Reviewerly/exHarmony huggingface dataset:
Reimagining the Mobile Phone: Investigating Speculative Approaches to Design in Human-Computer Interaction for Development (HCI4D)

Design has traditionally been used to solve problems in the field of Human-Computer Interaction for Development (HCI4D). Mobile phone applications intended to address health and education challenges in low and middle-income countries (LMICs) are prominent examples of these efforts. In this paper, I argue that design-especially, speculative approaches-can play a different role in HCI4D. Within HCI, speculative design has proven to be useful for raising critical questions. Here, I present a collection of nine speculative design concepts that are intended to raise questions about mobile phones; in particular, how they are designed and what they are used for. These concepts are inspired by my +10 years of studying mobile phone use in Kenya. In the discussion, I elaborate on how speculative design approaches can benefit HCI4D by prompting questions about whether the field is solving the right problems. I also discuss how speculative design is useful for drawing attention to other problems and topics of study.
ACapMed: Automatic Captioning for Medical Imaging

Medical image captioning is a very challenging task that has been rarely addressed in the literature on natural image captioning. Some existing image captioning techniques exploit objects present in the image next to the visual features while generating descriptions. However, this is not possible for medical image captioning when one requires following clinician-like explanations in image content descriptions. Inspired by the preceding, this paper proposes using medical concepts associated with images, in accordance with their visual features, to generate new captions. Our end-to-end trainable network is composed of a semantic feature encoder based on a multi-label classifier to identify medical concepts related to images, a visual feature encoder, and an LSTM model for text generation. Beam search is employed to ensure the best selection of the next word for a given sequence of words based on the merged features of the medical image. We evaluated our proposal on the ImageCLEF medical captioning dataset, and the results demonstrate the effectiveness and efficiency of the developed approach.
Modified collaborative filtering for hybrid recommender systems and personalized search: The case of digital library

Digital libraries constitute a considerable source of digital content providers, similar to video and music streaming services. Therefore, a solid, reliable and intelligent recommender system is essential to accommodate the plethora of different interests amongst its users. In view of this compelling need, this paper presents a modification to the classic collaborative filtering technique which incorporates the user’s actions into the recommendation production process. In this way, the user implicitly provides extra data to the collaborative filtering-based recommender system, resulting in higher quality recommendations and personalized search results, especially when combined with elements of content-based filtering. The results of the above-mentioned modification are presented by integrating the recommender system to a web-based digital lending library application. The evaluation of the application was made using the inspection method of cognitive walkthrough.
Sequence aware recommenders for fashion E-commerce

Abstract In recent years, fashion e-commerce has become more and more popular. Since there are so many fashion products provided by e-commerce retailers, it is necessary to provide recommendation services to users to minimize information overload. When users look for a product on an e-commerce website, they usually click the product information sequentially. Previous recommenders, such as content-based recommenders and collaborative filtering recommenders, do not consider this important behavioral characteristic. To take advantage of this important characteristic, this study proposes sequence-aware recommenders for fashion product recommendation using a gated recurrent unit (GRU) algorithm. We conducted an experiment using a dataset collected from an e-commerce website of a Korean fashion company. Experimental results show that sequence aware recommenders outperform non-sequence aware recommender, and multiple sequence-based recommenders outperform a single sequence-based recommender because they consider the attributes of fashion products. Finally, we discuss the implications of our study on fashion recommendations and propose further research topics.
Intelligent video surveillance using enhanced deep belief based multilayered convolution neural network classification techniques

Abstract Video surveillance has undergone numerous changes in the past few years and several pieces of research have been carried out in this field. Object tracking is the significant task in such systems, and hence it is essential to review the standard approaches dealing with object detection, classification and tracking. This work proposes a novel classification technique for a detected object of a moving scene from a video dataset. Initially the dataset has been processed and prepared for data training based on neural networks. The data has been classified using the proposed enhanced deep belief based multilayered convolutional neural network (EBMCNN). The major focus is on deep learning applications involved in estimating the count, total persons involved and the activities in a crowd where all criteria are taken into consideration, thereby achieving security through video analysis. Identifying theft and the detection of violence are some security measures where video is converted to frames which are then processed to analyse the individuals along with their activities. The classification is performed through comparative analysis of a real‐time dataset. Experimental results show the accuracy 97%, precision 93.8%, recall 87.7% and F‐1 score 87.5%.
An improved all-optical diffractive deep neural network with less parameters for gesture recognition

As a framework of optical machine learning, all-optical diffractive neural network (D 2 NN) has delivered an ideal outcome of feature detection and target classification, currently raising high interest in the optics and photonics community. In this paper, we applied an improved D 2 NN architecture to the field of gesture recognition, which features more complicated contour than the common MNIST handwriting recognition in the previous literature. The proposed network structure incorporates the wavelet-like phase modulation pattern technique and the highway network on the basis of all-optical neural network. Through modulating the phase of incident light, the wavelet-like pattern can substantially reduce the parameters in the network layer. In addition, a highway network is employed to address the vanishing gradient phenomenon in the training process. In the experiment, we numerically achieved blind testing accuracy of 95.6% for identifying ten different gestures, and the number of parameters is only 3% of the regular D 2 NN. Reliability test and analysis show that the proposed method is a high-efficiency solution with low-parameters expecting for implementation of various machine learning tasks. • All-optical diffractive deep neural network (D 2 NN) for gesture recognition. • The wavelet-like phase modulation pattern for D 2 NN to reduce the parameters. • The highway network structure to address the vanishing gradient phenomenon.
Evaluation of Hands-free Teleportation in VR

Teleportation is a popular locomotion technique that allows users to navigate beyond the confines of available tracking space with the smallest chance of inducing VR sickness. Users typically specify a teleportation destination by using a hand-held motion-sensing controller. However, for various reasons, it can be desirable or required to have a hands-free alternative to controller-based teleportation. We evaluate three different hands-free ways of teleporting with users selecting a destination using head gaze and activating teleport using: (1) eye-wink, (2) a mouth gesture, and (3) dwell. A user study with 20 participants compared all three techniques to controller-based teleportation using a waypoint based navigation task. Quantitative and subjective results showed eye-wink is the most viable alternative to using a controller and offered a lower selection error.
Prediction based cost estimation technique in agile development

Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis.
Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person's mental states and predict mood variations.
Robust, Secure and Semi-Blind Watermarking Technique Using Flexible Scaling Factor in Block-Based Wavelet Algorithm

Multimedia security has received much attention recently because of the rapid transmission of elements such as text, images, audio, video, software, animation and games. Security is becoming especially critical for content owners concerned about the illegal usage of their original products. Encryption and watermarking are two methodologies for digital applications. Spatial domain and frequency domain watermarking algorithms give very promising results in embedding binary images into the cover images. This paper proposed a new method of semi-blind watermarking technique. The digital images are divided into 4 × 4 blocks and converted into discrete Wavelet transformations (DWTs). The binary image is embedded into each block using the flexible scaling factor method. Experimental results show that the proposed method has higher peak signal to noise ratio (PSNR) and similarity ratio (SR) values compared to the standard Wavelet transformation and block-based Wavelet algorithms. The results prove that the proposed hybrid algorithm is more effective, robust, secure and resistant than DWT and block-based DWT algorithms.
Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods.
Design and Verification of Crab Steering System for High Clearance Self-Propelled Sprayer

A crab steering system aiming at improving the steering flexibility and operation efficiency of the high clearance sprayer was designed. First, a steering transmission mechanism of the high clearance sprayer was designed according to the operational and structural characteristics of the sprayer. Meanwhile, a crab steering hydraulic system based on load sensing was designed, and a mathematical model of the steering transmission mechanism and hydraulic system was established to describe the working characteristics of the crab steering system. On the basis of analyzing the operating environment of the sprayer and the operating characteristics of crab steering mode, a control strategy and algorithm of the crab steering system were proposed. The simulation model of the crab steering control system was built according to the established mathematical model, and the simulation analysis of the crab steering system was carried out. The simulation results showed that under the excitation of step signal, the average deviation of the two front wheels is 0.063°, and the absolute value of the maximum deviation is 2.75°, which is within the allowable range of deviation and meets the crab steering demand of the sprayer. Additionally, in order to verify the effectiveness of the designed system, an actual vehicle test platform of the crab steering system was built based on the 3WPG-3000 high clearance self-propelled sprayer independently developed by the research group, and the field test results revealed that the average deviation of the four wheels was 0.285°, the maximum absolute deviation was 2.587°, the rotation deviation was small, within the allowable and reasonable range. Altogether, the results of the simulation and field test verify the accuracy, stability and practicability of the crab steering system, which effectively improved the maneuverability of the sprayer.
Development of Usability Guidelines: A Systematic Literature Review

Usability guideline is a well-established topic of study, with numerous guidelines developed over the years. There is a lack of study that provides a comprehensive overview of the development of usability guidelines. Motivated by this shortcoming, a systematic review was conducted to establish a body of knowledge on the development of usability guidelines to assist researchers working in the field. The systematic review was conducted by analyzing forty-three (43) research papers selected from seven (7) digital libraries and assessed to identify; the current state of research, specific domains, data collection techniques, development methods, and validation techniques. The results showed an increasing research trend in the development of usability guidelines. The development of usability guidelines was undertaken in many different domains, where the medical domain recorded the highest number of activities. Literature survey is the dominant data collection technique for developing usability guidelines, while usability evaluation is the most common technique for validating newly developed guidelines. The findings concluded that there is a lack of standardization for developing, implementing, and evaluating usability guidelines. Future work can be carried out to establish a methodology for developing and implementing usability guidelines. Moreover, a tool can also be built using machine learning to develop usability guidelines automatically.
An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialised for a single domain. Metaheuristics and hyper-heuristics are typically used as high-level cross-domain search methods, utilising problem-specific low-level heuristics for each problem domain to modify a solution. Such methods have a number of parameters to control their behaviour, whose initial settings can influence their search behaviour significantly. Previous methods in the literature either fix these parameters based on previous experience, or set them specifically for particular problem instances. There is a lack of extensive research investigating the tuning of these parameters systematically. In this paper, F-Race is deployed as an automated cross-domain parameter tuning approach. The parameters of a steady-state memetic algorithm and the low-level heuristics used by this algorithm are tuned across nine single-objective problem domains, using different training strategies and budgets to investigate whether F-Race is capable of effectively tuning parameters for cross-domain search. The empirical results show that the proposed methods manage to find good parameter settings, outperforming many methods from the literature, with different configurations identified as the best depending upon the training approach used.
Temporal pattern mining from user-generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section.
Digital geographies of the bug: A case study of China's contact tracing systems in the COVID-19

The COVID-19 pandemic has radically expanded the role of algorithmic governance in everyday mobility. In China, urban and provincial governments have introduced health codes app as a national contract tracing and quarantine enforcement method to restrict the movements of "risky" individuals through malls, subways, railways, as well as between regions. Yet the health codes have been implemented with uneven efficacy and unexpected consequences. Drawing on glitch politics, we read these unintended consequences as "bugs" emerging from the introduction of platform-based management into everyday life. These bugs mediated individuals' lived experiences of the digital app and the hybrid space constituted by population governance, individual digital navigation, and technology. Drawing on a database of posts scraped from Zhihu, a popular Chinese question-and-answer site, we examine three dimensions of the bug: the algorithmic bug, the territorial bug, and the corporeal bug. This paper sheds light on the significance of end-user experiences in digital infrastructure and contributes to our understanding of the digital geographies of bugs in algorithmic governance and platform urbanism.
Widely flexible and finely adjustable nonlocal dispersion cancellation with wavelength tuning

In fiber-based quantum information processing with energy-time entangled photon pairs, optimized dispersion compensation is vital to preserve the strong temporal correlation of the photon pairs. We propose and experimentally verify that, by simply tuning the wavelength of the entangled photon pairs, nonlocal dispersion cancellation (NDC) can provide a widely flexible and finely adjustable solution for optimizing the dispersion compensation, which cannot be reached with the traditional local dispersion cancellation (LDC) instead. By way of example, when a 50 km-long single-mode fiber (SMF) is dispersion compensated by a 6.2-km-long commercial dispersion compensating fiber (DCF) based on the LDC configuration, it will lead to an almost invariant over-compensation in the wavelength range of 1500-1600 nm which restricts the observed temporal coincidence width of the self-developed energy-time entangled photon-pairs source to a minimum of ∼110 ps. While in the NDC configuration, the dispersion compensation can be readily optimized by tuning the signal wavelength to 1565.7 nm and a minimum coincidence width of 86.1 ± 0.7 ps is observed, which is mainly limited by the jitter of the single-photon detection system. Furthermore, such optimized dispersion compensation can also be achieved as the fiber length varies from 48 km to 60 km demonstrating the wide flexibility of NDC. Thanks to these capabilities, elaborate dispersion compensation modules are no longer required, which makes NDC a more versatile tool in fiber-based quantum information and metrology applications.