Reviewerly/exHarmony huggingface dataset:
Incremental Concept Learning via Online Generative Memory Recall

The ability to learn more concepts from incrementally arriving data over time is essential for the development of a lifelong learning system. However, deep neural networks often suffer from forgetting previously learned concepts when continually learning new concepts, which is known as the catastrophic forgetting problem. The main reason for catastrophic forgetting is that past concept data are not available, and neural weights are changed during incrementally learning new concepts. In this article, we propose an incremental concept learning framework that includes two components, namely, ICLNet and RecallNet. ICLNet, which consists of a trainable feature extractor and a dynamic concept memory matrix, aims to learn new concepts incrementally. We propose a concept-contrastive loss to alleviate the magnitude of neural weight changes and mitigate the catastrophic forgetting problems. RecallNet aims to consolidate old concepts memory and recall pseudo samples, whereas ICLNet learns new concepts. We propose a balanced online memory recall strategy to reduce the information loss of old concept memory. We evaluate the proposed approach on the MNIST, Fashion-MNIST, and SVHN data sets and compare it with other pseudorehearsal-based approaches. Extensive experiments demonstrate the effectiveness of our approach.
Secure End-to-End Sensing in Supply Chains

Trust along digitalized supply chains is challenged by the aspect that monitoring equipment may not be trustworthy or unreliable as respective measurements originate from potentially untrusted parties. To allow for dynamic relationships along supply chains, we propose a blockchain-backed supply chain monitoring architecture relying on trusted hardware. Our design provides a notion of secure end-to-end sensing of interactions even when originating from untrusted surroundings. Due to attested checkpointing, we can identify misinformation early on and reliably pinpoint the origin. A blockchain enables long-term verifiability for all (now trustworthy) IoT data within our system even if issues are detected only after the fact. Our feasibility study and cost analysis further show that our design is indeed deployable in and applicable to today’s supply chain settings.
Identification of Encrypted Video Streaming Based on Differential Fingerprints

Nowadays, because of the convenience and broad reach of social media platforms, such as YouTube, Facebook, and Twitter, terrorists have been taking advantage of these platforms to spread illegal videos. Existing researches extract video fingerprints based on statistical data and inevitably overlook some features, which makes it impossible to accurately represent the encrypted video streaming. Besides, almost no research can identify the target video from a large video dataset on the premise of high accuracy and low false-positive rate. Therefore, in this paper, we extract differential fingerprints to represent the encrypted video streaming more accurately and identify the encrypted video streaming using our local sequence alignment algorithm. We conduct our method on a large dataset containing 200,000 videos. The results show that by continuously sniffing the encrypted video streaming for 12 seconds, the precision of our method can exceed 98%, and the false-positive rate is nearly 0.
Clothing Classification Using Deep CNN Architecture Based on Transfer Learning

The need of a powerful visual analytics tools becomes a necessity today especially with the emergence of pictures on the Internet and their use several times instead of text. In this paper, a new approach for clothing style classification is presented. The types of clothing items we consider in the proposed system include shirt, pants, suit, dress and so on. Certainly, clothing style classification represents a recent computer vision research subject who has several attractive applications, including e-commerce, criminal law and on-line advertising. In our proposed approach, the classification has been carried out by Deep Convolutional Neural Networks (CNNs). This Deep Learning technique Inception-v3 has shown very good performances for different object recognition problems. For deep features extraction, we use a machine learning technique called Transfer learning to refine pretrained models. Experiments are performed on two clothing datasets, particularly on the large and public dataset ImageNet. According to the obtained results, the developed system provides better results than those proposed in the state of the art.
A Procedure for Complete Census Estimation of Rooftop Photovoltaic Potential in Urban Areas

Rooftop photovoltaic solar systems can be an essential tool to support the energy transition of Europe. The assessment of solar power generation potential in urban areas, necessary for smart grid planning, requires the processing of data of different types, such as building cadastral information, a detailed description of available roof areas, and solar irradiation data. We introduce an algorithm for the fast calculation of the building’s shadows and a procedure for the integration of solar irradiation in time. We therefore develop a methodology that allows a fast evaluation with minimal computational resources, and we apply it to an urban scenario of a medium-sized European city obtaining an estimate of the complete census PV power generation potential, with a spatial resolution of 1 m. We validate the results by comparison with a reference procedure, obtaining minimal deviation with a much lower demand for computational resources.
Endex: Degree of Mining Power Decentralization for Proof-of-Work Based Blockchain Systems

Proof of Work (PoW) is one of the key consensus mechanisms for blockchain systems to ensure trust and secure service. The PoW process, which is also known as mining, is computationally intensive, because the miners need to solve a well-crafted puzzle to add a new block to the chain. With cryptocurrency such as Bitcoin becoming more popular, it requires greater levels of mining power for successful mining. Most miners tend to join specific mining pools to share rewards contributed by their mining power. This leads to more centralized PoWbased blockchain systems, which contradicts the design goal of decentralization for blockchain. Mining power concentration may damage the trust and secure level of blockchain systems. However, no tools or studies could timely and quantitatively tell us how decentralized in terms of mining power the PoW blockchain system is. in this article, we design a decentralization index, called Endex, to efficiently quantify the degree of mining power decentralization and timely capture the abnormal phenomena of PoW blockchain systems. We then validate the effectiveness and accuracy of Endex over real mining datasets of four popular blockchain systems. The empirical study demonstrates that Endex can well capture the changes in mining power for these blockchain systems and detect in time the abnormal incidents such as double spending attack and hard fork.
True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Spatial anonymization of address points is critical to fields such as public health. There have been recent concerns about applications of geomasks that did not guarantee the level of k-anonymity theoretically expected. An analysis of the problem and a potential solution were previously proposed: Adaptive Areal Elimination (AAE). The present paper expands on AAE and proposes a modified version, Adaptive Areal Masking (AAM). A benchmark comparison of both methods is conducted, which shows that AAM outperforms AAE in most configurations tested. The discussion attempts to identify the application cases for which AAE might still be preferable and addresses documentation needs with both methods.
Verification Approaches for Learning-Enabled Autonomous Cyber–Physical Systems

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> Neural network control systems are often at the heart of autonomous systems. The authors classify existing verification methods for these systems and advocate the necessity of integrating verification techniques in the training process to enhance robustness. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Selma Saidi, TU Dortmund</i>
Elasticity management for capacity planning in software as a service cloud computing

Applications of cloud computing are increasing as companies shift from on-premise IT environments to public, private, or hybrid clouds. Consequently, cloud providers use capacity planning to maintain the capacity of computing resources (instances) required to meet the dynamic nature of the demand (queries). However, there is a trade-off between deploying too many costly instances, and deploying too few instances and paying penalties for not being able to process queries on-time. An instance has multiple resource dimensions and executing a query consumes multiple dimensions of an instance's capacity. This detailed multi-dimensional management of cloud computing resource capacity is known as elasticity management and is an important issue faced by all cloud providers. Determining the optimal number of instances needed in a given planning horizon is challenging, due to the combinatorial nature of the optimization problem involved. We develop an optimization model and related algorithms to capture the trade-off between the resource cost versus the delayed execution penalty in software as a service applications from the cloud provider's perspective. We develop an exact approach to solve small to medium sized applications and heuristics to solve large applications. We then evaluate their performance via extensive computational analyses with real-world data and current cloud provider approaches. We also develop a stochastic framework and methodology to deal with demand uncertainty, and using two different randomly generated data sets (representing problem instances in practice), we demonstrate that robust solutions can be obtained.
Cross-entropy based stochastic optimization of robot trajectories using heteroscedastic continuous-time Gaussian processes

High dimensional robot motion planning has recently been approached with trajectory optimization methods that efficiently minimize a suitable objective function in order to generate robot trajectories that are both optimal and feasible. However, finding a globally optimal solution is often an insurmountable problem in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, mainly in cluttered environments. In this paper, we propose a novel trajectory planning algorithm that employs stochastic optimization in order to find a collision-free trajectory generated from a continuous-time Gaussian process (GP). The contributions of the proposed motion planning method stem from introducing the heteroscedasticity of the GP, together with exploited sparsity for efficient covariance estimation, and a cross-entropy based stochastic optimization for importance sampling based trajectory optimization. We evaluate the proposed method on three simulated scenarios: a maze benchmark, a 7 DOF robot arm planning benchmark and a 10 DOF mobile manipulator trajectory planning example and compare it to a state-of-the-art GP trajectory optimization method, namely the Gaussian process motion planner 2 algorithm (GPMP2). Our results demonstrate the following: (i) the proposed method yields a more thorough exploration of the solution space in complex environments than GPMP2, while having comparable execution time, (ii) the introduced heteroscedasticity generates GP priors better suited for collision avoidance and (iii) the proposed method has the ability to efficiently tackle high-dimensional trajectory planning problems.
I-ViSE: Interactive Video Surveillance as an Edge Service Using Unsupervised Feature Queries

Situation awareness (SAW) is essential for many mission-critical applications. However, SAW is challenging when trying to immediately identify objects of interest or focus on suspicious activities from thousands of video frames. This article develops a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios, such as public safety monitoring, the features of objects of interest may be much more complicated than face features. In addition, human operators may not be always able to provide a descriptive, simple, and accurate query. Actually, it is more often that there are only rough, general descriptions of certain suspicious objects or accidents. This article proposes interactive video surveillance as an edge service (I-ViSE) based on unsupervised feature queries. Adopting unsupervised methods that do not reveal any private information, the I-ViSE scheme utilizes general features of a human body and color of clothes. An I-ViSE prototype is built following the edge-fog computing paradigm and the experimental results verified the I-ViSE scheme meets the design goal of scene recognition and target analysis in less than 2 s.
Exposure to Cyber Victimization: Results from a Swiss Survey

This study tests the assumptions of the routine activity theory on three online forms of victimization: computer virus, online fraud, and unauthorized use of personal data. Data come from a representative victimization survey conducted in the Swiss city of Lugano from January 2019 to April 2019. The analysis resulted in the following conclusions. First, victims are more common among regular internet and social media users, independent of the type of crime. Second, the installation of an antivirus program is more likely to work as a protective measure to detect and disarm digital threats rather than a preventive measure that reduces the opportunity for crime to occur. Third, computer literacy is associated with a lower risk of malware victimization. Fourth, users' characteristics partially reflect differences in internet consumption. These findings are consistent across different specifications.
Time-based Gap Analysis of Cybersecurity Trends in Academic and Digital Media

This study analyzes cybersecurity trends and proposes a conceptual framework to identify cybersecurity topics of social interest and emerging topics that need to be addressed by researchers in the field. The insights drawn from this framework allow for a more proactive approach to identifying cybersecurity patterns and emerging threats that will ultimately improve the collective cybersecurity posture of the modern society. To achieve this, cybersecurity-oriented content in both media and academic corpora, disseminated between 2008 and 2018, were morphologically analyzed via text mining. A total of 3,556 academic papers obtained from the top-10 highly reputable cybersecurity academic conferences, and 4,163 news articles collected from the New York Times were processed. The LDA topic modeling followed optimal perplexity and coherence scores resulted in 12 trendy topics. Next, the time-based gap between these trendy topics was analyzed to measure the correlation between media and trendy academic topics. Both convergences and divergences between the two cybersecurity corpora were identified, suggesting a strong time-based correlation between these resources. This framework demonstrates the effective use of automated techniques to provide insights about cybersecurity topics of social interest and emerging trends and informs the direction of future academic research in this field.
Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations

In data streams, the data distribution of arriving observations at different time points may change—a phenomenon called concept drift. While detecting concept drift is a relatively mature area of study, solutions to the uncertainty introduced by observations with missing values have only been studied in isolation. No one has yet explored whether or how these solutions might impact drift detection performance. We, however, believe that data imputation methods may actually increase uncertainty in the data rather than reducing it. We also conjecture that imputation can introduce bias into the process of estimating distribution changes during drift detection, which can make it more difficult to train a learning model. Our idea is to focus on estimating the distance between observations rather than estimating the missing values, and to define membership functions that allocate observations to histogram bins according to the estimation errors. Our solution comprises a novel masked distance learning (MDL) algorithm to reduce the cumulative errors caused by iteratively estimating each missing value in an observation and a fuzzy-weighted frequency (FWF) method for identifying discrepancies in the data distribution. The concept drift detection algorithm proposed in this article is a singular and unified algorithm that can handle missing values, but not an imputation algorithm combined with a concept drift detection algorithm. Experiments on both synthetic and real-world datasets demonstrate the advantages of this method and show its robustness in detecting drift in data with missing values. The results show that compared to the best-performing algorithm that handles imputation and drift detection separately, MDL-FWF reduced the average drift detection difference from 10.75% to 5.83%. This is a nearly 46% improvement. These findings reveal that missing values exert a profound impact on concept drift detection, but using fuzzy set theory to model observations can produce more reliable results than imputation.
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?

How can we make software analytics simpler and faster? One method is to match the complexity of analysis to the intrinsic complexity of the data being explored. For example, hyperparameter optimizers find the control settings for data miners that improve the predictions generated via software analytics. Sometimes, very fast hyperparameter optimization can be achieved by "DODGE-ing"; i.e. simply steering way from settings that lead to similar conclusions. But when is it wise to use that simple approach and when must we use more complex (and much slower) optimizers?} To answer this, we applied hyperparameter optimization to 120 SE data sets that explored bad smell detection, predicting Github issue close time, bug report analysis, defect prediction, and dozens of other non-SE problems. We find that the simple DODGE works best for data sets with low "intrinsic dimensionality" (u ~ 3) and very poorly for higher-dimensional data (u > 8). Nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks.
Accurate Long-Term Multiple People Tracking Using Video and Body-Worn IMUs

Most modern approaches for video-based multiple people tracking rely on human appearance to exploit similarities between person detections. Consequently, tracking accuracy degrades if this kind of information is not discriminative or if people change apparel. In contrast, we present a method to fuse video information with additional motion signals from body-worn inertial measurement units (IMUs). In particular, we propose a neural network to relate person detections with IMU orientations, and formulate a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. The fusion of visual and inertial cues provides several advantages. The association of detection boxes in the video and IMU devices is based on motion, which is independent of a person's outward appearance. Furthermore, inertial sensors provide motion information irrespective of visual occlusions. Hence, once detections in the video are associated with an IMU device, intermediate positions can be reconstructed from corresponding inertial sensor data, which would be unstable using video only. Since no dataset exists for this new setting, we release a dataset of challenging tracking sequences, containing video and IMU recordings together with ground-truth annotations. We evaluate our approach on our new dataset, achieving an average IDF1 score of 91.2%. The proposed method is applicable to any situation that allows one to equip people with inertial sensors.
How does code readability change during software evolution?

Code reading is one of the most frequent activities in software maintenance. Such an activity aims at acquiring information from the code and, thus, it is a prerequisite for program comprehension: developers need to read the source code they are going to modify before implementing changes. As the code changes, so does its readability; however, it is not clear yet how code readability changes during software evolution. To understand how code readability changes when software evolves, we studied the history of 25 open source systems. We modeled code readability evolution by defining four states in which a file can be at a certain point of time (non-existing, other-name, readable, and unreadable). We used the data gathered to infer the probability of transitioning from one state to another one. In addition, we also manually checked a significant sample of transitions to compute the performance of the state-of-the-art readability prediction model we used to calculate the transition probabilities. With this manual analysis, we found that the tool correctly classifies all the transitions in the majority of the cases, even if there is a loss of accuracy compared to the single-version readability estimation. Our results show that most of the source code files are created readable. Moreover, we observed that only a minority of the commits change the readability state. Finally, we manually carried out qualitative analysis to understand what makes code unreadable and what developers do to prevent this. Using our results we propose some guidelines (i) to reduce the risk of code readability erosion and (ii) to promote best practices that make code readable.
Recovering Depth from Still Images for Underwater Dehazing Using Deep Learning

Estimating depth from a single image is a challenging problem, but it is also interesting due to the large amount of applications, such as underwater image dehazing. In this paper, a new perspective is provided; by taking advantage of the underwater haze that may provide a strong cue to the depth of the scene, a neural network can be used to estimate it. Using this approach the depthmap can be used in a dehazing method to enhance the image and recover original colors, offering a better input to image recognition algorithms and, thus, improving the robot performance during vision-based tasks such as object detection and characterization of the seafloor. Experiments are conducted on different datasets that cover a wide variety of textures and conditions, while using a dense stereo depthmap as ground truth for training, validation and testing. The results show that the neural network outperforms other alternatives, such as the dark channel prior methods and it is able to accurately estimate depth from a single image after a training stage with depth information.