Reviewerly/exHarmony huggingface dataset:
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.
Coherence-based inequality for the discrimination of three-qubit GHZ and W class

Quantum coherence and entanglement originate from the superposition principle. We derive a rigorous relation between the $${l_1}$$ -norm of coherence and concurrence, in that we show that the former is always greater than the latter. This result highlights the hierarchical relationship between coherence and concurrence, and proves coherence to be a fundamental and ubiquitous resource. We derive an analogous form of monogamy inequality, which is based on the partial coherence of the reduced two-qubit and reduced single qubit of the particular class of three-qubit state. Moreover, we provide coherence-based inequality for the classification of GHZ class and W class of three-qubit states. Finally, we provide theoretical discussion for the possible implementation of the scheme in an experiment.
Fully Informed Grey Wolf Optimizer Algorithm

Grey wolf optimizer (GWO) is a newly generated metaheuristic search algorithm inspired by the social behaviour of the grey wolf, which resembles the social structure and hunting mechanism of grey wolves in nature, and is based on three main steps: searching for prey, encircling prey and attacking prey. This paper presents a new variant of GWO algorithm in which each wolf individually updates their respective positions when they get inspiration from the first fittest solution, that is the α wolf, and it proved to be a good algorithm in terms of convergence speed. But, in this strategy, each wolf is naturally influenced by the first fittest solution, which is responsible for decision-making. Therefore, in this new proposed paper, a new search strategy, namely ‘fully informed learning’, is used. The developed algorithm is described as the fully informed grey wolf optimizer algorithm (FIGWOA). To check the performance, the FIGWOA is tested on 14 well-known benchmark functions of different complexities. The results are compared with some optimization algorithms as grey wolf optimizer (GWO), differential evolution (DE), power low based local search in spider monkey optimization (PLSMO), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. The results are very assuring and show that the proposed algorithm is competitive in the field of algorithms based on swarm intelligence. Comparative results show that the GWO algorithm can provide very competitive results compared to other well-known conventional, heuristics and metaheuristics search algorithms.
On the Cluster Admission Problem for Cloud Computing

Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.
Human–machine interaction and design methods

This chapter addresses human-machine interaction and its associated conceptual frameworks and design methods. Human-machine interaction is a study of the interaction between humans and machines. Human-computer interaction research dwells on the usability of computer technology and interactive interfaces, whereas human-robot interaction research aims to develop robots that communicate socially and interact with people in everyday life settings. Well-known functions in interactive computer devices include haptic devices such as keyboards and touch screens, speech and auditory recognition systems, and gesture recognition. Designers of social robots therefore need to take into consideration the effects of form and appearance on a person’s appreciation, reaction and possible positive or negative interaction with a robot. Participatory design refers to a method emphasising the involvement of the end-user of a product from the very beginning of the design process.
Entrepreneurial Ecosystem and Performance of SMEs in Pakistan

Purpose: This study aims to measure the entrepreneurial ecosystem of Pakistan and examining its effect on SMEs' performance. A thriving business sector is indispensable for the economic growth of a country. Businesses nurture an environment that is supportive for the entry, survival, and growth of entrepreneurs. The concept of the entrepreneurial ecosystem, as a combination of interdependent factors, is relatively new in entrepreneurship, especially in developing countries. Research/Methodology/Design: The World Bank Enterprise Survey data of 2049 SMEs for the years 2006 and 2013 has been used to get entrepreneurs' perceptions of Pakistan's entrepreneurial ecosystem. The cluster analysis and canonical discriminant analysis are used to measure the entrepreneurial ecosystem of Pakistan, and regression analysis is used to estimate its effect on SMEs' performance. Findings: The findings suggest that the entrepreneurial ecosystem of Pakistan, as a system, is a constraint on SMEs' performance. However, the lack of a skilled workforce to meet the needs of modern industrial technologies is most negatively affecting SMEs' performance. Practical Implications: It can be concluded that there is no one perfect recipe for setting up an entrepreneurial economy, however, research should use a bottom-up approach to provide these practical yet imperfect roadmaps to the policymakers for devising a set of actions for gradually shaping the entrepreneurial ecosystems in a specific direction. Originality/Value: Entrepreneurial ecosystems is a novel way of looking at entrepreneurial activity in a region. Most of the research in this domain is descriptive and theoretical, lacking empirical support, especially in developing countries. Therefore, this research is a contribution to the body of knowledge on entrepreneurship and entrepreneurial ecosystems.
Improving Univariate Time Series Anomaly Detection Through Automatic Algorithm Selection and Human-in-the-Loop False-Positive Removement

The existence of a time series anomaly detection method that performs well for all domains is a myth. Given a massive library of available methods, how can one select the best method for their application? An extensive evaluation of every anomaly detection method is not feasible. Many existing anomaly detection systems do not include an avenue for human feedback, essential given the subjective nature of what even is anomalous. We present a technique for improving univariate time series anomaly detection through automatic algorithm selection and human-in-the-loop false-positive removement. These determinations were made by extensively experimenting with over 30 pre-annotated time series from the open-source Numenta Anomaly Benchmark repository. Once the highest performing anomaly detection methods are selected via these characteristics, humans can annotate the predicted outliers which are used to tune anomaly scores via subsequence similarity search and improve the selected methods for their data, increasing evaluation scores and reducing the need for annotation by 70% on predicted anomalies where annotation is used to improve F-scores.
IMPROVING STUDENTS’ WRITING SKILLS IN DESCRIPTIVE TEXT USING PICTURE WORD INDUCTIVE MODEL (PWIM) STRATEGY

The objective of the study is to provide the students treatment and make the students easy in learning process of writing descriptive text, therefore to improve the students writing skills in English by using PWIM strategy. The participants are the 7th grade students of Junior High School at Cimahi in academic year 2019/2020 which consist of 32 students. The study using CAR with two cycles to observe the activities and responses of students in classroom. Result of the study could be seen from the improvement score from pretest to posttest 2 with a minimum score of 75 and maximum score of 85 in posttest 2. Keywords: Writing, Descriptive Text, PWIM Strategy, CAR
Gamification as a basic tool in distance education

Currently there are various teaching-learning strategies, which are useful and can be implemented according to the objective that is had with the group of students, it is there, where gamification is implemented, whose main objective is to influence behavior of people, since it can be a motivator to continue experimenting with new learnings, which lead the student to solve problems through games, having an assignment of points or feedback, as a result of it, and with it taking gamification as a basic tool in distance education. Objectives, methodology. Through this research, it is sought to demonstrate that within distance education there are various strategies that can be implemented in the material that makes up each subject, but this time we wanted to focus on gamification as a tool that can be implemented in the designs of Each of the materials of the distance subjects, so that, with this, it can be seen as another motivational alternative, so that the student becomes even more integrated in the training process of their learning. Contribution. This research aims to demonstrate that gamification tools can be basic in the teaching-learning process of distance education, since this is where it is required that the student be motivated and generate a greater commitment in the process of their learning. The results of implementing gamification in distance education subjects bring us beneficial results in the implementation, thus having significant changes for the students, thus better professionals.
Community-Based Flood Risk Management in a Dense Populated Floodplain Area

Flood risk management in a floodplain area with high population density has complex challenges.Almost all communities in that area are unwilling to move even though they are aware of the potential for flooding.This study aims to: 1) develop knowledge and understanding of community-based flood risk management in Malang City, Indonesia; 2) assess the cost effectiveness of the flood risk management; 3) develop a framework for building community-based flood risk management.This study used a mixed method combining quantitative and qualitative approaches.The results show that the understanding of the flood risk and the potency of flood emergency response of the local government significantly influence building communitybased flood risk management system.This system can reduce the flood risk up to 30% compared to before the implementation of that system, and provides direct financial benefits.The total benefit can be cost effectiveness of up to 49.2% in a year.
Flow-Grounded Dynamic Texture Synthesis for Video Compression

The basic ingredients of modern video coding standards are block-based prediction and transforms. However, when dealing with video contents containing dynamic textures (DT), the existing prediction schemes usually failed due to temporal variability and randomness of DT, which results in more bit cost on residual coding compared with other contents. In view of this point, a novel video compression scheme for DT is proposed in this work. In particular, wavelet-based analysis on motion characteristics of DT is firstly presented and based on the analysis, we introduce a flow-grounded texture synthesis method for video compression. Instead of conventional inter prediction, synthesized DT contents are used for reconstruction at the decoder. The proposed scheme has been fully integrated into the test model of Versatile Video Coding standard, VTM-10.0, for validation and a subjective test has also been carried out. Experimental results show that bitrate savings can be achieved by 40% on average at comparable visual quality.
Stage-wise Fine-tuning for Graph-to-Text Generation

Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize the structure information of the input graph. In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes the model on Wikipedia before adapting to the graph-to-text generation. In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel tree-level embedding method to capture the inter-dependency structures of the input graph. This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.
Reliability validation for file system interpretation

This paper examines current best practices for Digital Forensic (DF) tool and method validation in the context of file system interpretation for digital evidence. In order to meet the legal and scientific requirements in criminal procedures file system (FS) reverse engineering (RE) is a necessity. Currently, there is no standard procedure for reliability testing of FS RE. Ideal validation requirements exist, but they are on high-level and practical implementation is missing. In this paper we propose a formal reliability validation procedure for file system reverse engineering, documenting the forensic process, including the tools used, ensuring reliability and reproducibility of the method and the results. The procedure is based on legal and scientific criteria and tested against file system reverse engineering methods. It is applicable to all types of reverse engineering methods in digital forensics.
Evaluation of Heavy Metals and Total Hydrocarbon Concentration in the Qua Iboe River Estuary, Akwa Ibom State, Nigeria

The research assessed the concentration level of heavy metals and total hydrocarbon in Qua Iboe River Estuary of Akwa Ibom State, Nigeria during the months of April, May and June. The experimental research design was adopted and a total of 72 water samples were collected on weekly basis from ST1, ST2 and ST3 during high and low tides in thoroughly-rinsed 1-litre plastic containers to avoid adulteration of the samples. Samples were analyzed in-situ and in the laboratory using standard methods. The results of the concentrations of the sampled parameters during high tide included: Cr (0.003-0.21mgl-1), Fe (0.123-0.511mgl-1); Mn (0.018-0.66mgl-1), Pb (0.105-0.603mgl-1), Mg (0.42-0.623mgl-1), Cu (0.00-0.32mgl-1) and THC (0.12-0.88mgl-1)while the low tide concentrations showed: Cr (0.001-0.061mgl-1), Fe (0.56-4.11mgl-1), Mn (0.002-0.13mgl-1), Pb (0.011-0.211mgl-1), Mg (0.108-1.342mgl-1), Cu (0.011-0.076mgl-1) and THC (0.23-0.80mgl-1). The concentrations of Hg and As were negligible throughout the study. Analysis revealed that THC varied significantly across the sampled stations (ANOVA, P &lt;0.001). Pollution Index, PI &lt;1 for Ni, Mn, Na, Hg, As, Mg and Cu, implied that there was no pollution in the estuary. Fe showed that the estuary was slightly polluted with PI 1-2; Cr, V and THC recorded PI 2-3 signifying that the water was moderately polluted; PI &gt;5 for Pb revealed that the estuary was seriously polluted with lead. The oil spills by ExxonMobil’s offshore platforms in the area, the heinous practices of waste dumping along the coast have resulted in the high THC and heavy metals concentrations found in the water samples. Hence, the water in Qua Iboe River Estuary is polluted and not safe for personal or domestic uses. The study recommended that Exxon Mobil and other oil companies in Nigeria should adhere strictly to international best practices in oil exploration/exploitation, environmental conservation, maintenance of equipment and safety of their infrastructure should be their priority. Prompt actions should be taken towards clean-up exercises in case of oil spill incidents in the area and proper, modern sewage and solid wastes disposal mechanism should be put in place by the host communities.&#x0D; &#x0D; Received: 31 January 2021 / Accepted: 27 February 2021 / Published: 7 March 2021
Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari

The process of accepting new cadet candidates at the Maritime Academy of Marine Sanctuary every year, produces a lot of data in the form of profiles of prospective cadets. The activity caused a large accumulation of data, it became difficult to identify prospective cadets. This research discusses the application of data mining to generate profiles that have similar attributes. One of the data mining techniques used to identify a group of objects that have the same characteristics is Cluster Analysis. The data clustering method is divided into one or more clusters that have the same characteristics called K-means. The method that the author uses is knowledge discovery in databases (KDD) consisting of Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge. Implementation of K-means Clustering process using Rapid Miner. Attributes used by NIT, Level, Name, Student Status, Type of Registration, Gender, Place of Birth, Date of Birth, Religion, School Origin, School Origin Department, Religion, GPA, Subdistrict, District/ City, Province. Returns the number of clusters 30 (k=30). From the research results based on davies bouldin test on K-means algorithm resulted in the closest value of 0 is k = 29 with Davies bouldin: 0.070, with the most cluster member distribution in cluster 16 containing cluster members 115 items.
Exploring the parameter space of human activity recognitionwith mobile devices

Motion sensors available on smart phones make it possible to recognize human activities. Accelerometer, gyroscope, magnetometer, and their various combinations are used to classify, particularly, locomotion activities, ranging from walking to biking. In most of the studies, the focus is on the collection of data and on the analysis of the impact of different parameters on the recognition performance. The parameter space includes the types of sensors used, features, classification algorithms, and position/orientation of the mobile device. In most of the studies, the impact of some of these parameters is partially analyzed; however, in this work, we investigate the parameter space in detail with a global focus. Particularly, we investigate the impact of using different feature-sets, the impact of using different sensors individually and in combination, the impact of different classifiers, and the impact of phone position. Using an ANOVA analysis, we investigate the importance of various parameters on the recognition performance. We show that these parameters are ranked according to their impact on the recognition performance in the following order: sensor, position, classifier, feature. We believe that such an analysis is important since we can statistically show how much a parameter is affecting the recognition performance. Our observations can be used in future studies by only focusing on the important parameters. We present our findings as a discussion to guide the further studies in this domain.
Assessment and Decision-Making in Universities: Analytics of the Administration-Staff Compromises.

Various processes in academic organizations include the decision points where selecting people through their assessment and ranking is performed, and the impact of wrong or right choices can be very high. How do we simultaneously ensure that these selection decisions are well balanced, fair, and unbiased by satisfying the key stakeholders' wishes? How much and what kinds of evidence must be used to make them? How can both the evidence and the procedures be made transparent and unambitious for everyone? In this paper, we suggest a set of so-called deep evidence-based analytics, which is applied on top of the collective awareness platform (portal for managing higher education processes). The deep evidence, in addition to the facts about the individual academic achievements of personnel, includes the evidence about individual rewards. However, what is more important is that such evidence also includes explicit individual value systems (formalized personal preferences in the self-assessment of both achievements and the rewards). We provide formalized procedures that can be used to drive the academic assessment and selection processes within universities based on advanced (deep) evidence and with different balances of decision power between administrations and personnel. We also show how this analytics enables computational evidence for some abstract properties of an academic organization related to its organizational culture, such as organizational democracy, justice, and work passion. We present the analytics together with examples of its actual use within Ukrainian higher education at the Trust portal.