Reviewerly/exHarmony huggingface dataset:
Optimization of PBKDF2 Using HMAC-SHA2 and HMAC-LSH Families in CPU Environment

Password-Based Key Derivation Function 2 (PBKDF2) is widely used cryptographic algorithm in order to generate secure keys to a password in various occasions. For example, it is used for file encryption and implementation of authentication systems, and so on. However, the generated derived key has a lower entropy than a general cryptography key, so its use is limited. To compensate for this the number of iteration counts of PBKDF2 should be increased. As the number of repetitive tasks increases, the entropy of the derived key increases, but it takes more time to generate the derived key. We present various optimization methods of PBKDF2. The main idea of our proposed method is reducing redundant block operations and optimizing the internal process of underlying Pseudo Random Function (PRF). In other words, we integrate several redundant operations and make full use of constant values used in PBKDF2. We use two HMAC algorithms: one using SHA-2 family and one using LSH family as the PRF of PBKDF2 (SHA-2 family is the most widely used hash functions, and LSH family is the latest hash function recently developed in South Korea). With our techniques, our implementations outperform Korea Internet & Security Agency (KISA) implementation by 121.26%, 325.91%, and 231.89% for using SHA256, LSH256, and LSH512 respectively; and also outperform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenSSL</i> implementation by 39.59% using SHA512. In addition, we show that the internal process of PBKDF2 can be computed independently. With our multi thread technique, our PBKDF2 implementations outperform KISA implementation by 2,152.66%, 1,986.85%, and 1,591.36% for using SHA256, LSH256, and LSH512 respectively; and our PBKDF2-HMAC-SHA512 implementation outperforms <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenSSL</i> implementation by 523.57%. With our proposed implementation techniques, higher security can be achieved with more iteration operations. Furthermore, our optimization techniques can be easily expanded to optimize the performance of PBKDF2 on GPGPU and embedded devices.
Generative adversarial image super‐resolution network for multiple degradations

The existing single image super-resolution methods based on deep learning cannot handle multiple degradations well, and the generated image tends to be blurred and over-smoothed due to poor generalisation ability. In this study, the authors propose a method based on a generative adversarial network (GAN) to deal with multiple degradations. In the generator network, blur kernel and noise level are used as input through dimensionality stretching strategy preprocessing to make full use of prior knowledge. In addition, three discriminators with different scales are used in the discriminator network to pay attention to the reconstruction of image details while focusing on the global consistency of the image. For the problems of vanishing gradient and mode collapse existing in GAN-based methods, a gradient penalty term is added in the loss function. Extensive experiments demonstrate that the proposed method not only can handle multiple degradations to obtain state-of-the-art performance, but also deliver visually credible results in real scenes.
Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage

The COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing.The goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020.We performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University's campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode.Between June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus.Digital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.
A Dynamic Motion Analysis of a Six-Wheel Ground Vehicle for Emergency Intervention Actions

To protect the personnel of the intervention units operating in high-risk areas, it is necessary to introduce (autonomous/semi-autonomous) robotic intervention systems. Previous studies have shown that robotic intervention systems should be as versatile as possible. Here, we focused on the idea of a robotic system composed of two vectors: a carrier vector and an operational vector. The proposed system particularly relates to the carrier vector. A simple analytical model was developed to enable the entire robotic assembly to be autonomous. To validate the analytical-numerical model regarding the kinematics and dynamics of the carrier vector, two of the following applications are presented: intervention for extinguishing a fire and performing measurements for monitoring gamma radiation in a public enclosure. The results show that the chosen carrier vector solution, i.e., the ground vehicle with six-wheel drive, satisfies the requirements related to the mobility of the robotic intervention system. In addition, the conclusions present the elements of the kinematics and dynamics of the robot.
A Lightweight Action Recognition Method for Unmanned-Aerial-Vehicle Video

In recent year, due to motility and wide coverage, unmanned aerial vehicle (UAV) has been widely applied in surveillance system. Human action recognition in UAV video is essential for surveillance video understanding. However, existing action recognition methods suffer from heavy computing, which makes it hard to deploy in real applications. In this paper, a lightweight action recognition method for UAV video(LARMUV) is proposed. This method is based on TSN and adopt Mo-bileNetV3 as backbone, which greatly reduces amount of computing and parameters. Self-attention mechanism is adopted to capture temporal structure among different frames. For loss function, Focal Loss is used to putting more focus on hard, misclassified examples. Last but not least, knowledge distillation is employed to enhance the performance of our model, which transfer knowledge from a larger teacher model to student model. Experimental results on HMDB51, UCF101 and UAV dataset show that our method can achieve competitive performance compared to baseline methods while run in real-time mode.
A Learning-based Dynamic Load Balancing Approach for Microservice Systems in Multi-cloud Environment

Multi-cloud environment has become common since companies manage to prevent cloud vendor lock-in for security and cost concerns. Meanwhile, the microservice architecture is often considered for its flexibility. Combining multi-cloud with microservice, the problem of routing requests among all possible microservice instances in multi-cloud environment arises. This paper presents a learning-based approach to route requests in order to balance the load. In our approach, the performance of microservice is modeled explicitly through machine learning models. The model can derive the response time from request volume, route decision, and other cloud metrics. Then the balanced route decision is obtained from optimizing the model with Bayesian Optimization. With this approach, the request route decision can adjust to dynamic runtime metrics instead of remaining static for all different circumstances. Explicit performance modeling avoids searching on an actual microservice system which is time-consuming. Experiments show that our approach reduces average response time by 10% at least.
Construction and Application of Knowledge Graph for Power System Dispatching

The knowledge graph is an important knowledge representation form of artificial intelligence in the big data era. In the knowledge graph, entities and their relations are represented in the form of a graph. This paper constructs a knowledge graph in the field of power system dispatching, which helps dispatchers in scenarios such as knowledge management and emergency determination. First, basic concepts for knowledge graph and its construction frameworks are introduced. Then, detailed construction method of the dispatch knowledge graph is proposed. Finally, a constructed knowledge graph for power system dispatching is detailed, and the benefits of the knowledge graph in real power system dispatching scenarios are discussed.
Developing vocabulary card base on Augmented Reality (AR) for learning English

Abstract The application of technology in the world of education can affect the quality of students in understanding learning material because technology is one of the important elements to assist in the teaching and learning process. One educational application that can be developed is Augmented Reality (AR) technology. AR is one of the learning media that can be used to combine virtual objects or objects into a real environment with the virtual world to be able to interact in realtime. English is a foreign language that must be mastered. In learning English, students need to be given adequate vocabulary learning that can be provided through reading or listening activities. The importance of adequate vocabulary in English to support the smoothness of oral communication, where there are 2 things that inhibit the ineffectiveness of oral communication namely students do not have adequate vocabulary mastery and do not know how to use vocabulary in their spoken language. Effective and interactive learning media will facilitate students in learning vocabulary. This research an instructional media based on augmented reality vocabulary cards will be made for learning English vocabulary. Learning English vocabulary developed is a basic vocabulary consisting of object recognition and greeting. The learning media will display real objects and sounds, so that the object looks real and the ease of pronunciation of English consonants correctly.
Residual-Network-Leveraged Vehicle-Thrown-Waste Identification in Real-Time Traffic Surveillance Videos

We attempt to intelligently identify violations of throwing waste from vehicles (TWV) in real-time traffic surveillance videos. In addition to polluting the environment, TWV easily causes injury to sanitation workers responsible for cleaning roads by passing vehicles. However, manual inspection is still the commonest way to recognize such uncivilized behavior in videos with very high time and labor-consuming. In answer to these challenges, we design a novel 20-layer residual network (Nov-ResNet-20) for training the vehicle-thrown-waste identification model (VTWIM). Then, incorporating Nov-ResNet-20, Selective Search, and Non-Maximum Suppression (NMS), we propose the deep-residual-network-leveraged vehicle-thrown-waste identification method (DRN-VTWI). Our method first splits one video frame into several regions matching suspected objects marked with location boxes via Selective Search. Then, in terms of the VTWIM trained by Nov-ResNet-20 our method identifies the regions containing TWV. Last, our method removes the redundant location boxes for each recognized, vehicle-thrown waste and only keeps the best one. The significance of our work is four-fold: 1) Nov-ResNet-20 has a moderate depth: 6 convolutional layers, 7 residual layers, and in total 20 weight layers. Due to the joint contribution of the residual, batch normalization, dropout, and cross-entropy loss, it is eligible to identify TWV using a small quantity of manually-annotated training samples. 2) Selective Search diversely marks all possible, suspected objects in video frames, whereas NMS keeps the best location box for each recognized vehicle-thrown waste, removing all redundancies. In this way, DRN-VTWI finds potential violations of TWV as many as possible and optimally annotates vehicle-thrown wastes in frames as well. 3) Combining the power of Nov-ResNet-20, Selective Search, and NMS, DRN-VTWI well solves the challenging, intelligent identification of vehicle-thrown wastes for real-time traffic surveillance. Experimental studies conducted on real-time traffic surveillance videos demonstrate the effectiveness as well as superiority of our efforts.
Reversible data hiding based on three shadow images using rhombus magic matrix

This paper proposes a multi-image-based reversible data hiding method using a rhombus magic matrix. It takes a pixel pair as a position coordinate of a 256×256 modulus function matrix and extracts a 5-order rhombus matrix. It first embeds a 5-ary secret digit by producing three shadow pixel pairs which satisfies the predefined distance condition. Then it embeds a 6-ary secret digit by permuting the three shadow pixel pairs and assigning them to three ordered shadow images. Third, it embeds another 5-ary secret digit by modifying the pixel pair within a 3-order rhombus magic matrix in a shadow image. The receiver can extract the secret data and recover the original cover image when obtaining all shadow images. It also introduces a new application scenario for hierarchical security data transmission. The experimental results and analysis show that the proposed security scheme provides high embedding capacity with good visual quality of shadow images.
Immersive Virtual Reality Training of Bioreactor Operations

This paper explores the application of immersive virtual reality (VR) as a training environment for biopharmaceutical engineering processes. An immersive simulation of a real-world industrial bioreactor setup was developed. This allows repeated practice in a safe and contextualized environment and mitigate limited access to equipment for hands-on training opportunities. It is hypothesized that using VR as an authentic context will assist in achieving the learning outcomes of performing operations in industry standard bioreactor setups, and understanding cellular processes. Two preliminary studies were conducted to evaluate the benefits of the VR application. Using pre- and post tests, there were comparable improvements using the VR application when compared to traditional lab lessons. Qualitatively, participants indicated benefits to learning through repeated practice and immersion in a realistic 3D lab environment. The introduction of gamification methods as motivators are areas to be further explored to better integrate students' theoretical and practical understanding compared to traditional approaches.
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs

Network visualisation techniques are important tools for the exploratory analysis of complex systems. While these methods are regularly applied to visualise data on complex networks, we increasingly have access to time series data that can be modelled as temporal networks or dynamic graphs. In dynamic graphs, the temporal ordering of time-stamped edges determines the causal topology of a system, i.e., which nodes can, directly and indirectly, influence each other via a so-called causal path. This causal topology is crucial to understand dynamical processes, assess the role of nodes, or detect clusters. However, we lack graph drawing techniques that incorporate this information into static visualisations. Addressing this gap, we present a novel dynamic graph visualisation algorithm that utilises higher-order graphical models of causal paths in time series data to compute time-aware static graph visualisations. These visualisations combine the simplicity and interpretability of static graphs with a time-aware layout algorithm that highlights patterns in the causal topology that result from the temporal dynamics of edges .
4.2 A 12nm Autonomous-Driving Processor with 60.4TOPS, 13.8TOPS/W CNN Executed by Task-Separated ASIL D Control

Autonomous driving systems, when deployed to market, will require accurate and high-speed recognition, judgment and operation. Convolutional neural networks (CNNs) require large amounts of computation for pattern recognition. The CNN performance required for level-3 autonomous driving systems is 120TOPS or higher. As shown in Fig. 4.2.1, recent CNN implementations are oriented toward high performance and low power [1] -[4]. In previously reported SoCs for autonomous driving [5], power consumption is more than 70W, requiring a heavy and expensive water-cooling system. To save weight and cost by using an air-cooling system for an in-vehicle electronics control unit (ECU), power consumption less than 25W is indispensable and around half of that can be assigned for the SoC. Consequently, achieving 120TOPS with 10TOPS/W is necessary for an autonomous driving system. At the same time, achieving the ASIL D standard, the highest safety level defined in ISO 26262, is also required for SoCs for autonomous driving. Dual-core lock step (DCLS) is a technique to satisfy ASIL D by comparing the results of parallel execution of the same process in duplicated hardware. However, simple full-time DCLS doubles power consumption and degrades power efficiency. In this paper, we achieve 60.4TOPS CNN performance with 13.8TOPS/W efficiency in an application processor having high-reliability ASIL D targeted safety mechanisms for autonomous driving system. One and two-device configurations achieving performance of 60 and 120TOPS, respectively, for ADAS and autonomous driving offer practical solutions for products.
Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study.
Defect Reduction Planning (Using TimeLIME)

Software comes in releases. An implausible change to software is something that has never been changed in prior releases. When planning how to reduce defects, it is better to use plausible changes, i.e., changes with some precedence in the prior releases. To demonstrate these points, this paper compares several defect reduction planning tools. LIME is a local sensitivity analysis tool that can report the fewest changes needed to alter the classification of some code module (e.g., from “defective” to “non-defective”). TimeLIME is a new tool, introduced in this paper, that improves LIME by restricting its plans to just those attributes which change the most within a project. In this study, we compared the performance of LIME and TimeLIME and several other defect reduction planning algorithms. The generated plans were assessed via (a) the similarity scores between the proposed code changes and the real code changes made by developers; and (b) the improvement scores seen within projects that followed the plans. For nine project trails, we found that TimeLIME outperformed all other algorithms (in 8 out of 9 trials). Hence, we strongly recommend using past releases as a source of knowledge for computing fixes for new releases (using TimeLIME). Apart from these specific results, the other lesson from this paper is that our community might be more careful about using off-the-shelf AI tools, without first applying SE knowledge (e.g., that past releases are a good source of knowledge for planning defect reductions). As shown here, once that SE knowledge is applied, this can result in dramatically better reasoning.