Reviewerly/exHarmony huggingface dataset:
Recursive Sentiment Detection Algorithm for Russian Sentences

The article is devoted to the task of sentiment detection of Russian sentences. The sentiment is conceived as the author's attitude to the topic of a sentence. This assay considers positive, neutral, and negative sentiment classes, i.e., the task of three-classes classification is solved. The article introduces a rule-based sentiment detection algorithm for Russian sentences. The algorithm is based on the assumption that the sentiment of a phrase can be determined by the sentiments of its parts by the recursive application of appropriate semantic rules to the sentiments of its parts organized as a constituency parse tree. The utilized set of semantic rules was constructed based on a discussion with experts in linguistics. The experiments showed that the proposed recursive algorithm performs slightly worse on the hotel reviews corpus than the adapted rule-based approach: weighted $F_1$-measures are 0.75 and 0.78, respectively. To measure the algorithm efficiency on complex sentences, we created OpenSentimentCorpus based on OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. On OpenSentimentCorpus the recursive algorithm performs better than the adapted approach does: $F_1$-measures are 0.70 and 0.63, respectively. This indicates that the proposed algorithm has an advantage in case of more complex sentences with more subtle ways of expressing the sentiment.
Squeeze, Twist, Stretch: Exploring Deformable Digital Musical Interfaces Design Through Non-Functional Prototypes

Deformable interfaces are an emerging area of Human-Computer Interaction (HCI) research that offers nuanced and responsive physical interaction with digital technologies. They are well suited to creative and expressive forms of HCI such as Digital Musical Interfaces (DMIs). However, research on the design of deformable DMIs is limited. This paper explores the role that deformable interfaces might play in DMI design. We conducted an online study with 23 DMI designers in which they were invited to create non-functional deformable DMIs together. Our results suggest forms of gestural input and sound mappings that deformable interfaces intuitively lend themselves to for DMI design. From our results, we highlight four styles of DMI that deformable interfaces might be most suited to, and suggest the kinds of experience that deformable DMIs might be most compelling for musicians and audiences. We discuss how DMI designers explore deformable materials and gestures input and the role of unexpected affordances in the design process.
Strategic Performance Measurement System and Its Impact on Organizational Effectiveness: A Study of United Arab Emirates Based Organizations

Performance measurement has been subject to a considerable amount of research and attention over the past 15 years. The inadequacy of traditional financially based performance measurement systems and the introduction of nonfinancial measures have been the triggers for much of this research. The purpose of this paper is to review the performance measurement framework (Input-process-output), their application in the UAE based organizations in quality enhancement and to identify gaps in knowledge and practice that suggest future research. The paper draws on the study of performance indicators used in the 6 UAE based organizations. The design of indicators has been covered by many publications, but the design of measures specific and appropriate to UAE based organizations has not been well addressed. Based on the study of these organizations, a conceptual model is proposed that treats performance measurement as an integrated system, based on the TQM philosophy of continuous improvement cycles.
Querying Temporal Property Graphs

Nowadays, in many real-world problems, objects are characterized by properties and interactions that evolve over time. Several temporal property graph models associated with query languages are proposed in the literature to manage the temporal and interconnectivity features of such problems. However, they are not widely used due to the lack of a conceptual view. To overcome this drawback, we propose user-oriented operators to analyze temporal evolution of property graphs. We also define translation rules between our operators and existing property graph query languages to implement them directly. To illustrate the feasibility of our solution, we present two case studies based on a Neo4j and an OrientDB implementations of our operators and show some real-world querying examples.
Machine Learning Based Password Strength Analysis

Passwords, as the most used method of authentication because to its ease of implementation, allow attackers to get access to the accounts owned by others by means of cracking passwords. This is cause of the similar patterns that users use to create a password, like dictionary words, common phrases, person and location names, keyboard pattern, and so on. Multiple password cracking techniques had been introduced to predict the password offline or online, with the majority of records say the one with weak password or familiar password patterns being cracked. This suggested prototype implements numerous machine learning methods such as Decision Tree (DT), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) on a web application in real time to force users to choose a secure password. This results in the user’s account being logged into if particularly the password strength from more than half of the algorithms is strong.
Reconstructing Training Data from Trained Neural Networks

Understanding to what extent neural networks memorize training data is an intriguing question with practical and theoretical implications. In this paper we show that in some cases a significant fraction of the training data can in fact be reconstructed from the parameters of a trained neural network classifier. We propose a novel reconstruction scheme that stems from recent theoretical results about the implicit bias in training neural networks with gradient-based methods. To the best of our knowledge, our results are the first to show that reconstructing a large portion of the actual training samples from a trained neural network classifier is generally possible. This has negative implications on privacy, as it can be used as an attack for revealing sensitive training data. We demonstrate our method for binary MLP classifiers on a few standard computer vision datasets.
Comparative analysis of error mitigation techniques for variational quantum eigensolver implementations on IBM quantum system

Quantum computers are anticipated to transcend classical supercomputers for computationally intensive tasks by exploiting the principles of quantum mechanics. However, the capabilities of the current generation of quantum devices are limited due to noise or errors, and therefore implementation of error mitigation and/or correction techniques is pivotal to reliably process quantum algorithms. In this work, we have performed a comparative analysis of the error mitigation capability of the [[4,2,2]] quantum error-detecting code (QEC method), duplicate circuit technique, and the Bayesian read-out error mitigation (BREM) approach in the context of proof-of-concept implementations of variational quantum eigensolver (VQE) algorithm for determining the ground state energy of H$_2$ molecule. Based on experiments on IBM quantum device, our results show that the duplicate circuit approach performs superior to the QEC method in the presence of the hardware noise. A significant impact of cross-talk noise was observed when multiple mappings of the duplicate circuit and the QEC method were implemented simultaneously $-$ again the duplicate circuit approach overall performed better than the QEC method. To gain further insights into the performance of the studied error mitigation techniques, we also performed quantum simulations on IBM system with varying strengths of depolarising circuit noise and read-out errors which further supported the main finding of our work that the duplicate circuit offer superior performance towards mitigating of errors when compared to the QEC method. Our work reports a first assessment of the duplicate circuit approach for a quantum algorithm implementation and the documented evidence will pave the way for future scalable implementations of the duplicated circuit techniques for the error-mitigated practical applications of near-term quantum computers.
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss Selection (RHO-LOSS), a simple but principled technique which selects approximately those points for training that most reduce the model's generalization loss. As a result, RHO-LOSS mitigates the weaknesses of existing data selection methods: techniques from the optimization literature typically select 'hard' (e.g. high loss) points, but such points are often noisy (not learnable) or less task-relevant. Conversely, curriculum learning prioritizes 'easy' points, but such points need not be trained on once learned. In contrast, RHO-LOSS selects points that are learnable, worth learning, and not yet learnt. RHO-LOSS trains in far fewer steps than prior art, improves accuracy, and speeds up training on a wide range of datasets, hyperparameters, and architectures (MLPs, CNNs, and BERT). On the large web-scraped image dataset Clothing-1M, RHO-LOSS trains in 18x fewer steps and reaches 2% higher final accuracy than uniform data shuffling.
An Investigation of Kripke-style Modal Type Theories

This technical report investigates Kripke-style modal type theories, both simply typed and dependently typed. We examine basic meta-theories of the type theories, develop their substitution calculi, and give normalization by evaluation algorithms.
Hydra: Practical Metadata Security for Contact Discovery, Messaging, and Voice Calls

Abstract Protecting communications’ metadata can be as important as protecting their content, i.e., recognizing someone contacting a medical service may already allow to infer sensitive information. There are numerous proposals to implement anonymous communications, yet none provides it in a strong (but feasible) threat model in an efficient way. We propose Hydra, an anonymity system that is able to efficiently provide metadata security for a wide variety of applications. Main idea is to use latency-aware, padded, and onion-encrypted circuits even for connectionless applications. This allows to implement strong metadata security for contact discovery and text-based messages with relatively low latency. Furthermore, circuits can be upgraded to support voice calls, real-time chat sessions, and file transfers—with slightly reduced anonymity in presence of global observers. We evaluate Hydra using an analytical model as well as call simulations. Compared to other systems for text-based messaging, Hydra is able to decrease end-to-end latencies by an order of magnitude without degrading anonymity. Using a dataset generated by performing latency measurements in the Tor network, we further show that Hydra is able to support anonymous voice calls with acceptable quality of service in real scenarios. A first prototype of Hydra is published as open source.
Application of Correlation Filter Tracking Algorithm Based on Multiple Color Features in Basketball Motion Capture System

The application of motion capture system in basketball game can intercept the highlights of the game and improve the viewing effect. In order to improve the tracking performance of target tracking algorithm and the efficiency of traditional basketball tracking system, a correlation filtering algorithm combining multiple color features is adopted in this study. A dhcf as algorithm based on multicolor feature is established. The adaptability of traditional KCF algorithm in scene attributes such as rapid target change and illumination change is mainly studied. The success rate of multicolor tracking algorithm can reach 78.2%. At the same time, based on the traditional kernel correlation filter, the algorithm combines the discriminant color descriptor and the direction gradient histogram. The results show that the success rate and accuracy of the algorithm in otb50 are 84.2% and 82.5%, respectively. In addition, excellent tracking performance is reflected in various scene attributes. Therefore, the algorithm can improve the target tracking performance of the traditional kernel correlation filter, especially solving the adaptability of the traditional algorithm in the scene of light change, fast target movement, and so on.
Metric Dimension of Graphs and its Application to Robotic Navigation

Metric dimension of graphs has several applications to networking such as network navigation, network discovery and verification, wireless sensor network localization, and locating intruders in a network.This paper investigates the metric dimension in terms of contraction and bijection when a robot is navigating a network modeled by the(2,1)C 4 -snake graph, 2∆2-snake graph and 3C4-snake graph.
Harnessing Task Usage Prediction and Latency Sensitivity for Scheduling Workloads in Wind-Powered Data Centers

The growing number of data centers consumes a vast amount of energy for processing. There is a desire to reduce the environmental footprint of the IT industry, and one way to achieve this is to use renewable energy sources. A challenge with using renewable resources is that the energy output is irregular as a consequence of the intermittent nature of this form of energy. In this paper, we propose a simple and yet efficient latency-aware workload scheduler that creates an energy-agile workload, by deferring tasks with low latency sensitivity to periods with excess renewable energy. The scheduler also increases the overall efficiency of the data center, by packing the workload into as few servers as possible, using neural-network-based predictions of resource usage on an individual task basis to avoid unnecessarily provisioning an excess number of servers. The scheduler was tested on a subset of real-world workload traces, and real-world wind-power generation data, simulating a small-scale data center co-located with a wind turbine. Extensive experimental results show that the devised scheduler reduced the number of servers doing work in periods of low wind-power production up to 93% of the time, by postponing tasks with a low latency sensitivity to a later interval.
Review of Deep Learning: Architectures, Applications and Challenges

Deep Learning is a continuously evolving subset of machine learning techniques.New technology has provided solutions to a wide range of complex problems that were once unsolvable due to limitations in human intelligence.Since its conception, several DL architectures have been developed, including recursive neural networks, recurrent neural networks, artificial neural networks, and convolution neural networks.Many of their contributions have been in the area of computer vision, natural language processing, sequence generation, etc.Despite their increasing popularity, many individuals cannot see the bigger picture or comprehend these techniques.In this paper, the various deep learning models are described, as well as how they work.In addition, the article explains a few prominent DL models and their relevance in contemporary technology.As with every rapidly changing technology, DL has some limitations.These limitations are mitigated to some extent in this paper.Further, it emphasizes their continued development, the challenges they face, and the possibilities for future research in their fields
Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial-temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial-temporal finger joint angle patterns. The third model consists of the spatial-temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial-temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.
THE EFFECTIVENESS OF PROWRITINGAID TOWARD STUDENTS WRITING SKILLS IN SENIOR HIGH SCHOOL

Teaching activities are the core activities in the learning process, where there is a transfer of knowledge carried out by teachers to students. Various methods can be used in the learning process. The various methods are adapted to the needs and characteristics of students. Media and technology are also used to make the learning process more interesting. This study aims to find out about the effectiveness of using the ProWritingAid toward writing skills of XI senior high school students. The sample used in this study was 35 students of class XI. This research was conducted using a quantitative approach using a one shot case study as a research model. Data analysis in this study used paired t-test. The results of the paired t-test show a significance value of 0.000 where the value is smaller than 0.05 (0.00 &lt; 0.05) so that H0 is rejected and Ha is accepted, meaning that the ProWritingAid application is effective to be used toward students' writing skills of XI senior high school.
Study Of Evaluation Of Drainage Waterways System In Catchment Area Kertomenanggal Kota Surabaya

Kertomenanggal secondary channel is a channel that passes from the upstream of the Pagesangan Timur 1 channel to the downstream of the Surabaya-Sidoarjo Border Channel Primary Channel along 2.68 km, has a catchment area of ​​1.3 km2. In the rainy season, inundation often occurs at STA 1,455 to STA 1,819 in the Kertomenanggal Channel. This can cause disruption of population activities. Inundation can occur due to flood discharge that is too high, so that the channel capacity is unable to accommodate the existing flood discharge. There are 4 tertiary channels leading to the Kertomenanggal Channel that need to be analyzed, namely, the Menanggal Indah channel, Wisma Menanggal 7, Cipta Menanggal 2, and Wisma Menanggal 1.The method used to find the capacity of the drainage channel includes hydrological analysis used to find the maximum daily average rainfall in the area and catchment area of ​​the Kertomenanggal channel, as well as analysis of channel calculations using the rational method to determine the existing discharge and the planned Kertomenanggal channel.The existing Kertomenanggal channel drainage is 9.530 m3/second, while the incoming design discharge is 11.252 m3/second. The existing size of the Kertomenanggal Channel which is now unable to accommodate the water discharge when it rains, so it is necessary to normalize the channel and change the dimensions of the overflowing channel. Pump optimization needs to be done in order to cope with the planned flood discharge which is greater than the existing discharge of the Kertomenanggal Channel.
High-efficiency 3D shape measurement based on redesigned Gray code and aligned phase unwrapping method

In Gray-code assisted phase-shifting algorithm, jump errors seriously influence the measurement accuracy, which is commonly solved by projecting more patterns, exacerbating the contradiction between accuracy and efficiency. To avoid the error and improve the efficiency, this paper proposes an aligned phase unwrapping (Ali-PU) method with novelly redesigned Gray code: (1) The Gray-code and fringe patterns are generated by the proposed gray alternation and space multiplexing Gray-code method (GSGC). (2) A new formula is deduced to extract wrapped phase from fringe patterns, which has half fringe period misalignment with the original phase order. (3) Using the deliberately constructed misalignment, the aligned and correct phase order can be constructed, consequently the absolute phase without jump errors will be calculated. The method can efficiently obtain accurate 3D shapes by reducing at least two Gray-code patterns. In addition, it is more suitable to the objects with sharply changing edges as well as high-speed dynamic scenes because of its half fringe period misalignment tolerance. Experimental results demonstrate the efficiency and simplicity by marking 58-period fringe patterns with only 4 redesigned Gray-code patterns.
Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes

A cancer diagnosis is a source of psychological and emotional stress, which are often maintained for sustained periods of time that may lead to depressive disorders. Depression is one of the most common psychological conditions in patients with cancer. According to the Global Cancer Observatory, breast and colorectal cancers are the most prevalent cancers in both sexes and across all age groups in Spain.This study aimed to compare the prevalence of depression in patients before and after the diagnosis of breast or colorectal cancer, as well as to assess the usefulness of the analysis of free-text clinical notes in 2 languages (Spanish or Catalan) for detecting depression in combination with encoded diagnoses.We carried out an analysis of the electronic health records from a general hospital by considering the different sources of clinical information related to depression in patients with breast and colorectal cancer. This analysis included ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis codes and unstructured information extracted by mining free-text clinical notes via natural language processing tools based on Systematized Nomenclature of Medicine Clinical Terms that mentions symptoms and drugs used for the treatment of depression.We observed that the percentage of patients diagnosed with depressive disorders significantly increased after cancer diagnosis in the 2 types of cancer considered-breast and colorectal cancers. We managed to identify a higher number of patients with depression by mining free-text clinical notes than the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.8% (441/1269). In addition, the number of patients with depression who received chemotherapy was higher than those who did not receive this treatment, with significant differences (P<.001).This study provides new clinical evidence of the depression-cancer comorbidity and supports the use of natural language processing for extracting and analyzing free-text clinical notes from electronic health records, contributing to the identification of additional clinical data that complements those provided by coded data to improve the management of these patients.