Reviewerly/exHarmony huggingface dataset:
Negotiation game for joint IT and energy management in green datacenters

As the power demand of datacenters is increasing sharply, a promising solution is to power datacenters locally by renewable energies. However, one of the main challenges when operating such green datacenters is to conciliate the intermittent power supply and the power demand. To deal with this problem, we view the green datacenter as two sub-systems, namely, Information Technology (IT) sub-system which consumes energy, and electrical sub-system which supplies energy. The objective is to find an efficient trade-off between the power demand and power supply, respecting the operational requirements of both sub-systems (i.e., the requirements on utility, or monetary gain, which includes monetary revenue and monetary cost). First, we analyze the problem by a black-box approach. In this approach, the models of the two sub-systems are unknown to each other, and the two sub-systems negotiate by exchanging their power preferences. However, we found that the black-box approach cannot guarantee stable solutions in term of execution time and generational distance (which is the distance between a solution and the Pareto front). Then we introduce a semi black-box approach, in which the two sub-systems are modeled as the buyer and the supplier in a buyer–supplier negotiation game. We propose an algorithm that allows the buyer and supplier to negotiate, seeking for an efficient trade-off between the power demand and power supply. The analytical results show that the semi black-box algorithm converges to equilibrium, and these results are then confirmed by experimental results. We conduct the experiments by implementing a middleware of a datacenter powered by renewable energies. The experimental results show that the semi black-box algorithm improves significantly the stability, quality of service (QoS) and utility of the datacenter, compared to other algorithms. In term of stability, compared to the black-box algorithm, the semi black-box algorithm reduces the standard deviation of execution time and generational distance by 23 and 27 times, respectively. In term of QoS and utility, the semi black-box algorithm outperforms the algorithms that do not consider joint IT-energy management, as well as the algorithms that do not utilize a semi black-box design.
Calibration based on ray-tracing for multi-line structured light projection system

Multi-line Structured Light Projection System (MSLPS) is widely used with the advantages of high-contrast stripes and high robustness in high-speed dynamic reconstruction in comparison with Phase Modulation System (PMS). However, existing calibration methods are either sophisticated or low-accuracy in large visual field application because the bending of line structured light plane caused by lens distortion is inevitable and projector calibration is necessary. Therefore, in this paper, we present an accurate and robust calibration method based on ray-tracing, which establishes accurate relationship between subpixel coordinates of each bending line structured light plane and 3D coordinates based on camera coordinate system without projector calibration. As long as placing a checkerboard at random positions, MSLPS can be calibrated and hence is more suitable in practical application. Experimental results demonstrated that, compared with existing line structured light strategies, the proposed method could achieve more accurate and robust 3D reconstruction and the calibration process is simpler and easier to implement.
A Critical Analysis of ISO 17825 (‘Testing Methods for the Mitigation of Non-invasive Attack Classes Against Cryptographic Modules’)

The ISO standardisation of 'Testing methods for the mitigation of non-invasive attack classes against cryptographic modules' (ISO/IEC 17825:2016) specifies the use of the Test Vector Leakage Assessment (TVLA) framework as the sole measure to assess whether or not an implementation of (symmetric) cryptography is vulnerable to differential side-channel attacks. It is the only publicly available standard of this kind, and the first side-channel assessment regime to exclusively rely on a TVLA instantiation. TVLA essentially specifies statistical leakage detection tests with the aim of removing the burden of having to test against an ever increasing number of attack vectors. It offers the tantalising prospect of 'conformance testing': if a device passes TVLA, then, one is led to hope, the device would be secure against all (first-order) differential side-channel attacks. In this paper we provide a statistical assessment of the specific instantiation of TVLA in this standard. This task leads us to inquire whether (or not) it is possible to assess the side-channel security of a device via leakage detection (TVLA) only. We find a number of grave issues in the standard and its adaptation of the original TVLA guidelines. We propose some innovations on existing methodologies and finish by giving recommendations for best practice and the responsible reporting of outcomes.
Distributed video coding scheme of multimedia data compression algorithm for wireless sensor networks

Abstract The emergence of multimedia data has enriched people’s lives and work and has penetrated into education, finance, medical, military, communications, and other industries. The text data takes up a small space, and the network transmission speed is fast. However, due to its richness, the multimedia data makes it occupy an ample space. Some high-definition multimedia information even reaches the GB level, and the multimedia data network transmission is relatively slow. Compared with the traditional scalar data, the multimedia data better describes the characteristics of the transaction, but at the same time, the multimedia data itself has a large capacity and must be compressed. Nodes of wireless multimedia sensor networks have limited ability to process data. Traditional data compression schemes require high processing power of nodes and are not suitable for sensor networks. Therefore, distributed video codec scheme in recent years becomes one of the hot multimedia sensor network technologies, which is a simple coding scheme, coding complexity of decoding performance. In this paper, distributed video codec and its associated knowledge based on the study present a distributed video coding scheme and its improvements. Aiming at the problem that the traditional distributed video coding scheme cannot accurately decode the motion severe region and the boundary region, a distributed video coding algorithm based on gradient-domain ROI is proposed, which can enhance the coding efficiency of the severe motion region and improve the decoded image while reducing the code rate and quality, ultimately reducing sensor node energy consumption.
A Comparative Approach of Dimensionality Reduction Techniques in Text Classification

This work deals with document classification. It is a supervised learning method (it needs a labeled document set for training and a test set of documents to be classified). The procedure of document categorization includes a sequence of steps consisting of text preprocessing, feature extraction, and classification. In this work, a self-made data set was used to train the classifiers in every experiment. This work compares the accuracy, average precision, precision, and recall with or without combinations of some feature selection techniques and two classifiers (KNN and Naive Bayes). The results concluded that the Naive Bayes classifier performed better in many situations.
On quantum methods for machine learning problems part II: Quantum classification algorithms

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, “quantum tools”, presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.
Topic modeling Twitter data using Latent Dirichlet Allocation and Latent Semantic Analysis

The industrial world has entered the era of industrial revolution 4.0. In this era, there is an urgent data requirement from the community to support service policies. Because of that, Surabaya Government made Media Center Surabaya. This media is used to accommodate all the aspiration of Surabaya citizen. To access this media, a citizen can use Twitter. The topic which is discussed in Twitter is important information that we need to know. The information can be used to improve the performance of Surabaya Government services. Twitter data is a text data that consists of thousands of variables. Text mining is frequently used to analyze this kind of data, including topic modeling and sentiment analysis. This study would work on topic modeling focused on the algorithm employing Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The evaluation of the algorithm performance uses the topic coherence. As unstructured data, the Twitter data need preprocessing before the analysis. The stages of preprocessing include cleansing, stemming, and stop words. The advantages of LSA are fast and easy to implement. LSA, on the other hand, doesn’t consider the relationship between documents in the corpus, while LDA does. This study shows that LDA gives a better result than LSA.
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization

We study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We examine fully-connected and convolutional networks (FCN and CNN), both linear and nonlinear, initialized randomly and then trained to minimize the reconstruction error. The trained networks stereotypically take one of two forms: the constant function (memorization) and the identity function (generalization). We formally characterize generalization in single-layer FCNs and CNNs. We show empirically that different architectures exhibit strikingly different inductive biases. For example, CNNs of up to 10 layers are able to generalize from a single example, whereas FCNs cannot learn the identity function reliably from 60k examples. Deeper CNNs often fail, but nonetheless do astonishing work to memorize the training output: because CNN biases are location invariant, the model must progressively grow an output pattern from the image boundaries via the coordination of many layers. Our work helps to quantify and visualize the sensitivity of inductive biases to architectural choices such as depth, kernel width, and number of channels.
Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model

Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.
Utility Mining across Multi-Sequences with Individualized Thresholds

Utility-oriented pattern mining has become an emerging topic since it can reveal high-utility patterns (e.g., itemsets, rules, sequences) from different types of data, which provides more information than the traditional frequent/confident-based pattern mining models. The utilities of various items are not exactly equal in realistic situations; each item has its own utility or importance. In general, user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results showed that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.
A Virtual Home for the Virtual Clinical Trial

Virtual clinical trials (VCTs) can satisfy the need for rigorous clinical trials by using distributed technological solutions that eliminate the need for a physical trial site. This report explores potential benefits of using virtual reality (VR) to provide a “virtual site” for VCTs, a shared immersive hub in which VCT participants could experience elements of the trial and interact with the trial team. VR is a communication technology that has been emerging alongside the development of VCTs, although they have never been merged in a substantial way. Many of the gaps within the VCT paradigm are areas in which VR excels. VR environments are standardized and precisely uniform, the technology allows introduction of an almost endless set of stimuli to participants’ visual and auditory systems, and VR systems are adept at capturing precise movement and behavioral data. Although VR has not yet found its way into VCTs, much of the groundwork for such integration has been laid through research and technological development achieved in the past few years. Future implementation of VR within VCTs could move us from site-less trials to those with a virtual site serving as a hub for trial information provision, interaction with trial representatives, administration of evaluations and assessments, and more.
Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights

We explore the area of fairness in clustering from the different perspective of modifying clusterings from existing algorithms to make them fairer whilst retaining their quality. We formulate the minimal cluster modification for fairness (MCMF) problem where the input is a given partitional clustering and the goal is to minimally change it so that the clustering is still of good quality and fairer. We show using an intricate case analysis that for a single protected variable, the problem is efficiently solvable (i.e., in the class P) by proving that the constraint matrix for an integer linear programming (ILP) formulation is totally unimodular (TU). Interestingly, we show that even for a single protected variable, the addition of simple pairwise guidance (to say ensure individual level fairness) makes the MCMF problem computationally intractable (i.e., NP-hard). Experimental results on Twitter, Census and NYT data sets show that our methods can modify existing clusterings for data sets in excess of 100,000 instances within minutes on laptops and find as fair but higher quality clusterings than fair by design clustering algorithms.
Leveraging Knowledge Graphs for Big Data Integration: the XI Pipeline

This article gives an overview of recent efforts focusing on integrating heterogeneous data using Knowledge Graphs. I introduce a pipeline consisting of five steps to integrate semi-structured or unstructured content. I discuss some of the key applications of this pipeline through three use-cases, and present the lessons learnt along the way while designing and building data integration systems.
Soft trees with neural components as image-processing technique for archeological excavations

Abstract There are situations when someone finds a certain object or its remains. Particularly the second case is complicated, because having only a part of the element, it is difficult to identify the full object. In the case of archeological excavations, the fragment should be classified in order to know what we are looking at. Unfortunately, such classification may be a difficult task. Hence, it is essential to focus on certain features which define it, and then to classify the complete object. In this paper, we proposed creating a novel soft tree decision structure. The idea is based on soft sets. In addition, we have introduced convolutional networks to the nodes to make decisions based on graphic files. A new archeological item can be photographed and evaluated by the proposed technique. As a result, the object will be classified depending on the amount of information obtained to the appropriate class. If the object cannot be classified, the method will return individual features and possible class.
Steganography of Encrypted Messages Inside Valid QR Codes

Steganography is considered the first line of defense in information security as it hides a secret message (payload) inside an innocent looking file (container) to transfer the payload under the adversary's nose without noticing it. Steganographic systems only use the container to hide the payload. In this paper, we present a steganographic system that uses the container not only to hide the payload, but also to give misleading information to the adversary. To achieve this goal, we use quick response (QR) code as a container. QR codes generated by our proposed system can carry its ordinary message in addition to the payload. Anyone can read the message, but the payload can only be obtained using a secret key. The message and the payload are unrelated; i.e. any message can be generated regardless of the payload and vise versa. We can take advantage of that by generating a message that gives misleading information to the adversary. We test the proposed system and show that the generated QR code is (valid) i.e indistinguishable from an ordinary QR code which makes it look innocent and less susceptible to an adversary's attack. Moreover, it is space-efficient, has an acceptable level of noise immunity and is prone to steganalysis attacks.
Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

Background Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). Objective This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. Methods We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (&lt;0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. Conclusions Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.
HandLeVR: Action-Oriented Learning in a VR Painting Simulator

The development of vocational competence has so far been inefficiently implemented in some trades, as for example in the training of vehicle painters. The HandLeVR project therefore underscores the use of Virtual Reality to promote action-oriented learning of techniques for carrying out vehicle painting work. This article describes both the instructional and technological aspects of a VR Painting Simulator developed in the project and presents intermediate results.
Comments on “Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption”

Cloud computing can provide a flexible way to effectively share data among multiple users since it can overcome the time and location constraints of computing resource usage. However, the users of cloud computing are still reluctant to share sensitive data to a cloud server since the cloud server should be treated as an untrusted entity. In order to support secure and efficient data sharing in a cloud computing environment, Wei et al. recently extended the concept of identity-based encryption (IBE) to support key revocation and ciphertext update functionalities, and proposed a revocable-storage identity-based encryption (RS-IBE) scheme. In this article, we show that the RS-IBE scheme of Wei et al. does not satisfy the correctness property of RS-IBE. We also propose a method to modify the existing RS-IBE scheme.
TRAK: A Testing Tool for Studying the Reliability of Data Delivery in Apache Kafka

In modern applications the demand for real-time processing of high-volume data streams is growing. Common application scenarios include market feed processing and electronic trading, maintenance of IoT devices and fraud detection. In some scenarios reliability is the utmost concern while in others speed and simplicity are the top priority. Apache Kafka is a high-throughput distributed messaging system and its reliable stream delivery capability makes it an ideal source of data for stream-processing systems. With various configurable parameters Kafka is very flexible in reliable data delivery thus allowing all kinds of reliability tradeoffs. In this paper we introduce a tool for Testing the Reliability of Apache Kafka (TRAK), to study different data delivery semantics in Kafka and compare their reliability under poor network quality. We build a Kafka testbed using Docker containers and use a network emulation tool to control the network delay and loss. Two metrics, message loss rate and duplicate rate, are used in our experiments to evaluate the reliability of data delivery in Kafka. The experimental results show that under high network delay the size of messages matters. The at-least-once semantics is more reliable than at-most-once in a network with high packet loss, but can lead to duplicated messages.