Reviewerly/exHarmony huggingface dataset:
Medical decision support systems based on Fuzzy Cognitive Maps

This paper determines the risk for cardiovascular diseases (CVDs), and nutrition level in infants aged 0–6 months using Fuzzy Cognitive Maps (FCMs). The aim of this study is to facilitates the medical experts to early detects these diseases with accuracy, so that overall death ratio can be reduced. Firstly, we have introduced the concepts of FCMs and briefly refer to the applications of these methods in medical. After that, two intelligent decision support systems for cardiovascular and malnutrition are developed using FCMs. The proposed cardiovascular risk assessment system takes six inputs: chest pain, cholesterol, heart rate, blood pressure, blood sugar, and old peak and determines CVDs risk. The second decision support system of malnutrition diagnosis takes twelve inputs: breastfeeding, daily income, maternal education, colostrum intake, energy intake, protein intake, vitamin A intake, iron intake, family size, height, weight, head circumference, and skin fold thickness and diagnoses the nutrition level in infants. We have explained the working of both decision support systems using case studies.
Modular divide-and-conquer parallelization of nested loops

We propose a methodology for automatic generation of divide-and-conquer parallel implementations of sequential nested loops. We focus on a class of loops that traverse read-only multidimensional collections (lists or arrays) and compute a function over these collections. Our approach is modular, in that, the inner loop nest is abstracted away to produce a simpler loop nest for parallelization. The summarized version of the loop nest is then parallelized. The main challenge addressed by this paper is that to perform the code transformations necessary in each step, the loop nest may have to be augmented (automatically) with extra computation to make possible the abstraction and/or the parallelization tasks. We present theoretical results to justify the correctness of our modular approach, and algorithmic solutions for automation. Experimental results demonstrate that our approach can parallelize highly non-trivial loop nests efficiently.
3D skeleton-based action recognition with convolutional neural networks

Activity recognition based on skeletons has drawn a lot of attention due to its wide applications in human-computer interaction, surveillance system. Compare with image data, a skeleton has a benefit of the robustness with background changing and computing efficiently dues to its low dimensional representation. With the rise of deep neural networks, a lot of works has been applied using both CNN and LSTM networks to solve this problem. In this paper, we proposed a framework for action recognition using skeleton data and evaluate it with different network architectures. We first modify the feature representation by adding motion information to a skeleton image, which gives useful information to the networks. After that, different networks architectures have been employed and evaluated to give insight into how well it will perform on this kind of data. Finally, we evaluated the system on two public datasets NTU-RGB+D and CMDFall to show the efficiency and feasibility of the system. The proposed method achieves 76.8% and 45.23% on NTU-RGB+D and CMDFall, respectively, which is competitive results.
Why do Users Issue Good Queries?

Despite advances in the past few decades in studying what kind of queries users input to search engines and how to suggest queries for the users, the fundamental question of what makes human cognition able to estimate goodness of query terms is largely unanswered. For example, a person searching information about "cats'' is able to choose query terms, such as "housecat'', "feline'', or "animal'' and avoid terms like "similar'', "variety'', and "distinguish''. We investigated the association between the specificity of terms occurring in documents and human brain activity measured via electroencephalography (EEG). We analyzed the brain activity data of fifteen participants, recorded in response to reading terms from Wikipedia documents. Term specificity was shown to be associated with the amplitude of evoked brain responses. The results indicate that by being able to determine which terms carry maximal information about, and can best discriminate between, documents, people have the capability to enter good query terms. Moreover, our results suggest that the effective query term selection process, often observed in practical search behavior studies, has a neural basis. We believe our findings constitute an important step in revealing the cognitive processing behind query formulation and evaluating informativeness of language in general.
Detection of Tampered Images Using Blockchain Technology

Multimedia data, especially images are increasing dramatically. Images published on the Web are more often facing the risk of being tampered or manipulated since their content is easily mutable. Using blockchain technology provides advantages and, at the same time, challenges when dealing with this issue due to the following reasons: (a) data in a blockchain are well saved and immutable and (b) adding the data directly to a blockchain may consume much time which makes it computationally and economically expensive. Thus, we propose a blockchain-based solution which considers two key aspects: First, using a blockchain to register information about ownership and copyrights for authors, as well as descriptive information of an image, used to detect copyright violations. Second, avoiding insertion of raw image data into the blockchain, but storing only unique descriptive metadata about the images, allowing for a more efficient implementation of the system. This work considers different well-known image matching approaches to validate the power of the proposed approach, which allows for an efficient checking of violations of copyrights for a given image.
Teaching User Centered Conceptual Design Using Cross-Cultural Personas and Peer Reviews for a Large Cohort of Students

It is important that Software Engineering and Information Technology students learn techniques to conceive solutions that are centered on the users. Users are often from many different cultures; hence an awareness of cultural differences is an important concept for students to grasp. Particularly when class sizes are large, access to users that would allow the students to practice User-Centered design principles is not feasible. Different methodologies must be pursued to teach students the principles of design. This paper presents peer review and personas, archetypical users of an application, as techniques for teaching User-Centered conceptual design to a large cohort of students. We provided a persona from a set of six personas, each authored to represent two different cultures (Australian or Vietnamese), to 153 undergraduate students at an Australian university to conceive design solutions for the persona. Later, the students were exposed to all of the personas and then they peer reviewed each other's design artifacts. From the comments left by the students we conclude that the technique has met its learning objectives.
Remixing Wakanda

While maker education offers a method for increasing interest in STEM domains among youth, underrepresented communities possess a rich cultural history of making and STEM practices too often ignored by popular articulations of making. In this paper we describe the 8-week ReMixing Wakanda Project, which brings together youth of color, artists, designers, educators, and researchers to construct a new vision for a culturally relevant and sustaining STEAM-centered learning and making framework situated in an Afrofuturist design aesthetic. Here we highlight how the design of the physical space, discussions with guest artists, and a collective art construction supported participants as they explored the past to confront the present and imagine futures for and by and about themselves.
Binarization of optical fringe patterns with intensity inhomogeneities based on modified FCM algorithm

Binarization of fringe patterns is a commonly used process in the fringe skeleton method for optical fringe pattern analysis. Most of the existing binarization algorithms of fringe patterns are based on the determination of gray level threshold. However, spatial intensity inhomogeneity induced by the uneven illumination makes the gray level threshold hard to determine. In this paper, we present a method for the binarization of fringe patterns based on a modified Fuzzy c-means clustering (FCM) algorithm. This method permits the clustering of pixels influenced by their immediate surroundings. The pixels in the fringe pattern are clustered into white fringes and black fringes without calculating thresholds. We evaluate the performance of this method by applying it to computer-simulated and real fringe patterns. Experimental results demonstrate that the proposed method can achieve the desired binarization results and perform better than the previous binarization methods, under non-uniform illumination conditions.
A simulated approach to evaluate side-channel attack countermeasures for the Advanced Encryption Standard

Modern networks have critical security needs and a suitable level of protection and performance is usually achieved with the use of dedicated hardware cryptographic cores. Although the Advanced Encryption Standard (AES) is considered the best approach when symmetric cryptography is required, one of its main weaknesses lies in its measurable power consumption. Side-Channel Attacks (SCAs) use this emitted power to analyse and revert the mathematical steps and extract the encryption key. Nowadays they exist several dedicated equipment and workstations for SCA weaknesses analysis and the evaluation of the related countermeasures, but they can present significant drawbacks as a high cost for the instrumentation or, in case of cheaper instrumentation, the need to underclock the physical circuit implementing the AES cipher, in order to adapt the circuit clock frequency accordingly to the power sampling rate of ADCs or oscilloscopes bandwidth. In this work, we proposed a methodology for Correlation and Differential Power Analysis against hardware implementations of an AES core, relying only on a simulative approach. Our solution extracts simulated power traces from a gate-level netlist and then elaborates them using mathematical-statistical procedures. The main advantage of our solution is that it allows to emulate a real attack scenario based on emitted power analysis, without requiring any additional physical circuit or dedicated equipment for power samples acquisition, neither modifying the working conditions of the target application context (such as the circuit clock frequency). Thus, our approach can be used to validate and benchmark any SCA countermeasure during an early step of the design, thereby shortening and helping the designers to find the best solution during a preliminary phase and potentially without additional costs.
Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average-dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.
A Study on SMIE Credit Evaluation Model Based on Blockchain Technology

As an important part of the national economy, (Small and Medium Industrial Enterprise)SMIE are an important factor that promotes China's economic growth, but financing is still a difficult problem that restricts their development. Dependent on its intelligent protocols, distributed features and consensus mechanism, blockchain has broad prospects in the field of enterprise credit reporting, but the previous credit evaluation system had problems such as forgery of corporate credit records and imperfect evaluation indicators. This paper studied the SMIE credit evaluation model and analyzed the evaluation indicators, and based on blockchain technology, it realized data sharing and eliminated the impact of false data on the model. Finally, it proposed the construction of an SMIE credit evaluation model with complete evaluation indicators so as to improve related research on credit evaluation system.
Automated Clustering for Data Analytics

ABSTRACT Today, auditors must consider the risks of material misstatement due to fraud during the financial statement audit (Messier, Glover, and Prawitt 2016). Current audit guidance recommends the use of data mining methods such as clustering to improve the likelihood of discovering irregularities during fraud risk assessment (ASB 2012). Unfortunately, significant challenges exist relative to using clustering in practice, including data preprocessing, model construction, model selection, and outlier detection. The traditional auditor is not trained to effectively address these complexities. One solution entails automation of clustering, thus eliminating the difficult, manual decision points within the clustering process. This would allow practitioners to focus on problem investigation and resolution, rather than being burdened with the technical aspects of clustering. In this paper, automated clustering is explored. In the process, each manual decision point is addressed, and a suitable automated solution is developed. Upon conclusion, a clustering application is formulated and demonstrated.
The optimal upper bound of the number of queries for Laplace mechanism under differential privacy

Differential privacy is a state-of-the-art technology for privacy preserving, and Laplace mechanism is a simple and powerful tool to realize differential privacy. However, there is an obvious flaw in differential privacy, which is each query function can only be executed finite times for the reason that adversary can recover the real query result if he executes the same query function many times. Unfortunately, how to set the upper bound for the number of linear queries is still an issue. In this paper, we focus on the linear query function in Laplace-based mechanisms, and we propose a method to set the upper bound for the number of linear queries from the perspective of information theory. The main idea is, firstly we find the most aggressive linear query that leaks the maximum information about the dataset to adversaries, and we set the upper bound of the number of queries so that even if the most aggressive linear query cannot leak the whole self-information about any individual to the adversary. On the other hand, the number of queries is also influenced by the type of dataset (continuous and discrete). In this paper, we also discuss the different upper bound for the number of queries for continuous datasets and discrete datasets. Finally, we conduct the experiments on the continuous dataset and the discrete dataset to prove our result.
Probabilistic Existence Results for Parent-Identifying Schemes

Parent-identifying schemes provide a way to identify causes from effects for some information systems, such as digital fingerprinting and group testing. In this paper, we consider the combinatorial structures for parent-identifying schemes. First, we establish an equivalent relationship between the parent-identifying schemes and forbidden configurations. Based on this relationship, we derive the probabilistic existence lower bounds for two related combinatorial structures, that is, t-parent-identifying set systems (t-IPPS) and t-multimedia parent-identifying codes (t-MIPPC), which are used in broadcast encryption and multimedia fingerprinting, respectively. The probabilistic lower bound for the maximum size of a t-IPPS has the asymptotically optimal order of magnitude in many cases, and that for t-MIPPC provides the asymptotically optimal code rate when t = 2 and the best known asymptotic code rate when t ≥ 3. Furthermore, we analyze the structure of 2-IPPS and prove some bounds for certain cases.
Fear the Reaper: how content moderation rules are enforced on social media

This paper investigates the enforcement stage of the content moderation process on social media platforms. It argues that the current approach adopted by most platforms is underdeveloped, poses serious human rights issues, and would benefit from a number of reforms. This paper will first explain what the role of the moderator entails and how the enforcement process occurs at social media platforms. The investigation will then turn to the enforcement process itself and identify a number of problems in how content rules are generally enforced including bias in decision-making, an over-reliance on efficiency as a solution, and inconsistent enforcement of terms and conditions. Finally, it will provide a number of suggestions for reform including moving away from the efficiency narrative to consider larger issues in enforcement (which are not so easily solved) such as human rights and rule of law issues and the adoption of a body of precedents as a tool for accountability and empowerment of users.
Lattice RingCT V2.0 with Multiple Input and Multiple Output Wallets

This paper presents the Lattice-based Ring Confidential Transactions "Lattice RingCT v2.0" protocol. Unlike the previous Lattice RingCT v1.0 (LRCT v1.0) protocol, the new protocol supports Multiple-Input and Multiple-Output (MIMO) wallets in transactions, and it is a fully functional protocol construction for cryptocurrency applications such as Hcash. Since the MIMO cryptocurrency setting introduces new balance security requirements (and in particular, security against out-of-range amount attacks), we give a refined balance security model to capture such attacks, as well as a refined anonymity model to capture amount privacy attacks. Our protocol extends a previously proposed ring signature scheme in the LRCT v1.0 protocol, to support the MIMO requirements while preserving the post-quantum security guarantees, and uses a lattice-based zero-knowledge range proof to achieve security against out-of-range attacks. Preliminary parameter estimates and signature sizes are proposed as a point of reference for future studies.
Outline Generation

In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potential sections and generating the corresponding section headings. Without loss of generality, the OG task can be viewed as a novel structured summarization task. To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings. The first one is the foundation for section identification, while the latter two are critical for consistent heading generation. In this work, we formulate the OG task as a hierarchical structured prediction problem, i.e., to first predict a sequence of section boundaries and then a sequence of section headings accordingly. We propose a novel hierarchical structured neural generation model, named HiStGen, for the task. Our model attempts to capture the three-level coherence via the following ways. First, we introduce a Markov paragraph dependency mechanism between context paragraphs for section identification. Second, we employ a section-aware attention mechanism to ensure the semantic coherence between a section and its heading. Finally, we leverage a Markov heading dependency mechanism and a review mechanism between context headings to improve the consistency and eliminate duplication between section headings. Besides, we build a novel Wriptsize IKI OG dataset, a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. Experimental results on our benchmark dataset demonstrate that our model can significantly outperform several state-of-the-art sequential generation models for the OG task.
Did It Change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates

Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input. Motivated by the broad availability of geo-tagged street-view images, we propose a new task aiming to make the map update process more proactive. We focus on automatically detecting changes of Points of Interest (POIs), specifically stores or shops of any kind, based on visual input. Faced with the lack of an appropriate benchmark, we build and release a large dataset, captured in two large shopping centers, that comprises 33K geo-localized images and 578 POIs. We then design a generic approach that compares two image sets captured in the same venue at different times and outputs POI changes as a ranked list of map locations. In contrast to logo or franchise recognition approaches, our system does not depend on an external franchise database. It is instead inspired by recent deep metric learning approaches that learn a similarity function fit to the task at hand. We compare various loss functions to learn a metric aligned with the POI change detection goal, and report promising results.
RapidRelease - A Dataset of Projects and Issues on Github with Rapid Releases

In the recent years, there has been a surge in the adoption of agile development model and continuous integration (CI) in software development. Recent trends have reduced average release cycle lengths to as low as 1-2 weeks, leading to an extensive number of studies in release engineering. Open-source development (OSD) has also witnessed a rapid increase in release rates, however, no large dataset of open-source projects exists which features high release rates. In this paper, we introduce the RapidRelease dataset, a data showcase of high release frequency open-source projects. The dataset hosts 994 projects from Github, with over 2 million issue reports. To the best of our knowledge, this is the first dataset that can facilitate researchers to empirically study release engineering and agile software development in open-source projects with rapid releases.