Reviewerly/exHarmony huggingface dataset:
An end-to-end face recognition method with alignment learning

Many effective methods have been proposed for face recognition in the past decade and the face recognition accuracy is also gradually improved, but these algorithms usually need to perform face alignment process based on the prior knowledge of facial structure before extracting facial features. The face recognition system usually consists of face detection, face alignment, facial feature extraction, etc., which are independent of each other, and it is difficult to design and train the end-to-end face recognition model. In this paper, an end-to-end face recognition method based on spatial transformation layer is proposed. Specifically, the spatial transformation layer is placed in front of the feature extraction layer of the face recognition network, and the face region is aligned by alignment learning which requires neither prior knowledge nor artificially defined geometric transformation. The face identity category information allows the convolutional neural network to automatically learn the most appropriate face alignment. Simulation experiments on CASIA-WebFace, LFW (Labeled Face in the Wild) and YTF (Youtube Face) face database have shown that the suggested alignment learning algorithm in this paper can realize the end-to-end face recognition and can effectively improve the face recognition rate as well.
Self-Calibrated Absolute Thickness Measurement of Opaque Specimen Based on Differential White Light Interferometry

In this article, we present a self-calibrated absolute thickness measurement method for opaque specimens based on fiber-optic white light interferometry. A new type of self-reference probe, which is composed of a fiber ferrule, an integrated beam splitter, and a gradient index lens, is presented. In our setup, two probes of this kind are fixed facing each other, and the distance between them can be measured. Then we insert a specimen between the probes and measure the distance between the specimen surfaces and probes on both sides. This way, a self-calibrated measurement of specimen thickness can be realized without the traditional calibration process using a standard thickness sample. Thus, the uncertainty component introduced by the standard thickness sample can be eliminated. The performance of the proposed method is evaluated by measuring standard thickness samples calibrated by the National Institute of Metrology of China, and the results show a good agreement with the nominal values. Also, we analyzed several key uncertainty components, as well as the reliability of our method. The calculated extended measurement uncertainty (k = 2) is 0.06 μm for Cr specimen with a nominal thickness of 10.56, and 0.08 μm for Ni specimen with a nominal thickness of 31.46 μm.
RIVA: Robust Integrity Verification Algorithm for High-Speed File Transfers

End-to-end integrity verification is designed to protect file transfers against silent data corruption by comparing checksum of files at source and destination end points using cryptographic hash functions such as MD5 and SHA1. However, existing implementations of end-to-end integrity verification for file transfers fall short to detect undetected disk errors that causes inconsistency between disk and cache memory. In this article, we propose Robust Integrity Verification Algorithm (RIVA) to strengthen the integrity of file transfers by forcing checksum computation tasks to read files directly from disk. RIVA achieves this by invalidating memory mappings of file pages after their transfer such that when the file is read again for checksum calculation, it will be fetched from disk and silent disk errors will be captured. We design and conduct extensive fault resilience experiments to evaluate the robustness of integrity verification algorithms against undetected disk write errors. The results indicate that while the state-of-the-art integrity verification algorithms fail to detect the injected errors for almost all file sizes, RIVA captures all of them with the help of cache invalidation. We further run statistical analysis to assess the probability of missing silent disk errors and find that RIVA reduces the likelihood by 10 to 15 orders of magnitude compared to the existing approaches. Finally, enforcing disk read in integrity verification introduces an inevitable overhead in exchange of increased robustness against silent disk errors, but RIVA keeps its overhead below 15 percent in most cases by running transfer, cache invalidation, and checksum computation processes concurrently for different portions of the same file.
Anchoring ocean literacy: participatory iBook design within secondary science classrooms

This article elucidates a participatory design process in which a prototype iBook was embedded within the context of secondary science classrooms through multiple interventions. The fundamental design intention of the iBook is to increase students’ ocean literacy in a curriculum that lacks marine science. Two e-learning ecologies were identified across two schools; an iPad school and non-iPad school. Participants include two second-year science classes (14–15-year-olds) and their respective teachers. Science teachers were asked to implement the iBook, based on their appraisal, as an accompaniment to their lessons throughout three interventions. Qualitative data took the form of teacher interviews, teacher reflections and student exit cards. Findings illuminate emergent design principles of iBooks that enthuse students with content; mainly to draw upon their personal interest corresponding to interactive technologies. The benefits of participatory design research are highlighted with reference to augmenting student ocean literacy in the science classroom.
Competitive Selfish Mining

Bitcoin mining is the process of generating new blocks in Bitcoin blockchain. This process is vulnerable to different types of attacks. One of the most famous attacks in this category is Selfish Mining, introduced by Eyal and Sirer [1] in 2014. This attack is essentially a strategy that a sufficiently powerful mining pool can follow to obtain more revenue than its fair share. This is a tempting attack to deploy because every pool is trying to increase its revenue and this is an excellent opportunity. However, in most of the works to date, the effect of only one selfish pool has been studied. This is an attempt to analyze selfish mining in a competitive environment, where there are more than one selfish miners in play. To do so, we created a Bitcoin network simulator and used it to simulate different configurations of miners to be able to address this problem. In short, our finding shows that in almost all of the configurations, with the presence of a more powerful selfish miner, selfish mining actually decreases the revenue of the weaker selfish miners and also helps the stronger selfish miner.
Fluid intake recognition using inertial sensors

As one of many uses of body-worn inertial sensors, health monitoring applications can have a significant impact on the quality of life for a user even with inexpensive consumer electronics. In this paper, we address fluid intake monitoring as an activity recognition problem and conduct a user-study with 41 participants. We show that while an approach with a wrist-mounted sensor outperforms an approach with a head-mounted sensor, they can both be considered viable options for such a system. Furthermore we compare the classification performance of a hybrid CNN-LSTM artificial neural network with simpler baseline classifiers.
Design of Trust Blockchain Consensus Protocol Based on Node Role Classification

The blockchain technology has broad application prospects in the financial field, public welfare industry, and Internet with its characteristics of decentralization, openness, autonomy, information unchangeability and anonymity. The most prominent problem it faces is the difficulty in reaching a credible consensus quickly. Based on the consensus mechanism of DPoS and PBFT, a new trust blockchain consensus protocol is proposed. This protocol adds trust value attributes to nodes, dynamically classifies node roles, tracks node behaviors in transactions, and scores trust values for the nodes. The node promotion and demotion mechanism is introduced and the roles of nodes are reclassified according to their latest trust values. Results show that the proposed consensus protocol has high efficiency, low consumption, high fault tolerance and dynamic adaptability, which can meet the needs of most alliance chains.
Dependence of Dynamics of Multi-robot System on Control Architecture

Nowadays multi-robot systems on the way to find widespread usage in industry, agriculture and other fields of human activity. Currently, there is multiple control systems architecture for multi-robot systems. Researches in which was carried out the mathematical description of robots group control systems by analogy with the electromechanical systems are known. E.g., analogies with the motion of charged particles in an electric potential field containing target points, and obstacles to avoid. In this paper, this approach has been developed and generalized. The problems of distributed, centralized and hybrid control of multi-robot systems in the presence of previously unknown environmental factors and changing operating conditions are considered. The main goal of the work is the development of an adaptive control system for a group of robots performing the assigned task regardless of external factors affecting the behavior of an individual robot. Unlike previously published works, a network model with a dynamic structure represents a mathematical model of the distributed control system of the robot group. The control system of an individual robot, including a number of subsystems (information processing, navigation, platform control, the user interface) described as a local network of finite-state machines. Algorithms for control the system as a whole, as well as algorithms for the behavior of each individual robot represented. The dynamic organization of clusters within the group control system, including situations with quantitative composition changing of the robot group, and with the occurrence of unknown obstacles in the working environment are developed. The results of the research can find application in solving various tasks of mobile robot group control, including UAVs in the tasks of exploration and monitoring of the environment; robots for agricultural purposes in the performance of a wide range of fieldwork; in the performance of extreme operations, including rescue operations, taken out after accidents and disasters.
Data Confidentiality and Information Credibility in On-line Ecosystems

Recent ICTs paradigms such as cloud computing, data outsourcing, digital data markets, and the spread of multiple social media based on Web 2.0 technologies, facilitate the exchange of large data and information flows among a myriad of interconnected devices and users, for different aims and purposes. This complex scenario underlies the development of on-line ecosystems of interacting entities, where the concepts of community, self-organization, evolution and knowledge are fundamental.
Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing

Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.
ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction

Abstract Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster‐based, and graph‐based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall‐oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n‐gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.
An empirical investigation of remittances and financial inclusion nexus in Sub-Saharan Africa

The scope of financial development has been expanding and moving gradually towards a more inclusive development thus attention is gradually shifting to financial inclusion. It is against this background that this study investigates the role of migrants’ remittances on financial inclusion in selected SSA countries. Pooled Mean Group (PMG) form of panel ARDL was employed but cross-sectional dependent characteristics of the data required the use of cross-sectional methods that cater for such properties. Consequently, XTDCCE: Dynamic Common Correlated Effects and XTCCE Common Correlated Effects estimator were used as robustness checks. For the purpose of this empirical investigation, we collected data on Remittances, Account Ownership and Income Per Capita for 27 SSA countries based on data availability. The conclusion is that remittances have no significant effect on financial inclusion in SSA. However, the variable demonstrates the potential to positively influence financial inclusion. Thus, there should be concrete policy efforts in the SSA to make remittances count for inclusive growth. The study contributes substantially by moving attention from a broad concept of financial development to Financial Inclusion and employed a more recent panel estimating techniques to investigate the nexus between remittances and Inclusion.
Closing the Loop between knowledge patterns in cognition and the Semantic Web

We discuss currently open issues in the discovery and representation of knowledge patterns in computational processing of meaning, in order to improve interoperability and cognitive validity of web-based semantics. We present the current state of knowledge patterns (KP) in Knowledge Representation, the Semantic Web and Cognitive Sciences, focusing on an intensional abstraction of heterogeneous predicates as a formal foundation for KP.
Energy-efficient face detection and recognition scheme for wireless visual sensor networks

Energy-efficient and robust face detection and recognition scheme can be useful for many application fields such as security and surveillance in multimedia and visual sensor network (VSN). VSN consists of wireless resources-constrained nodes that are equipped with low-energy CMOS cameras for monitoring. On the one hand, captured images are meaningful multimedia-data that impose high energy consumption to be processed and transmitted. On the other hand, visual sensor (VS) is a battery-powered node with limited life-time. This situation leads to a trade-off between detection-accuracy and power-consumption. This trade-off is considered as the most major challenge for applications using multimedia data in wireless environments such as VSN. For optimizing this trade-off, a novel face detection and recognition scheme has been proposed in this paper based on VSN. In this scheme, detection phase is performed at VS and recognition phase is accomplished at the base station (sink). The contributions of this paper are in three folds: 1. Fast and energy-aware face-detection algorithm is proposed based on omitting non-human blobs and feature-based face detection in the considered human-blobs. 2. A novel energy-aware and secure algorithm for extracting light-weight discriminative vector of detected face-sequence to be sent to sink with low transmission-cost and high security level. 3. An efficient face recognition algorithm has been performed on the received vectors at the sink. The performance of our proposed scheme has been evaluated in terms of energy-consumption, detection and recognition accuracy. Experimental results, performed on standard datasets (FERET, Yale and CDnet) and on personal datasets, demonstrate the superiority of our scheme over the recent state-of-the-art methods.
Survey on Ontology-Based Sentiment Analysis of Customer Reviews for Products and Services

In recent years, the use of Internet and World Wide Web increases the research on sentiment analysis. Few years ago, whenever customer wanted to buy any product or to use any services, he/she is used to take the review from those who are already using that products or services. In recent years, various e-commerce sites are available from where one can buy the products and get the customer reviews of various products and services. Ontology plays an important role to analyze the product reviews based on the semantics of the reviews. The people living around the world are not familiar with the English language. Various e-commerce sites are available on the Internet in different languages. The customers write the reviews of the products or services they purchased/used in their own local languages in the e-commerce sites. The main aim of this paper is to conduct the survey on the sentiment analysis of customer reviews present in different languages using ontology. Also mention the problems which exist in those languages to analyze the products and services' reviews. An experiment is performed on the sentiment analysis of Hindi online customer reviews of the mobile phones using ontology which tries to minimize the challenges occurred in the earlier systems.
Pre-processing for single image dehazing

Existing single image haze removal algorithms could suffer from noise amplification in sky regions and possible color distortion in restored images due to noise in haze images. In this paper, a simple pre-processing tool is introduced for single image haze removal so as to reduce the effect of noise in the restored image. The input image is first decomposed into base and detail layers by using a weighted guided image filter (WGIF). The airlight and transmission map are estimated from the base layer. In order to restore the objects close to the camera well, the decomposition of haze image is adaptive to the value of the transmission map. If the transmission map of a pixel is small, it is decomposed into two layers, otherwise, not decomposed. Since the noise is included in the detail layer, the base layer is amplified in the final image if the haze image is decomposed. Experiments show that the proposed pre-processing tool can indeed be applied to improve the state-of-the-art haze removal algorithms.
Novel Defense Method against Audio Adversarial Example for Speech-to-Text Transcription Neural Networks

With the developments in deep learning, the security of neural networks against vulnerabilities has become one of the most urgent research topics in deep learning. There are many types of security countermeasures. Adversarial examples and their defense methods, in particular, have been well-studied in recent years. An adversarial example is designed to make neural networks misclassify or produce inaccurate output. Audio adversarial examples are a type of adversarial example where the main target of attack is a speech-to-text transcription neural network. In this study, we propose a new defense method against audio adversarial examples for the speech-to-text transcription neural networks. It is difficult to determine whether an input waveform data representing the sound of voice is an audio adversarial example. Therefore, the main framework of the proposed defense method is based on a sandbox approach. To evaluate the proposed defense method, we used actual audio adversarial examples that were created on Deep Speech, which is a speech-to-text transcription neural network. We confirmed that our defense method can identify audio adversarial examples to protect speech-to-text systems.
Collocations and near-native competence: Lexical strategies of heritage speakers of Russian

This paper presents an exploratory study on the use of frequency-based probabilistic word combinations in Heritage Russian. The data used in the study are drawn from three small corpora of narratives, representing the language of Russian heritage speakers from three different dominant-language backgrounds, namely German, Finnish, and American English. The elicited narratives are based on video clips that the participants saw before the recording. Since the current study is based on a relatively small corpus, we conducted a manual corpus-based analysis of the heritage corpora and an automated analysis of the baseline (monolingual) corpus to investigate the differences between the heritage and monolingual language varieties. We hypothesize that heritage speakers deploy fewer probabilistic strategies in language production compared with native speakers and that their active knowledge of and access to ready-to-use multiword units are restricted compared with native speakers. When they cannot access a single lexical item or a collocation, heritage speakers are able to tap both into the resources of the dominant language and the resources of their home language. The connection to the dominant language results in transfer-based non-standard word combinations; when heritage speakers tap into the resources of their home language, they produce unattested in the monolingual variety, “heritage” collocations, many of which are nevertheless grammatically legitimate.
Multi‐sensor Poisson multi‐Bernoulli filter based on partitioned measurements

The single-sensor Poisson multi-Bernoulli (MB) mixture (PMBM) filter has been developed for multi-target tracking (MTT). However, there is a lack of research on the multi-sensor (MS) extensions of this filter. Because the conjugate density of PMBM filter is a hybrid form, which makes it difficult to extend directly using existing methods. In this study, a general MS Poisson MB filter based on an MS measurement likelihood is derived for MS-MTT. The MB mixture in the PMBM conjugate posterior is approximated as a single MB after each measurement update step. The likelihood function is designed for the partitioned measurements. Firstly, the authors employ the greedy measurement partition algorithm to derive an efficient implementation method; a Gibbs sampler is used to solve the data association problem subsequently. Secondly, they design a novel partition mechanism based on the Gibbs sampling algorithm dealing with those measurements generated by close targets. Various performance simulation and analysis are given in Sections 5 and 6, respectively.