Reviewerly/exHarmony huggingface dataset:
Adaptive sentiment analysis using multioutput classification: a performance comparison

The primary objective of this research is to create a multi-output classification model for sentiment analysis through the combination of 10 algorithms: BernoulliNB, Decision Tree, K-nearest neighbor, Logistic Regression, LinearSVC, Bagging, Stacking, Random Forest, AdaBoost, and ExtraTrees. In doing so, we aim to identify the optimal algorithm performance and role within the model. The data utilized in this study is derived from customer reviews of cryptocurrencies in Indonesia. Our results indicate that LinearSVC and Stacking exhibit a high accuracy (90%) compared to the other eight algorithms. The resulting multi-output model demonstrates an average accuracy of 88%, which can be considered satisfactory. This research endeavors to innovate in adaptive sentiment analysis classification by developing a multi-output model that utilizes a combination of 10 classification algorithms.
A consumer emotion analysis system based on support vector regression model

The effective means to stimulate economic growth is to enhance consumers' consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at different times. By mining the online comments of consumers on the online fitness platform, we can find the characteristics of fitness projects that consumers care about. Analyzing consumers' emotional tendencies towards the characteristics of fitness programs will help online fitness platforms adjust the quality and service direction of fitness programs in a timely manner. At the same time, it can also provide purchase advice and suggestions for other consumers. Based on this goal, this study uses an optimized support vector regression (SVR) model to build a consumer sentiment analysis system, so as to predict the consumer's willingness to pay. The optimized SVR model uses the region convolution neural network (RCNN) to extract features from the dataset, and uses feature data to train the SVR model. The experimental results show that the SVR model optimized by RCNN is more accurate. The improvement of the accuracy of consumer sentiment analysis can accurately help businesses promote and publicize, and increase sales. On the other hand, the increase in the accuracy of emotion analysis can also help users quickly locate their favorite fitness projects, saving browsing time. To sum up, the emotional analysis system for consumers in this paper has good practical value.
Salient object detection for RGBD video via spatial interaction and depth-based boundary refinement

Abstract Recently proposed state-of-the-art saliency detection models rely heavily on labeled datasets and rarely focus on perfect RGBD feature fusion, which lowers their generalization ability. In this paper, we propose a depth-based interaction and refinement network (DIR-Net) to fully leverage the depth information provided with RGB images to generate and refine the corresponding saliency segmentation maps. In total, three modules are included in our framework. A depth-based refinement module (DRM) and an RGB module work in parallel while coordinating via interactive spatial guidance modules (ISGMs), which utilize spatial and channel attention computed from both depth features and RGB features. In each layer, the features in both modules are refined and guided by the spatial information obtained from the other module through ISGMs. In the RGB module, before sending the depth-guided feature map to the decoder, a convolutional gated recurrent unit (ConvGRU)-based block is introduced to handle temporal information. Thinking about the clear movement information in RGB features, the block also guides temporal information in DRM. By merging the results from both the DRM and RGB modules, a segmentation map with distinct boundaries is generated. Considering the lack of depth images in popular public datasets, we utilize a depth estimation network that incorporates manual postprocessing-based correction to generate depth images on the DAVIS and UVSD datasets. The state-of-the-art performance achieved on both the original and new datasets illustrates the advantage of our RGBD feature fusion strategy, with a real-time speed of 19 fps on a single GPU.
Psychological Features of Forming the Life Prospects of First-Year Students

The modern space of professional training for qualified specialists is characterised by variability and changes in the value paradigm, as a result of which the development of a professional takes place in the context of psychological characteristics, which, in turn, requires new research. The purpose of the presented study is to empirically examine the psychological features of the development of students, values and life prospects. In the course of the study, the authors used general research methods, including quantitative analysis and generalisation, and psychodiagnostic methods. The paper considered the psychological features of the development of value orientations of first-year students based on personal characteristics, such as asociality, anxiety, reflexivity, internal conflict, reflected self-attitude, etc. The correlation analysis of the obtained empirical data confirmed the interdependence of anxiety, reflectivity, self-determination, and value orientations in adolescence, which have a certain influence on the coordination of life prospects of youth. The analysis allowed identifying the factors of development of students, value sphere: “sociality”; “Self-concept”; “autoreflexia”; “externality”, “self-development”. The essence of the components of the total variance for the selected 5 factors in accordance with the sum of squares of factor loads is substantiated. It was discovered that the obtained factors are formed based on individual psychological indicators. The prospect of further research is the compilation and testing of a correctional programme, which will be aimed at improving the selected factors that determine the development of the entire system of values of students, life prospects
Fast Relevant Feature Selection Based on Markov Boundary Learning from Data Stream for Classification Tasks

Learning a classification model from a dataset with the presence of irrelevant variables makes such a task more difficult and less accurate. To address this issue, feature selection has been proposed. However, existing feature selection struggles to select accurate and relevant sub-features or subspaces. It has been demonstrated in the literature that learning the Markov boundary (MB) of features helps to extract appropriate subsets of relevant features from the full data space. However, due to the fact that almost all existing methods learn MBs with static data, a state-of-the-art algorithm for learning MBs with streaming data, called SDMB (streaming data-based MB), has recently been proposed. SDMB has successfully improved online feature selection based on learning Markov boundary (MB) from data stream for classification tasks. Nevertheless, we found that SDMB has computational redundancy and there is room for improvement in terms of runtime. Therefore, we propose an improvement to SDMB. The main drawback of SDMB is that it entirely recomputes the conditional independence/dependence tests (i.e., the dependency scores) between a target variable and other variables in each iteration of learning MBs from the full data space. This paper proposes an incremental SDMB, called FastSDMB, to address the above bottleneck by reducing the dependency score computation time of SDMB. FastSDMB avoids the computational redundancy in SDMB by using a novel incremental computing collective dependency scoring process. This incremental process retains and reuses, in current iterations, the calculated conditional independence test score of variables that have not changed from previous iterations. To demonstrate FastSDMB's effectiveness and capability of handling fast data arrival scenarios, comparisons are made first over the state-of-the-art SDMB, and then we apply FastSDMB to various classification models in order to showcase the resulting performance improvement when implemented over a wide variety of synthetic and real-world datasets. The experimental results show that FastSDMB can run up to 4.8 × faster on average than SDMB and it maintains the same accuracy when learning Markov boundary. Thereby proving its efficiency in fast relevant feature selection based on Markov boundary learning from data stream.
System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics

Abstract Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system predictor (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of predictor when used as a guide for rewritings produced by the answer set programming rewriting tools projector and lpopt . The results demonstrate potential to this approach.
Block-based multi-scale image enhancement method for industrial inspection system

Automated inspections and intelligent image processing optimize quality control processes. Images captured in the industrial inspection system have low contrast and faint color. Many enhancement algorithms have recently been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The proposed method based on the frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is presented in obtaining a local-global estimate of high-contrast images, also used in the general fusion model. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than in the original image. The experiment results on the test dataset confirmed the high efficiency of the proposed enhancement method compared to the state-of-the-art techniques for industrial inspection systems.
Improving the phase modulation of spatial light modulator using Shack-Hartmann wavefront sensor

A phase-only spatial light modulator (SLM) is a device that is commonly used in various optical applications. Generally, SLM offers great advantages such as low power consumption and compact design. However, due to the manufacturing process, the main drawbacks of the SLM are surface non-uniformity and cross-talk between adjacent pixels, which add undesirable phase modulation. As a result, the SLM’s functionality is impacted, leading to image quality degradation, in terms of the signal-to-noise ratio (SNR), of optical reconstruction in holographic projection, for instance. Therefore, the aim of the current study is to measure and compensate for the surface non-uniformities of the SLM and improve its phase modulation. To achieve this, Shack-Hartmann wavefront sensor (SHWFS) is utilized. At first, a flat constant phase pattern is displayed on the SLM, and its surface phase shape is measured using a plane wave illumination. The reflected wavefront from the SLM is measured using SHWFS and then its phase information has been calculated. Hence, the calculated phase values are converted into a phase-only computer-generated hologram (CGH). The calculated CGH is displayed on the phase-only SLM to compensate for the phase errors of the SLM. The reflected wavefront has been measured after displaying the CGH to evaluate the compensation process. The experimental results reveal that the SHWFS provides high accuracy in the measurement of the phase distortion introduced by the surface of SLM. The SHWFS method is simple, robust, offers real-time performance, and is vibration-insensitive when compared with interferometric approaches.
Graphical Representation of Lipid Panel: A Simplified Time-Series Data Display

High cholesterol is a risk factor for developing Atherosclerotic Cardiovascular Disease. Poorly designed health data displays cause an undue cognitive burden on clinicians. Simplified line graphs (i.e., sparklines) could support efficient cognitive processing and interpretation of lipid panel results. Clinical concepts for cognitive tasks assessing low-density lipoprotein laboratory results were analyzed according to their internal representations and data scale types. A sparkline external representation aligns more closely with the internal representations for mental tasks associated with identifying abnormalities and assessing trends compared to traditional tabular displays. By simplifying the health data display with sparklines, faster cognitive processing is theoretically supported.
Towards (Really) Safe and Fast Confidential I/O

Confidential cloud computing enables cloud tenants to distrust their service provider. Achieving confidential computing solutions that provide concrete security guarantees requires not only strong mechanisms, but also carefully designed software interfaces. In this paper, we make the observation that confidential I/O interfaces, caught in the tug-of-war between performance and security, fail to address both at a time when confronted to interface vulnerabilities and observability by the untrusted host. We discuss the problem of safe I/O interfaces in confidential computing, its implications and challenges, and devise research paths to achieve confidential I/O interfaces that are both safe and fast.
Programming Fully Disaggregated Systems

With full resource disaggregation on the horizon, it is unclear what the most suitable programming model is that enables dataflow developers to fully harvest the potential that recent hardware developments offer. In our vision, we propose to raise the abstraction level to allow developers to primarily reason about their dataflow and the requirements that need to be met by the underlying system in a declarative fashion. Underneath, the system works with typed memory regions and uses the notion of ownership that allows for more flexible memory management across the different compute devices and the tasks mapped onto them. This requires a holistic approach that crosses multiple layers of the system stack, opening exciting systems research questions.
Towards Increased Datacenter Efficiency with Soft Memory

Memory is the bottleneck resource in today's datacenters because it is inflexible: low-priority processes are routinely killed to free up resources during memory pressure. This wastes CPU cycles upon re-running killed jobs and incentivizes datacenter operators to run at low memory utilization for safety. This paper introduces soft memory, a software-level abstraction on top of standard primary storage that, under memory pressure, makes memory revocable for re-allocation elsewhere. We prototype soft memory with the Redis key-value store, and find that it has low overhead.
Metal: An Open Architecture for Developing Processor Features

In recent years, an increasing number of hardware devices started providing programming interfaces to developers such as smart NICs. Processor vendors use microcode to extend processors' features such as Intel SGX and VT-x. This enables processor architects to quickly evolve processor designs and features. However, modern processors still lack general programmability as microcode is inaccessible to system developers. Developers still cannot define custom processor features. We argue that processors should expose this capability to developers, which enables new operating system and application designs.
CPU-free Computing: A Vision with a Blueprint

Since the inception of computing, we have been reliant on CPU-powered architectures. However, today this reliance is challenged by manufacturing limitations (CMOS scaling), performance expectations (stalled clocks, Turing tax), and security concerns (microarchitectural attacks). To re-imagine our computing architecture, in this work, we take a more radical, but pragmatic approach and propose to eliminate the CPU with its design baggage from data center computing. We integrate three primary pillars of computing, i.e., networking, storage, and computing, into a single, self-hosting, unified CPU-free Data Processing Unit (DPU) called Hyperion. The elimination of the CPU from computing necessitates re-thinking our computing, networking, and storage abstractions, and tackle the associated challenges which we sketch in this paper. We share the blueprint of our work-in-progress, Hyperion's hardware and software stack, and seek feedback.
Role of open innovation and strategy on SMEs in Dubai: review and theoretical framework

Purpose This study aims to examine and discuss the importance and benefits of Open Innovation (OI), Transformational Leadership (TL), Innovation Strategy (IS), Creative Climate (CC), Radical Innovation (RI) and Sustainable Competitive Advantage (SCA) for small and medium-sized enterprises (SMEs) in Dubai. This work also examines the mediating impact of future foresight drivers (FFD) on SMEs' SCA. The study provides a theoretical framework for enhancing SMEs' organizational performance and highlights the need for future empirical research. Design/methodology/approach The study uses a systematic literature review (SLR) approach and a bibliometric analysis approach to collect, examine and analyze data from previous research on OI, TL, IS, CC, RI and SCA. This work evaluated 110 publications from separate scholarly databases, Scopus and Web of Science (WoS). Findings The study finds a positive relationship between OI, TL, IS, CC, RI and SCA and that future empirical research is needed. While there is limited information on the impact of these concepts on SMEs in the Middle East and especially in Dubai, the study presents new concepts to be debated. The study provides a vital tool for businesses to improve their performance by adopting OI, TL and IS and analyzing their present competitive status to develop new strategies and build competitiveness. Originality/value The originality of this study lies in its contribution to understanding the relationships among OI, TL, IS, CC and RI and their impact on SMEs' SCA in Dubai. By emphasizing the importance of OI, TL and IS in improving SMEs' performance and competitiveness, this study provides valuable insights for SME managers seeking to enhance their organizations' sustainability and long-term success. The review also identifies a gap in the literature regarding the impact of these concepts on SMEs in the Middle East, emphasizing the need for further research in this area.
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

Abstract Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.
Nonverbal Communication in Immersive Virtual Reality through the Lens of Presence: A Critical Review

Abstract The emergence of metaverse signifies the transformation of virtual reality (VR) from an isolated digital experience into a social medium, which facilitates new contexts of information exchange and communication. In fact, VR comprises the first-ever computer-mediated communication paradigm that enables the transfer of a broad range of nonverbal cues, including some unique cues which are not even known from face-to-face social encounters. This highlights the urgency to theoretically and experimentally investigate aspects of nonverbal communication (NVC) in immersive virtual environments (IVEs). We provide a critical outlook on empirical studies aiming at widening the discussion on how presence, as a core social factor, is affected by the perception of nonverbal signals and how NVC may be effectively utilized to facilitate social interactions in immersive environments. Our review proposes a classification of the most fundamental cues and modalities of NVC, which we associate with conceptualizations of presence that are more relevant to interpersonal communication. We also investigate the NVC-related aspects that are essential to construct an “active” virtual self-concept and highlight associations among NVC-related aspects through forming a complex web of research topics coming from the field of IVEs. We establish that the key research challenge is to go beyond simply studying nonverbal cues and technological settings in isolation.
Lost in Afghanistan: Can the World Take ICT4D Seriously?

Seeking to maximize the value of information and communication technologies for development research.
Towards a Manageable Intra-Host Network

Intra-host networks, including heterogeneous devices and interconnect fabrics, have become increasingly complex and crucial. However, intra-host networks today do not provide sufficient manageability. This prevents data center operators from running a reliable and efficient end-to-end network, especially for multi-tenant clouds. In this paper, we analyze the main manageability deficiencies of intra-host networks and argue that a systematic solution should be implemented to bridge this function gap. We propose two key building blocks for a manageable intra-host network: a fine-grained monitoring system and a holistic resource manager. We discuss the research questions associated with realizing these two building blocks.
Skadi: Building a Distributed Runtime for Data Systems in Disaggregated Data Centers

Data-intensive systems are the backbone of today's computing and are responsible for shaping data centers. Over the years, cloud providers have relied on three principles to maintain cost-effective data systems: use disaggregation to decouple scaling, use domain-specific computing to battle waning laws, and use serverless to lower costs. Although they work well individually, they fail to work in harmony: an issue amplified by emerging data system workloads.
Evolving Operating System Kernels Towards Secure Kernel-Driver Interfaces

Our work explores the challenge of developing secure kernel-driver interfaces designed to protect the kernel from isolated kernel extensions. We first analyze a range of possible attack vectors that exist in current isolation frameworks. Then, we suggest a new approach to building secure isolation boundaries centered around ideas that originate in safe operating systems: isolation of heaps and single ownership.
Fragility in AIs Using Artificial Neural Networks

Suggesting ways to reduce fragility in AI systems that include artificial neural networks.
When SkyPilot meets Kubernetes

The Sky vision[3] aims to open a new era in cloud computing. Sky abstracts clouds and dynamically use multiple clouds to optimize workload execution. This enable users to focus on their business logic, rather than interact with multiple clouds, and manually optimize performance and costs.