Reviewerly/exHarmony huggingface dataset:
Evaluating The Number of Trainable Parameters on Deep Maxout and LReLU Networks for Visual Recognition

Object recognition research has made notable steps since the appearance of convolutional neural networks, and many activation functions have been proposed to enhance the classification performance of these networks. Maxout networks have achieved great success in many computer vision tasks, but there is limited information on whether an increase in the number of trainable parameters can increase the performance in Leaky Rectified Linear Unit (LReLU) networks compared to maxout networks. Our experiments compare LReLU, rectified linear unit, scaled exponential linear unit, and hyperbolic tangent to four maxout variants. We evaluate ReLU and LReLU with 2x, 3x and 6x the number of filters in each convolutional layer. We also evaluate ReLU, LReLU and maxout networks with approximately the same number of trainable parameters. Under equal conditions, we found that on average, across all datasets, LReLU performs better than any of the evaluated activation functions.
Deep Learning Based Classification Using Semantic Information for Polsar Image

The deep learning has been applied to PolSAR image classification tasks in many researches. But they are pixel-based or spatial-based. None of them take the polarimetric semantic information into consideration. In this paper, a hierarchical classification method based on the polarimetric semantic information and deep learning is proposed. The method combines deep learning and semantic model to benefit from both the discriminative deep features and the polarimetric semantic information. The semantic information can provide the priori knowledge to improve the classification result of deep learning. Experimental results show that the proposed method can well preserve image details meanwhile suppress the speckle noises.
Rent-a-Watt: Rethinking energy use feedback

This paper details our journey toward re-imagining eco-feedback at scale. Simple in-home displays are found to offer limited efficacy and become disused, yet it is simply not feasible (or at least politically acceptable) to deploy high quality feature-rich energy feedback to every household in a region. This paper presents Rent-a-Watt, a speculative, rentable, sharing economy-based model of energy use feedback which is envisaged as a means of providing energy literacy at scale. Based on findings from a preliminary field deployment, we identify limits to engagement with smart plug-based feedback and outline avenues for future work, arguing a case for a sharing economy model of energy use feedback and the potential for a marriage of HCI work on energy use feedback with that on user-led thermal performance audits.
Improving Text-Image Matching with Adversarial Learning and Circle Loss for Multi-modal Steganography

This paper proposes a multi-modal steganography method based on an improved text-image matching algorithm. At present, most of the steganography methods are based on single modality of carriers and embed confidential information into the carriers by cover modification or cover synthesis. Since the distortions between the covers after embedding and the original covers are inevitable, these steganography methods can be detected by the existing steganalysis methods. To solve this problem, we propose multi-modal steganography which hides the confidential information in the semantic relevancy between two modalities of original carriers. The semantic relevancy between the two modalities is measured by text-image matching, which affects the imperceptibility of the proposed method to a large extent. In order to increase the security of multi-modal steganography, we improve the current text-image matching algorithm with adversarial learning and circle loss. By selecting and transmitting the original multi-modal carriers with high relevancy, the proposed method can escape from the detection of current steganalysis methods. It is also illustrated by the theoretical analysis and experimental results that the semantic relevancy between the selected multi-modal carriers is enhanced.
«Amur mission control center»: Formation and modern development

The rapid development of space-based technologies and services continuously increases the requirements for specialists developing, testing, and operating space technology. Conventional teaching methods are not enough to train highly qualified and competitive specialists in the space industry. This paper presents an example of a project for improving the quality of educational programs for engineering students. The project is run based on the Amur State University. The purpose of the project is to train personnel to ensure the effective operation of the new Vostochny cosmodrome in Russia. The problem of improving the quality of students’ training is solved in a threefold manner. First, the first university center for spacecraft flight control was created in the Far East. Second, an educational program based on laboratory practices using telemetry information from small spacecraft “AIST” was introduced. Third, engineering training based on completing a project to develop a real CubeSat class spacecraft was tested and integrated into the curriculum. Solving these tasks allowed expanding and intensifying educational programs for engineering majors, increasing their competence in the development and operation of spacecraft. Thus, the educational program is capable of providing the fledging Vostochny cosmodrome with highly qualified young specialists.
Generating New Lower Abstract Task Operator using Grid-TLI

We propose a method of subdividing robot tasks into new lower abstract tasks. The description of robot tasks in an abstract manner is effective for motion planning for complex tasks and teaching robot movements in various environments. However, a more efficient task description may be obtained by using a lower abstraction according to the work environment. We argue that a higher abstract task can be expressed as a new lower abstract subtasks by applying Grid-based Signal Temporal Inference (Grid-TLI). We show that a new task can be completed using the Signal Temporal Logic formula for each cluster. We demonstrated the efficiency of our method through computer simulations using a 2-D security robot task.
Argumentation-Based Explanations of Multimorbidity Treatment Plans

We present an argumentation model to explain the optimal treatment plans recommended by a Satisfiability Modulo Theories solver for multimorbid patients. The resulting framework can be queried to obtain supporting reasons for nodes on a path following a model of argumentation schemes. The modelling approach is generic and can be used for justifying similar sequences.
Utilizing the Blackboard Learning Management System at the University of Ha’il from the Perspective of Faculty Members using Internet Of Things (IOT)

The study aims to investigate the perceptions of faculty members in the preparatory year at the University of Ha’il concerning the use of the Blackboard learning management system, and to identify the impact of the study variables (gender, discipline, teaching language, and number of training courses) on the responses of the study sample members. A descriptive methodology has been used, as the study was applied to a sample of (123) faculty members, and to collect information, a questionnaire of (32) items was developed. The results of the study showed that the level of using the Blackboard at the University of Ha’il by faculty members was high. It indicated that there were no statistically significant differences due to gender. Moreover, the results revealed that there were no statistically significant differences (p=0.218) in the use of Blackboard based on the number of training courses, and that there were no statistically significant differences (p=0.390) in the use of Blackboard based on academic ranking.
Simple and Secured Password Hiding Technique for Image-based Authentication using a Least Significant Bit based Embedding Scheme

This study proposes a secured and straightforward password hiding technique to address authentication problems. The idea of Least Significant Bit based image steganography and data hiding technique is utilized to come up with an image-based authentication to make it easier for the users to maintain their password. The proposed technique also resolve the burden of memorizing every password for all accounts and avoid “Shoulder surfing” by hiding the password in the cover image. Although there is a rising number of innovative ways to authenticate users and text-based passwords are still popular and generally used method, the proposed technique aims to improve existing authentication and address the drawbacks of text-based authentication. The use of randomized embedding, image partitioning, and columnar transposition help increase the complexity of the extraction of the password embedded in the cover image. At the same time, the use of the one-time pad Encryption added a layer of security in keeping the password. In this way, the password is kept secure and confidential. A series of testing was conducted to verify the outcome of the proposed technique. The Peak-to-Signal-Noise-ratio (PSNR) and Similarity Index (SSIM) average result is 72.5688 and 0.9999, respectively. The result proves that the generated image password has high imperceptibility. While the probability of detection of image password using statistical attacks is low since the majority of the stego images attained an acceptable degree with RS Analysis average result of 0.0627, Chi-Square Analysis average result of 0.174203882 and Sample Pair Analysis average result of 0.06229.
Napping Guard: Deanonymizing Tor Hidden Service in a Stealthy Way

In this paper, we propose the Napping Guard attack which can deanonymize hidden services in a stealthy way. The key insight of our method is utilizing a design flaw of hidden service's requests to build a simplex covert channel, which can send message from a malicious guard relay to the collusive Client-OP. With the help of this covert channel, the guard relay delivers the actual IP address of the hidden service to the collusive Client-OP. Considering the Client-OP knows the onion address of hidden service, the adversary is able to deanonymize the hidden service through correlating the actual IP address and onion address on Client-OP. In particular, compared with previous attacks, our covert channel utilizes latency signal instead of traffic signal, and eliminates the dependency of malicious Rend-Point, so as to achieve a better concealment and lower cost. Our experiment shows that the covert channel is reliable that has the precision and recall about 99.35% and 99.19%. In addition, we also propose a mitigation of Napping Guard attack, and report the design flaw to the Tor project.
Approximated Dynamic Trait Models for Heterogeneous Multi-Robot Teams

To realize effective heterogeneous multi-agent teams, we must be able to leverage individual agents' relative strengths. Recent work has addressed this challenge by introducing trait-based task assignment approaches that exploit the agents' relative advantages. These approaches, however, assume that the agents' traits remain static. Indeed, in real-world scenarios, traits are likely to vary as agents execute tasks. In this paper, we present a transformation-based modeling framework to bridge the gap between state-of-the-art task assignment algorithms and the reality of dynamic traits. We define a transformation as a function that approximates dynamic traits with static traits based on a specific statistical measure. We define different candidate transformations, investigate their effects on different dynamic trait models, and the resulting task performance. Further, we propose a variance-based transformation as a general solution that approximates a variety of dynamic models, eliminating the need for hand specification. Finally, we demonstrate the benefits of reasoning about dynamic traits both in simulation and in a physical experiment involving the game of capture-the-flag.
Applying text mining methods to extracting information from news articles

Abstract In the present paper, the text mining methods from the point of view of their application for extracting information from news articles, are discussed. For this purpose, research questions are formulated and opportunities to assist in obtaining their respective answers are presented, as they are illustrated by the application of text mining methods on a created dataset with articles in Bulgarian. The extracted information could be useful for research of the topics covered in the articles, exploration of customer interests, etc.
Multiple Backup Resource Allocation with Workload-Dependent Failure Probability

This paper proposes a multiple backup resource allocation model with a workload-dependent failure probability to minimize the maximum expected unavailable time (MEUT) under a protection priority policy. The workload-dependent failure probability is a monotonically increasing function which reveals the relationship between computing workload and failure probability. The proposed model adopts hot backup and cold backup strategies to provide protection. The cold backup strategy is a protection strategy, in which the requested loads of backup resources are not processed as active workloads before failures occur to reduce resource utilization with the cost of long recover time. The hot backup strategy is a protection strategy, in which the backup resources execute at the same time with functions to recover promptly with the cost of high workload. For protection of each function with multiple backup resources, it is required to adopt a suitable priority policy to determine the expected unavailable time. We analyze the superiority of the protection priority policy for multiple backup resources in the proposed model and provide the theorems that clarify the influence of policies on MEUT. The numerical results show that the proposed model reduces MEUT compared with the single backup model in which each function is protected by only one server without protection priority of servers. The priority policy adopted in the proposed model specifying that the server which adopts the hot backup strategy has higher priority than that with the cold backup strategy for multiple backup resources suppresses MEUT compared with other priority policies.
Contact Tracing Apps for COVID-19: Access Permission and User Adoption

Contact tracing apps are powerful software tools that can help to control the spread of COVID-19. In this article, we evaluated 53 COVID-19 contact tracing apps found on the Google Play Store in terms of their usage, rating, access permission, and user privacy. For each app we identified the country of origin, number of downloads, and access permissions to further understand other attributes and ratings of these apps. Our results showed that contact tracing apps had low overall ratings and nearly 40% of them were requesting “dangerous access permission” including access to storage, media files, and camera permissions. We discovered that user adoption rates were inversely correlated to access permission requirements. To the best of our knowledge, this article summarizes the most extensive collection of contact tracing apps for COVID-19. We recommend that future contact tracing apps be more transparent in permission requirements and justify permission requests to preserve the app users' privacy.
Domain Adaptation for Part-of-Speech Tagging of Indonesian Text Using Affix Information

Part-of-speech tagging is a process to apply word class of a word in texts. POS Tagger for specific language is usually built with generic domain corpus, for example using text from newspaper. If this POS Tagger tested against word from new domain or another specific domain, then the POS Tagger can possibly word class inaccurately. Solving specific domain adaptation can be done by using several methods, using clustering to change word representation or using model with big number of lexicon and using labelled texts from specific domain for training the model. In this research we apply domain adaptation method by using additional lexicon that built based on affix rule. Specific domain used is beauty product domain. Component for this system is a POS Tagger with generic domain and unlabeled lexicon from target domain. Word class in target domain lexicon applied based on affix information and the remains labelled manually. Based on observation to the dataset, words in English was often to be used, so the lexicon developed in Indonesian and English. The processed lexicon added in lexicon from original POS Tagger to give specific domain information to the POS Tagger with generic domain. The POS tags focused in this study are noun, proper noun, adjective and adverb because results from this POS Tagger are used for aspect and opinion extraction. Tagger with added lexicon achieve 68.99% accuracy and the percentage of words that are successfully recognized by tagger is 92.36%.
Mineral control on the geochemistry of the Rock Canyon Creek REE-F-Ba deposit, British Columbia, Canada

The Rock Canyon Creek carbonate-hosted REE-F-Ba deposit has tectonic, stratigraphic and structural similarities with Mississippi Valley-type and sparry magnesite deposits in the SE Rocky Mountains. The main REE-fluorite zone is a steeply dipping body, extending 1100 m along-strike, 50 m wide and 100 m deep. It spatially coincides with pre-existing crackle breccias in carbonate rocks, and consists of dolomite, fluorite, barite, pyrite, quartz, K-feldspar, calcite, porous apatite, REE-fluorocarbonates and REE-phosphates. The main fluorocarbonates are bastnaesite, parisite and synchysite. Monazite, crandallite group minerals and apatite are the main phosphates. Fluorite content varies from less than 1 to 13.5% (by weight) and ∑REE + Y concentrations vary from trace to 1.95% (by weight). The mineralized zone is heterogeneous on the deposit scale, as indicated by three-dimensional geochemical modelling combined with a geochemical assessment based on 89 mineralized samples and detailed downhole mineral and geochemical profiles of a key borehole. Chemical heterogeneity and key elemental co-variations are explained by strong mineralogical control and have implications for the design of exploration and development programmes for this type of deposit. The chondrite-normalized REE pattern of samples from the mineralized zone shows enrichment in LREE, similar to typical carbonatite-related mineralization; however, no carbonatite is exposed nearby.
Low bit‐based convolutional neural network for one‐class object detection

Low-performance systems such as mobile and embedded devices require an efficient deep neural network for object detection. In this letter, we propose a very efficient network made by both quantisation and model compression for detecting one class. First, our proposed network uses 1-bit weights to reduce the kernel parameter size and 8-bit activations to increase the speed. Second, we optimise the model size and computational power by compressing the maximum number of channels of the network. Therefore, compared to Darknet19, our proposed network infers 35 times faster on the CPU and saves over 7000 times memory. For fair evaluations, we built one-class object detection databases to detect subtitles of various videos and a specific class from the Pascal VOC database. We verify, compared to Darknet19 and Tiny you only look once (YOLO), that the proposed optimised network does not degrade in object detection accuracy with the efficient and applicable parameter sizes and computational complexity.
A Novel Support Vector Machine Based Radar Individual Recognition Algorithm Under Inconsistent Noise Condition

In modern electronic warfare, the complexity of electromagnetic environment makes it more and more difficult to identify the emitter. Because of the uncertainty of battlefield situation, the signal-to-noise ratio (SNR) of radar signal is different, which affects the accuracy of emitter identification process. Normally, the amount of the received emitter signal may be small, so the selection of classifier has a great impact on the recognition result. Traditional support vector machine (SVM) has high classification accuracy and strong generalization ability for small sample data, which can solve this problem well. Considering the different contribution of features to individual recognition, we propose an improved SVM algorithm with weight optimization, and apply it to the problem of individual recognition of emitter under the condition of different SNR. The correctness and validity of the algorithm are verified by simulation experiments.
Security and Performance of Elliptic Curve Cryptography in Resource-limited Environments: A Comparative Study

Nowadays, the usage of constrained-resources devices like sensors and IoT devices are increasing rapidly. These devices often deal with sensitive data and simultaneously have limited storage and low processing capabilities. That means achieving protocols with high levels of security can be relatively difficult in these limited environments, as we find ourselves faced with a trade-off between performance and security. Therefore, the ultimate goal is to find a cryptographic scheme which achieve good levels of security and consume little resources. This study seeks to analyze the performance and security of Elliptic Curve Cryptography in resource-limited environments by comparing ECC to other public key cryptographic schemes over key pair generation, key exchange and digital signature primitives. The comparison results of this paper promote using ECC in resource-limited environments and show the significant differences between the time and memory requirements of ECC comparing to other public key schemes especially when the security requirements increase.
A Novel Algorithm of Numbers to Text Conversion for Uzbek Language TTS Synthesizer

Numbers and counting have become an essential element of our daily living, particularly when we talk about the electronic computer. This article explains specific methods determining the Uzbek number rule and describes a computational technique able to transform decimal numbers to their equal Uzbek number names. First, we analyzed the scientific and morphological foundation of the Uzbek numbers to formalize its syntactic and arithmetic techniques. Second, a new algorithm was developed to convert numbers in the decimal number system into Uzbek words. Finally, a software tool was developed based on the proposed algorithm. This software tool is part of the software for translating Uzbek texts into speech.
Resource-aware On-device Deep Learning for Supermarket Hazard Detection

Supermarkets need to implement safety measures to create a safe environment for shoppers and employees. Many of these injuries, such as falls, are caused by a lack of safety precautions. Such incidents are preventable by timely detection of hazardous conditions such as undesirable objects on supermarket floors. In this paper, we describe EdgeLite, a new lightweight deep learning model specifically designed for local and fast inference on edge devices which have limited memory and compute power. We show how EdgeLite was deployed on three different edge devices for detecting hazards in images of supermarket floors. On our dataset of supermarket floor hazards, EdgeLite outperformed six state-of-the-art object detection models in terms of accuracy when deployed on the three small devices. Our experiments also showed that energy consumption, memory usage, and inference time of EdgeLite were comparable to that of the baseline models. Based on our experiments, we provide recommendations to practitioners for overcoming resource limitations and execution bottlenecks when deploying deep learning models in settings involving resource-constrained hardware.