Reviewerly/exHarmony huggingface dataset:
SimpleTran: Transferring Pre-Trained Sentence Embeddings for Low Resource Text Classification

Fine-tuning pre-trained sentence embedding models like BERT has become the default transfer learning approach for several NLP tasks like text classification. We propose an alternative transfer learning approach called SimpleTran which is simple and effective for low resource text classification characterized by small sized datasets. We train a simple sentence embedding model on the target dataset, combine its output embedding with that of the pre-trained model via concatenation or dimension reduction, and finally train a classifier on the combined embedding either by fixing the embedding model weights or training the classifier and the embedding models end-to-end. Keeping embeddings fixed, SimpleTran significantly improves over fine-tuning on small datasets, with better computational efficiency. With end-to-end training, SimpleTran outperforms fine-tuning on small and medium sized datasets with negligible computational overhead. We provide theoretical analysis for our method, identifying conditions under which it has advantages.
On the Applicability of Certificateless Public Key Cryptography (CL-PKC) for Securing the Internet of Things (IoT)

With the introduction of smart cities, transportation, industries, and even smart health care, the Internet of Things (IoT) has risen as a domain of great potential, impact and development with Gartner forecasting about 20.4 billion Internet-connected smart devices connected by 2020. However, most of these IoT devices can be easily compromised. Quintessentially, these IoT devices have limited computation, memory, and processing capacity, and thus they are more susceptible to attacks than conventional devices like computers, tablets, or smartphones. In this paper, we present and review significant security issues in IoT. We review and analyze security protocols and challenges with respect to standard IoT protocol architecture. More importantly, we discuss, how certificateless cryptography (CL-PKC) is being used to solve many security problems in IoT. Furthermore, we tabulate such proposals with an emphasis on open research problems in this domain.
Learning Spatio-Temporal Convolutional Network for Real-Time Object Tracking

Siamese series of tracking networks have shown great potentials in achieving balanced accuracy and beyond real-time speed. However, most of existing siamese trackers only consider appearance features of first frame, and hardly benefit from interframe information. The lack of latest temporal transformation degrades the tracking performance during challenges such as deformation and partial occlusion. In this paper we focus on making using of the rich information in latest consecutive frames to improve the feature representation of initial template frame. Specifically, the latest frames after 3d convolution are used to generate an attention map, which is then point-wise multiplied by the features of first frame to obtain the updated template. With the attention map, the template can adaptively cope with the deformation and occlusion of the target. Since the first frame is always used as the basis of the template, there is no cumulative error when using the latest frames for attention. Due to the shared 2d convolution of all frames, the feature map results can be reused so that the added module has almost no time-consuming effects. This module is easily embedded into different siamese trackers. Through verification, the module has significantly improved tracking performance in different backbone situations.
Design of Control Algorithms for Mobile Robots in an Environment with Static and Dynamic Obstacles

This article proposes the construction of autonomous mobile robots and designing of obstacle avoidance algorithms for them. Nowadays, mobile robots are gaining more and more popularity on the customer as well as industrial market, for example as automatic vacuum cleaners or lawnmowers. Obstacle avoidance algorithms play an important role in performance of this types of robots. The proposed algorithms were designed for builds with rather not expensive electronic components, especially sensors with limited precision and dynamics. The project began with the selection of needed parts and building materials as well as designing of the PCB and assembling the whole construction. The project included also designing and developing the software responsible for, among others, implementation of obstacle avoidance algorithms. After the project’s completion, a series of tests in a closed environment was conducted to verify the quality of vehicles’ performance. Results of tests were positive.
Research on Feature Extraction Method of Geometric Image Recognition

Abstract The existing image recognition feature extraction technology cannot effectively transmit the image information if the extracted features are not complete, sufficient and inaccurate. It will have an impact on image processing, such as reducing the accuracy and efficiency of subsequent image recognition and image tracking. At the same time, the existing technology for image processing destroys the topological structure between image pixels, and the process is complicated in the calculation of high-dimensional data space. Therefore, this paper proposes a method and steps of feature extraction for collective image recognition, and makes relevant supplementary explanation and analysis.
Tactile Guidance System for the Blind Based on Digital Image Processing

Abstract Science and technology are making earth-shaking changes in our daily lives, and as a special group, blind people should enjoy the fruits of technological progress. Since the traditional blind guiding device can only give rough information about the distance and orientation of the obstacle, the user obtains too little information, and there is still too much unknown fear during the traveling. The guiding effect is not great. Therefore, after considering the physical and psychological needs of the blind, this paper designs a system of tactile perception imaging instead of vision. The system collects obstacle information through the camera, performs digital image processing, and then displays it on the tactile imager through special encoding processing. Through the sense of touch, the blind person processes the tactile information into image information through the brain, thereby obtaining the size, shape and position information of the obstacle, and then forming a more concrete understanding of the obstacle, so that the blind can perfectly avoid the obstacle.
Learning sums of powers of low-degree polynomials in the non-degenerate case

We develop algorithms for writing a polynomial as sums of powers of low degree polynomials. Consider an $n$-variate degree-$d$ polynomial $f$ which can be written as $$f = c_1Q_1^{m} + \ldots + c_s Q_s^{m},$$ where each $c_i\in \mathbb{F}^{\times}$, $Q_i$ is a homogeneous polynomial of degree $t$, and $t m = d$. In this paper, we give a $\text{poly}((ns)^t)$-time learning algorithm for finding the $Q_i$'s given (black-box access to) $f$, if the $Q_i's$ satisfy certain non-degeneracy conditions and $n$ is larger than $d^2$. The set of degenerate $Q_i$'s (i.e., inputs for which the algorithm does not work) form a non-trivial variety and hence if the $Q_i$'s are chosen according to any reasonable (full-dimensional) distribution, then they are non-degenerate with high probability (if $s$ is not too large). Our algorithm is based on a scheme for obtaining a learning algorithm for an arithmetic circuit model from a lower bound for the same model, provided certain non-degeneracy conditions hold. The scheme reduces the learning problem to the problem of decomposing two vector spaces under the action of a set of linear operators, where the spaces and the operators are derived from the input circuit and the complexity measure used in a typical lower bound proof. The non-degeneracy conditions are certain restrictions on how the spaces decompose.
Creative interactions lab @ Carleton University

opinion Share on Creative interactions lab @ Carleton University Authors: Sara Nabil View Profile , Audrey Girouard View Profile Authors Info & Claims InteractionsVolume 27Issue 3May - June 2020 pp 16–19https://doi.org/10.1145/3389153Online:17 April 2020Publication History 1citation485DownloadsMetricsTotal Citations1Total Downloads485Last 12 Months209Last 6 weeks15 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Measuring Active Information in Biological Systems

In computer search optimization theory, active information is a measurement of a search algorithm’s internal information as it relates to its problem space. While it has been previously applied to evolutionary search algorithms on computers, it has not been applied yet to biological systems. Active information can be very useful in differentiating between mutational adaptations which are based on internally-coded information and those which are the results of happenstance. However, biological systems present many practical problems regarding measuring active information which are not present in digital systems. This paper describes active information, how it can be used in biology, and how some of these problems can be overcome in specific cases.
Solving String Constraints with Approximate Parikh Image

In this paper, we propose a refined version of the Parikh image abstraction of finite automata to resolve string length constraints. We integrate this abstraction into the string solver SLOTH, where on top of handling length constraints, our abstraction is also used to speed-up solving other types of constraints. The experimental results show that our extension of SLOTH has good results on simple benchmarks as well as on complex benchmarks that are real-word combinations of transducer and concatenation constraints.
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Recognition.

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this work, we propose a spatially and temporally aware tensor-based neural network for human pose recognition using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental validation of the proposed model indicates that it can achieve state-of-the-art performance. Although in this study, we consider the problem of human pose recognition, our methodology is general enough to be applied to any pattern recognition problem spatiotemporal data from sensor networks.
Research on New Intelligent Building Electrical Energy Saving Technology Based on Internet of Things Technology

Abstract In order to continuous the advancement of technology, the development of Internet of Things technology will greatly promote the intelligent, we make information and automation of buildings, which is of great significance for promoting intelligent buildings. The modern intelligent building electrical energy-saving design measures can not only realize the energy-saving design, the real purpose is to save energy and reduce emissions, promote energy-saving emission reduction can promote China’s sustainable development, implement scientific methods, and assist China’s economic development. When designing, we designers should consider various factors and rationally design electrical energy-saving measures.
The effect of absorptive capacity on innovation: the mediator role of human capital , telecommunications companies (Orange, Zain, Umnia) in Jordan

The aim of this paper is to investigate the effect of absorptive capacity on innovation, in telecommunications companies (Orange, Zain, Umnia) in Jordan, and whether human capital mediates the effect of absorptive capacity on innovation. A questionnaire that targets 361 respondents resulted in 346 usable ones with a response rate of 95.8% to test the research hypotheses. Multiple regressions were used to analyse the research data. The findings indicate that absorptive capacity relates positively to innovation, human capital emerged as having the strong effects on innovation. Moreover, all absorptive capacity dimensions had positive significant effects on human capital. The results also support the argument that human capital plays a mediate role in absorptive capacity and innovation. The present study provides managers with an empirical proof that possessing external knowledge through absorptive capacity seems to help reinforce their ability to innovation. In addition, possessing strong human capital effective will motivate and lead more innovation.
Design and Implementation of a Multistage Image Compression and Reconstruction System Based on the Orthogonal Matching Pursuit Using FPGA

Due to the vast developments in the media communication field and the quality of the visual imaging, image data compression has been one of the most interesting field. The main purpose of the image compression is to produce a very low bit rate while achieving a high quality of the reconstructed images. Image compression are used for all fields of media communication such as medical image recognition, multimedia, digital image processing. There are different algorithms for compression and reconstruction. One of these methods is the Orthogonal Matching Pursuit that is used mainly in the reconstruction of the radar signal. This paper discloses a new methodology for image compression and reconstruction to enhance the performance while at the same time reducing the bit data size. This kind of reconstruction algorithm is based upon multistage compression and OMP reconstruction. The Matlab Simulation and the FPGA implementation will be provided in this paper to validate the concept of this paper.
Effect of Crosslinker Concentration on the Comprehensive Properties of Polyacrylamide-aluminium Citrate/Water Glass Gel

Abstract In this paper, the authors discuss the polyacrylamide-aluminum citrate/water glass gel and set the gradient on the cross-linking agent aluminum citrate to study the cross-linking agent content of polyacrylamide-aluminum citrate/water glass gel. The influence of properties, viscosity, strength and resistance characteristics, found that the water retention rate, viscosity and strength resistance of polyacrylamide-aluminum citrate/water glass gel increased first with the increase of aluminum citrate. After the reduction. Among them, when the amount of the cross-linking agent aluminum citrate is 1.2 wt%, the polyacrylamide-aluminum citrate/water glass gel performs excellent in various aspects. The optimal ratio of polyacrylamide-aluminum citrate/water glass gel is finally confirmed, which provides theoretical support for its application in coal mines.
Highly Efficient SCA-Resistant Binary Field Multiplication on 8-Bit AVR Microcontrollers

Binary field ( B F ) multiplication is a basic and important operation for widely used crypto algorithms such as the GHASH function of GCM (Galois/Counter Mode) mode and NIST-compliant binary Elliptic Curve Cryptosystems (ECCs). Recently, Seo et al. proposed a novel SCA-resistant binary field multiplication method in the context of GHASH optimization in AES GCM mode on 8-bit AVR microcontrollers (MCUs). They proposed a concept of Dummy XOR operation with a kind of garbage registers and a concept of instruction level atomicity ( I L A ) for resistance against Timing Analysis (TA) and Simple Power Analysis (SPA) and used a Karatsuba Block-Comb multiplication approach for efficiency. Even though their method achieved a large performance improvement compared with previous works, it still has room for improvement on the 8-bit AVR platform. In this paper, we propose a more improved binary field multiplication method on 8-bit AVR MCUs. Our method basically adopts a Dummy XOR technique using a set of garbage registers for TA and SPA security; however, we save the number of used garbage registers from eight to one by using the fact that the number of used garbage registers does not affect TA and SPA security. In addition, we apply a multiplier encoding approach so as to decrease the number of required registers when accessing the multiplier, which enables the use of extended block size in the Karatsuba Block-Comb multiplication technique. Actually, the proposed technique extends the block size from four to eight and the proposed binary field multiplication method can compute a 128-bit B F multiplication with only 3816 clock cycles ( c c ) (resp. 3490 c c ) with (resp. without) the multiplier encoding process, which is almost a 32.8% (resp. 38.5%) improvement compared with 5675 c c of the best previous work. We apply the proposed technique to the GHASH function of the GCM mode with several additional optimization techniques. The proposed GHASH implementation provides improved performance by over 42% compared with the previous best result. The concept of the proposed B F method can be extended to other MCUs, including 16-bit MSP430 MCUs and 32-bit ARM MCUs.
Personalized Automated Itineraries Generator for Tourism

In this present era, people want everything to be quick and easy. In tourism, for example, people want an easy way to get information about attractions, events or the best city itinerary. With the huge data on the internet, a personalized recommender system is needed to filter the information and make it useful. In recent years, not only the recommendation for attractions are developed, but there is also the urgency of itinerary recommendation. In this paper, we would like to propose a system that can generate an itinerary that matches user preferences. The itinerary generation process will be dealt with using Ant Colony Optimization which is capable to solve the Traveling Salesman Problem in a good manner. On the other hand, to match user preferences, Personalized Technique will be introduced to create a recommendation that will be used to create a good itinerary that considers user preferences, optimal route, and preferred time to visit attractions.
Urgensi Pengembangan Koleksi Sebagai Upaya Menyediakan Koleksi yang Berkualitas di Perpustakaan

In the living environment of the library community as an institution providing information services provide easy access to the entire community to access information for free easily and quickly. In the develop- ment stage of the collection of procurement processes gave birth to a quality collection in accordance with the needs of information user and have relevance to the development of the latest information technology so that the library as an information center can compete with other information centers. Collection development policy is a tool / means to direct all activities related to the planning, funding, selection, and procurement of library materials. The collection development policy is also a guide to developing collections directed. In order for the collection development policy can be implemented directed librarian as a waiter of information should always give confidence to library user in using library as source of information.
Identification of Dynamical Systems Using Symbolic Regression

We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic programming to evolve a system of ordinary differential equations (ODE). The novelty is that we add a step of gradient-based optimization of the ODE parameters. For this we calculate the sensitivities of the solution to the initial value problem (IVP) using automatic differentiation. The proposed approach is tested on a set of 19 problem instances taken from the literature which includes datasets from simulated systems as well as datasets captured from mechanical systems. We find that gradient-based optimization of parameters improves predictive accuracy of the models. The best results are obtained when we first fit the individual equations to the numeric differences and then subsequently fine-tune the identified parameter values by fitting the IVP solution to the observed variable values.
Classification of Imbalanced Banking Dataset using Dimensionality Reduction

The classification is an important data mining technique. In classification, the classifier automatically learns the properties of classes or categories from the pre-defined training documents. In this paper we have used the imbalanced bank marketing dataset for direct marketing campaign available at UCI machine learning repository. In this paper, we compare the accuracy of different classification algorithms like Naive Bayes, J48, KNN and Bayesnet. CfsSubsetEval method is used for the dimensionality reduction by evaluating the subset of attributes based on the predictive ability and finding out the duplication among the selected features. Before the dimensionality reduction, J48 provide 89%. of accuracy. By using the dimensionality reduction, the accuracy is increased to 91.2% by J48 algorithm.
"Mutual sharing" - master and student in the individual and common scientific world

The article is an analysis of the master-student relationship in the dimension of academic cooperation. We attempt to diagnose the current situation from the perspective of young scientists and identify good practices in the form of potentially supporting activities for their development. We appeal, among others to the term "mutual sharing" as a key factor for the development of a mutually enriching relationship. The issues raised in the project are a response to changes taking place in the modern world, which shape the need to verify and update the perception of the master's definition and the specificity of his relationship with the student, so that it corresponds to current needs.
SIFT Feature Image Stitching Based on Improved Cuckoo Algorithm

Abstract Traditional image stitching method based on SIFT algorithm that the stitching time is long and the efficiency is low, and the stitching effect is not ideal. In this paper, the location of the parasitic nest in the cuckoo algorithm is mutated, and an evolutionary reduction function is proposed for the step factor and the probability of discovery. On the basis of this, an improved cuckoo algorithm is proposed, and the edge points of the image are solved by this algorithm, and the comparison between the edge points of the image and the feature points in the SIFT algorithm is made. The results show that the time of image stitching is obviously reduced and the speed and quality of the splicing are improved obviously, and it can fully meet the needs of practical application.