Household registration system and private enterprise’s hiring policy in Shenzhen, China

The purpose of the thesis was to examine how household registration (Hukou) system affects private enterprise’s hiring policy in Shenzhen, China. In addition, this thesis aimed to found out what make Shenzhen different than other city if Hukou play less important role in Shenzhen private company’s recruitment process. In order to reach this topic, this thesis adopted Taste for Discrimination Model and Discrimination in employment theory, and uses a qualitative study research design with eight case companies. From previous study, there are severe discriminations in employment in China and Hukou is one of the reasons, especially rural Hukou people got unfair treatment compare to urban Hukou people. However, based on this thesis finding, Hukou is not a major factor for Shenzhen private company to consider candidates. Furthermore, Hukou is even not a factor during the recruitment process; company can provide Shenzhen Hukou to them if they want to obtain a Shenzhen Hukou. in the end, this thesis argues that Shenzhen has its unique policy and culture environment which leads to the Hukou discrimination is not exist for Shenzhen private company

Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas

Implementasi Kontrol Integritas E-kiosk untuk Pengamanan Sistem Pemungutan Suara secara Elektronik (E-VOTING)

Pemungutan suara dalam pemilu di Indonesia masih dilakukan secara manual, yaitu menggunakan media kertas. Dalam sistem tersebut, terjadi risiko kesalahan yang tinggi dalam penghitungan suara mengingat surat suara yang diproses terbilang banyak. Selain itu, rawan terjadi kecurangan terhadap jumlah suara demi memenangkan kelompok atau golongan tertentu. Akibatnya, pelaksanaan pemilu menjadi tidak sesuai dengan asas yang berlaku dan hasilnya tidak akurat. Untuk mengatasinya, dirancanglah sistem pemungutan suara yang lebih modern, yang disebut dengan sistem pemungutan suara secara elektronik (e-voting)

Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.Comment: 12 pages, Accepted by www202

ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering

With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.Comment: EMNLP 202

ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.comComment: To appear at the IEEE Conference on Robotics and Automation (ICRA), 202