Reviewerly/exHarmony huggingface dataset:
Ranking-Based Reward Extrapolation without Rankings.

The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. Recent empirical results show that imitation learning via ranked demonstrations allows for better-than-demonstrator performance; however, ranked demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to outperform the demonstrator. To address these issues, we first contribute a sufficient condition for when better-than-demonstrator performance is possible and discuss why ranked demonstrations can contribute to better-than-demonstrator performance. Building on this theory, we then introduce Disturbance-based Reward Extrapolation (D-REX), a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations. By generating rankings automatically, ranking-based imitation learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available. We empirically validate our approach on standard MuJoCo and Atari benchmarks and show that D-REX can utilize automatic rankings to significantly surpass the performance of the demonstrator and outperform standard imitation learning approaches. D-REX is the first imitation learning approach to achieve significant extrapolation beyond the demonstrator's performance without additional side-information or supervision, such as rewards or human preferences.
A Novel Method for Location Privacy Protection in LBS Applications

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>, which is the user’s tolerance. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.
The Needs of Collaborative Tool for Practicing Pair Programming in Educational Setting

&lt;p class="0abstract"&gt;Pair programming is a technique which is introduced for helping programmers to develop high quality codes. Beside technical aspects, collaboration and working in team are the important skills needed in practicing the technique. These skills are essential to be instilled among our engineering students. Pair programming (known as PP) is one of Agile software development technique which introduces collaborative ways for programmers to develop software. Many advantages have been reported when one practiced the technique, which include improve team communication and productiveness, reduction in defect counts, and at the same time lessen development time. Among students, the technique was also reported to enhance students’ learning and satisfaction, as well as students’ learning skills. In order to get the full benefits of pair programming technique, however, correct guidance must be provided to the students. In this study, we proposed a collaborative pair programming tool for students in practicing the technique. The tool offers all features required to allow the collaboration for the pair programming technique to work. The tool is designed and developed using Visual Basic Studio software. A database is incorporated to store students’ information. An experiment was conducted to evaluate the students’ performance with and without using the tool. Results showed that the tool can help improving students’ performance in terms of quality of codes, and lessen their time in completing their coding. These are the consequences of collaboration and team work skills that they have been practicing when using the tool.&lt;/p&gt;
Can We Securely Outsource Big Data Analytics with Lightweight Cryptography?

Advances in cryptography such as secure multiparty computation (SMC) and fully-/somewhat-homomorphic encryption (FHE/SHE) have already provided a generic solution to the problem of processing encrypted data; however, they are still not that efficient if one directly applies them for big data analytics.
An Orthogonal QUasi-Affine TRansformation Evolution (O-QUATRE) Algorithm for Global Optimization

In this paper, a new Orthogonal QUasi-Affine TRansformation Evolution (O-QUATRE) algorithm was proposed for global optimization. The O-QUATRE algorithm is actually implemented as a combination of both the QUATRE algorithm and the orthogonal array, both of which together secured an overall better performance on complex optimization problems. The proposed algorithm is verified under CEC2013 test suite for real-parameter optimization. The experimental results indicated that the proposed O-QUATRE algorithm obtained better mean and standard deviation of fitness error than QUATRE algorithm, which means that the O-QUATRE algorithm was of more robustness and better stability.
Color Transfer Method for Efficient Enhancement of Color Images and Its Application to Peripheral Blood Smear Analysis

In this paper, we propose an efficient color transfer approach as a pre-processing step, and demonstrate use of the method for enhancement of peripheral blood smear images. Peripheral blood smear analysis is subjective, laborious and is error prone. Automation is highly desirable to obtain objective and accurate results. It also would help reduce the burden of pathologists. However, automation of peripheral blood smear analysis is challenging due to the fact that the microscopic images suffer from illumination variations. Also, variations in the process of manual staining leads to variations in colors expressed by the various components of the peripheral blood smear namely the red blood cells, white blood cells and platelets. Many research groups have reported various approaches to color transfer to enhance the quality of peripheral blood smear images to facilitate automation. Color transfer approach uses a template image with the desirable qualities and transfers the color characteristics of this image to the images under study. We propose an efficient color transfer approach and demonstrate its use for enhancement of peripheral blood smear images.
Preference elicitation: Obtaining gestural guidelines for PACS in neurosurgery

Accessing medical records is an integral part of neurosurgical procedures in the Operating Room (OR). Gestural interfaces can help reduce the risks for infections by allowing the surgical staff to browse Picture Archiving and Communication Systems (PACS) without touch. The main objectives of this work are to: a) Elicit gestures from neurosurgeons to analyze their preferences, b) Develop heuristics for gestural interfaces, and c) Produce a lexicon that maximizes surgeons' preferences.A gesture elicitation study was conducted with nine neurosurgeons. Initially, subjects were asked to outline the gestures on a drawing board for each of the PACS commands. Next, the subjects performed one of three imaging tasks using gestures instead of the keyboard and mouse. Each gesture was annotated with respect to the presence/absence of gesture descriptors. Next, K-nearest neighbor approach was used to obtain the final lexicon that complies with the preferred/popular descriptors.The elicitation study resulted in nine gesture lexicons, each comprised of 28 gestures. A paired t-test between the popularity of the overall gesture and the top three descriptors showed that the latter is significantly higher than the former (89.5%-59.7% vs 19.4%, p < 0.001), meaning more than half of the subjects agreed on these descriptors. Next, the gesture heuristics were generated for each command using the popular descriptors. Lastly, we developed a lexicon that complies with surgeons' preferences.Neurosurgeons do agree on fundamental characteristics of gestures to perform image manipulation tasks. The proposed heuristics could potentially guide the development of future gesture-based interaction of PACS for the OR.
Robust Temporal Difference Learning for Critical Domains

We present a new Q-function operator for temporal difference (TD) learning methods that explicitly encodes robustness against significant rare events (SRE) in critical domains. The operator, which we call the $\ko$-operator, allows to learn a robust policy in a model-based fashion without actually observing the SRE. We introduce single- and multi-agent robust TD methods using the operator $\ko$. We prove convergence of the operator to the optimal robust Q-function with respect to the model using the theory of Generalized Markov Decision Processes. In addition we prove convergence to the optimal Q-function of the original MDP given that the probability of SREs vanishes. Empirical evaluations demonstrate the superior performance of $\ko$-based TD methods both in the early learning phase as well as in the final converged stage. In addition we show robustness of the proposed method to small model errors, as well as its applicability in a multi-agent context.
Identification of The Tuberculosis (TB) Disease Based on XRay Images Using Probabilistic Neural Network (PNN)

Abstract Tuberculosis is an airborne contagious disease caused by the Bacterium Mycobacterium Tuberculosis which means major threat to global health. If Tuberculosis is not treated promptly and quickly, it will cause the disease to get worse. For that, the treatment should not be done carelessly. Therefore, a system is needed to identify the Tuberculosis diseases to help the doctors as an early stage in diagnosing the disease. In this research, Probabilistic Neural Network (PNN) method was used to identify the presence of Tuberculosis by system to help the doctor to make the decision. The stages before doing an identification such as the image acquisition, pre-processing, and feature extractions using Invariant Moment. This study was conducted using train data of 105 normal X-Ray images and 105 images of pulmonary Tuberculosis X-Rays. For the testing data used of 50 X-Ray images. The results showed that the proposed method was able to identify the Tuberculosis disease with an accuracy of 96%.
The Positive Impact in Changing of E-Learning Environment to M-Learning to Enhance Critical Thinking Skills in Foreign Language Learning

This study has investigated the impact of using Mobile Learning Model alias M-learning with the principles of the connectivist learning theory to enhance the critical thinking skills of students who learn Mandarin as a second foreign language at state-of-art- study level. M-learning is a new global trend in education of distance learning with the robust development of an innovative and interactive ICTs. As a highly popular possession in the usage of mobile phone for learning purposes in the context of language learning which has given the importance of attention practitioners and researchers especially in distance learning in education. However, promoting mobile phone integrated learning has been seen a very few efforts in the scope of Mandarin learning. Therefore, to find out the importance of m-learning in language learning in number 246 students have been interviewed face-to-face to collect qualitative data and then analyzed for themes that described the essence of experiences shared among the participants. The results have shown that m-learning can integrate more and more opportunities to mitigate challenges in a variety of ways to support the learning and teaching of a language. But, results have reflected that it has different perceptions about the M-learning use in language learning, therefore, some integrated measures were proposed for language learning in the M-learning environment.
DynMat, a network that can learn after learning

To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is ‘accumulative’ and ‘online’ without interruption. However, despite the recent success, artificial neural networks (ANNs) must be trained offline and suffer catastrophic interference between old and new learning, indicating that ANNs’ conventional learning algorithms may not be suitable for building intelligent agents comparable to our brain. In this study, we propose a novel neural network architecture (DynMat) consisting of dual learning systems inspired by the complementary learning system (CLS) theory suggesting that the brain relies on short- and long-term learning systems to learn continuously. Our empirical evaluations show that (1) DynMat can learn a new class without catastrophic interference and (2) it does not strictly require offline training.
A mechanical formalization of higher-ranked polymorphic type inference

Modern functional programming languages, such as Haskell or OCaml, use sophisticated forms of type inference. While an important topic in the Programming Languages research, there is little work on the mechanization of the metatheory of type inference in theorem provers. In particular we are unaware of any complete formalization of the type inference algorithms that are the backbone of modern functional languages. This paper presents the first full mechanical formalization of the metatheory for higher-ranked polymorphic type inference. The system that we formalize is the bidirectional type system by Dunfield and Krishnaswami (DK). The DK type system has two variants (a declarative and an algorithmic one) that have been manually proven sound , complete and decidable . We present a mechanical formalization in the Abella theorem prover of DK’s declarative type system with a novel algorithmic system. We have a few reasons to use a new algorithm. Firstly, our new algorithm employs worklist judgments , which precisely capture the scope of variables and simplify the formalization of scoping in a theorem prover. Secondly, while DK’s original formalization comes with very well-written manual proofs, there are several details missing and some incorrect proofs, which complicate the task of writing a mechanized proof. Despite the use of a different algorithm we prove the same results as DK, although with significantly different proofs and proof techniques. Since such type inference algorithms are quite subtle and have a complex metatheory, mechanical formalizations are an important advance in type-inference research.
Causal Inference Under Interference And Network Uncertainty.

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly [10, 36, 20], but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.
Integrating Domain-Knowledge into Deep Learning

In this talk, I will first discuss deep learning models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will show how we can encode external linguistic knowledge as an explicit memory in recurrent neural networks, and use it to model co-reference relations in text. I will further introduce methods that can augment neural representation of text with structured data from Knowledge Bases for question answering, and show how we can use structured prior knowledge from Knowledge Graphs for image classification. Finally, I will introduce the notion of structured memory as being a crucial part of an intelligent agent's ability to plan and reason in partially observable environments. I will present a modular hierarchical reinforcement learning agent that can learn to store arbitrary information about the environment over long time lags, perform efficient exploration and long-term planning, while generalizing across domains and tasks.
Analyze the effectiveness of an algorithm for identifying Polish characters in handwriting based on neural machine learning technologies

An approach is presented that generalize OCR task including polish letters using deep learning technique. The paper extends EMNIST dataset so that two new classes of polish diacritics “Ą” and “Ć” are attached. Using this new dataset and deep learning technique one can analyze sensitives of standard as well as presented extended approach with different parameters of convolutional neural network. The analysis of the results shows that the shadows and noises that Polish characters, leave with their hooks leads, can be properly recognized and two similar letters “A” and “Ą” can be distinguished by convolutional neural network. On the other hand a neural network trained on dataset without Polish characters do not treat letters “Ą” and “Ć” properly.
End-to-end speech recognition system based on improved CLDNN structure

In the field of end-to-end speech recognition technology based on deep learning, CLDNN (Convolutional Long Short-Term Memory Fully Connected Deep Neural Network) is a commonly used model structure. The fully connected LSTM (Long Short Term Memory) model is used in the traditional CLDNN structure to process the timing information in the speech signal, which is prone to over-fitting during the training process and affects the learning effect. Deeper models tend to perform better, but increasing the model depth by simply stacking the network layers can cause gradient disappearance, gradient explosion, and "degeneration" problems. Aiming at the above phenomena and problems, this paper proposes an improved CLDNN structure. It combines the residual network and ConvLSTM to establish the residual ConvLSTM model, and replaces the fully connected LSTM model in the traditional CLDNN structure. The model structure solves the problems of the traditional CLDNN model, and can increase the model depth by stacking residual ConvLSTM blocks without gradient disappearance, gradient explosion and "degeneration" problems, which makes the speech recognition system perform better. The experimental results show that the model structure has a word error rate (WER) decrease of more than 8% in both Chinese and English speech recognition tasks compared to the traditional CLDNN structure.
Point break: a study of bandwidth denial-of-service attacks against Tor

As the Tor network has grown in popularity and importance as a tool for privacy-preserving online communication, it has increasingly become a target for disruption, censorship, and attack. A large body of existing work examines Tor's susceptibility to attacks that attempt to block Tor users' access to information (e.g., via traffic filtering), identify Tor users' communication content (e.g., via traffic fingerprinting), and de-anonymize Tor users (e.g., via traffic correlation). This paper focuses on the relatively understudied threat of denialof-service (DoS) attacks against Tor, and specifically, DoS attacks that intelligently utilize bandwidth as a means to significantly degrade Tor network performance and reliability.We demonstrate the feasibility of several bandwidth DoS attacks through live-network experimentation and highfidelity simulation while quantifying the cost of each attack and its effect on Tor performance. First, we explore an attack against Tor's most commonly used default bridges (for censorship circumvention) and estimate that flooding those that are operational would cost $17K/mo. and could reduce client throughput by 44% while more than doubling bridge maintenance costs. Second, we explore attacks against the TorFlow bandwidth measurement system and estimate that a constant attack against all TorFlow scanners would cost $2.8K/mo. and reduce the median client download rate by 80%. Third, we explore how an adversary could use Tor to congest itself and estimate that such a congestion attack against all Tor relays would cost $1.6K/mo. and increase the median client download time by 47%. Finally, we analyze the effects of Sybil DoS and deanonymization attacks that have costs comparable to those of our attacks.
Another Look at a Proposed Cubic Chaotic Mapping

In this paper, we take another look at a cubic chaotic mapping proposed before, in which a case was left unconcerned. For this case, we could form a cubic mapping easily almost as same as we did before. Analysis illustrates that the mapping formed demonstrates perfect chaotic properties for anti-control of chaos. At last, a simple pseudorandom bit generator is designed and excellent results are achieved for all statistical tests.
Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples.

Deep learning models are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on benign inputs. However, under the black-box setting, most existing adversaries often have a poor transferability to attack other defense models. In this work, from the perspective of regarding the adversarial example generation as an optimization process, we propose two new methods to improve the transferability of adversarial examples, namely Nesterov Iterative Fast Gradient Sign Method (NI-FGSM) and Scale-Invariant attack Method (SIM). NI-FGSM aims to adapt Nesterov accelerated gradient into the iterative attacks so as to effectively look ahead and improve the transferability of adversarial examples. While SIM is based on our discovery on the scale-invariant property of deep learning models, for which we leverage to optimize the adversarial perturbations over the scale copies of the input images so as to avoid overfitting on the white-box model being attacked and generate more transferable adversarial examples. NI-FGSM and SIM can be naturally integrated to build a robust gradient-based attack to generate more transferable adversarial examples against the defense models. Empirical results on ImageNet dataset demonstrate that our attack methods exhibit higher transferability and achieve higher attack success rates than state-of-the-art gradient-based attacks.