learning classification is used to classify the flow based botnet traffic using network flow dataset. The proposed model is able to distinguish botnet traffic from normal traffic with an accuracy of 99.94% using the J48 decision tree machine learning algorithm. The result of the proposed algorithm is efficient to be implemented in real time scenario.
Component-based feature extraction and representation schemes for vehicle make and model recognition

This paper presents a novel feature representation and recognition scheme for vehicle make and model recognition (VMMR) from the frontal image of the vehicle. In general, some domain knowledge is introduced and further exploited to accomplish this task. The modular components in the frontal appearance of vehicles present distinct visual characteristics, and the varying discrimination ability of them made the main discriminant region changes when vehicles compared at inter- or intra-brand level. Inspired by the peculiar vehicle properties, this paper focuses on the localized visual characteristics in the discriminant subregions, and make the representation of the frontal vehicle in a multi-scale spatial manner. This representation scheme encodes local spatial information and component-specific characteristics to the feature descriptors, which enhances the discrimination ability of the feature representation and mitigates the multiplicity problem of VMMR to some extent. Extensive experiments on a large-scale vehicle image dataset have shown the efficiency of the methods in this work.
Tutorial on Explainable Recommendation and Search

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Wireframe Parsing With Guidance of Distance Map

We propose an end-to-end method for simultaneously detecting local junctions and global wireframe in man-made environment. Our pipeline consists of an anchor-free junction detection module, a distance map learning module, and a line segment proposing and verification module. A set of line segments are proposed from the predicted junctions with guidance of the learned distance map, and further verified by the proposal verification module. Experimental results show that our method outperforms the previous state-of-the-art wireframe parser by a descent margin. In terms of line segments detection, our method shows competitive performance on standard benchmarks. The proposed networks are end-to-end trainable and efficient. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aa</sup> The code will be released on github for reproduction of the results.
DSmT-Based Ultrasonic Detection Model for Estimating Indoor Environment Contour

As for uncertainties of ranging and direction angle in the ultrasonic sensor measurement, an ultrasonic distance measurement model is first proposed for representing these uncertainties through analyzing the working principle of the ultrasonic sensor, which can be adopted to detect the contour of wall plan and cylinder. Moreover, the Dezert–Smarandache Theory (DSmT) method is employed to fuse the uncertainty data measured by using the ultrasonic sensor. Next, extended Hough transform (EHT) and least-squares method (LSM) are combined to identify the environmental contour. Then, the measurement uncertainty of the ultrasonic sensor is analyzed for setting the threshold Th used for distinguishing line and cylinder, and the detection range of the cylinder radius is estimated. Finally, we build an indoor environment and design the ultrasonic sensor hardware system to detect the indoor environment for experimental verification. The indoor environment contour obtained in the experimental results is consistent with the real environment, which illustrates the feasibility and effectiveness of the proposed method. The proposed method has certain reference value for research of simultaneous localization and mapping (SLAM) of the mobile robot.
An optimized probabilistic neural network with unit hyperspherical crown mapping and adaptive kernel coverage

It is important to improve the classification accuracy and reduce the storage space when probabilistic neural networks are used for pattern classification tasks. Based on a unit hyperspherical crown mapping and adaptive kernel coverage strategy, this paper presents an optimized hyperspherical crown probabilistic neural network(HCPNN). To overcome the separability problem caused by the fusion of heterogeneous samples, we adopt an unconventional unit hyperspherical crown mapping model in the sample space. Theoretical analysis indicates that nonlinear mapping can improve the separability of the original sample set under certain conditions. In addition, to optimize the pattern layer structure of probabilistic neural networks, we adopt an adaptive kernel coverage method for the training sample space to generate initial pattern nodes. The accumulation potential of the sample in each training subclass is used to measure the distribution density of different classes, and an adaptive update mechanism of potential values is established. In each iteration, nodes with high accumulation potential values are searched as pattern nodes from the dense to sparse regions. The precise position of each pattern node and the corresponding kernel width are adjusted by the Expected Maximum algorithm. Experiments show that HCPNN outperforms other algorithms with respect to the classification performance.
Charge balancing symmetric pre‐resolve adiabatic logic against power analysis attacks

A novel, energy efficient and power analysis robust logic style called the charge balancing symmetric pre-resolve adiabatic logic (CBSPAL) is proposed to overcome the susceptibility of cryptosystems against side channel power analysis attacks. It employs differential cascode logic tree structure with a pre-resolving feature, which realises improved energy efficiency by minimising non-adiabatic loss and leakage current. The energy efficiency of the proposed logic against static complementary metal oxide semiconductor (CMOS) and other existing secure adiabatic logic styles is proved. Energy deviation for the different input transitions of the individual logic gates, namely, buffer/NOT, AND/NAND and XOR/XNOR is found to be very minimal and it validates the immunity of the proposed logic against power analysis attacks. SPICE simulation of 4-bit add-round structure implementation using CBSPAL shows an energy saving of 89.5% compared to static CMOS implementation at a frequency of 125 MHz. Security of the proposed logic against the side channel power analysis attack is demonstrated by performing the correlation power analysis attacks as applicable for the SPICE simulations. Exhaustive SPICE simulations have been performed using the 32 nm CMOS predictive technology model libraries.
Blockchain for Gaming

The gaming industry is being rapidly transformed by the introduction of new technologies such as AI, VR, and AR. Blockchain technology has the potential to redefine the future of gaming by turning what has been seen as a leisure activity into a potentially economic endeavor (Gilot 2018). According to an online survey of 5000 people released by Electronic Entertainment Design and Research in 2018, nearly 70% of the American population, or roughly 211 million people, play video games on at least one type of device. The results showed that 90% of those surveyed play games on their smartphones, tablets, or both. The study also found that U.S. gamers spend an average of 12 h per week on video games (Crecente 2018). Video game engagement continues to grow and that could mean big returns for the gaming industry.
Dual-FOFE-net Neural Models for Entity Linking with PageRank

This paper presents a simple and computationally efficient approach for entity linking (EL), compared with recurrent neural networks (RNNs) or convolutional neural networks (CNNs), by making use of feedforward neural networks (FFNNs) and the recent dual fixed-size ordinally forgetting encoding (dual-FOFE) method to fully encode the sentence fragment and its left/right contexts into a fixed-size representation. Furthermore, in this work, we propose to incorporate PageRank based distillation in our candidate generation module. Our neural linking models consist of three parts: a PageRank based candidate generation module, a dual-FOFE-net neural ranking model and a simple NIL entity clustering system. Experimental results have shown that our proposed neural linking models achieved higher EL accuracy than state-of-the-art models on the TAC2016 task dataset over the baseline system, without requiring any in-house data or complicated handcrafted features. Moreover, it achieves a competitive accuracy on the TAC2017 task dataset.
Security Reductions for White-Box Key-Storage in Mobile Payments

The goal of white-box cryptography is to provide security even when the cryptographic implementation is executed in adversarially controlled environments. White-box implementations nowadays appear in commercial products such as mobile payment applications, e.g., those certified by Mastercard. Interestingly, there, white-box cryptography is championed as a tool for secure storage of payment tokens, and importantly, the white-boxed storage functionality is bound to a hardware functionality to prevent code-lifting attacks. In this paper, we show that the approach of using hardware-binding and obfuscation for secure storage is conceptually sound. Following security specifications by Mastercard and also EMVCo, we first define security for a white-box key derivation functions (WKDF) that is bound to a hardware functionality. WKDFs with hardware-binding model a secure storage functionality, as the WKDFs in turn can be used to derive encryption keys for secure storage. We then provide a proof-of-concept construction of WKDFs based on pseudorandom functions (PRF) and obfuscation. To show that our use of cryptographic primitives is sound, we perform a cryptographic analysis and reduce the security of our WKDF to the cryptographic assumptions of indistinguishability obfuscation and PRF-security. The hardware-functionality that our WKDF is bound to is a PRF-like functionality. Obfuscation helps us to hide the secret key used for the verification, essentially emulating a signature functionality as is provided by the Android key store. We rigorously define the required security properties of a hardware-bound white-box payment application (WPAY) for generating and encrypting valid payment requests. We construct a WPAY, which uses a WKDF as a secure building block. We thereby show that a WKDF can be securely combined with any secure symmetric encryption scheme, including those based on standard ciphers such as AES.
Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution. 1
A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning

Reinforcement learning (RL) builds an effective agent that handles tasks in complex and uncertain environments by maximizing future reward. However, the efficiency is insufficient for practical use such as game AI and autonomous driving. An effective but selfish agent conflicts with other humans, and hence the demand of a human-like behavior arises. Imitation learning (IL) has been employed to train an agent to mimic the actions of expert behaviors provided as training data. However, IL tends to build an agent limited in performance by the expert skill, and even worse, the agent exhibits an inconsistent behavior since IL is not goal-oriented. In this paper, we propose a training scheme by mixing RL and IL for both discrete and continuous action space problems. The proposed scheme builds an agent that achieves a performance higher than an agent trained by only IL and exhibits a more human-like behavior than agents trained by RL or IL, validated by human sensitivity.
Overcoming Supply Chain Finance Challenges Via Blockchain Technology

The purpose of this chapter is to demonstrate how blockchain technology – which permits the Internet-based exchange of value (digital assets) – enables supply chain finance banks to overcome the challenges they face when attempting to create win–win transactions for supply chain participants. Traditionally, buyers and suppliers linked together in a supply chain have conflicting objectives as manifested by a zero-sum payoff structure. Suppliers want their invoices to be paid quickly in order to reduce their need for working capital. In contrast, buyers want to delay payment of invoices as long as possible in order to reduce their need for working capital. In other words, suppliers want a short cash conversion cycle; buyers want a long cash conversion cycle. This conflict is eliminated by the insertion of a financial intermediary (supply chain finance bank) between the buyer and the supplier. The bank eliminates the conflict by: (1) using its balance sheet to decouple the cash conversion cycles of the buyer and supplier; and (2) providing cheaper financing to impatient suppliers and reluctant buyers (since the bank has a higher credit rating than both the supplier and the buyer).
10 Reasons to Get Interested in Graph Drawing

This is an invitation to the research area of graph drawing. It encompasses basic research such as graph theory, complexity theory, data structures, and graph algorithms as well as applied research such as software libraries, implementations, and applications. Application domains include areas within computer science (e. g., information visualization, software engineering, model-based design, automated cartography) as well as outside (e. g., molecular biology and the social sciences). A selection of results demonstrates the influence of graph drawing on other areas and vice versa.
Dense-CAM: Visualize the Gender of Brains with MRI Images

Studying the gender differences of brains is important to understand the brain cognitive mechanism. With the increasing amounts of brain imaging data, researchers start to use deep learning for brain image processing. However, the interpretability of deep models is the big gap between them. At present, deep model visualization is the main method to solve the interpretability problem of deep networks. Class Activation Map (CAM) is a commonly-used deep model visualization method. But the resolution of CAMs are restricted since it only uses the last layer for visualization. In this paper, we proposed a novel convolutional network named Dense-CAM by combining DenseNet and CAM and realized the visualization of the whole network so that it can generate more accurate and more robust deep model visualization. The network was tested with the gender classification problem using more than 6000 samples and achieved an accuracy of 92.93%. Brain regions with significant differences between men and women are found with the proposed method, which can be used for future brain imaging studies.
Prediction Rules in E-Learning Systems Using Genetic Programming

This paper describes the use of Data Mining Techniques to improve teaching–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to model the student’s interaction with the study material using prediction rules whose interpretation will allow to detect the weaknesses of the educational process and evaluate the quality of the study material. The proposed rule discovery method is the Evolutionary Algorithms and particularly the Grammar-Based Genetic Programming (GB-GP), which is compared to association rules and decision tree construction for discovering prediction rules.
Weighted Optical Flow Prediction and Attention Model for Object Tracking

Object tracking has been a hot computer vision topic for many years.Although great process has been made, it still has large room to improve because of the complexity of the natural scene and the multiple interference.In this work, we improve the object tracking performance in two ways.First, a sequential scoring model is proposed to integrate the optical flow information of history video frames into the feature map of current frame.Second, an attention model with optical flow information is used for further improvement by differentiating the contribution of different positions in the template to the final response map.On the other hand, the entire model are end-to-end trainable.We test the methods on OTB (Object Tracking Benchmark) and VOT (Visual Object Tracking) tracking datasets.The experimental results demonstrate that the improved tracking accuracy and robustness to occlusion, strenuous motion and vanishing objects.