Reviewerly/exHarmony huggingface dataset:
How Online Whiteboard Promotes Students' Collaborative Skills in Laboratory Learning

This work reports on the use of an online whiteboard in producing a collaborative and interactive report in laboratory learning at the Faculty of Pharmacy, Silpakorn University, Thailand. In this strategy, 166 students worked in groups of 8-9 to create experimental flow charts, to record data and to discuss results with friends on the board. Along with the students' activities, the instructors accessed the boards to monitor student progress and to provide feedback. From the questionnaires, most students reported that they were satisfied with using the tool and agreed that the learning activities were effective. While most of them had never used an online whiteboard before, they reported that the tool was not difficult to use and had useful and attractive features. Furthermore, they gained digital skills and teamwork skills from learning with the whiteboard. Therefore, online whiteboard can be used a tool to engage students in collaborative learning, active learning and formative assessment.
Considering Wake Gestures for Smart Assistant Use

Smart speakers have become an almost ubiquitous technology as they enable users to access conversational agents easily. Yet, the agents can only be activated using specific voice commands, i.e. a wake word. This, in turn, requires the device to constantly listen to and process sound, which represents a privacy issue for some users. Further, using the trigger word for the agent in a conversation with another human may lead to accidental triggers. Here, we propose using gestural triggers for conversational agents. We conducted gesture elicitation to identify five candidate gestures. We then conducted a user study to investigate the acceptability and effort required to perform the gestures. Initial results indicate that the snap gesture shows the most potential. Our work contributes initial insights on using smart speakers with ubiquitous sensing.
Tourist Place Reviews Sentiment Classification Using Machine Learning Techniques

Social media is growing trend now a days. Every day millions of user review and rate tourist places on tourism websites. Sentiment analysis can be performed over these reviews which will be helpful to find tourist place popularity. Based on sentiment analysis result, tourist can easily decide tour destination to be visited. In this paper sentiment analysis has been implemented using machine learning approach. The Dataset has been collected from various tourism review websites. Here we have performed comparative study of feature extraction algorithms i.e. CountVectorization, TFIDFVectorization. Along with classification algorithms Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). Performance of algorithms has been compared using various parameters like accuracy, recall, precision and f1-score. From experiment we found that TFIDFVectorization feature extraction algorithm has improved accuracy of classification algorithm as compare to CountVectorization for given review dataset. In sentiment classification of tourist place reviews TFIDFVectorization+RF has given highest accuracy 86% for a research dataset used.
On the security and confidentiality of quantum key distribution

Abstract Cryptography is a scientific method that is used to transmit secret information. In contrast, quantum cryptography depends on physical laws to encrypt information; when the quantum computer appeared, the classic encryption method becomes inefficient. The quantum method is commonly used to distribute keys, a process called as quantum key distribution (QKD). In this paper, we consider the efficiency of the quantum method compared to classical methods. Also, we discuss the security of QKD against several attacks and provide security analysis based on probabilistic models. Additionally, the paper explains how to encrypt random numbers into a sequence of photons using a QKD system for the distribution of a key. This research demonstrates the efficiency and security of QKD in sending and distributing keys between communication parties. Thus, both the sender and the receiver would be able to obtain a security key using the quantum method rather than classical methods.
Kaskade: Graph Views for Efficient Graph Analytics

Graphs are a natural way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive sub-graph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. In this work, we leverage structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present Kaskade, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. Kaskade employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query set and to select the best query evaluation plan given a set of materialized views. We evaluate its performance over real-world graphs, including the provenance graph that we maintain at Microsoft to enable auditing, service analytics, and advanced system optimizations. Our results show that Kaskade substantially reduces the effective graph size and yields significant performance speedups (up to 50X), in some cases making otherwise intractable queries possible.
ScrAPIr

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.
Work Engagement: Trust and Respect to Engage your People

Objectives:The purpose of this study is to examine the impact of interpersonal relationships (i.e.respect and felt trust) on work engagement among employees from the manufacturing sector in Jordan represented by a critical Oil and energy supply company; Al-Manaseer Group in Jordan.The study attempts to uncover the impact of procedural justice on employees' work engagement.Methods/Statistical Analysis: Data collected from 181 respondents were analyzed using SEM/Amos for data analysis, conformity factor analysis, path analysis, regressions and correlations.Findings: Results indicated that respect that employees receive from their managers and employees' felt trust positively affected their work engagement.Both mediated the relationship between procedural justice and work engagement and felt trust mediated the relationship between respect that employees receive from direct manager/supervisor and job engagement.The results also revealed that procedural justice has a positive effect on perceived respect from their direct manager/supervisor, withan also positive effect on employees' felt trust.Application/Improvements: Empirical study serves manufacturing organizations and hence its findings may provide a better understanding of the impact of respect, trust and justice on employees' work engagement.Thus, effective interpersonal relationships may further predict significant organizational consequences that require further research and analysis.
Heterogeneous Scheduling of Deep Neural Networks for Low-power Real-time Designs

Deep neural networks have become the readiest answer to a range of application challenges including image recognition, stock analysis, natural language processing, and biomedical applications such as seizure detection. All while outperforming prior leading solutions that relied heavily on hand-engineered techniques. However, deployment of these neural networks often requires high-computational and memory-intensive solutions. These requirements make it challenging to deploy Deep Neural Networks (DNNs) in embedded, real-time low-power applications where classic architectures, GPUs and CPUs, still impose significant power burden. Systems-on-Chip (SoC) with Field-programmable Gate Arrays (FPGAs) can be used to improve performance and allow more fine-grain control of resources than CPUs or GPUs, but it is difficult to find the optimal balance between hardware and software to improve DNN efficiency. In the current research literature there have been few proposed solutions to address optimizing hardware and software deployments of DNNs in embedded low-power systems. To address the computation resource restriction and low-power needs for deploying these networks, we describe and implement a domain-specific metric model for optimizing task deployment on differing platforms, hardware and software. Next, we propose a DNN hardware accelerator called Scalable Low-power Accelerator for real-time deep neural Networks (SCALENet) that includes multithreaded software workers. Finally, we propose a heterogeneous aware scheduler that uses the DNN-specific metric models and the SCALENet accelerator to allocate a task to a resource based on solving a numerical cost for a series of domain objectives. To demonstrate the applicability of our contribution, we deploy nine modern deep network architectures, each containing a different number of parameters within the context of two different neural network applications: image processing and biomedical seizure detection. Utilizing the metric modeling techniques integrated into the heterogeneous aware scheduler and the SCALENet accelerator, we demonstrate the ability to meet computational requirements, adapt to multiple architectures, and lower power by providing an optimized task to resource allocation. Our heterogeneous aware scheduler improves power saving by decreasing power consumption by 10% of the total system power, does not affect the accuracy of the networks, and still meets the real-time deadlines. We demonstrate the ability to achieve parity with or exceed the energy efficiency of NVIDIA GPUs when evaluated against Jetson TK1 with embedded GPU SoC and with a 4× power savings in a power envelope of 2.0W. When compared to existing FPGA-based accelerators, SCALENet’s accelerator and heterogeneous aware scheduler achieves a 4× improvement in energy efficiency.
Evaluation of Machine Learning Techniques for Hand Pose Estimation on Handheld Device with Proximity Sensor

Tracking finger movement for natural interaction using hand is commonly studied. For vision-based implementations of finger tracking in virtual reality (VR) application, finger movement is occluded by a handheld device which is necessary for auxiliary input, thus tracking finger movement using cameras is still challenging. Finger tracking controllers using capacitive proximity sensors on the surface are starting to appear. However, research on estimating articulated hand pose from curved capacitance sensing electrodes is still immature. Therefore, we built a prototype with 62 electrodes and recorded training datasets using an optical tracking system. We have introduced 2.5D representation to apply convolutional neural network methods on a capacitive image of the curved surface, and two types of network architectures based on recent achievements in the computer vision field were evaluated with our dataset. We also implemented real-time interactive applications using the prototype and demonstrated the possibility of intuitive interaction using fingers in VR applications.
A survey on the formalisation of system requirements and their validation

System requirements define conditions and capabilities to be met by a system under design. They are a partial definition in natural language, with inevitable ambiguities. Formalisation concerns with the transformation of requirements into a specification with unique interpretation, for resolving ambiguities, underspecified references and for assessing whether requirements are consistent, correct (i.e. valid for an acceptable solution) and attainable. Formalisation and validation of system requirements provides early evidence of adequate specification, for reducing the validation tests and high-cost corrective measures in the later system development phases. This article has the following contributions. First, we characterise the specification problem based on an ontology for some domain. Thus, requirements represent a particular system among many possible ones, and their specification takes the form of mapping their concepts to a semantic model of the system. Second, we analyse the state-of-the-art of pattern-based specification languages, which are used to avoid ambiguity. We then discuss the semantic analyses (missing requirements, inconsistencies etc.) supported in such a framework. Third, we survey related research on the derivation of formal properties from requirements, i.e. verifiable specifications that constrain the system's structure and behaviour. Possible flaws in requirements may render the derived properties unsatisfiable or not realizable. Finally, this article discusses the important challenges for the current requirements analysis tools, towards being adopted in industrial-scale projects.
Target Embedding and Position Attention with LSTM for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) provides fine-grained sentiment information compared with traditional sentiment analysis. There are two approaches to the task, aspect term sentiment analysis (ATSA) and aspect category sentiment analysis (ACSA). In this paper, we propose a model, namely Recurrent Neural Network with Target Embedding (RTE) using target enhance technic to improve the accuracy of both the two kinds of approaches. Specifically, RTE involves two stacked LSTMs for target term extraction and sentiment analysis, and a target enhance unit for spreading target or aspect information. Experiments are conducted on several public datasets and the results illustrate that the proposed RTE outperforms several state-of-the-art models compared.
Metaprobes, Metaphysical Workshops and Sketchy Philosophy

The intersection of philosophy and HCI is a longstanding site of interest for the field that has been attracting special attention in recent years. In this paper, we present metaphysical probes (Metaprobes) as a tool for design-led philosophical inquiry. A Metaprobe is a design artifact used to study a metaphysical idea without concealing the philosophical tools mobilized by the designers or the designerly knowledge attained after deployment. We introduce the concept of a Metaphysical Workshop. This is the set of sketchy philosophical notions that a designer mobilizes in order to research a philosophical idea through design. We then present a case study that comprises: the philosophical issue under examination, the Metaprobes designed to study it, the metaphysical workshop used and the designerly insight produced. We conclude with a discussion of the potentials and weaknesses of Metaprobes in relation to other critical and speculative research-through-design practices. We aim to provide one way to make philosophies already present in design more explicit and make other philosophical concepts relevant to HCI more accessible and workable for designers.
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold

Dimensionality reduction (DR) on the manifold includes effective methods which project the data from an implicit relational space onto a vectorial space. Regardless of the achievements in this area, these algorithms suffer from the lack of interpretation of the projection dimensions. Therefore, it is often difficult to explain the physical meaning behind the embedding dimensions. In this research, we propose the interpretable kernel DR algorithm (I-KDR) as a new algorithm which maps the data from the feature space to a lower dimensional space where the classes are more condensed with less overlapping. Besides, the algorithm creates the dimensions upon local contributions of the data samples, which makes it easier to interpret them by class labels. Additionally, we efficiently fuse the DR with feature selection task to select the most relevant features of the original space to the discriminative objective. Based on the empirical evidence, I-KDR provides better interpretations for embedding dimensions as well as higher discriminative performance in the embedded space compared to the state-of-the-art and popular DR algorithms.
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

Labeling data comprehensively and efficiently is a widely needed but challenging task. With limited computing resources, given a data stream and a collection of deep-learning models, we propose to adaptively select and schedule a subset of these models to execute, aiming to maximize the value of the model output. Achieving this goal is nontrivial since a model's output on any data item is content-dependent and hard to predict. In this paper, we present an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule models under deadline or deadline-memory constraints. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on 30 popular image labeling models to demonstrate the effectiveness of our design.
PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension

Similarity search has been widely used in various fields, particularly in the Alibaba ecosystem. The open-source solutions to a similarity search of vectors can only support a query with a single vector, whereas real-life scenarios generally require a processing of compound queries. Moreover, existing open-source implementations only provide runtime libraries, which have difficulty meeting the requirements of industrial applications. To address these issues, we designed a novel scheme for extending the index-type of PostgreSQL (PG), which enables a similar vector search and achieves a high-performance level and strong reliability of PG. Two representative types of nearest neighbor search (NNS) algorithms are presented herein. These algorithms achieve a high performance, and afford advantages such as the support of composite queries and seamless integration of existing business data. The other NNS algorithms can be easily implemented under the proposed framework. Experiments were conducted on large datasets to illustrate the efficiency of the proposed retrieval mechanism.
A Solution for Secure Multi-Party Certified Electronic Mail Using Blockchain

In this article, we present the first solution for multi-party certified email that achieves fairness and confidentiality without the intervention of a TTP but with blockchain support when problems arise. Our solution preserves the confidentiality of the mail exchanged between the sender and the recipients even when the blockchain is involved. We provide a proof of concept implementation based on Ethereum smart contracts that shows it is feasible from a practical point of view. Because the functions of a smart contract are only executed on the blockchain in the case of conflict, costs are minimised compared to solutions that execute the functions on the blockchain in all cases (on-chain solutions). Moreover, the solution can be integrated into the existing email infrastructure.
Deep joint neural model for single image haze removal and color correction

The quality of an image affects the performance of computer vision applications. The presence of haze often greatly depreciates the visual effect of images. It is a traditional and critical vision challenge to remove haze from a single image. This paper proposes a trainable end-to-end de-hazing connectionist model with a special design. First, feature learning is conducted using hierarchical convolutional layers with nested structures. Cascaded haze-relevant tasks are then sequentially performed via a physics-driven sub-network. In particular, to break the assumption of a homogeneous atmosphere, a branch of the sub-network estimates the scattering factor in the form of a two-dimensional tensor. Finally, a chromatic adaptation layer is proposed for color adjustment, which is often neglected in existing de-hazing methods. In addition, we integrate different training criteria based on the characteristics of the haze-relevant variables in our model. For a fully actionable optimization, an asynchronous learning paradigm is designed for the fusion of different de-hazing tasks, and the joint model is further facilitated by a cyclic restoration. The effectiveness of the proposed de-hazing model was verified via extensive experiments, and most results of our method are remarkable.