Reviewerly/exHarmony huggingface dataset:
Hyperlocal user-generated video contributions on public displays

Public displays are being increasingly used to entice citizen participation, yet their content often lacks the true voice of local inhabitants. This paper investigates the potential of allowing user-generated videos to be shown unto public displays. We thus evaluated a preliminary version of 'Story Maker', a cross-media platform that invited local inhabitants to contribute video snippets in reaction to a thematic calls to action. The uploaded videos were edited into a weekly story by a professional media moderator, and shown on three large displays located in different semi-public contexts. Our qualitative analysis of a five-week in-the-wild study translates into design considerations that focus on how user-generated content should emphasize a clear purpose, valorise an already existing community, follow immediately actionable expectations with few practical complications, and should be edited by people with local knowledge. We expect these considerations to be helpful towards future public applications of citizen-empowered content creation.
Improvising with Machines - Designing Artistic Non-Human Actors

For musicians, improvising with other musicians is not uncommon. But what happens when musicians engage in musical improvisation with semi-autonomous machines? We investigated a seminar in which design students built machines for musicians to improvise with. We explored the experiences of musicians when improvising with non-human musicians, as well as the challenges of designing non-human musicians. Among other things, we found that while from an outside perspective, the machines appeared as independent actors that interact with the musicians, the musicians experienced them as additional instruments they controlled. The interaction design of non-human actors was challenging for designers.
Implementation of the level of detail method on augmented reality android-based applications

Abstract Many 3D games and applications use augmented reality technology in smartphones. When an application or game is running, it will increase The Workload of The Processor which is signed by the heat of The Smartphone. The processor and battery temperatures increase due to heavy workload of processor. This study uses the Level of Detail-method to reduce the detail of 3D objects from the Augmented Reality application. 3D objects that appear on the smartphone screen are adjusted to the distance of the camera to the marker. The distance of the camera to the marker triggers the change of details so that the processor workload decreases. The test results show that the average Workload Processor is reduced by 10,47% and the temperature decreases by 0.94°C.
MetaLink: A Travel Guide to the LOD Cloud

Graph-based traversal is an important navigation paradigm for the Semantic Web, where datasets are interlinked to provide context. While following links may result in the discovery of complementary data sources and enriched query results, it is widely recognized that traversing the LOD Cloud indiscriminately results in low quality answers. Over the years, approaches have been published that help to determine whether links are trustworthy or not, based on certain criteria. While such approaches are often useful for specific datasets and/or in specific applications, they are not yet widely used in practice or at the scale of the entire LOD Cloud. This paper introduces a new resource called MetaLink. MetaLink is a dataset that contains metadata for a very large set of owl:sameAs links that are crawled from the LOD Cloud. MetaLink encodes a previously published error metric for each of these links. MetaLink is published in combination with LOD-a-lot, a dataset that is based on a large crawl of a subset of the LOD Cloud. By combining MetaLink and LOD-a-lot, applications are able to make informed decisions about whether or not to follow specific links on the LOD Cloud. This paper describes our approach for creating the MetaLink dataset. It describes the vocabulary that it uses and provides an overview of multiple real-world use cases in which the MetaLink dataset can solve non-trivial research and application challenges that were not addressed before.
A General Methodology to Quantify Biases in Natural Language Data

Biases in data, such as gender and racial stereotypes, are propagated through intelligent systems and amplified at end-user applications. Existing studies detect and quantify biases based on pre-defined attributes. However, in real practices, it is difficult to gather a comprehensive list of sensitive concepts for various categories of biases. We propose a general methodology to quantify dataset biases by measuring the difference of its data distribution with a reference dataset using Maximum Mean Discrepancy. For the case of natural language data, we show that lexicon-based features quantify explicit stereotypes, while deep learning-based features further capture implicit stereotypes represented by complex semantics. Our method provides a more flexible way to detect potential biases.
Efficient Query Processing with Optimistically Compressed Hash Tables &amp; Strings in the USSR

Modern query engines rely heavily on hash tables for query processing. Overall query performance and memory footprint is often determined by how hash tables and the tuples within them are represented. In this work, we propose three complementary techniques to improve this representation: Domain-Guided Prefix Suppression bit-packs keys and values tightly to reduce hash table record width. Optimistic Splitting decomposes values (and operations on them) into (operations on) frequently-accessed and infrequently-accessed value slices. By removing the infrequently-accessed value slices from the hash table record, it improves cache locality. The Unique Strings Self-aligned Region (USSR) accelerates handling frequently-occurring strings, which are very common in real-world data sets, by creating an on-the-fly dictionary of the most frequent strings. This allows executing many string operations with integer logic and reduces memory pressure. We integrated these techniques into Vectorwise. On the TPC-H benchmark, our approach reduces peak memory consumption by 2-4× and improves performance by up to 1.5×. On a real-world BI workload, we measured a 2× improvement in performance and in micro-benchmarks we observed speedups of up to 25×.
GPU-Based Parallelism for ASP-Solving

Answer Set Programming (ASP) has become the paradigm of choice in the field of logic programming and non-monotonic reasoning. With the design of new and efficient solvers, ASP has been successfully adopted in a wide range of application domains. Recently, with the advent of GPU Computing, which allowed the use of modern parallel Graphical Processing Units (GPUs) for general-purpose computing, new opportunities for accelerating ASP computation has arisen. In this paper, we describe a new approach for solving ASP that exploits the parallelism provided by GPUs. The design of a GPU-based solver poses various challenges due to the peculiarities of GPU in terms of both programmability and architecture capabilities with respect to the intrinsic nature of the satisfiability problems, which exposes poor parallelism.
Crowd density estimation based on conditional random field and convolutional neural networks

Crowd density estimation has an important value in the management of public safety. This paper presents an algorithm of combining conditional random field (CRF) model and convolution neural networks (CNN) to estimate crowd density. Firstly, the CRF model with higher-order potentials is used to extract foreground information and get a binary graph. Then using the original image to recover the information of binary graph. Finally, the multi-stage CNN was designed to obtain quality feature of foreground for crowd density estimation. In the experiment, we validate the effectiveness of the proposed algorithm on Chun-xi road and Nanjing train station data sets. Experimental results indicated that the proposed method has a good performance for crowd density estimation in different scene.
Network Interface Architecture for Remote Indirect Memory Access (RIMA) in Datacenters

Remote Direct Memory Access (RDMA) fabrics such as InfiniBand and Converged Ethernet report latency shorter by a factor of 50 than TCP. As such, RDMA is a potential replacement for TCP in datacenters (DCs) running low-latency applications, such as Web search and memcached. InfiniBand’s Shared Receive Queues (SRQs), which use two-sided send/recv verbs (i.e., channel semantics ), reduce the amount of pre-allocated, pinned memory (despite optimizations such as InfiniBand’s on-demand paging (ODP)) for message buffers. However, SRQs are limited fundamentally to a single message size per queue, which incurs either memory wastage or significant programmer burden for typical DC traffic of an arbitrary number (level of burstiness) of messages of arbitrary size. We propose remote indirect memory access (RIMA) , which avoids these pitfalls by providing (1) network interface card (NIC) microarchitecture support for novel queue semantics and (2) a new “verb” called append . To append a sender’s message to a shared queue, the receiver NIC atomically increments the queue’s tail pointer by the incoming message’s size and places the message in the newly created space. As in traditional RDMA, the NIC is responsible for pointer lookup, address translation, and enforcing virtual memory protections. This indirection of specifying a queue (and not its tail pointer, which remains hidden from senders) handles the typical DC traffic of an arbitrary sender sending an arbitrary number of messages of arbitrary size. Because RIMA’s simple hardware adds only 1--2 ns to the multi-\mu s message latency, RIMA achieves the same message latency and throughput as InfiniBand SRQ with unlimited buffering. Running memcached traffic on a 30-node InfiniBand cluster, we show that at similar, low programmer effort, RIMA achieves significantly smaller memory footprint than SRQ. However, while SRQ can be crafted to minimize memory footprint by expending significant programming effort, RIMA provides those benefits with little programmer effort. For memcached traffic, a high-performance key-value cache ( FastKV ) using RIMA achieves either 3× lower 96 th-percentile latency or significantly better throughput or memory footprint than FastKV using RDMA.
Fall Alert: A Novel Approach to Detect Fall Using Base as a YOLO Object Detection

In this work, a novel approach to detect irrecoverable fall of a person especially during night by leveraging convolutional neural network (CNN)-based object recognition and image processing techniques are proposed. YOLO (You Only Look Once) object detection model is trained on the COCO dataset to detect the desired object from input frames extracted from a video stream and is tested whether it has succumbed to an irrecoverable fall or not for fall over multiple frames. The paper begins by introducing the core idea behind proposed approach and its importance followed by a review of previously done work in a nutshell. Then, the proposed approach which gave appreciable results is presented. Accuracy of the proposed approach is found to be 93.16%. The work also shows an experimental ratio of the height of camera to the distance of the person from the camera. It is found to be 5:6 for its successful fall detection that gives a competitive performance as compared to other state-of-the-art approaches.
Performance of RUS and SMOTE Method on Twitter Spam Data Using Random Forest

Abstract Spam data on Twitter has one problem, which is imbalanced data. This study proposes two resampling methods, namely RUS and SMOTE as one way to balance the data. In RUS, the data becomes less because random data is deleted while the data on SMOTE will be even greater because synthetic data is grown. To measure the impact of these two methods on the dataset, the Random Forest Classifier method will be applied. In the results, RUS and SMOTE can increase the value of the Area under Curve to 95.21% and 99.13%. The best performance is obtained when applying a balanced ratio of SMOTE.
An Attention-based Deep Network for CTR Prediction

Click-through rate (CTR) prediction is a crucial topic in online advertising system. Early researchers proposed numerous shallow models to analyze this issue, such as logistic regression, factorization machines and Gradient boosting decision tree. In order to improve the model performance furthermore, researchers propose some deep models, such as the factorization-machine supported neural networks, Wide&Deep, DeepFM. Normally, user's historical behavior data contains abundant feature information, but above models lack of modeling and analysis of historical data. To address this problem, the paper proposes a deep CTR prediction model based on attention mechanism and GRU model, which can make use of the users' historical behaviors. The experimental results demonstrate that compared with other popular models, our proposed model can improve the prediction performance by extracting the implied interest features from user historical behaviors.
Multipartite Gaussian entanglement of formation

Entanglement of formation is a fundamental measure that quantifies the entanglement of bipartite quantum states. This measure has recently been extended into multipartite states taking the name $\alpha$-entanglement of formation. In this work, we follow an analogous multipartite extension for the Gaussian version of entanglement of formation, and focusing on the the finest partition of a multipartite Gaussian state we show this measure is fully additive and computable for 3-mode Gaussian states.
Joint Image-text Representation Learning for Fashion Retrieval

There is a demand in the current fashion e-commerce field that users expect to find products that are similar to the query image and match the query text description simultaneously. Traditional text-based retrieval and image-based retrieval methods cannot deal with this issue. In this paper, we propose a fashion-oriented image-text representation learning model called FashionCorrNet. It is an improvement to correlational neural network (CorrNet) by deepening the network structure and merging Kendall correlation coefficient and Pearson correlation coefficient to the objective function to maximize the semantic similarity between the hidden representation of different modalities. In the model, the input consists of two parts, an image and a piece of text description, and the output returns a collection of images that meet the conditions. Experiments on Amazon dataset demonstrate that our model achieves a significant performance gain in image-text retrieval compared to state-of-the-art models.
“I Don’t Understand!”: Toward a Model to Evaluate the Role of User Story Quality

User stories are popular for conveying requirements in agile software projects. Despite existing quality criteria, authors make formal mistakes that result in “bad” user story quality. If developers have insufficient experience in balancing quality problems, the creation of a shared mental model is impossible, thus increasing the risk of impacts on the project’s success. This article provides a work-in-progress research model to set these variables in relation and establish a systematic method to uncover answers regarding their correlation. Details on the effects support research in agile requirements engineering to gain a better understanding of cognitive processes in the comprehension of user stories. In addition, insights can help to develop design recommendations and AI tools to improve user stories. A first evaluation of the model provides promising insights into the behavior and forms a basis for future research.
Mechanisms for optimization of detection and correction of text errors based on combining multilevel morphological analysis with n-gram models

Abstract In the article the problem of increasing the information reliability in electronic document management systems is formulated, and mechanisms for controlling and correcting spelling and errors with semantic values are developed on the basis of a combined multilevel morphological analysis with n-gram models, a typical search, recognition, and classification tools. Mechanisms for verifying the spelling of a word on the basis of a vector representation of variables and comparison with a standard analogue are proposed according to the principles of using statistical, natural, structural, technological, semantic information redundancy. The solutions to the problems of increasing the information reliability based on a set of keywords, phrases, terms by comparing with virtual, frequency dictionaries located in the electronic document database and knowledge base are obtained. A technique has been developed to optimize control mechanisms and correct spelling errors based on the use of logical, semantic and structural - technological links, cross-relationships between individual or groups of words, phrases in the text information. The obtained tools to increase the reliability of the texts of electronic documents are tested in real condition, the results are compared with the conclusions of the system experts.
A Quantitative Exploration of the 9-Factor Theory: Distribution of Leadership Roles Between Scrum Master and Agile Team

A number of qualitative studies find that team leadership is one essential success factor for evolving into a mature agile team. One such qualitative study suggests the 9-Factor Theory of Scrum Master roles, which claims that the Scrum Master performs a set of 9 leadership roles which are transferred to the team over time (Spiegler et al., 2019). We aimed at conducting a quantitative exploration that examines the presence and change of the 9-Factory Theory in relation to team maturity. We conducted an online survey with 67 individuals at the conglomerate Robert Bosch GmbH. Descriptive statistics reveal that the Scrum Master and the agile team score differently on the 9 factors and that the Scrum Master role is most often distributed in teams that had been working between 3 and 5 months in an agile manner. Yet, we also find that the leadership roles predominantly remain with one dedicated Scrum Master. Based on our results we suggest to group the 9-Factor Theory into three clusters: the Scrum Master is rather linked to psychological team factors (1), while the team tends to be linked to rather product-related factors (2). Organizational factors (3) are less often present. Our practical implications suggest an extension of the Scrum Master description. Furthermore, our study lays groundwork for future quantitative testing of leadership in agile teams.
CBCT: CryptoCurrency Based Blockchain Technology

With the continuous development in technology, the interest in Blockchain Technology has been increasing since 2008. It is a data management technology which is decentralized and mainly it is being used for Bitcoin cryptocurrency. Blockchain technology is having certain attributes like security, integrity and data preservation without the intervention of third party and in resultant; it provides its usage in various applications. Blockchain is an emerging technology in this era. Blockchain has gained prominence due to its highly secured features. Blockchain, as the name suggests, is a chain of blocks. Blockchain technology is made secured by incorporating the features of cryptographic hash. Each block maintains a hash value. Further, this hash value also depends on all the previous blocks. All the blocks are connected like a linked list. A single block contains a cryptographic hash of previous blocks, a timestamp and transaction data. A block is represented as a merkle tree root hash. All the blocks follow the same structure, only the data varies. The major application of blockchain due to which it has gained popularity is Bitcoin. Here, in the proposed research work, we would like to discuss various applications of Blockchain Technology like finance, academics, medical sciences, Intellectual Property Rights and how it is impacting the lives of people in day-to-day life. This paper focuses on the application of blockchain. Along with it, a comparative statistical literature review has been presented..
Curiosity-Driven Exploration Effectiveness on Various Environments

Hand Crafting Reward functions have never been scalable solutions for real world problems. The self-generated intrinsic rewards inspired by human curiosity may be one of scalable answers to solve sparse reward problem. The research thus investigated the effectiveness of some selected techniques based on the theory of curiosity-driven exploration. The Count-based, Prediction-based and other methods in total of six algorithms were experimented on various OpenAI gym environments. The results showed that the exploration algorithms have an impact on software agent in ability to find optimal solutions compared with the baseline in many cases. Still, there is no clear winner between the selected exploration methods and the best scalable exploration is not yet explored. The finding is that the added small intrinsic reward noise helps improve sample efficiency in the short run.
Fuzzy Multi-intent Classifier For User Generated Software Documents

User generated software documents (e.g. app reviews, bug reports, developer emails, code comment, etc) often contain a sense of purpose, or intent. They would help the analyst in categorizing, in assigning suitable resources, or in further extracting important information (e.g. user opinions in reviews). However, recent studies were using mostly human effort to extract and encode linguistic patterns to detect such intents from very specific domains (i.e. app reviews and developer emails). In this work, we formalize the problem statement of multi-intent classification for user generated software documents, and propose a Fuzzy Multi-Intent Classifier that can train a classification model on annotated text examples to predict intents using fuzzy logic on matched intent patterns. Our classifier utilizes the novel concept of Lexeme Sequence Pattern which can approximately match to similar text without having a human to encode its Parse Tree. We evaluated our approach against 6 common classification algorithms and out-performed them.
Crypto-Trading. Rechargeable Token-Based Smart Energy Market Enabled by Blockchain and IoT Technology

This paper presents the definition and the implementation of a decentralized system for the energy trading managed by blockchain technology. The system, called Crypto-Trading, is composed by three interacting subsystems: the trading platform, the blockchain, and the smart meters system. It is conceived to exploit the IoT technology of smart meters and the decentralization of smart contracts working inside the blockchain technology for managing exchange and trading of energy by means of specific tokens. The paper defines the system as a decentralized application, identifying system actors and describing user stories. Then provides the description of the use case concerning the rechargeable token, one of the main feature of our system, and its interaction with the other components of the system. Finally, the paper compares our implementation choice with other ongoing projects in the field of energy trading.
Seismic Assessment of Urban Buildings Using Data Mining Methods

Seismic assessment of an urban environment is a time-consuming and complicated task that also requires considerable financial resources. In this paper, we suggest an approach to the seismic evaluation of urban buildings based on data mining methods. Regarding this topic, researches have been conducted on 163 typical objects with 19 different features including various types of buildings. The k-means method was used to create clusters of similar objects, it also allowed us to determine dependencies between data. More accurate clustering models were obtained using a hierarchical algorithm. The quality of the proposed methods was evaluated to ensure the reliability of the obtained results.