Reviewerly/exHarmony huggingface dataset:
Democratizing Soap

This paper explores methodological considerations of using constructive assemblies as a participatory design tool in order to explore new conceptual and material possibilities in saturated product categories. Constructive assemblies are tangible, reconfigurable, modular physical sets that can be combined in multiple different ways. They are valuable as collaborative tools on account of how they facilitate social inclusion by having a low skill barrier to participation. They are also valuable as generative tools, as users are able to quickly build complex constructions with the constituent components in different configurations. In this paper, we present a study in which a series of eight Participatory Design (PD) workshops were conducted with these assemblies, in which stakeholders explored possibilities for re-designing the everyday use of soap. Participants were drawn from the Fast Moving Consumer Goods (FMCG) industry and everyday soap users, and the workshops took three different configurations: video-conference and shared location, video conference and distributed location, and physical workshop and shared location. Our analysis highlights the consequentiality of the materiality of the assembly, the interplay between the specific workshop tasks, their setup and the physical constraints of the toolset, and emergent social behaviour from the experiments. We discuss these findings in relation to existing frameworks
An Efficient Method for DPM Code Localization Based on Depthwise Separable Convolution

With the popular application of direct part mark (DPM) technology, DPM code inspection has been a hot issue in the machine vision.It mainly consists of two steps, namely, localization and decoding.DPM code localization is a key and complex step in the DPM code inspection.However, the traditional localization methods suffer from complex imaging environment, involving various imaging background, illumination, imaging distance, and exposures.Furthermore, the target itself, i.e., the DPM code, could be severely polluted or worn.Aiming at improving the performance and robustness of DPM code localization, an efficient method with depthwise separable convolution is proposed in this paper.The optimized network model has the advantages of a few parameters, high computational efficiency, high precision localization, and good generalization ability.Meanwhile, the precision of the DPM code region is improved with the help of multi-scale prediction.The experiments on our DPM code localization database demonstrate the effectiveness and flexibility of the proposed method in comparison with the YOLOv3 network and the Tiny_YOLO network.Furthermore, the proposed method can estimate the exposure level of the DPM code region, which is benefiting to the DPM code recognition and enables the adaptive ability.
Trustworthy media news content retrieval from web using truth content discovery algorithm

Nowadays, the internet plays a major role in online information retrieval. As, we are well aware the web provides information related all the fields like national, lifestyle, movies, shopping, spirituality, sports, entertainment and much more. One cannot assume that the web retrieved information is believable or trustworthy due to multiple answers to the same query. The paper aims to study many research articles which are related to truth information discovery. However, the paper found that there is no proper research to find trustworthiness of news content which is extracted from multiple information sources with minimum misclassification error and retrieval time. To alleviate these issues, the truth content discovery algorithm is proposed to produce trustworthy information with minimal time along with multiple domain news information. The system provides reliable information from a various source of news provider with minimum classification error. The proposed method ranked the news content index based on query matches from extracted information. It minimizes the query retrieval time and classification error. Based on the experimental evaluation, proposed TCD + J48 algorithm produced the best result compared to existing approaches. It minimizes reduces 7.54 ms the query retrieval time (QRT), 5% Error rate (ER) and 0.98 and Mean Normalized Absolute Distance (MNAD).
Pengendalian Temperatur dan Kelembaban dalam Kumbung Jamur Tiram (Pleurotus sp) Secara Otomatis Berbasis Mikrokontroler

Oyster mushroom can grow properly at temperatures of 16–30 °C and relative humidity of 80–95%. Environment conditioning by spraying of water in mushroom house manually in the morning and evening as the temperature and humidity controling is less effective and highly bothersome. Using of technology can controlling temperature and humidity in a mushroom house automatically. This research aims to design an automatic control system to control temperature and humidity in oyster mushroom house. Research is located at an altitude of 125 meters above sea level. Automatic control system with a setting point temperature of 25 – 30 °C and humidity of 80 – 95% was tested at mushroom house with dimensions of 4 × 2 × 2 m with a capacity of 600 baglog mushrooms. The results show that the performance of daily temperature and humidity without control is respectively 24.10 to 35.19 °C and 64.28 to 99.90%. While the temperature and humidity with the control system are 25.10 to 30.09 °C and 80.84 to 99.90%, respectively.
A Wearable Nebula Material Investigations of Implicit Interaction

In this paper, we present the Nebula, a garment that translates intentional gestures and implicit interaction into sound.Nebula is a studded cloak made from a heavy fabric that envelopes the wearer with many pendulous folds. We describe the design process, and specifically highlight three material investigations that show particularly important material connections that were fundamental to the experience of the garment: How the draping and construction of the garment allowed for implicit interaction, how the studs were used both as a computational sensing material and a strong visual component, and how the sound design exploited tangible material qualities in the garment. Finally, we discuss how such material investigations in general can be put to use. Both as a way to produce evocative connections in the materials available in design work, but also as a way to extract legible design intentions for other designers and researchers.
Ensemble learning using fast rule based fuzzy K –means pre clustering and classification for aquatic behavior-extracted tsunami prediction

Underwater seismic reactions have been pre-sensed by various mathematical analytical methods. Various warning systems that have deployed have sometimes proven to be effective. Tsunami-underwater seismicity is still considered as one of the major fears among mankind. In this paper we propose a Tsunami alert generation model based on Ensemble Clustering and Classification TPbEC2 for generating alerts. This model uses turtle behavioral dataset for classification of alert and No Alert. The model shown here works in two parts as: fast pre-clustering has been proposed based on Fuzzy K means modification followed by ensemble with FRBCS (Fuzzy Rule based Classification System). The first part of model method is validated in comparison to four base clustering methods with three indices. Similarly the Ensemble of MFKmeansIC along with base clustering methods (in total 25) with all five base FRBCS’s is evaluated for accuracy. To further validate, statistical test- Friedman is also performed for best ensembles from above accuracy matrix where MFKmeansIC+ FH.GBML shows the best rank.
Supporting the Co-design of Games for Privacy Awareness

Privacy is a well-known concern connected to teenagers' usage of e.g., social media, mobile apps, and wearables. However, providing proper learning in this area is challenging. Games have recently been proposed as a tool to increase awareness of privacy concerns. It is important that these games are relevant and engaging. In this paper, we present a workshop to involve teenagers in the co-design of games to promote privacy awareness, describing the workshop process together with the cards and the board that support the process. We evaluated the workshop together with students between 15–17 years of age divided in groups of 3–4 participants. Results show that all the groups were able to generate interesting game ideas and the workshop was perceived as entertaining.
Quantum-Inspired Bat Optimization Algorithm for Automatic Clustering of Grayscale Images

This paper introduces a quantum-inspired framework with the bat optimization algorithm for automatic clustering of image datasets. The aim of this work is to find out the optimal number of clusters from an image dataset on a run. A comparison has been produced between the quantum-inspired bat optimization algorithm and its classical counterpart. As a result, it is seen that the quantum-inspired version outperforms its classical counterpart. Computational experiments have been conducted on four Berkeley image datasets.
An algorithm for computing short-range forces in molecular dynamics simulations with non-uniform particle densities

We present projection sorting, an algorithmic approach to determining pairwise short-range forces between particles in molecular dynamics simulations. We show it can be more effective than the standard approaches when particle density is non-uniform. We implement tuned versions of the algorithm in the context of a biophysical simulation of chromosome condensation, for the modern Intel Broadwell and Knights Landing architectures, across multiple nodes. We demonstrate up to 5× overall speedup and good scaling to large problem sizes and processor counts.
Co-change patterns: A large scale empirical study

Co-Change Clustering is a modularity assessment technique that reveals how often changes are localized in modules and whether a change propagation represents design problems. This technique is centered on co-change clusters, which are highly inter-related source code files considering co-change relations. In this paper, we conduct a series of empirical analysis in a large corpus of 133 popular software projects on GitHub. We describe six co-change patterns by projecting them over the directory structure. We mine 1802 co-change clusters and 1719 co-change clusters (95%) are covered by the six co-change patterns. In this study, we aim to answer two central questions: (i) Are co-change patterns detected in different programming languages? (ii) How do different co-change patterns relate to rippling, activity density, ownership, and team diversity on clusters? We conclude that Encapsulated and Well-Confined clusters (Wrapped) implement well-defined and confined concerns. Octopus clusters are proportionally numerous regarding to other patterns. They relate significantly with ripple effect, activity, ownership, and diversity in development teams. Although Crosscutting are scattered over directories, they implement well-defined concerns. Despite they present higher activity compared to Wrapped clusters, it is not necessarily easy to get rid of them, suggesting that support tools may play a crucial role.
Fair service matching agent for federated cloud

A federated cloud is a group of cloud service providers operating together for service sharing. In the federation, cloud service providers obtain services from providers when their services are insufficient to meet the demand. The federated cloud uses agent-based, incentive-based or reciprocal resource fairness-based techniques for service selection. In agent-based service selection, the agent selects the best service provider for service sharing. Distributing the service requests among the service providers of the federation is a challenging task. The agent-based service selection techniques do not distribute service requests to all cloud providers in the federated cloud. Additionally, the agent-based service selection techniques rarely present service offers to the newly joined cloud provider. To fairly distribute the service requests, a new agent named “A Fair Service Matching Agent (FSMA)”, fairly distributes service requests to all cloud providers in the federated cloud and provides a fair chance to the newly joined cloud provider. The experimental results demonstrate FSMA outperforms the existing traditional Cloud Resource Bartering system.
Optimal design of photovoltaic solar systems considering shading effect and hourly radiation using a modified PSO algorithm

Regarding the significant potential of solar energy in Iran, implementation of optimally designed photovoltaic (PV) systems can be effective. Hence, this study proposes two objective functions: first, the maximum possible output energy for a given area and, second, the minimum area receiving a given yearly energy from PV fixed collectors in a solar field, both of which are calculated. In addition, the shading and masking effects are considered in the calculations. A modified particle swarm optimization (MPSO) algorithm is used to solve the optimization problem. The case study of this article is a shopping center in Isfahan-Iran (latitude 32.5°N) with the minimum yearly energy demand of 171 MWh and the 5000 m 2 roof area. To evaluate the yearly energy, the calculated hourly radiation approach is applied to the case study. The results show that the maximum possible generated energy is 881 MWh/year for the given area. In addition, to provide the minimum demand, 720 m 2 area of roof is needed. To verify the effectiveness of the proposed MPSO, the results are compared with those of obtained by the relevant commercial software.
Usability and shoulder surfing vulnerability of pattern passwords on mobile devices using camouflage patterns

With the revolution of smart devices that have become the basis of our daily lives, the majority of users rely on them to save their personal and sensitive information. As a result, users are increasingly interested in authentication processes, which is a challenge for designers to provide a secure and usable authentication process. The pattern password is one of the most selected authentication methods, since the recent development in alternative authentication interfaces for smart phones, tablets and touch screens laptops. Although drawing a pattern seems easier than typing a password, it has a major security drawback which is the shoulder-surfing attack. Therefore, this paper proposes a shoulder-surfing resistance approach for mobile devices using Camouflage Patterns method which allows choosing a very short password, while insuring that the password remains hidden amongst a large number of nodes draws. Based on this approach, three techniques are introduced and implemented using an Android platform. An experimental study is conducted for evaluating the security and usability aspects. The results showed that the proposed approach is reasonably resistant against shoulder-surfing attacks and usable for users. Accordingly, this approach is recommended for designers in order to provide very simple and yet very complicated passwords, to be observed by the attacker, at the same time.
Slow Education and Cognitive Agility

Governance of cyberpower from a military perspective are focused on the efforts to control and influence events occurring in cyberspace. For the Norwegian Defence, this means educating cyber engineers, responsible for governing cyberpower effects, beyond technical skills and competencies. To match the complexity of modern warfighting necessitates adaptive high-order thinking skills. Building on earlier cognitive engineering and human factors research in cyber defence this article suggests how Slow Education has the potential to improve cognitive performance among cyber cadets. Slow techniques were applied to 37 cyber cadets during a three-year bachelor programme at the Norwegian Defence Cyber Academy. The quantitative data for this study was gathered during a two-week Cyber Defence Exercise. Combining and applying a novel pedagogic method with psychological techniques suggests reflective pondering, self-regulation and metacognition as being associated with cognitive agility. This study helps develop and make metrics available that are suitable to evaluate human performance in cyber defence.
Learning Discriminative Embeddings for Object Recognition on-the-fly

We address the problem of learning to recognize new objects on-the-fly efficiently. When using CNNs, a typical approach for learning new objects is by fine-tuning the model. However, this approach relies on the assumption that the original training set is available and requires high-end computational resources for training the ever-growing dataset efficiently, which can be unfeasible for robots with limited hardware. To overcome these limitations, we propose a new architecture that: 1) Instead of predicting labels, it learns to generate discriminative and separable embeddings of an object's viewpoints by using a Supervised Triplet Loss, which is easier to implement than current smart mining techniques and the trained model can be applied to unseen objects. 2) Infers an object's identity efficiently by utilizing a lightweight classifier in the features embedding space, this keeps the inference time in the order of milliseconds and can be retrained efficiently when new objects are learned. We evaluate our approach on four real-world images datasets used for Robotics and Computer Vision applications: Amazon Robotics Challenge 2017 by MIT-Princeton, T-LESS, ToyBoX, and CORe50 datasets. Code available at [1].
Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments

This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.
Talking about interaction*

Recent research has exposed disagreements over the nature and usefulness of what may (or may not) be Human-Computer Interaction's fundamental phenomenon: 'interaction'. For some, HCI's theorising about interaction has been deficient, impacting its capacity to inform decisions in design, suggesting the need either to perform first-principles definition work or broader administrative clarification and formalisation of the multitude of formulations of the concepts of interaction and their particular uses. For others, there remain open questions over the continued relevance of certain 'versions' of interaction as a useful concept in HCI at all. We pursue a different perspective in this paper, reviewing how HCI treats interaction through examining its 'conceptual pragmatics' within HCI's discourse. We argue that articulations of the concepts of interaction can be a site of productive conflict for HCI that for many reasons may resist attempts of formalisation as well as attempts to dispense with them. The main contribution of this paper is in specifying how we might go about talking of interaction and the value of interaction language as promiscuous concepts.
Historical reasoning in authentic research tasks: Mapping cognitive and document spaces

To support historians in their work, we need to understand their work‐related needs and propose what is required to support those needs. Although the quantity of digitized historical documents available is increasing, historians' ways of working with the digital documents have not been widely studied, particularly in authentic work settings. To better support the historians' reasoning processes, we investigate history researchers' work tasks as the context of information interaction and examine their cognitive access points into information. The analysis is based on a longitudinal observational research and interviews in a task‐based research setting. Based on these findings in the historians' cognitive space, we build bridges into the document space. By studying the information interactions in real task contexts, we facilitate the provision of task‐specific handles into documents that can be used in designing digital research tools for historians.
Unsupervised Extractive Summarization for Product Description Using Coverage Maximization with Attribute Concept

This paper presents a system that generates unsupervised extractive summarization of product descriptions. The system utilizes a coverage maximization model with concepts introduced as a practical solution for extractive summarization. Product descriptions present more challenges to this task, including but not limited to, shorter paragraphs and infrequent concepts distributed across the paragraph. To overcome these challenges, we propose domain-specific concepts, namely product attributes, in addition to general linguistic concepts such as named entities and syntactic dependency. The result demonstrates that product attributes can help generate better summarization for product descriptions.
Block Ciphers Classification Based on Random Forest

Discriminant analysis is an important analytical method in cryptanalysis. When only known ciphertext, the encryption algorithm for identifying and classifying is an important part of distinguishing analysis. In this paper, a random forest classifier is used to classify eight block ciphers of ECB mode and eight block ciphers of CBC mode. By designing a feature based on ciphertext recombination and location-specific, the results show that the designed features can effectively extract the information of ciphertext data. In ECB mode, 8 algorithms can be successfully classified with an accuracy of more than 87%. In CBC mode, it can also be classified with higher accuracy than random.
A location-sensitive and network-aware broker for recommending Web services

Collaborative filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real “quality” of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, and Web services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values, a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations.