Reviewerly/exHarmony huggingface dataset:
Comparing fixed and variable segment durations for adaptive video streaming

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables dynamic adaptation of video quality by splitting a video into small segments and providing multiple quality levels per segment. So far, HAS services typically utilize a fixed segment duration. This reduces the encoding and streaming variability and thus allows a faster encoding of the video content and a reduced prediction complexity for adaptive bit rate algorithms. Due to the content-agnostic placement of I-frames at the beginning of each segment, additional encoding overhead is introduced. In order to mitigate this overhead, variable segment durations, which take encoder placed I-frames into account, have been proposed recently. Hence, a lower number of I-frames is needed, thus achieving a lower video bitrate without quality degradation. While several proposals exploiting variable segment durations exist, no comparative study highlighting the impact of this technique on coding efficiency and adaptive streaming performance has been conducted yet. This paper conducts such a holistic comparison within the adaptive video streaming eco-system. Firstly, it provides a broad investigation of video encoding efficiency for variable segment durations. Secondly, a measurement study evaluates the impact of segment duration variability on the performance of HAS using three adaptation heuristics and the dash.js reference implementation. Our results show that variable segment durations increased the Quality of Experience for 54% of the evaluated streaming sessions, while reducing the overall bitrate by 7% on average.
A Decision Tree Based Supervised Program Interpretation Technique for Gurmukhi Language

Deciphering the right context of the given word is one of the main challenges in Natural Language Processing. The study of Word Sense Disambiguation helps in deciphering the right context of the given word in use. Decision Tree is a methodology discussed under the supervised techniques used in WSD. Gurmukhi is one of the regional languages of India and much of the work done in this language is limited to knowledge-based mechanisms. The implementation of decision tree to correctly decipher the ambiguous word is new to this language and it has shown promising results with an average F-measure of 73.1%. These results will further help in Gurmukhi Word Sense Disambiguation.
"You Cannot Offer Such a Suggestion"

Previous work has looked closely at the challenges of using patient-generated data to enable remote assessment and monitoring by healthcare professionals. In this paper, we examine family caregivers who act as proxies for patients who may not have the capacity of capturing the necessary data. We worked with occupational therapists to develop an application for remote assessment of the safety of patients' homes by occupational therapists with the assistance of family caregivers. We evaluated the application with family caregivers and found two features unique to communication between family caregivers and healthcare professionals: Caregivers want to be able to direct healthcare professionals' attention to support problem-solving at home, and they include their perspective on how to best meet the patient's health needs. We discuss the importance of these findings for home systems in the domain of long-term chronic care.
Using Passive DNS to Detect Malicious Domain Name

With the prosperity of the Internet, the number of malicious domain name is enormous, and the scope and harm of the threats they create are increasing. Using traditional reputation systems and reverse engineering methods to detect malicious domain name cannot be real-time, and the process of detecting malicious domain name is complicated and cumbersome. In order to make up for the deficiencies and maintain accuracy, this paper adopts machine-learning method and uses passive DNS as the analytical data to construct a malicious domain name classification detection model. According to the access characteristics and character characteristics of domain name, we designed a complete feature analysis scheme and proposed a multi-dimensional DGA domain name detection method. We also propose a pornographic domain name detection method based on word vector in combination with the Chinese network environment. Finally, we implement prototype systems for malicious domain name detection and achieve good results.
Scenario-based XAI for Humanitarian Aid Forecasting

One domain application of artificial intelligence (AI) systems is humanitarian aid planning, where dynamically changing societal conditions need to be monitored and analyzed, so humanitarian organizations can coordinate efforts and appropriately support forcibly displaced peoples. Essential in facilitating effective human-AI collaboration is the explainability of AI system outputs (XAI). This late-breaking work presents an ongoing industrial research project aimed at designing, building, and implementing an XAI system for humanitarian aid planning. We draw on empirical data from our project and define current and future scenarios of use, adopting a scenario-based XAI design approach. These scenarios surface three central themes which shape human-AI collaboration in humanitarian aid planning: (1) Surfacing Causality, (2) Multifaceted Trust & Lack of Data Quality, (3) Balancing Risky Situations. We explore each theme and in doing so, further our understanding of how humanitarian aid planners can partner with AI systems to better support forcibly displaced peoples.
Utilizing Participant Voice in Volunteer Training

Delivering training to volunteers is a huge challenge for non-governmental organisations (NGOs). Traditional classroom-based approaches that dominate training are problematic due to the limited participation they offer to trainees. Peer-led approaches however, have shown promise in helping NGOs utilise trainee experiences within training. Although technologies are playing an increasing role in training, their benefits are not well understood. We describe our experience of designing peer-led training for community volunteers in rural India. Working alongside an NGO involved in community regeneration and social action, we collaboratively delivered a ten-day training workshop, deploying audio technologies to engage the participants in sharing lived experiences. We draw on reflections from trainers and trainees on how utilising participant voice can enhance training. We highlight opportunities around the usage of audio technologies for engaging with participant voice, including the ability to reclaim trainee agency within training and to work within cultural barriers.
Towards Traceable Design Rationale in Augmented Reality

When new things, such as buildings or physical products, are designed, the design process typically explores multiple design alternatives and undergoes several iterations. The associated artefacts typically grow from low-fidelity prototypes, such as paper sketches, to high-fidelity prototypes, such as 3D scale models. While previous work has focused on capturing the design rationale behind the decisions that happen during such a design process, this information typically remains secluded and is not easily accessible for the stakeholders. In this paper, we explore how to augment both physical and digital designs with their associated design rationale and decisions. Our exploratory inquiry with three experts in architecture provides qualitative feedback on our augmented reality tool and concepts. We expect that these preliminary results are valuable for future traceability tools for physical and digital designs, even beyond the domain of architecture.
Temporal Constraint Satisfaction Problems in Fixed-Point Logic

Finite-domain constraint satisfaction problems are either solvable by Datalog, or not even expressible in fixed-point logic with counting. The border between the two regimes can be described by a strong height-one Maltsev condition. For infinite-domain CSPs, the situation is more complicated even if the template structure of the CSP is model-theoretically tame. We prove that there is no Maltsev condition that characterizes Datalog already for the CSPs of first-order reducts of (Q; <); such CSPs are called temporal CSPs and are of fundamental importance in infinite-domain constraint satisfaction. Our main result is a complete classification of temporal CSPs that can be expressed in one of the following logical formalisms: Datalog, fixed-point logic (with or without counting), or fixed-point logic with the Boolean rank operator. The classification shows that many of the equivalent conditions in the finite fail to capture expressibility in Datalog or fixed-point logic already for temporal CSPs.
First time user experience of academic information system: An evaluation of usability

Abstract The use of information technology continues to grow, one of which is software development. The software does not only focus on functionality, but the ease of use becomes a heuristic development. The interface design cannot be underestimated because the wrong interface design can also result in repeated software development from an initial stage. Software development from the start is certainly not easy and time-consuming to revise it. The specific purpose of this study is to adapt various models of usability testing instruments to measure how easy it is to use the system being tested. This research aims to help the designer evaluate what he has designed. The first stage is the validation of the first user experience instrument of the Academic Information System Universitas Muhammadiyah Magelang. The results of this study found that the KRS system was able to be well received by the first user.
A Keyword-Based Literature Review Data Generating Algorithm—Analyzing a Field from Scientific Publications

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields—communication engineering, and lab on a chip technology—were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.
Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

Image-text hashing approaches have been widely applied in large-scale similarity search applications due to their efficiency in both search speed and storage efficiency. Most recent supervised hashing approaches learn a hash function by constructing a pairwise similarity matrix or directly learning the hash function and hash code (i.e.,1 or −1) procedure based on class labels. However, the former suffers from high training complexity and storage cost, and the latter ignores the semantic correlation of the original data, both of which prevent discriminative hash codes. To this end, we propose a novel discrete hashing algorithm called supervised matrix factorization hashing with quantitative loss (SMFH-QL). The proposed SMFH-QL first generates hash codes via the class label, avoiding the construction of a pairwise similarity; then, matrix factorization is used to design hash codes from original image-text data, thereby eliminating the impact of class labels and reducing the quantization error. Moreover, we introduce a quantitative loss function term to learn hash codes by incorporating class labels and the original data information, facilitating learning a similarity-preserving hash function in image-text search. Extensive experiments show that SMFH-QL outperforms several existing hashing methods on three representative datasets.
Cognitive Artificial Intelligence Countermeasure for Enhancing the Security of Big Data Hardware from Power Analysis Attack

Digital communication systems as the part of big data are utilized to transmit data and information. The increase of the digital communication system utilization will increase the value of information and on the other hand also induces an increase in the number of attacks on such systems. Side Channel Attack (SCA) is an attack model that could disrupt the information security when hardware implements a cryptographic algorithm. Differential Power Analysis (DPA), a kind of SCA, can reveal 75% of secret key used in encryption hardware. Other techniques called Correlation Power Analysis (CPA) which uses correlation factor between trace and hamming weight from the input of key generation can reveal the right secret key of Advanced Encryption Standard (AES) in significantly shorter span of time. The objective of this research is to design and implement an electronic countermeasure to deal with power analysis attack. The attacking aspect is reviewed as a form of identification of the correct countermeasure method against power analysis attack using Cognitive Artificial Intelligence (CAI)‘s method called cognitive countermeasure approach in an AES encryption device. Our main contribution is in the design of cognitive-countermeasure by altering the measured power consumption in affecting the secret key value of power analysis. The measured signal is altered by generating random masking value using CAI’s information fusion. CAI is a new perspective in Artificial Intelligence which is characterized by its capability to grow new knowledge based on the information from the sensory system. The random alteration of measured signal and continuous evolution of the masking value by using CAI’s information fusion is very significant in tackling the risk of power analysis. We also succeeded in implementing an AES encryption device based on CAI method on the Field-Programmable Gate Array (FPGA) platform.
Detection of white blood cells using YOLOV3 network

White blood cells are a very important part of medical research diagnosis. With the rise of artificial intelligence, it is also widely used in the medical field. At present, deep learning is the main research method. Most neural networks do not perform well on white blood cells. In this paper, we use YOLOv3 to improve the accuracy rate. In yolov3, Darknet extracts features and k-mean produces boxes. Yolov3 is Multi-scale prediction, so YOLOv3 is better for small target detection. At the same time, in this paper, we also preprocessed the data set by portioning them into blocks according to a certain ratio to improve the accuracy rate. As the results shown, the processed data set is better performance than the original data set in training.
Toward A Pervasive Gaze-Contingent Assistance System

Mobile devices with high-speed connectivity provide us with access to gigabytes of high resolution images, videos, and graphics. For instance, a head-worn display can be used to augment the real view with digitized visual information (Figure 1). Eye tracking helps us to understand how we process visual information and it allows us to develop gaze-enabled interactive systems. For instance, foveated gaze-contingent displays (GCDs) dynamically adjust the level of detail according to the user’s point-of-interest. We propose that GCDs should take users’ attention and cognitive load into account, augment their vision with contextual information and provide personalized assistance in solving visual tasks. Grounded on existing literature, we identified several research questions that need to be discussed before developing such displays.
HeadReach: Using Head Tracking to Increase Reachability on Mobile Touch Devices

People often operate their smartphones with only one hand, using just their thumb for touch input. With today's larger smartphones, this leads to a reachability issue: Users can no longer comfortably touch everywhere on the screen without changing their grip. We investigate using the head tracking in modern smartphones to address this reachability issue. We developed three interaction techniques, pure head (PH), head + touch (HT), and head area + touch (HA), to select targets beyond the reach of one's thumb. In two user studies, we found that selecting targets using HT and HA had higher success rates than the default direct touch (DT) while standing (by about 9%) and walking (by about 12%), while being moderately slower. HT and HA were also faster than one of the best techniques, BezelCursor (BC) (by about 20% while standing and 6% while walking), while having the same success rate.
Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations

Automated opinion mining of consumer reviews is becoming increasingly important due to the rising influence of reviews on online retail shopping. Existing approaches to automated opinion classification rely either on sentiment lexicons or supervised machine learning. Deep neural networks perform this classification task particularly well by utilizing dense document representation in terms of word embeddings. However, this representation model does not consider the sentiment polarity or sentiment intensity of the words. To overcome this problem, we propose a novel model of deep neural network with word-sentiment associations. This model produces richer document representation that incorporates both word context and word sentiment. Specifically, our model utilizes pre-trained word embeddings and lexicon-based sentiment indicators to provide inputs to a deep feed-forward neural network. To verify the effectiveness of the proposed model, a benchmark dataset of Amazon reviews is used. Our results strongly support integrated document representation, which shows that the proposed model outperforms other existing machine learning approaches to opinion mining of consumer reviews.
Spark Performance Optimization Analysis In Memory Management with Deploy Mode In Standalone Cluster Computing

As data is growing in different dimensions, it is difficult to get appropriate data analytic tools. Spark is one of high speed "in-memory computing" big data analytic tool designed to improve the efficiency of data computing in both batch and realtime data analytic. Spark is memory bottleneck problem which degrades the performance of applications due to in memory computation and uses of storing intermediate and output result in memory. Investigating how performance is increased in relation to spark executor memory, number of executors, number of cores, and deploy mode parameters configuration in a standalone cluster model is our primary goal. Three representative spark applications are used as workloads to evaluates performance in relation to changing these parameters value. Experimental result show, submitting the job in cluster deploy mode is faster to finish than a submitting job in client deploy mode under two workloads. This implies spark performance does not depend on deploy mode rather it depends on types of application. However, increasing number of executor per worker, a number of core per executor and memory fraction will increase spark performance under all workloads in any deploy mode.
Adaptive middleware in go - a software architecture-based approach

Abstract Adaptive middleware is essential for developing distributed systems in several applications domains. The design and implementation of this kind of middleware, however, it is still a challenge due to general adaptation issues, such as When to adapt? Where to include the adaptation code? What to adapt?, and How to guarantee safe adaptations?. Current solutions commonly face these challenges at the implementation level and do not focus on the safety aspects of the adaptation. This paper proposes a holistic solution implemented in Go programming language for developing adaptive middleware centred on the adoption of software architecture principles combined with lightweight use of formalisms. Software architecture concepts work as an enabling approach for structuring and adapting the middleware. Meanwhile, the formalisation helps in providing some guarantees before and during the middleware execution. The proposed solution is evaluated by implementing an adaptive middleware and comparing its performance against existing middleware systems. As shown in the experimental evaluation, the proposed solution enables us to design and implement safe adaptive middleware systems without compromising their performance.
SBTM: A joint sentiment and behaviour topic model for online course discussion forums

Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.
PositionPeak: Stimulating Position Changes During Meetings

In office environments, workers spend the majority of their workday sitting in a static position behind a desk or around a meeting table. Prolonged sitting time and sedentary behavior have severe negative health effects. Through this explorative study, we studied how different postures can be stimulated during meetings. We designed PositionPeak: three pieces of furniture aimed at composing a 'dynamic meeting room', subtly encouraging participants to avoid static postures. We video-recorded 5 meetings (N=16) and coded the number of position changes per participant. Participants also filled out a pre- and post-questionnaire about their experience. Our findings show that PositionPeak triggers people to adopt a variety of postures. Participants on average experienced a more efficient meeting but reported physical discomfort with some objects. We discuss the influence of PositionPeak on the meetings' social dynamics, the acceptance of new conventions and design recommendations for new meeting facilities.