Reviewerly/exHarmony huggingface dataset:
More than Looking: Using Eye Movements Behind the Eyelids as a New Game Mechanic

The number of mainstream gaze-enabled games is rapidly increasing. They introduce gaze interactions based on the alignment of the eyes' focus on locations of interest. We challenge this traditional use of gaze and propose to play without looking. We developed KryptonEyed, a mini-game prototype that requires the player to close their eyes and perform eye movements behind the eyelids before opening them to aim with gaze for interaction. To validate the interaction mechanic and the game experience, we conducted two user studies. We evaluated the feasibility of using the proposed dynamic, its performance and the game experience. Results show how participants could successfully use the proposed gaze technique and overcome the game challenge. Based on our findings, we discuss the metaphors to "not look" for interaction and give directions to introduce the dynamic in gaze-enabled games.
An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer

A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed.
Extended Planning Poker: A Proposed Model

Agile is the leading type of software development methodology and have a higher chance of success than non-agile projects. Collaborative and cooperative are characteristic of the Agile software development method. It is demonstrated by involving users actively, empower the team to make decisions, capture requirements in lightweight and visual, focus on frequent delivery of products through developing small, incremental release, and iterate. This characteristic is causing effort estimation remains the main challenge in agile software development. Planning poker is an effort estimation technique that applied extensively with the ability to empower all team developers to decide, especially in estimate effort. Still, the result is very subjective and takes a long time. This research proposes a new model to improve actual planning poker performance. A proposed model modify two parts of the planning poker is the estimation process and consensus process. Three variables added to the estimation process as well as implementing weight and score for its variables. The consensus process proposed done automatically based on five attributes that are related to the estimator.
“Always on the Table”: Revealing Smartphone Usages in everyday Eating Out Situations

Research on food practices and technology use is becoming more common, albeit with a constant technological determinism with respect to the support of individual practices. Nevertheless, there are only a few empirical studies that outline the use of current technologies within eating contexts. We therefore conducted an empirical study on the practice of eating out and the use of mobile technologies before, during, and after eating. Our investigation consists of a qualitative interview study (n=29) complemented by a large observational study (n=458) within several restaurant settings. Our results indicate a strong reluctance to use technology while eating and highlights several design spaces focusing on before and after the actual eating. Within our paper, we uncover a strong relationship between smartphone use and the social settings in which the interaction takes place. We contribute to the emerging research field of Human-Food Interaction by outlining design spaces for supporting practices around food consumption when eating out.
Development of an IoT-Based Health Promotion System for Seniors

With a reducing fertility rate and the increase in life expectancy in Taiwan, the changing structure of the population has pushed Taiwan into an aged society. The demand for elderly care environments is rapidly increasing. However, to prevent an illness for the elderly is better than to treat it. Monitoring of an individual’s health is required as a periodic activity. Using wireless technology combined with physiological measurement techniques and home care equipment can assist elderly individuals to promote health and the health care service at home. In this research, a wireless sensor network has been adopted to develop context aware health promotion for an elderly-care system. The system is divided into three subsystems: the IoT-based physiological information subsystem, the context awareness-based service subsystem, and the elderly nutrition diet and health promotion subsystem. In this system, users also can select an Aging Diet Module or an Aging Exercise Module in our app. After the user selects the Aging Diet Module, the system will record and give suggestions. Moreover, the system can integrate long-term elderly diet and exercise records, and then, assist elderly to complete personal nutrition assessment and health management. The research goal is to construct a health promotion system utilizing IoT technology. In this paper, the system’s functions and implementation approaches are introduced.
On the Security and Performance of Proof-based Consensus Protocols

The following topics are dealt with: Internet of Things; telecommunication traffic; cloud computing; security of data; software defined networking; public domain software; distributed databases; learning (artificial intelligence); fault tolerant computing; and distributed algorithms.
Blockchain-Integrated Virtual Power Plant Demonstration

The penetration of distributed energy resources keeps increasing in most of electricity markets, becoming an essential part of smart grid systems. This, along with advancements in power converters and control systems, led to formation of various aggregation mechanisms, such as Virtual Power Plants (VPP) or demand response (DR) aggregators, enabling participation of small and medium scale distributed energy resources (DER) in electricity markets. Such mechanisms typically entail control of DER assets at specified time periods to provide grid services such as peak shaving. However, the transparency of operations when controlling the aggregated DERs is a risk from the asset owner's perspective and may lead to various types of disputes with the aggregator or operator. In order to tackle this issue, we have developed a blockchain-based mechanism that handles all transactions within a VPP on a distributed data ledger, enabling full transparency of the system. The blockchain system is integrated with an actual VPP setup with a total aggregated size of 1.8MWs composed of renewables, energy storage systems and controllable loads. The proposed mechanism contributes to grid digitalization and enables new applications in power systems, incentivizing larger penetration of DERs and their participation in ancillary services.
DSFMA: deeply supervised fully convolutional neural networks based on multi-level aggregation for saliency detection

In recent years, the emergence of fully convolutional neural networks (FCNs) has delivered significant success in the field of saliency detection. Although the different levels of FCNs layers can hold different types of information for salient object detection, it is still a challenging issue to find a generic method while integrating all relevant information synthetically with multi-level aggregation. In this paper, we present a novel multi-level aggregation method by following a U-shaped architecture of the VGG-16 network. As the shallower layers of FCNs contain the low-level integrated features which are capable of capturing the more details of salient objects, while the more profound layers that hold the high-level integrated features have more contextual information. To exploit all the relevant information, we extend the last four side-outputs of U-Net at the encoder and decoder sides and then utilize the concept of skip and short-connections to incorporate the high-level contextual knowledge with low-level details. Besides, we also integrate the recurrent convolutional layers (RCLs) into our model, which provide more deepness and enhance the capability to integrate more contextual knowledge. At last, we combine all the side-outputs into a final saliency map together for salient object detection. We evaluate the performance of the proposed model on six broadly used saliency detection benchmarks by comparing it with the other 11 state-of-the-art approaches. Experimental outcomes determine that our method achieves a favorable performance for all compared evaluation measures.
CreepyLeaks: Participatory Speculation Through Demos

Data leakage and privacy are well known but abstract terms for most people. To grant people a real-time and on-body experience of data leakage as it happens, we constructed CreepyLeaks. CreepyLeaks turns data leakage into a visceral embodied experience that augments visual information about what data are leaked, where and via which application. Conducting evaluations of CreepyLeaks we learned the value of the public demonstrations over individual encounters with privacy technologies. Rather than inspiring the audience to think positively about a design or functionality, the demo became an opportunity to develop collective ideas of concepts and problems that are difficult to grasp individually. By conducting privacy technology demos in public and semi-public events, we demonstrate how demos can create conditions for participatory speculation – a practice involving a confluence of human and non-human actors in speculative experiences, acknowledging the politics inherent in technology design and collectively considering possible futures.
SAGA-Bench: Software and Hardware Characterization of Streaming Graph Analytics Workloads

Many application scenarios such as social network analysis and real-time financial fraud detection involve performing batched updates and analytics on a time-evolving or streaming graph. Despite their importance, streaming graph analytics workloads have not been systematically studied at either the software or the architecture levels. This paper fills this gap through three contributions. First, we develop and open-source SAGA-Bench, a benchmark for streaming graph analytics, which puts together different data structures and compute models on the same platform for a fair and systematic characterization. Second, we perform software-level characterization using SAGA-Bench. Our profiling reveals that the best data structure for a streaming graph depends on the per-batch degree distribution of the graph. We also observe that the incremental compute model provides performance benefits especially for larger graphs. Finally, we show that the graph update phase contributes at least 40% of the streaming graph processing latency in many cases. Third, we perform workload characterization at the architecture level. Our study reveals that the graph update phase exhibits lower utilization of architecture resources than the compute phase. Furthermore, the hardware resource utilization of the update phase strongly depends on the underlying structure of the batches of the graph. Finally, between compute and update phases, the former exhibits a higher L3 cache hit ratio, whereas the latter shows a higher L2 cache hit ratio.
A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNet

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question answering, sentiment analysis, and emotion recognition. EWN includes sets of cognitive synonyms called synsets, which are interlinked by means of conceptual-semantic and lexical relations and where each synset expresses a distinct concept. However, other languages are still lagging behind in having large-scale and rich lexical resources similar to EWN. In this article, we focus on enabling the development of such resources for Arabic. While there have been efforts in developing an Arabic WordNet (AWN), the current version of AWN has its limitations in size and in lacking transliteration standards, which are important for compatibility with Arabic NLP tools. Previous efforts for extending AWN resulted in a lexicon, called ArSenL, that overcame the size and the transliteration standard limitation but was limited in accuracy due to the heuristic approach that only considered surface matching between the English definitions from the Standard Arabic Morphological Analyzer (SAMA) and EWN synset terms, and that resulted in inaccurate mapping of Arabic lemmas to EWN’s synsets. Furthermore, there has been limited exploration of other expansion methods due to expensive manual validation needed. To address these limitations of simultaneously having large-scale size with high accuracy and standard representations, the mapping problem is formulated as a link prediction problem between a large-scale Arabic lexicon and EWN, where a word in one lexicon is linked to a word in another lexicon if the two words are semantically related. We use a semi-supervised approach to create a training dataset by finding common terms in the large-scale Arabic resource and AWN. This set of data becomes implicitly linked to EWN and can be used for training and evaluating prediction models. We propose the use of a two-step Boosting method, where the first step aims at linking English translations of SAMA’s terms to EWN’s synsets. The second step uses surface similarity between SAMA’s glosses and EWN’s synsets. The method results in a new large-scale Arabic lexicon that we call ArSenL 2.0 as a sequel to the previously developed sentiment lexicon ArSenL. A comprehensive study covering both intrinsic and extrinsic evaluations shows the superiority of the method compared to several baseline and state-of-the-art link prediction methods. Compared to previously developed ArSenL, ArSenL 2.0 included a larger set of sentimentally charged adjectives and verbs. It also showed higher linking accuracy on the ground truth data compared to previous ArSenL. For extrinsic evaluation, ArSenL 2.0 was used for sentiment analysis and showed, here, too, higher accuracy compared to previous ArSenL.
Basic level of concepts in formal concept analysis 1: formalization and utilization

Basic level of concepts represents a significant, thoroughly explored psychological phenomenon in the psychology of concepts. We argue that formal concept analysis provides a convenient formal framework for considerations regarding the basic level. In this first part of our study, we present a direct formalization of a rudimentary view of basic level due to Rosch. We also demonstrate by experiments that among all formal concepts the basic level concepts represent natural and interesting ones and are therefore of interest from the data analysis viewpoint. In the second part, we shall utilize the present approach to examine existing psychological views of basic level and explore relationships among them.
Spanning attack: reinforce black-box attacks with unlabeled data

Adversarial black-box attacks aim to craft adversarial perturbations by querying input–output pairs of machine learning models. They are widely used to evaluate the robustness of pre-trained models. However, black-box attacks often suffer from the issue of query inefficiency due to the high dimensionality of the input space, and therefore incur a false sense of model robustness. In this paper, we relax the conditions of the black-box threat model, and propose a novel technique called the spanning attack. By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks. Extensive experiments show that the proposed method works favorably in both soft-label and hard-label black-box attacks.
An examination of electronic resource management in academic libraries in Ghana through the Techniques of Electronic Resource Management (TERMS) framework

The management of electronic resources (ERs) has become a core function of academic libraries and researchers have revealed how academic libraries in developed countries are struggling to successfully manage these resources. The scholarly literature on the management of ERs in libraries has predominantly focused on developed country context. Little research has been conducted on how academic libraries in developing countries are managing their online databases. This paper presents the findings of a study that examines ER workflows and the extent to which ER workflows or the practices of managing online databases in academic libraries in Ghana compare with the Techniques of Electronic Resource Management (TERMS) framework. Data were collected from library staff in two public and two private universities, and consortium executives using semi-structured interviews. The findings revealed that although efforts had been made to follow standard procedures, some components of the TERMS framework were absent in the ER workflow in both public and private case libraries while other aspects of the workflow were responsibilities of parties external to the libraries. Opportunities exist for improving ERM practices in academic libraries in Ghana. In particular, by involving users in the selection and assessment of ERs, increasing promotional activities, and improving the documentation of ER workflow. The findings also revealed interconnections between various stages of TERMS. The findings provide a basis from which academic libraries can improve their practices and extends the application and revision of the TERMS framework to an African context.
JSON-LD Based Web API Semantic Annotation Considering Distributed Knowledge

Based on semantically annotated Web APIs, automatic Web API composition can be implemented easily.The operation can greatly improve efficiency of building a software system.However, in real world, semantic annotation for Web APIs will encounter various difficulties, because of their distribution and function diversity, such as disunited API description formats, response result with complex structure, shortage of business domain ontologies, semantic conflicts among distributed knowledge, and so on.To solve these difficulties, we propose a JSON-LD based Web API semantic annotation approach (JWASA).JWASA can assist professional developers to semi-automatically complete semantic annotation of Web APIs.A common Web API description ontology is firstly defined, including necessary vocabularies about invocation information, functional semantics, and non-functional features.Then, JWASA automatically converts a Web API description into a document in an united JSON format, and assist developers to semi-automatically embed semantic information of crucial elements of the API by means of a lightweight Linked Data format JSON-LD.Meanwhile, a semantic annotation specification is proposed to deal with various complex situations in Web API description, e.g: too many response parameters, no request parameters, etc.In addition, to improve efficiency of annotation, JWASA provides some extra operations, including automatic new ontology or vocabulary creation, automatic functional semantics extraction etc.Also, JWASA provides semiautomatically bridge rule generation algorithm, which can infer implied relationships among vocabularies (e.g: sub-class, super-class, equality).JWASA focuses on the semantic annotation of functionality of Web APIs, and can create effective semantic Web APIs for future API automatic composition.We implement a prototype system and carry out a series of experiments to evaluate JWASA on real Web APIs crawled from Internet.Experiments show that our approach is effective and efficient.
Transformation of Ontology-Based Conceptual Models into Relational Schemas

Despite the existence of several strategies for transforming structural conceptual models into relational schemas, there are a number of features of ontology-based conceptual models that have not been taken into account in the existing literature. Most approaches fail to support conceptual models that: (i) include overlapping or incomplete generalizations; (ii) support dynamic classification; (iii) have multiple inheritance; and (iv) have orthogonal hierarchies. This is because many of the approaches discussed in the literature are based on the object-relational mapping and, as a consequence, assume primitives underlying object-oriented programming languages (instead of conceptual modeling languages). This paper addresses this gap, focusing on the realization of taxonomic hierarchies of ontology-based conceptual models. We explore some ontological meta-properties that characterize classes in these models (sortality and rigidity) to guide the transformation and avoid some problems in existing approaches.
Data Augmentation for Handwritten Character Recognition of MODI Script Using Deep Learning Method

Deep learning-based methods such as convolutional neural networks are extensively used for various pattern recognition tasks. To successfully carry out these tasks, a large amount of training data is required. The scarcity of a large number of handwritten images is a major problem in handwritten character recognition; this problem can be tackled using data augmentation techniques. In this paper, we have proposed a convolutional neural network-based character recognition method for MODI script in which the data set is subjected to augmentation. The MODI script was an official script used to write Marathi, until 1950, the script is no more used as an official script. The preparation of a large number of handwritten characters is a tedious and time-consuming task. Data augmentation is very useful in such situations. Our study uses different types of augmentation techniques, such as on-the-fly (real-time) augmentation and off-line method (data set expansion method or traditional method). A performance comparison between these methods is also performed.