Reviewerly/exHarmony huggingface dataset:
Bi-directional Relevance Matching between Medical Corpora

Readily available, trustworthy, and usable medical information is vital to promoting global health. Cochrane is a non-profit medical organization that conducts and publishes systematic reviews of medical research findings. Over 3000 Cochrane Reviews are presently used as evidence in Wikipedia articles. Currently, Cochrane's researchers manually search Wikipedia pages related to medicine in order to identify Wikipedia articles that can be improved with Cochrane evidence. Our aim is to streamline this process by applying existing document similarity and information retrieval methods to automatically link Wikipedia articles and Cochrane Reviews. Potential challenges to this project include document length and the specificity of the corpora. These challenges distinguish this problem from ordinary document representation and retrieval problems. For our methodology, we worked with data from 7400 Cochrane Reviews, ranging from one to several pages in length, and 33,000 Wikipedia articles categorized as medical. We explored different methods of document vectorization including TFIDF, LDA, LSA, word2Vec, and doc2Vec. For every document in both corpora, their similarity to each document in the opposing set was calculated using established vector similarity metrics such as cosine similarity and KL-divergence. Labeled data for this unsupervised task was not available. Models were evaluated by comparing the results to two standards: (1) Cochrane Reviews currently cited in Wikipedia articles and (2) a data set provided by a medical expert that indicates which Cochrane Reviews could be considered for specific Wikipedia articles. Our system performs best using TFIDF document representation and cosine similarity.
Combining ANFIS and Digital Coaching for Good Decisions in Industrial Processes

The context we address is the digitalization of industry and industrial processes. Digitalization brings enhanced logistics network and value chain integration, which are effective instruments to meet increasing competition and slimmer margins for productivity and profitability. Digitalization also brings pronounced requirements for effective planning, problem solving and decision-making. Decision analytics, including soft computing, will meet the challenges from growing global competition that major industrial corporations face and will help solve the problems of big data/fast data that digitalization is generating as a by-product. A new mantra is gaining support - powerful, intelligent systems will be effective for the digitalization of industrial processes. The discussion has paid less attention to the fact that users need advanced knowledge and skills to benefit from the intelligent systems. We need both an effective transfer of knowledge from developers, experts and researchers to users and support for daily use and operations as automated, intelligent industrial systems are complex to operate. We call this knowledge mobilization, and work out how ANFIS models and digital coaching contribute to good decisions in large, complex industrial processes.
The Research of Four Rotor Aircraft based on STM32

Four rotor aircraft has been extensively studied in recent years. Especially the small Four-rotor aircraft has a very broad application prospects in real life, many companies and scientific research institutions are studying. The technology involved in the Four-rotor aircraft includes flight dynamics, mechanical structure, navigation, automatic control, circuit design and so on. By developing a Four-rotor aircraft, we can not only learn a variety of knowledge and technology, but also broaden our horizons and understand the development of the current automatic control system in the process of production, which will be of great help to future work and learning. The hardware design of the Four-rotor aircraft studied in this paper includes: using STM32 series MCU as the main controller, 6-axis attitude sensor MPU6050 to collect the acceleration and angular velocity of the vehicle, electronic compass HMC5883L to collect magnetic field, USB serial port chip CP2104, TP4056 to provide 4.2V lithium batteries. The charging voltage and charging current can be controlled, and the internal universal timer interface is used to realize the capture function of remote control input PWM waveform and the output PWM waveform to control the speed of the motor. The software design in this paper includes the configuration of serial communication, the connection between the flight control board and the host computer, the reading of sensor data, the calculation of the current attitude by the strapdown navigation control method, the output of Euler angle, the configuration of timer to achieve the function of input capture and PWM waveform output, and the application of digital PID. The controller calculates the deviation between the desired attitude input by the remote controller and the current attitude of the aircraft, calculates the duty cycle of the output PWM waveform, and adjusts the rotation rate of the four rotors to realize the six-degree-of-freedom flight state of the Four-rotor aircraft.
Wwall

Water is an essential nutritional source. Its deficiency negatively influences our mental and physical health. Despite this, people may forget drinking enough water due to the everyday rush in places like office environments. Previously, HCI researchers addressed this problem via developing interactive technologies aimed at motivating regular water intake. However, these technologies heavily rely on giving individual feedback on water intake and self-monitoring as a behavior change strategy. We expanded this existing design space with a new concept: Wwall, a smart water dispenser system which differs from previous examples as it 1) gives collective feedback along with individual feedback via an ambient display, and 2) uses cooperation to motivate regular water intake. In this WIP, we present our design and prototyping process as well as directions for future work
A Tent Map and Logistic Map Based Approach for Chaos-Based Image Encryption and Decryption

A cryptosystem incorporating multiple chaotic maps is proposed. Visual cryptography is necessary in today’s world to combat a large number of data thefts. Image encryption using chaotic maps is a common procedure, but it does not ensure absolute security. We use two maps in this paper, to curb the problem of security. The use of two chaotic maps has made the process of image encryption more secure. Image encryption is done using tent map, which is superimposed on another image encrypted using the logistic map. The encryption scheme has a greater keyspace because of two chaotic maps instead of one and thus has better security against statistical attacks.
Case Study The Internet of Things and Ethics

The Internet of Things (IoT) may be defined as a network of networks, where the end devices are not user-handled devices but can be computing devices, mechanical and digital machines. In many businesses, IoT-based software is used increasingly as a means to deliver enhanced customer service and improved business management procedures. By using IoT to monitor business operations, through tracking-capable software, businesses are, for instance, able to track products and employees. The issue is further explored through literature review and a case study of a company developing IoT based monitoring software. The review focuses on the effects of using IoT as part of Smart Information Systems, especially systems supported by 5G networks in the near future. The effects on the users of SIS are referred to by the term Quality of Experience (QoE) and the specific effects of 5G networks on QoE are discussed in this background review. Since the user experience is also affected by such actions as employee and asset monitoring with the use of IoT, a brief overview of legal aspects follows the technological details of QoE in an IoT-aware 5G system. The legal/human rights analysis is presented through the literature, and takes into account some suggestions for guidelines and policies on monitoring is offered. A discussion on ethics and perceptions around monitoring and tracking is further presented. The CRM.COM case focuses thereafter on how the company provides tracking software as a service and as a product for businesses nationally and in several countries worldwide. The case study discusses the ethics of such IoT-powered software, by considering both their design and their usage. Overall, the area of using IoT-based tracking and monitoring applications to assist and enhance specific business processes is growing and becoming increasingly popular, both in terms of development and use. Being a new research area, however, it lacks sufficient literature that examines the ethical, social, economic and legal implications of the use of this technology. Such studies into the design, development and use of such IoT-based applications present important relevant information that enriches the state-of-the-art literature on the topic both from an academic and a practical perspective. This report offers an original case study on the use of an IoT related SIS in the software design and development area. Many of the ethical and legal issues discussed in this report have been analysed more generally within academia and assessed in other areas of application, but have rarely been associated with the IoT usage for tracking and monitoring. Therefore, this report will be highly valuable for the development and furthering of theory, knowledge and application for designing, developing and using such IoT based applications.
Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis

We propose a Bayesian sparse multivariate regression method to model the relationship between microbe abundance and environmental factors for microbiome data. We model abundance counts of operational taxonomic units (OTUs) with a negative binomial distribution and relate covariates to the counts through regression. Extending conventional nonlocal priors, we construct asymmetric nonlocal priors for regression coefficients to efficiently identify relevant covariates and their effect directions. We build a hierarchical model to facilitate pooling of information across OTUs that produces parsimonious results with improved accuracy. We present simulation studies that compare variable selection performance under the proposed model to those under Bayesian sparse regression models with asymmetric and symmetric local priors and two frequentist models. The simulations show the proposed model identifies important covariates and yields coefficient estimates with favorable accuracy compared with the alternatives. The proposed model is applied to analyze an ocean microbiome dataset collected over time to study the association of harmful algal bloom conditions with microbial communities.
A Subjective Comparison of Broadcast and Unicast Transmission Impairments

This paper describes a subjective test performed with 60 viewers over the age of 18 years and 570 young students, the majority being 10 or 11 years old, to understand the relative annoyance caused by various transmission impairments when viewing television content. Our goal was to compare errors associated with broadcast and multicast delivery, which appear as areas of extreme color or tearing of the image, with errors associated with adaptive bitrate unicast, such as interruptions and quality variations. We also wanted to study the impact of the type of screen the content is viewed on, considering viewing on tablets and on a TV. A significant level of consistency was found over all of the results obtained. Interruptions were the most annoying impairment, with multiple short interruptions being more annoying than a single longer one, and with missing content being more annoying than simply pausing. We found more tolerance to quality variation on tablets and found that younger adults are more annoyed by impairments on tablets and older adults are more annoyed when watching on the TV. We have found young students to have similar views to adults, and have found them to be capable of participating in subjective tests.
Registration uncertainty quantification via low-dimensional characterization of geometric deformations

This paper presents an efficient approach to quantifying image registration uncertainty based on a low-dimensional representation of geometric deformations. In contrast to previous methods, we develop a Bayesian diffeomorphic registration framework in a bandlimited space, rather than a high-dimensional image space. We show that a dense posterior distribution on deformation fields can be fully characterized by much fewer parameters, which dramatically reduces the computational complexity of model inferences. To further avoid heavy computation loads introduced by random sampling algorithms, we approximate a marginal posterior by using Laplace's method at the optimal solution of log-posterior distribution. Experimental results on both 2D synthetic data and real 3D brain magnetic resonance imaging (MRI) scans demonstrate that our method is significantly faster than the state-of-the-art diffeomorphic registration uncertainty quantification algorithms, while producing comparable results.
Tangible Tensors

Advances in biology and computational power have led to the availability of large biological data sets, yet these advances raise new design challenges. Designers must build effective tools that cater to the needs of biologists and data scientists in order to visually explore and manipulate data for modeling and analysis. We present the Tangible Tensors system, a new tensor-based visualization and tangible manipulation tool that serves to improve functionality over previous data analytics approaches. We designed a platform that supports iterative exploration of the solution space and better interpretation tools for biologists. User study results indicate that our system is easy to learn and use, and useful for data modeling and analysis tasks.
At the Intersection of Culture and Method

Boundaries and borders continue to draw political attention and create conflict on a global scale. They interrupt or facilitate people and money, and influence how designers and politicians alike work in opposition to oppression. This one-day workshop will explore the use of design research and computing practice in resisting and reifying inequalities. Recognizing that technology production is only a partial response to caring for ourselves and our environment, participants of this workshop will consider the design commitments, pedagogies, and labor that produce new strategies for more equitable futures. We will collectively ask and grapple with the following questions: 1) what are the current concerns and pressures of our community in producing feminist means and ends, 2) how do we work as academics, activists, allies and advocates through our design activities; and 3) whose opportunities, hopes, fears, innovations, and futures are we building?
The Settlers of “Catanbinatorics”

Summary.Catan is a dynamic property-building and trading board game in which players build a new board every time they play by arranging tiles, number tokens, and port markers. In this paper, we count the number of possible boards, consider different ways of counting the number tokens based on probability, and count the number of non-equivalent tile arrangements in two ways: one using a direct approach, the other taking advantage of more elegant techniques from abstract algebra.
Similarity Search in 3D Human Motion Data

Motion capture technologies can digitize human movements into a discrete sequence of 3D skeletons. Such spatio-temporal data have a great application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. The objective of this tutorial is to explain fundamental principles and technologies designed for searching, subsequence matching, classification and action detection in the 3D human motion data. These operations inherently require the concept of similarity to determine the degree of accordance between pairs of 3D skeleton sequences. Such similarity can be modeled using a generic approach of metric space by extracting effective deep features and comparing them by efficient distance functions. The metric-space approach also enables applying traditional index structures to efficiently access large datasets of skeleton sequences. We demonstrate the functionality of selected motion-processing operations by interactive web applications.
Enhancing an Attack to DSA Schemes

In this paper, we improve the theoretical background of the attacks on the DSA schemes of a previous paper, and we present some new more practical attacks.
Turning HATE into LOVE: Compact Homomorphic Ad Hoc Threshold Encryption for Scalable MPC

In a public-key threshold encryption scheme, the sender produces a single ciphertext, and any $$t+1$$ out of $$n$$ intended recipients can combine their partial decryptions to obtain the plaintext. Ad hoc threshold encryption (ATE) schemes require no correlated setup, enabling each party to simply generate its own key pair. In this paper, we initiate a systematic study of the possibilities and limitations of ad-hoc threshold encryption, and introduce a key application to scalable multiparty computation (MPC). Assuming indistinguishability obfuscation (iO), we construct the first ATE that is sender-compact—that is, with ciphertext length independent of $$n$$ . This allows for succinct communication once public keys have been shared. We also show a basic lower bound on the extent of key sharing: every sender-compact scheme requires that recipients of a message know the public keys of other recipients in order to decrypt. We then demonstrate that threshold encryption that is ad hoc and homomorphic can be used to build efficient large-scale fault-tolerant multiparty computation (MPC) on a minimal (star) communication graph. We explore several homomorphic schemes, in particular obtaining one iO-based ATE scheme that is both sender-compact and homomorphic: each recipient can derive what they need for evaluation from a single short ciphertext. In the resulting MPC protocol, once the public keys have been distributed, all parties in the graph except for the central server send and receive only short messages, whose size is independent of the number of participants. Taken together, our results chart new possibilities for threshold encryption and raise intriguing open questions.
Use of Neumann series decomposition to fit the Weighted Euclidean distance and Inner product scoring models in automatic speaker recognition

It is commonly agreed that the main performance indicators in real life biometric applications are: accuracy, calibration quality and computation cost in the case of large databases use. Thus, the Gaussian Probabilistic Linear Discriminant Analysis (GPLDA) has been considered, during the last decade, as one of the most promising scoring techniques in Automatic Speaker Recognition. In this paper, the Algerian Modern Colloquial Arabic Speech Corpus has been used to evaluate a new i-vectors scoring approach based on Neumann series decomposition to fit the Weighted Euclidean Distance and Weighted Inner Product scoring models. This realistic evaluation investigates how this new approach can improve the global recognition performance compared to the GPLDA baseline system, considered as a reference point. The obtained results have shown that the proposed method outperforms the GPLDA baseline system in terms of recognition accuracy and calibration quality. More interestingly, the computational cost of the proposed method is significantly reduced compared to the GPLDA baseline counterpart. These results have an important implication in real-life speaker verification scenarios, especially in some tracking applications, where the computation cost is one of the main concerns besides performance.
Accurate Positioning Siamese Network for Real-Time Object Tracking

Though end-to-end siamese networks have achieved great performance on object tracking owing to offline pre-training with large datasets. They are still liable to fail to track fast moving object and their accuracy suffers from the cosine window for mitigating background interference. The cosine window will aggravate the boundary effect and have a negative impact on track. In this paper, we propose an accurate positioning siamese network (FPSiam) for real-time object tracking. This approach can better estimate the search candidate area of the object in the current frame from the previous tracking position and can keep tracking well for the fast moving object. The unbalanced penalty mechanism of the cosine window in the tracking process is avoided. Furthermore, the features of the low-level layers are high-resolution, which is suitable for positioning the object. High-level layers are full of rich semantic features which are suitable for classifying the objects. In order to utilize the advantages of high-level features and low-level features, we introduce a densely connected tracking network. A number of experiments were conducted on five challenging tracking datasets: OTB50, OTB2013, OTB2015, VOT2015, and VOT2016, and the proposed method achieved excellent results on these benchmarks.
A Fuzzy Modeling Approach for Group Decision Making in Social Networks

Social networks have been commonly used, people use social networks with various purposes, such as, enjoying time, making business, and contacting their friends. All these activities are mainly based on sharing data. In social networks, making decision on data sharing process has become one of the main challenge because it involves people who have different opinions on the same problem. Diversified opinions cause uncertainties in decision making process. Fuzzy logic is used to overcome uncertainties’ situations. In this work, we provide a fuzzy logic based decision making framework for SNs. The proposed fuzzy logic based framework uses data sensitivity value and trust value (confidence value) to make the group decision. Users express their opinions on data security features to obtain aggregated decision. Facebook data sharing process is chosen as a case study.
SOM Clustering Collaborative Filtering Algorithm Based on Singular Value Decomposition

The application of traditional collaborative filtering algorithm on large-scale commercial websites is very mature. However, the data sparsity and extensibility problems that occur in the algorithm affect the recommendation accuracy of the algorithm. In order to solve this problem, a SOM clustering collaborative filtering algorithm based on singular value decomposition is proposed. Firstly, the original sparse matrix is reduced by the singular value decomposition, and the items are evaluated in the low-dimensional space, the prediction results are filled in the original matrix, which alleviates the problem of data sparseness. Then use SOM to cluster the users, which reduces the range of users searching for neighbors and improves the scalability of the algorithm. The experimental results on MovieLens-100k show that the algorithm can effectively improve the accuracy of the recommendation.
A synopsis of static analysis alerts on open source software

Static application security testing (SAST) tools detect potential code defects (alerts) without having to execute the code. SASTs are now widely used in practice by both commercial and open source software (OSS). Prior work found that half of the state-of-the-art OSS projects have already employed automated static analysis [1]. However, little public information is available regarding the actionability (important to developers to act upon) of SAST alerts.