Reviewerly/exHarmony huggingface dataset:
DualPrune: A Dual Purpose Pruning of Convolutional Neural Networks for Resource-Constrained Devices

Many successful applications of deep learning have been witnessed in various domains. However, the use of deep learning models in edge-devices is still limited. Deploying a large model onto small devices for real-time inference requires an adequate amount of resources. In the last couple of years, pruning has evolved as an important and widely used technique to reduce the inference cost and compress the storage-intensive deep learning models for small devices. In this paper, we proposed a novel dual-purpose pruning approach to accelerate the model performance and reduce the storage requirement of the model. The experiments on the CIFAR10 dataset with AlexNet and VGG16 models show that our proposed approach is effective and can be used to make the deployment of the trained model easier for edge-devices with marginal loss of accuracy. For the VGG16 experiment, our approach reduces parameters from 14.98M to 3.7M resulting in a 74.73% reduction in floating-point operations with only 0.8% loss in the accuracy.
Feature Extraction and Recognition Method of Industrial Internet of Things Terminal Based on MGA-SVM Dimension Reduction

The Industrial Internet of Things (IIoT) enables massive industrial control terminals to be interconnected. In order to avoid the intrusion of illegal terminals into the system, the terminals must be accurately identified. Aiming at the problem of low recognition accuracy of current terminal recognition scheme, a feature extraction and recognition method of industrial control terminal based on MGA-SVM dimension reduction is proposed. By collecting the message information returned by the scanned object from all kinds of terminals in the scanning network, a hybrid method of multi parent cross mutation genetic algorithm and support vector machine is proposed to extract and optimize the acquired data to obtain the best subset that can mark the terminal features. On this basis, the method of industrial control terminal recognition based on integrated learning is proposed. Experimental results show that this method can effectively improve the recognition accuracy.
Design and Verification of Ultra-small Processor with Specializes Binarized Neural Network

A lot of multiplication circuits and a large-scale memory are required to operate a neural network. However, it is also necessary to use a low-power consumption and small-scale processor with a neural network for an embedded image-processing system. A binarized neural network (BNN) operating on the basis of the binary operation method is one of the candidates for solving such problems because the multiplication circuit is unnecessary, and a relatively small memory is required. In this study, an ultrasmall processor was designed with a specialized BNN based on the processor with a very small area for function, “Pilaf.” The processor designed is called “Pulin,” in which a control circuit is added that can access a large memory area and an image-processing arithmetic circuit. Despite the addition of some circuits, the design of Pulin demonstrates the same chip area as that of Pilaf because the architecture and the state machine are optimized. In addition, the processing time taken in the case of the neural network execution was reduced to approximately half that of the conventional processor. In addition, a large-scale integration (LSI) circuit was fabricated, and the delay time and operation speed of Pulin were analyzed using an LSI tester.
A HES for Low Speed Processors

Adaptation of e-commerce in third world countries requires more secure computing facilities. Online data is vulnerable and susceptible to active attacks. Hundreds of security mechanisms and services have been proposed to curb this challenge. However, available security mechanisms, sufficiently strong, are heavy for the machines used. To secure online data where machines' processing power and memory are deficient, a Hybrid Encryption Standard (HES) is proposed. The HES is built on the Data Encryption Standard (DES) algorithm and its siblings. The component units of the DES are redesigned towards reduced demands for processing power and memory. Precisely, white box designs of IP tables, PC tables, Expansion tables, Rotation tables, S-boxes and P-boxes are proposed, all aimed at reducing the processing time and memory demands. Evaluation of the performance of the HES algorithm against the performance of the traditional DES algorithm reveal that the HES out-performs the DES with regards to speed, memory demands, and general acceptance by novice practitioners in the cryptography field. In addition, reproducibility and flexibility are attractive features of the HES over the DES.
An Improved Trajectory Estimation Algorithm in Boost Phase

Aiming at reducing the trajectory estimation error in boost phase by using some typical dynamic characteristic parameters of the ballistic missile, an improved trajectory estimation algorithm in boost phase for two satellites detection is proposed. The thrust acceleration profile is constructed by the ballistic missile's dynamic characteristic parameters, then an accurate motion model of the missile in boost phase is established. According to the ballistic missile's motion characteristics in boost phase, the Cubature Kalman Filtering is introduced as the filtering algorithm. Simulation results show that, compared with the traditional trajectory estimation algorithm based on the CS model in boost phase, the estimation root mean square error of the proposed algorithm is reduced by about 50%, and the filtering stability is significantly improved.
Exploiting Inconsistency Problem in Multi-label Classification via Metric Learning

Multi-label classification problem has gained growing attention in recent years due to its diverse applications to real-world problems such as image annotation and query suggestions. However, traditional multi-label classification methods tend to fail due to the inconsistency between input and output space, where similar instances in the feature space may have distinct semantic labels in the output space. To eliminate the inconsistency problem, in this paper, we propose a supervised metric learning approach for multi-label classification, called MLMLI, which attempts to learn a similarity metric for multi-label data. The basic idea is to incorporate label similarity in output space as weak supervision to assign higher similarity to the pairs of instances with more similar labels. To this end, a weighted triple loss, and a step-specified coordinate descent method are employed. Different from traditional dimensionality reduction approaches, MLMLI is independent of any prior information of data, and thus enjoys a high capacity of generalization. Moreover, the metric learned by MLMLI offers a new venue for feature learning. Experiments on real-world datasets have further demonstrated the effectiveness of MLMLI and show its superiority over many state-of-the-art algorithms.
A Path to More Meaningful Technical Services Assessment

In recent years, there have been many initiatives aiming to improve library assessment by making better use of operational data. While most LIS administrators and stakeholders believe assessment is important, too often many of the quantitative measures traditionally used may satisfy demands from above, come up short in terms of oﬀering direction for meaningful or positive change.This may be especially true when it comes to technical services departments. This article will discuss the need for meaningful assessment in order to support decision-making in technical services, investigate the traditional data collected, and suggest an approach utilizing information from performance appraisals that could lead to a more holistic assessment of library departments.
«dovrebbe essere essenziale dire [ɲɔkki] e non [ɲɔtʃi]» Riflessioni soggettive sull’insegnamento della grammatica italiana

In the teaching of Italian in Germany, grammar is nowadays considered as a tool that serves language acquisition, but that is subordinated to the communicative goals of the language lessons. Teachers have to take these as a starting point for their didactic decisions. However, decision procedures do not only depend on educational policy or scientific findings, but also on individual hypotheses of various types. Based on 31 interviews of (future) teachers of Italian (2016-17), the present study aims at analysing (future) teachers’ decision making considering the teaching of grammar. The results show that a dialect and migrant background can play a significant role for didactic decisions.
Designing Artificial Intelligence/International Relations (AI/IR) Platform: Foreign Policy Decision-Making Simulation in ASEAN Negotiation

The growing interest in artificial intelligence (AI) application into foreign policy decision making and negotiation among scholars and policy-makers demonstrates an inherent transition of International Relations both as a field and practice into the so-called Fourth Industrial Revolution. This study examines the application of AI in providing a computerized decision-making support system and how the production of such a system would assist both practitioners and learners of International Relations in understanding the state's foreign policy decision making process and strategies during diplomatic negotiations. This study offers a design of AI/IR systems which adopts the Expert System framework and employs the strategy of Forward Chaining. This study provides a framework for developing a rule-based system to identify possible foreign policy alternatives and their consequences by adopting the Rational Choice model as the basis of knowledge engineering. Given the remarkable growth of the Southeast Asia region in recent years, this study is particularly interested in developing a computerized decision-making system that can promptly respond to the region's dynamics.
МОДЕЛЬ ПРИЙНЯТТЯ РІШЕНЬ ЩОДО ФУНКЦІОНАЛЬНОГО СТАНУ СКЛАДНИХ ОБ”ЄКТІВ

Effective assessment of the condition of complex objects is important for various industries and science. Almost allcharacteristics of a biological object are characterized by variability - a specific and distinctive feature of all biomedicalmeasurements. Variation of the trait occurs if its values vary from individual to individual or for one individual over time, whichmakes it impossible to repeat control measurements of the parameters of the same biological object, as is usually accepted in theart, which leads to great difficulties in estimating random variables. measurement and, as a consequence, diagnostic errors. Theaim of the work is to formalize the decision-making process in assessing the functional state, such a complex object as the humanbody, using a statistical method. The possibility of estimating the state of the organism as a lot of parametric system in thecorrelation of its indicators, the use of T2 – Hotelling statistics, which allows by determining the individual characteristics of thetransformation of informative parameters, to provide more reliable results on changes in body stability compared to the traditionalapproach and age. The effectiveness of using the developed approach to assessing the state of the organism is confirmed byexperimental results. The results of the research presented in the article can be used to assess the degree of deviation from thenorm of both technical and biological objects, including the body of humans or animals.The article presents the results of research in the field of experimental methods and proposes a new approach toassessing the functional state of complex objects based on the processing of the results of measuring control of their parameters.The paper proposes the formalization of the decision-making process in assessing the functional state, such a complex object as thehuman body, using a statistical method. The possibility of estimating the state of the organism as a lot of parametric system in thecorrelation of its indicators, the use of T2 – Hotelling statistics, which allows by determining the individual characteristics of thetransformation of informative parameters, to provide more reliable results on changes in body stability compared to the traditionalapproach and age. Medical indicators of 12 operators performing professional duties, in which blood tests were performed duringone year, were calculated. Peculiarities of using personalized medicine within the framework of the 3P concept are given.However, the use of T2 – Hotelling statistics is proposed, which makes it possible to determine the limits of controlled values at agiven level of significance according to the chi-square distribution quantile table.
Mobile Technologies, Input Hypothesis and Second Language Acquisition in the Rwandan Context

The use of technologies in education started in the 1950s specifically with the use of audio-visual tools in language learning. Those tools which were made of big heavy hardware with limited software, accessible only from a specific location, have currently evolved into handy portable and mobile devices which are accessible at any place and time with unlimited variety of software, and this gave birth to mobile-assisted language learning as a young field of study. As the world rapidly changes, there is a need for studies which integrate these technologies with the available traditional theories that were primarily based on traditional ways of teaching and learning. This article is an attempt to integrate the use of modern mobile technologies with Input Hypothesis of second language acquisition in the context of Rwanda, one of the monolingual world countries in matters of national language, but which use foreign languages for various purposes.Keywords: ICT in education; mobile assisted language learning; second language acquisition; mobile technologies in language learning (MTLL);  emerging technologies; input hypothesis
Multivariate Independence Set Search via Progressive Addition for Conditional Markov Acyclic Networks

Estimation of conditional dependencies over a joint multivariate probability distribution is a difficult task for big data, e.g. -omics datasets, and it becomes quickly intractable when the number of variables involved grows large. For instance, structure learning in Bayesian networks has super-exponential complexity. Dimension reduction techniques such as principal component analysis can be useful but often transform the original space and can still pose problems with scalability. This substantially limits characterization of joint probability: in general, only pairwise or k-level correlations can be analyzed efficiently. We introduce the Multivariate Independence Set Search via Progressive Addition for Conditional Markov Acyclic Networks (MISS-PACMAN), which operates a greedy selection of jointly independent feature sets from a larger set of covariates, given a variable ordering. The method is non-parametric and can be used with any kernel function. MISS-PACMAN is therefore suitable for heterogeneous big data, as it combines flexibility and scalability. In our tests on both simulated and real-world data, using random forests as kernel, MISS-PACMAN was able to select independence feature sets linearly with the number of features. Further, by combining multiple independence sets, MISS-PACMAN well-approximates the underlying conditional structure of data variables (according to the generating Bayesian network), and compares favorably with other network structure discovery algorithms, such as the Peter-Clark and the fast incremental association Markov blanket.
Robust Incremental Kalman Filter for the System under Poor Observation Conditions

When the measurement equation or noise variance of the system is uncertain, the performance of the filter will deteriorate and even cause the divergence of the filter. The introduction of incremental equation is an effective method to eliminate the unknown measurement error. In this paper, a robust incremental Kalman filter based on incremental equations is proposed for linear discrete systems with unknown measurement errors and unknown noise variances, and its robustness is analyzed. Simulation results show the effectiveness and feasibility of the proposed algorithm.
Reducing Tail Latency In Cassandra Cluster Using Regression Based Replica Selection Algorithm

Online applications adoption, and success are driven by a multitude of factors among them the service response time. This is natural as users tend to prefer a faster service than a slower. However, it is challenging to deliver consistently fast response times due to performance variability inherent to the infrastructure running the application; This performance variability causes a fraction of user requests to experience unusual latency called tail latency. In this work, a Linear Regression Based Replica Selection Algorithm is proposed. The regression model helps to estimate how long a specific query is going to take to be serviced, and based on this information, a server with more or less resources is chosen to service the query. Experiments done using data generated by a fleet of buses show that the proposed approach is successful in reducing the higher percentiles latency up to 30 % in some cases while not impacting negatively the throughput.
Systematic Evaluation of Deep Learning Models for Human Activity Recognition Using Accelerometer

Human Activity Recognition (HAR) based on data from wearable sensors has become an attractive research topic thanks to its applications in different fields such as healthcare and smart environments. Recently, the advancement of deep learning with capability to perform automatically high-level feature extraction has achieved promising results. However, the performance of the deep learning models depends deeply on the characteristics of the datasets such as the number of classes, the inter-similarity and intra-variation. Therefore, directly comparing these models has become difficult since a wide variety of experimental protocols, evaluation metrics, and datasets are employed. In this paper, for the first time, a systematic evaluation of several deep learning models for HAR from wearable sensors is provided. In particular, three models named Convolutional Neural Network (CNN) [1], DeepConvLSTM - a combination of CNN and Long Short Term Memory (LSTM) [2], and SensCapsNet - a Capsule Neural Network for wearable sensor-based HAR [3] were implemented and evaluated on three benchmark datasets that are 19NonSens, CMDFall, and UCI-HAR dataset. Moreover, to have an intuitive explanation of deep learning models, a visualization of features learnt from these models is given. The evaluation codebase and results will be made publicly available for community use.
Comparing Adversary Defense Mechanisms in Cognitive Radio Networks

BACKGROUND: In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter. METHODS: Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter. This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiver-based defense mechanism is developed against the jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK when transmission is not successful and vice versa, to poison the training process of the attacker. Results: We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter when compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the medium. CONCLUSION: A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.
The wheel-step planetary exploration vehicle with two-way stable obstacle-crossing function

Abstract Planetary exploration has always been an important topic in China’s aerospace field. On the rugged planet surface, having a good obstacle-crossing ability is crucial to the mission of the planetary exploration mobile system In this paper, after consulting the data of planetary vehicles at home and abroad, and analyzing the current suspension structure of wheeled planetary exploration vehicles at home and abroad, a six-wheel planetary exploration vehicle with a certain obstacle-crossing capability and capable of bidirectional driving is designed.
Using a zettelkasten to manage your ideas

It can be hard to keep a record of notes from lectures and conferences over several years. Jason Ratcliffe provides more information on creating a zettelkasten—a way that information can not only be easily managed, but also a way to build upon ideas
An Approach of LPI Radar Signal Detection Based on Visibility Graph

There is an urgent need to address the crucial problems caused by more and more low energy and complex of radar signals in the radar countermeasure field. In 2009, Luque et al. introduced a visibility algorithm to describe time series. In this paper, the visibility graph is applied to radar signal detection. Adjacency matrix of visibility graph is adapted to complex and various electromagnetic environment. On the other hand, differential power spectral density has advantage on low probability of intercept (LPI) radar signal detection. Except for linear frequency modulation (LFM), the proposed approach is efficient to most of LPI modulation types, such as frequency shift keying (FSK), sinusoidal frequency modulation (SFM), binary phase shift keying (BPSK) and LFM-BPSK combined modulation. 1000 Monte Carlo simulation results shows detection probability can reach 92% under -5 dB signal noise ratio (SNR).
Quantized Warping and Residual Temporal Integration for Video Super-Resolution on Fast Motions

In recent years, numerous deep learning approaches to video super resolution have been proposed, increasing the resolution of one frame using information found in neighboring frames. Such methods either warp frames into alignment using optical flow, or else forgo warping and use optical flow as an additional network input. In this work we point out the disadvantages inherent in these two approaches and propose one that inherits the best features of both, warping with the integer part of the flow and using the fractional part as network input. Moreover, an iterative residual super-resolution approach is proposed to incrementally improve quality as more neighboring frames are provided. Incorporating the above in a recurrent architecture, we train, evaluate and compare the proposed network to the SotA, and note its superior performance in faster motion sequences.
Finite Difference Approximation to Fuzzy Heat Equation in Cylindrical and Spherical Polar Co-ordinates

In this paper, we investigate the stability of the heat equation in the polar Coordinate fuzzy parametric system, first we write the heat equation in a polar system, then express it as a parametric fuzzy, and then drive it using definitions Bede and et al., we have written the equation and finally, tested its stability using the matrices method. These method has higher accuracy and less error, than non-fuzzy methods.