Reviewerly/exHarmony huggingface dataset:
Distributed robust statistical learning: Byzantine mirror descent

We consider the distributed statistical learning problem in a high-dimensional adversarial scenario. At each iteration, m worker machines compute stochastic gradients and send them to a master machine. However, an α-fraction of m worker machines, called Byzantine machines, may act adversarially and send faulty gradients. To guard against faulty information sharing, we develop a distributed robust learning algorithm based on mirror descent. This algorithm is provably robust against Byzantine machines whenever α ∈ [0, 1/2). For smooth convex functions, we show that running the proposed algorithm for T iterations achieves a statistical error bound Õ(1/√mT + α/√T). This result holds for a large class of normed spaces and it matches the known statistical error bound for Byzantine stochastic gradient in the Euclidean space setting. A key feature of the algorithm is that the dimension dependence of the bound scales with the dual norm of the gradient; in particular, for probability simplex, we show that it depends logarithmically on the problem dimension d. Such a weak dependence is desirable in high-dimensional statistical learning and it has been known to hold for the classical mirror descent but it appears to be new for the Byzantine gradient scenario.
A Comparative Study of Pretrained Language Models on Thai Social Text Categorization

The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language model on a large noisy Thai social media corpus of over 1.26 billion tokens and later fine-tuned on the downstream classification tasks. Due to the linguistically noisy and domain-specific nature of the content, our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model. We compared four modern language models: ULMFiT, ELMo with biLSTM, OpenAI GPT, and BERT. We systematically compared the models across different dimensions including speed of pretraining and fine-tuning, perplexity, downstream classification benchmarks, and performance in limited pretraining data.
Policy Gradient using Weak Derivatives for Reinforcement Learning

This paper considers policy search in continuous state-action reinforcement learning problems. Typically, one computes search directions using a classic expression for the policy gradient called the Policy Gradient Theorem, which decomposes the gradient of the value function into two factors: the score function and the Q-function. This paper presents four results: (i) an alternative policy gradient theorem using weak (measure-valued) derivatives instead of score-function is established; (ii) the stochastic gradient estimates thus derived are shown to be unbiased and to yield algorithms that converge almost surely to stationary points of the non-convex value function of the reinforcement learning problem; (iii) the sample complexity of the algorithm is derived and is shown to be O(1/ k); (iv) finally, the expected variance of the gradient estimates obtained using weak derivatives is shown to be lower than those obtained using the popular score-function approach. Experiments on OpenAI gym pendulum environment illustrate the superior performance of the proposed algorithm.
Improving Mandarin Prosody Boundary Detection by Using Phonetic Information and Deep LSTM Model

Automatic prosodic boundary detection is useful for automatic speech processing, such as automatic speech recognition (ASR) and speech synthesis. In this paper, we propose two techniques to improve the boundary detection performance. First, in addition to prosody features (e.g, pitch, duration and energy), phonetic information (word/articulatory information) is integrated into the framework of prosodic boundary detection. We compared two forms of phonetic information: word form and articulatory form. Moreover, boundary detection can be regarded as a sequence labeling task. A deep Long Short-Term Memory (LSTM) is adopted for this task, which replaces the traditional Deep Neural Networks (DNN) model. The experimental results showed that the boundary detection performance can be improved by the additional phonetic information, with relative 5.9% (word form) and 9.8% (articulatory form) improvements respectively in contrast with the system that only used prosody features modeled. The articulatory information and prosody features with deep LSTM achieved the best result, with further performance enhancement from 76.35% to 77.85% (relative 6.3%) compared with that modeled by DNN.
An Approach of Handling Verbal Inflections of Bengali Text: Conversion of Sadhu to Cholito Form of Language

Bengali language follows Subject-Object-Verb (SOV) structure for constructing sentences where verb plays a crucial role. Machine translation for Bengali becomes easier if the verb is identified correctly in a semantically valid sentence. However, Bengali verbs are affected by various complex inflections while using in a sentence. Those inflections are determined according to tense (time of occurring event), person (nature of the subject), Sadhu and Cholito forms (historical evolution of writing styles of Bengali) of language. In this study, we propose an algorithm to identify different inflections of Bengali verbs to retrieve verb roots and present an approach to convert Sadhu to Cholito form of writing Bengali sentences. The methodology shows satisfying accuracy and fluency in the context of language evaluation.
Facial Age Estimation using Deep neural networks: A Survey

Facial age estimation techniques are extensively used in vitality applications nowadays; however, they are a time-consuming task. Compared to other methods, deep learning algorithms showed a superior performance to solve face related age estimation problem. The current surveys are incomplete, not up-to-date and are not concerned with deep learning in particular. In this paper, a comprehensive updated summary of the deep age estimation frameworks are introduced. We review the previous research efforts in deep age estimation systems and present the popular datasets. Convolution Neural Networks (CNN) are introduced with their models. The performance criteria widely used in evaluation are declared. Finally, the challenges and the future directions are discussed to reach a robust facial age estimation system.
Location Based Place Recommendation using Social Network

In recent times, due to the massive choices on the internet, there is need of the recommender system. It can overcome the information overload problem to convey pertinent information. It gives users the suggestion of items or customized services according to their preferences and past history. Previously, recommender systems were based on demographic, content-based and collaborative filtering. In current time, recommender systems are integrating social information. Recently the online social networks have become very popular on the internet. Due to the increased use of online social network, information exchange has been increased. Users can initiate new relationships with other users based on features such as groups, hobbies and interests. With the help of social network, people can share their visit and check-in information, feelings, views and ideas. As the social network and global positioning system (GPS) technology are growing, location based social networks (LBSNs) are growing rapidly. Location based social networks can offer customized location services to the users with a new point-of-interest (POI). Point-of-interest recommendation can combine location and social network, which integrates online users and physical locations. Point-of-interest recommendation assists users to search new locations as per their preferences or choices. In the recent study of location based social networks, influence factors are taken into consideration. Influence factors like social influence, spatial/geographical influence, temporal influence and popularity features can give better recommendation result. This paper includes the literature survey on recommender system, social network, influence analysis and recommendation of places for location based social networks.
Solving Knights Covering Problem by a Hybrid Algorithm

There are problems which do not have analytical solutions and require algorithms (such as Backtracking) that use brute force. The time complexity of these algorithms is polynomial, so they lose their practical advantage for even small systems. In this study, we apply the hybrid algorithm to find the number of knights required to cover every square of a chessboard-looking table (a.k.a. Knights Covering Problem) to reduce time complexity. This approach simplifies the problem by removing the attacked cells in order to decrease the number of trial-and-error steps. The hybrid algorithm increases efficiency by reducing computation time. Our analysis shows 50% performance increment for 4-KCP.
Fusion of LiDAR and Camera Images in End-to-end Deep Learning for Steering an Off-road Unmanned Ground Vehicle

We consider the task of learning the steering policy based on deep learning for an off-road autonomous vehicle. The goal is to train a system in an end-to-end fashion to make steering predictions from input images delivered by a single optical camera and a LiDAR sensor. To achieve this, we propose a neural network-based information fusion approach and study several architectures. In one study focusing on late fusion, we investigate a system comprising two convolutional networks and a fully-connected network. The convolutional nets are trained on camera images and LiDAR images, respectively, whereas the fully-connected net is trained on combined features from each of these networks. Our experimental results show that fusing image and LiDAR information yields more accurate steering predictions on our dataset, than considering each data source separately. In another study we consider several architectures performing early fusion that circumvent the expensive full concatenation at raw image level. Even though the proposed early fusion approaches performed better than unimodal systems, they were significantly inferior to the best system based on late fusion. Overall, through fusion of camera and LiDAR images in an off-road setting, the normalized RMSE errors can be brought down to a region comparable to that for on-road environments.
SDR Demonstration of Signal Classification in Real-Time Using Deep Learning

In this paper, we demonstrate a software defined radio (SDR) prototype with the capability of signal classification in real-time. Detection, classification, and characterization of wireless signals is a key step to efficiently utilize and effectively share spectrum for enabling SDR-based cognitive radio and intelligent radio. Convolutional neural network (CNN), a deep learning (DL) algorithm, is trained, tested, and used for wireless signal modulation classification. Two types of radio frequency (RF) front-ends, ADALM- PLUTO and Universal Software Radio Peripherals (USRP), are used to transmit and receive over-the-air signals in the demonstration. A classification accuracy of 95.5% is achieved using ADALM-PLUTO; and 96.25% is achieved using USRP N210 with SBX daughterboard.
Implementation of a Robotic Control System for Fault Tolerance and Safety using Human Robot Interaction

Fault tolerance is important in a robotic system architecture because of the advancements in robotics which makes the robots much smarter and more capable on executing tasks. Thus fault tolerant and self-healing robotic control systems help prevent any cascade errors that can lead to the robot becoming inoperable or causing a safety hazard. The current paper is a work in progress describing the initial efforts of implementing a fault tolerant robotic system architecture and presents initial progress of this implementation where the robotic control system demonstrates a fault tolerance and a self-healing algorithm using human robot interaction in the process.
School zoning system using K-Means algorithm for high school students in Makassar City

The process of admitting High School Students in Makassar City produces a lot of student data, in the form of student learning activities data and also student profile data. This affects the search for information on the data. This study discusses the grouping of students towards Makassar City Public High Schools by utilizing the data mining process using clustering techniques. The algorithm used for cluster formation is the K-Means algorithm. K-Means is a nonhierarchical data clustering method that can group school data into several clusters based on the similarity of the data. Euclidean Distance is used to determine the distance of school points and address points for students. The proposed system is a zoning area determination system for acceptance of high school students on a noncircle basis using student data and school data. The data used are 22 school data and 1547 student data. The results of this study are used as a basis for decision making to determine optimal school zoning so that student distribution is evenly distributed based on the cluster formed. The aim is so that the data distribution does not overlap for schools that are close together so that schools that have the closest distance are grouped in one cluster.
SVM Model for Feature Selection to Increase Accuracy and Reduce False Positive Rate in Falls Detection

Falls are a dangerous problem for people of all ages. Thus, accurate falls detection with minimized false alarms is very important. This study aims to detect falls and activities of daily living (ADLs) using acceleration data and to introduce an effective feature selection criterion to reduce the false positive rate of the falls detection systems. The falls detection system in this study consists of three stages. At the first stage, we have harnessed some feature extraction techniques to have discriminative features from the acceleration data. Then we have used feature selection criterions to select effective features in the detection task. At the last stage, we used Support Vector Machine (SVM) to classify the selected features in falls and ADLs. We have used raw acceleration data and extracted all the features. Then we selected features based on the Minimum Redundancy Maximum Relevance (MRMR) criterion and Double Input Symmetrical Relevance (DISR) in the fall detection experiment. We have found that the DISR feature selection criterion is more effective in acceleration based fall detection system. The results show 100% classification accuracy and zero false positive rates in fall detection for the DISR based selected features.
Shamir's Secret Sharing for Authentication without Reconstructing Password

Shamir's Secret sharing is a quantum attack proof algorithm and is used heavily for secret sharing. But it can also be used for authentication protocols as a replacement of hashing. In this paper, we propose an authentication protocol which will use Shamir's secret sharing method to authenticate with server. Hashing may not be able to hide data as effective in post quantum era. So in post quantum era, if any data server get exposed, users credentials can be also compromised as they were hidden by using hashing as an one way encryption. Our protocol will be able to solve this problem in a way that complete data exposure from server will not reveal the actual password provided by the user. So, even if the user uses same password for other online services/systems, these services and systems will not be effected.
Twitter Data Visualization and Sentiment Analysis of Article 370

Twitter is a platform widely used by people to express their opinion and share their thoughts based on any topic or event. Over the years, Twitter has only gained popularity. In this paper, the opinion of users about the revocation of Article 370 and its impact on trade in Pakistan, terrorism in India and Pakistan, and tweets regarding Kashmir have been mined. Article 370 of the Indian Constitution gave special status to Jammu and Kashmir and was revoked on 5 August 2019. Sentiment analysis is performed for these topics to analyze the positivity, negativity, and neutrality of the tweets on a global basis. Two thousand two hundred tweets between 5 August and 30 August are analyzed, and it is found that people have a slightly more positive response about this topic. People from Pakistan are concerned about the impact on trade while people from India are worried about the impact on terrorism. The tweets and data regarding them are stored and visualized using various graphs and plots.
Efficiently Transform Contracts Written in Peyton Jones Contract Descriptive Language to Solidity

Financial contracts play a major role in the modern economy. Due to a large variation of contracts being traded in financial markets, a standard representation for contracts was required in order to eliminate the ambiguity imposed by natural languages. Peyton Jones et al. catered this need by proposing a combinator library embedded in Haskell which enabled contract representation and valuation. However, every financial contract represented as such suffered from the same fundamental problem. If a contract is executable, the parties involved in the contract needed to trust a central counterparty to give them the correct results after execution. The interference of this middleman introduced certain risks as well as a significant amount of overhead. In this paper, a novel approach to facilitate autonomous contract execution was proposed, exploiting the features and use cases of the Ethereum blockchain and its scripting language, Solidity. The approach involves transforming contracts written in Peyton Jones Contract Descriptive language (CDL) to Solidity using a special purpose compiler. The result of this transformation is a smart contract equivalent to the traditional financial contract. The transformed smart contract is deployed and executed on the Ethereum blockchain using an Ethereum client. The proposed solution was evaluated against existing attempts to design autonomous financial contracts. The research shows that a contract written using CDL can be transformed into a smart contract which executes autonomously in a trust-less environment. As a result, it was concluded that financial contracts could operate without a central counterparty with increased performance and reduced overheads in comparison to traditional financial contracts.
NetForager: Geographically-Distributed Network Performance Monitoring of Web Applications

Users connect to web applications for various content and services. In addition to the internet for the global user/ customer base, in today's enterprises, most business needs are also served by web applications. The application performance monitoring, testing, and maintenance is a considerable effort when serving customers and business needs. Ensuring a high level of user experience, isolation of network issues from application service performance, and providing a seamless experience independent of the proximity of the end user to the application server are some of the challenges faced in performance assurance efforts. In this respect, a reliable testing and monitoring mechanism for application performance is essential to business continuity. Furthermore, virtualization technologies along with extensive mobility adds tremendous challenge to collection of reliable and isolated data in conducting performance monitoring for any application. In this paper, we present the results of a comprehensive network traffic analysis conducted in a geographically distributed manner for over 15 representative web applications. We report that web applications utilize a wide range of bandwidth capacity due to location of the dynamic content and the time of day that the data may be retrieved. The variations create an inconsistent performance level in their network usage metrics. Our reported metrics such as the duration of complete web data transfer and bandwidth utilization can help enterprises, network engineers, and service providers in fine tuning their services. Our approach does not contain sensitive user information, allows for extensive configuration and customization of the framework, and finally, data capture processes have a built-in sharing mechanism for repeatable network experiments through a data collection description language.
Research on Performance of Artificial Bee Colony Algorithm Based on Benchmark Test Function

Abstract Because the artificial bee colony(ABC) algorithm has problems of poor development ability, slow convergence rate and easy to fall into local optimum, an improved ABC algorithm is proposed. The algorithm combines the global optimal guidance strategy, we propose a new location update formula to improve the efficiency of the iterative optimization process, and introduce the mining coefficient in the formula to improve the accuracy of global optimization. In addition, the beta distribution is introduced during the scouting bee phase, which improves the disturbance ability of the algorithm and prevents it from falling into local extremum effectively. Finally, the simulation results of four benchmark functions of Sphere, Rastrigin, Rosenbrock and Griewank show that compared with the traditional algorithm and other improved artificial bee colony algorithm, the proposed algorithm has faster convergence speed and better optimization ability.
A Hybrid System for Securing Data Communication

This paper presents a One-Time Pad (OTP) encryption and a genetic algorithm (GA) audio steganography, respectively. In the proposed scheme, genetic algorithm is used for both encryption and steganography. In the encryption process, the key is randomly generated using genetic algorithm. The generated key is used as a key for the One-Time Pad algorithm. Then, the encrypted message is embedded in audio in the best position for embedding using signal-to-noise ratio (SNR) in genetic algorithm fitness function. Results had been discussed for the proposed scheme. It dissolves the drawbacks of key generating and distribution for One-Time Pad by using genetic algorithm. The proposed method satisfies the main steganography requirements pointed in perceptual transparency, hidden data capacity and robustness. Experimental results had been made, namely text analysis, waveform analysis and signal-to-noise ratio. Besides, a comparison of SNR for the proposed method with some known algorithm. All are to indicate that the proposed method can significantly outperform many steganography techniques in terms of security and audio quality.
Development of an anomaly detection method with a novel hidden semi-Markov model incorporating unlearned states

This paper outlines a novel pattern recognition approach incorporating consideration of unexpected anomaly patterns in time-series data. In this approach, probability density functions of unlearned states are incorporated in a hidden semi-Markov model allowing consideration for the temporal dependence of data, as the general classifier used misidentifies abnormal data not belonging to classes predefined in training. In the experiments performed, the proposed method was applied to the classification of artificial time-series data and motion recognition problems for simulated care tasks. The characteristics seen in motion recognition were extracted from physical data obtained from a care worker via a motion capture system. The proposed method produced higher levels of motion recognition than previous approaches, and the results demonstrated the effectiveness of the technique.