Reviewerly/exHarmony huggingface dataset:
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms.

We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier. This has become a topic of recent interest as a way to query a trained model and suggest possible actions to overturn its decision. Mathematically, the problem is formally equivalent to that of finding adversarial examples, which also has attracted significant attention recently. Most work on either counterfactual explanations or adversarial examples has focused on differentiable classifiers, such as neural nets. We focus on classification trees, both axis-aligned and oblique (having hyperplane splits). Although here the counterfactual optimization problem is nonconvex and nondifferentiable, we show that an exact solution can be computed very efficiently, even with high-dimensional feature vectors and with both continuous and categorical features, and demonstrate it in different datasets and settings. The results are particularly relevant for finance, medicine or legal applications, where interpretability and counterfactual explanations are particularly important.
Bang-bang control as a design principle for classical and quantum optimization algorithms

Physically motivated classical heuristic optimization algorithms such as simulated annealing (SA) treat the objective function as an energy landscape, and allow walkers to escape local minima. It h...
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals

We study the problem of agnostic learning under the Gaussian distribution in the Statistical Query (SQ) model. We develop a method for finding hard families of examples for a wide range of concept classes by using LP duality. For Boolean-valued concept classes, we show that the $L^1$-polynomial regression algorithm is essentially best possible among SQ algorithms, and therefore that the SQ complexity of agnostic learning is closely related to the polynomial degree required to approximate any function from the concept class in $L^1$-norm. Using this characterization along with additional analytic tools, we obtain explicit optimal SQ lower bounds for agnostically learning linear threshold functions and the first non-trivial explicit SQ lower bounds for polynomial threshold functions and intersections of halfspaces. We also develop an analogous theory for agnostically learning real-valued functions, and as an application prove near-optimal SQ lower bounds for agnostically learning ReLUs and sigmoids.
Recommender System for Sentiment Analysis using Machine Learning Models

In recent years, with the rapid growth of Internet technology, online shopping has become a rapid way for users to purchase and consume desired products. Large volume of user-generated content on social media sites like twitter has resulted in tweet sentiment analysis. Sentiment analysis supports as base for decision support systems and recommendation systems and it becomes an essential tool on online platforms to extract the information on user emotional state to improve user satisfaction. This paper proposes an effective sentiment analysis recommender system framework using machine learning models.
PENGARUH TEKNIK PEMBIMBINGAN GURU TERHADAP HASIL BELAJAR BAHASA INDONESIA SISWA KELAS V SDN 2 MALINO

Abstract: The Effect of Teacher Guidance Techniques on the Learning Outcomes of Class V Students of SDN 2 Malino. The One Group Pretest-Posttest type experiment with five stages, namely planning, giving pretest, giving treatment, giving posttest, and analyzing results. 25 teachers and students of class V SDN 2 Malino, totaling 25 people as respondents, were conducted in the odd semester 2020/2021. The research instruments used in this study were tests, observation, and documentation. The data obtained were processed using descriptive statistical analysis and inferential statistical analysis (Normality Test, Homogeneity Test, and Hypothesis Test). The variables examined in this study were tutoring techniques and learning outcomes. The results of this study indicate that: (1) The mathematics learning outcomes of SDN 2 Malino students before the treatment fall into the poor category, namely the mean 50.60. While the learning outcomes after treatment in the form of tutoring techniques showed an increase, namely the mean 80.20 including the good category. (2) The data obtained can be seen that the implementation of tutoring techniques affects student learning outcomes in class V SDN 2 Malino. This can be seen from the value of Sig. (2-tailed) 0.05, it is known that there is a significant difference between students 'pretest learning outcomes and students' posttest learning outco Keywords:Teacher Guidance and Indonesian Language Learning Outcomes Abstrak: Pengaruh Teknik Pembimbingan Guru Terhadap Hasil Belajar Bahasa Indonesia Siswa Kelas V SDN 2 Malino.eksperimen tipe The One Group Pretest-Posttest dengan lima tahapan, yaitu perencanaan, pemberian pretest, pemberian perlakuan (treatment), pemberian posttest, dan analisis hasil. guru dan siswa kelas V SDN 2 Malino yang berjumlah 25 orang sebagai responden penelitian ini dilaksanakan pada semester ganjil 2020/2021. Instrumen penelitian yang digunakan dalam penelitian ini adalah tes, observasi, dan dokumentasi. Data yang diperoleh diolah dengan menggunakan analisis statistik deskriptif dan analisis statistik inferensial (Uji Normalitas, Uji Homogenitas, dan Uji Hipotesis). Variabel yang diteliti dalam penelitian ini adalah teknik bimbingan belajar dan hasil belajar. Hasil penelitian ini menunjukkan bahwa: (1) Hasil belajar matematika siswa SDN 2 Malino sebelum perlakuan masuk dalam kategori kurang yaitu mean 50,60. Sedangkan hasil belajar setelah perlakuan berupa teknik bimbingan belajar menunjukan adanya peningkatan yaitu mean 80,20 termasuk kategori baik. (2) Data yang diperoleh dapat diketahui bahwa pelaksanaan teknik bimbingan belajar berpengaruh terhadap hasil belajar siswa di kelas V SDN 2 Malino. Hal ini dapat dilihat dari nilai Sig.(2-tailed) 0,05, diketahui bahwa ada perbedaan yang signifikan antara hasil belajar pretest siswa dengan hasil belajar posttest siswa. Kata Kunci: Pembimbingan Guru dan Hasil Belajar Bahasa Indonesia
Retrieve &amp; Memorize: Dialog Policy Learning with Multi-Action Memory

Dialogue policy learning, a subtask that determines the content of system response generation and then the degree of task completion, is essential for task-oriented dialogue systems.However, the unbalanced distribution of system actions in dialogue datasets often causes difficulty in learning to generate desired actions and responses.In this paper, we propose a retrieve-and-memorize framework to enhance the learning of system actions.Specially, we first design a neural context-aware retrieval module to retrieve multiple candidate system actions from the training set given a dialogue context.Then, we propose a memoryaugmented multi-decoder network to generate the system actions conditioned on the candidate actions, which allows the network to adaptively select key information in the candidate actions and ignore noises.We conduct experiments on the large-scale multi-domain task-oriented dialogue dataset MultiWOZ 2.0 and MultiWOZ 2.1.Experimental results show that our method achieves competitive performance among several state-of-the-art models in the context-to-response generation task.
Application of improved recursive regression algorithm on I&amp;O model under COVID-19

Abstract Regression algorithm is an important data prediction method of machine learning. It can establish calculation matrix through the existing data and obtain the hidden characteristics in the data and other valuable information. Due to the excellent data processing ability, regression is widely used in gray data prediction, algorithm model building, data analysis and other fields. In this paper, the multi-layer recursive regression is adopted and improved for the regression algorithm with double influence factors, which increases the processing ability of multi class data. Data of import and export trade between China and the United States in the past five years is collected to create the future prediction model when the epidemic does not occur. Meanwhile, the data of four months after the outbreak to calculate the condition of the future prediction model of the epidemic situation in COVID-19. Using two opposite conditions as different data of regression algorithm calculation, different prediction algorithm formulas are obtained. After multiple calculations, the average value increases the sensitivity of the model. The calculation results show that the improved recursive regression algorithm has higher stability and sensitivity.
Contents Preference Model Combined with Matrix Factorization for Movie Recommendation

With the growth of the media market, companies that provide contents services such as movies, music and video are providing various content to satisfy users. While these changes have allowed users to enjoy richer content, a new problem has emerged that they have to spend much more time than before to find content that suits their taste among the overflowing content. Recommender system has become an important key to solve these problems. Matrix Factorization (MF) is the most well-known and widely used for identifying users’ preference on contents. However, MF has a drawback of data sparsity and is not capable of utilizing meta-data. In this study, we proposed a two-stage contents preference model with Matrix Factorization (CPMF). The proposed method combines MF and contents preference models that utilize a variety of meta-data (e.g., actors, directors, and genres) to identify users’ preferences. A numerical analysis is conducted to evaluate the performance of the proposed method for movie recommendation domains.
Near Optimal Distributed Learning of Halfspaces with Two Parties

Distributed learning protocols are designed to train on distributed data without gathering it all on a single centralized machine, thus contributing to the efficiency of the system and enhancing its privacy. We study a central problem in distributed learning, called distributed learning of halfspaces: let $U \subseteq \R^d$ be a known domain of size $n$ and let $h:\R^d\to \R$ be an unknown target affine function. A set of examples $\{(u,b)\}$ is distributed between several parties, where~$u \in U$ is a point and $b = \sign(h(u)) \in \{\pm 1\}$ is its label. The parties' goal is to agree on a classifier~$f: U\to\{\pm 1\}$ such that~$f(u)=b$ for every input example~$(u,b)$.We design a protocol for the distributed halfspace learning problem in the two-party setting, communicating only $\tilde O(d\log n)$ bits. To this end, we introduce a new tool called halfspace containers, that is closely related to bracketing numbers in statistics and to hyperplane cuttings in discrete geometry, and allows for a compressed approximate representation of every halfspace. We complement our upper bound result by an almost matching $\tilde \Omega(d\log n)$ lower bound on the communication complexity of any such protocol.Since the distributed halfspace learning problem is closely related to the convex set disjointness problem in communication complexity and the problem of distributed linear programming in distributed optimization, we also derive upper and lower bounds of $\tilde O(d^2\log n)$ and~$\tilde{\Omega}(d\log n)$ on the communication complexity of both of these basic problems.
Learning with Group Noise

Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object w.r.t. the target, meanwhile considers the Non-IID characteristics among objects in the group. Only the most confident one of the objects is used to learn the model, so that the ﬁne-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching.
ONBOARD/ON-GROUND IMAGE PROCESSING CHAIN FOR HIGH-RESOLUTION EARTH OBSERVATION SATELLITES

Abstract. Over the last decade, the French space agency (CNES) has designed and successfully operated high-resolution satellites such as Pléiades. High-resolution satellites typically acquire panchromatic images with fine spatial resolutions and multispectral images with coarser samplings for downlink constraints. The multispectral image is reconstructed on the ground, using pan-sharpening techniques. Onboard compression and ground processing affect however the quality of the final product. In this paper, we describe our next-generation onboard/on-ground image processing chain for high-resolution satellites. This paper focuses on onboard compression, compression artefacts correction, denoising, deconvolution and pan-sharpening. In the first part, we detail our fixed-quality compression approach, which limits compression effects to a fraction of the noise, thus preserving the useful information in an image. This approach optimises the bitrate at the cost of image size, which depends on the scene complexity. This technique requires however pre- and post-processing steps. The noisy HR images obtained after decompression are suited for non-local denoising algorithms. We show in the second part of this paper that non-local denoising outperforms previous techniques by 15% in terms of root mean-squared error when tested on simulated noiseless references. Deconvolution is also detailed. In the final part of this paper, we put forward an adaptation of this chain to low-cost CMOS Bayer colour matrices. We demonstrate that the concept of our image chain remains valid, provided slight modifications (in particular dedicated transformations of the colour planes and demosaicing). A similar chain is under investigation for future missions.
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates

Behavior of deep neural networks can be inconsistent between different versions.Regressions 1 during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain.This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates.Using negative flip rate as regression measure, we show that regression has a prevalent presence across tasks in the GLUE benchmark.We formulate the regression-free model updates into a constrained optimization problem, and further reduce it into a relaxed form which can be approximately optimized through knowledge distillation training method.We empirically analyze how model ensemble reduces regression.Finally, we conduct CHECKLIST behavioral testing to understand the distribution of regressions across linguistic phenomena, and the efficacy of ensemble and distillation methods.* * Work done while at Amazon AWS AI. 1 Here regression refers to bugs in software testing instead of the statistical estimation method. Negative No Flip
LM-cut and Operator Counting Heuristics for Optimal Numeric Planning with Simple Conditions

We consider optimal numeric planning with numeric conditions consisting of linear expressions of numeric state variables and actions that increase or decrease numeric state variables by constant quantities. We build on previous research to introduce a new variant of the numeric hmax heuristic based on the delete-relaxed version of such planning tasks. Although our hmax heuristic is inadmissible, it yields a numeric version of the classical LM-cut heuristic which is admissible. Further, we prove that our LM-cut heuristic neither dominates nor is dominated by the existing numeric heuristic hmax(hbd). We show that admissibility also holds when integrating the numeric cuts into the operator-counting (OC) heuristic producing an admissible numeric version of the OC heuristic. Through experiments, we demonstrate that both these heuristics compete favorably with the state-of-the-art heuristics: in particular, while sometimes expanding more nodes than other heuristics, numeric OC solves 19 more problem instances than the next closest heuristic.
Domestic Capital Flow VS Foreign Capital InFlow in Nigeria: Which One Matter Most?

The path to achieving economic expansion as well as the ultimate economic development is the greatest macroeconomic goal sought after by all global economies. Traditionally, these resources are classified as labour and capital. Therefore, we focus on the capital factor and its impact on economic advancement. This study classifies the capital resource capital into two categories namely domestic and foreign capital and to compare their respective influence on economic expansion. The results show that domestic capital is more influential in promoting the cause of economic expansion whereas external debt demonstrates the serious level of threat to the course of economic expansion in Nigeria. Thus, this study recommends the need for the authority concern to look inward in resolving it economic challenges through domestic mobilization of resources . Sourcing for external capital should strictly take the form of FDI and foreign aids to augment the domestic capital for economic reasons with the view to expand the productive capacity of the economy.
Structure Learning from Related Data Sets with a Hierarchical Bayesian Score.

Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they comprise different related, but not homogeneous, data sets collected in different ways. In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD). The proposed score is based on a hierarchical model that pools information across data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. We derive a closed-form expression for BHD using a variational approximation of the marginal likelihood and we study its performance using simulated data. We find that, when data comprise multiple related data sets, BHD outperforms the Bayesian Dirichlet equivalent uniform (BDeu) score in terms of reconstruction accuracy as measured by the Structural Hamming distance, and that it is as accurate as BDeu when data are homogeneous. Moreover, the estimated networks are sparser and therefore more interpretable than those obtained with BDeu, thanks to a lower number of false positive arcs.
EVALUATING DEEP SEMI-SUPERVISED LEARNING METHODS FOR COMPUTER VISION APPLICATIONS

Deep semi-supervised learning (SSL) have been significantly investigated in the past few years due to its broad spectrum of theory, algorithms, and applications. The extensive use of the SSL methods is dominant in the field of computer vision, for example, image classification, human activity recognition, object detection, scene segmentation, and image generation. In spite of the significant success achieved in these domains, critically analyzing SSL methods on benchmark datasets still presents important challenges. In the literature, very limited reviews and surveys are available. In this paper, we present short but focused review about the most significant SSL methods. We analyze the basic theory of SSL and the differences among various SSL methods. Then, we present experimental analysis to compare these SSL methods using standard datasets. We also provide an insight into the challenges of the SSL methods.
Distributed Position-Force Control for Cooperative Transportation with Multiple Mobile Manipulators

This paper presents a distributed position-force control framework for multiple mobile manipulators in charge of achieving a tightly cooperative transportation task. Since the effect of each robot is different in the whole system, a three-layer control framework is designed. For the first layer, mobile bases run distributed observer which uses global states. At the second layer, the position deviation is adopted to improve the accuracy of general manipulators. Then, position control works in combination with force to ensure that the most important manipulator achieves cooperative transportation accurately and compliantly. The designed controller is extensible, which suits not only for pure transportation tasks but can also be exploited in those cases where a closed kinematic chain is generated by multi-robots manipulations. An analysis of the proposed controller is validated by simulation with three UR5 manipulators mounted on differential driven mobile bases separately.
Brecha educativa con las clases virtuales en México durante la pandemia del Covid-19.

In “Education gap with virtual learning in Mexico during Covid-19 pandemic” we analyze the shutting down of schools around the world because of the coronavirus pandemic, the institutional projects of global and local virtual education, in universities of Mexico and Spain, and the teachers’ challenges in this period of distance and blended learning. Through an anthropological approach we analyze the school community’s sociocultural situation in Mexico during virtual education. Therefore, with this article we will understand that it is necessary to strength the state education system to battle education gap, educational lagging and ensure technology access to low-income students. Key words: Covid-19. Virtual education. Blended learning. Challenges. Institutional projects. Educational lagging. Low-income students. Education gap.
Do YouTubers Hate Asians?: An Analysis of YouTube Users’ Anti-Asian Hatred on Major U.S. News Channels during the COVID-19 Pandemic

The outbreak of the Coronavirus Disease (COVID-19) has been widely covered on major U.S. media. “Chinese Virus” or “Wuhan Virus” became media buzz words especially at the beginning stage of the outbreak, which was feared to fuel anti-Asian hatred both in the U.S. and worldwide. This study examines the news coverage about COVID-19 in relation to Asians, mainly Chinese and China, on YouTube channels of major U.S. media outlets, and explores the relationship between the media framing and anti-Asian sentiments embedded in the comments beneath the news video. By content analyzing 50 news videos covering COVID-19 and Asians from 5 U.S. media organizations and 5000 comments, the findings suggest that attribution of responsibility and conflict are the most frequently used frames by the news reporting. The results also reveal that suspicion of conspiracy, rather than blaming, emerged as the most frequent theme embedded in hateful comments. One promising finding is that the frequency of hateful comments is significantly lower than that of non-hateful comments across all news frame categories.