Reviewerly/exHarmony huggingface dataset:
Geometrically Mappable Image Features

Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning. Features that uniquely describe the visual contents of images have a wide range of applications, including image retrieval and understanding. In this work, we propose a method that learns image features targeted for image-retrieval-based localization. Retrieval-based localization has several benefits, such as easy maintenance and quick computation. However, the state-of-the-art features only provide visual similarity scores which do not explicitly reveal the geometric distance between query and retrieved images. Knowing this distance is highly desirable for accurate localization, especially when the reference images are sparsely distributed in the scene. Therefore, we propose a novel loss function for learning image features which are both visually representative and geometrically relatable. This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional. In our experiments we show that our features not only offer significantly better localization accuracy, but also allow to estimate the trajectory of a query sequence in absence of the reference images.
IoT System Accomplishment using BlockChain in Validating and Data Security with Cloud

In a block channel IoT system, sensitive details can be leaked by means of the proof of work or address check, as data or application Validation data is applied on the blockchain. In this, the zero-knowledge evidence is applied to a smart metering system to show how to improve the anonymity of the blockchain for privacy safety without disclosing information as a public key. Within this article, a blockchain has been implemented to deter security risks such as data counterfeiting by utilizing intelligent meters. Zero-Knowledge Proof, an anonymity blockchain technology, has been implemented through block inquiry to prevent threats to security like personal information infringement. It was suggested that intelligent contracts would be used to avoid falsification of intelligent meter data and abuse of personal details.
An Ensemble of Ensembles Approach to Author Attribution for Internet Relay Chat Forensics

With the advances in Internet technologies and services, social media has been gained extreme popularity, especially because these technologies provide potential anonymity, which in turn harbors hacker discussion forums, underground markets, dark web, and so on. Internet relay chat (IRC) is a real-time communication protocol actively used by cybercriminals for hacking, cracking, and carding. Hence, it is particularly urgent to identify the authors of threat messages and malicious activities in IRC. Unfortunately, author identification studies in IRC remain as an underexplored area. In this research, we perform novel IRC text feature extraction methods and propose the first author attribution version of the deep forest (DF) model that is an ensemble of ensembles that utilizes the fusion of ensemble learning techniques. Our approach is supported by autonomic IRC monitoring. Experiments show that our approach is highly effective for author attribution and attains high accuracy even when the number of candidates is large while training data is limited.
WITHDRAWN: Investigating the progress of human e-healthcare systems with understanding the necessity of using emerging blockchain technology

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
How governance structure, terrorism, and internationalisation affect innovation? Evidence from Pakistan

The existing studies on innovation extensively focused on such economic factors as foreign direct investment (FDI) inflows, trade openness, and R&D investments. Contrastingly, non-economic factors have received less research attention in the innovation literature. Lousy governance may lead to political and institutional ineffectiveness. Widespread corruption weakens the accountability practices and rule of law and may consequently hamper innovation. Similarly, terrorism deters flows of investment, increases unproductive costs, causes brain drains, decreases R&D investment and entrepreneurial activities and finally inhibits rates of innovation. This study empirically investigates the impacts of governance structure, terrorism, FDI inflows, and trade openness upon innovation in a laggard country: Pakistan. The findings via the ARDL co-integration approach show that lousy governance, terrorism and FDI inflows have adverse impacts on innovation. Trade openness positively influences innovation, but only in the long run. The results across several robust checks remain the same. Policy implications are also discussed.
Synthesis of Parallel Synchronous Software

In typical embedded applications, the precise execution time of the program does not matter, and it is sufficient to meet a real-time deadline. However, modern applications in information security have become much more time-sensitive, due to the risk of timing side-channel leakage. The timing of such programs needs to be data-independent and precise. We describe a parallel synchronous software model, which executes as N parallel threads on a processor with word-length N. Each thread is a single-bit synchronous machine with precise, contention-free timing, while each of the N threads still executes as an independent machine. The resulting software supports fine-grained parallel execution. In contrast to earlier work to obtain precise and repeatable timing in software, our solution does not require modifications to the processor architecture nor specialized instruction scheduling techniques. In addition, all threads run in parallel and without contention, which eliminates the problem of thread scheduling. We use hardware (HDL) semantics to describe a thread as a single-bit synchronous machine. Using logic synthesis and code generation, we derive a parallel synchronous implementation of this design. We illustrate the synchronous parallel programming model with practical examples from cryptography and other applications with precise timing requirements.
Computation Resource Allocation in Mobile Blockchain-enabled Edge Computing Networks

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.
Dynamic Hand Gesture Recognition Using Temporal-Stream Convolutional Neural Networks

Movement recognition is a hot issue in machine learning. The gesture recognition is related to video processing, which gives problems in various aspects. Some of them are separating the image against the background firmly. This problem has consequences when there are incredibly different settings from the training data. The next challenge is the number of images processed at a time that forms motion. Previous studies have conducted experiments on the Deep Convolutional Neural Network architecture to detect actions on sequential model balancing each other on frames and motion between frames. The challenge of identifying objects in a temporal video image is the number of parameters needed to do a simple video classification so that the estimated motion of the object in each picture frame is needed. This paper proposed the classification of hand movement patterns with the Single Stream Temporal Convolutional Neural Networks approach. This model was robust against extreme non-training data, giving an accuracy of up to 81,7%. The model used a 50 layers ResNet architecture with recorded video training.
Accounting for Effects of Variation in Luminance in Pupillometry for Field Measurements of Cognitive Workload

Eye-tracking is now above and beyond the sole measurement of visual attention. Amongst the multiple measures it provides, some have been explored as a measure of cognitive workload (CW). One such measure is pupil diameter. Although the relationship between pupil size and CW has been extensively documented, pupil diameter is primarily impacted by luminance variations while the cognitive workload has a relatively minor influence. Therefore luminance variations have to be accounted for, either in the experimental design or in the data processing to avoid the masking of the CW effects. This has meant that the use of pupillometry for the measurement of anything but the pupillary light response, has been restricted to highly controlled lighting conditions in a laboratory. This study proposes a new method that uses point of view (POV) video in conjunction with a luminance measurement sensor to dynamically estimate the luminance of the visual stimuli. As currently available off the shelf eye trackers are usually not equipped to record luminance variations, a luminance sensor was added to a commercial eye tracker. Eye-tracking gaze data, POV video recording of the operator/observer and a head-mounted (POV) luminance sensor together estimate the expected pupil diameter. This estimate over time is due to sole influence of luminance variations. This expected pupil diameter is used as baseline for the cognitive workload. The method was validated in laboratory conditions with controlled visual stimuli. The method reliably measures induced cognitive workload despite luminance variation.
Sentiment Analysis Using Ensemble-Hybrid Model with Hypernym Based Feature Engineering

Sentimental analysis helps in interpretation and classification of textual data using various techniques applied in deep learning models. The processing of movie reviews given by users is enhanced using feature engineering by adding hypernyms to the data to enhance the semantic meaning and establish semantic relationships between words of the reviews. This would help the system to identify the context used by the user in his review as positive or negative. This paper implements a hybrid Bi-LSTM-ANN model to match the spatial aspect of hypernym features with the temporal aspects of the reviews. The Bi-LSTM part models the semantic meaning of the reviews and the ANN part improves the efficiency of the model by adding the hypernyms of words in each review. The model determines the positive or negative sentiment of movie reviews with an accuracy of 83.16%. The paper also determines the rating of movie reviews on a scale of 10 with an accuracy of 41.03%.
The hybrid recommendation of digital educational resources in a distance learning environment

The accompaniment and the follow-up of the learners in an online training aim at helping the learner to carry out his or her training and to guarantee an adapted and quality learning. During a learning process, personalized search and recommendation of digital educational resources form aspects of this accompaniment. This article presents a search engine and a hybrid recommendation of digital educational resources. This engine allows for filtering and personalized search by providing adapted resources to the users' profiles on the one hand; on the other hand, to making a combination of the collaborative, the content-based and the semantic filtering to propose other additional resources. The semantic filtering is based on the exploitation of SPARQL queries from the system that we propose. They are executed on a remote server containing reusable vocabularies and formalized according to the linked data principles and technologies, such as the Lod Cloud. The result obtained is a set of linked terms to the keywords specified in the search query. These terms are then used to extend the search. We used a search test-set by keywords entered via a form and then we manually analyzed the linked terms obtained and the documents returned. The results obtained by our approach are satisfactory.
Process estimation in qubit systems: a quantum decision theory approach

We address quantum decision theory as a convenient framework to analyze process discrimination and estimation in qubit systems. In particular we discuss the following problems: i) how to discriminate whether or not a given unitary perturbation has been applied to a qubit system; ii) how to determine the amplitude of the minimum detectable perturbation. In order to solve the first problem, we exploit the so-called Bayes strategy, and look for the optimal measurement to discriminate, with minimum error probability, whether or not the unitary transformation has been applied to a given signal. Concerning the second problem, the strategy of Neyman and Pearson is used to determine the ultimate bound posed by quantum mechanics to the minimum detectable amplitude of the qubit transformation. We consider both pure and mixed initial preparations of the qubit, and solve the corresponding binary decision problems. We also analyze the use of entangled qubits in the estimation protocol and found that entanglement, in general, improves stability rather than precision. Finally, we take into account the possible occurrence of different kinds of background noise and evaluate the corresponding effects on the discrimination strategies.
Mining large‐scale high utility patterns in vehicular ad hoc network environments

Abstract One well‐known type of mobile ad hoc network is known as a vehicular ad hoc network (VANET). The functions of such a network are integrated into a new generation of wireless networks for vehicles, which has established a robust self‐organizing network that exists between roadside units and mobile vehicles. In this article, we research the comfort applications in VANET with a new proposed algorithm, EHUM, short form for efficient high utility itemset mining, to mine patterns of the more popular Points of Interest (POIs). This algorithm is based on the traditional high‐utility itemset mining (HUIM) algorithm, and we propose a more reasonable pruning strategy. Concurrently, for solving the problem of excessive data volume in VANET, we applied this algorithm to the MapReduce architecture used for improving the feasibility in practical applications. Our in‐depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well to mine the POIs pattern in a big data data set and shows great performance in a Hadoop computing cluster.
A personal learning environment (PLE) approach to mobile teaching and learning on a short-term study abroad

This Scholarship of Teaching and Learning (SoTL) research examines the effectiveness of a personal learning environment (PLE) approach in using mobile technology for experiential learning during a study abroad to China. Thematic qualitative analysis of 21 undergraduate students' written post-trip reflections on their experiences revealed valuable benefits and challenges of the PLE utilizing students' personal mobile devices in real time. Recommendations are offered for program leaders, educators, and trainers with a vested interest in learners' engagement and experiential learning. Sharing these findings with the teaching community at large makes unique and significant contributions to the SoTL literature.
Learned Model Composition With Critical Sample Look-Ahead for Semi-Supervised Learning on Small Sets of Labeled Samples

In this work, we propose to push the performance limit of semi-supervised learning on very small sets of labeled samples by developing a new method called learned model composition with critical sample look-ahead (LMCS). Training efficient deep neural networks on much smaller sets of labeled samples is a challenging problem. With a small labeled set, the initial network suffers from low accuracy. Based on this error-prone network, the subsequent semi-supervised learning process will be fragile and unstable. To address this issue, we propose to introduce a look-ahead master model to identify the correct direction of model evolution to effectively guide the semi-supervised learning process of the student model. Specifically, our proposed LMCS method explores two major ideas. First, it introduces a new learned model composition structure so that we can compose a more efficient master network from student models of past iterations through a network learning process. Second, we develop a new method, called confined maximum entropy search, to discover new critical samples near the model decision boundary and provide the master model with look-ahead access to these samples to enhance its guidance capability. Our extensive experimental results demonstrate that the proposed LMCS method outperforms the state-of-the-art semi-supervised learning methods, especially on small sets of labeled samples. For example, on the CIFAR-10 dataset, with a very small set of 80 labeled samples, our method outperforms Google's MixMatch method, reducing the error rate by more than 10%.
FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection

We need robots more aware of the unintended outcomes of their actions for ensuring safety. This can be achieved by an onboard failure detection system to monitor and detect such cases. Onboard failure detection is challenging with a limited set of onboard sensor setup due to the limitations of sensing capabilities of each sensor. To alleviate these challenges, we propose FINO-Net, a novel multimodal sensor fusion based deep neural network to detect and identify manipulation failures. We also introduce FAILURE, a multimodal dataset, containing 229 real-world manipulation data recorded with a Baxter robot. Our network combines RGB, depth and audio readings to effectively detect failures. Results indicate that fusing RGB with depth and audio modalities significantly improves the performance. FINO-Net achieves %98.60 detection accuracy on our novel dataset. Code and data are publicly available at https://github.com/ardai/fino-net.
Entanglement preserving local thermalization

We investigate whether entanglement can survive the thermalization of subsystems. We present two equivalent formulations of this problem: (1) Can two isolated agents, accessing only pre-shared randomness, locally thermalize arbitrary input states while maintaining some entanglement? (2) Can thermalization with local heat baths, which may be classically correlated but do not exchange information, locally thermalize arbitrary input states while maintaining some entanglement? We answer these questions in the positive at every nonzero temperature and provide bounds on the amount of preserved entanglement. We provide explicit protocols and discuss their thermodynamic interpretation: we suggest that the underlying mechanism is a speed-up of the subsystem thermalization process. We also present extensions to multipartite systems. Our findings show that entanglement can survive locally performed thermalization processes accessing only classical correlations as a resource. They also suggest a broader study of the channel's ability to preserve resources and of the compatibility between global and local dynamics.
Implementing a transactional design model to ensure the mindful development of public-facing science communication projects

This paper introduces the concept of transactional design---integrating Druschke's "transactional" model of rhetoric and science and Kinsella's model of "public expertise"---to demonstrate how technical communication and user experience (UX) designers and researchers can play an essential role in helping scientists cultivate meaningful relationships with members of the public toward the goal of making scientific content more accessible and actionable. This paper reports on the challenges that arose when a water modeling system built for experts was adapted for a public museum audience. It discusses specific issues the UX team had in contending with outdated "deficit" and "conduit" models of communication when working with scientists to adapt the system; it provides a checklist for steps that technical communication and UX designers and researchers---as those who best understand audiences and work directly with users---can champion the idea of transactional design to setup knowledge-making partnerships toward the co-construction of public-facing scientific communication projects.
Self-Similarity Action Proposal

Temporal action proposal generation, which aims to locate temporal segments that may contain actions, is a key prepositive step of various video analysis tasks, like temporal action detection. In this letter, we present Self-Similarity Action Proposal (SSAP), a simple method that generates action proposals using the self-similarity of videos. Specifically, a basic low-level index, structural similarity, is adopted to measure the similarity between adjacent frames. Potential action boundaries are located by thresholding the similarity values and candidate action segments are successively generated by grouping the boundaries. A segment evaluation module (SEM) is further employed to score and refine the segments. The framework achieves state-of-the-art performance on THUMOS14 and competitive results on ActivityNet v1.3. Notably, on THUMOS14, it achieves over 4% improvement on the average recall at 50 proposals and 3.3% gain in mAP@0.7 when combined with an existing action classifier for temporal action detection.
Quantum walks on graphs of the ordered Hamming scheme and spin networks

It is shown that the hopping of a single excitation on certain triangular spin lattices with non-uniform couplings and local magnetic fields can be described as the projections of quantum walks on graphs of the ordered Hamming scheme of depth 2. For some values of the parameters the models exhibit perfect state transfer between two summits of the lattice. Fractional revival is also observed in some instances. The bivariate Krawtchouk polynomials of the Tratnik type that form the eigenvalue matrices of the ordered Hamming scheme of depth 2 give the overlaps between the energy eigenstates and the occupational basis vectors.