Reviewerly/exHarmony huggingface dataset:
MQSMER: a mixed quadratic shape model with optimal fuzzy membership functions for emotion recognition

The traditional geometrical-based approaches used in facial emotion recognition fail to capture the uncertainty present in the quadrilateral shape of emotions under analysis, which reduces the recognition accuracy rate. Furthermore, these approaches require extensive computational time to extract the facial features and to train the models. This article proposes a novel geometrical fuzzy-based approach to accurately recognize facial emotions in images in less time. The four corner vertices of the mouth are the most important features to recognize facial emotions and can be extracted without the need of a reference face. These extracted features can then be used to define the quadrilateral shape, and the associated degree of impreciseness in the shape can be accessed using the proposed geometric fuzzy membership functions. Hence, four fuzzy features are derived from the membership functions and given to classifiers for emotion evaluations. In our tests, the fuzzy features achieved an accuracy rate of 96.17% in the Japanese Female Facial Expression database, and 98.32% in the Cohn-Kanade Facial Expression database, which are higher than the ones achieved by other common up-to-date methods. In terms of computational time, the proposed method required an average of 0.375 s to build the used model in a common PC.
Student User Experience with the IBM QISKit Quantum Computing Interface

The field of quantum computing is rapidly expanding. As manufacturers and researchers grapple with the limitations of classical silicon central processing units (CPUs), quantum computing sheds these limitations and promises a boom in computational power and efficiency. The quantum age will require many skilled engineers, mathematicians, physicists, developers, and technicians with an understanding of quantum principles and theory. There is currently a shortage of professionals with a deep knowledge of computing and physics able to meet the demands of companies developing and researching quantum technology. This study provides a brief history of quantum computing, an in-depth review of recent literature and technologies, an overview of IBMs QISKit for implementing quantum computing programs, and two successful programming examples. These two programs along with the associated Jupyter notebook pages will provide additional intermediate samples for IBM Q Experience.
Hardware Accelerated Semantic Declarative Memory Systems through CUDA and MapReduce

Declarative memory enables cognitive agents to effectively store and retrieve factual memory in real-time. Increasing the capacity of a real-time agent's declarative memory increases an agent's ability to interact intelligently with its environment but requires a scalable retrieval system. This work represents an extension of the Accelerated Declarative Memory (ADM) system, referred to as Hardware Accelerated Declarative Memory (HADM), to execute retrievals on a GPU. HADM also presents improvements over ADM's CPU execution and considers critical behavior for indefinitely running declarative memories. The negative effects of a constant maximum associative strength are considered, and mitigating solutions are proposed. HADM utilizes a GPU to process the entire semantic network in parallel during retrievals, yielding significantly faster declarative retrievals. The resulting GPU-accelerated retrievals show an average speedup of approximately 70 times over the previous Service Oriented Architecture Declarative Memory (soaDM) implementation and an average speedup of approximately 5 times over ADM. HADM is the first GPU-accelerated declarative memory system in existence.
Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy

Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Enabling End Users to Define the Behavior of Smart Objects in AAL Environments

In Ambient Assisted Living (AAL), Internet of Things (IoT) technology is exploited to equip living environments with smart objects that communicate with the outside world in an intelligent and goal-oriented manner and can support the occupants’ activities. Currently, providing such objects with new capabilities requires several programming efforts. In this paper, we present an approach to combine IoT technologies and End-User Development (EUD) paradigms and tools to identify innovative scenarios where end-users are directly involved in the creation and customization of the AAL systems they use. We will present EFESTO, a Task Automation tool that offers novel visual interaction paradigms to enable end users to easily express rules for smart object configuration and discuss how the overall approach can support daily practices of non-frail elderlies.
A Multimodal Text Matching Model for Obfuscated Language Identification in Adversarial Communication?

Obfuscated language is created to avoid censorship in adversarial communication such as sensitive information conveying, strong sentiment expression, secret actions plan, and illegal trading. The obfuscated sentences are usually generated by replacing one word with another to conceal the textual content. Intelligence and security agencies identify such adversarial messages by scanning with a watch-list of red-flagged terms. Though semantic expansion techniques are adopted, the precision and recall of the identification is limited due to the ambiguity and the unbounded creation way. To this end, this paper frames the obfuscated language identification problem as a text matching task, where each message is checked whether matches a red-flagged term. We propose a multimodal text matching model which combining textual and visual features. The proposed model extends a Bi-directional Long Short Term Memory network with a visual-level representation component to achieve the given task. Comparative experiments on real-world dataset demonstrate that the proposed method could achieve a better performance than the previous methods.
Continuous Software Engineering of Innovative Automotive Functions: An Industrial Perspective

One major challenge in the automotive industry is to deliver innovative functions more frequently. Hence, the standard development process with a fixed release plan is likely to be turned into a more continuous procedure. From a methodological perspective, this change includes applying well-established agile development techniques. In contrast to pure software-related domains, the successful implementation of software-based functions in embedded systems highly depends on non-functional requirements, and therefore we see the need for extending the pure code-centric notion of agility. In order to do so, we reflect on architectural drivers that are beneficial for the transformation of OEMs into software companies. Finally, we present our perspective on future automotive software engineering by illustrating how continuous integration is applied by a software engineer not only on the level of source code, but also on the system architecture level and the respective role.
Interpreting Library Chat Reference Service Transactions

This paper discusses live chat instances as patron participatory learning interactions in a medium-sized non-Western university library. Widespread and popular in the regional commercial sector, it is not so in local academic libraries whose library websites were visited. Rather, chat is used primarily as a service for general university enquiries. For this paper, library live chat scripts covering a five-year period from February 2013 to February 2018 were examined, a literature review done, and insights derived from those scripts. Findings contribute towards context-specific understanding of university student library use habits.
Global Convergence of the (1 + 1) Evolution Strategy to a Critical Point

Abstract We establish global convergence of the (1 + 1) evolution strategy, that is, convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a critical point to measurable functions. At its core, the analysis is based on a novel progress guarantee for elitist, rank-based evolutionary algorithms. By applying it to the (1 + 1) evolution strategy we are able to provide an accurate characterization of whether global convergence is guaranteed with full probability, or whether premature convergence is possible. We illustrate our results on a number of example applications ranging from smooth (non-convex) cases over different types of saddle points and ridge functions to discontinuous and extremely rugged problems.
Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology

This paper presents a study that analyses the effect of financing costs on grid parity in photovoltaic (PV) installations by applying a probabilistic methodology. Three different case studies, located in Spain, have been considered, with 500 kW, 50 kW and 5 kW grid-connected PV generators. The technical and economic calculations were performed, considering the interest rate, yield across the Spanish geography, and PV module cost as parameters. The Monte Carlo method was applied to consider the full probabilistic range of values given to the different variables. The goal of this study was to determine, for the studied cases, the levelised cost of energy (LCOE) and the internal rate of return by considering realistic values of the variables. A success rate parameter was calculated, which determined the likelihood of the number of times that the LCOE was below the retail cost of electricity. All the cases were evaluated by applying 10,000 iterations, considering the standard deviations and means defined.
Enabling Multiple Applications to Simultaneously Augment Reality

Augmented reality (AR) platforms are evolving to support immersive 3D experiences. Most modern AR platforms support only a single immersive app at a time, but users may also benefit from the ability to engage with multiple apps at once. The ability of different apps to simultaneously augment a user's world raises critical questions: how might apps visually conflict with each other, and how can we design AR platforms to support rich behaviors while mediating conflicts? In this work, we pose and explore these questions, identifying means of visual conflict as well as platform design strategies to mediate conflicts. We then analyze state-of-the-art AR platforms (HoloLens, Magic Leap One, and Meta 2) to understand their trade-offs and identify unexplored gaps in the broader design space. Our exploration reveals key guidelines and lessons to inform future multi-app AR efforts.
A Reconfigurable Accelerator for Sparse Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been shown to be very useful in image recognition and other AI applications. CNNs are usually computationally intensive. To address the challenge of overwhelming calculation requirements, researchers have proposed network compression methods to reduce the number of synaptic weights and the amount of computations. In this paper, we propose an input row based sparse convolution neural network accelerator on FPGAs that performs sparse CNN computing efficiently. Similar to the DNNWEAVER architecture, our accelerator also uses two-level architecture hierarchy, with multiple Processing Units (PUs) and each PU comprises a set of basic Processing Elements (PEs). The number of PEs in a single PU and the number of PUs in a design are reconfigurable for different CNNs for best performance. Our architecture does not require the large multiplexer for data selection as needed in Cambricon-X, thus, is more suitable for larger accelerator designs for high performance. Besides, we propose a weight merging method to balance the computation load on different PUs to maximize the overall computation efficiency. For evaluation, we implement our design with 32 PUs and each with 14 PEs. When compared with the DNNWEAVER implementation for non-sparse VGG16 network, we get an overall performance of 3.6x speedup running at 100MHz on a Xilinx ZC706 board and reach the speed of 297 GOPS.
Slim: {OS} Kernel Support for a Low-Overhead Container Overlay Network

Containers have become the de facto method for hosting large-scale distributed applications. Container overlay networks are essential to providing portability for containers, yet they impose significant overhead in terms of throughput, latency, and CPU utilization. The key problem is a reliance on packet transformation to implement network virtualization. As a result, each packet has to traverse the network stack twice in both the sender and the receiver’s host OS kernel. We have designed and implemented Slim, a low-overhead container overlay network that implements network virtualization by manipulating connection-level metadata. Our solution maintains compatibility with today’s containerized applications. Evaluation results show that Slim improves the throughput of an in-memory key-value store by 66% while reducing the latency by 42%. Slim reduces the CPU utilization of the in-memory key-value store by 54%. Slim also reduces the CPU utilization of a web server by 28%-40%, a database server by 25%, and a stream processing framework by 11%.
Genetic programming and evolvable machines at 20

The journal and in particular the resource reviews have been running for 20 years. We summarise the GP literature, including top papers and authors, as seen by users of the genetic programming bibliography. Then revisit our original goals for GPEM book reviews and compare them with what has achieved.
Experiential Learning of Software Project Management and Software Development via Course Collaboration

The skills to effectively manage software development teams and to productively interact with a project manager are important in the computing professions. Teaching these skills in a traditional academic setting, however, is challenging. To support the experiential learning of these skills, we established a collaboration between two computing courses, a sophomore-level one and a senior-level one, where the seniors serve as managers of teams of sophomores on a semester-long project. This paper describes the collaboration and evaluates it in terms of student learning and experience based on three iterations of that collaboration. The associated course materials and evaluation instruments are made publicly available.
A Study of the Application of Word Cloud Visualization in College English Teaching

Nowadays, the cultivation of students' reading ability has become one of the essential aspects in College English Teaching, in which the methodological guidance to help students grasp effectively the key words of articles emerges particularly as the significant point in classroom interactivity.As the visualization mechanism analyzing and researching the English text quickly, Word Cloud has been put forth as English teachers' new tool in reading training.This thesis tends to present a dynamically interactive system of Word Cloud Visualization based on the technique of D3 and JQuery in the field of Computer Science, and attempts to elaborate its advantages, such as the comfortable interface and the prominent key points with several examples of recent US presidential inaugural speeches as well as the test analysis of the CET 4 .
A Novel Repetition Normalized Adversarial Reward for Headline Generation

While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases [1]. In this paper, we propose a novel repetition normalized adversarial reward to mitigate these problems. Our repetition penalized reward can greatly reduce the repetition rate and adversarial training mitigates generating incoherent phrases. Our model significantly outperforms the baseline model on ROUGE-1 (+3.24), ROUGE-L (+2.25), and a decreased repetition-rate (-4.98%).
Utilizing response times in cognitive diagnostic computerized adaptive testing under the higher‐order deterministic input, noisy ‘and’ gate model

Methods of cognitive diagnostic computerized adaptive testing ( CD ‐ CAT ) under higher‐order cognitive diagnosis models have been developed to simultaneously provide estimates of the attribute mastery statuses of examinees for formative assessment and estimates of a latent continuous trait for overall summative evaluation. In a typical CD ‐ CAT environment, examinees are often subject to a time limit, and the examinees’ response times ( RT s) for specific test items can be routinely recorded by custom‐made programs. Because examinees are individually administered tailored sets of test items from the item pool, they may experience different levels of speededness during testing and different levels of risk of running out of time. In this study, RT s were considered during the item‐selection procedure to control the test speededness and the RTs were treated as useful information for improving latent trait estimation in CD ‐ CAT under the higher‐order deterministic input, noisy ‘and’ gate ( DINA ) model. A modified posterior‐weighted Kullback–Leibler ( PWKL ) method that maximizes the item information per time unit and a shadow‐test method that assembles a provisional test subject to a specified time constraint were developed. Two simulation studies were conducted to assess the effects of the proposed methods on the quality of CD ‐ CAT for fixed‐ and variable‐length exams. The results show that, compared with the traditional PWKL method, the proposed methods preserve a lower risk of running out of time while ensuring satisfactory attribute estimation and providing more accurate estimates of the latent trait and speed parameters. Finally, several suggestions for future research are proposed.
On Determining Variability Annotations In Partially Annotated Models

In model-driven software product line engineering (SPLE) the superset of products is developed over models. A feature model typically states the discriminating and common factors of the software. In annotative approaches model elements are associated with variability annotations which are boolean expressions over the features defining in which products the elements are visible. When the product line is defined over different models, the developer wants to annotate the model of one type and transform it to different representations, e.g., a (UML) class diagram into a relational database schema for establishing an object-relational mapping. Assigning the annotations manually to the target model is an error-prone and laborious task. In a black-box approach we automatically assign the correct annotations to the target model without analyzing the transformation specification. It is an easy task in the case of 1:1 mappings where the annotation of the source element is copied to the corresponding element in the target model. Typically this kind of information is available, e.g., in traces written during the transformation execution. In reality, more complex mappings are frequent but the correspondences harder to determine. Assuming a target model is already annotated with the annotation of 1:1 correspondences, a certain number of elements remains without annotations. This paper contributes strategies to determine missing annotations in partially annotated models. We compare a global include strategy with more sophisticated ones which take the model structure into account. Since we apply missing annotations locally on one model, we solve a general SPLE problem where completely annotated models reduce the manual user effort and are desirable for filtering.
Integrating Feature Extractors for the Estimation of Human Facial Age

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Flood disaster and risk anticipation strategy

The purpose of this study is to describe the risk anticipation strategy in facing flood as an action by local people particularly in the settlement area of Makassar suburbs. This is urgent to describe due that flood area is the densely populated area. This research used interviews, observation and literature study. Interviewing is for residents about their strategy in anticipating the risk of floods. Results of interviews that are mapped are based on the indicators of risk anticipating strategy. Observation technique is about the settlement condition before and after the flood. Literature study is about related theory and the same problem of this research for comparing each other. This study found that the residents as local people in risk anticipating flood as strategy, such as refuge to relatives and neighbors whose homes are free of the flood, store goods on higher ground, or tie to keep them from flood flowing, pet animals, preparing rubber tires for transportation.
Multivariate geochemical classification of chromitite layers in the Bushveld Complex, South Africa

The Bushveld Complex, the largest layered mafic-ultramafic intrusion worldwide, is host of numerous, laterally continuous and chemically similar chromitite layers. Based on their stratigraphic position the layers are subdivided into a lower, middle and upper group (LG, MG and UG). Within these groups the layers are numbered successively – from the base to the top of each group. Attempts of discriminating between single layers based on their composition have failed – mainly due to the significant overlap of compositional fields, e.g. of chromitite mineral assemblages and chromite mineral chemistry between (neighboured) layers. In this contribution a tailored and easy to use multivariate classification scheme for the chromitite layers is proposed, based on a comprehensive classification routine for the LG and MG chromitites. This routine allows a clear attribution with known uncertainty of eight distinct chromitite layers. The study was carried out at the Thaba Mine, a chromite mine located on the western limb of the Bushveld Complex. The classification is based on a large geochemical database (N = 1205) from Thaba Mine. It comprises of a hierarchical discrimination approach relying on linear discriminant analysis and involves five distinct steps. Using default homogeneous prior probabilities, classification results are excellent for the first discrimination steps (LGs vs. MGs, 97 %; LG-6 vs. LG-6A, 94 %) and very good for the following steps (MG-1/2 vs. MG-3/4, 86 %; MG-1 vs. MG-2, 92 %; MG-3 vs. MG-4, 93 %; MG-4 vs. MG-4Z, 97 %; MG-4 vs. MG-4A, 88 %). The classification scheme was tested using the same sample set as a training set with unknown composition. Overall classification results for unknown samples belonging to one of the layers are 81 %. Hence, the classification scheme is at least valid for the Thaba mine. The approach may, however, be extended across the entire Bushveld, provided that an appropriate geochemical data set is available.