Reviewerly/exHarmony huggingface dataset:
Automated curriculum generation through setter-solver interactions

Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations are often too small, or rewarding events are too infrequent to make learning feasible. Human education instead relies on curricula –the breakdown of tasks into simpler, static challenges with dense rewards– to build up to complex behaviors. While curricula are also useful for artificial agents, hand-crafting them is time consuming. This has lead researchers to explore automatic curriculum generation. Here we explore automatic curriculum generation in rich,dynamic environments. Using a setter-solver paradigm we show the importance of considering goal validity, goal feasibility, and goal coverage to construct useful curricula. We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work. Finally, we demonstrate the value of a novel technique that guides agents towards a desired goal distribution. Altogether, these results represent a substantial step towards applying automatic task curricula to learn complex, otherwise unlearnable goals, and to our knowledge are the first to demonstrate automated curriculum generation for goal-conditioned agents in environments where the possible goals vary between episodes.
Improving Neural Language Generation with Spectrum Control

Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks. However, contextualized embeddings at the output layer of these powerful models tend to degenerate and occupy an anisotropic cone in the vector space, which is called the representation degeneration problem. In this paper, we propose a novel spectrum control approach to address this degeneration problem. The core idea of our method is to directly guide the spectra training of the output embedding matrix with a slow-decaying singular value prior distribution through a reparameterization framework. We show that our proposed method encourages isotropy of the learned word representations while maintains the modeling power of these contextual neural models. We further provide a theoretical analysis and insight on the benefit of modeling singular value distribution. We demonstrate that our spectrum control method outperforms the state-of-the-art Transformer-XL modeling for language model, and various Transformer-based models for machine translation, on common benchmark datasets for these tasks.
Measuring the Reliability of Reinforcement Learning Algorithms

Inadequate reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have grown substantially. To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a novel set of metrics that quantitatively measure different aspects of reliability. In this work, we address variability and risk, both during training and after learning (on a fixed policy). We designed these metrics to be general-purpose, and we also designed complementary statistical tests to enable rigorous comparisons on these metrics. In this paper, we first describe the desired properties of the metrics and their design, the aspects of reliability that they measure, and their applicability to different scenarios. We then describe the statistical tests and make additional practical recommendations for reporting results. Finally, we apply our metrics to a set of common RL algorithms and environments, compare them, and analyze the results.
Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition

In recent years, deep learning (DL) based systems have become very popular for constructing hierarchical representations from unlabeled data. Moreover, DL approaches have been shown to exceed foregoing state of the art machine learning models in various areas, by pattern recognition being one of the more important cases. This paper applies Convolutional Deep Belief Networks (CDBN) to textual image data containing Arabic handwritten script (AHS) and evaluated it on two different databases characterized by the low/high-dimension property. In addition to the benefits provided by deep networks, the system is protected against over-fitting. Experimentally, the authors demonstrated that the extracted features are effective for handwritten character recognition and show very good performance comparable to the state of the art on handwritten text recognition. Yet using Dropout, the proposed CDBN architectures achieved a promising accuracy rates of 91.55% and 98.86% when applied to IFN/ENIT and HACDB databases, respectively.
Challenges of Cloud Computing Adoption Model for Higher Education Level in Zanzibar (the Case Study of SUZA and ZU)

The progressive education department in creating nations, including Zanzibar, is experiencing the issue of conveying dimension of the data and interchanges innovation. Technology like cloud computing encourages educating, learning, what’s more, on motion exercising, which is essential for instructive necessities. Based on these difficulties the study attempts to find those challenges in implementing the cloud computing adoption model to Zanzibar’s universities such as SUZA and ZU to overcome the existing problems. In the effort of identifying those challenges the study proposes a special model named as ITOETAM to break down different challenges and precise complications faced by SUZA and ZU in implementing clouding system. The model validated via an online survey of 84 respondents from students, lecturers and staff of Zanzibar’s universities (SUZA and ZU), partial least squares demonstrating (PLS-SEM) led to affirm the cloud computing adoption model (ITOETAM) measurements. The model consists of five constructs named CCAM, IEF, TF, OF, and EF, where all were significant to CCAM with T-value more than 1.98 and p-value less than 0.05 for example, (IEF (β= 0.709), TF (β= 0.596), and EF (β= -0.317)) have proven the proper challenges of cloud computing adoption model at university level in Zanzibar’s universities except OF (β= 0.027) which rejecting the model. The rejected factors were due to the size is big enough and not considered as matter, also the age of the institution is too long since they start providing services. Based on validation of measurement and structural model test, the study found that data security and risk among technological factors, peer pressure and government regulation categorized as environmental factors, privacy and management as internal-external factors proved as a statistical significant challenge in implementing the cloud computing adoption model to the higher educational entity especially SUZA and ZU. This model is usable and significance to researchers and planners for policy implication.
A Blockchain Based Decentralized Authentication Framework for Resource Constrained IOT devices

Internet of Things (IoT) is excessively influencing our day to day lives in many domains be it small smart device like a smart-watch or large industrial enterprises. Thus, a number of different IoT based applications are developed and deployed for these sectors. An IoT system enables devices to communicate among each other without human assistance/involvement. Therefore, verifying the authenticity of devices and ensuring integrity of the communicated messages should be given major importance in this type of autonomous systems. Apart from this, it is almost impractical to develop a centralized authentication scheme for a system whose size growing day by day. For this reason, a decentralized framework based on Blockchain technology that can efficiently manage such a large number of smart devices has been proposed in this paper. An authentication scheme using which devices as well as end users can prove their legitimacy to each other is also designed.
Trustworthy Management in Decentralized IoT Application using Blockchain

Internet of Things (IoT) as per estimated will connect 50 billion devices by 2020. Since its evolution, IoT technology provides lots of flexibility to develop and implement any application. Most of the application improves the human living standard and also makes life easy to access and monitoring the things in real time. Though there exist some security and privacy issues in IoT system like authentication, computation, data modification, trust among users. In this paper, we have identified the IoT application like insurance, supply chain system, smart city and smart car where trust among associated users is an major issue. The current centralized system does not provide enough trust between users. Using Blockchain technology we have shown that trust issue among users can be managed in a decentralized way so that information can be traceable and identify/verify any time. Blockchain has properties like distributed, digitally share and immutable which enhance security. For Blockchain implementation, Ethereum platform is used.
Single-shot freeform surface profiler

We propose a novel and simple method of single-shot freeform surface profiler based on spatially phase-shifted lateral shearing interferometry. By the adoption of birefringent materials, the laterally shearing waves are simply generated without any bulky and complicated optical components. Moreover, the phase maps that lead to the 3D profile of the freeform surface can be instantly obtained by the spatial phase-shifting technique using a pixelated polarizing camera. The proposed method was theoretically described and verified by measuring several samples in comparison to the measurement results with a well-established stylus probe.
Breast Tumor Detection in Digital Mammogram Based on Efficient Seed Region Growing Segmentation

Breast cancer is increasing rapidly in women these days. It is diagnosed after the symptoms appear. The detection at an initial stage and the procedure of the treatment are the most significant strategies curing it effectively. For the determination of breast cancer, breast mammography is required. It can be one of the methods to recognize tumor in women's breasts because it lessens the risk of death by recognizing the tumor at an early level. This paper shows a combination of selection methods for step by step evaluation for Efficient Seeded Region Growing with enhanced performance to spot breast cancer. The suggested method has been divided into the following parts: First, preprocessing of breast image is performed to estimate the automatic extraction of Region of Interest. Second, an automatic threshold is calculated for the binarization process. Third, the numbers of seed points are determined automatically and their positions in the breast image are identified using the density of pixels value. Fourth, a method calculating the threshold value is proposed for the purpose of region creation in seed region growing. The given method is applied and tested on the two publicly available mammogram data sets; Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). It was observed that the proposed algorithm gives 94.6% and 95.3% True Positive Fraction (TPF) in case of MIAS and DDSM images, respectively. The parameters demonstrate that the given algorithm gives better results than the previously available methods.
Secret-Shared Shuffle

Generating additive secret shares of a shuffled dataset - such that neither party knows the order in which it is permuted - is a fundamental building block in many protocols, such as secure collaborative filtering, oblivious sorting, and secure function evaluation on set intersection. Traditional approaches to this problem either involve expensive public-key based crypto or using symmetric crypto on permutation networks. While public-key-based solutions are bandwidth efficient, they are computation-heavy. On the other hand, constructions based on permutation networks are communication-bound, especially when the dataset contains large elements, for e.g., feature vectors in an ML context. We design a new 2-party protocol for this task of computing secret shares of shuffled data, which we refer to as secret-shared shuffle. Our protocol is secure against a static semi-honest adversary. At the heart of our approach is a new primitive we define (which we call “Share Translation”) that generates two sets of pseudorandom values “correlated via the permutation”. This allows us to reduce the problem of shuffling the dataset to the problem of shuffling pseudorandom values, which enables optimizations both in computation and communication. We then design a Share Translation protocol based on oblivious transfer and puncturable PRFs. Our final protocol for secret-shared shuffle uses lightweight operations like XOR and PRGs, and in particular doesn’t use public-key operations besides the base OTs. As a result, our protocol is concretely more efficient than the existing solutions. In particular, we are two-three orders of magnitude faster than public-key-based approach and one order of magnitude faster compared to the best known symmetric-key approach when the elements are moderately large.
Ultrasound Image Denoising Using Wavelet Thresholding Methods in Association with the Bilateral Filter

Ultrasound imaging has been widely used in medical diagnosis in modern medicine. Although widespread, it presents low-resolution images, typically degraded by speckle noise, which requires the use of image-processing techniques aiming at improving the image quality and allowing a proper medical diagnosis use. This paper presents a new association based on S-median thresholding and the fast bilateral filter for speckle noise reduction. In order to validate the results, the proposed method was analyzed and compared with two others thresholding methods. The image quality improvement is evidenced by an increase of 14.13% in PSNR while the structural features and contour preservation have increased 4.96% in MSSIM and 0.70% in β, respectively. As the obtained results shown, the proposed method is able to reduce the speckle noise, to preserve the spatial characteristics and the contour information of the images and it is indicated for noise reduction at noise levels commonly found in ultrasound equipment.
Aspect-Context Interactive Attention Representation for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to determine sentiment polarities of various aspects in reviews, where each review typically contains multiple aspects, that may correspond to different polarities. Aspect-level sentiment classification, unlike document-level sentiment classification, requires different context representations for different aspects. Existing methods normally use Long Short-Term Memory (LSTM) network to model aspects and contexts separately, and they combine attention mechanisms to extract features of a specific aspect in its context. Attention mechanisms are not used for sequence modeling, so aspects are not considered when generating context sequence representations. This study proposes a novel aspect-context interactive representation structure that only relies on an attention mechanism for generating sequence-to-sequence representations in both context and aspect. It is capable of extracting features related to the specific aspect during the process of its context sequence modeling, and generating a high quality aspect representation simultaneously. We have conducted comprehensive experiments to compare with thirteen existing methods. Our experimental results show that the proposed model is able to achieve significantly better performance on Restaurant dataset, as well as very competitive results on Laptop and Twitter datasets.
Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics

The most popular face recognition benchmarks assume a distribution of subjects without much attention to their demographic attributes. In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recognition. The main aim of this study is focused on a better understanding of the feature space generated by deep models, and the performance achieved over different demographic groups. We also propose a general formulation of algorithmic discrimination with application to face biometrics. The experiments are conducted over the new DiveFace database composed of 24K identities from six different demographic groups. Two popular face recognition models are considered in the experimental framework: ResNet-50 and VGG-Face. We experimentally show that demographic groups highly represented in popular face databases have led to popular pre-trained deep face models presenting strong algorithmic discrimination. That discrimination can be observed both qualitatively at the feature space of the deep models and quantitatively in large performance differences when applying those models in different demographic groups, e.g. for face biometrics.
Planning of the Web-based E-Raport Assessment System

In the world of information technology has changed the lifestyle in the aspect of our lives especially in the educational world. E-Raport is an academic information system used to input web-based student value data. The current value data input system is a benchmark for the creation of quality output and able to compete with other schools. One of the benchmarks that can be the competitiveness for other schools is the result of student learning assessments that are handled by each class. Problems that occur in the input of the value of Raport at SDN Sukanagara is currently still a manual that is the value of Raport recorded in the book Raport, so it takes a long time and a lot of energy needed. Research has the purpose of designing a value information system that facilitates the checking, recording and reporting of the computerized value of the students. Computerized Raport value reports can minimize errors in student data processing. This WEB based online raport is very easy data information and can be accessed anytime and anywhere, the system design works to insert and save value Report data and attendance and display info from school. The system uses data collection methods such as interviews, library studies, and system design, which are context diagrams, and prototypes. The information system of online Raport SDN Sukanagara is expected to be able to bring positive impact to the educational world, especially for SDN Sukanagara in the development of information technology applied in the world of education.
A Secure Platform for IoT Devices based on ARM Platform Security Architecture

Recent IoT services are being used in various fields such as smart homes, smart factories, smart cars and industrial systems. These various IoT services are implemented through hyper-connected IoT devices, and accordingly, security requirements of these devices are being highlighted. In order to satisfy the security requirements of IoT devices, various studies have been conducted such as HSM, Security SoC, and TrustZone. In particular, ARM proposed Platform Security Architecture (PSA), which is a security architecture that provide execution isolation to safely manage and protect the computing resources of low- end IoT devices. PSA can ensure confidentiality and integrity of IoT devices based on its structural features, but conversely, it has the problem of increasing development difficulty in using the security functions of PSA. To solve this problem, this paper analyzes the security requirements of an IoT platform and proposes secure platform based on PSA. To evaluate the proposed secure platform, a PoC implementation is provided based on hardware prototype consisting of FPGA. Our experiments with the PoC implementation verify that the proposed secure platform offers not only high security but also convenience of application development for IoT devices.
Image Sharpening Detection Based on Difference Sets

Image sharpening is one of the basic operations used to improve the visual effect of images. Image editing makes it impossible to confirm the authenticity of an image, and the purpose of image forensics is to detect whether an image has been artificially edited. To solve this problem, a forensic algorithm based on global image pixel values is proposed. Twelve difference sets composed of first-order and second-order differences in different directions are used as the image feature to reveal the difference between image pixels. Additionally, the feature is used to determine whether the image has been sharpened or not. To verify the performance of the algorithm, a series of experiments are performed on different formats of image datasets. Compared with the existing forensic algorithms based on traditional machine learning, the detection accuracy of the proposed method is greatly improved.
Adaptive Reward-Poisoning Attacks against Reinforcement Learning

In reward-poisoning attacks against reinforcement learning (RL), an attacker can perturb the environment reward $r_t$ into $r_t+\delta_t$ at each step, with the goal of forcing the RL agent to learn a nefarious policy. We categorize such attacks by the infinity-norm constraint on $\delta_t$: We provide a lower threshold below which reward-poisoning attack is infeasible and RL is certified to be safe; we provide a corresponding upper threshold above which the attack is feasible. Feasible attacks can be further categorized as non-adaptive where $\delta_t$ depends only on $(s_t,a_t, s_{t+1})$, or adaptive where $\delta_t$ depends further on the RL agent's learning process at time $t$. Non-adaptive attacks have been the focus of prior works. However, we show that under mild conditions, adaptive attacks can achieve the nefarious policy in steps polynomial in state-space size $|S|$, whereas non-adaptive attacks require exponential steps. We provide a constructive proof that a Fast Adaptive Attack strategy achieves the polynomial rate. Finally, we show that empirically an attacker can find effective reward-poisoning attacks using state-of-the-art deep RL techniques.
Evaluation of Phishing Techniques Based on Machine Learning

Phishing sites is the major problems for online security challenges because of large number of online transactions is done every day. The objective of the paper is to do survey about the phishing: A social attack and its detection and to make aware of the users who doesn't know about this major attack as many of them are still falling in the trap. Most of the users are unaware about this problem; they unknowingly fill many forms that belong to phishing website which are hidden. This leads to the leaking of sensitive information of the victim. This study also gives brief knowledge about several machine learning techniques such as kNN Algorithm, Naïve Bayes, Decision Tree, Support Vector Machines, Neural Network and Random Forest algorithm for predicting phishing sites.