Reviewerly/exHarmony huggingface dataset:
Medical Image Enhancement Algorithm Based on Histogram Equalization and Dyadic Wavelet Transform

Medical image are often deteriorated by various noises during acquisition or transmission, this will leads to low contrast and poor quality of the medical image, which further seriously affects clinical diagnosis and interpretation, In order to solve the image blur and edge loss problem in the process of collecting and transmitting medical image, a novel medical image enhancement algorithm based on histogram equalization and dyadic wavelet transform is proposed in this paper, Firstly, histogram equalization is performed to enhance medical image contrast, Secondly, the medical image is decomposed into four sub bands (LL, HL, LH, HH) by dyadic wavelet transform four times, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and the diagonal line high frequency component, and then The single-threshold enhancement function is used after equalizing the low-frequency coefficients of the fourth layer again. the double threshold enhancement function is performed on the horizontal high frequency coefficients of each layer which further enhance the image quality and compansate for the information lost during histogram equalization, adaptive enhancement function is performed on high frequency coefficients in vertical and diagonal directions of each layer, Finally, The enhanced image was obtained through the inverse dyadic wavelet transform by using processed low frequency and high frequency coefficients, Many simulation experiments show that our proposed algorithm achieves more favorable performance in handing medical images, this method can not only enhance an image's details but can also preserve its edge to increase human visibility, which can raise the rate of conformity in clinical diagnoses.
Technical capacities of digitisation centres within ICEDIG participating institutions

DiSSCo, the Distributed System of Scientific Collections, is seeking to centralise certain infrastructure and activities relating to the digitisation of natural science collections. Deciding what activities to distribute, what to centralise, and what geographic level of aggregation (e.g. regional, national or pan European) is most appropriate for each task, was one of the challenges set out within the EC-funded ICEDIG project. In this paper we present the results of a survey of several European collections to establish current digitisation capacity, strengths and skills associated with existing digitisation infrastructure. Our results indicate that most of the institutions surveyed are engaged in large-scale digitisation of collections and that this is usually being undertaken by dedicated teams of digitisers within each institution. Some cross institutional collaboration is happening, but this is still the exception for a variety of funder and practical reasons. These results inform future work that establishes a set of principles to determine how digitisation infrastructure might be most efficiently organised across European organisations in order to maximise progress on the digitisation of the estimated 1.5 billion specimens held within European natural science collections.
InsideOut

Imaging capsules are ingestible sensors that capture the video of one's gastrointestinal tract for medical diagnosis. We believe that the capsule's experiential perspective is often overlooked by associated medical applications. This work explores the design of this experiential perspective through combining imaging capsules with digital play. We designed a playful wearable system called "InsideOut", where users play with the real-time video of their gastrointestinal tract captured by an imaging capsule. Based on an in-the-wild study, we derived four themes articulating the play experiences and discussed key design implications to guide future playful designs using imaging capsules. Our research highlights the opportunity of using medical imaging technologies to enable intriguing bodily play experiences. Furthermore, such experiences can deepen the players' engagement with and understanding of their bodies, ultimately contributing to a more playful and humanized health care agenda.
Identifying Victim Blaming Language in Discussions about Sexual Assaults on Twitter

Increasing instances of sexual assault have presented an opportunity for these heinous crimes to be discussed on social platforms. Oftentimes, victims are slut shamed and held culpable for the assault by the community which further discourages such personal disclosures and assault reporting. Victim Blaming has multiple psychological effects on the victim and further discourages formal reporting of such crimes. Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims. Additionally, specific datasets to devise models should also be developed. In our work, we present an exhaustive statistical analysis of victim blaming and gender attributes along with a single step transfer learning based classification method to identify victim blaming language on Twitter. Finally, we compare the performance of the proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.
Fine-tuning ERNIE for chest abnormal imaging signs extraction

Chest imaging reports describe the results of chest radiography procedures. Automatic extraction of abnormal imaging signs from chest imaging reports has a pivotal role in clinical research and a wide range of downstream medical tasks. However, there are few studies on information extraction from Chinese chest imaging reports. In this paper, we formulate chest abnormal imaging sign extraction as a sequence tagging and matching problem. On this basis, we propose a transferred abnormal imaging signs extractor with pretrained ERNIE as the backbone, named EASON (fine-tuning ERNIE with CRF for Abnormal Signs ExtractiON), which can address the problem of data insufficiency. In addition, to assign the attributes (the body part and degree) to corresponding abnormal imaging signs from the results of the sequence tagging model, we design a simple but effective tag2relation algorithm based on the nature of chest imaging report text. We evaluate our method on the corpus provided by a medical big data company, and the experimental results demonstrate that our method achieves significant and consistent improvement compared to other baselines.
Plant Classification Based on Extraction Feature Gray Level Co-Occurrence Matrix Using k-nearest Neighbour

Abstract Indonesia is one of the countries with high plant diversity. Almost every region in Indonesia has distinctive plants and may not be present in other countries. Based on these facts required a strategic step to record and identify plants in Indonesia. One method that can be used to leaf image feature extraction is the Gray Level Co-occurrence Matrix (GLCM). This research will implement k-Nearest Neighbor (k-NN) method to classify type of plants based on leaf texture. The classification result based on GLCM using k-NN classifier showed that the accuracy using k = 3 was 83%. The use of parameter k influence classification results, the greater the value of k then the accuracy would be smaller. Classification errors for some types of leaf images occurred because the value extraction traits generated by GLCM was very similar and had a small range of values.
Phase-shift speckle-shearing interferometry

Abstract We have numerically simulated the process of measuring stress – strain states by the method of speckle-shearing interferometry using the phase-shift technique. A computer model with the possibility of setting its strain and roughness is developed, which includes a model of a diffusely reflecting test object corresponding to the characteristics of a real membrane, as well as a speckle interferometer model that allows speckle interferograms to be obtained for different speckle sizes and angles between interfering beams. The process of reconstructing the object surface topogram from model speckle interferograms by the phase-shift technique is implemented. Using the developed models, a two-dimensional shearogram are obtained, which is a derivative of the strain field of a circular membrane. Comparison of the results of numerical simulation with experimental data shows that the differences (rms deviations) do not exceed 0.02 μm. It is also shown that the error of interferogram reconstruction by the phase-shift technique increases significantly when the test object strains exceed 12 μm.
A Complexity Analysis Approach for Model-based System Engineering

With the increasing complexity of the highly engineered products, Model-based Systems Engineering (MBSE) is proposed to support the complexity management of the product development. As the basic of complexity management, complexity analysis is used to measure the system complexity for system solution trade-offs. Using traditional MBSE approaches, system architectures of product are formalized as MBSE models whose complexity measurement provides cues to quantitative trade-offs. In this paper, an MBSE approach is proposed to support complex analysis using qualitative and quantitative approaches. A GOPPRR approach is first proposed to support MBSE formalisms. Then a complexity measurement formula is used to calculate the structure complexity of the MBSE models. Finally, through a tool-chain developed based on Open Services for Lifecycle Collaboration (OSLC), a visualization tool is used to analyze the system complexity by measuring and visualizing the model complexity. A case study is proposed to evaluate the potentials of this approach for supporting product trade-offs. From the results, the approach enables to calculate complexity of MBSE models and virtualizes the model topologies.
Communicating Sustainable Consumption and Production in 360° Video

Research at the intersection of technology and sustainability is increasing across disciplines. Virtual reality is one technology used to address social issues, though less work has explored how immersive environments might impact a viewer's impression of complex environmental issues like sustainability.
Tracing Design

The role of design in organizations has risen in prominence since the turn of the century. Our collective understanding of this role, identification of designerly practices, and ability to trace their value and influence within an organizational landscape however remains problematic. Both industry and academia have made distinct efforts to study design in practice, acknowledging the importance of the value of design as a competitive instrument but with little agreement on how it is understood, communicated and operationalised. This paper takes a pragmatic orientation, exploring the contemporary challenges design practitioners face in terms of documenting, accounting for and communicating the value of designerly work. We adopt a bottom-up approach, conducting 11 semi-structured interviews with design practitioners, to study how practitioners in situ negotiate the design-value space. Our findings present design value communication strategies, how design knowledge is consumed, what documentation practices are used, and the organizational challenges encountered.
Design and Development of Parking Motor Parking Information System at Muhammadiyah University, Sidoarjo

Abstract The problem that often occurs in high-rise motorbike parking lots is the lack of information on the availability of motorbike parking lots. Currently at Muhammadiyah University in Sidoarjo there is no information system in the motorbike parking lot so users are often forced to surround parking lots to find empty parking lots, in the face of problems this requires a good information system so that motorbike parking users can find out information on the availability of parking spaces. Through this research entitled the design of a motorbike parking information system in a multi-storey building, it is expected to make it easier for users to park at Muhammadiyah University in finding a parking space. This research is done by making a design simulation by utilizing Arduino module components, Ethernet shield modules &amp; infrared obstacle sensors, the working principle of the system is the infrared obstacle sensor installed at the entrance and exit on each floor whose function is to provide data to the Arduino then Arduino processes the sensor by sending data to the database, the database is used to store data and sensor values in the database that will be displayed in the Web browser which will be displayed on the monitor installed on the ground floor to notify the driver of the empty parking slot so as to produce efficiency BBM and time efficiency.
Message-Locked Searchable Encryption: A New Versatile Tool for Secure Cloud Storage

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting deduplication and enabling users to perform search operations over encrypted data. In addition, it supports both multi-keyword and negative keyword searches. We formulate the security notions of MLSE and prove our scheme satisfies all the security requirements. Moreover, we provide an interesting extension of our construction to support Proof of Storage (PoS). Compared with the existing solutions, MLSE achieves better functionalities and efficiency, and hence enables more versatile and efficient cloud storage service.
tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports

Background The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports. Methods We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard. Results tbiExtractor displayed high sensitivity (0.92–0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score ≥ 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report). Conclusion tbiExtractor is a validated algorithm for extraction of TBI common data elements from radiology reports. This automation reduces the time spent to extract structured data and improves the consistency of data extracted. Lastly, tbiExtractor can be used to stratify subjects into groups based on visible damage by partitioning the annotations of the pertinent clinical conditions on a radiology report.
Reliability, Validity, and Strength of a Unified Model for Information Security Policy Compliance

The purpose of this paper was to first examine the reliability, validity, and strength of a unified model for information security policy compliance that includes eight constructs explaining information security policy compliance. Second, seven hypotheses were developed and tested based on the constructs in the model. An instrument with eight constructs was used to reflect the traditional survey statements method that measures current or retrospective behavior for the items of each construct. A sample of 187 faculty and staff from a mid-sized university in the USA participated in the study. Data were collected electronically and analyzed through SmartPLS 3.0, a partial least square structural equation modeling software. The results indicated strong support for the models’ reliability, validity, and strength. The results also showed support for five hypotheses. Two hypotheses were rejected. The findings with implications are discussed. Recommendations for future research are made.
SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners

While several tools exist for training and evaluating narrow machine learning (ML) algorithms, their design generally does not follow a particular or explicit evaluation methodology or theory. Inversely so for more general learners, where many evaluation methodologies and frameworks have been suggested, but few specific tools exist. In this paper we introduce a new framework for broad evaluation of artificial intelligence (AI) learners, and a new tool that builds on this methodology. The platform, called SAGE (Simulator for Autonomy & Generality Evaluation), works for training and evaluation of a broad range of systems and allows detailed comparison between narrow and general ML and AI. It provides a variety of tuning and task construction options, allowing isolation of single parameters across complexity dimensions. SAGE is aimed at helping AI researchers map out and compare strengths and weaknesses of divergent approaches. Our hope is that it can help deepen understanding of the various tasks we want AI systems to do and the relationship between their composition, complexity, and difficulty for various AI systems, as well as contribute to building a clearer research road map for the field. This paper provides an overview of the framework and presents results of an early use case.
Access to Land and Food Security: Analysis of ‘Priority Crops’ Production in Ogun State, Nigeria

Using Ogun State located in South-western Nigeria, this chapter draws attention to the increase in output productivity of priority crops in the State from 2003 to 2015 due to the acquisitions of over 47,334 hectares of agricultural land across 28 communities in different Local Government Areas (LGAs). From Ogun State Agriculture Data, eight priority crops are analyzed: cassava, maize, rice, melon, yam, cocoyam, potato, and cowpea. Statistics reveal that the cultivation of cassava gives the highest average output of 4,515,620 metric tonnes and yield per hectare of 16.41 relative to other produce which affirms that Ogun State has the most comparative advantage in the cultivation of cassava followed by maize. The chapter further explores other pro-poor programmes directed at ensuring food security in the State.
AR-glasses-based attention guiding for complex environments

Augmented Reality (AR) based assistance has a huge potential in the context of Industry 4.0: AR links digital information to physical objects and processes in a mobile and, in the case of AR glasses, hands-free way. In most companies, order-picking is still done using paper lists. With the rapid development of AR hardware during the last years, the interest in digitizing picking processes using AR rises. AR-based guiding for picking tasks can reduce the time needed for visual search and reduce errors, such as wrongly picked items or false placements.
Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.
BuSiNec - Studying the Effects of a Busyness Signifying Necklace in the Wild

Workplace stress is a growing problem, which is often not identified by sufferers and those around them until it becomes chronic. Moreover, admitting to feel stressed is a highly private and therefore sensitive topic, particularly at work. Informed by the findings from six in-depth interviews, we designed the manually self-adjusted wearable BuSiNec: a Busyness Signifying Necklace. 18 participants wore it at their workplace and reported on their experiences and their usage behavior through a diary and in a focus group interview. Our findings indicate that BuSiNec supports self-reflection on stress and further stimulates valuable discussions among co-users which in turn increased mutual consideration. Participants were conscious of their their displayed busyness status towards others and used the display to encourage or discourage discussions and interruptions, or as a warning of upcoming hurry. From these findings, we derive recommendations for the integration of affective state information in wearable displays.
Symmetric Key Generation and Distribution Using Diffie-Hellman Algorithm

TransferPurohit, Kaustubh of data overKumar, Avanish a networkUpadhyay, Mayank is one of the most endangeredKumar, Krishan things to look for. As the cases of data leaks and theft are increasing exponentially, there is a need of a secure gateway that can prevent the data from getting misused. In group-oriented application such as video conferencing and multi-player gaming, it is necessary that only the trusted member can access the data and that too in a confidential manner in such a way that the prowler is not able to modify or ingress it. The Diffie–Hellman algorithm was one of the first and foremost schemes used for key exchange using a symmetric key encryption technique. The proposed algorithm is an advanced version of the traditional Diffie–Hellman algorithm that can be used efficiently by increasing the time complexity of the current by a fair margin thus making it difficult for the intruder to decrypt the common key.
Improved Deep Learning in OFDM Systems With Imperfect Timing Synchronization

The paper proposes a deep neural network (DNN) based receiver to outperform the state-of-the-art w/o timing synchronization error in orthogonal frequency-division multiplexing (OFDM) systems. Moreover, the closed-form of a traditional minimum mean square error (MMSE) receiver is derived in the presence of inter-symbol-interference. The derived receiver is used to benchmark the performance of the proposed DNN.