Reviewerly/exHarmony huggingface dataset:
Data-Driven Solutions for Digital Communications

In this paper, we show that data-driven communication methods can overcome limitations of pure model-based designs. Classical communication theory works well with ‘ideal’ channels (AWGN, Rayleigh, etc.). However, an open area of research exists in how to transmit across non-ideal channels, such as high Doppler, nonlinear, or contested. Communications researchers have already started to make great progress using machine learning to address a variety of communications problems. We build on their findings and propose a variety of machine-learned communication techniques. We show a transmitter that uses reinforcement learning algorithms to learn efficient modulation schemes. The benefit of a reinforcement learned system is that it can change its transmission scheme based on the current characteristics of the channel. We show our reinforcement learned transmitter successfully able to learn an efficient modulation scheme given power constraints. We show our transmitter not only learning good modulation schemes for AWGN channels, but also adapting when suddenly presented with a non-linear amplifier in its path. In addition, while it takes a long time to initially train the transmitter, it relatively quickly adapts to the introduction and removal of non-linear amplification. This approach outperforms static modulation schemes that suffer when the non-linearities are introduced. Furthermore, we propose a receiver that uses a machine-learning classifier to decode either a fixed transmitter scheme or a learned transmitter scheme. We also implement and propose neural networks to simultaneously learn transmitter and receiving algorithms via supervised learning. Finally we implement a learned parameter selection model to select communication system parameters such as modulation order and other values dynamically based on current channel state. In this way we can utilize machine learning to improve the robustness and resiliency of critical SATCOM links.
Privacy threats in low-cost people counting devices

As evident from an in-depth analysis of the state of the art concerning device tracking through Wi-Fi probes and MAC addresses, these techniques represent an increasingly relevant privacy threat. In this paper we provide design and implementation details of a low-cost and low-power people counter based on the Espressif ESP8266 board, and we explicitly analyze the overall cost of the introduced solution. The proposed device can gather MAC addresses from Wi-Fi packets and is designed to circumvent MAC address randomization, as we demonstrate through practical experiments. Our study also shows that, as IoT devices and components are less and less expensive, even a single person could set up a personal people counting systems to be maliciously installed in urban areas or indoor environments.
Learning Segmentation from Object Color

We propose Color Shift GAN (CSGAN), a method that allows learning to segment an object class without the need for pixel-wise annotations. We exploit a single textual annotation of the basic object color per image to learn the semantics of an object class. By using only a textual basic color annotation of each object, we are able to drastically reduce labeling efforts. We created a dataset of 29,910 images of cars and annotated the basic color of their body works. Our model reaches 61.4 percent IoU on our test data. CSGAN trained with additional 128 pixel-wise annotations reaches 62.0 percent. By adding 45,150 unlabeled images to the training of CSGAN we are able to increase IoU to 65.0 percent without using a single pixel-wise annotation. This verifies that our weak objective is sufficient for learning segmentation.
GPU-BTM: A Topic Model for Short Text using Auxiliary Information

Recently, short texts become very popular in social life. To understand short texts, researchers develop topic models to extract topic information. However, conventional topic models mainly focus on long documents which cannot deal with the sparsity problem of short text. In this paper, we propose a novel topic model for short text called GPU-BTM, which incorporates Generalized Pólya Urn technique into Biterm Topic Model. GPU-BTM utilizes the similarity information and the co-occurrence pattern of words simultaneously to handle the sparsity problem. Specifically, the GPU module considers the similarity information among words, so that GPU-BTM generates more coherent topics. On the other hand, BTM module tries to capture the co-occurrence pattern of words so that the enriched contexts relieve the data sparsity problem. In the experiment part, the results demonstrate that GPU-BTM model outperforms four latest comparison models on two real world short text datasets.
Hiding Identities: Estimation Under Local Differential Privacy

In this paper, we study an estimation problem under the local differential privacy (LDP) framework: There is an ordered list of d values (e.g., real numbers); a set of n users, where each user observes an element from this list and each value in the list is observed by at least one user; and an untrusted server, who wants to estimate the values that the users possess, without learning (in the sense of LDP) the actual value that each user has and its corresponding index in the list. Towards this, we propose two LDP estimation schemes: The first one is under the assumption that the server knows the number of users that observe each value; and the second one is for the general scenario, in which the server does not have this prior information. We show that the minimax risk decreases with the total number of users under a very mild condition on the number of users observing each value.
A Deep Learning Cognitive Architecture: Towards a Unified Theory of Cognition

This work suggests a novel approach to autonomous systems development linking autonomous technology to an integrated cognitive architecture with the aim of supporting a common artificial general intelligence (AGI) development. The paper provides a summary of strengths and weaknesses of some of the most known cognitive architecture and highlights how to support a generic artificial intelligent approach rather than ad hoc solutions. It also proposes objective evaluation criteria to assess a cognitive architecture. Finally, the proposed cognitive architecture is introduced: a Deep-Learning Artificial Neural Cognitive Architecture (D-LANCA), which aims to overcome current limits of cognitive frameworks for autonomous systems with the view to create a common artificial general intelligent (AGI) cognitive approach across industries.
Development of a Framework for Effective Surveillance and Human Intrusion Detection in Border regions covered with Dry leaves

Terrorist intrusion creates problems to the national security. The casualty induced by previous terror attacks against Indian Military Force in Pathankot, Uri and the recent Pulwama attack demands the need of developing a multi-sensing system for detecting the presence and movement of intruders in the border areas. Huge human involvement is needed for the conventional patrolling systems and the latest surveillance systems are equipped with high-tech and costly devices. Line of sight issues and high false warning rates are the major problem experienced by using any of this single technique. This lead to the need of developing an effective surveillance and human intruder detection system integrated with multi sensing approach and different technologies. The proposed system is a part of the PANCHENDRIYA framework, only for detecting a human intruder in the border region covered by dry leaves. This framework aims to reduce the work force and cost of the traditional border surveillance by integrating multiple sensors and various wireless technologies. Microphone sensor is used to detect the presence of human intruder in dry leaves through footstep signal detection and surveillance camera sensor is used for getting a clear evidence of the intrusion in the border region through visual information. The overall system performance is enhanced by integrating these sensors into a microcontroller development board with high computational capability. Once the human intrusion in border region covered with dry leaves is detected, then the system initiates early alerts.
Asymptotically Scale-invariant Multi-resolution Quantization

A multi-resolution quantizer is a sequence of quantizers where the output of a coarser quantizer can be deduced from the output of a finer quantizer. In this paper, we propose an asymptotically scale-invariant multi-resolution quantizer, which performs uniformly across any choice of average quantization step, when the length of the range of input numbers is large. Scale invariance is especially useful in worst case or adversarial settings, ensuring that the performance of the quantizer would not be affected greatly by small changes of storage or error requirements.
The Peril of Artificial Intelligence

The integration of AI technology is with the hope of reducing human errors, improving efficiency, and providing faster result. Nonetheless, research indicates that AI may become hazardous over time and would not perform the intended roles. To recognize the perilous effects of AI, the study draws from the dynamic programming theory developed by Richard Bellman. The study proves that while AI is programmed to do a useful task, a malfunction may lead to negative consequences of the entire system. It may also attract indirect consequences such as traffic jams. The best way to explain this correlation is via the dynamic programming theory, which asserts that as systems become complex, they may sway from their targeted objective. Computer-related dangers may emanate from the cognitive complexity of the system as opposed to the specific properties of which the system was meant to perform. The immediate risks of AI are autonomous weapons, although other long-term effects such as gradual replacement of humans by robots and AI drug addition will also result.
Smart card readers and the quality of the 2019 general elections in Nigeria: successes and challenges

(2020). Smart card readers and the quality of the 2019 general elections in Nigeria: successes and challenges. The Round Table: Vol. 109, Nigeria’s 2019 general elections – a shattered hope?, pp. 396-405.
An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index

Graph similarity search is a common operation of graph database, and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms.
An IoT Virtual eLearning Space

This paper presents the Virtual eLearning Space implemented as an Internet of Things ecosystem. The components of the space inhabit three layers – a sensing layer, an operative and analytical layer, and a cognitive layer, which are described in more detail. One of the advantages of the space is that integrating the virtual world with the physical world of the university campus provides effective support to disabled students. This new opportunity is demonstrated by an example scenario. At the same time, VeLS is enhanced to be a reference architecture that can be adapted for new IoT applications. In the reference architecture, virtualization of “things” is supported by three formal tools – AmbiNet, TNet, and ENet. Future directions are also briefly discussed.
Comparison of the Schemes in Digital Image Watermarking Techniques

Abstract With the rapid developing of network techniques and multimedia technology, the data security becomes an important and challenging research topic. The emergence of digital image watermarking technology provides an effective way for multimedia copyright protection, which embeds specific information. This paper analyses the characteristics of digital watermarking technology firstly, then studies some important digital image watermarking algorithms, finally compares the schemes in digital image watermarking techniques. The work aims to compare the differences of the schemes in digital image watermarking techniques and improve the performance of digital image watermarking.
Consistent query answering with prioritized active integrity constraints

Consistent query answering is a principled approach for querying inconsistent databases. It relies on two basic notions: the notion of a repair, that is, a consistent database that "minimally" differs from the original one, and the notion of a consistent query answer, that is, a query answer that can be derived from every repair. In general, an inconsistent database can admit multiple repairs, each corresponding to a different way of restoring consistency, and the consistent query answering framework does not make any discrimination among them. However, in many applications it is natural and desired to express preferences among the different choices that can be made to resolve inconsistency.
Real-time Processing for Remote Sensing Satellite Data Based on Stream Computing

With the development of data achieving ability of high resolution remote sensing satellites and the enhancement of data receiving ability on the ground, the data processing workload of the existing ground application system for remote sensing satellites is growing, and the demand of real-time data processing is increasingly higher. In recent years, stream computing has become a research hotspot due to the high performance for real-time concurrent processing and distributed computing. In this paper, a new real-time processing method of remote sensing satellite data is proposed by the framework of stream computing. Firstly, according to the characteristics of remote sensing data processing, the stream computing is modeled, the processing time of the instance is abstracted, and the multi-task optimization and scheduling methods are given. Then the real-time data processing system of remote sensing satellite is implemented by using this method, and the data tuples, processing components and task topology are redesigned. Finally, the data processing time and throughput rate of the system are tested and analyzed. Experimental results show that the real-time performance of the system is greatly improved.
Accelerating Beta Weil Pairing with Precomputation and Multi-pairing Techniques

In this paper, we present the efficient computation methods of $$\beta $$ Weil pairing with the precomputation and multi-pairing techniques. We use the following two ideas to facilitate applying those techniques to $$\beta $$ Weil pairing. 1. Elimination of denominators of $$\beta $$ Weil pairing: The original $$\beta $$ Weil pairing proposed by Aranha et al. is the products of quotients of extended Miller functions. Then, we propose the method to eliminate denominators of $$\beta $$ Weil pairing. 2. Elimination of exponents of $$\beta $$ Weil pairing: The original $$\beta $$ Weil pairing has distinct exponent in each term. Then, we propose the method to eliminate exponents of the $$\beta $$ Weil pairing. Thereby, we can apply the precomputation and multi-pairing technique to $$\beta $$ Weil pairing in order to accelerate the computation. Moreover, we estimate the computational costs of the proposed methods for the BLS-48 curve with 256 bit level of security against the Kim-Barbulescu attack, proposed by Kiyomura et al. Then, we compare the results with the normal $$\beta $$ Weil pairing and the optimal ate pairing. Furthermore, we also provide implementation results.
Multi-camera Panoramic Stitching with Real-time Chromatic Aberration Correction

Abstract Due to uneven lighting conditions and differences between camera lenses, in actual engineering applications, there is a difference in brightness of the image collected by the camera, which will cause obvious gaps in video stitching and poor stitching effect of panoramic pictures. This paper designs and implements an algorithm based on the gray statistics of overlapping areas to achieve global correction, and combined with the fade-in and fade-out image fusion algorithm to make the splicing transition natural. The experimental results show that in the Visual Studio 2015 platform environment, real-time stitching of 8-channel cameras has a fast algorithm speed and can achieve a stitching speed of 25 frames per second. The video is smooth and the picture quality is good without obvious color difference.
Multi-factor Fusion POI Recommendation Model

In the context of the rapid development of location-based social networks (LBSN), point of interest (POI) recommendation becomes an important service in LBSN. The POI recommendation service aims to recommend some new places that may be of interest to users, help users to better understand their cities, and improve users' experience of the platform. Although the geographic influence, similarity of POIs, and user check-ins information have been used in the existing work recommended by POI, little existing work considered combing the aforementioned information. In this paper, we propose to make recommendations by combing user ratings with the above information. We model four types of information under a unified POI recommendation framework and this model is called extended user preference model based on matrix factorization, referred to as UPEMF. Experiments were conducted on two real world datasets, and the results show that the proposed method improves the accuracy of POI recommendations compared to other recent methods.
The Wisdom of the Gaming Crowd

In this paper, we report on three projects in which we are applying natural language processing techniques to analyse video game reviews. We present our process, techniques, and progress for extracting and analysing player reviews from the gaming platform Steam. Analysing video game reviews presents great opportunity to assist players to choose games to buy, to help developers to improve their games, and to aid researchers in further understanding player experience in video games. With limited previous research that specifically focuses on game reviews, we aim to provide a baseline for future research to tackle some of the key challenges. Our work shows promise for using natural language processing techniques to automatically identify features, sentiment, and spam in video game reviews on the Steam platform.
Feature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context

Feature Extraction and Efficiency Comparison Using Dimension Reduction Methods in Sentiment Analysis Context
Feature Set Ensembles for Sentiment Analysis of Tweets

In recent years, sentiment analysis has attracted a lot of research attention due to the explosive growth of online social media usage and the abundant user data they generate. Twitter is one of the most popular online social networks and a microblogging platform where users share their thoughts and opinions on various topics. Twitter enforces a character limit on tweets, which makes users find creative ways to express themselves using acronyms, abbreviations, emoticons, etc. Additionally, communication on Twitter does not always follow standard grammar or spelling rules. These peculiarities can be used as features for performing sentiment classification of tweets. In this chapter, we propose a Maximum Entropy classifier that uses an ensemble of feature sets that encompass opinion lexicons, n-grams and word clusters to boost the performance of the sentiment classifier. We also demonstrate that using several opinion lexicons as feature sets provides a better performance than using just one, at the same time as adding word cluster information enriches the feature space.
Accuracy' Measures of Sentiment Analysis Algorithms for Spanish Corpus generated in Peer Assessment

The purpose of this study is to test a model that classifies some sentiment as positive or negative from some feedback in Spanish that are generated through peer assessment in Higher Education. The Supervised Machine Learning method is implemented. Several experiments are performed with a manually tagged data set to test different combinations of N-grams with Term Frequency-Inverse Document Frequency (TF-IDF), and classification algorithms: Multinomial Naive Bayes, Support Vector Machine, Logistic Regression, and also Random Forest, in order to obtain the right combination that gives the best performance. The simulation results displayed that the Support Vector Machine classifier with the combination of 1-grams + 2-grams + TF-IDF is the best model in Precision, Recall and F-Measure.
A Study of Perceptual and Cognitive Models Applied to Prediction of Eye Gaze within Statistical Graphs

In theory, visual saliency in a graph should be used to draw attention to its most important component(s). Thus salience is commonly viewed both as a basis for predicting where graph readers are likely to look, and as a core design technique for emphasizing what a reader is intended to see among competing elements in a given chart or plot. We briefly review models, metrics, and applicable theories as they pertain to graphs. We then introduce new saliency models based on perceptual and cognitive theories that, to our knowledge, have not been previously applied to models for viewing statistical graphics. The resulting frameworks can be broadly classified as bottom-up perceptual models or top-down cognitive models. We report the results of evaluating these new theory-informed approaches on gaze data collected for statistical graphs and for more general information visualizations. Interestingly, the new models fare no better than previous ones. We review the experience, noting why we expected these hypotheses to be effective, and discuss how and why their performance did not match our aspirations. We suggest directions for future research that may help to clarify some of the issues raised.
Underwater Maneuvering Target Tracking Algorithm Based on Interacting Multiple Model Strong Tracking Square-Root Cubature Kalman Filter

In the maneuvering target tracking, the primary problem is to have high stability, strong robustness and convergence for the tracking filtering algorithms. Based on Interacting Multiple Model, and by combining the strong tracking filtering (STF) and the square-root cubature Kalman filtering (SRCKF), an IMM strong tracking square-root cubature Kalman filtering (IMM-ST-SRCKF) was proposed for nonlinear maneuvering target tracking to overcome the precision decreasing and even divergency caused by target maneuvering. The IMM-ST-SRCKF tracking algorithm is based on the combination of the interacting multiple models (IMM) algorithm and strong tracking square-root cubature Kalman filtering (ST-SRCKF) to cope with the underwater maneuvering target tracking problem with the time of arrive (TOA) measurements. The simulation result demonstrate that the proposed algorithm provides better tracking precision comparing with the other algorithms.