Reviewerly/exHarmony huggingface dataset:
Identification of VoIP Speech With Multiple Domain Deep Features

Identifying whether a phone call comes from VoIP (Voice over Internet Protocol) is a challenging but less-investigated audio forensic issue. As shown in a previous study, existing feature based methods do not work well. In this paper, we propose a robust data-driven approach, called CNN-MLS (convolutional neural network based multi-domain learning scheme), to distinguish VoIP calls from mobile phone calls. To better explore the differences between VoIP and mobile phone calls, we first process data with high-pass filtering, and then extract deep features from both temporal domain and spectral domain. Two CNN architectures are designed for accepting data from respective domains, and some tricks such as auxiliary classifiers and individual subnet training are used for accelerating network convergence. The deep features are finally fused in a classification module for identifying the phone call type. The proposed method is evaluated on VPCID (VoIP Phone Call Identification Database) dataset, under various testing conditions. We pay particular attention to tests on data belonging to a source mismatched with the training sources. Experimental results show that, compared with existing methods, our method can achieve satisfactory and better accuracy on two-second-long inputs, implying that an alert may be activated shortly after a VoIP call is made.
The Trade-Off Between Privacy and Fidelity via Ehrhart Theory

As an increasing amount of data is gathered nowadays and stored in databases (DBs), the question arises of how to protect the privacy of individual records in a DB even while providing accurate answers to queries on the DB. Differential Privacy (DP) has gained acceptance as a framework to quantify vulnerability of algorithms to privacy breaches. We consider the problem of how to sanitize an entire DB via a DP mechanism, on which unlimited further querying is performed. While protecting privacy, it is important that the sanitized DB still provide accurate responses to queries. The central contribution of this work is to characterize the amount of information preserved in an optimal DP DB sanitizing mechanism (DSM). We precisely characterize the utility-privacy trade-off of mechanisms that sanitize DBs in the asymptotic regime of large DBs. We study this in an information-theoretic framework by modeling a generic distribution on the data, and a measure of fidelity between the histograms of the original and sanitized DBs. We consider the popular $\mathbb{L}_{1}-$distortion metric that leads to the formulation as a linear program (LP). This optimization problem is prohibitive in complexity with the number of constraints growing exponentially in the parameters of the problem. Leveraging tools from discrete geometry, analytic combinatorics, and duality theorems of optimization, we fully characterize the optimal solution in terms of a power series whose coefficients are the number of integer points on a multidimensional convex polytope studied by Ehrhart in 1967. Employing Ehrhart theory, we determine a simple closed form computable expression for the asymptotic growth of the optimal privacy-fidelity trade-off to infinite precision. At the heart of the findings is a deep connection between the minimum expected distortion and the Ehrhart series of an integral convex polytope.
Dynamic Tuple Scheduling with Prediction for Data Stream Processing Systems

For data stream processing systems such as Apache Heron, workload imbalance across processing instances often causes significant system performance degradation. To mitigate such issues, Apache Heron leverages a naive throttling-based back-pressure scheme, which may lead to unexpected system disruption. This calls for a finer-grained control to distribute data stream units (tuples) between successive instances, a.k.a. tuple scheduling, which well adapts to data stream variations and workload discrepancy. Besides, the benefits of predictive scheduling to data stream processing systems still remain unexplored. In this paper, we formulate tuple scheduling problem as a stochastic network optimization problem, with careful choices in the granularity of system modeling and decision making. With non-trivial transformation, we decouple the problem into a series of online subproblems. By exploiting unique subproblem structure, we propose POTUS, an efficient, online, and distributed scheduling scheme that employs the power of predictive scheduling but requires only limited system dynamics to achieve a tunable trade-off between communication cost reduction and system queue stability. Theoretical analysis and simulations show that POTUS effectively shortens response time with mild-value of future information, even in the face of misprediction. Our solution is also applicable to other data stream processing systems.
Triple - Entry Bookkeeping: History and Benefits of the Concept

With the emergence of the blockchain technology another concept is revived -triple-entry bookkeeping.The tTerm first occurred in 1989 in a paper written by Yuji Ijiri, this alternative accounting system was expanded at a later time.The Double entry bookkeeping evolved around the 1300s AD in Italy and is still being used to this day.Having in mind that this system is prone to error and fraud, this paper examines how triple entry bookkeeping contributes to transparency, future reference, reconciliation, assurance and auditing.Therefore, the analysis is carried out systematically, uniformly and consistently.Additionally, the changing role of external auditors in everyday operations will be evaluated and the paper concludes with a discussion of a proposed solution for the principalagent problem
Soundness of a Dataflow Analysis for Memory Monitoring

An important concern addressed by runtime verification tools for C code is related to detecting memory errors. It requires to monitor some properties of memory locations (e.g., their validity and initialization) along the whole program execution. Static analysis based optimizations have been shown to significantly improve the performances of such tools by reducing the monitoring of irrelevant locations. However, soundness of the verdict of the whole tool strongly depends on the soundness of the underlying static analysis technique. This paper tackles this issue for the dataflow analysis used to optimize the E-ACSL runtime assertion checking tool.We formally define the core dataflow analysis used by E-ACSL and prove its soundness.
Cryptanalysis of the Lightweight Block Cipher BORON

This paper provides security evaluations of a lightweight block cipher called BORON proposed by Bansod et al. There is no third-party cryptanalysis towards BORON. Designers only provided coarse and simple security analysis. To fill this gap, security bounds of BORON against differential and linear cryptanalysis are presented in this paper. By automatic models based on the SMT solver STP, we search for differential and linear trails with the minimal number of active S-boxes and trails with optimal probability and bias. Then, we present key-recovery attacks towards round-reduced BORON. This paper is the first third-party cryptanalysis towards BORON.
AUTONOMOUS UNMANNED AERIAL VEHICLE FLIGHT ACCURACY EVALUATION FOR THREE DIFFERENT PATH-TRACKING ALGORITHMS

This paper shows mathematical results of three methods, which can be used for Unmanned Aerial Vehicle (UAV) to make transition from one flight leg to another. In paper, we present general equations, which can be used for generating waypoint-switching methods when for experiment purpose mathematical UAV model is used. UAV is modelled as moving dot, which eliminates all of the aerodynamics factors and we can concentrate only on the navigation problems. Lots of attention is dedicated to show possible flight path error values with representation of modelled flight path trajectories and deviations from the flight mission path. All of the modelled flight missions are done in two-dimensional space and all the results are evaluated by looking at Probability Density Function (PDF) values, as we are mostly interested in the probability of the error.
The Effect of Self Efficacy and Parent Support on Learning Motivation in Management Business Students in Padang's Private Vocational School

This study departs from the expectation that high or low student learning motivation are influenced by self-efficacy and family environment. The results of the study show that: 1) self-efficacy has a significant effect on learning motivation. 2) self-efficacy and parental support have a significant effect on learning motivation. This research is an associative descriptive study. The sample in this study is the business management students of class X and XI that are registered at Padang Private Vocational School. The findings from this study are expected to be a reference for teachers and other academic communities to be able to pay more attention to students in learning by giving attention to the family environment and paying attention to their learning attitudes and also help increase student motivation in learning so that maximum learning outcomes are obtained
Improved Adaptive Holonic Particle Swarm Optimization

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.
Modeling Confidence in Sequence-to-Sequence Models

Recently, significant improvements have been achieved in various natural language processing tasks using neural sequence-to-sequence models.While aiming for the best generation quality is important, ultimately it is also necessary to develop models that can assess the quality of their output.In this work, we propose to use the similarity between training and test conditions as a measure for models' confidence.We investigate methods solely using the similarity as well as methods combining it with the posterior probability.While traditionally only target tokens are annotated with confidence measures, we also investigate methods to annotate source tokens with confidence.By learning an internal alignment model, we can significantly improve confidence projection over using stateof-the-art external alignment tools.We evaluate the proposed methods on downstream confidence estimation for machine translation (MT).We show improvements on segmentlevel confidence estimation as well as on confidence estimation for source tokens.In addition, we show that the same methods can also be applied to other tasks using sequence-tosequence models.On the automatic speech recognition (ASR) task, we are able to find 60% of the errors by looking at 20% of the data.
La formación docente en línea: experiencias con MOOCs en Sonora (México)

The purpose of this study was to know the perspectives that a group of teachers from the state of Sonora, Mexico, have on online training and on themassive open online courses(MOOCs)"Key Learning Collection". These MOOCs were designed to make basic education teachers aware of the approaches of the new educational model derived from the 2013 reform. The approach applied in the research was the qualitative one; the data was collected through semi-structured interviews with six primary school teachers. Teachers' perspectives regarding online training focused on the categories of analysis: online versus face-to-face in-service teacher training, participation requirements of online in-service teacher training, and challenges of online in-service teacher training; and regarding MOOCs, they were categorized into: didactic-pedagogical dimension of the courses, massiveness and absence of tutor, implemented technological infrastructure, and the role of the school authority for its implementation. The findings show an open position of teachers towards online training;,however, their willingness to participate in it is influenced by their previous experiences in this modality. Likewise, the findings highlight the need to promote a joint commitment, both of educational authorities and teachers, to successfully implement massive teacher training strategies such as MOOCs.
Intelligent Optical Network with AI and Blockchain

The inevitable trend to develop 5G and beyond puts higher requirements on the intelligence of optical networks. In the operation of an optical network, many processes can take advantage of emerging technologies for effective optimization, such as resource allocation, fault operation, traffic classification and prediction and also security convincement.Artificial intelligence (AI) is a method which establishes digital logical topologies following the structure of biological neural network to reproduce its analysis and learning process. Besides, blockchain is also a secure, decentralized architecture that supports the trustworthiness of distributed communications and storage. These two technologies are considered to have great potential in optimizing the intelligent optical network in different aspects.In this paper, we will demonstrate some possible approaches of how AI and blockchain can separately be integrated with optical network to improve its performance.
In Search of the Factors Behind Naive Sentence Judgments: A State Trace Analysis of Grammaticality and Acceptability Ratings

We present a state-trace analysis of sentence ratings elicited by asking participants to evaluate the overall acceptability of a sentence and those elicited by asking participants to focus on structural well-formedness only. Appealing to literature on `grammatical illusion' sentences, we anticipated that a simple instruction manipulation might prompt people to apply different weights to different aspects of acceptability in the two conditions. The state trace analysis of the rating data indicates that some dissociations between subjective judgments of acceptability and grammaticality are consistent with both judgment types accessing a single underlying factor. These results add to the existing comparisons between analytic and probabilistic modeling approaches to predicting rating judgments.
What Do You Mean ‘Why?’: Resolving Sluices in Conversations

In conversation, we often ask one-word questions such as ‘Why?’ or ‘Who?’. Such questions are typically easy for humans to answer, but can be hard for computers, because their resolution requires retrieving both the right semantic frames and the right arguments from context. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures.
Grammar Filtering for Syntax-Guided Synthesis

Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.
Design and Performance Analysis of a Dual Axis Solar Tracker

This paper is aimed at the method of raising the efficiency of the solar panel by using a dual axis solar tracker. This is designed with four LDRs, which are the main sensor inputs, an Arduino Uno microcontroller, which is the main processing unit and a well-planned platform design consisting of two servo motors and a metal structure. The structure is suitable for maneuvering a 20W solar panel that weights around 2 Kg, while consuming very little energy to function. The main objective of this paper is to prove the fact that this dual axis solar tracker design is a more efficient one for harnessing the maximum amount of solar energy than existing similar trackers. A 37.76 percent increase in efficiency has been analyzed when compared to a fixed configuration solar panel that is tilted at 25 degrees throughout the day.
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call “IB learning”. We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a “vector quantization” approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, “Aggregated Learning”, for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.
Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases

We present RARL, an approach to discover rules of the form body ⇒ head in large knowledge bases (KBs) that typically include a set of terminological facts (TBox) and a set of TBox-compliant assertional facts (ABox). RARL's main intuition is to learn rules by leveraging TBox-information and the semantic relatedness between the predicate(s) in the atoms of the body and the predicate in the head. RARL uses an efficient relatedness-driven TBox traversal algorithm, which given an input rule head, generates the set of most semantically related candidate rule bodies. Then, rule confidence is computed in the ABox based on a set of positive and negative examples. Decoupling candidate generation and rule quality assessment offers greater flexibility than previous work.
Learning with Unsure Responses

Many annotation systems provide to add an unsure option in the labels, because the annotators have different expertise, and they may not have enough confidence to choose a label for some assigned instances. However, all the existing approaches only learn the labels with a clear class name and ignore the unsure responses. Due to the unsure response also account for a proportion of the dataset (e.g., about 10-30% in real datasets), existing approaches lead to high costs such as paying more money or taking more time to collect enough size of labeled data. Therefore, it is a significant issue to make use of these unsure.In this paper, we make the unsure responses contribute to training classifiers. We found a property that the instances corresponding to the unsure responses always appear close to the decision boundary of classification. We design a loss function called unsure loss based on this property. We extend the conventional methods for classification and learning from crowds with this unsure loss. Experimental results on realworld and synthetic data demonstrate the performance of our method and its superiority over baseline methods.
Linear convergence of cyclic SAGA

In this work, we present and analyze C-SAGA, a (deterministic) cyclic variant of SAGA. C-SAGA is an incremental gradient method that minimizes a sum of differentiable convex functions by cyclically accessing their gradients. Even though the theory of stochastic algorithms is more mature than that of cyclic counterparts in general, practitioners often prefer cyclic algorithms. We prove C-SAGA converges linearly under the standard assumptions. Then, we compare the rate of convergence with the full gradient method, (stochastic) SAGA, and incremental aggregated gradient (IAG), theoretically and experimentally.
Attention-over-Attention Field-Aware Factorization Machine

Factorization Machine (FM) has been a popular approach in supervised predictive tasks, such as click-through rate prediction and recommender systems, due to its great performance and efficiency. Recently, several variants of FM have been proposed to improve its performance. However, most of the state-of-the-art prediction algorithms neglected the field information of features, and they also failed to discriminate the importance of feature interactions due to the problem of redundant features. In this paper, we present a novel algorithm called Attention-over-Attention Field-aware Factorization Machine (AoAFFM) for better capturing the characteristics of feature interactions. Specifically, we propose the field-aware embedding layer to exploit the field information of features, and combine it with the attention-over-attention mechanism to learn both feature-level and interaction-level attention to estimate the weight of feature interactions. Experimental results show that the proposed AoAFFM improves FM and FFM with large margin, and outperforms state-of-the-art algorithms on three public benchmark datasets.