Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Distributed Machine Learning Towards Statistical Challenges

Deep learning based artificial intelligence has made many breakthroughs. The training process of deep learning usually requires a lot of data. The availability of big data, especially privacy-sensitive data, is impeding the application of deep learning. Collecting the data may cause big privacy concerns. Some privacy-preserving deep learning methods have emerged in academia and industry. In this paper, we propose BDML+, a decentralized framework based on consortium Blockchain for privacy-preserving distributed deep learning. It focuses on statistical challenges such as different data distributions and data amounts among participants. Statistical challenges are tackled by several techniques. In the first block, a small amount of publicly shared data and a bootstrap warm-up model are given. During the training process, the local training epochs are automatically adjusted with an adaptive boosting method to prevent local training from non-convergence or overfitting. Besides, factors such as local data amount, the base block and the number of training steps are considered to avoid integrating parameter weights with large divergence. The experimental results show that BDML+ has strong adaptability to various data distributions and data amounts.
Constrained episodic reinforcement learning in concave-convex and knapsack settings

We propose an algorithm for tabular episodic reinforcement learning with constraints. We provide a modular analysis with strong theoretical guarantees for settings with concave rewards and convex constraints, and for settings with hard constraints (knapsacks). Most of the previous work in constrained reinforcement learning is limited to linear constraints, and the remaining work focuses on either the feasibility question or settings with a single episode. Our experiments demonstrate that the proposed algorithm significantly outperforms these approaches in existing constrained episodic environments.
Smart Prison - Video Analysis for Human Action Detection

Prisons or correctional facilities require a high amount of manpower for maintaining safety whilst ensuring service quality. However, high turn-over rate (5% per year) commonly seen in these institutions often brings operational challenges, which may entail risk to safety. Prisoner's abnormal behaviors, such as self-harming and fighting, are the major concerns posing the highest safety hazards to prisoners and front-end staff. Traditional human-dependent method used for monitoring prisoners' abnormal behaviors is labor-intensive, and may give rise to problem such as misdetection. A Smart Prison system has been developed to assist front-end staff in detecting prisoners' abnormal behaviors. Results shows that over 95% of the abnormal behaviors can be detected by the system. This supporting system can lower the operational pressure resulted from shortage of manpower, and reduce the rate of abnormal behavior misdetection. This will improve the safety of both front-end staff and prisoners.
Analysis of Minimax Algorithm Using Tic-Tac-Toe

The game industry has been on exponential growth, has different businesses of varying size, ethos, scope and beyond. Success of these video-games comes from a lot of labor-intensive work by developers. Every little nuance of each character, the objects within a character’s environment must be hand-coded. Repetitive work takes up a significant part of development time, which leads to an increase in glitches and logical flaws. Artificial intelligence has been used to simulate human players in software games, provides an opportunity for game developers to create unique experiences and different outcomes for each player. Computer chess players are well-known examples, wherein modern chess programs are trained to defeat best human players. AI based algorithms that can be implemented for games, but a need for optimal solutions is on a rise. We require a comparative analysis of multiple algorithms for understanding the most efficient and ideal one. In our work, through use of a game Tic-Tac-Toe various algorithms will be carried out with its prototype compared in terms of effective rate and optimality.
Implementation of VPN Using Router MikroTik at Al-Basyariah Education Foundation Bogor

VPN is a connection between one network with another network privately through a public network. At Al-Basyariah Education Foundation is a school that has several buildings far apart. The problems faced today that are in several areas related to and communicating using flash and using file shares for those who already have their own LAN. Meanwhile, to connect with the main building and the branch building using the internet and email to send data and communicate. By using a VPN (Virtual Private Network) build a network using the PPTP (Point-To-Point Tunneling Protocol) method with the Mikrotik Router RB2011L then the main building and branch building can be connected. Thus the Al-Basyariah Education Foundation can be connected and can access computer networks anywhere via the internet.
An HVS-Oriented Saliency Map Prediction Modeling.

Visual attention is one of the most significant characteristics for selectingand understanding the outside world. The nature complex scenes, includinglarger redundancy and human vision, can't be processing all informationsimultaneously because of the information bottleneck. The visual system mainlyfocuses on dominant parts of the scenes to reduce the input visual redundancyinformation. It's commonly known as visual attention prediction or visualsaliency map. This paper proposes a new saliency prediction architectureinspired by human low-level visual cortex function. The model considered theopponent color channel, wavelet energy map, and contrast sensitivity functionfor extract image features and maximum approach to real visual neural networkfunction in the brain. The proposed model is evaluated several datasets,including MIT1003, MIT300, TORONTO, and SID4VAM to explain its efficiency. Theproposed model results are quantitatively and qualitatively compared to otherstate-of-the-art salience prediction models and their achieved out-performingof visual saliency prediction.
The Amazing World of Neural Language Generation

Neural Language Generation (NLG) -using neural network models to generate coherent text -is among the most promising methods for automated text creation.Recent years have seen a paradigm shift in neural text generation, caused by the advances in deep contextual language modeling (e.g., LSTMs, GPT, GPT2) and transfer learning (e.g., ELMo, BERT).While these tools have dramatically improved the state of NLG, particularly for low resources tasks, state-of-the-art NLG models still face many challenges: a lack of diversity in generated text, commonsense violations in depicted situations, difficulties in making use of factual information, and difficulties in designing reliable evaluation metrics.In this tutorial, we will present an overview of the current state-of-the-art in neural network architectures, and how they shaped recent research directions in text generation.We will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications.
An environment based approach for the ant colony convergence

Ant colony optimization (ACO) algorithms are a bio inspired solutions which have been very successful in combinatorial problem solving, also known as NP-hard problems, including transportation system optimization. As opposed to exact methods, which could give the best results of a tested problem, this meta-heuristics is based on the stochastic logic but not on theoretical mathematics demonstration (or only on certain well defined applications). According to this, the weak point of this meta-heuristics is his convergence, its termination condition. We can finds many different termination criteria in the scientific literature, yet most of them are costly in resources and unsuitable for practical problems. On the other hand, given the fact that the ACO is a stochastic approach, it seems difficult to decide whether to stop the algorithms in order to have the optimal result of the tested problems. Therefore, the thesis of this paper is to propose an approach based on the environment in order to determine the best termination criteria of the ACO, for an optimized solution.
Multi-complement feature network for infrared-visible cross-modality person re-identification

Infrared-visible cross-modality person re-identification (IV-ReID) is a challenging task that aims to match infrared person images with visible person images of same identity. The person images of two modalities are captured by visible cameras and infrared cameras, respectively. Due to the variation between two modalities, most existing methods tend to extract common features of different modalities by shared network. However, on account of ignoring the effect of single-modality features, the way of merely extracting common features loses part of single-modality information. To address this problem, we propose an end-to-end model, multi-complement feature network (MFN), to complement common features with single-modality features. We divide MFN into two modules, feature extracting module (FEM) and feature complementing module (FCM). At the stage of FEM, we employ a two-stream network with architecture of multiple granularities to extract single-modality features and common features. Afterward, at the stage of FCM, we utilize the characteristic of graph convolution network (GCN) to associate multiple features of different modalities. In FCM, we design a concise but effective graph structure that takes the features extracted by FEM as input of GCN. Compared with previous methods, our method reserves single-modality features and makes them work with common features. Extensive experiments implemented on two mainstream datasets of IV-ReID, SYSU-MM01, and RegDB demonstrate that our method achieves state-of-the-art performance.
Neo-colonialism and Africa’s Development: A Critical Review

This research work accessed Nkrumah's thought on neo-colonialism and Africa's development.The broad objective of the study is to develop a framework for the complete liberation of Africa from the negative impacts of neo-colonial powers in the socio-economic and political spheres of the region.This study is set to provide answers to such questions like: How has neo-colonialism impacted on Africa's development?How can African continent be liberated from neo-colonial influences in order to achieve its economic and political independence?The study adopted the secondary source of data collection in answering the above questions.The dependency theory was adopted as its framework of analysis.The study found amongst others that: African continent are still under the shackles of neo-colonialism because in most cases, the major political economy and socio-cultural ideas, belief, norms, values, practices, structures and institutions introduced by the respective colonial imperialist powers were not dismantled after their independence.The study concluded that, for Africa to overcome the problems of neo-colonialism and its instruments, in order to gain complete economic and political independence, they should fight and struggle against the external forces which have a vested interest in keeping them underdeveloped.This study therefore, recommended amongst others that, all the ideas, belief, norms, values, practices, structures and institutions introduced by the respective colonial imperialist powers in Africa before independence, should be dismantled since the colonial days is over in Africa.
Efficient Privacy-Preserving Approaches for Trajectory Datasets

The use of credit cards or RFID cards for transaction payment has become ubiquitous in recent years. This transactional data is stored in the form of a trajectory, including a sequence of locations at which the customer used the credit card. Such datasets are sensitive since they store customer's locations and purchasing patterns. Releasing this type of dataset without proper anonymization may breach individual's privacy. Moreover, an adversary can attack the dataset with partial trajectory knowledge to reveal sensitive information about a person. We study trajectory data anonymization techniques for this problem, where a data publisher can construct a safe dataset with minimum information loss. A global suppression algorithm was proposed in [1] for trajectory data anonymization. The algorithm is computationally expensive for large trajectory datasets. We propose a tree-based data structure that significantly reduces the computational cost of the global suppression algorithm. In an experiment with a real-world dataset, our data structure performs global suppression in 50% less time than the state- of-the-art algorithm.
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples

The robustness of a neural network to adversarial examples can be provably certified by solving a convex relaxation. If the relaxation is loose, however, then the resulting certificate can be too conservative to be practically useful. Recently, a less conservative robustness certificate was proposed, based on a semidefinite programming (SDP) relaxation of the ReLU activation function. In this paper, we describe a geometric technique that determines whether this SDP certificate is exact, meaning whether it provides both a lower-bound on the size of the smallest adversarial perturbation, as well as a globally optimal perturbation that attains the lower-bound. Concretely, we show, for a least-squares restriction of the usual adversarial attack problem, that the SDP relaxation amounts to the nonconvex projection of a point onto a hyperbola. The resulting SDP certificate is exact if and only if the projection of the point lies on the major axis of the hyperbola. Using this geometric technique, we prove that the certificate is exact over a single hidden layer under mild assumptions, and explain why it is usually conservative for several hidden layers. We experimentally confirm our theoretical insights using a general-purpose interior-point method and a custom rank-2 Burer-Monteiro algorithm.
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function based on Watson's perceptual model, which computes a weighted distance in frequency space and accounts for luminance and contrast masking. We extend the model to color images, increase its robustness to translation by using the Fourier Transform, remove artifacts due to splitting the image into blocks, and make it differentiable. In experiments, VAEs trained with the new loss function generated realistic, high-quality image samples. Compared to using the Euclidean distance and the Structural Similarity Index, the images were less blurry; compared to deep neural network based losses, the new approach required less computational resources and generated images with less artifacts.
Margins are Insufficient for Explaining Gradient Boosting.

Boosting is one of the most successful ideas in machine learning, achieving great practical performance with little fine-tuning. The success of boosted classifiers is most often attributed to improvements in margins. The focus on margin explanations was pioneered in the seminal work by Schapire et al. (1998) and has culminated in the $k$'th margin generalization bound by Gao and Zhou (2013), which was recently proved to be near-tight for some data distributions (Gronlund et al. 2019). In this work, we first demonstrate that the $k$'th margin bound is inadequate in explaining the performance of state-of-the-art gradient boosters. We then explain the short comings of the $k$'th margin bound and prove a stronger and more refined margin-based generalization bound for boosted classifiers that indeed succeeds in explaining the performance of modern gradient boosters. Finally, we improve upon the recent generalization lower bound by Gronlund et al. (2019).
Uncertainty-based Deep Object Detection from Aerial Images

Object detection in aerial images is an important task because it is used in various applications such as land management, disaster monitoring, national security, and map production, However, owing to the characteristics of aerial images, such as high resolution, data imbalance between classes, lack of data, and densely appearing objects, it is difficult to improve the performance even with the recent deep learning-based object detection models. To overcome these challenges, this paper proposes an uncertainty-based max-margin learning method and a data augmentation method based on attribute transformation specialized for aerial images. The superiority of the proposed methods based on a deep learning-based object detection model is revealed by it winning the aerial image object detection contest 2020.
BPS-VSS: A Blockchain-Based Publish/Subscribe Video Surveillance System with Fine Grained Access Control

In recent years, the video surveillance system has become an indispensable management tool for cities. The surveillance system can improve the effectiveness of management and supervision and reduce the possibility of crime. Video surveillance as a service is a type of cloud service that can reduce redundant information and communications technology (ICT) facilities. Users use video surveillance services through a publish/subscribe (P/S) model. In this paper, we propose a blockchain-based publish/subscribe video surveillance system with fine grained access control. A P/S service model for the video surveillance is given and the matching efficiency is improved using a multi-level index mechanism. Ciphertext-policy attribute-based encryption (CP-ABE) is adopted to encrypt the access control policy for a published video surveillance, and only the subscriber with satisfied attributes can decrypt and subscribe video services. In traditional CP-ABE schemes, access policy is stored and granted by the cloud, which lacks credibility due to centralization. We use blockchain to record the access policy, realizing user self certification. The video received from the camera is encrypted and stored in InterPlanetary File System (IPFS) node connected to the blockchain network. The decrypted key of the video is encrypted by CP-ABE. Finally, the access policy, decrypted key, video metadata, the hash value returned by IPFS are recorded in the blockchain.
Classification of Holy Quran Verses based on Imbalanced Learning

Imbalanced Learning (IL) is considered as a special case of text classification. It is applied in order to classify Imbalanced classes that are not equal in the number of samples. There are many researches on classified Quranic text which depends on different methods of classification. However, there is no study that classifies the Quranic topics based on Imbalanced Leaning. So, this paper aims to apply the concept of IL to assign corresponding topics for the Quranic verses according to their contents. In this paper, two Quranic datasets have been classified by using Imbalanced Learning consecutively; the first dataset is Unification of God “Tawheed” and Polytheism of God “Shirk” verses, the second dataset is Meccan, and Medinan chapters. Imbalanced Classification is applied here since these topics have imbalanced classes which cannot be classified correctly by traditional methods. The results showed that applying Imbalanced Classification produces better outcomes than the results that are executed without using Imbalanced Classification techniques.
A Study on career decision process of the major-desired career mismatch among physical education majoring students

This study aims at confirming the characteristics and strategies of the career decision process of college students majoring in physical education and contextual factors that are influencing the process. In this study, grounded theory was implemented to analyze in-depth the career decision process of the informants when their major and their desired career were inconsistent. Informants of the study were 8 students who met the appropriate requirements among the students who received career counseling or career interviews. Data collected from college students majoring in physical education whose major and desired career did not match in career decision process. As a result, 145 concepts and 42 subcategories were classified, and 22 categories were derived by integrating them. In the paradigm model of the career disagreement between majors and career-determining college students, the central phenomenon was ‘Searching the career I want’, and the causal condition that resulted in this phenomenon was ‘Realizing the difference between the major and the desired career’. The contextual condition was ‘Choose a brighter future than uncomfortable college life’. In addition, as an intervening condition suggested by the participants to overcome the phenomenon, ‘Expectation for a better future’ appeared, and as an action/interaction strategy, ‘Finding a path that suits me’ was drawn from the analysis. Through this process, the participants made ‘Desired career decision’ as a result. The types of career decisions they want were ‘Conflict type’, ‘Independent type’ and ‘Stable seeking type’.
Distributed Newton Can Communicate Less and Resist Byzantine Workers

We develop a distributed second order optimization algorithm that is communication-efficient as well as robust against Byzantine failures of the worker machines. We propose COMRADE (COMunication-efficient and Robust Approximate Distributed nEwton), an iterative second order algorithm, where the worker machines communicate only once per iteration with the center machine. This is in sharp contrast with the state-of-the-art distributed second order algorithms like GIANT [34] and DINGO[7], where the worker machines send (functions of) local gradient and Hessian sequentially; thus ending up communicating twice with the center machine per iteration. Moreover, we show that the worker machines can further compress the local information before sending it to the center. In addition, we employ a simple norm based thresholding rule to filter-out the Byzantine worker machines. We establish the linear-quadratic rate of convergence of COMRADE and establish that the communication savings and Byzantine resilience result in only a small statistical error rate for arbitrary convex loss functions. To the best of our knowledge, this is the first work that addresses the issue of Byzantine resilience in second order distributed optimization. Furthermore, we validate our theoretical results with extensive experiments on synthetic and benchmark LIBSVM [5] data-sets and demonstrate convergence guarantees.