Reviewerly/exHarmony huggingface dataset:
Simple Expert System for Intelligent Control and HCI for a Wheelchair fitted with Ultrasonic Sensors

Using an expert system to make driving a powered-wheelchair less problematic is investigated. The system interprets sensor and joystick signals and then mixes them and improves that collaboration to control speed and direction. Ultrasonic sensors are used to identify hazardous circumstances and suggest a safer direction and speed. Results from drivers completing a series of timed routes are presented. Users completed tests using joysticks to control their chair with and then without a microcomputer and sensor system. A recent system is used to compare and contrast the results. This new system consistently performed quicker than the recent system. It also appears that the quantity of support provided by the sensors and microcomputer should be adjusted depending on situations and surroundings.
The Role of Cue Utilization and Cognitive Load in the Recognition of Phishing Emails

Phishing emails represent a major threat to online information security. While the prevailing research is focused on users' susceptibility, few studies have considered the decision-making strategies that account for skilled detection. One relevant facet of decision-making is cue utilization, where users retrieve feature-event associations stored in long-term memory. High degrees of cue utilization help reduce the demands placed on working memory (i.e., cognitive load), and invariably improve decision performance (i.e., the information-reduction hypothesis in expert performance). The current study explored the effect of cue utilization and cognitive load when detecting phishing emails. A total of 50 undergraduate students completed: (1) a rail control task; (2) a phishing detection task; and (3) a survey of the cues used in detection. A cue utilization assessment battery (EXPERTise 2.0) then classified participants with either higher or lower cue utilization. As expected, higher cue utilization was associated with a greater likelihood of detecting phishing emails. However, variation in cognitive load had no effect on phishing detection, nor was there an interaction between cue utilization and cognitive load. Further, the findings revealed no significant difference in the types of cues used across cue utilization groups or performance levels. These findings have implications for our understanding of cognitive mechanisms that underpin the detection of phishing emails and the role of factors beyond the information-reduction hypothesis.
From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address.
An Arable Field for Benchmarking of Metaheuristic Algorithms for Capacitated Coverage Path Planning Problems

This study specifies an agricultural field (Latitude = 56°30′0.8″ N, Longitude = 9°35′27.88″ E) and provides the absolute optimal route for covering that field. The calculated absolute optimal solution for this field can be used as the basis for benchmarking of metaheuristic algorithms used for finding the most efficient route in the field. The problem of finding the most efficient route that covers a field can be formulated as a Traveling Salesman Problem (TSP), which is an NP-hard problem. This means that the optimal solution is infeasible to calculate, except for very small fields. Therefore, a range of metaheuristic methods has been developed that provide a near-optimal solution to a TSP in a “reasonable” time. The main challenge with metaheuristic methods is that the quality of the solutions can normally not be compared to the absolute optimal solution since this “ground truth” value is unknown. Even though the selected benchmarking field requires only eight tracks, the solution space consists of more than 1.3 billion solutions. In this study, the absolute optimal solution for the capacitated coverage path planning problem was determined by calculating the non-working distance of the entire solution space and determining the solution with the shortest non-working distance. This was done for four scenarios consisting of low/high bin capacity and short/long distance between field and storage depot. For each scenario, the absolute optimal solution and its associated cost value (minimum non-working distance) were compared to the solutions of two metaheuristic algorithms; Simulated Annealing Algorithm (SAA) and Ant Colony Optimization (ACO). The benchmarking showed that neither algorithm could find the optimal solution for all scenarios, but they found near-optimal solutions, with only up to 6 pct increasing non-working distance. SAA performed better than ACO, concerning quality, stability, and execution time.
Deep neural networks for computational optical form measurements

Deep neural networks have been successfully applied in many different fields like computational imaging, medical healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine learning approach is explored to solve an inverse problem in the accurate measurement of optical surfaces. The approach is developed and tested using virtual measurements with known ground truth.
Platform Teams

Software-producing organizations are seeking to release faster and more efficiently new versions of their products to their customers to remain competitive in the fierce software market. Continuous delivery practices arise as a potential solution since every commit to the repository could result in a production-candidate version of a product, accelerating time to market, and improving customer satisfaction. In this work, we employed Grounded Theory to investigate how organizations pursuing continuous delivery should organize their development and operations teams. We collected data from 27 IT professionals. After a careful analysis, we started the elaboration of a taxonomy with four patterns of organizational structures: (1) siloed departments, (2) classical DevOps, (3) cross-functional teams, and (4) platform teams. We observed that the platform team structure is the most distinctive classification of our taxonomy, and it has promising results regarding delivery performance. Some relevant aspects we found out about platform teams include: infrastructure specialists need coding skills; product teams have to operate their business services; and much of the non-functional concerns are handled by the platform, alleviating product teams.
Sign Language to Text Conversion Using Deep Learning

Sign Languages also popularly called Signed Languages. This is the only language by which deaf and dumb can communicate with each other and one of the most unexplored areas. So the proposed paper represents a model that can be used to translate given sign language to simple English text so that it could be used for dumb people. By late proceedings in the field of profound learning, there exist many applications that neural network systems can solve one such problem is this sign language. To meet the requirements, the proposed model uses deep learning techniques to solve the problem. One such technique is Convolutional Neural Networks for image depiction and classification. The model designed in the proposed paper has a dataset that consists of 87,002 images. The dataset has been split into training data and test data in the ratio of 9:1. The proposed model was trained on 78,300 images and the testing was performed on 8700 images which were classified in 29 classes. The model has produced a training accuracy of 98.67%.
Two classes of permutation trinomials with Niho exponents over finite fields with even characteristic

In this paper, we consider two classes of permutation trinomials with Niho-type exponents over the finite field F22m. Some sufficient conditions are obtained to characterize the coefficients of the permutation trinomials. Our numerical result suggests that those sufficient conditions for one class are also necessary.
gsufsort: constructing suffix arrays, LCP arrays and BWTs for string collections

The construction of a suffix array for a collection of strings is a fundamental task in Bioinformatics and in many other applications that process strings. Related data structures, as the Longest Common Prefix array, the Burrows-Wheeler transform, and the document array, are often needed to accompany the suffix array to efficiently solve a wide variety of problems. While several algorithms have been proposed to construct the suffix array for a single string, less emphasis has been put on algorithms to construct suffix arrays for string collections.In this paper we introduce gsufsort, an open source software for constructing the suffix array and related data indexing structures for a string collection with N symbols in O(N) time. Our tool is written in ANSI/C and is based on the algorithm gSACA-K (Louza et al. in Theor Comput Sci 678:22-39, 2017), the fastest algorithm to construct suffix arrays for string collections. The tool supports large fasta, fastq and text files with multiple strings as input. Experiments have shown very good performance on different types of strings.gsufsort is a fast, portable, and lightweight tool for constructing the suffix array and additional data structures for string collections.
Evaluating Pose Estimation as a Solution to the Fall Detection Problem

In this age of evolving technological capabilities, assisted living has proven useful to ease the frailty that comes with aging. Within this context, the detection and prevention of accidents are paramount to ensure a longer life expectancy for the elderly. Over the years, many approaches for fall detection have been proposed, such as ambient sensors, wearable devices, and automated camera monitoring. A recent approach is to use pose estimation software to identify humans and pinpoint the location of their most important joints. This pose information can be later used as features for an effective fall detection system. This scenario begs the question: Can pose estimation methods be as effective as the sensor or other camera-based ones? To answer this question, we analyzed three pose estimation frameworks, totalizing eleven models, paired with a simple neural network classifier. In our experiments, we have obtained competitive results among the state-of-the-art on the UR Fall Detection dataset, a multi-modal fall detection benchmark, comprised of RGB, depth, and acceleration data. More specifically, our best model achieved a sensitivity rate of 94.5% and a specificity rate of 99.9%, in line with the best camera and sensor-based solutions.
PP-OCQ: A distributed privacy-preserving optimal closeness query scheme for social networks

Optimal closeness query in social networks requires obtaining the social datasets from each user so that he/she finds out a shortest social distance with any target user. For example, we can make friends in terms of the most similar social relationship of family background, education level and hobbies etc. Unfortunately, social data concerning user’s attributes might reveal personal sensitive information and be exploited maliciously. Considering the above privacy-revealing issues, this paper proposes a Privacy-Preserving Optimal Closeness Query (PP-OCQ) scheme, which achieves the secure optimal closeness query in a distributed manner without revealing the users’ sensitive information. We construct an equivalent cost graph where all users’ information are encrypted by his/her public key and the data are authenticated by signature. It employs the ElGamal Cryptosystem to achieve the privacy protection in social networks, and gives an optimal closeness query protocol without leaking the users’ sensitive information on homomorphic user ciphertexts. Then it follows the routing protocol, distributed Bellman-Ford shortest-paths protocol, to query the optimal closeness through the users’ message propagation in multiple iterations. The direction of propagation is controlled by some indicators so that each user performs corresponding operations based on homomorphism property and fails to obtain other user’s information due to the masking of random numbers. Our analysis and simulations show that the proposed scheme is efficient in terms of computation cost and communication overhead.
Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices

We present <inline-formula><tex-math notation="LaTeX">${\sf Supremo}$</tex-math></inline-formula> , a cloud-assisted system for low-latency image super-resolution (SR) in mobile devices. As SR is extremely compute-intensive, we first further optimize state-of-the-art DNN to reduce the inference latency. Furthermore, we design a mobile-cloud cooperative execution pipeline composed of specialized data compression algorithms to minimize end-to-end latency with minimal image quality degradation. Finally, we extend <inline-formula><tex-math notation="LaTeX">${\sf Supremo}$</tex-math></inline-formula> to video applications by formulating a dynamic optimal control algorithm to design <inline-formula><tex-math notation="LaTeX">${\sf Supremo-Opt}$</tex-math></inline-formula> , which aims to maximize the impact of SR while satisfying latency and resource constraints under practical network conditions. <inline-formula><tex-math notation="LaTeX">${\sf Supremo}$</tex-math></inline-formula> upscales 360p image to 1080p in 122 ms, which is 43.68× faster than on-device GPU execution. Compared to cloud offloading-based solutions, <inline-formula><tex-math notation="LaTeX">${\sf Supremo}$</tex-math></inline-formula> reduces wireless network bandwidth consumption and end-to-end latency by 15.23× and 4.85× compared to baseline approach of sending and receiving whole images, and achieves 2.39 dB higher PSNR compared to using conventional JPEG to achieve similar data size compression. Furthermore, <inline-formula><tex-math notation="LaTeX">${\sf Supremo-Opt}$</tex-math></inline-formula> guarantees robust performance in practical scenarios.
“Don’t Judge a Book by its Cover”: Exploring Book Traits Children Favor

We present the preliminary exploration we conducted to identify traits that can influence children's preferences in books. Findings offer insights for the design of recommender algorithms that would look beyond patterns inferred from traditional user-system interactions (e.g., ratings) for recommendation purposes, since when it comes to children such data is rarely, if at all, available.
The design thinking of co-located vs. distributed software developers

Context: Designing software is an activity in which software developers think and make design decisions that ultimately shape the structure and behavior of software products. Currently, designing software is one of the least understood activities in which software developers engage. In a collaborative design setting, distances such as geographic, cultural, or social distance can lead to socio-technical challenges that potentially affect the way software is designed.
Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

Image registration is an important part of many clinical workflows and inclusion of segmentation information of structures of interest improves registration performance. We propose to integrate segmentation information in a registration framework using fine grained feature maps obtained in a self supervised manner. Self supervised feature maps enables use of segmentation information despite the unavailability of manual segmentations. Experimental results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.
Design of Hybrid Cryptography System based on Vigenère Cipher and Polybius Cipher

The Cryptography is gotten from a Greek word which implies the craft of ensuring data by changing it into a muddled organization and unreadable format. It is a mix of arithmetic and software engineering. The dynamite growth of the Internet has made an expanded familiarity with intrigue uncertainty issues. Even though security is the measure worries over the internet, numerous applications have been created and structured without considering fundamental destinations of data security that is confidentiality, authentication, and protection. As our day by day exercises become increasingly more dependent upon data networks, the significance of an understanding of such security issues and trouble will also increase. To forestall some undesirable clients or individuals to gain admittance to the data, cryptography is required. .This paper introduces a new hybrid security cipher by combining the two most important Ciphers such as Polybius Cipher and Vigenère Cipher. This hybrid encryption cipher provides greater security as compared to classic ciphers.
Low bit rate underwater video image compression and coding method based on wavelet decomposition

In view of the limited bandwidth of underwater video image transmission, a low bit rate underwater video compression coding method is proposed. Based on the preprocessing process of wavelet transform and coefficient down-sampling, the visual redundancy of underwater image is removed and the computational coefficients and coding bits are reduced. At the same time, combined with multi-level wavelet decomposition, inter frame motion compensation, entropy coding and other methods, according to the characteristics of different types of frame image data, reduce the number of calculations and improve the coding efficiency. The experimental results show that the reconstructed image quality can meet the visual requirements, and the average compression ratio of underwater video can meet the requirements of underwater acoustic channel transmission rate.
Neural Reasoning, Fast and Slow, for Video Question Answering

What does it take to design a machine that learns to answer natural questions about a video? A Video QA system must simultaneously understand language, represent visual content over space-time, and iteratively transform these representations in response to lingual content in the query, and finally arriving at a sensible answer. While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process. Inspired by the dual-process account of human reasoning, we design a dual process neural architecture, which is composed of a question-guided video processing module (System 1, fast and reactive) followed by a generic reasoning module (System 2, slow and deliberative). System 1 is a hierarchical model that encodes visual patterns about objects, actions and relations in space-time given the textual cues from the question. The encoded representation is a set of high-level visual features, which are then passed to System 2. Here multi-step inference follows to iteratively chain visual elements as instructed by the textual elements. The system is evaluated on the SVQA (synthetic) and TGIF-QA datasets (real), demonstrating competitive results, with a large margin in the case of multi-step reasoning.
Minimal 3D Dubins Path with Bounded Curvature and Pitch Angle

In this paper, we address the problem of finding cost-efficient three-dimensional paths that satisfy the maximum allowed curvature and the pitch angle of the vehicle. For any given initial and final configurations, the problem is decoupled into finding the horizontal and vertical parts of the path separately. Although the individual paths are modeled as two-dimensional Dubins curves using closed-form solutions, the final 3D path is constructed using the proposed local optimization to find a cost-efficient solution. Moreover, based on the decoupled approach, we provide a lower bound estimation of the optimal path that enables us to determine the quality of the found heuristic solution. The proposed solution has been evaluated using existing benchmark instances and compared with state-of-the-art approaches. Based on the reported results and lower bounds, the proposed approach provides paths close to the optimal solution while the computational requirements are in hundreds of microseconds. Besides, the proposed method provides paths with fewer turns than others, which make them easier to be followed by the vehicle's controller.
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive approach for EXPLaining black-box classifiers using Adaptive Neighborhood generation (EXPLAN). EXPLAN is a module-based algorithm consisted of dense data generation, representative data selection, data balancing, and rule-based interpretable model. It takes into account the adjacency information derived from the black-box decision function and the structure of the data for creating a representative neighborhood for the instance being explained. As a local model-agnostic explanation method, EXPLAN generates explanations in the form of logical rules that are highly interpretable and well-suited for qualitative analysis of the model's behavior. We discuss fidelity-interpretability trade-offs and demonstrate the performance of the proposed algorithm by a comprehensive comparison with state-of-the-art explanation methods LIME, LORE, and Anchor. The conducted experiments on real-world data sets show our method achieves solid empirical results in terms of fidelity, precision, and stability of explanations.