Reviewerly/exHarmony huggingface dataset:
No free lunch but a cheaper supper: A general framework for streaming anomaly detection

In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure to overcome the limitations of one-size-fits-all solutions. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.
Frequency-Dependent Depth Map Enhancement via Iterative Depth-Guided Affine Transformation and Intensity-Guided Refinement

Recently, deep convolutional neural network sho-ws significant improvement for intensity-guided depth map enhancement. The most networks focus on either increasing depth or easing features propagation via residual learning and dense connection. However, it has not been explicitly considered yet to mitigate the artifacts caused by the differences of the distributions between the depth map and the corresponding color image, e.g., edge misalignment. In this paper, a novel depth-guided affine transformation is used to filter out the unrelated intensity features, which is further used to refine the depth features. Since the quality of initial depth features is low, the depth-guided intensity features filtering and the intensity-guided depth features refinement are iteratively performed, which progressively promotes effects of such tasks. To make full use of the iterations, all the refined depth features are dense connected followed by a 1 × 1 convolution layer. In addition, to improve the performance in the case of large upsampling factors (e.g., 16×), the depth features are enhanced from coarse to fine. In each frequency-dependent refinement of the depth features, the above iterative subnetwork as well as the residual learning are introduced. The proposed method is tested for the noise-free and noisy cases which compares against 16 state-of-the-art methods. Our experimental results show the improved performances based on the qualitative and quantitative evaluations.
Pseudo-2D superconducting quantum computing circuit for the surface code: proposal and preliminary tests

Of the many potential hardware platforms, superconducting quantum circuits have become the leading contender for constructing a scalable quantum computing system. All current architecture designs necessitate a 2D arrangement of superconducting qubits with nearest neighbour interactions, compatible with powerful quantum error correction using the surface code. A major hurdle for scalability in superconducting systems is the so called wiring problem, where qubits internal to a chip-set become inaccessible for external control/readout lines. Current approaches resort to intricate and exotic 3D wiring and packaging technology which is a significant engineering challenge to realize, while maintaining qubit fidelity. Here we solve this problem and present a modified superconducting scalable micro-architecture that does not require any 3D external line technology and reverts back to a completely planar design. This is enabled by a new pseudo-2D resonator network that provides inter-qubit connections via airbridges. We carried out experiments to examine the feasibility of the newly introduced airbridge component. The measured quality factor of these new inter-qubit resonators is sufficient for high fidelity gates, below the threshold for the surface code, with negligible measured cross-talk. The resulting physical separation of the external wirings and the inter-qubit connections on-chip should reduce cross-talk and decoherence as the chip-set increases in size. This result demonstrates that a large-scale, fully error corrected quantum computer can be constructed by monolithic integration technologies without additional overhead and without special packaging know-hows.
Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis

Solar radiation measurements are highly important for achieving energy efficiency in smart buildings as well as solar energy production. They are commonly acquired with pyranometer sensor device. However, due to its high initial and maintenance costs it is not densely deployed in the field. Consequently, it provides only limited coverage as a data source for solar radiation. Hence, theoretical, empirical and/or data-driven models are utilized to estimate solar radiation in areas without pyranometers using only data from meteorological sensor stations, which on the other hand are widely available and obtained from sustainable sensor networks. In this paper, end to end process is described for building hybrid models for solar radiation using Artificial Intelligence (AI), or more specifically Machine Learning (ML) methods, after which a detailed analysis is performed on (1) the accuracy of the models regards to their parameters and input features, (2) the sustainability of the models in the real world, and finally (3) their feasibility in (near) real-time monitoring. The results are expressed with relative root mean squared error (RRMSE) and they show that hybrid models outperform model- and data-driven ones, with artificial neural network giving the best results (RRMSE = 0.0393). Additionally, the models can be enhanced by performing an informed feature selection, where a posteriori selection proves to be better than a priori selection (RRMSE = 0.0371). Further investigation shows that randomly selected input data gives faster model convergence as expected. However, sequential input data can match it if model training starts with autumn or spring data when weather exhibits sufficient variety. When applied on different times scales, all models perform best on 3h scale rather than daily, where random forest (RRMSE = 0.0275) outperforms neural network (RRMSE = 0.0315). However, for (near) real time usage the models perform almost the same as for daily, with RRMSE of 0.0469 for 1min scale with neural network. This demonstrates the feasibility of the hybrid models in Internet of Things (IoT) applications, which commonly require at least hourly intervals for solar radiation.
A Critical Review of Blockchain Consensus Model

The state-of-art Blockchain technology has been receiving enormous attention from researchers and industry practitioners in recent years. Basically Blockchain is a distributed immutable ledgerutilized by user mainly to perform transactions across various nodes in a blockchain network after a mutual consensus of all the associated nodes in the network. Noticeably, Blockchain is made famous by the Bitcoin cryptocurrency. As of 2017 Blockchain has seen to disrupt many applications to a global worth of 150B. The consensus models are responsible for the process of reaching an agreement between all nodes in blockchain to accept a new block. The consensus model plays a vital role in sustaining Blockchain efficiency. Using the right consensus model can significantly increase the performance of blockchain operation. The consensus models can be divided into two categories. The first category is the proof-based consensus model whereby a node in the Blockchain performs enough proof over other nodes to gain a chance to append the next block to the chain. The second category is the voting-based model in which nodes need to share their results in verifying a transaction of a new block before a final decision is reached. In this paper we discuss the characteristics of some major existing consensus models and provide some critical reviews on their performances. The reviewis mainly concerning these aspects: transaction throughput, latency, network bandwidth, and storage.
Optimizing Regularized Cholesky Score for Order-Based Learning of Bayesian Networks

Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes, for a high-scoring Bayesian network. Our scoring function is derived from regularizing Gaussian DAG likelihood, and its optimization gives an alternative formulation of the sparse Cholesky factorization problem from a statistical viewpoint, which is of independent interest. We combine global simulated annealing over permutations with a fast proximal gradient algorithm, operating on triangular matrices of edge coefficients, to compute the score of any permutation. Combined, the two approaches allow us to quickly and effectively search over the space of DAGs without the need to verify the acyclicity constraint or to enumerate possible parent sets given a candidate topological sort. The annealing aspect of the optimization is able to consistently improve the accuracy of DAGs learned by local search algorithms. In addition, we develop several techniques to facilitate the structure learning, including pre-annealing data-driven tuning parameter selection and post-annealing constraint-based structure refinement. Through extensive numerical comparisons, we show that ARCS achieves substantial improvements over existing methods, demonstrating its great potential to learn Bayesian networks from both observational and experimental data.
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks

In this paper, we propose a channel-wise interaction based binary convolutional neural networks (CI-BCNN) approach for efficient inference. Conventional binary convolutional neural networks usually apply the xnor and bitcount operations in the binary convolution with notable quantization errors, which obtain opposite signs of pixels in binary feature maps compared to their full-precision counterparts and lead to significant information loss. In our proposed CI-BCNN method, we exploit the channel-wise interactions with the prior knowledge which aims to alleviate inconsistency of signs in binary feature maps and preserves the information of input samples during inference. Specifically, we mine the channel-wise interactions by using a reinforcement learning model, and impose channel-wise priors on the intermediate feature maps to correct inconsistent signs through the interacted bitcount. Since CI-BCNN mines the channel-wise interactions in a large search space where each channel may correlate with others, the search deficiency caused by sparse interactions obstacles the agent to obtain the optimal policy. To address this, we further present a hierarchical channel-wise interaction based binary convolutional neural networks (HCI-BCNN) method to shrink the search space via hierarchical reinforcement learning. Moreover, we propose a denoising interacted bitcount operation in binary convolution by smoothing the channel-wise interactions, so that noise in channel-wise priors can be alleviated. Extensive experimental results on the CIFAR-10 and ImageNet datasets demonstrate the effectiveness of the proposed CI-BCNN and HCI-BCNN.
A Deep CNN Approach with Transfer Learning for Image Recognition

This paper presents a model of Deep Convolutional Neural Networks (CNN) based on transfer learning for image recognition. This means to use a Deep CNN system pretrained on the large ImageNet dataset of 14 million images and 1000 classes in order to learn feature selection. The results of the pretraining phase are transferred to the problem of classification for the images belonging to the UC Merced Land Use dataset with 21 classes. As benchmark, we have considered a Deep CNN trained with a fraction of the same UC Merced Land Use dataset containing the test images for classification. The experimental results have pointed out the obvious advantage of the Deep CNN with transfer learning (accuracy of 0.87 using pretraining over 0.46 for fully training on the same dataset).
Stubborn and Dead-Zone Redesign for Nonlinear Observers and Filters

We propose a redesign paradigm for stable estimators by introducing a saturation or a dead-zone nonlinearity with adaptive thresholds on the output injection term.Such nonlinearities allow improving the sensitivity to measurement noise in different scenarios (impulsive disturbances or persistent noise such as sensor bias), while preserving the asymptotic convergence properties of the original observer.These redesigns apply to a broad class of state estimators, including linear observers, observers for input-affine systems, observers for Lipschitz systems, observers based on the circle criterion, high-gain observers, standard and extended Kalman filters.Simulation results confirm the effectiveness of both the stubborn and dead-zone redesigns.
A Machine Learning based Spam Detection Mechanism

In today's internet-oriented data; receiving spam email messages are quite obvious. Most of the time such emails are commercial. But many times, such emails may contain some phishing links that have malware. This arises the need for proposing prudent mechanism to detect or identify such spam emails so that time and memory space of the system can be saved up to a great extent. In this paper, we presented the same mechanism which can filter spam and non-spam emails. Our proposed algorithm generates dictionary and features and trains them through machine learning for effective results.
The Technology Adoption and Governance of Artificial Intelligence in the Philippines

Artificial intelligence is primed to disrupt our society and the industry. The AI trend of technological singularity is continuously accelerating and is being employed to the different facets of humanity from education, medicine, business, engineering, arts and the like. Government and private companies have been hooked up with this fast pacing technology. AI may displace some non-digital jobs that performs heavy load and repetitive tasks, but it certainly augments labor shortage by realigning the workforce competitiveness to what the technology requires. The diffusion of AI technology is necessary for mental shift of the government and industry leaders to adopt the technology. Research and development is very promising to uplift mankind to faster productivity and positively affect the industries in international perspective. The Philippines is still coping up with the adoption of AI system, but it can steer up globally by strengthening the technology governance of strictly implementing the policies with measures the PDP 2017-2022 and its HNRDA.
DTMMN: Deep transfer multi-metric network for RGB-D action recognition

In the field of action recognition, fusing multi-modality information is a major research direction. Although many state-of-the-art methods have emerged recently, they lack consideration for the problem of modality deficiency and modality distribution. This paper proposes a novel two-stage method which named deep transfer multi-metric network (DTMMN) to investigate these two questions. First, we train a transfer network to learn the mapping from RGB to depth modality for addressing modality deficiency, and then we use the multi-metric learning to get the relation and differences of feature distribution for actions recognition. We experiment on two challenge datasets and our approach outperforms the state-of-the-art methods on both datasets.
The role of emoticons in sarcasm comprehension in younger and older adults: Evidence from an eye-tracking experiment

We present an eye-tracking experiment examining moment-to-moment processes underlying the comprehension of emoticons. Younger (18–30) and older (65+) participants had their eye movements recorded while reading scenarios containing comments that were ambiguous between literal or sarcastic interpretations (e.g., But you’re so quick though). Comments were accompanied by wink emoticons or full stops. Results showed that participants read earlier parts of the wink scenarios faster than those with full stops, but then spent more time reading the text surrounding the emoticon. Thus, readers moved more quickly to the end of the text when there was a device that may aid interpretation but then spent more time processing the conflict between the superficially positive nature of the comment and the tone implied by the emoticon. Interestingly, the wink increased the likelihood of a sarcastic interpretation in younger adults only, suggesting that perceiver-related factors play an important role in emoticon interpretation.
“We Have Been Thrown Under the Bus”: Corporate Versus Individual Defense Mechanisms Against Transnational Corporate Bribery Charges

The telecommunication company Telia’s dealings in Uzbekistan have resulted in bribery accusations both in Sweden and in abroad. The article analyzes the defense mechanisms produced by both the corporation and the prosecuted former executives of the company. Telia’s initially denial eventually changed into a partial acknowledgment in combination with a scapegoating discourse. While Telia hardly defended itself at all in the Swedish court, the company’s former executives employed a defense of legality, denial of knowledge, of deviance, and of responsibility as well as a claim of being scapegoated. We discuss these developments in the light of the transformation of the Telia case from a mediated corporate scandal to a criminal court case and from a focus on organizational to individual responsibility.
Cloud data integrity verification scheme for associated tags

In typical cloud storage systems such as storage systems for electronic health records (EHRs), users must regularly update sensitive files that are stored in the cloud. Leakage of the operation logs (OLs) will cause severe damage to users. Therefore, it is critical to develop secure auditing techniques for verifying users OLs. Under this scenario, we propose a novel auditing scheme that realizes secure detection for users OLs. In this design, the OLs are grouped according to the types of operations, the verification tags (VTs) of the OLs are calculated by associating an operation relationship, and the cloud data integrity verification is realized by randomly detecting the correctness of VTs. We introduce a homomorphic hash function into the integrity verification of the OLs, and the verification result for the sensitive information is derived from the verification result for the public information, thereby realizing stronger privacy protection. Furthermore, our scheme also achieves efficient handling of data dynamics. The security analysis and experimental verification indicate that our scheme is secure and efficient.
A Preventive Secure Software Development Model for a Software Factory: A Case Study

The number of cyberattacks has greatly increased in in the last years, as well as their sophistication and impact. For this reason, new emerging software development models are demanded, which help in developing secure by default software. To achieve this, the analysis and comparison in depth of the current models of secure software development is especially important. In this paper, a review of the most popular secure software models is presented, and a new secure software methodology is proposed, adapted to all current environments. A practical experiment in a software development company is tested, as a case study, considering data from real software projects. The results are presented and compared in two development scenarios: a classic one with a reactive security approach, and another one, emerging and preventive, that applies security by default in all phases of the software life cycle. In the case study, the total amount of vulnerabilities is reduced by 68,42%, decreasing their criticality and the temporal impact of their resolutions. In this way, software security and quality are methodologically improved with the proposed model, proving that the new emerging approach provides a more secure software.
Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi

Population explosion leads to an unprecedented increase in the number of physical objects or vehicles on road. As a result, the number of road accidents increases due to a very heavy traffic flow. In this paper, traffic flow is monitored by using computer vision paradigm, where images or sequence of images provides a betterment on the road view. In order to detect vehicles, monitor and estimate traffic flow using low cost electronic devices, this research work utilizes camera module of raspberry pi along with Raspberry Pi 3. It also aims to develop a remote access using raspberry-pi to detect, track and count vehicles only when some variations occur in the monitored area. The proposed system captures video stream like vehicles in the monitored area to compute the information and transfer the compressed video stream for providing video based solution that is mainly implemented in Open CV by Python Programming. The proposed method is considered as an economical solution for industries in which cost-effective solutions are developed for traffic management.
i-BIN: An Intelligent Trash Bin for Automatic Waste Segregation and Monitoring System

In the recent years, one of the underlying effects of a growing population is environmental hazard. Of these problems, the increasing amount of trash is the most critical. As the world's population continues to rise at an unprecedented rate, waste management is seen as an effective solution to mitigate the issue. In this project, Internet-of- Things were utilized to design an automatic waste segregation system. Specifically, we developed a trash bin equipped with sensors, which can intelligently segregate waste system that provides monitoring report of waste collection. Image recognition was used to process automatic classification of trash using machine learning technique. Upon training more than 2000 samples for biodegradable and non-biodegradable waste, the developed prototyped were able to classify trash efficiently.
Suppression of switching errors in weakly damped Josephson junctions

The task of the Josephson junction switching is numerically solved in the framework of the classical RCSJ model with white Gaussian noise. In order to switch the junction to the resistive state a periodic supra-threshold signal is applied. The operational mode is similar to the single photon counter proposed earlier. Here we consider a purely classical situation in order to find out how far the typical aluminium Josephson junction is from the single photon operational mode. We have found that for the considered parameters, if the switching happens due to external periodic signal, it occurs mostly during the first two periods of driving, and the number of photons, needed for switching, is more than one hundred. Also we observe the double minima of the mean switching time and the standard deviation versus the driving frequency. This determines the parameter range, optimal for reliable switching, which can be used as a guidance in quasi-classical and quantum approaches.