Reviewerly/exHarmony huggingface dataset:
A hybrid approach of intelligent systems to help predict absenteeism at work in companies

In recent years, several surveys have been conducted on absenteeism and how this affects the routine of conducting productive operations in companies. Therefore, having criteria for predicting absenteeism at work can help managers in contingency actions reduce financial losses due to the absence of a worker in their workplace. The objective of this work is to apply the artificial intelligence concepts of a regularized fuzzy neural network, which combines the benefits of artificial neural networks with the fuzzy set theory to obtain more accurate results in predicting corporate absenteeism. The database called absenteeism at work, taken from the UCI Machine Learning Repository, which captured elements of a Brazilian company, was applied in a fuzzy neural network model that allows the calculation of the regressors, defining the estimate of the lack of hours of an employee. The results of the experiments prove that the intelligent model can help in the creation of a specialist system that assists in the prediction of absenteeism.
Solar energy potential according to climatic and geometrical parameters of cities and buildings: A case-study from Tabriz City- Iran

This paper focused on the solar energy utilization in cities and buildings as exemplified by Tabriz City in Iran. All the geographic and climatic factors have been considered to estimate the solar energy potential for Tabriz City using angstrom model in SPSS. Then, the solar energy potential in the Blue Mosque (BM) of Tabriz investigated in detail. Decisive parameters in solar energy evaluation such as geographic parameters, climatic factors, radiation types, and orientation techniques and geometry analysis of buildings, were considered based on Tabriz regarding Duffie Beckman and Stephenson's cousin methods. The method of Lagrange interpolation regarded to define the elements geometrical equations. The rate of irradiation solar energy was analyzed for domed, horizontal and vertical surfaces of the BM in Tabriz City using Ladybug for Rhino and MS Excel software programs. The results of this study showed that it is possible to domesticate noticeable rate of solar energy in a cloudy climate like Tabriz by appropriate orientation and geometry of building elements.
Can Virtual Assistants Produce Recommendations?

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.
Tracy: Tracing Facts over Knowledge Graphs and Text

In order to accurately populate and curate Knowledge Graphs (KGs), it is important to distinguish ?s?p?o? facts that can be traced back to sources from facts that cannot be verified. Manually validating each fact is time-consuming. Prior work on automating this task relied on numerical confidence scores which might not be easily interpreted. To overcome this limitation, we present Tracy, a novel tool that generates human-comprehensible explanations for candidate facts. Our tool relies on background knowledge in the form of rules to rewrite the fact in question into other easier-to-spot facts. These rewritings are then used to reason over the candidate fact creating semantic traces that can aid KG curators. The goal of our demonstration is to illustrate the main features of our system and to show how the semantic traces can be computed over both text and knowledge graphs with a simple and intuitive user interface.
Robust Online Multilabel Learning Under Dynamic Changes in Data Distribution With Labels

In this paper, a robust online multilabel learning method dealing with dynamically changing multilabel data streams is proposed. The proposed method has three advantages: 1) higher accuracy due to a newly defined objective function based on labels ranking; 2) fast training and update based on a newly derived closed-form (rather than gradient descent based) solution for the new objective function; and 3) high robustness to a newly identified concept drift in multilabel data streams, namely, changes in data distribution with labels (CDDL). The high robustness benefits from two novel works: 1) a new sequential update rule that preserves the labels ranking information learned from all old (but discarded) samples while updating the model only based on new incoming samples and 2) a fixed threshold for label bipartition that is insensitive to any kind of changes in data distribution including CDDL. The proposed method has been evaluated over 13 benchmark datasets from various domains. As shown in the experimental results, the proposed work is highly robust to CDDL in both the sequential model update and multilabel thresholding. Furthermore, the proposed method improves the performance in different evaluation measures, including Hamming loss, F1-measure, Precision, and Recall while taking short training time on most evaluated datasets.
Computational Design of Steady 3D Dissection Puzzles

Abstract Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles . We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state‐of‐the‐art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
DELR: A double-level ensemble learning method for unsupervised anomaly detection

Although the anomaly detection problem has been widely studied in data mining and machine learning, most algorithms in this domain have been performed with limited generalization ability. To that end, ensemble learning has been proven to effectively improve the generalization ability of anomaly detection algorithms. However, there is room for further improvement in existing anomaly ensemble methods. For example, these methods are based on a single-level ensemble strategy that only considers the combination of the final results and usually neglects the loss of information during the generation of multiple subspaces. In this paper, we propose a double-level ensemble learning method using linear regression as the base detector called DELR, which has better robustness and can reduce the risk of information loss. The first level is used to reduce the loss of information, and the second level is used to improve the generalization ability. To better satisfy the diversity requirement for the anomaly ensemble, we present a diversity loss function to retrain the base models. Furthermore, we devise a novel weighted average strategy to ensure effectiveness in the second level. Our experimental results and analysis demonstrate that the DELR algorithm obtains better generalization ability over real-world datasets compared to several state-of-art anomaly algorithms.
Research for practice

Some ML papers suffer from flaws that could mislead the public and stymie future research.
Machine Learning in Adversarial RF Environments

With more and more autonomous deployments of wireless networks, accurate knowledge of the RF environment is becoming indispensable. Various techniques have been developed over the years that can not only assess the RF environment but can also characterize the various radio transmitters (sources) that define the ambient RF environment. Machine learning techniques have shown promise for such characterizations through the development of RF machine learning (RFML) systems delivering autonomous control. Although classical machine learning techniques work well for a large variety of tasks, they have not done as well for RFML. For RFML tasks, deep feature learners with an inherent recurrent structure have been shown to perform well. Even so, the field of RFML is still very young, and a lot needs to be done to bridge the gap between the ML community and the wireless community for RFML to be successfully applied for solving large-scale real-life problems. This article is a step in that direction.
Throughput Optimized Scheduler for Dispersed Computing Systems

Dispersed computing is promising paradigm to supplement the conventional cloud computing. Performing computation on the edge leads to significant reduction in communication with the remote cloud. However, challenges exist to fully exploit the advantages of dispersed computing systems: The edge devices are heterogeneous in computation and communication capacity; Tasks decomposed from the target applications can have complex precedence requirements captured by a Directed Acyclic Graph (DAG). With these challenges in mind, we propose a throughput optimized task scheduler, targeting at applications (such as computer vision and video processing) where input data are continuously and steadily fed into the execution pipeline. The scheduler incorporates two innovative techniques: task duplication and task splitting. To circumvent low bandwidth data links in the highly heterogeneous environment, we duplicate critical tasks to reroute communication paths. To load-balance the various tasks in complicated target applications, we split heavy-loaded tasks to allow cooperation of multiple nearby edge devices. Through simulation, we thoroughly evaluate the performance of the scheduler under various configuration of tasks and dispersed systems. Task duplication improves throughput of the baseline schedule significantly (> 1.2×), for systems with large variance in data link bandwidth and tasks with large communication-to-computation ratio. Task splitting leads to significant throughput improvement (> 1.25×) for systems with heterogeneous processing power and tasks with large variance in workload. On average, our scheduler improves throughput of the baseline schedule by > 1.6×.
Likelihood Ratio Tests for a Large Directed Acyclic Graph

Inference of directional pairwise relations between interacting units in a directed acyclic graph (DAG), such as a regulatory gene network, is common in practice, imposing challenges because of lack of inferential tools. For example, inferring a specific gene pathway of a regulatory gene network is biologically important. Yet, frequentist inference of directionality of connections remains largely unexplored for regulatory models. In this article, we propose constrained likelihood ratio tests for inference of the connectivity as well as directionality subject to nonconvex acyclicity constraints in a Gaussian directed graphical model. Particularly, we derive the asymptotic distributions of the constrained likelihood ratios in a high-dimensional situation. For testing of connectivity, the asymptotic distribution is either chi-squared or normal depending on if the number of testable links in a DAG model is small. For testing of directionality, the asymptotic distribution is the minimum of d independent chi-squared variables with one-degree of freedom or a generalized Gamma distribution depending on if d is small, where d is number of breakpoints in a hypothesized pathway. Moreover, we develop a computational method to perform the proposed tests, which integrates an alternating direction method of multipliers and difference convex programming. Finally, the power analysis and simulations suggest that the tests achieve the desired objectives of inference. An analysis of an Alzheimer’s disease gene expression dataset illustrates the utility of the proposed method to infer a directed pathway in a gene network.
Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network

Deformable registration is very significant for various clinical image applications. Unfortunately, existing conventional medical image registration approaches, which involve time-consuming iterative optimization, have not reached the level of routine clinical practice in terms of registration time and robustness. The aim of this study is to propose a tuning-free 3D image registration model based on adversarial deep network, and to achieve rapid and high-accurate registration. We propose a fully convolutional network (FCN) to regress the 3D dense deformation field in one shot from the to-be-registered image pair. To precisely regress the complex deformation and produce optimal registration, we design the FCN as a novel multi-scale frame to capture the complementary multi-scale image features and effectively characterize the spatial correspondence between the image pair. Moreover, we learn a discriminator network simultaneously to discriminate the registered two images, where the discrimination loss helps further update the FCN. Thus by the adversarial training strategy, the registration network is urged to produce well-registered two images that are indistinguishable for the discriminator. We perform registration experiments on four different brain MR datasets using the model trained by ANDI database. Compared with some state-of-the-art registration algorithms including other newest deeplearning-based methods, the proposed method provides a considerable increase of large than 4% in terms of Dice similarity coefficient (DSC). Moreover, our model also obtains comparable distance errors. More significantly, our model can achieve a high-accurate 3D registration result in average 0.74 s, with roughly hundred speed-up over conventional registration methods. The proposed model shows consistent high performance for various registration tasks under a second without any additional parameter tuning, which proves its potential for real-time clinical applications.
From two types of electrostatic position-dependent semiconductor qubits to quantum universal gates and hybrid semiconductor-superconducting quantum computer

Properties of two types of position-dependent electrostatic qubits: eigenenergy-based and Wannier-based, are treated with Schrodinger formalism. Their operating principles are given. The corresponding quantum universal gates for selected qubit types are described and their possible implementation is suggested. The modeling methodology of setting and reading semiconductor qubit is suggested. The interface between superconducting and semiconductor quantum computer is proposed and its implementation and operating principles are described.
IoT Devices Security Using RF Fingerprinting

Internet of Things (IoT) devices industry is rapidly growing, with an accelerated increase in the list of manufacturers offering a wide range of smart devices selected to enhance end-users' standard of living. Security remains an after-thought in these devices resulting in vulnerabilities. While there exists a cryptographic protocol designed to solve such authentication problem, the computational complexity of cryptographic protocols and scalability problems make almost all cryptography-based authentication protocols impractical for IoT. Wireless RFF (Radio Frequency Fingerprinting) comes as a physical layer-based security authentication method that improves wireless security authentication, which is especially useful for the power and computing limited devices. As a proof-of-concept, this paper proposes a universal SDR (software defined Radio)-based inexpensive implementation intended to sense emitted wireless signals from IoT devices. Our approach is validated by extracting mobile phone signal bursts under different user-dedicated modes. The proposed setup is well adapted to accurately capture signals from different telecommunication standards. To ensure a unique identification of IoT devices, this paper also provides an optimum set of features useful to generate the device identity fingerprint.
Effect of Transparency and Trust on Acceptance of Automatic Online Comment Moderation Systems

User-generated online comments and posts increasingly contain abusive content that needs moderation from an ethical but also legislative perspective. The amount of comments and the need for moderation in our digital world often overpower the capacity of manual moderation. To remedy this, platforms often adopt semi-automated moderation systems. However, because such systems are typically black boxes, user trust in and acceptance of the system is not easily achieved, as black box systems can be perceived as nontransparent and moderating user comments is easily associated with censorship. Therefore, we investigate the relationship of system transparency through explanations, user trust and system acceptance with an online experiment. Our results show that the transparency of an automatic online comment moderation system is a prerequisite for user trust in the system. However, the objective transparency of the moderation system does not influence the user's acceptance.
Malboard: A novel user keystroke impersonation attack and trusted detection framework based on side-channel analysis

Concealing malicious components within widely used USB peripherals has become a popular attack vector utilizing social engineering techniques and exploiting users' trust in USB devices. This vector enables the attacker to easily penetrate an organization's computers even when the target is secured or in an air-gapped network. Such malicious concealment can be done as part of a supply chain attack or during the device manufacturing process. In cases where the device allows the user to update its firmware, a supply chain attack may involve changing just the device's firmware, thus compromising the device without the need for concealment. A compromised device can impersonate other devices like keyboards in order to send malicious keystrokes to the computer. However, the keystrokes generated maliciously do not match human keystroke characteristics, and therefore they can be easily detected by security tools that are designed to continuously verify the user's identity based on his/her keystroke dynamics. In this paper, we present Malboard, a sophisticated attack based on designated hardware concealment, which automatically generates keystrokes that have the attacked user's behavioral characteristics; in this attack these keystrokes are injected into the computer in the form of malicious commands and thus can evade existing detection mechanisms designed to continuously verify the user's identity based on keystroke dynamics. We implemented this novel attack and evaluated its performance on 30 subjects performing three different keystroke tasks; we evaluated the attack against three existing detection mechanisms, and the results show that our attack managed to evade detection in 83–100% of the cases, depending on the detection tools in place. Malboard was proven to be effective in two scenarios: either by a remote attacker using wireless communication to communicate with Malboard or by an inside attacker (malicious employee) that physically operates and uses Malboard. In addition, in order to address the evasion gap, we developed three different modules aimed at detecting keystroke injection attacks in general, and particularly, the more sophisticated Malboard attack. Our proposed detection modules are trusted and secured, because they are based on three side-channel resources which originate from the interaction between the keyboard, user, and attacked host. These side-channel resources include (1) the keyboard's power consumption, (2) the keystrokes' sound, and (3) the user's behavior associated with his/her ability to respond to displayed textual typographical errors. Our results showed that each of the proposed detection modules is capable of detecting the Malboard attack in 100% of the cases, with no misses and no false positives; using them together as an ensemble detection framework will assure that an organization is immune to the Malboard attack in particular and other keystroke injection attacks in general.
Performance Analysis of Supervised Classifiers Using PCA Based Techniques on Breast Cancer

Focus of this paper is to recognize tumorous (malignant) and non-tumorous (benign) from the dataset. Wisconsin breast cancer data (WBCD) has been used and taken from UCI machine learning repository. Most popular supervised learning classifiers with PCA based dimensionality rebate techniques applied. Support Vector Machine, K Nearest Neighbor, Decision Tree, Naïve Bayes and Logistic Regression used with Linear, Sigmoid, Cosine, Poly and Radial basis function based PCA's. Numerous performance metrics tested after getting confusion matrix. Among them accuracy, sensitivity, specificity, false positive rate, false omission rate, precision, prevalence, f1-score, negative predicted value, false negative rate, false discovery rate and markedness. Our best performing models then relatively compared with other existing models. Sigmoid based Naïve Bayes exhibits best accuracy of 99.20%. K Nearest Neighbor also illustrate superb performance with all kernel PCA based techniques. Accuracy ranges from 96.4% to 97.8%.
Advances in Measurement and Cognition

Situative, sociocognitive (SC) psychology is forcing a reconception of educational assessment. The SC perspective emphasizes the interplay between across-person linguistic, cultural, and substantive patterns that human activity is organized around and within-person cognitive resources that individuals develop to participate in activities. Rather than seeing assessment primarily as measurement, we are increasingly seeing it as an evidentiary argument, situated in social contexts, shaped by purposes, and centered on students’ developing capabilities for valued activities. Developments in technology and analytic methods support new practices and familiar practices as reconceived. Implications follow for current challenges such as assessing higher-order skills, performance in digital environments, and diverse student populations.
Cloud Functions for Fast and Robust Resource Auto-Scaling

We design and build FEAT, a new scaling approach that uses (1) cloud functions as interim processing resources to compensate for VM launch delays and (2) a reactive, knobless, auto-scaling algorithm that requires no pre-specified thresholds or parameters, making it robust against changing load. We implement FEAT on Amazon Web Services (AWS) and Microsoft Azure. Our evaluations clearly demonstrate the higher performance and robustness of FEAT in comparison to existing approaches.