Reviewerly/exHarmony huggingface dataset:
Regression models for distributions of dockage and foreign materials in grain silos filled by central spouts

Flat or hopper bottom cylindrical silos are mostly used for storing grains on farms. When grain is loaded into a silo by a central spout, a heap forms and segregation occurs, which may result in different distributions of dockage and foreign materials (DFM) at different locations inside the silo. Using the data reported in the literature, regression models were developed to predict the DFM distributions inside the silos. The regression models were used as a tool to compare the DFM distributions among different studies published in literature. It was found that the best regression models to predict the distributions of other grains, other particles, and small impurities were the 3- parameter exponential decay, 5- parameter rational, and 3- parameter exponential decay, respectively. These models predicted the DFM distributions well in generally, but there were some discrepancies at the silo center and walls regardless the direction of distribution, silo size, dropping height, crop type, and loading method. Specifically, the DFM distributions at the center and walls had larger variations than other locations.
Image Captioning for the Visually Impaired

The Paper proposes an idea for enhancing the usefulness of the technology for the betterment of the visually impaired. The project includes an Android app which captures the image of the surrounding to the blind person and send it to an Image captioning algorithm. The image captioning algorithm processes the image and converts it to text. It uses both Computer Vision and Natural Language Processing to generate the captions. The textual description of the image is then converted to speech which is then sent to the user. Technologies such as Deep Learning, Cloud Computing and tools like Flask and Tensorflow are been used at the backend. Cloud is been used as a serving for the Image Captioning API. The Android app is made using Android Studio. The textual description which is converted to speech is sent back to the user so that it gives them an idea of what is present in their surrounding.
Malicious Profile Detection on Social Media: A Survey Paper

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.
Immutable Smart Contracts on Blockchain Technology: Its Benefits and Barriers

Blockchain is an emerging technology which works on decentralized peer-to-peer network and allows to make transactions securely by eliminating the need of a central authority. Blockchain is renowned for exchange of crypto-currencies but with the advent of smart contracts it is now widely used in other applications such as smart supply chain, healthcare, smart transportation, online voting, public donations, will management, buying and selling property and insurance sector. The smart contracts are digitally signed and make applications immutable and transparent. This paper is aimed at unveiling Blockchain technology, smart contracts and its applications. This paper is also disclosing the barriers in adopting Blockchain and smart contracts. The recent literature was reviewed to find the answers to the research questions. Huge power consumption, slow transaction rate, complex architecture and lack of awareness and expertise are identified as the barriers in Blockchain adoption whereas legal issues and high transaction cost are the main barriers in adopting smart contracts.
1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning

In this paper we present a new approach for pupil segmentation. It can be computed and trained very efficiently, making it ideal for online use for high speed eye trackers as well as for energy saving pupil detection in mobile eye tracking. The approach is inspired by the BORE and CBF algorithms and generalizes the binary comparison by Haar features. Since these features are intrinsically very susceptible to noise and fluctuating light conditions, we combine them with conditional pupil shape probabilities. In addition, we also rank each feature according to its importance in determining the pupil shape. Another advantage of our method is the use of statistical learning, which is very efficient and can even be used online. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list.
Multi-camera joint spatial self-organization for intelligent interconnection surveillance

The construction of smart city makes information interconnection play an increasingly important role in intelligent surveillance systems. Especially the interconnection among massive cameras is the key to realizing the evolution from current fragmented monitoring to interconnection surveillance. However, it remains a challenging problem in practical systems due to large sensor quantity, various camera types, and complex spatial layout. Aimed at this problem, this paper proposes a novel multi-camera joint spatial self-organization approach, which realizes interconnection surveillance by unifying cameras into one imaging space. Differing from existing back-end data association strategy, our method takes front-end data calibration as a breakthrough to relate surveillance data. Specifically, this paper first initials camera spatial parameter by sequence complementary feature integration. Through integrating complementarity and redundancy among sequence features, our method has robustness under scene dynamic changes and noise. Then, we propose a multi-camera joint optimization method based on common monitoring coverage correlation analysis to estimate a more accurate relative relationship. By leveraging the two strategies, the spatial relationship and visual data association across monitoring cameras are returned finally. Our system organizes all cameras into a unified imaging space by itself. Extensive experimental evaluations on an actual campus environment demonstrate our method achieves remarkable performance.
Network Countermeasure Selection Under Blockchain Based Privacy Preserving

Due to the blooming trend of information technologies, attacks have become widespread and sophisticated, which calls to build a strong defense design that works with the attack evolution. The countermeasure selection enables the system to retrieve its health. However, privacy concerns are also introduced in the selection process, which makes the business companies less credible, which affects both users and services. In this paper we propose a dynamic defense framework that allows selecting the optimal countermeasure which brings between the blockchain-based privacy preserving and the countermeasure selection in order to estimate the privacy coverage in the quantitative metric.
Y-Net: Dual-branch Joint Network for Semantic Segmentation

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.
Optimal Load Balancing in Cloud Environment of Virtual Machines

Cloud resource scheduling is gaining prominence with the increasing trends of reliance on cloud infrastructure solutions. Numerous sets of cloud resource scheduling models were evident in the literature. Cloud resource scheduling refers to the distinct set of algorithms or programs the service providers engage to maintain the service level allocation for various resources over a virtual environment. The model proposed in this manuscript schedules resources of virtual machines under potential volatility aspects, which can be applied for any priority metric chosen by the server administrators. Also, the model can be flexible for any time frame-based analysis of the load factor. The model discussed in this manuscript relies on the Bollinger Bands tool for understanding the potential volatility aspects of a Virtual Machine. The experimental study of the model compared to the contemporary load balancing model called STLB (Starvation Threshold-based Load Balancing) refers to a simple and potential model that can be more pragmatic for sustainable ways of load balancing.
Origami Inference: Private Inference Using Hardware Enclaves

This work presents Origami, a framework which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blinding, interspersed with accelerator-based computation. Origami partitions the ML model into multiple partitions. The first partition receives the encrypted user input within an SGX enclave. The enclave decrypts the input and then applies cryptographic blinding to the input data and the model parameters. The layer computation is offloaded to a GPU/CPU and the computed output is returned to the enclave, which decodes the computation on noisy data using the unblinding factors privately stored within SGX. This process may be repeated for each DNN layer, as has been done in prior work Slalom. However, the overhead of blinding and unblinding the data is a limiting factor to scalability. Origami relies on the empirical observation that the feature maps after the first several layers can not be used, even by a powerful conditional GAN adversary to reconstruct input. Hence, Origami dynamically switches to executing the rest of the DNN layers directly on an accelerator. We empirically demonstrate that using Origami, a conditional GAN adversary, even with an unlimited inference budget, cannot reconstruct the input. Compared to running the entire VGG-19 model within SGX, Origami inference improves the performance of private inference from 11x while using Slalom to 15. 1x.
Intuitively adaptable outlier detector

Abstract Nowadays, we have been dealing with a large amount of data in which anomalies occur naturally for many reasons, both due to hardware and humans. Therefore, it is necessary to develop efficient tools that are easily adaptable to various data. The paper presents an innovative use of classical statistical tools to detect outliers in multidimensional data sets. The proposed approach uses well‐known statistical methods in an innovative way and allows for a high level of efficiency to be achieved using multi‐level aggregation. The effectiveness of the proposed innovative method is demonstrated by a series of numerical experiments.
Ikshana: Intelligent Assisting System for Visually Challenged People

People with visual impairment experience numerous difficulties in their everyday life. With the advancements in technologies, many developers have engineered various devices and aids for visually challenged people. However, many such assistive devices fall short of the user requirements concerning level of support and price. This paper presents Ikshana for visually challenged people, which intends to assist them with multiple everyday tasks using the advantage of an affordable and wearable design. Ikshana is a Sanskrit origin noun, which means sight. Ikshana, as a product, serves as a vocalized assistant which implements features like image processing, text detection and recognition, face identification, currency denomination recognition, and obstruction detection. The text reader system employs OCR software to help interpret the printed texts and online articles. The hardware architecture comprises a Raspberry pi 3B model as the processing unit, Raspberry Pi camera, buttons, and ultrasonic sensors. As a narrating agent, the headphone directs the audio output to the recipient.
Monocular 3-D Object Detection Based on Depth-Guided Local Convolution for Smart Payment in D2D Systems

3-D object detection from mobile phones in Device-to-Device (D2D) system provides a new smart payment tool for the next generation of fintech, which is more flexible and efficient than the traditional barcode. In this article, we propose a monocular 3-D object detection method based on depth-guided local convolution. The method combines the information of RGB image mode and depth mode by using a convolution kernel through depth image and works on a single RGB image locally. According to the multiscale input information, the convolution kernel is adaptively adjusted to capture the target objects of different scales, so as to improve the performance of 3-D object detection. In addition, we use the soft-non-maximum suppression algorithm instead of traditional non-maximum suppression to select the best prediction box. In order to further improve the accuracy of 3-D object detection, the depth estimation network and 3-D object detection network are jointly trained in this method to make the two networks constrain each other and achieve the best performance.
Increasing resilience via the use of personal data: Lessons from COVID-19 dashboards on data governance for the public good

Abstract Contemporary data tools such as online dashboards have been instrumental in monitoring the spread of the COVID-19 pandemic. These real-time interactive platforms allow citizens to understand the local, regional, and global spread of COVID-19 in a consolidated and intuitive manner. Despite this, little research has been conducted on how citizens respond to the data on the dashboards in terms of the pandemic and data governance issues such as privacy. In this paper, we seek to answer the research question: how can governments use data tools, such as dashboards, to balance the trade-offs between safeguarding public health and protecting data privacy during a public health crisis? This study used surveys and semi-structured interviews to understand the perspectives of the developers and users of COVID-19 dashboards in Hong Kong. A typology was also developed to assess how Hong Kong’s dashboards navigated trade-offs between data disclosure and privacy at a time of crisis compared to dashboards in other jurisdictions. Results reveal that two key factors were present in the design and improvement of COVID-19 dashboards in Hong Kong: informed actions based on open COVID-19 case data, and significant public trust built on data transparency. Finally, this study argues that norms surrounding reporting on COVID-19 cases, as well as cases for future pandemics, should be co-constructed among citizens and governments so that policies founded on such norms can be acknowledged as salient, credible, and legitimate.
Voronoi Evolving Treemaps

Since their invention, treemaps have been widely used to visualize hierarchical structures, due to their intuitive nature and their scaling capability: Indeed, given a maximum treemap size, one can depict hierarchical structures, such as file and software systems, of arbitrary size and depth. To make up for the rather blocky nature of treemaps, Voronoi treemaps were introduced, leading to depictions that look more "organic". However, hierarchical structures in general, evolve over time, and this is especially the case for software.We present Voronoi Evolving Treemaps (VET), a novel approach inspired by the Voronoi power-weighted treemap algorithm, that takes into account the evolution of hierarchical structures. VET is able to display the complete evolution of a software system in terms of its hierarchical structure, and enriches the visualization with additional information. We detail VET’s evolutionary layout algorithm, discuss the architecture, implementation, and the features of VET, and illustrate how VET can be used to analyze the evolution of different systems.
Remote Sensing Image Captioning with Continuous Output Neural Models

Remote sensing image captioning involves generating a concise textual description for an input aerial image. Most previous methods are based on neural encoder-decoder models trained to generate a sequence of discrete outputs with the standard cross-entropy token-level loss. This paper explores an alternative method based on continuous outputs, generating sequences of embedding vectors instead of directly predicting discrete word tokens. We argue that continuous outputs can facilitate the optimization of semantic similarity, as opposed to exact word-by-word matches. It also facilitates the use of loss functions that compare different views of the data. This includes comparing representations for individual tokens and for the entire captions, and also comparing captions against intermediate image representations. We experimentally compared discrete versus continuous output methods over the RSICD dataset, extensively used in the area. Results show that continuous outputs can indeed lead to better results, and our approach performs competitively with the state-of-the-art model in the area.
UI/UX Design of Portable Simulation Pet ‘KEDAMA’ Hairball for Relieving Pressure

Nowadays, the number of people who keep pets is increasing, and keeping pets is entertaining and relieves the stress of life. However, since the owner has less time to meet his pet on business trips or overtime, we would like to make a hairball which is called 'KEDAMA' in Japanese that can be near the owner instead of the pet at such times. This research is a UI/UX design aimed at relieving stress that acts on the five senses. We would like to select the effect of pets on the five senses of people and reduce the stress of people of the present generation. This KEDAMA system in addition to allowing users to choose existing options, can also be customized in the hair length, color, curl and other aspects. After the practical application of the system to the target people and the customized use of the KEDAMA, through our subjective questionnaire survey, the results show that 85% of the people would relieve the pressure to a certain extent after using it.
BitMine: An End-to-End Tool for Detecting Rowhammer Vulnerability

Rowhammer is a destructive software-induced DRAM fault, which an attacker can leverage to break system security. Both individual customers and enterprise users (e.g., cloud providers) might refrain from using a computing system if it is vulnerable to rowhammer vulnerability. In this paper, we provide the first end-to-end tool, coined BitMine, that systematically assesses a DRAM chip’s vulnerability to rowhammer bit flips. BitMine is an extension of DRAMDig. As DRAM address mappings are proprietary techniques and critical in inducing rowhammer bit flips, DRAMDig, our prior work, leverages domain knowledge to efficiently and deterministically reverse-engineer DRAM address mappings on Intel machines. By incorporating DRAMDig, BitMine configures three key parameters, i.e., hammer methods, hammer patterns, data patterns, on the effectiveness of finding rowhammer bit flips. BitMine by default implements 13 hammer methods, 4 hammer patterns and 16 data patterns and is extensible to support more. We evaluate DRAMDig and BitMine against multiple machine models that combine different DRAM chips and Intel microarchitectures. Our experiment results show that DRAMDig efficiently uncovers a deterministic DRAM address mapping for each machine model, and every implemented parameter in BitMine has its distinct effectiveness in triggering bit flips for different machine models.
Stepwise Help and Scaffolding for Java Code Tracing Problems With an Interactive Trace Table

In this paper, we describe the integration of a step-by-step interactive trace table into an existing practice system for introductory Java programming. These autogenerated trace problems provide help and scaffolding for students who have trouble in solving traditional one-step code tracing problems, accommodating a wider variety of learners. Findings from classroom deployments suggest the scaffolding provided by the trace table is a plausible form of help, most notably increases in performance and persistence and lower task difficulty. Based on usage data, we propose future implications for an adaptive version of the interactive trace table based on learner modeling.
Using Blockchain for Enhancing Collaboration among Independent Enterprises: A Knowledge-based Approach

Collaboration is essential to improve product quality, lower delays, and maximize profits. However, for effective collaboration, the data needs to be shared across organizations. Two problems arise: what and how much data to share and how to synchronize changes to shared data efficiently. Additionally, shared information must be independently verifiable to enhance trust. To address these issues, we present Pegasus. It provides ontology, heuristic rules and, smart contract templates. A case study in collaborative design from a large Original Equipment Manufacturer (OEM) illustrates Pegasus’s application, strengths, and weaknesses. The evaluation uses Hyperledger Fabric on Amazon Web Services.